From 424ae1421a6aa11fbea26dfd5e42ab1e870ae1be Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Mon, 22 Dec 2025 06:16:47 +0000 Subject: [PATCH 01/21] can modify cosyvoice --- NOTICE | 22 ++++++++++++++++++++++ README.md | 7 ++++++- cosyvoice | 1 - 3 files changed, 28 insertions(+), 2 deletions(-) create mode 100644 NOTICE delete mode 160000 cosyvoice diff --git a/NOTICE b/NOTICE new file mode 100644 index 0000000..c7663a3 --- /dev/null +++ b/NOTICE @@ -0,0 +1,22 @@ +Light TTS +Copyright (c) 2024 Light TTS Contributors + +This project contains code from the following third-party projects: + +================================================================================ + +CosyVoice (cosyvoice/) +https://github.com/FunAudioLLM/CosyVoice +Copyright (c) Alibaba, Inc. and its affiliates. +Licensed under the Apache License, Version 2.0 +Original commit: 9f27b42 + + The cosyvoice/ directory contains a modified copy of the CosyVoice project. + We have integrated and adapted this code for use in Light TTS. + The original LICENSE file is preserved in cosyvoice/LICENSE. + + All modifications to the original CosyVoice code are also licensed under + the Apache License, Version 2.0. + +================================================================================ + diff --git a/README.md b/README.md index 907603b..1503a60 100755 --- a/README.md +++ b/README.md @@ -149,4 +149,9 @@ When your service is started, you can call the service through the http API. We - bi-streaming. We use the websocket interface implementation, and we can find usage examples in `test/test_bistream.py`. ## License -This repository is released under the [Apache-2.0](LICENSE) license. \ No newline at end of file + +This repository is released under the [Apache-2.0](LICENSE) license. + +### Third-Party Code Attribution + +This project includes code from [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) (Copyright Alibaba, Inc. and its affiliates), which is also licensed under Apache-2.0. The CosyVoice code is located in the `cosyvoice/` directory and has been integrated and modified as part of Light TTS. See [NOTICE](NOTICE) file for complete attribution details. \ No newline at end of file diff --git a/cosyvoice b/cosyvoice deleted file mode 160000 index c939c80..0000000 --- a/cosyvoice +++ /dev/null @@ -1 +0,0 @@ -Subproject commit c939c80480d64a7963b0a0dad253fddeb04294bb From b81a44349c3c00ebaaa0bc214e19191bc018c068 Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Mon, 22 Dec 2025 06:17:44 +0000 Subject: [PATCH 02/21] delete submodule cosyvoice --- .gitmodules | 3 --- 1 file changed, 3 deletions(-) diff --git a/.gitmodules b/.gitmodules index 9b0161b..3d1b157 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,6 +1,3 @@ -[submodule "cosyvoice"] - path = cosyvoice - url = https://github.com/FunAudioLLM/CosyVoice.git [submodule "third_party/Matcha-TTS"] path = third_party/Matcha-TTS url = https://github.com/shivammehta25/Matcha-TTS.git From 130a68b0780fc711b14df3586c5a3b1662c1f0b4 Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Mon, 22 Dec 2025 06:20:19 +0000 Subject: [PATCH 03/21] add cosyvoice bc34459 --- .../.github/ISSUE_TEMPLATE/bug_report.md | 38 + .../.github/ISSUE_TEMPLATE/feature_request.md | 20 + cosyvoice/.github/workflows/lint.yml | 56 + cosyvoice/.github/workflows/stale-issues.yml | 22 + cosyvoice/.gitignore | 52 + cosyvoice/.gitmodules | 3 + cosyvoice/CODE_OF_CONDUCT.md | 76 + cosyvoice/FAQ.md | 16 + cosyvoice/LICENSE | 201 + cosyvoice/README.md | 260 + cosyvoice/asset/dingding.png | Bin 0 -> 122824 bytes cosyvoice/cosyvoice/__init__.py | 0 cosyvoice/cosyvoice/bin/average_model.py | 93 + cosyvoice/cosyvoice/bin/export_jit.py | 101 + cosyvoice/cosyvoice/bin/export_onnx.py | 114 + cosyvoice/cosyvoice/bin/train.py | 195 + cosyvoice/cosyvoice/cli/__init__.py | 0 cosyvoice/cosyvoice/cli/cosyvoice.py | 238 + cosyvoice/cosyvoice/cli/frontend.py | 219 + cosyvoice/cosyvoice/cli/model.py | 430 + cosyvoice/cosyvoice/dataset/__init__.py | 0 cosyvoice/cosyvoice/dataset/dataset.py | 151 + cosyvoice/cosyvoice/dataset/processor.py | 443 + cosyvoice/cosyvoice/flow/DiT/dit.py | 176 + cosyvoice/cosyvoice/flow/DiT/modules.py | 616 + cosyvoice/cosyvoice/flow/decoder.py | 494 + cosyvoice/cosyvoice/flow/flow.py | 432 + cosyvoice/cosyvoice/flow/flow_matching.py | 228 + cosyvoice/cosyvoice/flow/length_regulator.py | 70 + cosyvoice/cosyvoice/hifigan/discriminator.py | 230 + cosyvoice/cosyvoice/hifigan/f0_predictor.py | 103 + cosyvoice/cosyvoice/hifigan/generator.py | 746 + cosyvoice/cosyvoice/hifigan/hifigan.py | 67 + cosyvoice/cosyvoice/llm/llm.py | 739 + .../multilingual_zh_ja_yue_char_del.tiktoken | 58836 ++++++++++++++++ cosyvoice/cosyvoice/tokenizer/tokenizer.py | 327 + cosyvoice/cosyvoice/transformer/__init__.py | 0 cosyvoice/cosyvoice/transformer/activation.py | 84 + cosyvoice/cosyvoice/transformer/attention.py | 330 + .../cosyvoice/transformer/convolution.py | 258 + cosyvoice/cosyvoice/transformer/decoder.py | 396 + .../cosyvoice/transformer/decoder_layer.py | 132 + cosyvoice/cosyvoice/transformer/embedding.py | 302 + cosyvoice/cosyvoice/transformer/encoder.py | 474 + .../cosyvoice/transformer/encoder_layer.py | 236 + .../transformer/label_smoothing_loss.py | 96 + .../transformer/positionwise_feed_forward.py | 115 + .../cosyvoice/transformer/subsampling.py | 383 + .../cosyvoice/transformer/upsample_encoder.py | 321 + cosyvoice/cosyvoice/utils/__init__.py | 0 cosyvoice/cosyvoice/utils/class_utils.py | 85 + cosyvoice/cosyvoice/utils/common.py | 213 + cosyvoice/cosyvoice/utils/executor.py | 176 + cosyvoice/cosyvoice/utils/file_utils.py | 118 + cosyvoice/cosyvoice/utils/frontend_utils.py | 136 + cosyvoice/cosyvoice/utils/losses.py | 57 + cosyvoice/cosyvoice/utils/mask.py | 265 + cosyvoice/cosyvoice/utils/scheduler.py | 738 + cosyvoice/cosyvoice/utils/train_utils.py | 367 + cosyvoice/cosyvoice/vllm/cosyvoice2.py | 103 + cosyvoice/docker/Dockerfile | 51 + cosyvoice/example.py | 106 + cosyvoice/examples/grpo/cosyvoice2/Dockerfile | 6 + cosyvoice/examples/grpo/cosyvoice2/README.md | 125 + .../cosyvoice2/huggingface_to_pretrained.py | 71 + .../examples/grpo/cosyvoice2/infer_dataset.py | 397 + .../examples/grpo/cosyvoice2/prepare_data.py | 86 + .../cosyvoice2/pretrained_to_huggingface.py | 133 + .../examples/grpo/cosyvoice2/requirements.txt | 31 + .../examples/grpo/cosyvoice2/reward_tts.py | 233 + cosyvoice/examples/grpo/cosyvoice2/run.sh | 159 + .../grpo/cosyvoice2/scripts/compute_wer.sh | 33 + .../scripts/offline-decode-files.py | 754 + .../grpo/cosyvoice2/token2wav_asr_server.py | 346 + .../libritts/cosyvoice/conf/cosyvoice.yaml | 257 + .../libritts/cosyvoice/conf/ds_stage2.json | 42 + .../examples/libritts/cosyvoice/cosyvoice | 1 + .../cosyvoice/local/download_and_untar.sh | 97 + .../libritts/cosyvoice/local/prepare_data.py | 62 + .../cosyvoice/local/prepare_reject_sample.py | 50 + cosyvoice/examples/libritts/cosyvoice/path.sh | 3 + cosyvoice/examples/libritts/cosyvoice/run.sh | 109 + cosyvoice/examples/libritts/cosyvoice/tools | 1 + .../examples/libritts/cosyvoice/tts_text.json | 5 + .../libritts/cosyvoice2/conf/cosyvoice2.yaml | 234 + .../libritts/cosyvoice2/conf/ds_stage2.json | 42 + .../examples/libritts/cosyvoice2/cosyvoice | 1 + cosyvoice/examples/libritts/cosyvoice2/local | 1 + .../examples/libritts/cosyvoice2/path.sh | 1 + cosyvoice/examples/libritts/cosyvoice2/run.sh | 111 + .../examples/libritts/cosyvoice2/run_dpo.sh | 123 + cosyvoice/examples/libritts/cosyvoice2/tools | 1 + .../libritts/cosyvoice2/tts_text.json | 1 + .../libritts/cosyvoice3/conf/cosyvoice3.yaml | 224 + .../libritts/cosyvoice3/conf/ds_stage2.json | 42 + .../examples/libritts/cosyvoice3/cosyvoice | 1 + cosyvoice/examples/libritts/cosyvoice3/local | 1 + .../examples/libritts/cosyvoice3/path.sh | 1 + cosyvoice/examples/libritts/cosyvoice3/run.sh | 112 + cosyvoice/examples/libritts/cosyvoice3/tools | 1 + .../examples/magicdata-read/cosyvoice/conf | 1 + .../magicdata-read/cosyvoice/cosyvoice | 1 + .../cosyvoice/local/download_and_untar.sh | 97 + .../cosyvoice/local/prepare_data.py | 52 + .../examples/magicdata-read/cosyvoice/path.sh | 1 + .../examples/magicdata-read/cosyvoice/run.sh | 109 + .../examples/magicdata-read/cosyvoice/tools | 1 + .../magicdata-read/cosyvoice/tts_text.json | 18 + cosyvoice/requirements.txt | 42 + cosyvoice/runtime/python/Dockerfile | 13 + cosyvoice/runtime/python/fastapi/client.py | 92 + cosyvoice/runtime/python/fastapi/server.py | 101 + cosyvoice/runtime/python/grpc/client.py | 106 + cosyvoice/runtime/python/grpc/cosyvoice.proto | 43 + cosyvoice/runtime/python/grpc/server.py | 96 + .../runtime/triton_trtllm/Dockerfile.server | 8 + cosyvoice/runtime/triton_trtllm/README.DIT.md | 141 + cosyvoice/runtime/triton_trtllm/README.md | 146 + .../runtime/triton_trtllm/client_grpc.py | 922 + .../runtime/triton_trtllm/client_http.py | 172 + .../triton_trtllm/docker-compose.dit.yml | 20 + .../runtime/triton_trtllm/docker-compose.yml | 20 + .../model_repo/audio_tokenizer/1/model.py | 97 + .../model_repo/audio_tokenizer/config.pbtxt | 53 + .../model_repo/cosyvoice2/1/model.py | 452 + .../model_repo/cosyvoice2/config.pbtxt | 73 + .../model_repo/cosyvoice2_dit/1/model.py | 394 + .../model_repo/cosyvoice2_dit/config.pbtxt | 73 + .../model_repo/speaker_embedding/1/model.py | 153 + .../model_repo/speaker_embedding/config.pbtxt | 48 + .../model_repo/tensorrt_llm/1/.gitkeep | 0 .../model_repo/tensorrt_llm/config.pbtxt | 857 + .../model_repo/token2wav/1/model.py | 277 + .../model_repo/token2wav/config.pbtxt | 80 + .../model_repo/token2wav_dit/1/model.py | 142 + .../token2wav_dit/1/token2wav_dit.py | 510 + .../model_repo/token2wav_dit/config.pbtxt | 69 + .../triton_trtllm/offline_inference.py | 652 + .../runtime/triton_trtllm/requirements.txt | 14 + cosyvoice/runtime/triton_trtllm/run.sh | 142 + .../run_stepaudio2_dit_token2wav.sh | 225 + .../scripts/convert_checkpoint.py | 330 + .../triton_trtllm/scripts/fill_template.py | 69 + .../runtime/triton_trtllm/scripts/test_llm.py | 138 + .../triton_trtllm/streaming_inference.py | 122 + cosyvoice/runtime/triton_trtllm/token2wav.py | 335 + .../runtime/triton_trtllm/token2wav_dit.py | 1 + cosyvoice/tools/extract_embedding.py | 77 + cosyvoice/tools/extract_speech_token.py | 72 + cosyvoice/tools/make_parquet_list.py | 136 + cosyvoice/vllm_example.py | 39 + cosyvoice/webui.py | 180 + 152 files changed, 83244 insertions(+) create mode 100644 cosyvoice/.github/ISSUE_TEMPLATE/bug_report.md create mode 100644 cosyvoice/.github/ISSUE_TEMPLATE/feature_request.md create mode 100644 cosyvoice/.github/workflows/lint.yml create mode 100644 cosyvoice/.github/workflows/stale-issues.yml create mode 100644 cosyvoice/.gitignore create mode 100644 cosyvoice/.gitmodules create mode 100644 cosyvoice/CODE_OF_CONDUCT.md create mode 100644 cosyvoice/FAQ.md create mode 100644 cosyvoice/LICENSE create mode 100644 cosyvoice/README.md create mode 100644 cosyvoice/asset/dingding.png create mode 100644 cosyvoice/cosyvoice/__init__.py create mode 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cosyvoice/cosyvoice/hifigan/discriminator.py create mode 100644 cosyvoice/cosyvoice/hifigan/f0_predictor.py create mode 100644 cosyvoice/cosyvoice/hifigan/generator.py create mode 100644 cosyvoice/cosyvoice/hifigan/hifigan.py create mode 100644 cosyvoice/cosyvoice/llm/llm.py create mode 100644 cosyvoice/cosyvoice/tokenizer/assets/multilingual_zh_ja_yue_char_del.tiktoken create mode 100644 cosyvoice/cosyvoice/tokenizer/tokenizer.py create mode 100644 cosyvoice/cosyvoice/transformer/__init__.py create mode 100644 cosyvoice/cosyvoice/transformer/activation.py create mode 100644 cosyvoice/cosyvoice/transformer/attention.py create mode 100644 cosyvoice/cosyvoice/transformer/convolution.py create mode 100644 cosyvoice/cosyvoice/transformer/decoder.py create mode 100644 cosyvoice/cosyvoice/transformer/decoder_layer.py create mode 100644 cosyvoice/cosyvoice/transformer/embedding.py create mode 100644 cosyvoice/cosyvoice/transformer/encoder.py create mode 100644 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cosyvoice/runtime/triton_trtllm/run_stepaudio2_dit_token2wav.sh create mode 100644 cosyvoice/runtime/triton_trtllm/scripts/convert_checkpoint.py create mode 100644 cosyvoice/runtime/triton_trtllm/scripts/fill_template.py create mode 100644 cosyvoice/runtime/triton_trtllm/scripts/test_llm.py create mode 100644 cosyvoice/runtime/triton_trtllm/streaming_inference.py create mode 100644 cosyvoice/runtime/triton_trtllm/token2wav.py create mode 120000 cosyvoice/runtime/triton_trtllm/token2wav_dit.py create mode 100755 cosyvoice/tools/extract_embedding.py create mode 100755 cosyvoice/tools/extract_speech_token.py create mode 100755 cosyvoice/tools/make_parquet_list.py create mode 100644 cosyvoice/vllm_example.py create mode 100644 cosyvoice/webui.py diff --git a/cosyvoice/.github/ISSUE_TEMPLATE/bug_report.md b/cosyvoice/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..dd84ea7 --- /dev/null +++ b/cosyvoice/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,38 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. Go to '...' +2. Click on '....' +3. Scroll down to '....' +4. See error + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - OS: [e.g. iOS] + - Browser [e.g. chrome, safari] + - Version [e.g. 22] + +**Smartphone (please complete the following information):** + - Device: [e.g. iPhone6] + - OS: [e.g. iOS8.1] + - Browser [e.g. stock browser, safari] + - Version [e.g. 22] + +**Additional context** +Add any other context about the problem here. diff --git a/cosyvoice/.github/ISSUE_TEMPLATE/feature_request.md b/cosyvoice/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..bbcbbe7 --- /dev/null +++ b/cosyvoice/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,20 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: '' +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/cosyvoice/.github/workflows/lint.yml b/cosyvoice/.github/workflows/lint.yml new file mode 100644 index 0000000..4002222 --- /dev/null +++ b/cosyvoice/.github/workflows/lint.yml @@ -0,0 +1,56 @@ +name: Lint + +on: + pull_request: + push: + +jobs: + quick-checks: + runs-on: ubuntu-latest + steps: + - name: Fetch CosyVoice + uses: actions/checkout@v1 + - name: Checkout PR tip + run: | + set -eux + if [[ "${{ github.event_name }}" == "pull_request" ]]; then + # We are on a PR, so actions/checkout leaves us on a merge commit. + # Check out the actual tip of the branch. + git checkout ${{ github.event.pull_request.head.sha }} + fi + echo ::set-output name=commit_sha::$(git rev-parse HEAD) + id: get_pr_tip + - name: Ensure no tabs + run: | + (! git grep -I -l $'\t' -- . ':(exclude)*.txt' ':(exclude)*.svg' ':(exclude)**Makefile' ':(exclude)**/contrib/**' ':(exclude)third_party' ':(exclude).gitattributes' ':(exclude).gitmodules' || (echo "The above files have tabs; please convert them to spaces"; false)) + - name: Ensure no trailing whitespace + run: | + (! git grep -I -n $' $' -- . ':(exclude)*.txt' ':(exclude)third_party' ':(exclude).gitattributes' ':(exclude).gitmodules' || (echo "The above files have trailing whitespace; please remove them"; false)) + + flake8-py3: + runs-on: ubuntu-latest + steps: + - name: Setup Python + uses: actions/setup-python@v1 + with: + python-version: 3.9 + architecture: x64 + - name: Fetch CosyVoice + uses: actions/checkout@v1 + - name: Checkout PR tip + run: | + set -eux + if [[ "${{ github.event_name }}" == "pull_request" ]]; then + # We are on a PR, so actions/checkout leaves us on a merge commit. + # Check out the actual tip of the branch. + git checkout ${{ github.event.pull_request.head.sha }} + fi + echo ::set-output name=commit_sha::$(git rev-parse HEAD) + id: get_pr_tip + - name: Run flake8 + run: | + set -eux + pip install flake8==3.8.2 flake8-bugbear flake8-comprehensions flake8-executable flake8-pyi==20.5.0 mccabe pycodestyle==2.6.0 pyflakes==2.2.0 + flake8 --version + flake8 --max-line-length 180 --ignore B006,B008,B905,C408,E402,E731,E741,W503,W504,F401,F403,F405,F722,F841 --exclude ./third_party/,./runtime/python/grpc/cosyvoice_pb2*py + if [ $? != 0 ]; then exit 1; fi \ No newline at end of file diff --git a/cosyvoice/.github/workflows/stale-issues.yml b/cosyvoice/.github/workflows/stale-issues.yml new file mode 100644 index 0000000..d5cd3cf --- /dev/null +++ b/cosyvoice/.github/workflows/stale-issues.yml @@ -0,0 +1,22 @@ +name: Close inactive issues +on: + schedule: + - cron: "30 1 * * *" + +jobs: + close-issues: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + steps: + - uses: actions/stale@v5 + with: + days-before-issue-stale: 30 + days-before-issue-close: 14 + stale-issue-label: "stale" + stale-issue-message: "This issue is stale because it has been open for 30 days with no activity." + close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale." + days-before-pr-stale: -1 + days-before-pr-close: -1 + repo-token: ${{ secrets.GITHUB_TOKEN }} diff --git a/cosyvoice/.gitignore b/cosyvoice/.gitignore new file mode 100644 index 0000000..4a9f23f --- /dev/null +++ b/cosyvoice/.gitignore @@ -0,0 +1,52 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# Visual Studio Code files +.vscode +.vs + +# PyCharm files +.idea + +# Eclipse Project settings +*.*project +.settings + +# Sublime Text settings +*.sublime-workspace +*.sublime-project + +# Editor temporaries +*.swn +*.swo +*.swp +*.swm +*~ + +# IPython notebook checkpoints +.ipynb_checkpoints + +# macOS dir files +.DS_Store + +exp +data +raw_wav +tensorboard +**/*build* + +# Clangd files +.cache +compile_commands.json + +# train/inference files +*.wav +*.m4a +*.aac +*.pt +pretrained_models/* +*_pb2_grpc.py +*_pb2.py +*.tar \ No newline at end of file diff --git a/cosyvoice/.gitmodules b/cosyvoice/.gitmodules new file mode 100644 index 0000000..3d1b157 --- /dev/null +++ b/cosyvoice/.gitmodules @@ -0,0 +1,3 @@ +[submodule "third_party/Matcha-TTS"] + path = third_party/Matcha-TTS + url = https://github.com/shivammehta25/Matcha-TTS.git diff --git a/cosyvoice/CODE_OF_CONDUCT.md b/cosyvoice/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..66c2a4c --- /dev/null +++ b/cosyvoice/CODE_OF_CONDUCT.md @@ -0,0 +1,76 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to making participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or + advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies both within project spaces and in public spaces +when an individual is representing the project or its community. Examples of +representing a project or community include using an official project e-mail +address, posting via an official social media account, or acting as an appointed +representative at an online or offline event. Representation of a project may be +further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at mikelei@mobvoi.com. All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq diff --git a/cosyvoice/FAQ.md b/cosyvoice/FAQ.md new file mode 100644 index 0000000..53df058 --- /dev/null +++ b/cosyvoice/FAQ.md @@ -0,0 +1,16 @@ +## ModuleNotFoundError: No module named 'matcha' + +Matcha-TTS is a third_party module. Please check `third_party` directory. If there is no `Matcha-TTS`, execute `git submodule update --init --recursive`. + +run `export PYTHONPATH=third_party/Matcha-TTS` if you want to use `from cosyvoice.cli.cosyvoice import CosyVoice` in python script. + +## cannot find resource.zip or cannot unzip resource.zip + +Please make sure you have git-lfs installed. Execute + +```sh +git clone https://www.modelscope.cn/iic/CosyVoice-ttsfrd.git pretrained_models/CosyVoice-ttsfrd +cd pretrained_models/CosyVoice-ttsfrd/ +unzip resource.zip -d . +pip install ttsfrd-0.3.6-cp38-cp38-linux_x86_64.whl +``` diff --git a/cosyvoice/LICENSE b/cosyvoice/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/cosyvoice/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/cosyvoice/README.md b/cosyvoice/README.md new file mode 100644 index 0000000..a1d3d5e --- /dev/null +++ b/cosyvoice/README.md @@ -0,0 +1,260 @@ +![SVG Banners](https://svg-banners.vercel.app/api?type=origin&text1=CosyVoice🤠&text2=Text-to-Speech%20💖%20Large%20Language%20Model&width=800&height=210) + +## 👉🏻 CosyVoice 👈🏻 + +**Fun-CosyVoice 3.0**: [Demos](https://funaudiollm.github.io/cosyvoice3/); [Paper](https://arxiv.org/pdf/2505.17589); [Modelscope](https://www.modelscope.cn/models/FunAudioLLM/Fun-CosyVoice3-0.5B-2512); [Huggingface](https://huggingface.co/FunAudioLLM/Fun-CosyVoice3-0.5B-2512); [CV3-Eval](https://github.com/FunAudioLLM/CV3-Eval) + +**CosyVoice 2.0**: [Demos](https://funaudiollm.github.io/cosyvoice2/); [Paper](https://arxiv.org/pdf/2412.10117); [Modelscope](https://www.modelscope.cn/models/iic/CosyVoice2-0.5B); [HuggingFace](https://huggingface.co/FunAudioLLM/CosyVoice2-0.5B) + +**CosyVoice 1.0**: [Demos](https://fun-audio-llm.github.io); [Paper](https://funaudiollm.github.io/pdf/CosyVoice_v1.pdf); [Modelscope](https://www.modelscope.cn/models/iic/CosyVoice-300M); [HuggingFace](https://huggingface.co/FunAudioLLM/CosyVoice-300M) + +## Highlight🔥 + +**Fun-CosyVoice 3.0** is an advanced text-to-speech (TTS) system based on large language models (LLM), surpassing its predecessor (CosyVoice 2.0) in content consistency, speaker similarity, and prosody naturalness. It is designed for zero-shot multilingual speech synthesis in the wild. +### Key Features +- **Language Coverage**: Covers 9 common languages (Chinese, English, Japanese, Korean, German, Spanish, French, Italian, Russian), 18+ Chinese dialects/accents (Guangdong, Minnan, Sichuan, Dongbei, Shan3xi, Shan1xi, Shanghai, Tianjin, Shandong, Ningxia, Gansu, etc.) and meanwhile supports both multi-lingual/cross-lingual zero-shot voice cloning. +- **Content Consistency & Naturalness**: Achieves state-of-the-art performance in content consistency, speaker similarity, and prosody naturalness. +- **Pronunciation Inpainting**: Supports pronunciation inpainting of Chinese Pinyin and English CMU phonemes, providing more controllability and thus suitable for production use. +- **Text Normalization**: Supports reading of numbers, special symbols and various text formats without a traditional frontend module. +- **Bi-Streaming**: Support both text-in streaming and audio-out streaming, and achieves latency as low as 150ms while maintaining high-quality audio output. +- **Instruct Support**: Supports various instructions such as languages, dialects, emotions, speed, volume, etc. + + +## Roadmap + +- [x] 2025/12 + + - [x] release Fun-CosyVoice3-0.5B-2512 base model, rl model and its training/inference script + - [x] release Fun-CosyVoice3-0.5B modelscope gradio space + +- [x] 2025/08 + + - [x] Thanks to the contribution from NVIDIA Yuekai Zhang, add triton trtllm runtime support and cosyvoice2 grpo training support + +- [x] 2025/07 + + - [x] release Fun-CosyVoice 3.0 eval set + +- [x] 2025/05 + + - [x] add CosyVoice2-0.5B vllm support + +- [x] 2024/12 + + - [x] 25hz CosyVoice2-0.5B released + +- [x] 2024/09 + + - [x] 25hz CosyVoice-300M base model + - [x] 25hz CosyVoice-300M voice conversion function + +- [x] 2024/08 + + - [x] Repetition Aware Sampling(RAS) inference for llm stability + - [x] Streaming inference mode support, including kv cache and sdpa for rtf optimization + +- [x] 2024/07 + + - [x] Flow matching training support + - [x] WeTextProcessing support when ttsfrd is not available + - [x] Fastapi server and client + +## Evaluation + +| Model | Open-Source | Model Size | test-zh
CER (%) ↓ | test-zh
Speaker Similarity (%) ↑ | test-en
WER (%) ↓ | test-en
Speaker Similarity (%) ↑ | test-hard
CER (%) ↓ | test-hard
Speaker Similarity (%) ↑ | +| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| Human | - | - | 1.26 | 75.5 | 2.14 | 73.4 | - | - | +| Seed-TTS | ❌ | - | 1.12 | 79.6 | 2.25 | 76.2 | 7.59 | 77.6 | +| MiniMax-Speech | ❌ | - | 0.83 | 78.3 | 1.65 | 69.2 | - | - | +| F5-TTS | ✅ | 0.3B | 1.52 | 74.1 | 2.00 | 64.7 | 8.67 | 71.3 | +| Spark TTS | ✅ | 0.5B | 1.2 | 66.0 | 1.98 | 57.3 | - | - | +| CosyVoice2 | ✅ | 0.5B | 1.45 | 75.7 | 2.57 | 65.9 | 6.83 | 72.4 | +| FireRedTTS2 | ✅ | 1.5B | 1.14 | 73.2 | 1.95 | 66.5 | - | - | +| Index-TTS2 | ✅ | 1.5B | 1.03 | 76.5 | 2.23 | 70.6 | 7.12 | 75.5 | +| VibeVoice-1.5B | ✅ | 1.5B | 1.16 | 74.4 | 3.04 | 68.9 | - | - | +| VibeVoice-Realtime | ✅ | 0.5B | - | - | 2.05 | 63.3 | - | - | +| HiggsAudio-v2 | ✅ | 3B | 1.50 | 74.0 | 2.44 | 67.7 | - | - | +| VoxCPM | ✅ | 0.5B | 0.93 | 77.2 | 1.85 | 72.9 | 8.87 | 73.0 | +| GLM-TTS | ✅ | 1.5B | 1.03 | 76.1 | - | - | - | - | +| GLM-TTS RL | ✅ | 1.5B | 0.89 | 76.4 | - | - | - | - | +| Fun-CosyVoice3-0.5B-2512 | ✅ | 0.5B | 1.21 | 78.0 | 2.24 | 71.8 | 6.71 | 75.8 | +| Fun-CosyVoice3-0.5B-2512_RL | ✅ | 0.5B | 0.81 | 77.4 | 1.68 | 69.5 | 5.44 | 75.0 | + + +## Install + +### Clone and install + +- Clone the repo + ``` sh + git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git + # If you failed to clone the submodule due to network failures, please run the following command until success + cd CosyVoice + git submodule update --init --recursive + ``` + +- Install Conda: please see https://docs.conda.io/en/latest/miniconda.html +- Create Conda env: + + ``` sh + conda create -n cosyvoice -y python=3.10 + conda activate cosyvoice + pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com + + # If you encounter sox compatibility issues + # ubuntu + sudo apt-get install sox libsox-dev + # centos + sudo yum install sox sox-devel + ``` + +### Model download + +We strongly recommend that you download our pretrained `Fun-CosyVoice3-0.5B` `CosyVoice2-0.5B` `CosyVoice-300M` `CosyVoice-300M-SFT` `CosyVoice-300M-Instruct` model and `CosyVoice-ttsfrd` resource. + +``` python +# modelscope SDK model download +from modelscope import snapshot_download +snapshot_download('FunAudioLLM/Fun-CosyVoice3-0.5B-2512', local_dir='pretrained_models/Fun-CosyVoice3-0.5B') +snapshot_download('iic/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B') +snapshot_download('iic/CosyVoice-300M', local_dir='pretrained_models/CosyVoice-300M') +snapshot_download('iic/CosyVoice-300M-SFT', local_dir='pretrained_models/CosyVoice-300M-SFT') +snapshot_download('iic/CosyVoice-300M-Instruct', local_dir='pretrained_models/CosyVoice-300M-Instruct') +snapshot_download('iic/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd') + +# for oversea users, huggingface SDK model download +from huggingface_hub import snapshot_download +snapshot_download('FunAudioLLM/Fun-CosyVoice3-0.5B-2512', local_dir='pretrained_models/Fun-CosyVoice3-0.5B') +snapshot_download('FunAudioLLM/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B') +snapshot_download('FunAudioLLM/CosyVoice-300M', local_dir='pretrained_models/CosyVoice-300M') +snapshot_download('FunAudioLLM/CosyVoice-300M-SFT', local_dir='pretrained_models/CosyVoice-300M-SFT') +snapshot_download('FunAudioLLM/CosyVoice-300M-Instruct', local_dir='pretrained_models/CosyVoice-300M-Instruct') +snapshot_download('FunAudioLLM/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd') +``` + +Optionally, you can unzip `ttsfrd` resource and install `ttsfrd` package for better text normalization performance. + +Notice that this step is not necessary. If you do not install `ttsfrd` package, we will use wetext by default. + +``` sh +cd pretrained_models/CosyVoice-ttsfrd/ +unzip resource.zip -d . +pip install ttsfrd_dependency-0.1-py3-none-any.whl +pip install ttsfrd-0.4.2-cp310-cp310-linux_x86_64.whl +``` + +### Basic Usage + +We strongly recommend using `Fun-CosyVoice3-0.5B` for better performance. +Follow the code in `example.py` for detailed usage of each model. +```sh +python example.py +``` + +#### CosyVoice2 vllm Usage +If you want to use vllm for inference, please install `vllm==v0.9.0`. Older vllm version do not support CosyVoice2 inference. + +Notice that `vllm==v0.9.0` has a lot of specific requirements, for example `torch==2.7.0`. You can create a new env to in case your hardward do not support vllm and old env is corrupted. + +``` sh +conda create -n cosyvoice_vllm --clone cosyvoice +conda activate cosyvoice_vllm +pip install vllm==v0.9.0 transformers==4.51.3 numpy==1.26.4 -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com +python vllm_example.py +``` + +#### Start web demo + +You can use our web demo page to get familiar with CosyVoice quickly. + +Please see the demo website for details. + +``` python +# change iic/CosyVoice-300M-SFT for sft inference, or iic/CosyVoice-300M-Instruct for instruct inference +python3 webui.py --port 50000 --model_dir pretrained_models/CosyVoice-300M +``` + +#### Advanced Usage + +For advanced users, we have provided training and inference scripts in `examples/libritts/cosyvoice/run.sh`. + +#### Build for deployment + +Optionally, if you want service deployment, +You can run the following steps. + +``` sh +cd runtime/python +docker build -t cosyvoice:v1.0 . +# change iic/CosyVoice-300M to iic/CosyVoice-300M-Instruct if you want to use instruct inference +# for grpc usage +docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/grpc && python3 server.py --port 50000 --max_conc 4 --model_dir iic/CosyVoice-300M && sleep infinity" +cd grpc && python3 client.py --port 50000 --mode +# for fastapi usage +docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/fastapi && python3 server.py --port 50000 --model_dir iic/CosyVoice-300M && sleep infinity" +cd fastapi && python3 client.py --port 50000 --mode +``` + +#### Using Nvidia TensorRT-LLM for deployment + +Using TensorRT-LLM to accelerate cosyvoice2 llm could give 4x acceleration comparing with huggingface transformers implementation. +To quick start: + +``` sh +cd runtime/triton_trtllm +docker compose up -d +``` +For more details, you could check [here](https://github.com/FunAudioLLM/CosyVoice/tree/main/runtime/triton_trtllm) + +## Discussion & Communication + +You can directly discuss on [Github Issues](https://github.com/FunAudioLLM/CosyVoice/issues). + +You can also scan the QR code to join our official Dingding chat group. + + + +## Acknowledge + +1. We borrowed a lot of code from [FunASR](https://github.com/modelscope/FunASR). +2. We borrowed a lot of code from [FunCodec](https://github.com/modelscope/FunCodec). +3. We borrowed a lot of code from [Matcha-TTS](https://github.com/shivammehta25/Matcha-TTS). +4. We borrowed a lot of code from [AcademiCodec](https://github.com/yangdongchao/AcademiCodec). +5. We borrowed a lot of code from [WeNet](https://github.com/wenet-e2e/wenet). + +## Citations + +``` bibtex +@article{du2024cosyvoice, + title={Cosyvoice: A scalable multilingual zero-shot text-to-speech synthesizer based on supervised semantic tokens}, + author={Du, Zhihao and Chen, Qian and Zhang, Shiliang and Hu, Kai and Lu, Heng and Yang, Yexin and Hu, Hangrui and Zheng, Siqi and Gu, Yue and Ma, Ziyang and others}, + journal={arXiv preprint arXiv:2407.05407}, + year={2024} +} + +@article{du2024cosyvoice, + title={Cosyvoice 2: Scalable streaming speech synthesis with large language models}, + author={Du, Zhihao and Wang, Yuxuan and Chen, Qian and Shi, Xian and Lv, Xiang and Zhao, Tianyu and Gao, Zhifu and Yang, Yexin and Gao, Changfeng and Wang, Hui and others}, + journal={arXiv preprint arXiv:2412.10117}, + year={2024} +} + +@article{du2025cosyvoice, + title={CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training}, + author={Du, Zhihao and Gao, Changfeng and Wang, Yuxuan and Yu, Fan and Zhao, Tianyu and Wang, Hao and Lv, Xiang and Wang, Hui and Shi, Xian and An, Keyu and others}, + journal={arXiv preprint arXiv:2505.17589}, + year={2025} +} + +@inproceedings{lyu2025build, + title={Build LLM-Based Zero-Shot Streaming TTS System with Cosyvoice}, + author={Lyu, Xiang and Wang, Yuxuan and Zhao, Tianyu and Wang, Hao and Liu, Huadai and Du, Zhihao}, + booktitle={ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, + pages={1--2}, + year={2025}, + organization={IEEE} +} +``` + +## Disclaimer +The content provided above is for academic purposes only and is intended to demonstrate technical capabilities. Some examples are sourced from the internet. 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z+rgLjSJ18m$(q5PBG803<|tn-^NJp9F6I76W?4+!C$w*l>(3oer3dw+T4A$83+NbZ z?QH{TYV)z4kJ(CjNs0?MTI^45!Bf4oD59f2C6J++2PE?YKcR2DMq}W}t|O2;_OQ2p z>O4lExSmadIXbKzE%PYpq~4p2A9`BdcB)n{E;O(Pq>?QbH>rP) znny>$yqg=MX0nJQlm*AGNXlH}EpdjwQZ>HAb$dSF0j8(gIA<`t#z@aU7G{7^qqx}o z7!OD7YKG<~f|HT&K+?6dOv#>gmI{CY1wrv!o>=BOv_4-*`DnDcOG>;uQR7c;IOz>Y z2psW0#&0eivNk9i8a!m|FX?oJ`%0bqJkJLyzJ@lr@?`d0s2GXe8}p#Q&j9=%%s exHqx)QoUJ>Y$ltNGuPdMzo3yflUj%={Qm)B%4IbG literal 0 HcmV?d00001 diff --git a/cosyvoice/cosyvoice/__init__.py b/cosyvoice/cosyvoice/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/cosyvoice/cosyvoice/bin/average_model.py b/cosyvoice/cosyvoice/bin/average_model.py new file mode 100644 index 0000000..b7140c1 --- /dev/null +++ b/cosyvoice/cosyvoice/bin/average_model.py @@ -0,0 +1,93 @@ +# Copyright (c) 2020 Mobvoi Inc (Di Wu) +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import argparse +import glob + +import yaml +import torch + + +def get_args(): + parser = argparse.ArgumentParser(description='average model') + parser.add_argument('--dst_model', required=True, help='averaged model') + parser.add_argument('--src_path', + required=True, + help='src model path for average') + parser.add_argument('--val_best', + action="store_true", + help='averaged model') + parser.add_argument('--num', + default=5, + type=int, + help='nums for averaged model') + + args = parser.parse_args() + print(args) + return args + + +def main(): + args = get_args() + val_scores = [] + if args.val_best: + yamls = glob.glob('{}/*.yaml'.format(args.src_path)) + yamls = [ + f for f in yamls + if not (os.path.basename(f).startswith('train') + or os.path.basename(f).startswith('init')) + ] + for y in yamls: + with open(y, 'r') as f: + dic_yaml = yaml.load(f, Loader=yaml.BaseLoader) + loss = float(dic_yaml['loss_dict']['loss']) + epoch = int(dic_yaml['epoch']) + step = int(dic_yaml['step']) + tag = dic_yaml['tag'] + val_scores += [[epoch, step, loss, tag]] + sorted_val_scores = sorted(val_scores, + key=lambda x: x[2], + reverse=False) + print("best val (epoch, step, loss, tag) = " + + str(sorted_val_scores[:args.num])) + path_list = [ + args.src_path + '/epoch_{}_whole.pt'.format(score[0]) + for score in sorted_val_scores[:args.num] + ] + print(path_list) + avg = {} + num = args.num + assert num == len(path_list) + for path in path_list: + print('Processing {}'.format(path)) + states = torch.load(path, map_location=torch.device('cpu')) + for k in states.keys(): + if k not in ['step', 'epoch']: + if k not in avg.keys(): + avg[k] = states[k].clone() + else: + avg[k] += states[k] + # average + for k in avg.keys(): + if avg[k] is not None: + # pytorch 1.6 use true_divide instead of /= + avg[k] = torch.true_divide(avg[k], num) + print('Saving to {}'.format(args.dst_model)) + torch.save(avg, args.dst_model) + + +if __name__ == '__main__': + main() diff --git a/cosyvoice/cosyvoice/bin/export_jit.py b/cosyvoice/cosyvoice/bin/export_jit.py new file mode 100644 index 0000000..0013d64 --- /dev/null +++ b/cosyvoice/cosyvoice/bin/export_jit.py @@ -0,0 +1,101 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import argparse +import logging +logging.getLogger('matplotlib').setLevel(logging.WARNING) +import os +import sys +import torch +ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append('{}/../..'.format(ROOT_DIR)) +sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR)) +from cosyvoice.cli.cosyvoice import AutoModel +from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model, CosyVoice3Model +from cosyvoice.utils.file_utils import logging +from cosyvoice.utils.class_utils import get_model_type + + +def get_args(): + parser = argparse.ArgumentParser(description='export your model for deployment') + parser.add_argument('--model_dir', + type=str, + default='pretrained_models/CosyVoice-300M', + help='local path') + args = parser.parse_args() + print(args) + return args + + +def get_optimized_script(model, preserved_attrs=[]): + script = torch.jit.script(model) + if preserved_attrs != []: + script = torch.jit.freeze(script, preserved_attrs=preserved_attrs) + else: + script = torch.jit.freeze(script) + script = torch.jit.optimize_for_inference(script) + return script + + +def main(): + args = get_args() + logging.basicConfig(level=logging.DEBUG, + format='%(asctime)s %(levelname)s %(message)s') + + torch._C._jit_set_fusion_strategy([('STATIC', 1)]) + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + + model = AutoModel(model_dir=args.model_dir) + + if get_model_type(model.model) == CosyVoiceModel: + # 1. export flow encoder + flow_encoder = model.model.flow.encoder + script = get_optimized_script(flow_encoder) + script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir)) + script = get_optimized_script(flow_encoder.half()) + script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir)) + logging.info('successfully export flow_encoder') + elif get_model_type(model.model) == CosyVoice2Model: + # 1. export llm text_encoder + llm_text_encoder = model.model.llm.text_encoder + script = get_optimized_script(llm_text_encoder) + script.save('{}/llm.text_encoder.fp32.zip'.format(args.model_dir)) + script = get_optimized_script(llm_text_encoder.half()) + script.save('{}/llm.text_encoder.fp16.zip'.format(args.model_dir)) + logging.info('successfully export llm_text_encoder') + + # 2. export llm llm + llm_llm = model.model.llm.llm + script = get_optimized_script(llm_llm, ['forward_chunk']) + script.save('{}/llm.llm.fp32.zip'.format(args.model_dir)) + script = get_optimized_script(llm_llm.half(), ['forward_chunk']) + script.save('{}/llm.llm.fp16.zip'.format(args.model_dir)) + logging.info('successfully export llm_llm') + + # 3. export flow encoder + flow_encoder = model.model.flow.encoder + script = get_optimized_script(flow_encoder) + script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir)) + script = get_optimized_script(flow_encoder.half()) + script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir)) + logging.info('successfully export flow_encoder') + else: + raise ValueError('unsupported model type') + + +if __name__ == '__main__': + main() diff --git a/cosyvoice/cosyvoice/bin/export_onnx.py b/cosyvoice/cosyvoice/bin/export_onnx.py new file mode 100644 index 0000000..58e7708 --- /dev/null +++ b/cosyvoice/cosyvoice/bin/export_onnx.py @@ -0,0 +1,114 @@ +# Copyright (c) 2024 Antgroup Inc (authors: Zhoubofan, hexisyztem@icloud.com) +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import argparse +import logging +logging.getLogger('matplotlib').setLevel(logging.WARNING) +import os +import sys +import onnxruntime +import random +import torch +from tqdm import tqdm +ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append('{}/../..'.format(ROOT_DIR)) +sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR)) +from cosyvoice.cli.cosyvoice import AutoModel +from cosyvoice.utils.file_utils import logging + + +def get_dummy_input(batch_size, seq_len, out_channels, device): + x = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device) + mask = torch.ones((batch_size, 1, seq_len), dtype=torch.float32, device=device) + mu = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device) + t = torch.rand((batch_size), dtype=torch.float32, device=device) + spks = torch.rand((batch_size, out_channels), dtype=torch.float32, device=device) + cond = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device) + return x, mask, mu, t, spks, cond + + +def get_args(): + parser = argparse.ArgumentParser(description='export your model for deployment') + parser.add_argument('--model_dir', + type=str, + default='pretrained_models/CosyVoice-300M', + help='local path') + args = parser.parse_args() + print(args) + return args + + +@torch.no_grad() +def main(): + args = get_args() + logging.basicConfig(level=logging.DEBUG, + format='%(asctime)s %(levelname)s %(message)s') + + model = AutoModel(model_dir=args.model_dir) + + # 1. export flow decoder estimator + estimator = model.model.flow.decoder.estimator + estimator.eval() + + device = model.model.device + batch_size, seq_len = 2, 256 + out_channels = model.model.flow.decoder.estimator.out_channels + x, mask, mu, t, spks, cond = get_dummy_input(batch_size, seq_len, out_channels, device) + torch.onnx.export( + estimator, + (x, mask, mu, t, spks, cond), + '{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir), + export_params=True, + opset_version=18, + do_constant_folding=True, + input_names=['x', 'mask', 'mu', 't', 'spks', 'cond'], + output_names=['estimator_out'], + dynamic_axes={ + 'x': {2: 'seq_len'}, + 'mask': {2: 'seq_len'}, + 'mu': {2: 'seq_len'}, + 'cond': {2: 'seq_len'}, + 'estimator_out': {2: 'seq_len'}, + } + ) + + # 2. test computation consistency + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider'] + estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir), + sess_options=option, providers=providers) + + for _ in tqdm(range(10)): + x, mask, mu, t, spks, cond = get_dummy_input(batch_size, random.randint(16, 512), out_channels, device) + output_pytorch = estimator(x, mask, mu, t, spks, cond) + ort_inputs = { + 'x': x.cpu().numpy(), + 'mask': mask.cpu().numpy(), + 'mu': mu.cpu().numpy(), + 't': t.cpu().numpy(), + 'spks': spks.cpu().numpy(), + 'cond': cond.cpu().numpy() + } + output_onnx = estimator_onnx.run(None, ort_inputs)[0] + torch.testing.assert_allclose(output_pytorch, torch.from_numpy(output_onnx).to(device), rtol=1e-2, atol=1e-4) + logging.info('successfully export estimator') + + +if __name__ == "__main__": + main() diff --git a/cosyvoice/cosyvoice/bin/train.py b/cosyvoice/cosyvoice/bin/train.py new file mode 100644 index 0000000..3e4016f --- /dev/null +++ b/cosyvoice/cosyvoice/bin/train.py @@ -0,0 +1,195 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function +import argparse +import datetime +import logging +logging.getLogger('matplotlib').setLevel(logging.WARNING) +from copy import deepcopy +import os +import torch +import torch.distributed as dist +import deepspeed + +from hyperpyyaml import load_hyperpyyaml + +from torch.distributed.elastic.multiprocessing.errors import record + +from cosyvoice.utils.losses import DPOLoss +from cosyvoice.utils.executor import Executor +from cosyvoice.utils.train_utils import ( + init_distributed, + init_dataset_and_dataloader, + init_optimizer_and_scheduler, + init_summarywriter, save_model, + wrap_cuda_model, check_modify_and_save_config) + + +def get_args(): + parser = argparse.ArgumentParser(description='training your network') + parser.add_argument('--train_engine', + default='torch_ddp', + choices=['torch_ddp', 'deepspeed'], + help='Engine for paralleled training') + parser.add_argument('--model', required=True, help='model which will be trained') + parser.add_argument('--ref_model', required=False, help='ref model used in dpo') + parser.add_argument('--config', required=True, help='config file') + parser.add_argument('--train_data', required=True, help='train data file') + parser.add_argument('--cv_data', required=True, help='cv data file') + parser.add_argument('--qwen_pretrain_path', required=False, help='qwen pretrain path') + parser.add_argument('--checkpoint', help='checkpoint model') + parser.add_argument('--model_dir', required=True, help='save model dir') + parser.add_argument('--tensorboard_dir', + default='tensorboard', + help='tensorboard log dir') + parser.add_argument('--ddp.dist_backend', + dest='dist_backend', + default='nccl', + choices=['nccl', 'gloo'], + help='distributed backend') + parser.add_argument('--num_workers', + default=0, + type=int, + help='num of subprocess workers for reading') + parser.add_argument('--prefetch', + default=100, + type=int, + help='prefetch number') + parser.add_argument('--pin_memory', + action='store_true', + default=False, + help='Use pinned memory buffers used for reading') + parser.add_argument('--use_amp', + action='store_true', + default=False, + help='Use automatic mixed precision training') + parser.add_argument('--dpo', + action='store_true', + default=False, + help='Use Direct Preference Optimization') + parser.add_argument('--deepspeed.save_states', + dest='save_states', + default='model_only', + choices=['model_only', 'model+optimizer'], + help='save model/optimizer states') + parser.add_argument('--timeout', + default=60, + type=int, + help='timeout (in seconds) of cosyvoice_join.') + parser = deepspeed.add_config_arguments(parser) + args = parser.parse_args() + return args + + +@record +def main(): + args = get_args() + logging.basicConfig(level=logging.DEBUG, + format='%(asctime)s %(levelname)s %(message)s') + # gan train has some special initialization logic + gan = True if args.model == 'hifigan' else False + + override_dict = {k: None for k in ['llm', 'flow', 'hift', 'hifigan'] if k != args.model} + if gan is True: + override_dict.pop('hift') + try: + with open(args.config, 'r') as f: + configs = load_hyperpyyaml(f, overrides={**override_dict, 'qwen_pretrain_path': args.qwen_pretrain_path}) + except Exception: + with open(args.config, 'r') as f: + configs = load_hyperpyyaml(f, overrides=override_dict) + if gan is True: + configs['train_conf'] = configs['train_conf_gan'] + configs['train_conf'].update(vars(args)) + + # Init env for ddp + init_distributed(args) + + # Get dataset & dataloader + train_dataset, cv_dataset, train_data_loader, cv_data_loader = \ + init_dataset_and_dataloader(args, configs, gan, args.dpo) + + # Do some sanity checks and save config to arsg.model_dir + configs = check_modify_and_save_config(args, configs) + + # Tensorboard summary + writer = init_summarywriter(args) + + # load checkpoint + if args.dpo is True: + configs[args.model].forward = configs[args.model].forward_dpo + model = configs[args.model] + start_step, start_epoch = 0, -1 + if args.checkpoint is not None: + if os.path.exists(args.checkpoint): + state_dict = torch.load(args.checkpoint, map_location='cpu') + model.load_state_dict(state_dict, strict=False) + if 'step' in state_dict: + start_step = state_dict['step'] + if 'epoch' in state_dict: + start_epoch = state_dict['epoch'] + else: + logging.warning('checkpoint {} do not exsist!'.format(args.checkpoint)) + + # Dispatch model from cpu to gpu + model = wrap_cuda_model(args, model) + + # Get optimizer & scheduler + model, optimizer, scheduler, optimizer_d, scheduler_d = init_optimizer_and_scheduler(args, configs, model, gan) + scheduler.set_step(start_step) + if scheduler_d is not None: + scheduler_d.set_step(start_step) + + # Save init checkpoints + info_dict = deepcopy(configs['train_conf']) + info_dict['step'] = start_step + info_dict['epoch'] = start_epoch + save_model(model, 'init', info_dict) + + # DPO related + if args.dpo is True: + ref_model = deepcopy(configs[args.model]) + state_dict = torch.load(args.ref_model, map_location='cpu') + ref_model.load_state_dict(state_dict, strict=False) + dpo_loss = DPOLoss(beta=0.01, label_smoothing=0.0, ipo=False) + # NOTE maybe it is not needed to wrap ref_model as ddp because its parameter is not updated + ref_model = wrap_cuda_model(args, ref_model) + else: + ref_model, dpo_loss = None, None + + # Get executor + executor = Executor(gan=gan, ref_model=ref_model, dpo_loss=dpo_loss) + executor.step = start_step + + # Init scaler, used for pytorch amp mixed precision training + scaler = torch.cuda.amp.GradScaler() if args.use_amp else None + print('start step {} start epoch {}'.format(start_step, start_epoch)) + + # Start training loop + for epoch in range(start_epoch + 1, info_dict['max_epoch']): + executor.epoch = epoch + train_dataset.set_epoch(epoch) + dist.barrier() + group_join = dist.new_group(backend="gloo", timeout=datetime.timedelta(seconds=args.timeout)) + if gan is True: + executor.train_one_epoc_gan(model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader, + writer, info_dict, scaler, group_join) + else: + executor.train_one_epoc(model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, scaler, group_join, ref_model=ref_model) + dist.destroy_process_group(group_join) + + +if __name__ == '__main__': + main() diff --git a/cosyvoice/cosyvoice/cli/__init__.py b/cosyvoice/cosyvoice/cli/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/cosyvoice/cosyvoice/cli/cosyvoice.py b/cosyvoice/cosyvoice/cli/cosyvoice.py new file mode 100644 index 0000000..316b5f1 --- /dev/null +++ b/cosyvoice/cosyvoice/cli/cosyvoice.py @@ -0,0 +1,238 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import time +from typing import Generator +from tqdm import tqdm +from hyperpyyaml import load_hyperpyyaml +from modelscope import snapshot_download +import torch +from cosyvoice.cli.frontend import CosyVoiceFrontEnd +from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model, CosyVoice3Model +from cosyvoice.utils.file_utils import logging +from cosyvoice.utils.class_utils import get_model_type + + +class CosyVoice: + + def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, trt_concurrent=1): + self.model_dir = model_dir + self.fp16 = fp16 + if not os.path.exists(model_dir): + model_dir = snapshot_download(model_dir) + hyper_yaml_path = '{}/cosyvoice.yaml'.format(model_dir) + if not os.path.exists(hyper_yaml_path): + raise ValueError('{} not found!'.format(hyper_yaml_path)) + with open(hyper_yaml_path, 'r') as f: + configs = load_hyperpyyaml(f) + assert get_model_type(configs) == CosyVoiceModel, 'do not use {} for CosyVoice initialization!'.format(model_dir) + self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'], + configs['feat_extractor'], + '{}/campplus.onnx'.format(model_dir), + '{}/speech_tokenizer_v1.onnx'.format(model_dir), + '{}/spk2info.pt'.format(model_dir), + configs['allowed_special']) + self.sample_rate = configs['sample_rate'] + if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True): + load_jit, load_trt, fp16 = False, False, False + logging.warning('no cuda device, set load_jit/load_trt/fp16 to False') + self.model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'], fp16) + self.model.load('{}/llm.pt'.format(model_dir), + '{}/flow.pt'.format(model_dir), + '{}/hift.pt'.format(model_dir)) + if load_jit: + self.model.load_jit('{}/llm.text_encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'), + '{}/llm.llm.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'), + '{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32')) + if load_trt: + self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'), + '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir), + trt_concurrent, + self.fp16) + del configs + + def list_available_spks(self): + spks = list(self.frontend.spk2info.keys()) + return spks + + def add_zero_shot_spk(self, prompt_text, prompt_wav, zero_shot_spk_id): + assert zero_shot_spk_id != '', 'do not use empty zero_shot_spk_id' + model_input = self.frontend.frontend_zero_shot('', prompt_text, prompt_wav, self.sample_rate, '') + del model_input['text'] + del model_input['text_len'] + self.frontend.spk2info[zero_shot_spk_id] = model_input + return True + + def save_spkinfo(self): + torch.save(self.frontend.spk2info, '{}/spk2info.pt'.format(self.model_dir)) + + def inference_sft(self, tts_text, spk_id, stream=False, speed=1.0, text_frontend=True): + for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)): + model_input = self.frontend.frontend_sft(i, spk_id) + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.tts(**model_input, stream=stream, speed=speed): + speech_len = model_output['tts_speech'].shape[1] / self.sample_rate + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() + + def inference_zero_shot(self, tts_text, prompt_text, prompt_wav, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True): + prompt_text = self.frontend.text_normalize(prompt_text, split=False, text_frontend=text_frontend) + for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)): + if (not isinstance(i, Generator)) and len(i) < 0.5 * len(prompt_text): + logging.warning('synthesis text {} too short than prompt text {}, this may lead to bad performance'.format(i, prompt_text)) + model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_wav, self.sample_rate, zero_shot_spk_id) + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.tts(**model_input, stream=stream, speed=speed): + speech_len = model_output['tts_speech'].shape[1] / self.sample_rate + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() + + def inference_cross_lingual(self, tts_text, prompt_wav, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True): + for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)): + model_input = self.frontend.frontend_cross_lingual(i, prompt_wav, self.sample_rate, zero_shot_spk_id) + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.tts(**model_input, stream=stream, speed=speed): + speech_len = model_output['tts_speech'].shape[1] / self.sample_rate + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() + + def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0, text_frontend=True): + assert isinstance(self.model, CosyVoiceModel), 'inference_instruct is only implemented for CosyVoice!' + instruct_text = self.frontend.text_normalize(instruct_text, split=False, text_frontend=text_frontend) + for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)): + model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text) + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.tts(**model_input, stream=stream, speed=speed): + speech_len = model_output['tts_speech'].shape[1] / self.sample_rate + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() + + def inference_vc(self, source_wav, prompt_wav, stream=False, speed=1.0): + model_input = self.frontend.frontend_vc(source_wav, prompt_wav, self.sample_rate) + start_time = time.time() + for model_output in self.model.tts(**model_input, stream=stream, speed=speed): + speech_len = model_output['tts_speech'].shape[1] / self.sample_rate + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() + + +class CosyVoice2(CosyVoice): + + def __init__(self, model_dir, load_jit=False, load_trt=False, load_vllm=False, fp16=False, trt_concurrent=1): + self.model_dir = model_dir + self.fp16 = fp16 + if not os.path.exists(model_dir): + model_dir = snapshot_download(model_dir) + hyper_yaml_path = '{}/cosyvoice2.yaml'.format(model_dir) + if not os.path.exists(hyper_yaml_path): + raise ValueError('{} not found!'.format(hyper_yaml_path)) + with open(hyper_yaml_path, 'r') as f: + configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')}) + assert get_model_type(configs) == CosyVoice2Model, 'do not use {} for CosyVoice2 initialization!'.format(model_dir) + self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'], + configs['feat_extractor'], + '{}/campplus.onnx'.format(model_dir), + '{}/speech_tokenizer_v2.onnx'.format(model_dir), + '{}/spk2info.pt'.format(model_dir), + configs['allowed_special']) + self.sample_rate = configs['sample_rate'] + if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or load_vllm is True or fp16 is True): + load_jit, load_trt, load_vllm, fp16 = False, False, False, False + logging.warning('no cuda device, set load_jit/load_trt/load_vllm/fp16 to False') + self.model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'], fp16) + self.model.load('{}/llm.pt'.format(model_dir), + '{}/flow.pt'.format(model_dir), + '{}/hift.pt'.format(model_dir)) + if load_vllm: + self.model.load_vllm('{}/vllm'.format(model_dir)) + if load_jit: + self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32')) + if load_trt: + self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'), + '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir), + trt_concurrent, + self.fp16) + del configs + + def inference_instruct2(self, tts_text, instruct_text, prompt_wav, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True): + for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)): + model_input = self.frontend.frontend_instruct2(i, instruct_text, prompt_wav, self.sample_rate, zero_shot_spk_id) + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.tts(**model_input, stream=stream, speed=speed): + speech_len = model_output['tts_speech'].shape[1] / self.sample_rate + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() + + +class CosyVoice3(CosyVoice2): + + def __init__(self, model_dir, load_trt=False, load_vllm=False, fp16=False, trt_concurrent=1): + self.model_dir = model_dir + self.fp16 = fp16 + if not os.path.exists(model_dir): + model_dir = snapshot_download(model_dir) + hyper_yaml_path = '{}/cosyvoice3.yaml'.format(model_dir) + if not os.path.exists(hyper_yaml_path): + raise ValueError('{} not found!'.format(hyper_yaml_path)) + with open(hyper_yaml_path, 'r') as f: + configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')}) + assert get_model_type(configs) == CosyVoice3Model, 'do not use {} for CosyVoice3 initialization!'.format(model_dir) + self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'], + configs['feat_extractor'], + '{}/campplus.onnx'.format(model_dir), + '{}/speech_tokenizer_v3.onnx'.format(model_dir), + '{}/spk2info.pt'.format(model_dir), + configs['allowed_special']) + self.sample_rate = configs['sample_rate'] + if torch.cuda.is_available() is False and (load_trt is True or fp16 is True): + load_trt, fp16 = False, False + logging.warning('no cuda device, set load_trt/fp16 to False') + self.model = CosyVoice3Model(configs['llm'], configs['flow'], configs['hift'], fp16) + self.model.load('{}/llm.pt'.format(model_dir), + '{}/flow.pt'.format(model_dir), + '{}/hift.pt'.format(model_dir)) + if load_vllm: + self.model.load_vllm('{}/vllm'.format(model_dir)) + if load_trt: + if self.fp16 is True: + logging.warning('DiT tensorRT fp16 engine have some performance issue, use at caution!') + self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'), + '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir), + trt_concurrent, + self.fp16) + del configs + + +def AutoModel(**kwargs): + if not os.path.exists(kwargs['model_dir']): + kwargs['model_dir'] = snapshot_download(kwargs['model_dir']) + if os.path.exists('{}/cosyvoice.yaml'.format(kwargs['model_dir'])): + return CosyVoice(**kwargs) + elif os.path.exists('{}/cosyvoice2.yaml'.format(kwargs['model_dir'])): + return CosyVoice2(**kwargs) + elif os.path.exists('{}/cosyvoice3.yaml'.format(kwargs['model_dir'])): + return CosyVoice3(**kwargs) + else: + raise TypeError('No valid model type found!') diff --git a/cosyvoice/cosyvoice/cli/frontend.py b/cosyvoice/cosyvoice/cli/frontend.py new file mode 100644 index 0000000..0942da6 --- /dev/null +++ b/cosyvoice/cosyvoice/cli/frontend.py @@ -0,0 +1,219 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from functools import partial +from typing import Generator +import json +import onnxruntime +import torch +import numpy as np +import whisper +from typing import Callable +import torchaudio.compliance.kaldi as kaldi +import torchaudio +import os +import re +import inflect +try: + import ttsfrd + use_ttsfrd = True +except ImportError: + print("failed to import ttsfrd, use wetext instead") + from wetext import Normalizer as ZhNormalizer + from wetext import Normalizer as EnNormalizer + use_ttsfrd = False +from cosyvoice.utils.file_utils import logging, load_wav +from cosyvoice.utils.frontend_utils import contains_chinese, replace_blank, replace_corner_mark, remove_bracket, spell_out_number, split_paragraph, is_only_punctuation + + +class CosyVoiceFrontEnd: + + def __init__(self, + get_tokenizer: Callable, + feat_extractor: Callable, + campplus_model: str, + speech_tokenizer_model: str, + spk2info: str = '', + allowed_special: str = 'all'): + self.tokenizer = get_tokenizer() + self.feat_extractor = feat_extractor + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + self.campplus_session = onnxruntime.InferenceSession(campplus_model, sess_options=option, providers=["CPUExecutionProvider"]) + self.speech_tokenizer_session = onnxruntime.InferenceSession(speech_tokenizer_model, sess_options=option, + providers=["CUDAExecutionProvider" if torch.cuda.is_available() else + "CPUExecutionProvider"]) + if os.path.exists(spk2info): + self.spk2info = torch.load(spk2info, map_location=self.device) + else: + self.spk2info = {} + self.allowed_special = allowed_special + self.use_ttsfrd = use_ttsfrd + if self.use_ttsfrd: + self.frd = ttsfrd.TtsFrontendEngine() + ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) + assert self.frd.initialize('{}/../../pretrained_models/CosyVoice-ttsfrd/resource'.format(ROOT_DIR)) is True, \ + 'failed to initialize ttsfrd resource' + self.frd.set_lang_type('pinyinvg') + else: + self.zh_tn_model = ZhNormalizer(remove_erhua=False) + self.en_tn_model = EnNormalizer() + self.inflect_parser = inflect.engine() + + def _extract_text_token(self, text): + if isinstance(text, Generator): + logging.info('get tts_text generator, will return _extract_text_token_generator!') + # NOTE add a dummy text_token_len for compatibility + return self._extract_text_token_generator(text), torch.tensor([0], dtype=torch.int32).to(self.device) + else: + text_token = self.tokenizer.encode(text, allowed_special=self.allowed_special) + text_token = torch.tensor([text_token], dtype=torch.int32).to(self.device) + text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.int32).to(self.device) + return text_token, text_token_len + + def _extract_text_token_generator(self, text_generator): + for text in text_generator: + text_token, _ = self._extract_text_token(text) + for i in range(text_token.shape[1]): + yield text_token[:, i: i + 1] + + def _extract_speech_token(self, prompt_wav): + speech = load_wav(prompt_wav, 16000) + assert speech.shape[1] / 16000 <= 30, 'do not support extract speech token for audio longer than 30s' + feat = whisper.log_mel_spectrogram(speech, n_mels=128) + speech_token = self.speech_tokenizer_session.run(None, + {self.speech_tokenizer_session.get_inputs()[0].name: + feat.detach().cpu().numpy(), + self.speech_tokenizer_session.get_inputs()[1].name: + np.array([feat.shape[2]], dtype=np.int32)})[0].flatten().tolist() + speech_token = torch.tensor([speech_token], dtype=torch.int32).to(self.device) + speech_token_len = torch.tensor([speech_token.shape[1]], dtype=torch.int32).to(self.device) + return speech_token, speech_token_len + + def _extract_spk_embedding(self, prompt_wav): + speech = load_wav(prompt_wav, 16000) + feat = kaldi.fbank(speech, + num_mel_bins=80, + dither=0, + sample_frequency=16000) + feat = feat - feat.mean(dim=0, keepdim=True) + embedding = self.campplus_session.run(None, + {self.campplus_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist() + embedding = torch.tensor([embedding]).to(self.device) + return embedding + + def _extract_speech_feat(self, prompt_wav): + speech = load_wav(prompt_wav, 24000) + speech_feat = self.feat_extractor(speech).squeeze(dim=0).transpose(0, 1).to(self.device) + speech_feat = speech_feat.unsqueeze(dim=0) + speech_feat_len = torch.tensor([speech_feat.shape[1]], dtype=torch.int32).to(self.device) + return speech_feat, speech_feat_len + + def text_normalize(self, text, split=True, text_frontend=True): + if isinstance(text, Generator): + logging.info('get tts_text generator, will skip text_normalize!') + return [text] + # NOTE skip text_frontend when ssml symbol in text + if '<|' in text and '|>' in text: + text_frontend = False + if text_frontend is False or text == '': + return [text] if split is True else text + text = text.strip() + if self.use_ttsfrd: + texts = [i["text"] for i in json.loads(self.frd.do_voicegen_frd(text))["sentences"]] + text = ''.join(texts) + else: + if contains_chinese(text): + text = self.zh_tn_model.normalize(text) + text = text.replace("\n", "") + text = replace_blank(text) + text = replace_corner_mark(text) + text = text.replace(".", "。") + text = text.replace(" - ", "，") + text = remove_bracket(text) + text = re.sub(r'[，,、]+$', '。', text) + texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "zh", token_max_n=80, + token_min_n=60, merge_len=20, comma_split=False)) + else: + text = self.en_tn_model.normalize(text) + text = spell_out_number(text, self.inflect_parser) + texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "en", token_max_n=80, + token_min_n=60, merge_len=20, comma_split=False)) + texts = [i for i in texts if not is_only_punctuation(i)] + return texts if split is True else text + + def frontend_sft(self, tts_text, spk_id): + tts_text_token, tts_text_token_len = self._extract_text_token(tts_text) + embedding = self.spk2info[spk_id]['embedding'] + model_input = {'text': tts_text_token, 'text_len': tts_text_token_len, 'llm_embedding': embedding, 'flow_embedding': embedding} + return model_input + + def frontend_zero_shot(self, tts_text, prompt_text, prompt_wav, resample_rate, zero_shot_spk_id): + tts_text_token, tts_text_token_len = self._extract_text_token(tts_text) + if zero_shot_spk_id == '': + prompt_text_token, prompt_text_token_len = self._extract_text_token(prompt_text) + speech_feat, speech_feat_len = self._extract_speech_feat(prompt_wav) + speech_token, speech_token_len = self._extract_speech_token(prompt_wav) + if resample_rate == 24000: + # cosyvoice2, force speech_feat % speech_token = 2 + token_len = min(int(speech_feat.shape[1] / 2), speech_token.shape[1]) + speech_feat, speech_feat_len[:] = speech_feat[:, :2 * token_len], 2 * token_len + speech_token, speech_token_len[:] = speech_token[:, :token_len], token_len + embedding = self._extract_spk_embedding(prompt_wav) + model_input = {'prompt_text': prompt_text_token, 'prompt_text_len': prompt_text_token_len, + 'llm_prompt_speech_token': speech_token, 'llm_prompt_speech_token_len': speech_token_len, + 'flow_prompt_speech_token': speech_token, 'flow_prompt_speech_token_len': speech_token_len, + 'prompt_speech_feat': speech_feat, 'prompt_speech_feat_len': speech_feat_len, + 'llm_embedding': embedding, 'flow_embedding': embedding} + else: + model_input = self.spk2info[zero_shot_spk_id] + model_input['text'] = tts_text_token + model_input['text_len'] = tts_text_token_len + return model_input + + def frontend_cross_lingual(self, tts_text, prompt_wav, resample_rate, zero_shot_spk_id): + model_input = self.frontend_zero_shot(tts_text, '', prompt_wav, resample_rate, zero_shot_spk_id) + # in cross lingual mode, we remove prompt in llm + del model_input['prompt_text'] + del model_input['prompt_text_len'] + del model_input['llm_prompt_speech_token'] + del model_input['llm_prompt_speech_token_len'] + return model_input + + def frontend_instruct(self, tts_text, spk_id, instruct_text): + model_input = self.frontend_sft(tts_text, spk_id) + # in instruct mode, we remove spk_embedding in llm due to information leakage + del model_input['llm_embedding'] + instruct_text_token, instruct_text_token_len = self._extract_text_token(instruct_text) + model_input['prompt_text'] = instruct_text_token + model_input['prompt_text_len'] = instruct_text_token_len + return model_input + + def frontend_instruct2(self, tts_text, instruct_text, prompt_wav, resample_rate, zero_shot_spk_id): + model_input = self.frontend_zero_shot(tts_text, instruct_text, prompt_wav, resample_rate, zero_shot_spk_id) + del model_input['llm_prompt_speech_token'] + del model_input['llm_prompt_speech_token_len'] + return model_input + + def frontend_vc(self, source_speech_16k, prompt_wav, resample_rate): + prompt_speech_token, prompt_speech_token_len = self._extract_speech_token(prompt_wav) + prompt_speech_feat, prompt_speech_feat_len = self._extract_speech_feat(prompt_wav) + embedding = self._extract_spk_embedding(prompt_wav) + source_speech_token, source_speech_token_len = self._extract_speech_token(source_speech_16k) + model_input = {'source_speech_token': source_speech_token, 'source_speech_token_len': source_speech_token_len, + 'flow_prompt_speech_token': prompt_speech_token, 'flow_prompt_speech_token_len': prompt_speech_token_len, + 'prompt_speech_feat': prompt_speech_feat, 'prompt_speech_feat_len': prompt_speech_feat_len, + 'flow_embedding': embedding} + return model_input diff --git a/cosyvoice/cosyvoice/cli/model.py b/cosyvoice/cosyvoice/cli/model.py new file mode 100644 index 0000000..8e67b0c --- /dev/null +++ b/cosyvoice/cosyvoice/cli/model.py @@ -0,0 +1,430 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# 2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +from typing import Generator +import torch +import numpy as np +import threading +import time +from torch.nn import functional as F +from contextlib import nullcontext +import uuid +from cosyvoice.utils.common import fade_in_out +from cosyvoice.utils.file_utils import convert_onnx_to_trt, export_cosyvoice2_vllm +from cosyvoice.utils.common import TrtContextWrapper + + +class CosyVoiceModel: + + def __init__(self, + llm: torch.nn.Module, + flow: torch.nn.Module, + hift: torch.nn.Module, + fp16: bool = False): + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.llm = llm + self.flow = flow + self.hift = hift + self.fp16 = fp16 + self.token_min_hop_len = 2 * self.flow.input_frame_rate + self.token_max_hop_len = 4 * self.flow.input_frame_rate + self.token_overlap_len = 20 + # mel fade in out + self.mel_overlap_len = int(self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256) + self.mel_window = np.hamming(2 * self.mel_overlap_len) + # hift cache + self.mel_cache_len = 20 + self.source_cache_len = int(self.mel_cache_len * 256) + # speech fade in out + self.speech_window = np.hamming(2 * self.source_cache_len) + # rtf and decoding related + self.stream_scale_factor = 1 + assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf' + self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext() + self.lock = threading.Lock() + # dict used to store session related variable + self.tts_speech_token_dict = {} + self.llm_end_dict = {} + self.mel_overlap_dict = {} + self.flow_cache_dict = {} + self.hift_cache_dict = {} + + def load(self, llm_model, flow_model, hift_model): + self.llm.load_state_dict(torch.load(llm_model, map_location=self.device), strict=True) + self.llm.to(self.device).eval() + self.flow.load_state_dict(torch.load(flow_model, map_location=self.device), strict=True) + self.flow.to(self.device).eval() + # in case hift_model is a hifigan model + hift_state_dict = {k.replace('generator.', ''): v for k, v in torch.load(hift_model, map_location=self.device).items()} + self.hift.load_state_dict(hift_state_dict, strict=True) + self.hift.to(self.device).eval() + + def load_jit(self, llm_text_encoder_model, llm_llm_model, flow_encoder_model): + llm_text_encoder = torch.jit.load(llm_text_encoder_model, map_location=self.device) + self.llm.text_encoder = llm_text_encoder + llm_llm = torch.jit.load(llm_llm_model, map_location=self.device) + self.llm.llm = llm_llm + flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device) + self.flow.encoder = flow_encoder + + def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, trt_concurrent, fp16): + assert torch.cuda.is_available(), 'tensorrt only supports gpu!' + if not os.path.exists(flow_decoder_estimator_model) or os.path.getsize(flow_decoder_estimator_model) == 0: + convert_onnx_to_trt(flow_decoder_estimator_model, self.get_trt_kwargs(), flow_decoder_onnx_model, fp16) + del self.flow.decoder.estimator + import tensorrt as trt + with open(flow_decoder_estimator_model, 'rb') as f: + estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read()) + assert estimator_engine is not None, 'failed to load trt {}'.format(flow_decoder_estimator_model) + self.flow.decoder.estimator = TrtContextWrapper(estimator_engine, trt_concurrent=trt_concurrent, device=self.device) + + def get_trt_kwargs(self): + min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4)] + opt_shape = [(2, 80, 500), (2, 1, 500), (2, 80, 500), (2, 80, 500)] + max_shape = [(2, 80, 3000), (2, 1, 3000), (2, 80, 3000), (2, 80, 3000)] + input_names = ["x", "mask", "mu", "cond"] + return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names} + + def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid): + with self.llm_context, torch.cuda.amp.autocast(self.fp16 is True and hasattr(self.llm, 'vllm') is False): + if isinstance(text, Generator): + assert isinstance(self, CosyVoice2Model) and not hasattr(self.llm, 'vllm'), 'streaming input text is only implemented for CosyVoice2 and do not support vllm!' + for i in self.llm.inference_bistream(text=text, + prompt_text=prompt_text.to(self.device), + prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device), + prompt_speech_token=llm_prompt_speech_token.to(self.device), + prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device), + embedding=llm_embedding.to(self.device)): + self.tts_speech_token_dict[uuid].append(i) + else: + for i in self.llm.inference(text=text.to(self.device), + text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device), + prompt_text=prompt_text.to(self.device), + prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device), + prompt_speech_token=llm_prompt_speech_token.to(self.device), + prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device), + embedding=llm_embedding.to(self.device), + uuid=uuid): + self.tts_speech_token_dict[uuid].append(i) + self.llm_end_dict[uuid] = True + + def vc_job(self, source_speech_token, uuid): + self.tts_speech_token_dict[uuid] = source_speech_token.flatten().tolist() + self.llm_end_dict[uuid] = True + + def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0): + with torch.cuda.amp.autocast(self.fp16): + tts_mel, self.flow_cache_dict[uuid] = self.flow.inference(token=token.to(self.device, dtype=torch.int32), + token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), + prompt_token=prompt_token.to(self.device), + prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), + prompt_feat=prompt_feat.to(self.device), + prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device), + embedding=embedding.to(self.device), + flow_cache=self.flow_cache_dict[uuid]) + + # mel overlap fade in out + if self.mel_overlap_dict[uuid].shape[2] != 0: + tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window) + # append hift cache + if self.hift_cache_dict[uuid] is not None: + hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source'] + tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2) + else: + hift_cache_source = torch.zeros(1, 1, 0) + # keep overlap mel and hift cache + if finalize is False: + self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:] + tts_mel = tts_mel[:, :, :-self.mel_overlap_len] + tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) + if self.hift_cache_dict[uuid] is not None: + tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) + self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:], + 'source': tts_source[:, :, -self.source_cache_len:], + 'speech': tts_speech[:, -self.source_cache_len:]} + tts_speech = tts_speech[:, :-self.source_cache_len] + else: + if speed != 1.0: + assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode' + tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear') + tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) + if self.hift_cache_dict[uuid] is not None: + tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) + return tts_speech + + def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192), + prompt_text=torch.zeros(1, 0, dtype=torch.int32), + llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), + flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), + prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs): + # this_uuid is used to track variables related to this inference thread + this_uuid = str(uuid.uuid1()) + with self.lock: + self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False + self.hift_cache_dict[this_uuid] = None + self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0) + self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2) + if source_speech_token.shape[1] == 0: + p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid)) + else: + p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid)) + p.start() + if stream is True: + token_hop_len = self.token_min_hop_len + while True: + time.sleep(0.1) + if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len: + this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_hop_len + self.token_overlap_len]) \ + .unsqueeze(dim=0) + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + uuid=this_uuid, + finalize=False) + yield {'tts_speech': this_tts_speech.cpu()} + with self.lock: + self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:] + # increase token_hop_len for better speech quality + token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor)) + if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len: + break + p.join() + # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None + this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0) + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + uuid=this_uuid, + finalize=True) + yield {'tts_speech': this_tts_speech.cpu()} + else: + # deal with all tokens + p.join() + this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0) + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + uuid=this_uuid, + finalize=True, + speed=speed) + yield {'tts_speech': this_tts_speech.cpu()} + with self.lock: + self.tts_speech_token_dict.pop(this_uuid) + self.llm_end_dict.pop(this_uuid) + self.mel_overlap_dict.pop(this_uuid) + self.hift_cache_dict.pop(this_uuid) + self.flow_cache_dict.pop(this_uuid) + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.current_stream().synchronize() + + +class CosyVoice2Model(CosyVoiceModel): + + def __init__(self, + llm: torch.nn.Module, + flow: torch.nn.Module, + hift: torch.nn.Module, + fp16: bool = False): + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.llm = llm + self.flow = flow + self.hift = hift + self.fp16 = fp16 + # NOTE must matching training static_chunk_size + self.token_hop_len = 25 + # hift cache + self.mel_cache_len = 8 + self.source_cache_len = int(self.mel_cache_len * 480) + # speech fade in out + self.speech_window = np.hamming(2 * self.source_cache_len) + # rtf and decoding related + self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext() + self.lock = threading.Lock() + # dict used to store session related variable + self.tts_speech_token_dict = {} + self.llm_end_dict = {} + self.hift_cache_dict = {} + + def load_jit(self, flow_encoder_model): + flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device) + self.flow.encoder = flow_encoder + + def load_vllm(self, model_dir): + export_cosyvoice2_vllm(self.llm, model_dir, self.device) + from vllm import EngineArgs, LLMEngine + engine_args = EngineArgs(model=model_dir, + skip_tokenizer_init=True, + enable_prompt_embeds=True, + gpu_memory_utilization=0.2) + self.llm.vllm = LLMEngine.from_engine_args(engine_args) + self.llm.lock = threading.Lock() + del self.llm.llm.model.model.layers + + def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0): + with torch.cuda.amp.autocast(self.fp16): + tts_mel, _ = self.flow.inference(token=token.to(self.device, dtype=torch.int32), + token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), + prompt_token=prompt_token.to(self.device), + prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), + prompt_feat=prompt_feat.to(self.device), + prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device), + embedding=embedding.to(self.device), + streaming=stream, + finalize=finalize) + tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:] + # append hift cache + if self.hift_cache_dict[uuid] is not None: + hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source'] + tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2) + else: + hift_cache_source = torch.zeros(1, 1, 0) + # keep overlap mel and hift cache + if finalize is False: + tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) + if self.hift_cache_dict[uuid] is not None: + tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) + self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:], + 'source': tts_source[:, :, -self.source_cache_len:], + 'speech': tts_speech[:, -self.source_cache_len:]} + tts_speech = tts_speech[:, :-self.source_cache_len] + else: + if speed != 1.0: + assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode' + tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear') + tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) + if self.hift_cache_dict[uuid] is not None: + tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) + return tts_speech + + def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192), + prompt_text=torch.zeros(1, 0, dtype=torch.int32), + llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), + flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), + prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs): + # this_uuid is used to track variables related to this inference thread + this_uuid = str(uuid.uuid1()) + with self.lock: + self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False + self.hift_cache_dict[this_uuid] = None + if source_speech_token.shape[1] == 0: + p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid)) + else: + p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid)) + p.start() + if stream is True: + token_offset = 0 + prompt_token_pad = int(np.ceil(flow_prompt_speech_token.shape[1] / self.token_hop_len) * self.token_hop_len - flow_prompt_speech_token.shape[1]) + while True: + time.sleep(0.1) + this_token_hop_len = self.token_hop_len + prompt_token_pad if token_offset == 0 else self.token_hop_len + if len(self.tts_speech_token_dict[this_uuid]) - token_offset >= this_token_hop_len + self.flow.pre_lookahead_len: + this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_offset + this_token_hop_len + self.flow.pre_lookahead_len]).unsqueeze(dim=0) + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + token_offset=token_offset, + uuid=this_uuid, + stream=stream, + finalize=False) + token_offset += this_token_hop_len + yield {'tts_speech': this_tts_speech.cpu()} + if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) - token_offset < this_token_hop_len + self.flow.pre_lookahead_len: + break + p.join() + # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None + this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0) + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + token_offset=token_offset, + uuid=this_uuid, + finalize=True) + yield {'tts_speech': this_tts_speech.cpu()} + else: + # deal with all tokens + p.join() + this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0) + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + token_offset=0, + uuid=this_uuid, + finalize=True, + speed=speed) + yield {'tts_speech': this_tts_speech.cpu()} + with self.lock: + self.tts_speech_token_dict.pop(this_uuid) + self.llm_end_dict.pop(this_uuid) + self.hift_cache_dict.pop(this_uuid) + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.current_stream().synchronize() + + +class CosyVoice3Model(CosyVoice2Model): + + def __init__(self, + llm: torch.nn.Module, + flow: torch.nn.Module, + hift: torch.nn.Module, + fp16: bool = False): + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.llm = llm + self.flow = flow + self.hift = hift + self.fp16 = fp16 + # NOTE must matching training static_chunk_size + self.token_hop_len = 25 + # rtf and decoding related + self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext() + self.lock = threading.Lock() + # dict used to store session related variable + self.tts_speech_token_dict = {} + self.llm_end_dict = {} + self.hift_cache_dict = {} + + def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0): + with torch.cuda.amp.autocast(self.fp16): + tts_mel, _ = self.flow.inference(token=token.to(self.device, dtype=torch.int32), + token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), + prompt_token=prompt_token.to(self.device), + prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), + prompt_feat=prompt_feat.to(self.device), + prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device), + embedding=embedding.to(self.device), + streaming=stream, + finalize=finalize) + tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:] + # append mel cache + if self.hift_cache_dict[uuid] is not None: + hift_cache_mel = self.hift_cache_dict[uuid]['mel'] + tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2) + self.hift_cache_dict[uuid]['mel'] = tts_mel + else: + self.hift_cache_dict[uuid] = {'mel': tts_mel, 'speech_offset': 0} + if speed != 1.0: + assert token_offset == 0 and finalize is True, 'speed change only support non-stream inference mode' + tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear') + tts_speech, _ = self.hift.inference(speech_feat=tts_mel, finalize=finalize) + tts_speech = tts_speech[:, self.hift_cache_dict[uuid]['speech_offset']:] + self.hift_cache_dict[uuid]['speech_offset'] += tts_speech.shape[1] + return tts_speech diff --git a/cosyvoice/cosyvoice/dataset/__init__.py b/cosyvoice/cosyvoice/dataset/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/cosyvoice/cosyvoice/dataset/dataset.py b/cosyvoice/cosyvoice/dataset/dataset.py new file mode 100644 index 0000000..6b54184 --- /dev/null +++ b/cosyvoice/cosyvoice/dataset/dataset.py @@ -0,0 +1,151 @@ +# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import random +import math +from functools import partial + +import torch +import torch.distributed as dist +from torch.utils.data import IterableDataset +from cosyvoice.utils.file_utils import read_lists + + +class Processor(IterableDataset): + + def __init__(self, source, f, *args, **kw): + assert callable(f) + self.source = source + self.f = f + self.args = args + self.kw = kw + + def set_epoch(self, epoch): + self.source.set_epoch(epoch) + + def __iter__(self): + """ Return an iterator over the source dataset processed by the + given processor. + """ + assert self.source is not None + assert callable(self.f) + return self.f(iter(self.source), *self.args, **self.kw) + + def apply(self, f): + assert callable(f) + return Processor(self, f, *self.args, **self.kw) + + +class DistributedSampler: + + def __init__(self, shuffle=True, partition=True): + self.epoch = -1 + self.update() + self.shuffle = shuffle + self.partition = partition + + def update(self): + assert dist.is_available() + if dist.is_initialized(): + self.rank = dist.get_rank() + self.world_size = dist.get_world_size() + else: + self.rank = 0 + self.world_size = 1 + worker_info = torch.utils.data.get_worker_info() + if worker_info is None: + self.worker_id = 0 + self.num_workers = 1 + else: + self.worker_id = worker_info.id + self.num_workers = worker_info.num_workers + return dict(rank=self.rank, + world_size=self.world_size, + worker_id=self.worker_id, + num_workers=self.num_workers) + + def set_epoch(self, epoch): + self.epoch = epoch + + def sample(self, data): + """ Sample data according to rank/world_size/num_workers + + Args: + data(List): input data list + + Returns: + List: data list after sample + """ + data = list(range(len(data))) + # force datalist even + if self.partition: + if self.shuffle: + random.Random(self.epoch).shuffle(data) + if len(data) < self.world_size: + data = data * math.ceil(self.world_size / len(data)) + data = data[:self.world_size] + data = data[self.rank::self.world_size] + if len(data) < self.num_workers: + data = data * math.ceil(self.num_workers / len(data)) + data = data[:self.num_workers] + data = data[self.worker_id::self.num_workers] + return data + + +class DataList(IterableDataset): + + def __init__(self, lists, shuffle=True, partition=True): + self.lists = lists + self.sampler = DistributedSampler(shuffle, partition) + + def set_epoch(self, epoch): + self.sampler.set_epoch(epoch) + + def __iter__(self): + sampler_info = self.sampler.update() + indexes = self.sampler.sample(self.lists) + for index in indexes: + data = dict(src=self.lists[index]) + data.update(sampler_info) + yield data + + +def Dataset(data_list_file, + data_pipeline, + mode='train', + gan=False, + dpo=False, + shuffle=True, + partition=True): + """ Construct dataset from arguments + + We have two shuffle stage in the Dataset. The first is global + shuffle at shards tar/raw file level. The second is global shuffle + at training samples level. + + Args: + data_type(str): raw/shard + tokenizer (BaseTokenizer): tokenizer to tokenize + partition(bool): whether to do data partition in terms of rank + """ + lists = read_lists(data_list_file) + dataset = DataList(lists, + shuffle=shuffle, + partition=partition) + # map partial arg to padding func + data_pipeline[-1] = partial(data_pipeline[-1], gan=gan, dpo=dpo) + for func in data_pipeline: + dataset = Processor(dataset, func, mode=mode) + return dataset diff --git a/cosyvoice/cosyvoice/dataset/processor.py b/cosyvoice/cosyvoice/dataset/processor.py new file mode 100644 index 0000000..f186ed2 --- /dev/null +++ b/cosyvoice/cosyvoice/dataset/processor.py @@ -0,0 +1,443 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +import random + +import pyarrow.parquet as pq +from io import BytesIO +import torch +import torchaudio +from torch.nn.utils.rnn import pad_sequence +import torch.nn.functional as F +import pyworld as pw + + +AUDIO_FORMAT_SETS = {'flac', 'mp3', 'm4a', 'ogg', 'opus', 'wav', 'wma'} + + +def parquet_opener(data, mode='train', tts_data={}): + """ Give url or local file, return file descriptor + Inplace operation. + + Args: + data(Iterable[str]): url or local file list + + Returns: + Iterable[{src, stream}] + """ + for sample in data: + assert 'src' in sample + url = sample['src'] + try: + for df in pq.ParquetFile(url).iter_batches(batch_size=64): + df = df.to_pandas() + for i in range(len(df)): + sample.update(dict(df.loc[i])) + if mode == 'train': + # NOTE do not return sample directly, must initialize a new dict + yield {**sample} + else: + for index, text in enumerate(tts_data[df.loc[i, 'utt']]): + yield {**sample, 'tts_index': index, 'tts_text': text} + except Exception as ex: + logging.warning('Failed to open {}, ex info {}'.format(url, ex)) + + +def filter(data, + max_length=10240, + min_length=10, + token_max_length=200, + token_min_length=1, + min_output_input_ratio=0.0005, + max_output_input_ratio=1, + mode='train'): + """ Filter sample according to feature and label length + Inplace operation. + + Args:: + data: Iterable[{key, wav, label, sample_rate}] + max_length: drop utterance which is greater than max_length(10ms) + min_length: drop utterance which is less than min_length(10ms) + token_max_length: drop utterance which is greater than + token_max_length, especially when use char unit for + english modeling + token_min_length: drop utterance which is + less than token_max_length + min_output_input_ratio: minimal ration of + token_length / feats_length(10ms) + max_output_input_ratio: maximum ration of + token_length / feats_length(10ms) + + Returns: + Iterable[{key, wav, label, sample_rate}] + """ + for sample in data: + sample['speech'], sample['sample_rate'] = torchaudio.load(BytesIO(sample['audio_data'])) + sample['speech'] = sample['speech'].mean(dim=0, keepdim=True) + del sample['audio_data'] + # sample['wav'] is torch.Tensor, we have 100 frames every second + num_frames = sample['speech'].size(1) / sample['sample_rate'] * 100 + if num_frames < min_length: + continue + if num_frames > max_length: + continue + if len(sample['text_token']) < token_min_length: + continue + if len(sample['text_token']) > token_max_length: + continue + if len(sample['speech_token']) == 0: + continue + if 'reject_speech_token' in sample and len(sample['reject_speech_token']) == 0: + continue + if num_frames != 0: + if len(sample['text_token']) / num_frames < min_output_input_ratio: + continue + if len(sample['text_token']) / num_frames > max_output_input_ratio: + continue + yield sample + + +def resample(data, resample_rate=22050, min_sample_rate=16000, mode='train'): + """ Resample data. + Inplace operation. + + Args: + data: Iterable[{key, wav, label, sample_rate}] + resample_rate: target resample rate + + Returns: + Iterable[{key, wav, label, sample_rate}] + """ + for sample in data: + assert 'sample_rate' in sample + assert 'speech' in sample + sample_rate = sample['sample_rate'] + waveform = sample['speech'] + if sample_rate != resample_rate: + if sample_rate < min_sample_rate: + continue + sample['sample_rate'] = resample_rate + sample['speech'] = torchaudio.transforms.Resample( + orig_freq=sample_rate, new_freq=resample_rate)(waveform) + max_val = sample['speech'].abs().max() + if max_val > 1: + sample['speech'] /= max_val + yield sample + + +def truncate(data, truncate_length=24576, mode='train'): + """ Truncate data. + + Args: + data: Iterable[{key, wav, label, sample_rate}] + truncate_length: truncate length + + Returns: + Iterable[{key, wav, label, sample_rate}] + """ + for sample in data: + waveform = sample['speech'] + if waveform.shape[1] > truncate_length: + start = random.randint(0, waveform.shape[1] - truncate_length) + waveform = waveform[:, start: start + truncate_length] + else: + waveform = torch.concat([waveform, torch.zeros(1, truncate_length - waveform.shape[1])], dim=1) + sample['speech'] = waveform + yield sample + + +def compute_fbank(data, + feat_extractor, + token_mel_ratio=0, + mode='train'): + """ Extract fbank + + Args: + data: Iterable[{key, wav, label, sample_rate}] + + Returns: + Iterable[{key, feat, label}] + """ + for sample in data: + assert 'sample_rate' in sample + assert 'speech' in sample + assert 'utt' in sample + assert 'text_token' in sample + waveform = sample['speech'] + feat = feat_extractor(waveform).squeeze(dim=0).transpose(0, 1) + if token_mel_ratio != 0: + # trim to align speech_token and speech_feat + token_len = int(min(feat.shape[0] / token_mel_ratio, sample["speech_token"].shape[0])) + feat = feat[:token_mel_ratio * token_len] + sample["speech_token"] = sample["speech_token"][:token_len] + sample['speech_feat'] = feat + yield sample + + +def compute_f0(data, sample_rate, hop_size, mode='train'): + """ Extract f0 + + Args: + data: Iterable[{key, wav, label, sample_rate}] + + Returns: + Iterable[{key, feat, label}] + """ + frame_period = hop_size * 1000 / sample_rate + for sample in data: + assert 'sample_rate' in sample + assert 'speech' in sample + assert 'utt' in sample + assert 'text_token' in sample + waveform = sample['speech'] + _f0, t = pw.harvest(waveform.squeeze(dim=0).numpy().astype('double'), sample_rate, frame_period=frame_period) + if sum(_f0 != 0) < 5: # this happens when the algorithm fails + _f0, t = pw.dio(waveform.squeeze(dim=0).numpy().astype('double'), sample_rate, frame_period=frame_period) # if harvest fails, try dio + f0 = pw.stonemask(waveform.squeeze(dim=0).numpy().astype('double'), _f0, t, sample_rate) + f0 = F.interpolate(torch.from_numpy(f0).view(1, 1, -1), size=sample['speech_feat'].shape[0], mode='linear').view(-1) + sample['pitch_feat'] = f0 + yield sample + + +def parse_embedding(data, normalize, mode='train'): + """ Parse utt_embedding/spk_embedding + + Args: + data: Iterable[{key, wav, label, sample_rate}] + + Returns: + Iterable[{key, feat, label}] + """ + for sample in data: + sample['utt_embedding'] = torch.tensor(sample['utt_embedding'], dtype=torch.float32) + sample['spk_embedding'] = torch.tensor(sample['spk_embedding'], dtype=torch.float32) + if normalize: + sample['utt_embedding'] = F.normalize(sample['utt_embedding'], dim=0) + sample['spk_embedding'] = F.normalize(sample['spk_embedding'], dim=0) + yield sample + + +def tokenize(data, get_tokenizer, allowed_special, mode='train'): + """ Decode text to chars or BPE + Inplace operation + + Args: + data: Iterable[{key, wav, txt, sample_rate}] + + Returns: + Iterable[{key, wav, txt, tokens, label, sample_rate}] + """ + tokenizer = get_tokenizer() + for sample in data: + assert 'text' in sample + sample['text_token'] = tokenizer.encode(sample['text'], allowed_special=allowed_special) + if 'instruct' in sample: + sample['instruct_token'] = tokenizer.encode(sample['instruct'], allowed_special=allowed_special) + else: + sample['instruct_token'] = tokenizer.encode('', allowed_special=allowed_special) + yield sample + + +def shuffle(data, shuffle_size=10000, mode='train'): + """ Local shuffle the data + + Args: + data: Iterable[{key, feat, label}] + shuffle_size: buffer size for shuffle + + Returns: + Iterable[{key, feat, label}] + """ + buf = [] + for sample in data: + buf.append(sample) + if len(buf) >= shuffle_size: + random.shuffle(buf) + for x in buf: + yield x + buf = [] + # The sample left over + random.shuffle(buf) + for x in buf: + yield x + + +def sort(data, sort_size=500, mode='train'): + """ Sort the data by feature length. + Sort is used after shuffle and before batch, so we can group + utts with similar lengths into a batch, and `sort_size` should + be less than `shuffle_size` + + Args: + data: Iterable[{key, feat, label}] + sort_size: buffer size for sort + + Returns: + Iterable[{key, feat, label}] + """ + + buf = [] + for sample in data: + buf.append(sample) + if len(buf) >= sort_size: + buf.sort(key=lambda x: x['speech_feat'].size(0)) + for x in buf: + yield x + buf = [] + # The sample left over + buf.sort(key=lambda x: x['speech_feat'].size(0)) + for x in buf: + yield x + + +def static_batch(data, batch_size=16): + """ Static batch the data by `batch_size` + + Args: + data: Iterable[{key, feat, label}] + batch_size: batch size + + Returns: + Iterable[List[{key, feat, label}]] + """ + buf = [] + for sample in data: + buf.append(sample) + if len(buf) >= batch_size: + yield buf + buf = [] + if len(buf) > 0: + yield buf + + +def dynamic_batch(data, max_frames_in_batch=12000, mode='train'): + """ Dynamic batch the data until the total frames in batch + reach `max_frames_in_batch` + + Args: + data: Iterable[{key, feat, label}] + max_frames_in_batch: max_frames in one batch + + Returns: + Iterable[List[{key, feat, label}]] + """ + buf = [] + longest_frames = 0 + for sample in data: + assert 'speech_feat' in sample + assert isinstance(sample['speech_feat'], torch.Tensor) + new_sample_frames = sample['speech_feat'].size(0) + longest_frames = max(longest_frames, new_sample_frames) + frames_after_padding = longest_frames * (len(buf) + 1) + if frames_after_padding > max_frames_in_batch: + yield buf + buf = [sample] + longest_frames = new_sample_frames + else: + buf.append(sample) + if len(buf) > 0: + yield buf + + +def batch(data, batch_type='static', batch_size=16, max_frames_in_batch=12000, mode='train'): + """ Wrapper for static/dynamic batch + """ + if batch_type == 'static': + return static_batch(data, batch_size) + elif batch_type == 'dynamic': + return dynamic_batch(data, max_frames_in_batch) + else: + logging.fatal('Unsupported batch type {}'.format(batch_type)) + + +def padding(data, use_spk_embedding, mode='train', gan=False, dpo=False): + """ Padding the data into training data + + Args: + data: Iterable[List[{key, feat, label}]] + + Returns: + Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)] + """ + for sample in data: + assert isinstance(sample, list) + speech_feat_len = torch.tensor([x['speech_feat'].size(1) for x in sample], + dtype=torch.int32) + order = torch.argsort(speech_feat_len, descending=True) + + utts = [sample[i]['utt'] for i in order] + speech = [sample[i]['speech'].squeeze(dim=0) for i in order] + speech_len = torch.tensor([i.size(0) for i in speech], dtype=torch.int32) + speech = pad_sequence(speech, batch_first=True, padding_value=0) + speech_token = [torch.tensor(sample[i]['speech_token']) for i in order] + speech_token_len = torch.tensor([i.size(0) for i in speech_token], dtype=torch.int32) + speech_token = pad_sequence(speech_token, + batch_first=True, + padding_value=0) + speech_feat = [sample[i]['speech_feat'] for i in order] + speech_feat_len = torch.tensor([i.size(0) for i in speech_feat], dtype=torch.int32) + speech_feat = pad_sequence(speech_feat, + batch_first=True, + padding_value=0) + text = [sample[i]['text'] for i in order] + text_token = [torch.tensor(sample[i]['text_token']) for i in order] + text_token_len = torch.tensor([i.size(0) for i in text_token], dtype=torch.int32) + text_token = pad_sequence(text_token, batch_first=True, padding_value=0) + instruct_token = [torch.tensor(sample[i]['instruct_token']) for i in order] + instruct_token_len = torch.tensor([i.size(0) for i in instruct_token], dtype=torch.int32) + instruct_token = pad_sequence(instruct_token, batch_first=True, padding_value=0) + utt_embedding = torch.stack([sample[i]['utt_embedding'] for i in order], dim=0) + spk_embedding = torch.stack([sample[i]['spk_embedding'] for i in order], dim=0) + batch = { + "utts": utts, + "speech": speech, + "speech_len": speech_len, + "speech_token": speech_token, + "speech_token_len": speech_token_len, + "speech_feat": speech_feat, + "speech_feat_len": speech_feat_len, + "text": text, + "text_token": text_token, + "text_token_len": text_token_len, + "instruct_token": instruct_token, + "instruct_token_len": instruct_token_len, + "utt_embedding": utt_embedding, + "spk_embedding": spk_embedding, + } + if gan is True: + # in gan train, we need pitch_feat + pitch_feat = [sample[i]['pitch_feat'] for i in order] + pitch_feat_len = torch.tensor([i.size(0) for i in pitch_feat], dtype=torch.int32) + pitch_feat = pad_sequence(pitch_feat, + batch_first=True, + padding_value=0) + batch["pitch_feat"] = pitch_feat + batch["pitch_feat_len"] = pitch_feat_len + else: + # only gan train needs speech, delete it to save memory + del batch["speech"] + del batch["speech_len"] + if dpo is True: + reject_speech_token = [torch.tensor(sample[i]['reject_speech_token']) for i in order] + reject_speech_token_len = torch.tensor([i.size(0) for i in reject_speech_token], dtype=torch.int32) + reject_speech_token = pad_sequence(reject_speech_token, + batch_first=True, + padding_value=0) + batch['reject_speech_token'] = reject_speech_token + batch['reject_speech_token_len'] = reject_speech_token_len + if use_spk_embedding is True: + batch["embedding"] = batch["spk_embedding"] + else: + batch["embedding"] = batch["utt_embedding"] + yield batch diff --git a/cosyvoice/cosyvoice/flow/DiT/dit.py b/cosyvoice/cosyvoice/flow/DiT/dit.py new file mode 100644 index 0000000..0d637e4 --- /dev/null +++ b/cosyvoice/cosyvoice/flow/DiT/dit.py @@ -0,0 +1,176 @@ + +""" +ein notation: +b - batch +n - sequence +nt - text sequence +nw - raw wave length +d - dimension +""" + +from __future__ import annotations + +import torch +from torch import nn +import torch.nn.functional as F +from einops import repeat +from x_transformers.x_transformers import RotaryEmbedding +from cosyvoice.utils.mask import add_optional_chunk_mask +from cosyvoice.flow.DiT.modules import ( + TimestepEmbedding, + ConvNeXtV2Block, + CausalConvPositionEmbedding, + DiTBlock, + AdaLayerNormZero_Final, + precompute_freqs_cis, + get_pos_embed_indices, +) + + +# Text embedding + + +class TextEmbedding(nn.Module): + def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2): + super().__init__() + self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim) # use 0 as filler token + + if conv_layers > 0: + self.extra_modeling = True + self.precompute_max_pos = 4096 # ~44s of 24khz audio + self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False) + self.text_blocks = nn.Sequential( + *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)] + ) + else: + self.extra_modeling = False + + def forward(self, text: int["b nt"], seq_len, drop_text=False): # noqa: F722 + batch, text_len = text.shape[0], text.shape[1] + text = text + 1 # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx() + text = text[:, :seq_len] # curtail if character tokens are more than the mel spec tokens + text = F.pad(text, (0, seq_len - text_len), value=0) + + if drop_text: # cfg for text + text = torch.zeros_like(text) + + text = self.text_embed(text) # b n -> b n d + + # possible extra modeling + if self.extra_modeling: + # sinus pos emb + batch_start = torch.zeros((batch,), dtype=torch.long) + pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos) + text_pos_embed = self.freqs_cis[pos_idx] + text = text + text_pos_embed + + # convnextv2 blocks + text = self.text_blocks(text) + + return text + + +# noised input audio and context mixing embedding + + +class InputEmbedding(nn.Module): + def __init__(self, mel_dim, text_dim, out_dim, spk_dim=None): + super().__init__() + spk_dim = 0 if spk_dim is None else spk_dim + self.spk_dim = spk_dim + self.proj = nn.Linear(mel_dim * 2 + text_dim + spk_dim, out_dim) + self.conv_pos_embed = CausalConvPositionEmbedding(dim=out_dim) + + def forward( + self, + x: float["b n d"], + cond: float["b n d"], + text_embed: float["b n d"], + spks: float["b d"], + ): + to_cat = [x, cond, text_embed] + if self.spk_dim > 0: + spks = repeat(spks, "b c -> b t c", t=x.shape[1]) + to_cat.append(spks) + + x = self.proj(torch.cat(to_cat, dim=-1)) + x = self.conv_pos_embed(x) + x + return x + + +# Transformer backbone using DiT blocks + + +class DiT(nn.Module): + def __init__( + self, + *, + dim, + depth=8, + heads=8, + dim_head=64, + dropout=0.1, + ff_mult=4, + mel_dim=80, + mu_dim=None, + long_skip_connection=False, + spk_dim=None, + out_channels=None, + static_chunk_size=50, + num_decoding_left_chunks=2 + ): + super().__init__() + + self.time_embed = TimestepEmbedding(dim) + if mu_dim is None: + mu_dim = mel_dim + self.input_embed = InputEmbedding(mel_dim, mu_dim, dim, spk_dim) + + self.rotary_embed = RotaryEmbedding(dim_head) + + self.dim = dim + self.depth = depth + + self.transformer_blocks = nn.ModuleList( + [DiTBlock(dim=dim, heads=heads, dim_head=dim_head, ff_mult=ff_mult, dropout=dropout) for _ in range(depth)] + ) + self.long_skip_connection = nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None + + self.norm_out = AdaLayerNormZero_Final(dim) # final modulation + self.proj_out = nn.Linear(dim, mel_dim) + self.out_channels = out_channels + self.static_chunk_size = static_chunk_size + self.num_decoding_left_chunks = num_decoding_left_chunks + + def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False): + x = x.transpose(1, 2) + mu = mu.transpose(1, 2) + cond = cond.transpose(1, 2) + spks = spks.unsqueeze(dim=1) + batch, seq_len = x.shape[0], x.shape[1] + if t.ndim == 0: + t = t.repeat(batch) + + # t: conditioning time, c: context (text + masked cond audio), x: noised input audio + t = self.time_embed(t) + x = self.input_embed(x, cond, mu, spks.squeeze(1)) + + rope = self.rotary_embed.forward_from_seq_len(seq_len) + + if self.long_skip_connection is not None: + residual = x + + if streaming is True: + attn_mask = add_optional_chunk_mask(x, mask.bool(), False, False, 0, self.static_chunk_size, -1).unsqueeze(dim=1) + else: + attn_mask = add_optional_chunk_mask(x, mask.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1).unsqueeze(dim=1) + + for block in self.transformer_blocks: + x = block(x, t, mask=attn_mask.bool(), rope=rope) + + if self.long_skip_connection is not None: + x = self.long_skip_connection(torch.cat((x, residual), dim=-1)) + + x = self.norm_out(x, t) + output = self.proj_out(x).transpose(1, 2) + return output diff --git a/cosyvoice/cosyvoice/flow/DiT/modules.py b/cosyvoice/cosyvoice/flow/DiT/modules.py new file mode 100644 index 0000000..be8caec --- /dev/null +++ b/cosyvoice/cosyvoice/flow/DiT/modules.py @@ -0,0 +1,616 @@ + +""" +ein notation: +b - batch +n - sequence +nt - text sequence +nw - raw wave length +d - dimension +""" + +from __future__ import annotations +from typing import Optional +import math + +import torch +from torch import nn +import torch.nn.functional as F +import torchaudio + +from x_transformers.x_transformers import apply_rotary_pos_emb + + +# raw wav to mel spec +class MelSpec(nn.Module): + def __init__( + self, + filter_length=1024, + hop_length=256, + win_length=1024, + n_mel_channels=100, + target_sample_rate=24_000, + normalize=False, + power=1, + norm=None, + center=True, + ): + super().__init__() + self.n_mel_channels = n_mel_channels + + self.mel_stft = torchaudio.transforms.MelSpectrogram( + sample_rate=target_sample_rate, + n_fft=filter_length, + win_length=win_length, + hop_length=hop_length, + n_mels=n_mel_channels, + power=power, + center=center, + normalized=normalize, + norm=norm, + ) + + self.register_buffer("dummy", torch.tensor(0), persistent=False) + + def forward(self, inp): + if len(inp.shape) == 3: + inp = inp.squeeze(1) # 'b 1 nw -> b nw' + + assert len(inp.shape) == 2 + + if self.dummy.device != inp.device: + self.to(inp.device) + + mel = self.mel_stft(inp) + mel = mel.clamp(min=1e-5).log() + return mel + + +# sinusoidal position embedding + + +class SinusPositionEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x, scale=1000): + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb) + emb = scale * x.unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +# convolutional position embedding + + +class ConvPositionEmbedding(nn.Module): + def __init__(self, dim, kernel_size=31, groups=16): + super().__init__() + assert kernel_size % 2 != 0 + self.conv1d = nn.Sequential( + nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2), + nn.Mish(), + nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2), + nn.Mish(), + ) + + def forward(self, x: float["b n d"], mask: bool["b n"] | None = None): # noqa: F722 + if mask is not None: + mask = mask[..., None] + x = x.masked_fill(~mask, 0.0) + + x = x.permute(0, 2, 1) + x = self.conv1d(x) + out = x.permute(0, 2, 1) + + if mask is not None: + out = out.masked_fill(~mask, 0.0) + + return out + + +class CausalConvPositionEmbedding(nn.Module): + def __init__(self, dim, kernel_size=31, groups=16): + super().__init__() + assert kernel_size % 2 != 0 + self.kernel_size = kernel_size + self.conv1 = nn.Sequential( + nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=0), + nn.Mish(), + ) + self.conv2 = nn.Sequential( + nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=0), + nn.Mish(), + ) + + def forward(self, x: float["b n d"], mask: bool["b n"] | None = None): # noqa: F722 + if mask is not None: + mask = mask[..., None] + x = x.masked_fill(~mask, 0.0) + + x = x.permute(0, 2, 1) + x = F.pad(x, (self.kernel_size - 1, 0, 0, 0)) + x = self.conv1(x) + x = F.pad(x, (self.kernel_size - 1, 0, 0, 0)) + x = self.conv2(x) + out = x.permute(0, 2, 1) + + if mask is not None: + out = out.masked_fill(~mask, 0.0) + + return out + + +# rotary positional embedding related + + +def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0): + # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning + # has some connection to NTK literature + # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ + # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py + theta *= theta_rescale_factor ** (dim / (dim - 2)) + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) + t = torch.arange(end, device=freqs.device) # type: ignore + freqs = torch.outer(t, freqs).float() # type: ignore + freqs_cos = torch.cos(freqs) # real part + freqs_sin = torch.sin(freqs) # imaginary part + return torch.cat([freqs_cos, freqs_sin], dim=-1) + + +def get_pos_embed_indices(start, length, max_pos, scale=1.0): + # length = length if isinstance(length, int) else length.max() + scale = scale * torch.ones_like(start, dtype=torch.float32) # in case scale is a scalar + pos = ( + start.unsqueeze(1) + + (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long() + ) + # avoid extra long error. + pos = torch.where(pos < max_pos, pos, max_pos - 1) + return pos + + +# Global Response Normalization layer (Instance Normalization ?) + + +class GRN(nn.Module): + def __init__(self, dim): + super().__init__() + self.gamma = nn.Parameter(torch.zeros(1, 1, dim)) + self.beta = nn.Parameter(torch.zeros(1, 1, dim)) + + def forward(self, x): + Gx = torch.norm(x, p=2, dim=1, keepdim=True) + Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6) + return self.gamma * (x * Nx) + self.beta + x + + +# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py +# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108 + + +class ConvNeXtV2Block(nn.Module): + def __init__( + self, + dim: int, + intermediate_dim: int, + dilation: int = 1, + ): + super().__init__() + padding = (dilation * (7 - 1)) // 2 + self.dwconv = nn.Conv1d( + dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation + ) # depthwise conv + self.norm = nn.LayerNorm(dim, eps=1e-6) + self.pwconv1 = nn.Linear(dim, intermediate_dim) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.grn = GRN(intermediate_dim) + self.pwconv2 = nn.Linear(intermediate_dim, dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + residual = x + x = x.transpose(1, 2) # b n d -> b d n + x = self.dwconv(x) + x = x.transpose(1, 2) # b d n -> b n d + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.grn(x) + x = self.pwconv2(x) + return residual + x + + +# AdaLayerNormZero +# return with modulated x for attn input, and params for later mlp modulation + + +class AdaLayerNormZero(nn.Module): + def __init__(self, dim): + super().__init__() + + self.silu = nn.SiLU() + self.linear = nn.Linear(dim, dim * 6) + + self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + + def forward(self, x, emb=None): + emb = self.linear(self.silu(emb)) + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1) + + x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None] + return x, gate_msa, shift_mlp, scale_mlp, gate_mlp + + +# AdaLayerNormZero for final layer +# return only with modulated x for attn input, cuz no more mlp modulation + + +class AdaLayerNormZero_Final(nn.Module): + def __init__(self, dim): + super().__init__() + + self.silu = nn.SiLU() + self.linear = nn.Linear(dim, dim * 2) + + self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + + def forward(self, x, emb): + emb = self.linear(self.silu(emb)) + scale, shift = torch.chunk(emb, 2, dim=1) + + x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :] + return x + + +# FeedForward + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"): + super().__init__() + inner_dim = int(dim * mult) + dim_out = dim_out if dim_out is not None else dim + + activation = nn.GELU(approximate=approximate) + project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation) + self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)) + + def forward(self, x): + return self.ff(x) + + +# Attention with possible joint part +# modified from diffusers/src/diffusers/models/attention_processor.py + + +class Attention(nn.Module): + def __init__( + self, + processor: JointAttnProcessor | AttnProcessor, + dim: int, + heads: int = 8, + dim_head: int = 64, + dropout: float = 0.0, + context_dim: Optional[int] = None, # if not None -> joint attention + context_pre_only=None, + ): + super().__init__() + + if not hasattr(F, "scaled_dot_product_attention"): + raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") + + self.processor = processor + + self.dim = dim + self.heads = heads + self.inner_dim = dim_head * heads + self.dropout = dropout + + self.context_dim = context_dim + self.context_pre_only = context_pre_only + + self.to_q = nn.Linear(dim, self.inner_dim) + self.to_k = nn.Linear(dim, self.inner_dim) + self.to_v = nn.Linear(dim, self.inner_dim) + + if self.context_dim is not None: + self.to_k_c = nn.Linear(context_dim, self.inner_dim) + self.to_v_c = nn.Linear(context_dim, self.inner_dim) + if self.context_pre_only is not None: + self.to_q_c = nn.Linear(context_dim, self.inner_dim) + + self.to_out = nn.ModuleList([]) + self.to_out.append(nn.Linear(self.inner_dim, dim)) + self.to_out.append(nn.Dropout(dropout)) + + if self.context_pre_only is not None and not self.context_pre_only: + self.to_out_c = nn.Linear(self.inner_dim, dim) + + def forward( + self, + x: float["b n d"], # noised input x # noqa: F722 + c: float["b n d"] = None, # context c # noqa: F722 + mask: bool["b n"] | None = None, # noqa: F722 + rope=None, # rotary position embedding for x + c_rope=None, # rotary position embedding for c + ) -> torch.Tensor: + if c is not None: + return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope) + else: + return self.processor(self, x, mask=mask, rope=rope) + + +# Attention processor + + +class AttnProcessor: + def __init__(self): + pass + + def __call__( + self, + attn: Attention, + x: float["b n d"], # noised input x # noqa: F722 + mask: bool["b n"] | None = None, # noqa: F722 + rope=None, # rotary position embedding + ) -> torch.FloatTensor: + batch_size = x.shape[0] + + # `sample` projections. + query = attn.to_q(x) + key = attn.to_k(x) + value = attn.to_v(x) + + # apply rotary position embedding + if rope is not None: + freqs, xpos_scale = rope + q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0) + + query = apply_rotary_pos_emb(query, freqs, q_xpos_scale) + key = apply_rotary_pos_emb(key, freqs, k_xpos_scale) + + # attention + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + # mask. e.g. inference got a batch with different target durations, mask out the padding + if mask is not None: + attn_mask = mask + if attn_mask.dim() == 2: + attn_mask = attn_mask.unsqueeze(1).unsqueeze(1) # 'b n -> b 1 1 n' + attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2]) + else: + attn_mask = None + + x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False) + x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) + x = x.to(query.dtype) + + # linear proj + x = attn.to_out[0](x) + # dropout + x = attn.to_out[1](x) + + if mask is not None: + if mask.dim() == 2: + mask = mask.unsqueeze(-1) + else: + mask = mask[:, 0, -1].unsqueeze(-1) + x = x.masked_fill(~mask, 0.0) + + return x + + +# Joint Attention processor for MM-DiT +# modified from diffusers/src/diffusers/models/attention_processor.py + + +class JointAttnProcessor: + def __init__(self): + pass + + def __call__( + self, + attn: Attention, + x: float["b n d"], # noised input x # noqa: F722 + c: float["b nt d"] = None, # context c, here text # noqa: F722 + mask: bool["b n"] | None = None, # noqa: F722 + rope=None, # rotary position embedding for x + c_rope=None, # rotary position embedding for c + ) -> torch.FloatTensor: + residual = x + + batch_size = c.shape[0] + + # `sample` projections. + query = attn.to_q(x) + key = attn.to_k(x) + value = attn.to_v(x) + + # `context` projections. + c_query = attn.to_q_c(c) + c_key = attn.to_k_c(c) + c_value = attn.to_v_c(c) + + # apply rope for context and noised input independently + if rope is not None: + freqs, xpos_scale = rope + q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0) + query = apply_rotary_pos_emb(query, freqs, q_xpos_scale) + key = apply_rotary_pos_emb(key, freqs, k_xpos_scale) + if c_rope is not None: + freqs, xpos_scale = c_rope + q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0) + c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale) + c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale) + + # attention + query = torch.cat([query, c_query], dim=1) + key = torch.cat([key, c_key], dim=1) + value = torch.cat([value, c_value], dim=1) + + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + # mask. e.g. inference got a batch with different target durations, mask out the padding + if mask is not None: + attn_mask = F.pad(mask, (0, c.shape[1]), value=True) # no mask for c (text) + attn_mask = attn_mask.unsqueeze(1).unsqueeze(1) # 'b n -> b 1 1 n' + attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2]) + else: + attn_mask = None + + x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False) + x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) + x = x.to(query.dtype) + + # Split the attention outputs. + x, c = ( + x[:, : residual.shape[1]], + x[:, residual.shape[1]:], + ) + + # linear proj + x = attn.to_out[0](x) + # dropout + x = attn.to_out[1](x) + if not attn.context_pre_only: + c = attn.to_out_c(c) + + if mask is not None: + mask = mask.unsqueeze(-1) + x = x.masked_fill(~mask, 0.0) + # c = c.masked_fill(~mask, 0.) # no mask for c (text) + + return x, c + + +# DiT Block + + +class DiTBlock(nn.Module): + def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1): + super().__init__() + + self.attn_norm = AdaLayerNormZero(dim) + self.attn = Attention( + processor=AttnProcessor(), + dim=dim, + heads=heads, + dim_head=dim_head, + dropout=dropout, + ) + + self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh") + + def forward(self, x, t, mask=None, rope=None): # x: noised input, t: time embedding + # pre-norm & modulation for attention input + norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t) + + # attention + attn_output = self.attn(x=norm, mask=mask, rope=rope) + + # process attention output for input x + x = x + gate_msa.unsqueeze(1) * attn_output + + ff_norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + ff_output = self.ff(ff_norm) + x = x + gate_mlp.unsqueeze(1) * ff_output + + return x + + +# MMDiT Block https://arxiv.org/abs/2403.03206 + + +class MMDiTBlock(nn.Module): + r""" + modified from diffusers/src/diffusers/models/attention.py + + notes. + _c: context related. text, cond, etc. (left part in sd3 fig2.b) + _x: noised input related. (right part) + context_pre_only: last layer only do prenorm + modulation cuz no more ffn + """ + + def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_pre_only=False): + super().__init__() + + self.context_pre_only = context_pre_only + + self.attn_norm_c = AdaLayerNormZero_Final(dim) if context_pre_only else AdaLayerNormZero(dim) + self.attn_norm_x = AdaLayerNormZero(dim) + self.attn = Attention( + processor=JointAttnProcessor(), + dim=dim, + heads=heads, + dim_head=dim_head, + dropout=dropout, + context_dim=dim, + context_pre_only=context_pre_only, + ) + + if not context_pre_only: + self.ff_norm_c = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_c = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh") + else: + self.ff_norm_c = None + self.ff_c = None + self.ff_norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_x = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh") + + def forward(self, x, c, t, mask=None, rope=None, c_rope=None): # x: noised input, c: context, t: time embedding + # pre-norm & modulation for attention input + if self.context_pre_only: + norm_c = self.attn_norm_c(c, t) + else: + norm_c, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.attn_norm_c(c, emb=t) + norm_x, x_gate_msa, x_shift_mlp, x_scale_mlp, x_gate_mlp = self.attn_norm_x(x, emb=t) + + # attention + x_attn_output, c_attn_output = self.attn(x=norm_x, c=norm_c, mask=mask, rope=rope, c_rope=c_rope) + + # process attention output for context c + if self.context_pre_only: + c = None + else: # if not last layer + c = c + c_gate_msa.unsqueeze(1) * c_attn_output + + norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None] + c_ff_output = self.ff_c(norm_c) + c = c + c_gate_mlp.unsqueeze(1) * c_ff_output + + # process attention output for input x + x = x + x_gate_msa.unsqueeze(1) * x_attn_output + + norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None] + x_ff_output = self.ff_x(norm_x) + x = x + x_gate_mlp.unsqueeze(1) * x_ff_output + + return c, x + + +# time step conditioning embedding + + +class TimestepEmbedding(nn.Module): + def __init__(self, dim, freq_embed_dim=256): + super().__init__() + self.time_embed = SinusPositionEmbedding(freq_embed_dim) + self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim)) + + def forward(self, timestep: float["b"]): # noqa: F821 + time_hidden = self.time_embed(timestep) + time_hidden = time_hidden.to(timestep.dtype) + time = self.time_mlp(time_hidden) # b d + return time diff --git a/cosyvoice/cosyvoice/flow/decoder.py b/cosyvoice/cosyvoice/flow/decoder.py new file mode 100644 index 0000000..97768a4 --- /dev/null +++ b/cosyvoice/cosyvoice/flow/decoder.py @@ -0,0 +1,494 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Tuple +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import pack, rearrange, repeat +from cosyvoice.utils.common import mask_to_bias +from cosyvoice.utils.mask import add_optional_chunk_mask +from matcha.models.components.decoder import SinusoidalPosEmb, Block1D, ResnetBlock1D, Downsample1D, TimestepEmbedding, Upsample1D +from matcha.models.components.transformer import BasicTransformerBlock + + +class Transpose(torch.nn.Module): + def __init__(self, dim0: int, dim1: int): + super().__init__() + self.dim0 = dim0 + self.dim1 = dim1 + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = torch.transpose(x, self.dim0, self.dim1) + return x + + +class CausalConv1d(torch.nn.Conv1d): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = 'zeros', + device=None, + dtype=None + ) -> None: + super(CausalConv1d, self).__init__(in_channels, out_channels, + kernel_size, stride, + padding=0, dilation=dilation, + groups=groups, bias=bias, + padding_mode=padding_mode, + device=device, dtype=dtype) + assert stride == 1 + self.causal_padding = kernel_size - 1 + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = F.pad(x, (self.causal_padding, 0), value=0.0) + x = super(CausalConv1d, self).forward(x) + return x + + +class CausalBlock1D(Block1D): + def __init__(self, dim: int, dim_out: int): + super(CausalBlock1D, self).__init__(dim, dim_out) + self.block = torch.nn.Sequential( + CausalConv1d(dim, dim_out, 3), + Transpose(1, 2), + nn.LayerNorm(dim_out), + Transpose(1, 2), + nn.Mish(), + ) + + def forward(self, x: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + output = self.block(x * mask) + return output * mask + + +class CausalResnetBlock1D(ResnetBlock1D): + def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int = 8): + super(CausalResnetBlock1D, self).__init__(dim, dim_out, time_emb_dim, groups) + self.block1 = CausalBlock1D(dim, dim_out) + self.block2 = CausalBlock1D(dim_out, dim_out) + + +class ConditionalDecoder(nn.Module): + def __init__( + self, + in_channels, + out_channels, + channels=(256, 256), + dropout=0.05, + attention_head_dim=64, + n_blocks=1, + num_mid_blocks=2, + num_heads=4, + act_fn="snake", + ): + """ + This decoder requires an input with the same shape of the target. So, if your text content + is shorter or longer than the outputs, please re-sampling it before feeding to the decoder. + """ + super().__init__() + channels = tuple(channels) + self.in_channels = in_channels + self.out_channels = out_channels + + self.time_embeddings = SinusoidalPosEmb(in_channels) + time_embed_dim = channels[0] * 4 + self.time_mlp = TimestepEmbedding( + in_channels=in_channels, + time_embed_dim=time_embed_dim, + act_fn="silu", + ) + self.down_blocks = nn.ModuleList([]) + self.mid_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + output_channel = in_channels + for i in range(len(channels)): # pylint: disable=consider-using-enumerate + input_channel = output_channel + output_channel = channels[i] + is_last = i == len(channels) - 1 + resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + dim=output_channel, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + for _ in range(n_blocks) + ] + ) + downsample = ( + Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1) + ) + self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample])) + + for _ in range(num_mid_blocks): + input_channel = channels[-1] + out_channels = channels[-1] + resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + + transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + dim=output_channel, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + for _ in range(n_blocks) + ] + ) + + self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks])) + + channels = channels[::-1] + (channels[0],) + for i in range(len(channels) - 1): + input_channel = channels[i] * 2 + output_channel = channels[i + 1] + is_last = i == len(channels) - 2 + resnet = ResnetBlock1D( + dim=input_channel, + dim_out=output_channel, + time_emb_dim=time_embed_dim, + ) + transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + dim=output_channel, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + for _ in range(n_blocks) + ] + ) + upsample = ( + Upsample1D(output_channel, use_conv_transpose=True) + if not is_last + else nn.Conv1d(output_channel, output_channel, 3, padding=1) + ) + self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample])) + self.final_block = Block1D(channels[-1], channels[-1]) + self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1) + self.initialize_weights() + + def initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv1d): + nn.init.kaiming_normal_(m.weight, nonlinearity="relu") + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.GroupNorm): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.kaiming_normal_(m.weight, nonlinearity="relu") + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False): + """Forward pass of the UNet1DConditional model. + + Args: + x (torch.Tensor): shape (batch_size, in_channels, time) + mask (_type_): shape (batch_size, 1, time) + t (_type_): shape (batch_size) + spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None. + cond (_type_, optional): placeholder for future use. Defaults to None. + + Raises: + ValueError: _description_ + ValueError: _description_ + + Returns: + _type_: _description_ + """ + + t = self.time_embeddings(t).to(t.dtype) + t = self.time_mlp(t) + + x = pack([x, mu], "b * t")[0] + + if spks is not None: + spks = repeat(spks, "b c -> b c t", t=x.shape[-1]) + x = pack([x, spks], "b * t")[0] + if cond is not None: + x = pack([x, cond], "b * t")[0] + + hiddens = [] + masks = [mask] + for resnet, transformer_blocks, downsample in self.down_blocks: + mask_down = masks[-1] + x = resnet(x, mask_down, t) + x = rearrange(x, "b c t -> b t c").contiguous() + attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1) + attn_mask = mask_to_bias(attn_mask, x.dtype) + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=attn_mask, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t").contiguous() + hiddens.append(x) # Save hidden states for skip connections + x = downsample(x * mask_down) + masks.append(mask_down[:, :, ::2]) + masks = masks[:-1] + mask_mid = masks[-1] + + for resnet, transformer_blocks in self.mid_blocks: + x = resnet(x, mask_mid, t) + x = rearrange(x, "b c t -> b t c").contiguous() + attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1) + attn_mask = mask_to_bias(attn_mask, x.dtype) + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=attn_mask, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t").contiguous() + + for resnet, transformer_blocks, upsample in self.up_blocks: + mask_up = masks.pop() + skip = hiddens.pop() + x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0] + x = resnet(x, mask_up, t) + x = rearrange(x, "b c t -> b t c").contiguous() + attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1) + attn_mask = mask_to_bias(attn_mask, x.dtype) + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=attn_mask, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t").contiguous() + x = upsample(x * mask_up) + x = self.final_block(x, mask_up) + output = self.final_proj(x * mask_up) + return output * mask + + +class CausalConditionalDecoder(ConditionalDecoder): + def __init__( + self, + in_channels, + out_channels, + channels=(256, 256), + dropout=0.05, + attention_head_dim=64, + n_blocks=1, + num_mid_blocks=2, + num_heads=4, + act_fn="snake", + static_chunk_size=50, + num_decoding_left_chunks=2, + ): + """ + This decoder requires an input with the same shape of the target. So, if your text content + is shorter or longer than the outputs, please re-sampling it before feeding to the decoder. + """ + torch.nn.Module.__init__(self) + channels = tuple(channels) + self.in_channels = in_channels + self.out_channels = out_channels + self.time_embeddings = SinusoidalPosEmb(in_channels) + time_embed_dim = channels[0] * 4 + self.time_mlp = TimestepEmbedding( + in_channels=in_channels, + time_embed_dim=time_embed_dim, + act_fn="silu", + ) + self.static_chunk_size = static_chunk_size + self.num_decoding_left_chunks = num_decoding_left_chunks + self.down_blocks = nn.ModuleList([]) + self.mid_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + output_channel = in_channels + for i in range(len(channels)): # pylint: disable=consider-using-enumerate + input_channel = output_channel + output_channel = channels[i] + is_last = i == len(channels) - 1 + resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + dim=output_channel, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + for _ in range(n_blocks) + ] + ) + downsample = ( + Downsample1D(output_channel) if not is_last else CausalConv1d(output_channel, output_channel, 3) + ) + self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample])) + + for _ in range(num_mid_blocks): + input_channel = channels[-1] + out_channels = channels[-1] + resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + + transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + dim=output_channel, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + for _ in range(n_blocks) + ] + ) + + self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks])) + + channels = channels[::-1] + (channels[0],) + for i in range(len(channels) - 1): + input_channel = channels[i] * 2 + output_channel = channels[i + 1] + is_last = i == len(channels) - 2 + resnet = CausalResnetBlock1D( + dim=input_channel, + dim_out=output_channel, + time_emb_dim=time_embed_dim, + ) + transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + dim=output_channel, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + for _ in range(n_blocks) + ] + ) + upsample = ( + Upsample1D(output_channel, use_conv_transpose=True) + if not is_last + else CausalConv1d(output_channel, output_channel, 3) + ) + self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample])) + self.final_block = CausalBlock1D(channels[-1], channels[-1]) + self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1) + self.initialize_weights() + + def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False): + """Forward pass of the UNet1DConditional model. + + Args: + x (torch.Tensor): shape (batch_size, in_channels, time) + mask (_type_): shape (batch_size, 1, time) + t (_type_): shape (batch_size) + spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None. + cond (_type_, optional): placeholder for future use. Defaults to None. + + Raises: + ValueError: _description_ + ValueError: _description_ + + Returns: + _type_: _description_ + """ + t = self.time_embeddings(t).to(t.dtype) + t = self.time_mlp(t) + + x = pack([x, mu], "b * t")[0] + + if spks is not None: + spks = repeat(spks, "b c -> b c t", t=x.shape[-1]) + x = pack([x, spks], "b * t")[0] + if cond is not None: + x = pack([x, cond], "b * t")[0] + + hiddens = [] + masks = [mask] + for resnet, transformer_blocks, downsample in self.down_blocks: + mask_down = masks[-1] + x = resnet(x, mask_down, t) + x = rearrange(x, "b c t -> b t c").contiguous() + if streaming is True: + attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, self.static_chunk_size, -1) + else: + attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1) + attn_mask = mask_to_bias(attn_mask, x.dtype) + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=attn_mask, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t").contiguous() + hiddens.append(x) # Save hidden states for skip connections + x = downsample(x * mask_down) + masks.append(mask_down[:, :, ::2]) + masks = masks[:-1] + mask_mid = masks[-1] + + for resnet, transformer_blocks in self.mid_blocks: + x = resnet(x, mask_mid, t) + x = rearrange(x, "b c t -> b t c").contiguous() + if streaming is True: + attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, self.static_chunk_size, -1) + else: + attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1) + attn_mask = mask_to_bias(attn_mask, x.dtype) + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=attn_mask, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t").contiguous() + + for resnet, transformer_blocks, upsample in self.up_blocks: + mask_up = masks.pop() + skip = hiddens.pop() + x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0] + x = resnet(x, mask_up, t) + x = rearrange(x, "b c t -> b t c").contiguous() + if streaming is True: + attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, self.static_chunk_size, -1) + else: + attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1) + attn_mask = mask_to_bias(attn_mask, x.dtype) + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=attn_mask, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t").contiguous() + x = upsample(x * mask_up) + x = self.final_block(x, mask_up) + output = self.final_proj(x * mask_up) + return output * mask diff --git a/cosyvoice/cosyvoice/flow/flow.py b/cosyvoice/cosyvoice/flow/flow.py new file mode 100644 index 0000000..d07c181 --- /dev/null +++ b/cosyvoice/cosyvoice/flow/flow.py @@ -0,0 +1,432 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +import random +from typing import Dict, Optional +import torch +import torch.nn as nn +from torch.nn import functional as F +from omegaconf import DictConfig +from cosyvoice.utils.mask import make_pad_mask + + +class MaskedDiffWithXvec(torch.nn.Module): + def __init__(self, + input_size: int = 512, + output_size: int = 80, + spk_embed_dim: int = 192, + output_type: str = "mel", + vocab_size: int = 4096, + input_frame_rate: int = 50, + only_mask_loss: bool = True, + encoder: torch.nn.Module = None, + length_regulator: torch.nn.Module = None, + decoder: torch.nn.Module = None, + decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1, + 'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine', + 'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}), + 'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64, + 'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}): + super().__init__() + self.input_size = input_size + self.output_size = output_size + self.decoder_conf = decoder_conf + self.vocab_size = vocab_size + self.output_type = output_type + self.input_frame_rate = input_frame_rate + logging.info(f"input frame rate={self.input_frame_rate}") + self.input_embedding = nn.Embedding(vocab_size, input_size) + self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size) + self.encoder = encoder + self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size) + self.decoder = decoder + self.length_regulator = length_regulator + self.only_mask_loss = only_mask_loss + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + token = batch['speech_token'].to(device) + token_len = batch['speech_token_len'].to(device) + feat = batch['speech_feat'].to(device) + feat_len = batch['speech_feat_len'].to(device) + embedding = batch['embedding'].to(device) + + # xvec projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + + # concat text and prompt_text + mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device) + token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # text encode + h, h_lengths = self.encoder(token, token_len) + h = self.encoder_proj(h) + h, h_lengths = self.length_regulator(h, feat_len) + + # get conditions + conds = torch.zeros(feat.shape, device=token.device) + for i, j in enumerate(feat_len): + if random.random() < 0.5: + continue + index = random.randint(0, int(0.3 * j)) + conds[i, :index] = feat[i, :index] + conds = conds.transpose(1, 2) + + mask = (~make_pad_mask(feat_len)).to(h) + # NOTE this is unnecessary, feat/h already same shape + loss, _ = self.decoder.compute_loss( + feat.transpose(1, 2).contiguous(), + mask.unsqueeze(1), + h.transpose(1, 2).contiguous(), + embedding, + cond=conds + ) + return {'loss': loss} + + @torch.inference_mode() + def inference(self, + token, + token_len, + prompt_token, + prompt_token_len, + prompt_feat, + prompt_feat_len, + embedding, + flow_cache): + assert token.shape[0] == 1 + # xvec projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + + # concat speech token and prompt speech token + token_len1, token_len2 = prompt_token.shape[1], token.shape[1] + token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len + mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding) + token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # text encode + h, h_lengths = self.encoder(token, token_len) + h = self.encoder_proj(h) + mel_len1, mel_len2 = prompt_feat.shape[1], int(token_len2 / self.input_frame_rate * 22050 / 256) + h, h_lengths = self.length_regulator.inference(h[:, :token_len1], h[:, token_len1:], mel_len1, mel_len2, self.input_frame_rate) + + # get conditions + conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype) + conds[:, :mel_len1] = prompt_feat + conds = conds.transpose(1, 2) + + mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h) + feat, flow_cache = self.decoder( + mu=h.transpose(1, 2).contiguous(), + mask=mask.unsqueeze(1), + spks=embedding, + cond=conds, + n_timesteps=10, + prompt_len=mel_len1, + cache=flow_cache + ) + feat = feat[:, :, mel_len1:] + assert feat.shape[2] == mel_len2 + return feat.float(), flow_cache + + +class CausalMaskedDiffWithXvec(torch.nn.Module): + def __init__(self, + input_size: int = 512, + output_size: int = 80, + spk_embed_dim: int = 192, + output_type: str = "mel", + vocab_size: int = 4096, + input_frame_rate: int = 50, + only_mask_loss: bool = True, + token_mel_ratio: int = 2, + pre_lookahead_len: int = 3, + encoder: torch.nn.Module = None, + decoder: torch.nn.Module = None, + decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1, + 'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine', + 'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}), + 'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64, + 'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}): + super().__init__() + self.input_size = input_size + self.output_size = output_size + self.decoder_conf = decoder_conf + self.vocab_size = vocab_size + self.output_type = output_type + self.input_frame_rate = input_frame_rate + logging.info(f"input frame rate={self.input_frame_rate}") + self.input_embedding = nn.Embedding(vocab_size, input_size) + self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size) + self.encoder = encoder + self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size) + self.decoder = decoder + self.only_mask_loss = only_mask_loss + self.token_mel_ratio = token_mel_ratio + self.pre_lookahead_len = pre_lookahead_len + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + token = batch['speech_token'].to(device) + token_len = batch['speech_token_len'].to(device) + feat = batch['speech_feat'].to(device) + feat_len = batch['speech_feat_len'].to(device) + embedding = batch['embedding'].to(device) + + # NOTE unified training, static_chunk_size > 0 or = 0 + streaming = True if random.random() < 0.5 else False + + # xvec projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + + # concat text and prompt_text + mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device) + token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # text encode + h, h_lengths = self.encoder(token, token_len, streaming=streaming) + h = self.encoder_proj(h) + + # get conditions + conds = torch.zeros(feat.shape, device=token.device) + for i, j in enumerate(feat_len): + if random.random() < 0.5: + continue + index = random.randint(0, int(0.3 * j)) + conds[i, :index] = feat[i, :index] + conds = conds.transpose(1, 2) + + mask = (~make_pad_mask(h_lengths.sum(dim=-1).squeeze(dim=1))).to(h) + loss, _ = self.decoder.compute_loss( + feat.transpose(1, 2).contiguous(), + mask.unsqueeze(1), + h.transpose(1, 2).contiguous(), + embedding, + cond=conds, + streaming=streaming, + ) + return {'loss': loss} + + @torch.inference_mode() + def inference(self, + token, + token_len, + prompt_token, + prompt_token_len, + prompt_feat, + prompt_feat_len, + embedding, + streaming, + finalize): + assert token.shape[0] == 1 + # xvec projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + + # concat text and prompt_text + token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len + mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding) + token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # text encode + if finalize is True: + h, h_lengths = self.encoder(token, token_len, streaming=streaming) + else: + token, context = token[:, :-self.pre_lookahead_len], token[:, -self.pre_lookahead_len:] + h, h_lengths = self.encoder(token, token_len, context=context, streaming=streaming) + mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1] + h = self.encoder_proj(h) + + # get conditions + conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype) + conds[:, :mel_len1] = prompt_feat + conds = conds.transpose(1, 2) + + mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h) + feat, _ = self.decoder( + mu=h.transpose(1, 2).contiguous(), + mask=mask.unsqueeze(1), + spks=embedding, + cond=conds, + n_timesteps=10, + streaming=streaming + ) + feat = feat[:, :, mel_len1:] + assert feat.shape[2] == mel_len2 + return feat.float(), None + + +class CausalMaskedDiffWithDiT(torch.nn.Module): + def __init__(self, + input_size: int = 512, + output_size: int = 80, + spk_embed_dim: int = 192, + output_type: str = "mel", + vocab_size: int = 4096, + input_frame_rate: int = 50, + only_mask_loss: bool = True, + token_mel_ratio: int = 2, + pre_lookahead_len: int = 3, + pre_lookahead_layer: torch.nn.Module = None, + decoder: torch.nn.Module = None, + decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1, + 'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine', + 'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}), + 'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64, + 'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}): + super().__init__() + self.input_size = input_size + self.output_size = output_size + self.decoder_conf = decoder_conf + self.vocab_size = vocab_size + self.output_type = output_type + self.input_frame_rate = input_frame_rate + logging.info(f"input frame rate={self.input_frame_rate}") + self.input_embedding = nn.Embedding(vocab_size, input_size) + self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size) + self.pre_lookahead_len = pre_lookahead_len + self.pre_lookahead_layer = pre_lookahead_layer + self.decoder = decoder + self.only_mask_loss = only_mask_loss + self.token_mel_ratio = token_mel_ratio + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + token = batch['speech_token'].to(device) + token_len = batch['speech_token_len'].to(device) + feat = batch['speech_feat'].to(device) + feat_len = batch['speech_feat_len'].to(device) + embedding = batch['embedding'].to(device) + + # NOTE unified training, static_chunk_size > 0 or = 0 + streaming = True if random.random() < 0.5 else False + + # xvec projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + + # concat text and prompt_text + mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device) + token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # text encode + h, h_lengths = self.encoder(token, token_len, streaming=streaming) + h = self.encoder_proj(h) + + # get conditions + conds = torch.zeros(feat.shape, device=token.device) + for i, j in enumerate(feat_len): + if random.random() < 0.5: + continue + index = random.randint(0, int(0.3 * j)) + conds[i, :index] = feat[i, :index] + conds = conds.transpose(1, 2) + + mask = (~make_pad_mask(h_lengths.sum(dim=-1).squeeze(dim=1))).to(h) + loss, _ = self.decoder.compute_loss( + feat.transpose(1, 2).contiguous(), + mask.unsqueeze(1), + h.transpose(1, 2).contiguous(), + embedding, + cond=conds, + streaming=streaming, + ) + return {'loss': loss} + + @torch.inference_mode() + def inference(self, + token, + token_len, + prompt_token, + prompt_token_len, + prompt_feat, + prompt_feat_len, + embedding, + streaming, + finalize): + assert token.shape[0] == 1 + # xvec projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + + # concat text and prompt_text + token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len + mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding) + token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # text encode + if finalize is True: + h = self.pre_lookahead_layer(token) + else: + h = self.pre_lookahead_layer(token[:, :-self.pre_lookahead_len], context=token[:, -self.pre_lookahead_len:]) + h = h.repeat_interleave(self.token_mel_ratio, dim=1) + mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1] + + # get conditions + conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype) + conds[:, :mel_len1] = prompt_feat + conds = conds.transpose(1, 2) + + mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h) + feat, _ = self.decoder( + mu=h.transpose(1, 2).contiguous(), + mask=mask.unsqueeze(1), + spks=embedding, + cond=conds, + n_timesteps=10, + streaming=streaming + ) + feat = feat[:, :, mel_len1:] + assert feat.shape[2] == mel_len2 + return feat.float(), None + + +if __name__ == '__main__': + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + from hyperpyyaml import load_hyperpyyaml + with open('./pretrained_models/Fun-CosyVoice3-0.5B/cosyvoice3.yaml', 'r') as f: + configs = load_hyperpyyaml(f, overrides={'llm': None, 'hift': None}) + model = configs['flow'] + device = 'cuda' if torch.cuda.is_available() else 'cpu' + model.to(device) + model.eval() + max_len = 10 * model.decoder.estimator.static_chunk_size + chunk_size = model.decoder.estimator.static_chunk_size + context_size = model.pre_lookahead_layer.pre_lookahead_len + token = torch.randint(0, 6561, size=(1, max_len)).to(device) + token_len = torch.tensor([max_len]).to(device) + prompt_token = torch.randint(0, 6561, size=(1, chunk_size)).to(device) + prompt_token_len = torch.tensor([chunk_size]).to(device) + prompt_feat = torch.rand(1, chunk_size * 2, 80).to(device) + prompt_feat_len = torch.tensor([chunk_size * 2]).to(device) + prompt_embedding = torch.rand(1, 192).to(device) + pred_gt, _ = model.inference(token, token_len, prompt_token, prompt_token_len, prompt_feat, prompt_feat_len, prompt_embedding, streaming=True, finalize=True) + for i in range(0, max_len, chunk_size): + finalize = True if i + chunk_size + context_size >= max_len else False + pred_chunk, _ = model.inference(token[:, :i + chunk_size + context_size], torch.tensor([token[:, :i + chunk_size + context_size].shape[1]]).to(device), + prompt_token, prompt_token_len, prompt_feat, prompt_feat_len, prompt_embedding, streaming=True, finalize=finalize) + pred_chunk = pred_chunk[:, :, i * model.token_mel_ratio:] + print((pred_gt[:, :, i * model.token_mel_ratio: i * model.token_mel_ratio + pred_chunk.shape[2]] - pred_chunk).abs().max().item()) diff --git a/cosyvoice/cosyvoice/flow/flow_matching.py b/cosyvoice/cosyvoice/flow/flow_matching.py new file mode 100644 index 0000000..a45337a --- /dev/null +++ b/cosyvoice/cosyvoice/flow/flow_matching.py @@ -0,0 +1,228 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# 2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn.functional as F +from matcha.models.components.flow_matching import BASECFM +from cosyvoice.utils.common import set_all_random_seed + + +class ConditionalCFM(BASECFM): + def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None): + super().__init__( + n_feats=in_channels, + cfm_params=cfm_params, + n_spks=n_spks, + spk_emb_dim=spk_emb_dim, + ) + self.t_scheduler = cfm_params.t_scheduler + self.training_cfg_rate = cfm_params.training_cfg_rate + self.inference_cfg_rate = cfm_params.inference_cfg_rate + in_channels = in_channels + (spk_emb_dim if n_spks > 0 else 0) + # Just change the architecture of the estimator here + self.estimator = estimator + + @torch.inference_mode() + def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0, cache=torch.zeros(1, 80, 0, 2)): + """Forward diffusion + + Args: + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + n_timesteps (int): number of diffusion steps + temperature (float, optional): temperature for scaling noise. Defaults to 1.0. + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + + Returns: + sample: generated mel-spectrogram + shape: (batch_size, n_feats, mel_timesteps) + """ + + z = torch.randn_like(mu).to(mu.device).to(mu.dtype) * temperature + cache_size = cache.shape[2] + # fix prompt and overlap part mu and z + if cache_size != 0: + z[:, :, :cache_size] = cache[:, :, :, 0] + mu[:, :, :cache_size] = cache[:, :, :, 1] + z_cache = torch.concat([z[:, :, :prompt_len], z[:, :, -34:]], dim=2) + mu_cache = torch.concat([mu[:, :, :prompt_len], mu[:, :, -34:]], dim=2) + cache = torch.stack([z_cache, mu_cache], dim=-1) + + t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype) + if self.t_scheduler == 'cosine': + t_span = 1 - torch.cos(t_span * 0.5 * torch.pi) + return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), cache + + def solve_euler(self, x, t_span, mu, mask, spks, cond, streaming=False): + """ + Fixed euler solver for ODEs. + Args: + x (torch.Tensor): random noise + t_span (torch.Tensor): n_timesteps interpolated + shape: (n_timesteps + 1,) + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + """ + t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0] + t = t.unsqueeze(dim=0) + + # I am storing this because I can later plot it by putting a debugger here and saving it to a file + # Or in future might add like a return_all_steps flag + sol = [] + + # Do not use concat, it may cause memory format changed and trt infer with wrong results! + # NOTE when flow run in amp mode, x.dtype is float32, which cause nan in trt fp16 inference, so set dtype=spks.dtype + x_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype) + mask_in = torch.zeros([2, 1, x.size(2)], device=x.device, dtype=spks.dtype) + mu_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype) + t_in = torch.zeros([2], device=x.device, dtype=spks.dtype) + spks_in = torch.zeros([2, 80], device=x.device, dtype=spks.dtype) + cond_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype) + for step in range(1, len(t_span)): + # Classifier-Free Guidance inference introduced in VoiceBox + x_in[:] = x + mask_in[:] = mask + mu_in[0] = mu + t_in[:] = t.unsqueeze(0) + spks_in[0] = spks + cond_in[0] = cond + dphi_dt = self.forward_estimator( + x_in, mask_in, + mu_in, t_in, + spks_in, + cond_in, + streaming + ) + dphi_dt, cfg_dphi_dt = torch.split(dphi_dt, [x.size(0), x.size(0)], dim=0) + dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt - self.inference_cfg_rate * cfg_dphi_dt) + x = x + dt * dphi_dt + t = t + dt + sol.append(x) + if step < len(t_span) - 1: + dt = t_span[step + 1] - t + + return sol[-1].float() + + def forward_estimator(self, x, mask, mu, t, spks, cond, streaming=False): + if isinstance(self.estimator, torch.nn.Module): + return self.estimator(x, mask, mu, t, spks, cond, streaming=streaming) + else: + [estimator, stream], trt_engine = self.estimator.acquire_estimator() + # NOTE need to synchronize when switching stream + torch.cuda.current_stream().synchronize() + with stream: + estimator.set_input_shape('x', (2, 80, x.size(2))) + estimator.set_input_shape('mask', (2, 1, x.size(2))) + estimator.set_input_shape('mu', (2, 80, x.size(2))) + estimator.set_input_shape('t', (2,)) + estimator.set_input_shape('spks', (2, 80)) + estimator.set_input_shape('cond', (2, 80, x.size(2))) + data_ptrs = [x.contiguous().data_ptr(), + mask.contiguous().data_ptr(), + mu.contiguous().data_ptr(), + t.contiguous().data_ptr(), + spks.contiguous().data_ptr(), + cond.contiguous().data_ptr(), + x.data_ptr()] + for i, j in enumerate(data_ptrs): + estimator.set_tensor_address(trt_engine.get_tensor_name(i), j) + # run trt engine + assert estimator.execute_async_v3(torch.cuda.current_stream().cuda_stream) is True + torch.cuda.current_stream().synchronize() + self.estimator.release_estimator(estimator, stream) + return x + + def compute_loss(self, x1, mask, mu, spks=None, cond=None, streaming=False): + """Computes diffusion loss + + Args: + x1 (torch.Tensor): Target + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): target mask + shape: (batch_size, 1, mel_timesteps) + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + spks (torch.Tensor, optional): speaker embedding. Defaults to None. + shape: (batch_size, spk_emb_dim) + + Returns: + loss: conditional flow matching loss + y: conditional flow + shape: (batch_size, n_feats, mel_timesteps) + """ + b, _, t = mu.shape + + # random timestep + t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype) + if self.t_scheduler == 'cosine': + t = 1 - torch.cos(t * 0.5 * torch.pi) + # sample noise p(x_0) + z = torch.randn_like(x1) + + y = (1 - (1 - self.sigma_min) * t) * z + t * x1 + u = x1 - (1 - self.sigma_min) * z + + # during training, we randomly drop condition to trade off mode coverage and sample fidelity + if self.training_cfg_rate > 0: + cfg_mask = torch.rand(b, device=x1.device) > self.training_cfg_rate + mu = mu * cfg_mask.view(-1, 1, 1) + spks = spks * cfg_mask.view(-1, 1) + cond = cond * cfg_mask.view(-1, 1, 1) + + pred = self.estimator(y, mask, mu, t.squeeze(), spks, cond, streaming=streaming) + loss = F.mse_loss(pred * mask, u * mask, reduction="sum") / (torch.sum(mask) * u.shape[1]) + return loss, y + + +class CausalConditionalCFM(ConditionalCFM): + def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None): + super().__init__(in_channels, cfm_params, n_spks, spk_emb_dim, estimator) + set_all_random_seed(0) + self.rand_noise = torch.randn([1, 80, 50 * 300]) + + @torch.inference_mode() + def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, streaming=False): + """Forward diffusion + + Args: + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + n_timesteps (int): number of diffusion steps + temperature (float, optional): temperature for scaling noise. Defaults to 1.0. + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + + Returns: + sample: generated mel-spectrogram + shape: (batch_size, n_feats, mel_timesteps) + """ + + z = self.rand_noise[:, :, :mu.size(2)].to(mu.device).to(mu.dtype) * temperature + # fix prompt and overlap part mu and z + t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype) + if self.t_scheduler == 'cosine': + t_span = 1 - torch.cos(t_span * 0.5 * torch.pi) + return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond, streaming=streaming), None diff --git a/cosyvoice/cosyvoice/flow/length_regulator.py b/cosyvoice/cosyvoice/flow/length_regulator.py new file mode 100644 index 0000000..e1b6c1b --- /dev/null +++ b/cosyvoice/cosyvoice/flow/length_regulator.py @@ -0,0 +1,70 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Tuple +import torch.nn as nn +import torch +from torch.nn import functional as F +from cosyvoice.utils.mask import make_pad_mask + + +class InterpolateRegulator(nn.Module): + def __init__( + self, + channels: int, + sampling_ratios: Tuple, + out_channels: int = None, + groups: int = 1, + ): + super().__init__() + self.sampling_ratios = sampling_ratios + out_channels = out_channels or channels + model = nn.ModuleList([]) + if len(sampling_ratios) > 0: + for _ in sampling_ratios: + module = nn.Conv1d(channels, channels, 3, 1, 1) + norm = nn.GroupNorm(groups, channels) + act = nn.Mish() + model.extend([module, norm, act]) + model.append( + nn.Conv1d(channels, out_channels, 1, 1) + ) + self.model = nn.Sequential(*model) + + def forward(self, x, ylens=None): + # x in (B, T, D) + mask = (~make_pad_mask(ylens)).to(x).unsqueeze(-1) + x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='linear') + out = self.model(x).transpose(1, 2).contiguous() + olens = ylens + return out * mask, olens + + def inference(self, x1, x2, mel_len1, mel_len2, input_frame_rate=50): + # in inference mode, interploate prompt token and token(head/mid/tail) seprately, so we can get a clear separation point of mel + # NOTE 20 corresponds to token_overlap_len in cosyvoice/cli/model.py + # x in (B, T, D) + if x2.shape[1] > 40: + x2_head = F.interpolate(x2[:, :20].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear') + x2_mid = F.interpolate(x2[:, 20:-20].transpose(1, 2).contiguous(), size=mel_len2 - int(20 / input_frame_rate * 22050 / 256) * 2, + mode='linear') + x2_tail = F.interpolate(x2[:, -20:].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear') + x2 = torch.concat([x2_head, x2_mid, x2_tail], dim=2) + else: + x2 = F.interpolate(x2.transpose(1, 2).contiguous(), size=mel_len2, mode='linear') + if x1.shape[1] != 0: + x1 = F.interpolate(x1.transpose(1, 2).contiguous(), size=mel_len1, mode='linear') + x = torch.concat([x1, x2], dim=2) + else: + x = x2 + out = self.model(x).transpose(1, 2).contiguous() + return out, mel_len1 + mel_len2 diff --git a/cosyvoice/cosyvoice/hifigan/discriminator.py b/cosyvoice/cosyvoice/hifigan/discriminator.py new file mode 100644 index 0000000..bb8e85f --- /dev/null +++ b/cosyvoice/cosyvoice/hifigan/discriminator.py @@ -0,0 +1,230 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +try: + from torch.nn.utils.parametrizations import weight_norm, spectral_norm +except ImportError: + from torch.nn.utils import weight_norm, spectral_norm +from typing import List, Optional, Tuple +from einops import rearrange +from torchaudio.transforms import Spectrogram + +LRELU_SLOPE = 0.1 + + +class MultipleDiscriminator(nn.Module): + def __init__( + self, mpd: nn.Module, mrd: nn.Module + ): + super().__init__() + self.mpd = mpd + self.mrd = mrd + + def forward(self, y: torch.Tensor, y_hat: torch.Tensor): + y_d_rs, y_d_gs, fmap_rs, fmap_gs = [], [], [], [] + this_y_d_rs, this_y_d_gs, this_fmap_rs, this_fmap_gs = self.mpd(y.unsqueeze(dim=1), y_hat.unsqueeze(dim=1)) + y_d_rs += this_y_d_rs + y_d_gs += this_y_d_gs + fmap_rs += this_fmap_rs + fmap_gs += this_fmap_gs + this_y_d_rs, this_y_d_gs, this_fmap_rs, this_fmap_gs = self.mrd(y, y_hat) + y_d_rs += this_y_d_rs + y_d_gs += this_y_d_gs + fmap_rs += this_fmap_rs + fmap_gs += this_fmap_gs + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class MultiResolutionDiscriminator(nn.Module): + def __init__( + self, + fft_sizes: Tuple[int, ...] = (2048, 1024, 512), + num_embeddings: Optional[int] = None, + ): + """ + Multi-Resolution Discriminator module adapted from https://github.com/descriptinc/descript-audio-codec. + Additionally, it allows incorporating conditional information with a learned embeddings table. + + Args: + fft_sizes (tuple[int]): Tuple of window lengths for FFT. Defaults to (2048, 1024, 512). + num_embeddings (int, optional): Number of embeddings. None means non-conditional discriminator. + Defaults to None. + """ + + super().__init__() + self.discriminators = nn.ModuleList( + [DiscriminatorR(window_length=w, num_embeddings=num_embeddings) for w in fft_sizes] + ) + + def forward( + self, y: torch.Tensor, y_hat: torch.Tensor, bandwidth_id: torch.Tensor = None + ) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[List[torch.Tensor]], List[List[torch.Tensor]]]: + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + + for d in self.discriminators: + y_d_r, fmap_r = d(x=y, cond_embedding_id=bandwidth_id) + y_d_g, fmap_g = d(x=y_hat, cond_embedding_id=bandwidth_id) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorR(nn.Module): + def __init__( + self, + window_length: int, + num_embeddings: Optional[int] = None, + channels: int = 32, + hop_factor: float = 0.25, + bands: Tuple[Tuple[float, float], ...] = ((0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)), + ): + super().__init__() + self.window_length = window_length + self.hop_factor = hop_factor + self.spec_fn = Spectrogram( + n_fft=window_length, hop_length=int(window_length * hop_factor), win_length=window_length, power=None + ) + n_fft = window_length // 2 + 1 + bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands] + self.bands = bands + convs = lambda: nn.ModuleList( + [ + weight_norm(nn.Conv2d(2, channels, (3, 9), (1, 1), padding=(1, 4))), + weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))), + weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))), + weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))), + weight_norm(nn.Conv2d(channels, channels, (3, 3), (1, 1), padding=(1, 1))), + ] + ) + self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))]) + + if num_embeddings is not None: + self.emb = torch.nn.Embedding(num_embeddings=num_embeddings, embedding_dim=channels) + torch.nn.init.zeros_(self.emb.weight) + + self.conv_post = weight_norm(nn.Conv2d(channels, 1, (3, 3), (1, 1), padding=(1, 1))) + + def spectrogram(self, x): + # Remove DC offset + x = x - x.mean(dim=-1, keepdims=True) + # Peak normalize the volume of input audio + x = 0.8 * x / (x.abs().max(dim=-1, keepdim=True)[0] + 1e-9) + x = self.spec_fn(x) + x = torch.view_as_real(x) + x = rearrange(x, "b f t c -> b c t f") + # Split into bands + x_bands = [x[..., b[0]: b[1]] for b in self.bands] + return x_bands + + def forward(self, x: torch.Tensor, cond_embedding_id: torch.Tensor = None): + x_bands = self.spectrogram(x) + fmap = [] + x = [] + for band, stack in zip(x_bands, self.band_convs): + for i, layer in enumerate(stack): + band = layer(band) + band = torch.nn.functional.leaky_relu(band, 0.1) + if i > 0: + fmap.append(band) + x.append(band) + x = torch.cat(x, dim=-1) + if cond_embedding_id is not None: + emb = self.emb(cond_embedding_id) + h = (emb.view(1, -1, 1, 1) * x).sum(dim=1, keepdims=True) + else: + h = 0 + x = self.conv_post(x) + fmap.append(x) + x += h + + return x, fmap + + +class MultiResSpecDiscriminator(torch.nn.Module): + + def __init__(self, + fft_sizes=[1024, 2048, 512], + hop_sizes=[120, 240, 50], + win_lengths=[600, 1200, 240], + window="hann_window"): + + super(MultiResSpecDiscriminator, self).__init__() + self.discriminators = nn.ModuleList([ + SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window), + SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window), + SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window)]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for _, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +def stft(x, fft_size, hop_size, win_length, window): + """Perform STFT and convert to magnitude spectrogram. + Args: + x (Tensor): Input signal tensor (B, T). + fft_size (int): FFT size. + hop_size (int): Hop size. + win_length (int): Window length. + window (str): Window function type. + Returns: + Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). + """ + x_stft = torch.stft(x, fft_size, hop_size, win_length, window, return_complex=True) + + # NOTE(kan-bayashi): clamp is needed to avoid nan or inf + return torch.abs(x_stft).transpose(2, 1) + + +class SpecDiscriminator(nn.Module): + """docstring for Discriminator.""" + + def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False): + super(SpecDiscriminator, self).__init__() + norm_f = weight_norm if use_spectral_norm is False else spectral_norm + self.fft_size = fft_size + self.shift_size = shift_size + self.win_length = win_length + self.window = getattr(torch, window)(win_length) + self.discriminators = nn.ModuleList([ + norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))), + norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))), + norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))), + norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))), + norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), + ]) + + self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1)) + + def forward(self, y): + + fmap = [] + y = y.squeeze(1) + y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.device)) + y = y.unsqueeze(1) + for _, d in enumerate(self.discriminators): + y = d(y) + y = F.leaky_relu(y, LRELU_SLOPE) + fmap.append(y) + + y = self.out(y) + fmap.append(y) + + return torch.flatten(y, 1, -1), fmap diff --git a/cosyvoice/cosyvoice/hifigan/f0_predictor.py b/cosyvoice/cosyvoice/hifigan/f0_predictor.py new file mode 100644 index 0000000..c896890 --- /dev/null +++ b/cosyvoice/cosyvoice/hifigan/f0_predictor.py @@ -0,0 +1,103 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn as nn +try: + from torch.nn.utils.parametrizations import weight_norm +except ImportError: + from torch.nn.utils import weight_norm +from cosyvoice.transformer.convolution import CausalConv1d + + +class ConvRNNF0Predictor(nn.Module): + def __init__(self, + num_class: int = 1, + in_channels: int = 80, + cond_channels: int = 512 + ): + super().__init__() + + self.num_class = num_class + self.condnet = nn.Sequential( + weight_norm( + nn.Conv1d(in_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + weight_norm( + nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + weight_norm( + nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + weight_norm( + nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + weight_norm( + nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1) + ), + nn.ELU(), + ) + self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.condnet(x) + x = x.transpose(1, 2) + return torch.abs(self.classifier(x).squeeze(-1)) + + +class CausalConvRNNF0Predictor(nn.Module): + def __init__(self, + num_class: int = 1, + in_channels: int = 80, + cond_channels: int = 512 + ): + super().__init__() + + self.num_class = num_class + self.condnet = nn.Sequential( + weight_norm( + CausalConv1d(in_channels, cond_channels, kernel_size=4, causal_type='right') + ), + nn.ELU(), + weight_norm( + CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left') + ), + nn.ELU(), + weight_norm( + CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left') + ), + nn.ELU(), + weight_norm( + CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left') + ), + nn.ELU(), + weight_norm( + CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left') + ), + nn.ELU(), + ) + self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class) + + def forward(self, x: torch.Tensor, finalize: bool = True) -> torch.Tensor: + if finalize is True: + x = self.condnet[0](x) + else: + x = self.condnet[0](x[:, :, :-self.condnet[0].causal_padding], x[:, :, -self.condnet[0].causal_padding:]) + for i in range(1, len(self.condnet)): + x = self.condnet[i](x) + x = x.transpose(1, 2) + return torch.abs(self.classifier(x).squeeze(-1)) diff --git a/cosyvoice/cosyvoice/hifigan/generator.py b/cosyvoice/cosyvoice/hifigan/generator.py new file mode 100644 index 0000000..045cb4e --- /dev/null +++ b/cosyvoice/cosyvoice/hifigan/generator.py @@ -0,0 +1,746 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""HIFI-GAN""" + +from typing import Dict, Optional, List +import numpy as np +from scipy.signal import get_window +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import Conv1d +from torch.nn import ConvTranspose1d +from torch.nn.utils import remove_weight_norm +try: + from torch.nn.utils.parametrizations import weight_norm +except ImportError: + from torch.nn.utils import weight_norm +from torch.distributions.uniform import Uniform +from cosyvoice.transformer.convolution import CausalConv1d, CausalConv1dDownSample, CausalConv1dUpsample +from cosyvoice.transformer.activation import Snake +from cosyvoice.utils.common import get_padding +from cosyvoice.utils.common import init_weights + + +"""hifigan based generator implementation. + +This code is modified from https://github.com/jik876/hifi-gan + ,https://github.com/kan-bayashi/ParallelWaveGAN and + https://github.com/NVIDIA/BigVGAN + +""" + + +class ResBlock(torch.nn.Module): + """Residual block module in HiFiGAN/BigVGAN.""" + def __init__( + self, + channels: int = 512, + kernel_size: int = 3, + dilations: List[int] = [1, 3, 5], + causal: bool = False, + ): + super(ResBlock, self).__init__() + self.causal = causal + self.convs1 = nn.ModuleList() + self.convs2 = nn.ModuleList() + + for dilation in dilations: + self.convs1.append( + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation, + padding=get_padding(kernel_size, dilation)) if causal is False else + CausalConv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation, + causal_type='left' + ) + ) + ) + self.convs2.append( + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1)) if causal is False else + CausalConv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + causal_type='left' + ) + ) + ) + self.convs1.apply(init_weights) + self.convs2.apply(init_weights) + self.activations1 = nn.ModuleList([ + Snake(channels, alpha_logscale=False) + for _ in range(len(self.convs1)) + ]) + self.activations2 = nn.ModuleList([ + Snake(channels, alpha_logscale=False) + for _ in range(len(self.convs2)) + ]) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + for idx in range(len(self.convs1)): + xt = self.activations1[idx](x) + xt = self.convs1[idx](xt) + xt = self.activations2[idx](xt) + xt = self.convs2[idx](xt) + x = xt + x + return x + + def remove_weight_norm(self): + for idx in range(len(self.convs1)): + remove_weight_norm(self.convs1[idx]) + remove_weight_norm(self.convs2[idx]) + + +class SineGen(torch.nn.Module): + """ Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-wavefrom (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_thoreshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, harmonic_num=0, + sine_amp=0.1, noise_std=0.003, + voiced_threshold=0): + super(SineGen, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + + def _f02uv(self, f0): + # generate uv signal + uv = (f0 > self.voiced_threshold).type(torch.float32) + return uv + + @torch.no_grad() + def forward(self, f0): + """ sine_tensor, uv = forward(f0) + input F0: tensor(batchsize=1, dim=1, length) + f0 for unvoiced steps should be 0 + output sine_tensor: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + """ + f0 = f0.transpose(1, 2) + F_mat = torch.zeros((f0.size(0), self.harmonic_num + 1, f0.size(-1))).to(f0.device) + for i in range(self.harmonic_num + 1): + F_mat[:, i: i + 1, :] = f0 * (i + 1) / self.sampling_rate + + theta_mat = 2 * np.pi * (torch.cumsum(F_mat, dim=-1) % 1) + u_dist = Uniform(low=-np.pi, high=np.pi) + phase_vec = u_dist.sample(sample_shape=(f0.size(0), self.harmonic_num + 1, 1)).to(F_mat.device) + phase_vec[:, 0, :] = 0 + + # generate sine waveforms + sine_waves = self.sine_amp * torch.sin(theta_mat + phase_vec) + + # generate uv signal + uv = self._f02uv(f0) + + # noise: for unvoiced should be similar to sine_amp + # std = self.sine_amp/3 -> max value ~ self.sine_amp + # . for voiced regions is self.noise_std + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + noise = noise_amp * torch.randn_like(sine_waves) + + # first: set the unvoiced part to 0 by uv + # then: additive noise + sine_waves = sine_waves * uv + noise + return sine_waves.transpose(1, 2), uv.transpose(1, 2), noise + + +class SineGen2(torch.nn.Module): + """ Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-wavefrom (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_thoreshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, upsample_scale, harmonic_num=0, + sine_amp=0.1, noise_std=0.003, + voiced_threshold=0, + flag_for_pulse=False, + causal=False): + super(SineGen2, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.dim = self.harmonic_num + 1 + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + self.flag_for_pulse = flag_for_pulse + self.upsample_scale = upsample_scale + self.causal = causal + if causal is True: + self.rand_ini = torch.rand(1, 9) + self.rand_ini[:, 0] = 0 + self.sine_waves = torch.rand(1, 300 * 24000, 9) + + def _f02uv(self, f0): + # generate uv signal + uv = (f0 > self.voiced_threshold).type(torch.float32) + return uv + + def _f02sine(self, f0_values): + """ f0_values: (batchsize, length, dim) + where dim indicates fundamental tone and overtones + """ + # convert to F0 in rad. The interger part n can be ignored + # because 2 * np.pi * n doesn't affect phase + rad_values = (f0_values / self.sampling_rate) % 1 + + # initial phase noise (no noise for fundamental component) + if self.training is False and self.causal is True: + rad_values[:, 0, :] = rad_values[:, 0, :] + self.rand_ini.to(rad_values.device) + else: + rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], device=f0_values.device) + rand_ini[:, 0] = 0 + rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini + + # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) + if not self.flag_for_pulse: + rad_values = torch.nn.functional.interpolate(rad_values.transpose(1, 2), + scale_factor=1 / self.upsample_scale, + mode="linear").transpose(1, 2) + + phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi + phase = torch.nn.functional.interpolate(phase.transpose(1, 2) * self.upsample_scale, + scale_factor=self.upsample_scale, mode="nearest" if self.causal is True else 'linear').transpose(1, 2) + sines = torch.sin(phase) + else: + # If necessary, make sure that the first time step of every + # voiced segments is sin(pi) or cos(0) + # This is used for pulse-train generation + + # identify the last time step in unvoiced segments + uv = self._f02uv(f0_values) + uv_1 = torch.roll(uv, shifts=-1, dims=1) + uv_1[:, -1, :] = 1 + u_loc = (uv < 1) * (uv_1 > 0) + + # get the instantanouse phase + tmp_cumsum = torch.cumsum(rad_values, dim=1) + # different batch needs to be processed differently + for idx in range(f0_values.shape[0]): + temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] + temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] + # stores the accumulation of i.phase within + # each voiced segments + tmp_cumsum[idx, :, :] = 0 + tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum + + # rad_values - tmp_cumsum: remove the accumulation of i.phase + # within the previous voiced segment. + i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) + + # get the sines + sines = torch.cos(i_phase * 2 * np.pi) + return sines + + def forward(self, f0): + """ sine_tensor, uv = forward(f0) + input F0: tensor(batchsize=1, length, dim=1) + f0 for unvoiced steps should be 0 + output sine_tensor: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + """ + # fundamental component + fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)) + + # generate sine waveforms + sine_waves = self._f02sine(fn) * self.sine_amp + + # generate uv signal + uv = self._f02uv(f0) + + # noise: for unvoiced should be similar to sine_amp + # std = self.sine_amp/3 -> max value ~ self.sine_amp + # . for voiced regions is self.noise_std + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + if self.training is False and self.causal is True: + noise = noise_amp * self.sine_waves[:, :sine_waves.shape[1]].to(sine_waves.device) + else: + noise = noise_amp * torch.randn_like(sine_waves) + + # first: set the unvoiced part to 0 by uv + # then: additive noise + sine_waves = sine_waves * uv + noise + return sine_waves, uv, noise + + +class SourceModuleHnNSF(torch.nn.Module): + """ SourceModule for hn-nsf + SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0) + sampling_rate: sampling_rate in Hz + harmonic_num: number of harmonic above F0 (default: 0) + sine_amp: amplitude of sine source signal (default: 0.1) + add_noise_std: std of additive Gaussian noise (default: 0.003) + note that amplitude of noise in unvoiced is decided + by sine_amp + voiced_threshold: threhold to set U/V given F0 (default: 0) + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + uv (batchsize, length, 1) + """ + + def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0, sinegen_type='1', causal=False): + super(SourceModuleHnNSF, self).__init__() + + self.sine_amp = sine_amp + self.noise_std = add_noise_std + + # to produce sine waveforms + if sinegen_type == '1': + self.l_sin_gen = SineGen(sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod) + else: + self.l_sin_gen = SineGen2(sampling_rate, upsample_scale, harmonic_num, sine_amp, add_noise_std, voiced_threshod, causal=causal) + + # to merge source harmonics into a single excitation + self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) + self.l_tanh = torch.nn.Tanh() + self.causal = causal + if causal is True: + self.uv = torch.rand(1, 300 * 24000, 1) + + def forward(self, x): + """ + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + """ + # source for harmonic branch + with torch.no_grad(): + sine_wavs, uv, _ = self.l_sin_gen(x) + sine_merge = self.l_tanh(self.l_linear(sine_wavs)) + + # source for noise branch, in the same shape as uv + if self.training is False and self.causal is True: + noise = self.uv[:, :uv.shape[1]] * self.sine_amp / 3 + else: + noise = torch.randn_like(uv) * self.sine_amp / 3 + return sine_merge, noise, uv + + +class HiFTGenerator(nn.Module): + """ + HiFTNet Generator: Neural Source Filter + ISTFTNet + https://arxiv.org/abs/2309.09493 + """ + def __init__( + self, + in_channels: int = 80, + base_channels: int = 512, + nb_harmonics: int = 8, + sampling_rate: int = 22050, + nsf_alpha: float = 0.1, + nsf_sigma: float = 0.003, + nsf_voiced_threshold: float = 10, + upsample_rates: List[int] = [8, 8], + upsample_kernel_sizes: List[int] = [16, 16], + istft_params: Dict[str, int] = {"n_fft": 16, "hop_len": 4}, + resblock_kernel_sizes: List[int] = [3, 7, 11], + resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + source_resblock_kernel_sizes: List[int] = [7, 11], + source_resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5]], + lrelu_slope: float = 0.1, + audio_limit: float = 0.99, + f0_predictor: torch.nn.Module = None, + ): + super(HiFTGenerator, self).__init__() + + self.out_channels = 1 + self.nb_harmonics = nb_harmonics + self.sampling_rate = sampling_rate + self.istft_params = istft_params + self.lrelu_slope = lrelu_slope + self.audio_limit = audio_limit + + self.num_kernels = len(resblock_kernel_sizes) + self.num_upsamples = len(upsample_rates) + # NOTE in CosyVoice2, we use the original SineGen implementation + self.m_source = SourceModuleHnNSF( + sampling_rate=sampling_rate, + upsample_scale=np.prod(upsample_rates) * istft_params["hop_len"], + harmonic_num=nb_harmonics, + sine_amp=nsf_alpha, + add_noise_std=nsf_sigma, + voiced_threshod=nsf_voiced_threshold, + sinegen_type='1' if self.sampling_rate == 22050 else '2', + causal=False) + self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * istft_params["hop_len"]) + + self.conv_pre = weight_norm( + Conv1d(in_channels, base_channels, 7, 1, padding=3) + ) + + # Up + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): + self.ups.append( + weight_norm( + ConvTranspose1d( + base_channels // (2**i), + base_channels // (2**(i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + # Down + self.source_downs = nn.ModuleList() + self.source_resblocks = nn.ModuleList() + downsample_rates = [1] + upsample_rates[::-1][:-1] + downsample_cum_rates = np.cumprod(downsample_rates) + for i, (u, k, d) in enumerate(zip(downsample_cum_rates[::-1], source_resblock_kernel_sizes, source_resblock_dilation_sizes)): + if u == 1: + self.source_downs.append( + Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), 1, 1) + ) + else: + self.source_downs.append( + Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), u * 2, u, padding=(u // 2)) + ) + + self.source_resblocks.append( + ResBlock(base_channels // (2 ** (i + 1)), k, d) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = base_channels // (2**(i + 1)) + for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): + self.resblocks.append(ResBlock(ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, istft_params["n_fft"] + 2, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + self.reflection_pad = nn.ReflectionPad1d((1, 0)) + self.stft_window = torch.from_numpy(get_window("hann", istft_params["n_fft"], fftbins=True).astype(np.float32)) + self.f0_predictor = f0_predictor + + def remove_weight_norm(self): + print('Removing weight norm...') + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + self.m_source.remove_weight_norm() + for l in self.source_downs: + remove_weight_norm(l) + for l in self.source_resblocks: + l.remove_weight_norm() + + def _stft(self, x): + spec = torch.stft( + x, + self.istft_params["n_fft"], self.istft_params["hop_len"], self.istft_params["n_fft"], window=self.stft_window.to(x.device), + return_complex=True) + spec = torch.view_as_real(spec) # [B, F, TT, 2] + return spec[..., 0], spec[..., 1] + + def _istft(self, magnitude, phase): + magnitude = torch.clip(magnitude, max=1e2) + real = magnitude * torch.cos(phase) + img = magnitude * torch.sin(phase) + inverse_transform = torch.istft(torch.complex(real, img), self.istft_params["n_fft"], self.istft_params["hop_len"], + self.istft_params["n_fft"], window=self.stft_window.to(magnitude.device)) + return inverse_transform + + def decode(self, x: torch.Tensor, s: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor: + s_stft_real, s_stft_imag = self._stft(s.squeeze(1)) + s_stft = torch.cat([s_stft_real, s_stft_imag], dim=1) + + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, self.lrelu_slope) + x = self.ups[i](x) + + if i == self.num_upsamples - 1: + x = self.reflection_pad(x) + + # fusion + si = self.source_downs[i](s_stft) + si = self.source_resblocks[i](si) + x = x + si + + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + + x = F.leaky_relu(x) + x = self.conv_post(x) + magnitude = torch.exp(x[:, :self.istft_params["n_fft"] // 2 + 1, :]) + phase = torch.sin(x[:, self.istft_params["n_fft"] // 2 + 1:, :]) # actually, sin is redundancy + + x = self._istft(magnitude, phase) + x = torch.clamp(x, -self.audio_limit, self.audio_limit) + return x + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + speech_feat = batch['speech_feat'].transpose(1, 2).to(device) + # mel->f0 + f0 = self.f0_predictor(speech_feat) + # f0->source + s = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t + s, _, _ = self.m_source(s) + s = s.transpose(1, 2) + # mel+source->speech + generated_speech = self.decode(x=speech_feat, s=s) + return generated_speech, f0 + + @torch.inference_mode() + def inference(self, speech_feat: torch.Tensor, cache_source: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor: + # mel->f0 + f0 = self.f0_predictor(speech_feat) + # f0->source + s = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t + s, _, _ = self.m_source(s) + s = s.transpose(1, 2) + # use cache_source to avoid glitch + if cache_source.shape[2] != 0: + s[:, :, :cache_source.shape[2]] = cache_source + generated_speech = self.decode(x=speech_feat, s=s) + return generated_speech, s + + +class CausalHiFTGenerator(HiFTGenerator): + """ + HiFTNet Generator: Neural Source Filter + ISTFTNet + https://arxiv.org/abs/2309.09493 + """ + def __init__( + self, + in_channels: int = 80, + base_channels: int = 512, + nb_harmonics: int = 8, + sampling_rate: int = 22050, + nsf_alpha: float = 0.1, + nsf_sigma: float = 0.003, + nsf_voiced_threshold: float = 10, + upsample_rates: List[int] = [8, 8], + upsample_kernel_sizes: List[int] = [16, 16], + istft_params: Dict[str, int] = {"n_fft": 16, "hop_len": 4}, + resblock_kernel_sizes: List[int] = [3, 7, 11], + resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + source_resblock_kernel_sizes: List[int] = [7, 11], + source_resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5]], + lrelu_slope: float = 0.1, + audio_limit: float = 0.99, + conv_pre_look_right: int = 4, + f0_predictor: torch.nn.Module = None, + ): + torch.nn.Module.__init__(self) + + self.out_channels = 1 + self.nb_harmonics = nb_harmonics + self.sampling_rate = sampling_rate + self.istft_params = istft_params + self.lrelu_slope = lrelu_slope + self.audio_limit = audio_limit + + self.num_kernels = len(resblock_kernel_sizes) + self.num_upsamples = len(upsample_rates) + self.m_source = SourceModuleHnNSF( + sampling_rate=sampling_rate, + upsample_scale=np.prod(upsample_rates) * istft_params["hop_len"], + harmonic_num=nb_harmonics, + sine_amp=nsf_alpha, + add_noise_std=nsf_sigma, + voiced_threshod=nsf_voiced_threshold, + sinegen_type='1' if self.sampling_rate == 22050 else '2', + causal=True) + self.upsample_rates = upsample_rates + self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * istft_params["hop_len"]) + + self.conv_pre = weight_norm( + CausalConv1d(in_channels, base_channels, conv_pre_look_right + 1, 1, causal_type='right') + ) + + # Up + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): + self.ups.append( + weight_norm( + CausalConv1dUpsample( + base_channels // (2**i), + base_channels // (2**(i + 1)), + k, + u, + ) + ) + ) + + # Down + self.source_downs = nn.ModuleList() + self.source_resblocks = nn.ModuleList() + downsample_rates = [1] + upsample_rates[::-1][:-1] + downsample_cum_rates = np.cumprod(downsample_rates) + for i, (u, k, d) in enumerate(zip(downsample_cum_rates[::-1], source_resblock_kernel_sizes, source_resblock_dilation_sizes)): + if u == 1: + self.source_downs.append( + CausalConv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), 1, 1, causal_type='left') + ) + else: + self.source_downs.append( + CausalConv1dDownSample(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), u * 2, u) + ) + + self.source_resblocks.append( + ResBlock(base_channels // (2 ** (i + 1)), k, d, causal=True) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = base_channels // (2**(i + 1)) + for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): + self.resblocks.append(ResBlock(ch, k, d, causal=True)) + + self.conv_post = weight_norm(CausalConv1d(ch, istft_params["n_fft"] + 2, 7, 1, causal_type='left')) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + self.reflection_pad = nn.ReflectionPad1d((1, 0)) + self.stft_window = torch.from_numpy(get_window("hann", istft_params["n_fft"], fftbins=True).astype(np.float32)) + self.conv_pre_look_right = conv_pre_look_right + self.f0_predictor = f0_predictor + + def decode(self, x: torch.Tensor, s: torch.Tensor = torch.zeros(1, 1, 0), finalize: bool = True) -> torch.Tensor: + s_stft_real, s_stft_imag = self._stft(s.squeeze(1)) + if finalize is True: + x = self.conv_pre(x) + else: + x = self.conv_pre(x[:, :, :-self.conv_pre_look_right], x[:, :, -self.conv_pre_look_right:]) + s_stft_real = s_stft_real[:, :, :-int(np.prod(self.upsample_rates) * self.conv_pre_look_right)] + s_stft_imag = s_stft_imag[:, :, :-int(np.prod(self.upsample_rates) * self.conv_pre_look_right)] + s_stft = torch.cat([s_stft_real, s_stft_imag], dim=1) + + for i in range(self.num_upsamples): + x = F.leaky_relu(x, self.lrelu_slope) + x = self.ups[i](x) + + if i == self.num_upsamples - 1: + x = self.reflection_pad(x) + + # fusion + si = self.source_downs[i](s_stft) + si = self.source_resblocks[i](si) + x = x + si + + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + + x = F.leaky_relu(x) + x = self.conv_post(x) + magnitude = torch.exp(x[:, :self.istft_params["n_fft"] // 2 + 1, :]) + phase = torch.sin(x[:, self.istft_params["n_fft"] // 2 + 1:, :]) # actually, sin is redundancy + + x = self._istft(magnitude, phase) + if finalize is False: + x = x[:, :-int(np.prod(self.upsample_rates) * self.istft_params['hop_len'])] + x = torch.clamp(x, -self.audio_limit, self.audio_limit) + return x + + @torch.inference_mode() + def inference(self, speech_feat: torch.Tensor, finalize: bool = True) -> torch.Tensor: + # mel->f0 NOTE f0_predictor precision is crucial for causal inference, move self.f0_predictor to cpu if necessary + self.f0_predictor.to('cpu') + f0 = self.f0_predictor(speech_feat.cpu(), finalize=finalize).to(speech_feat) + # f0->source + s = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t + s, _, _ = self.m_source(s) + s = s.transpose(1, 2) + if finalize is True: + generated_speech = self.decode(x=speech_feat, s=s, finalize=finalize) + else: + generated_speech = self.decode(x=speech_feat[:, :, :-self.f0_predictor.condnet[0].causal_padding], s=s, finalize=finalize) + return generated_speech, s + + +if __name__ == '__main__': + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + from hyperpyyaml import load_hyperpyyaml + with open('./pretrained_models/Fun-CosyVoice3-0.5B/cosyvoice3.yaml', 'r') as f: + configs = load_hyperpyyaml(f, overrides={'llm': None, 'flow': None}) + model = configs['hift'] + device = 'cuda' if torch.cuda.is_available() else 'cpu' + model.to(device) + model.eval() + max_len, chunk_size, context_size = 300, 30, 8 + mel = torch.rand(1, 80, max_len).to(device) + pred_gt, _ = model.inference(mel) + for i in range(0, max_len, chunk_size): + finalize = True if i + chunk_size + context_size >= max_len else False + pred_chunk, _ = model.inference(mel[:, :, : i + chunk_size + context_size], finalize=finalize) + pred_chunk = pred_chunk[:, i * 480:] + print((pred_gt[:, i * 480:i * 480 + pred_chunk.shape[1]] - pred_chunk).abs().max().item()) diff --git a/cosyvoice/cosyvoice/hifigan/hifigan.py b/cosyvoice/cosyvoice/hifigan/hifigan.py new file mode 100644 index 0000000..046c2cf --- /dev/null +++ b/cosyvoice/cosyvoice/hifigan/hifigan.py @@ -0,0 +1,67 @@ +from typing import Dict, Optional +import torch +import torch.nn as nn +import torch.nn.functional as F +from matcha.hifigan.models import feature_loss, generator_loss, discriminator_loss +from cosyvoice.utils.losses import tpr_loss, mel_loss + + +class HiFiGan(nn.Module): + def __init__(self, generator, discriminator, mel_spec_transform, + multi_mel_spectral_recon_loss_weight=45, feat_match_loss_weight=2.0, + tpr_loss_weight=1.0, tpr_loss_tau=0.04): + super(HiFiGan, self).__init__() + self.generator = generator + self.discriminator = discriminator + self.mel_spec_transform = mel_spec_transform + self.multi_mel_spectral_recon_loss_weight = multi_mel_spectral_recon_loss_weight + self.feat_match_loss_weight = feat_match_loss_weight + self.tpr_loss_weight = tpr_loss_weight + self.tpr_loss_tau = tpr_loss_tau + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + if batch['turn'] == 'generator': + return self.forward_generator(batch, device) + else: + return self.forward_discriminator(batch, device) + + def forward_generator(self, batch, device): + real_speech = batch['speech'].to(device) + pitch_feat = batch['pitch_feat'].to(device) + # 1. calculate generator outputs + generated_speech, generated_f0 = self.generator(batch, device) + # 2. calculate discriminator outputs + y_d_rs, y_d_gs, fmap_rs, fmap_gs = self.discriminator(real_speech, generated_speech) + # 3. calculate generator losses, feature loss, mel loss, tpr losses [Optional] + loss_gen, _ = generator_loss(y_d_gs) + loss_fm = feature_loss(fmap_rs, fmap_gs) + loss_mel = mel_loss(real_speech, generated_speech, self.mel_spec_transform) + if self.tpr_loss_weight != 0: + loss_tpr = tpr_loss(y_d_gs, y_d_rs, self.tpr_loss_tau) + else: + loss_tpr = torch.zeros(1).to(device) + loss_f0 = F.l1_loss(generated_f0, pitch_feat) + loss = loss_gen + self.feat_match_loss_weight * loss_fm + \ + self.multi_mel_spectral_recon_loss_weight * loss_mel + \ + self.tpr_loss_weight * loss_tpr + loss_f0 + return {'loss': loss, 'loss_gen': loss_gen, 'loss_fm': loss_fm, 'loss_mel': loss_mel, 'loss_tpr': loss_tpr, 'loss_f0': loss_f0} + + def forward_discriminator(self, batch, device): + real_speech = batch['speech'].to(device) + # 1. calculate generator outputs + with torch.no_grad(): + generated_speech, generated_f0 = self.generator(batch, device) + # 2. calculate discriminator outputs + y_d_rs, y_d_gs, fmap_rs, fmap_gs = self.discriminator(real_speech, generated_speech.detach()) + # 3. calculate discriminator losses, tpr losses [Optional] + loss_disc, _, _ = discriminator_loss(y_d_rs, y_d_gs) + if self.tpr_loss_weight != 0: + loss_tpr = tpr_loss(y_d_rs, y_d_gs, self.tpr_loss_tau) + else: + loss_tpr = torch.zeros(1).to(device) + loss = loss_disc + self.tpr_loss_weight * loss_tpr + return {'loss': loss, 'loss_disc': loss_disc, 'loss_tpr': loss_tpr} diff --git a/cosyvoice/cosyvoice/llm/llm.py b/cosyvoice/cosyvoice/llm/llm.py new file mode 100644 index 0000000..3d9ae01 --- /dev/null +++ b/cosyvoice/cosyvoice/llm/llm.py @@ -0,0 +1,739 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# 2025 Alibaba Inc (authors: Xiang Lyu, Yabin Li, Qihua, Shengqiang Li) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import queue +import random +import time +import threading +from typing import Dict, Optional, Callable, List, Generator +import numpy as np +import torch +from torch import nn +import torch.nn.functional as F +from transformers import Qwen2ForCausalLM +from torch.nn.utils.rnn import pad_sequence, unpad_sequence +from cosyvoice.utils.common import IGNORE_ID +from cosyvoice.transformer.label_smoothing_loss import LabelSmoothingLoss +from cosyvoice.utils.common import th_accuracy +from cosyvoice.utils.file_utils import logging +from cosyvoice.utils.mask import make_pad_mask + + +class TransformerLM(torch.nn.Module): + def __init__( + self, + text_encoder_input_size: int, + llm_input_size: int, + llm_output_size: int, + text_token_size: int, + speech_token_size: int, + text_encoder: torch.nn.Module, + llm: torch.nn.Module, + sampling: Callable, + length_normalized_loss: bool = True, + lsm_weight: float = 0.0, + spk_embed_dim: int = 192, + ): + super().__init__() + self.llm_input_size = llm_input_size + self.speech_token_size = speech_token_size + # 1. build text token inputs related modules + self.text_embedding = torch.nn.Embedding(text_token_size, text_encoder_input_size) + self.text_encoder = text_encoder + self.text_encoder_affine_layer = nn.Linear( + self.text_encoder.output_size(), + llm_input_size + ) + + # 2. build speech token language model related modules + self.sos = 0 + self.task_id = 1 + self.eos_token = self.speech_token_size + self.llm_embedding = torch.nn.Embedding(2, llm_input_size) + self.llm = llm + self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 1) + self.criterion_ce = LabelSmoothingLoss( + size=speech_token_size + 1, + padding_idx=IGNORE_ID, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + + # 3. [Optional] build speech token related modules + self.speech_embedding = torch.nn.Embedding(speech_token_size, llm_input_size) + self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, llm_input_size) + + # 4. sampling method + self.sampling = sampling + + def encode( + self, + text: torch.Tensor, + text_lengths: torch.Tensor, + ): + encoder_out, encoder_mask = self.text_encoder(text, text_lengths, decoding_chunk_size=1, num_decoding_left_chunks=-1) + encoder_out_lens = encoder_mask.squeeze(1).sum(1) + encoder_out = self.text_encoder_affine_layer(encoder_out) + return encoder_out, encoder_out_lens + + def pad_unpad_sequence(self, sos_emb, embedding, text_token, text_token_len, task_id_emb, speech_token, speech_token_len): + text_token = unpad_sequence(text_token, text_token_len.cpu(), batch_first=True) + speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True) + lm_input = [torch.concat([sos_emb.squeeze(dim=0), embedding[i], text_token[i], task_id_emb.squeeze(dim=0), speech_token[i]], dim=0) + for i in range(len(text_token))] + lm_input_len = torch.tensor([i.size(0) for i in lm_input], dtype=torch.int32) + lm_input = pad_sequence(lm_input, batch_first=True, padding_value=IGNORE_ID) + return lm_input, lm_input_len + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + """ + Args: + text: (B, L, D) + text_lengths: (B,) + audio: (B, T, N) or (B, T) + audio_lengths: (B,) + """ + text_token = batch['text_token'].to(device) + text_token_len = batch['text_token_len'].to(device) + speech_token = batch['speech_token'].to(device) + speech_token_len = batch['speech_token_len'].to(device) + embedding = batch['embedding'].to(device) + + # 1. prepare llm_target + lm_target = [torch.tensor([IGNORE_ID] * (2 + text_token_len[i]) + speech_token[i, :speech_token_len[i]].tolist() + + [self.speech_token_size]) for i in range(text_token.size(0))] + lm_target = pad_sequence(lm_target, batch_first=True, padding_value=IGNORE_ID).to(device) + + # 1. encode text_token + text_token = self.text_embedding(text_token) + text_token, text_token_len = self.encode(text_token, text_token_len) + + # 2. embedding projection + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + embedding = embedding.unsqueeze(1) + + # 3. sos and task_id + sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) + + # 4. encode speech_token + speech_token = self.speech_embedding(speech_token) + + # 5. unpad and pad + lm_input, lm_input_len = self.pad_unpad_sequence(sos_emb, embedding, text_token, text_token_len, + task_id_emb, speech_token, speech_token_len) + + # 6. run lm forward + lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device)) + logits = self.llm_decoder(lm_output) + loss = self.criterion_ce(logits, lm_target) + acc = th_accuracy(logits.view(-1, self.speech_token_size + 1), lm_target, ignore_label=IGNORE_ID) + return {'loss': loss, 'acc': acc} + + def sampling_ids( + self, + weighted_scores: torch.Tensor, + decoded_tokens: List, + sampling: int, + ignore_eos: bool = True, + ): + num_trials, max_trials = 0, 100 + while True: + top_ids = self.sampling(weighted_scores, decoded_tokens, sampling) + if (not ignore_eos) or (top_ids < self.speech_token_size): + break + num_trials += 1 + if num_trials > max_trials: + raise RuntimeError('sampling reaches max_trials {} and still get eos when ignore_eos is True, check your input!'.format(max_trials)) + return top_ids + + @torch.inference_mode() + def inference( + self, + text: torch.Tensor, + text_len: torch.Tensor, + prompt_text: torch.Tensor, + prompt_text_len: torch.Tensor, + prompt_speech_token: torch.Tensor, + prompt_speech_token_len: torch.Tensor, + embedding: torch.Tensor, + sampling: int = 25, + max_token_text_ratio: float = 20, + min_token_text_ratio: float = 2, + uuid: str = '', + ) -> Generator[torch.Tensor, None, None]: + device = text.device + text = torch.concat([prompt_text, text], dim=1) + text_len += prompt_text_len + text = self.text_embedding(text) + + # 1. encode text + text, text_len = self.encode(text, text_len) + + # 2. encode embedding + if embedding.shape[0] != 0: + embedding = F.normalize(embedding, dim=1) + embedding = self.spk_embed_affine_layer(embedding) + embedding = embedding.unsqueeze(dim=1) + else: + embedding = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device).to(text.dtype) + + # 3. concat llm_input + sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) + if prompt_speech_token_len != 0: + prompt_speech_token_emb = self.speech_embedding(prompt_speech_token) + else: + prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device) + lm_input = torch.concat([sos_emb, embedding, text, task_id_emb, prompt_speech_token_emb], dim=1) + + # 4. cal min/max_length + min_len = int((text_len - prompt_text_len) * min_token_text_ratio) + max_len = int((text_len - prompt_text_len) * max_token_text_ratio) + + # 5. step by step decode + out_tokens = [] + offset = 0 + att_cache, cnn_cache = torch.zeros((0, 0, 0, 0), device=lm_input.device), torch.zeros((0, 0, 0, 0), device=lm_input.device) + for i in range(max_len): + y_pred, att_cache, cnn_cache = self.llm.forward_chunk(lm_input, offset=offset, required_cache_size=-1, + att_cache=att_cache, cnn_cache=cnn_cache, + att_mask=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]), + device=lm_input.device)).to(torch.bool)) + logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1) + top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False) + if top_ids == self.eos_token: + break + # in stream mode, yield token one by one + yield top_ids + out_tokens.append(top_ids) + offset += lm_input.size(1) + lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1) + + +class Qwen2Encoder(torch.nn.Module): + def __init__(self, pretrain_path): + super().__init__() + self.model = Qwen2ForCausalLM.from_pretrained(pretrain_path) + + def forward(self, xs: torch.Tensor, xs_lens: torch.Tensor): + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T) + outs = self.model( + inputs_embeds=xs, + attention_mask=masks, + output_hidden_states=True, + return_dict=True, + ) + return outs.hidden_states[-1], masks.unsqueeze(1) + + def forward_one_step(self, xs, masks, cache=None): + input_masks = masks[:, -1, :] + outs = self.model( + inputs_embeds=xs, + attention_mask=input_masks, + output_hidden_states=True, + return_dict=True, + use_cache=True, + past_key_values=cache, + ) + xs = outs.hidden_states[-1] + new_cache = outs.past_key_values + return xs, new_cache + + +class Qwen2LM(TransformerLM): + def __init__( + self, + llm_input_size: int, + llm_output_size: int, + speech_token_size: int, + llm: torch.nn.Module, + sampling: Callable, + length_normalized_loss: bool = True, + lsm_weight: float = 0.0, + mix_ratio: List[int] = [5, 15], + ): + torch.nn.Module.__init__(self) + self.llm_input_size = llm_input_size + self.llm_output_size = llm_output_size + self.speech_token_size = speech_token_size + # 2. build speech token language model related modules + self.sos = 0 + self.task_id = 1 + self.eos_token = speech_token_size + self.fill_token = speech_token_size + 2 + + self.llm_embedding = torch.nn.Embedding(2, llm_input_size) + self.llm = llm + self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 3) + self.criterion_ce = LabelSmoothingLoss( + size=speech_token_size + 3, + padding_idx=IGNORE_ID, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + + # 3. [Optional] build speech token related modules + self.speech_embedding = torch.nn.Embedding(speech_token_size + 3, llm_input_size) + + # 4. sampling method + self.sampling = sampling + self.mix_ratio = mix_ratio + + # 5. vllm related + self.stop_token_ids = [speech_token_size + i for i in range(3)] + self.vllm_output_queue = {} + + def prepare_lm_input_target(self, sos_emb, text_token, text_token_emb, text_token_len, task_id_emb, speech_token, speech_token_emb, speech_token_len): + lm_target, lm_input = [], [] + text_token = unpad_sequence(text_token, text_token_len.cpu(), batch_first=True) + speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True) + text_token_emb = unpad_sequence(text_token_emb, text_token_len.cpu(), batch_first=True) + speech_token_emb = unpad_sequence(speech_token_emb, speech_token_len.cpu(), batch_first=True) + for i in range(len(text_token)): + # bistream sequence + if random.random() < 0.5 and speech_token_len[i] / text_token_len[i] > self.mix_ratio[1] / self.mix_ratio[0]: + this_lm_target, this_lm_input = [], [] + this_lm_target.append(IGNORE_ID) + this_lm_input.append(sos_emb.squeeze(dim=0)) + for j in range(((text_token_len[i] + 1) / self.mix_ratio[0]).ceil().int().item()): + this_text_token = text_token[i][j * self.mix_ratio[0]: (j + 1) * self.mix_ratio[0]].tolist() + this_speech_token = speech_token[i][j * self.mix_ratio[1]: (j + 1) * self.mix_ratio[1]].tolist() + if len(this_text_token) == self.mix_ratio[0]: + assert len(this_speech_token) == self.mix_ratio[1] + this_lm_target += [IGNORE_ID] * (self.mix_ratio[0] - 1) + this_lm_target += this_speech_token + this_lm_target.append(self.fill_token) + this_lm_input.append(text_token_emb[i][j * self.mix_ratio[0]: (j + 1) * self.mix_ratio[0]]) + this_lm_input.append(speech_token_emb[i][j * self.mix_ratio[1]: (j + 1) * self.mix_ratio[1]]) + else: + this_lm_target += [-1] * len(this_text_token) + this_lm_target += speech_token[i][j * self.mix_ratio[1]:].tolist() + this_lm_target.append(self.eos_token) + this_lm_input.append(text_token_emb[i][j * self.mix_ratio[0]:]) + this_lm_input.append(task_id_emb.squeeze(dim=0)) + this_lm_input.append(speech_token_emb[i][j * self.mix_ratio[1]:]) + this_lm_target, this_lm_input = torch.tensor(this_lm_target), torch.concat(this_lm_input, dim=0) + # unistream sequence + else: + this_lm_target = torch.tensor([IGNORE_ID] * (1 + text_token_len[i]) + speech_token[i].tolist() + [self.eos_token]) + this_lm_input = torch.concat([sos_emb.squeeze(dim=0), text_token_emb[i], task_id_emb.squeeze(dim=0), speech_token_emb[i]], dim=0) + lm_target.append(this_lm_target) + lm_input.append(this_lm_input) + lm_input_len = torch.tensor([i.size(0) for i in lm_input], dtype=torch.int32) + lm_input = pad_sequence(lm_input, batch_first=True, padding_value=IGNORE_ID) + lm_target = pad_sequence(lm_target, batch_first=True, padding_value=IGNORE_ID) + return lm_target, lm_input, lm_input_len + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + """ + Args: + text: (B, L, D) + text_lengths: (B,) + audio: (B, T, N) or (B, T) + audio_lengths: (B,) + """ + text_token = batch['text_token'].to(device) + text_token_len = batch['text_token_len'].to(device) + speech_token = batch['speech_token'].to(device) + speech_token_len = batch['speech_token_len'].to(device) + + # 1. encode text_token + text_token_emb = self.llm.model.model.embed_tokens(text_token) + + # 3. sos and task_id + sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) + + # 2. encode speech_token + speech_token_emb = self.speech_embedding(speech_token) + + # 3. prepare llm_input/target + lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(sos_emb, text_token, text_token_emb, text_token_len, task_id_emb, + speech_token, speech_token_emb, speech_token_len) + lm_target = lm_target.to(device) + + # 4. run lm forward + lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device)) + logits = self.llm_decoder(lm_output) + loss = self.criterion_ce(logits, lm_target.to(device)) + acc = th_accuracy(logits.view(-1, self.speech_token_size + 3), lm_target, ignore_label=IGNORE_ID) + return {'loss': loss, 'acc': acc} + + def forward_dpo( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + text_token = batch['text_token'].to(device) + text_token_len = batch['text_token_len'].to(device) + speech_token = batch['speech_token'].to(device) + speech_token_len = batch['speech_token_len'].to(device) + reject_speech_token = batch['reject_speech_token'].to(device) + reject_speech_token_len = batch['reject_speech_token_len'].to(device) + + # 1. encode text_token + text_token_emb = self.llm.model.model.embed_tokens(text_token) + + # 3. sos and task_id + sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) + + # 2. encode speech_token + speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True) + reject_speech_token = unpad_sequence(reject_speech_token, reject_speech_token_len.cpu(), batch_first=True) + speech_token_combined = speech_token + reject_speech_token + speech_token_combined = pad_sequence(speech_token_combined, batch_first=True, padding_value=0) + speech_token_combined_len = torch.concat([speech_token_len, reject_speech_token_len], dim=0) + speech_token_combined_emb = self.speech_embedding(speech_token_combined) + + # 3. prepare llm_input/target + lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(sos_emb, text_token.repeat(2, 1), text_token_emb.repeat(2, 1, 1), text_token_len.repeat(2), + task_id_emb, speech_token_combined, speech_token_combined_emb, speech_token_combined_len) + lm_target = lm_target.to(device) + + # 4. run lm forward + lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device)) + logits = self.llm_decoder(lm_output) + chosen_logits = logits[:text_token.shape[0]] + rejected_logits = logits[text_token.shape[0]:] + chosen_lm_target = lm_target[:text_token.shape[0]] + rejected_lm_target = lm_target[text_token.shape[0]:] + loss = self.criterion_ce(chosen_logits, chosen_lm_target.to(device)) + acc = th_accuracy(chosen_logits.view(-1, self.speech_token_size + 3), chosen_lm_target, ignore_label=IGNORE_ID) + + # 5. calculate dpo logits + chosen_lm_mask = chosen_lm_target == IGNORE_ID + rejected_lm_mask = rejected_lm_target == IGNORE_ID + chosen_logps = torch.gather(chosen_logits.log_softmax(dim=-1), dim=2, index=chosen_lm_target.masked_fill(chosen_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1) + rejected_logps = torch.gather(rejected_logits.log_softmax(dim=-1), dim=2, index=rejected_lm_target.masked_fill(rejected_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1) + chosen_logps = (chosen_logps * chosen_lm_mask).sum(dim=-1) / chosen_lm_mask.sum(dim=-1) + rejected_logps = (rejected_logps * rejected_lm_mask).sum(dim=-1) / rejected_lm_mask.sum(dim=-1) + return {'loss': loss, 'acc': acc, 'chosen_logps': chosen_logps, 'rejected_logps': rejected_logps} + + @torch.inference_mode() + def inference( + self, + text: torch.Tensor, + text_len: torch.Tensor, + prompt_text: torch.Tensor, + prompt_text_len: torch.Tensor, + prompt_speech_token: torch.Tensor, + prompt_speech_token_len: torch.Tensor, + embedding: torch.Tensor, + sampling: int = 25, + max_token_text_ratio: float = 20, + min_token_text_ratio: float = 2, + uuid: str = '', + ) -> Generator[torch.Tensor, None, None]: + device = text.device + text = torch.concat([prompt_text, text], dim=1) + text_len += prompt_text_len + text = self.llm.model.model.embed_tokens(text) + + # 3. concat llm_input + sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) + if prompt_speech_token_len != 0: + prompt_speech_token_emb = self.speech_embedding(prompt_speech_token) + else: + prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device) + lm_input = torch.concat([sos_emb, text, task_id_emb, prompt_speech_token_emb], dim=1) + + # 4. cal min/max_length + min_len = int((text_len - prompt_text_len) * min_token_text_ratio) + max_len = int((text_len - prompt_text_len) * max_token_text_ratio) + + # 5. step by step decode + for token in self.inference_wrapper(lm_input, sampling, min_len, max_len, uuid): + yield token + + @torch.inference_mode() + def inference_wrapper(self, lm_input, sampling, min_len, max_len, uuid): + if hasattr(self, 'vllm'): + from vllm import SamplingParams, RequestOutput + sampling_params = SamplingParams(top_k=sampling, + stop_token_ids=self.stop_token_ids, + min_tokens=min_len, + max_tokens=max_len) + with self.lock: + self.vllm.add_request(uuid, {"prompt_embeds": lm_input.squeeze(0).to(torch.bfloat16).to(lm_input.device)}, sampling_params) + self.vllm_output_queue[uuid] = queue.Queue() + out_tokens = [] + while True: + with self.lock: + if self.vllm_output_queue[uuid].empty() is True: + request_outputs: List[RequestOutput] = self.vllm.step() + for request_output in request_outputs: + top_ids = list(request_output.outputs[0].token_ids)[-1] + self.vllm_output_queue[request_output.request_id].put(top_ids) + if self.vllm_output_queue[uuid].empty() is False: + top_ids = self.vllm_output_queue[uuid].get() + if top_ids in self.stop_token_ids: + break + # in stream mode, yield token one by one + yield top_ids + out_tokens.append(top_ids) + if len(out_tokens) == max_len: + break + time.sleep(0.001) + with self.lock: + self.vllm_output_queue.pop(uuid) + else: + out_tokens = [] + cache = None + for i in range(max_len): + y_pred, cache = self.llm.forward_one_step(lm_input, + masks=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]), device=lm_input.device)).to(torch.bool), + cache=cache) + logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1) + top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False) + if top_ids in self.stop_token_ids: + break + # in stream mode, yield token one by one + yield top_ids + out_tokens.append(top_ids) + lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1) + + @torch.inference_mode() + def inference_bistream( + self, + text: Generator, + prompt_text: torch.Tensor, + prompt_text_len: torch.Tensor, + prompt_speech_token: torch.Tensor, + prompt_speech_token_len: torch.Tensor, + embedding: torch.Tensor, + sampling: int = 25, + max_token_text_ratio: float = 20, + min_token_text_ratio: float = 2, + ) -> Generator[torch.Tensor, None, None]: + + device = prompt_text.device + # 1. prepare input + sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) + if prompt_speech_token_len != 0: + prompt_speech_token_emb = self.speech_embedding(prompt_speech_token) + else: + prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=prompt_text.dtype).to(device) + lm_input = torch.concat([sos_emb], dim=1) + + # 2. iterate text + out_tokens = [] + cache = None + # NOTE init prompt_text as text_cache as it is basically impossible prompt_speech_token/prompt_text < 15/5 + text_cache = self.llm.model.model.embed_tokens(prompt_text) + next_fill_index = (int(prompt_speech_token.shape[1] / self.mix_ratio[1]) + 1) * self.mix_ratio[1] - prompt_speech_token.shape[1] + for this_text in text: + text_cache = torch.concat([text_cache, self.llm.model.model.embed_tokens(this_text)], dim=1) + # prompt_speech_token_emb not empty, try append to lm_input + while prompt_speech_token_emb.size(1) != 0: + if text_cache.size(1) >= self.mix_ratio[0]: + lm_input_text, lm_input_speech = text_cache[:, :self.mix_ratio[0]], prompt_speech_token_emb[:, :self.mix_ratio[1]] + logging.info('append {} text token {} speech token'.format(lm_input_text.size(1), lm_input_speech.size(1))) + lm_input = torch.concat([lm_input, lm_input_text, lm_input_speech], dim=1) + text_cache, prompt_speech_token_emb = text_cache[:, self.mix_ratio[0]:], prompt_speech_token_emb[:, self.mix_ratio[1]:] + else: + logging.info('not enough text token to decode, wait for more') + break + # no prompt_speech_token_emb remain, can decode some speech token + if prompt_speech_token_emb.size(1) == 0: + if (len(out_tokens) != 0 and out_tokens[-1] == self.fill_token) or (len(out_tokens) == 0 and lm_input.size(1) == 1): + logging.info('get fill token, need to append more text token') + if text_cache.size(1) >= self.mix_ratio[0]: + lm_input_text = text_cache[:, :self.mix_ratio[0]] + logging.info('append {} text token'.format(lm_input_text.size(1))) + if len(out_tokens) != 0 and out_tokens[-1] == self.fill_token: + lm_input = lm_input_text + else: + lm_input = torch.concat([lm_input, lm_input_text], dim=1) + text_cache = text_cache[:, self.mix_ratio[0]:] + else: + logging.info('not enough text token to decode, wait for more') + continue + while True: + seq_len = lm_input.shape[1] if cache is None else lm_input.shape[1] + cache[0][0].size(2) + y_pred, cache = self.llm.forward_one_step(lm_input, + masks=torch.tril(torch.ones((1, seq_len, seq_len), device=lm_input.device)).to(torch.bool), + cache=cache) + logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1) + if next_fill_index != -1 and len(out_tokens) == next_fill_index: + top_ids = self.fill_token + next_fill_index += (self.mix_ratio[1] + 1) + else: + top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True) + if top_ids == self.fill_token: + next_fill_index = len(out_tokens) + self.mix_ratio[1] + 1 + logging.info('fill_token index {} next fill_token index {}'.format(len(out_tokens), next_fill_index)) + out_tokens.append(top_ids) + if top_ids >= self.speech_token_size: + if top_ids == self.fill_token: + break + else: + raise ValueError('should not get token {}'.format(top_ids)) + yield top_ids + lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1) + + # 3. final decode + lm_input = torch.concat([lm_input, text_cache, task_id_emb], dim=1) + logging.info('no more text token, decode until met eos') + while True: + seq_len = lm_input.shape[1] if cache is None else lm_input.shape[1] + cache[0][0].size(2) + y_pred, cache = self.llm.forward_one_step(lm_input, + masks=torch.tril(torch.ones((1, seq_len, seq_len), device=lm_input.device)).to(torch.bool), + cache=cache) + logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1) + top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=False) + out_tokens.append(top_ids) + if top_ids >= self.speech_token_size: + if top_ids == self.eos_token: + break + else: + raise ValueError('should not get token {}'.format(top_ids)) + # in stream mode, yield token one by one + yield top_ids + lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1) + + +class CosyVoice3LM(Qwen2LM): + def __init__( + self, + llm_input_size: int, + llm_output_size: int, + speech_token_size: int, + llm: torch.nn.Module, + sampling: Callable, + length_normalized_loss: bool = True, + lsm_weight: float = 0.0, + mix_ratio: List[int] = [5, 15], + ): + torch.nn.Module.__init__(self) + self.llm_input_size = llm_input_size + self.llm_output_size = llm_output_size + self.speech_token_size = speech_token_size + # 2. build speech token language model related modules + self.sos = speech_token_size + 0 + self.eos_token = speech_token_size + 1 + self.task_id = speech_token_size + 2 + self.fill_token = speech_token_size + 3 + + self.llm = llm + self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 200, bias=False) + self.criterion_ce = LabelSmoothingLoss( + size=speech_token_size + 200, + padding_idx=IGNORE_ID, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + + # 3. [Optional] build speech token related modules + self.speech_embedding = torch.nn.Embedding(speech_token_size + 200, llm_input_size) + + # 4. sampling method + self.sampling = sampling + self.mix_ratio = mix_ratio + + # 5. vllm related + self.stop_token_ids = [speech_token_size + i for i in range(200)] + self.vllm_output_queue = {} + + def forward( + self, + batch: dict, + device: torch.device, + ) -> Dict[str, Optional[torch.Tensor]]: + """ + Args: + text: (B, L, D) + text_lengths: (B,) + audio: (B, T, N) or (B, T) + audio_lengths: (B,) + """ + text_token = batch['text_token'].to(device) + text_token_len = batch['text_token_len'].to(device) + speech_token = batch['speech_token'].to(device) + speech_token_len = batch['speech_token_len'].to(device) + # NOTE should append instruct_token to sequence, not implemented yet + instruct_token = batch['instruct_token'].to(device) + instruct_token_len = batch['instruct_token_len'].to(device) + + # 1. encode text_token + text_token_emb = self.llm.model.model.embed_tokens(text_token) + + # 3. sos and task_id + sos_emb = self.speech_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.speech_embedding.weight[self.task_id].reshape(1, 1, -1) + + # 2. encode speech_token + speech_token_emb = self.speech_embedding(speech_token) + + # 3. prepare llm_input/target + lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(sos_emb, text_token, text_token_emb, text_token_len, task_id_emb, + speech_token, speech_token_emb, speech_token_len) + lm_target = lm_target.to(device) + + # 4. run lm forward + lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device)) + logits = self.llm_decoder(lm_output) + loss = self.criterion_ce(logits, lm_target.to(device)) + acc = th_accuracy(logits.view(-1, self.speech_token_size + 200), lm_target, ignore_label=IGNORE_ID) + return {'loss': loss, 'acc': acc} + + @torch.inference_mode() + def inference( + self, + text: torch.Tensor, + text_len: torch.Tensor, + prompt_text: torch.Tensor, + prompt_text_len: torch.Tensor, + prompt_speech_token: torch.Tensor, + prompt_speech_token_len: torch.Tensor, + embedding: torch.Tensor, + sampling: int = 25, + max_token_text_ratio: float = 20, + min_token_text_ratio: float = 2, + uuid: str = '', + ) -> Generator[torch.Tensor, None, None]: + device = text.device + text = torch.concat([prompt_text, text], dim=1) + text_len += prompt_text_len + text = self.llm.model.model.embed_tokens(text) + + # 3. concat llm_input + sos_emb = self.speech_embedding.weight[self.sos].reshape(1, 1, -1) + task_id_emb = self.speech_embedding.weight[self.task_id].reshape(1, 1, -1) + if prompt_speech_token_len != 0: + prompt_speech_token_emb = self.speech_embedding(prompt_speech_token) + else: + prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device) + lm_input = torch.concat([sos_emb, text, task_id_emb, prompt_speech_token_emb], dim=1) + + # 4. cal min/max_length + min_len = int((text_len - prompt_text_len) * min_token_text_ratio) + max_len = int((text_len - prompt_text_len) * max_token_text_ratio) + + # 5. step by step decode + for token in self.inference_wrapper(lm_input, sampling, min_len, max_len, uuid): + yield token diff --git a/cosyvoice/cosyvoice/tokenizer/assets/multilingual_zh_ja_yue_char_del.tiktoken b/cosyvoice/cosyvoice/tokenizer/assets/multilingual_zh_ja_yue_char_del.tiktoken new file mode 100644 index 0000000..ac47fe6 --- /dev/null +++ b/cosyvoice/cosyvoice/tokenizer/assets/multilingual_zh_ja_yue_char_del.tiktoken @@ -0,0 +1,58836 @@ +IQ== 0 +Ig== 1 +Iw== 2 +JA== 3 +JQ== 4 +Jg== 5 +Jw== 6 +KA== 7 +KQ== 8 +Kg== 9 +Kw== 10 +LA== 11 +LQ== 12 +Lg== 13 +Lw== 14 +MA== 15 +MQ== 16 +Mg== 17 +Mw== 18 +NA== 19 +NQ== 20 +Ng== 21 +Nw== 22 +OA== 23 +OQ== 24 +Og== 25 +Ow== 26 +PA== 27 +PQ== 28 +Pg== 29 +Pw== 30 +QA== 31 +QQ== 32 +Qg== 33 +Qw== 34 +RA== 35 +RQ== 36 +Rg== 37 +Rw== 38 +SA== 39 +SQ== 40 +Sg== 41 +Sw== 42 +TA== 43 +TQ== 44 +Tg== 45 +Tw== 46 +UA== 47 +UQ== 48 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index 0000000..6ecf4ae --- /dev/null +++ b/cosyvoice/cosyvoice/tokenizer/tokenizer.py @@ -0,0 +1,327 @@ +import base64 +import os +from functools import lru_cache +from typing import Optional +import torch +from transformers import AutoTokenizer +from whisper.tokenizer import Tokenizer + +import tiktoken + +LANGUAGES = { + "en": "english", + "zh": "chinese", + "de": "german", + "es": "spanish", + "ru": "russian", + "ko": "korean", + "fr": "french", + "ja": "japanese", + "pt": "portuguese", + "tr": "turkish", + "pl": "polish", + "ca": "catalan", + "nl": "dutch", + "ar": "arabic", + "sv": "swedish", + "it": "italian", + "id": "indonesian", + "hi": "hindi", + "fi": "finnish", + "vi": "vietnamese", + "he": "hebrew", + "uk": "ukrainian", + "el": "greek", + "ms": "malay", + "cs": "czech", + "ro": "romanian", + "da": "danish", + "hu": "hungarian", + "ta": "tamil", + "no": "norwegian", + "th": "thai", + "ur": "urdu", + "hr": "croatian", + "bg": "bulgarian", + "lt": "lithuanian", + "la": "latin", + "mi": "maori", + "ml": "malayalam", + "cy": "welsh", + "sk": "slovak", + "te": "telugu", + "fa": "persian", + "lv": "latvian", + "bn": "bengali", + "sr": "serbian", + "az": "azerbaijani", + "sl": "slovenian", + "kn": "kannada", + "et": "estonian", + "mk": "macedonian", + "br": "breton", + "eu": "basque", + "is": "icelandic", + "hy": "armenian", + "ne": "nepali", + "mn": "mongolian", + "bs": "bosnian", + "kk": "kazakh", + "sq": "albanian", + "sw": "swahili", + "gl": "galician", + "mr": "marathi", + "pa": "punjabi", + "si": "sinhala", + "km": "khmer", + "sn": "shona", + "yo": "yoruba", + "so": "somali", + "af": "afrikaans", + "oc": "occitan", + "ka": "georgian", + "be": "belarusian", + "tg": "tajik", + "sd": "sindhi", + "gu": "gujarati", + "am": "amharic", + "yi": "yiddish", + "lo": "lao", + "uz": "uzbek", + "fo": "faroese", + "ht": "haitian creole", + "ps": "pashto", + "tk": "turkmen", + "nn": "nynorsk", + "mt": "maltese", + "sa": "sanskrit", + "lb": "luxembourgish", + "my": "myanmar", + "bo": "tibetan", + "tl": "tagalog", + "mg": "malagasy", + "as": "assamese", + "tt": "tatar", + "haw": "hawaiian", + "ln": "lingala", + "ha": "hausa", + "ba": "bashkir", + "jw": "javanese", + "su": "sundanese", + "yue": "cantonese", + "minnan": "minnan", + "wuyu": "wuyu", + "dialect": "dialect", + "zh/en": "zh/en", + "en/zh": "en/zh", +} + +# language code lookup by name, with a few language aliases +TO_LANGUAGE_CODE = { + **{language: code for code, language in LANGUAGES.items()}, + "burmese": "my", + "valencian": "ca", + "flemish": "nl", + "haitian": "ht", + "letzeburgesch": "lb", + "pushto": "ps", + "panjabi": "pa", + "moldavian": "ro", + "moldovan": "ro", + "sinhalese": "si", + "castilian": "es", + "mandarin": "zh", +} + +AUDIO_EVENT = { + "ASR": "ASR", + "AED": "AED", + "SER": "SER", + "Speech": "Speech", + "/Speech": "/Speech", + "BGM": "BGM", + "/BGM": "/BGM", + "Laughter": "Laughter", + "/Laughter": "/Laughter", + "Applause": "Applause", + "/Applause": "/Applause", +} + +EMOTION = { + "HAPPY": "HAPPY", + "SAD": "SAD", + "ANGRY": "ANGRY", + "NEUTRAL": "NEUTRAL", +} + +TTS_Vocal_Token = { + "TTS/B": "TTS/B", + "TTS/O": "TTS/O", + "TTS/Q": "TTS/Q", + "TTS/A": "TTS/A", + "TTS/CO": "TTS/CO", + "TTS/CL": "TTS/CL", + "TTS/H": "TTS/H", + **{f"TTS/SP{i:02d}": f"TTS/SP{i:02d}" for i in range(1, 14)} +} + + +@lru_cache(maxsize=None) +def get_encoding(name: str = "gpt2", num_languages: int = 99): + vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken") + ranks = { + base64.b64decode(token): int(rank) + for token, rank in (line.split() for line in open(vocab_path) if line) + } + n_vocab = len(ranks) + special_tokens = {} + + specials = [ + "<|endoftext|>", + "<|startoftranscript|>", + *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]], + *[f"<|{audio_event}|>" for audio_event in list(AUDIO_EVENT.keys())], + *[f"<|{emotion}|>" for emotion in list(EMOTION.keys())], + "<|translate|>", + "<|transcribe|>", + "<|startoflm|>", + "<|startofprev|>", + "<|nospeech|>", + "<|notimestamps|>", + *[f"<|SPECIAL_TOKEN_{i}|>" for i in range(1, 31)], # register special tokens for ASR + *[f"<|{tts}|>" for tts in list(TTS_Vocal_Token.keys())], # register special tokens for TTS + *[f"<|{i * 0.02:.2f}|>" for i in range(1501)], + ] + + for token in specials: + special_tokens[token] = n_vocab + n_vocab += 1 + + return tiktoken.Encoding( + name=os.path.basename(vocab_path), + explicit_n_vocab=n_vocab, + pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""", + mergeable_ranks=ranks, + special_tokens=special_tokens, + ) + + +@lru_cache(maxsize=None) +def get_tokenizer( + multilingual: bool, + *, + num_languages: int = 99, + language: Optional[str] = None, + task: Optional[str] = None, # Literal["transcribe", "translate", None] +) -> Tokenizer: + if language is not None: + language = language.lower() + if language not in LANGUAGES: + if language in TO_LANGUAGE_CODE: + language = TO_LANGUAGE_CODE[language] + else: + raise ValueError(f"Unsupported language: {language}") + + if multilingual: + encoding_name = "multilingual_zh_ja_yue_char_del" + language = language or "en" + task = task or "transcribe" + else: + encoding_name = "gpt2" + language = None + task = None + + encoding = get_encoding(name=encoding_name, num_languages=num_languages) + + return Tokenizer( + encoding=encoding, num_languages=num_languages, language=language, task=task + ) + + +class CosyVoice2Tokenizer(): + def __init__(self, token_path, skip_special_tokens=True): + super().__init__() + # NOTE: non-chat model, all these special tokens keep randomly initialized. + special_tokens = { + 'eos_token': '<|endoftext|>', + 'pad_token': '<|endoftext|>', + 'additional_special_tokens': [ + '<|im_start|>', '<|im_end|>', '<|endofprompt|>', + '[breath]', '', '', '[noise]', + '[laughter]', '[cough]', '[clucking]', '[accent]', + '[quick_breath]', + "", "", + "[hissing]", "[sigh]", "[vocalized-noise]", + "[lipsmack]", "[mn]" + ] + } + self.special_tokens = special_tokens + self.tokenizer = AutoTokenizer.from_pretrained(token_path) + self.tokenizer.add_special_tokens(special_tokens) + self.skip_special_tokens = skip_special_tokens + + def encode(self, text, **kwargs): + tokens = self.tokenizer([text], return_tensors="pt") + tokens = tokens["input_ids"][0].cpu().tolist() + return tokens + + def decode(self, tokens): + tokens = torch.tensor(tokens, dtype=torch.int64) + text = self.tokenizer.batch_decode([tokens], skip_special_tokens=self.skip_special_tokens)[0] + return text + + +class CosyVoice3Tokenizer(CosyVoice2Tokenizer): + def __init__(self, token_path, skip_special_tokens=True): + # NOTE: non-chat model, all these special tokens keep randomly initialized. + special_tokens = { + 'eos_token': '<|endoftext|>', + 'pad_token': '<|endoftext|>', + 'additional_special_tokens': [ + '<|im_start|>', '<|im_end|>', '<|endofprompt|>', + '[breath]', '', '', '[noise]', + '[laughter]', '[cough]', '[clucking]', '[accent]', + '[quick_breath]', + "", "", + "[hissing]", "[sigh]", "[vocalized-noise]", + "[lipsmack]", "[mn]", "<|endofsystem|>", + "[AA]", "[AA0]", "[AA1]", "[AA2]", "[AE]", "[AE0]", "[AE1]", "[AE2]", "[AH]", "[AH0]", "[AH1]", "[AH2]", + "[AO]", "[AO0]", "[AO1]", "[AO2]", "[AW]", "[AW0]", "[AW1]", "[AW2]", "[AY]", "[AY0]", "[AY1]", "[AY2]", + "[B]", "[CH]", "[D]", "[DH]", "[EH]", "[EH0]", "[EH1]", "[EH2]", "[ER]", "[ER0]", "[ER1]", "[ER2]", "[EY]", + "[EY0]", "[EY1]", "[EY2]", "[F]", "[G]", "[HH]", "[IH]", "[IH0]", "[IH1]", "[IH2]", "[IY]", "[IY0]", "[IY1]", + "[IY2]", "[JH]", "[K]", "[L]", "[M]", "[N]", "[NG]", "[OW]", "[OW0]", "[OW1]", "[OW2]", "[OY]", "[OY0]", + "[OY1]", "[OY2]", "[P]", "[R]", "[S]", "[SH]", "[T]", "[TH]", "[UH]", "[UH0]", "[UH1]", "[UH2]", "[UW]", + "[UW0]", "[UW1]", "[UW2]", "[V]", "[W]", "[Y]", "[Z]", "[ZH]", + "[a]", "[ai]", "[an]", "[ang]", "[ao]", "[b]", "[c]", "[ch]", "[d]", "[e]", "[ei]", "[en]", "[eng]", "[f]", + "[g]", "[h]", "[i]", "[ian]", "[in]", "[ing]", "[iu]", "[ià]", "[iàn]", "[iàng]", "[iào]", "[iá]", "[ián]", + "[iáng]", "[iáo]", "[iè]", "[ié]", "[iòng]", "[ióng]", "[iù]", "[iú]", "[iā]", "[iān]", "[iāng]", "[iāo]", + "[iē]", "[iě]", "[iōng]", "[iū]", "[iǎ]", "[iǎn]", "[iǎng]", "[iǎo]", "[iǒng]", "[iǔ]", "[j]", "[k]", "[l]", + "[m]", "[n]", "[o]", "[ong]", "[ou]", "[p]", "[q]", "[r]", "[s]", "[sh]", "[t]", "[u]", "[uang]", "[ue]", + "[un]", "[uo]", "[uà]", "[uài]", "[uàn]", "[uàng]", "[uá]", "[uái]", "[uán]", "[uáng]", "[uè]", "[ué]", "[uì]", + "[uí]", "[uò]", "[uó]", "[uā]", "[uāi]", "[uān]", "[uāng]", "[uē]", "[uě]", "[uī]", "[uō]", "[uǎ]", "[uǎi]", + "[uǎn]", "[uǎng]", "[uǐ]", "[uǒ]", "[vè]", "[w]", "[x]", "[y]", "[z]", "[zh]", "[à]", "[ài]", "[àn]", "[àng]", + "[ào]", "[á]", "[ái]", "[án]", "[áng]", "[áo]", "[è]", "[èi]", "[èn]", "[èng]", "[èr]", "[é]", "[éi]", "[én]", + "[éng]", "[ér]", "[ì]", "[ìn]", "[ìng]", "[í]", "[ín]", "[íng]", "[ò]", "[òng]", "[òu]", "[ó]", "[óng]", "[óu]", + "[ù]", "[ùn]", "[ú]", "[ún]", "[ā]", "[āi]", "[ān]", "[āng]", "[āo]", "[ē]", "[ēi]", "[ēn]", "[ēng]", "[ě]", + "[ěi]", "[ěn]", "[ěng]", "[ěr]", "[ī]", "[īn]", "[īng]", "[ō]", "[ōng]", "[ōu]", "[ū]", "[ūn]", "[ǎ]", "[ǎi]", + "[ǎn]", "[ǎng]", "[ǎo]", "[ǐ]", "[ǐn]", "[ǐng]", "[ǒ]", "[ǒng]", "[ǒu]", "[ǔ]", "[ǔn]", "[ǘ]", "[ǚ]", "[ǜ]" + ] + } + self.special_tokens = special_tokens + self.tokenizer = AutoTokenizer.from_pretrained(token_path) + self.tokenizer.add_special_tokens(special_tokens) + self.skip_special_tokens = skip_special_tokens + + +@lru_cache(maxsize=None) +def get_qwen_tokenizer( + token_path: str, + skip_special_tokens: bool, + version: str = 'cosyvoice2' +): + if version == 'cosyvoice2': + return CosyVoice2Tokenizer(token_path=token_path, skip_special_tokens=skip_special_tokens) + elif version == 'cosyvoice3': + return CosyVoice3Tokenizer(token_path=token_path, skip_special_tokens=skip_special_tokens) + else: + raise ValueError diff --git a/cosyvoice/cosyvoice/transformer/__init__.py b/cosyvoice/cosyvoice/transformer/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/cosyvoice/cosyvoice/transformer/activation.py b/cosyvoice/cosyvoice/transformer/activation.py new file mode 100644 index 0000000..8cea548 --- /dev/null +++ b/cosyvoice/cosyvoice/transformer/activation.py @@ -0,0 +1,84 @@ +# Copyright (c) 2020 Johns Hopkins University (Shinji Watanabe) +# 2020 Northwestern Polytechnical University (Pengcheng Guo) +# 2020 Mobvoi Inc (Binbin Zhang) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Swish() activation function for Conformer.""" + +import torch +from torch import nn, sin, pow +from torch.nn import Parameter + + +class Swish(torch.nn.Module): + """Construct an Swish object.""" + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Return Swish activation function.""" + return x * torch.sigmoid(x) + + +# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license. +# LICENSE is in incl_licenses directory. +class Snake(nn.Module): + ''' + Implementation of a sine-based periodic activation function + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter + References: + - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://arxiv.org/abs/2006.08195 + Examples: + >>> a1 = snake(256) + >>> x = torch.randn(256) + >>> x = a1(x) + ''' + def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): + ''' + Initialization. + INPUT: + - in_features: shape of the input + - alpha: trainable parameter + alpha is initialized to 1 by default, higher values = higher-frequency. + alpha will be trained along with the rest of your model. + ''' + super(Snake, self).__init__() + self.in_features = in_features + + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # log scale alphas initialized to zeros + self.alpha = Parameter(torch.zeros(in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = Parameter(torch.ones(in_features) * alpha) + + self.alpha.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + ''' + Forward pass of the function. + Applies the function to the input elementwise. + Snake ∶= x + 1/a * sin^2 (xa) + ''' + alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] + if self.alpha_logscale: + alpha = torch.exp(alpha) + x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2) + + return x diff --git a/cosyvoice/cosyvoice/transformer/attention.py b/cosyvoice/cosyvoice/transformer/attention.py new file mode 100644 index 0000000..8c0c098 --- /dev/null +++ b/cosyvoice/cosyvoice/transformer/attention.py @@ -0,0 +1,330 @@ +# Copyright (c) 2019 Shigeki Karita +# 2020 Mobvoi Inc (Binbin Zhang) +# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Multi-Head Attention layer definition.""" + +import math +from typing import Tuple + +import torch +from torch import nn + + +class MultiHeadedAttention(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, + n_head: int, + n_feat: int, + dropout_rate: float, + key_bias: bool = True): + """Construct an MultiHeadedAttention object.""" + super().__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + self.linear_q = nn.Linear(n_feat, n_feat) + self.linear_k = nn.Linear(n_feat, n_feat, bias=key_bias) + self.linear_v = nn.Linear(n_feat, n_feat) + self.linear_out = nn.Linear(n_feat, n_feat) + self.dropout = nn.Dropout(p=dropout_rate) + + def forward_qkv( + self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor, size + (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor, size + (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor, size + (#batch, n_head, time2, d_k). + + """ + n_batch = query.size(0) + q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) + k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) + v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) + q = q.transpose(1, 2) # (batch, head, time1, d_k) + k = k.transpose(1, 2) # (batch, head, time2, d_k) + v = v.transpose(1, 2) # (batch, head, time2, d_k) + + return q, k, v + + def forward_attention( + self, + value: torch.Tensor, + scores: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool) + ) -> torch.Tensor: + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value, size + (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score, size + (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask, size (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + # NOTE(xcsong): When will `if mask.size(2) > 0` be True? + # 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the + # 1st chunk to ease the onnx export.] + # 2. pytorch training + if mask.size(2) > 0: # time2 > 0 + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + # For last chunk, time2 might be larger than scores.size(-1) + mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2) + scores = scores.masked_fill(mask, -float('inf')) + attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0) # (batch, head, time1, time2) + # NOTE(xcsong): When will `if mask.size(2) > 0` be False? + # 1. onnx(16/-1, -1/-1, 16/0) + # 2. jit (16/-1, -1/-1, 16/0, 16/4) + else: + attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = (x.transpose(1, 2).contiguous().view(n_batch, -1, + self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + 1.When applying cross attention between decoder and encoder, + the batch padding mask for input is in (#batch, 1, T) shape. + 2.When applying self attention of encoder, + the mask is in (#batch, T, T) shape. + 3.When applying self attention of decoder, + the mask is in (#batch, L, L) shape. + 4.If the different position in decoder see different block + of the encoder, such as Mocha, the passed in mask could be + in (#batch, L, T) shape. But there is no such case in current + CosyVoice. + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + """ + q, k, v = self.forward_qkv(query, key, value) + + # NOTE(xcsong): + # when export onnx model, for 1st chunk, we feed + # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode) + # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode). + # In all modes, `if cache.size(0) > 0` will alwayse be `True` + # and we will always do splitting and + # concatnation(this will simplify onnx export). Note that + # it's OK to concat & split zero-shaped tensors(see code below). + # when export jit model, for 1st chunk, we always feed + # cache(0, 0, 0, 0) since jit supports dynamic if-branch. + # >>> a = torch.ones((1, 2, 0, 4)) + # >>> b = torch.ones((1, 2, 3, 4)) + # >>> c = torch.cat((a, b), dim=2) + # >>> torch.equal(b, c) # True + # >>> d = torch.split(a, 2, dim=-1) + # >>> torch.equal(d[0], d[1]) # True + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, + cache.size(-1) // 2, + dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's + # non-trivial to calculate `next_cache_start` here. + new_cache = torch.cat((k, v), dim=-1) + + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) + return self.forward_attention(v, scores, mask), new_cache + + +class RelPositionMultiHeadedAttention(MultiHeadedAttention): + """Multi-Head Attention layer with relative position encoding. + Paper: https://arxiv.org/abs/1901.02860 + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + """ + + def __init__(self, + n_head: int, + n_feat: int, + dropout_rate: float, + key_bias: bool = True): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_head, n_feat, dropout_rate, key_bias) + # linear transformation for positional encoding + self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k)) + self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k)) + torch.nn.init.xavier_uniform_(self.pos_bias_u) + torch.nn.init.xavier_uniform_(self.pos_bias_v) + + def rel_shift(self, x: torch.Tensor) -> torch.Tensor: + """Compute relative positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1). + time1 means the length of query vector. + + Returns: + torch.Tensor: Output tensor. + + """ + zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1), + device=x.device, + dtype=x.dtype) + x_padded = torch.cat([zero_pad, x], dim=-1) + + x_padded = x_padded.view(x.size()[0], + x.size()[1], + x.size(3) + 1, x.size(2)) + x = x_padded[:, :, 1:].view_as(x)[ + :, :, :, : x.size(-1) // 2 + 1 + ] # only keep the positions from 0 to time2 + return x + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): Positional embedding tensor + (#batch, time2, size). + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + """ + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + + # NOTE(xcsong): + # when export onnx model, for 1st chunk, we feed + # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode) + # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode). + # In all modes, `if cache.size(0) > 0` will alwayse be `True` + # and we will always do splitting and + # concatnation(this will simplify onnx export). Note that + # it's OK to concat & split zero-shaped tensors(see code below). + # when export jit model, for 1st chunk, we always feed + # cache(0, 0, 0, 0) since jit supports dynamic if-branch. + # >>> a = torch.ones((1, 2, 0, 4)) + # >>> b = torch.ones((1, 2, 3, 4)) + # >>> c = torch.cat((a, b), dim=2) + # >>> torch.equal(b, c) # True + # >>> d = torch.split(a, 2, dim=-1) + # >>> torch.equal(d[0], d[1]) # True + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, + cache.size(-1) // 2, + dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's + # non-trivial to calculate `next_cache_start` here. + new_cache = torch.cat((k, v), dim=-1) + + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) + p = p.transpose(1, 2) # (batch, head, time1, d_k) + + # (batch, head, time1, d_k) + q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) + # (batch, head, time1, d_k) + q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, time2) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + # NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used + if matrix_ac.shape != matrix_bd.shape: + matrix_bd = self.rel_shift(matrix_bd) + + scores = (matrix_ac + matrix_bd) / math.sqrt( + self.d_k) # (batch, head, time1, time2) + + return self.forward_attention(v, scores, mask), new_cache diff --git a/cosyvoice/cosyvoice/transformer/convolution.py b/cosyvoice/cosyvoice/transformer/convolution.py new file mode 100644 index 0000000..edb32b2 --- /dev/null +++ b/cosyvoice/cosyvoice/transformer/convolution.py @@ -0,0 +1,258 @@ +# Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""ConvolutionModule definition.""" + +from typing import Tuple + +import torch +from torch import nn +import torch.nn.functional as F + + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model.""" + + def __init__(self, + channels: int, + kernel_size: int = 15, + activation: nn.Module = nn.ReLU(), + norm: str = "batch_norm", + causal: bool = False, + bias: bool = True): + """Construct an ConvolutionModule object. + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernel size of conv layers. + causal (int): Whether use causal convolution or not + """ + super().__init__() + + self.pointwise_conv1 = nn.Conv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + # self.lorder is used to distinguish if it's a causal convolution, + # if self.lorder > 0: it's a causal convolution, the input will be + # padded with self.lorder frames on the left in forward. + # else: it's a symmetrical convolution + if causal: + padding = 0 + self.lorder = kernel_size - 1 + else: + # kernel_size should be an odd number for none causal convolution + assert (kernel_size - 1) % 2 == 0 + padding = (kernel_size - 1) // 2 + self.lorder = 0 + self.depthwise_conv = nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + padding=padding, + groups=channels, + bias=bias, + ) + + assert norm in ['batch_norm', 'layer_norm'] + if norm == "batch_norm": + self.use_layer_norm = False + self.norm = nn.BatchNorm1d(channels) + else: + self.use_layer_norm = True + self.norm = nn.LayerNorm(channels) + + self.pointwise_conv2 = nn.Conv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.activation = activation + + def forward( + self, + x: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + cache: torch.Tensor = torch.zeros((0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute convolution module. + Args: + x (torch.Tensor): Input tensor (#batch, time, channels). + mask_pad (torch.Tensor): used for batch padding (#batch, 1, time), + (0, 0, 0) means fake mask. + cache (torch.Tensor): left context cache, it is only + used in causal convolution (#batch, channels, cache_t), + (0, 0, 0) meas fake cache. + Returns: + torch.Tensor: Output tensor (#batch, time, channels). + """ + # exchange the temporal dimension and the feature dimension + x = x.transpose(1, 2) # (#batch, channels, time) + + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + if self.lorder > 0: + if cache.size(2) == 0: # cache_t == 0 + x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0) + else: + assert cache.size(0) == x.size(0) # equal batch + assert cache.size(1) == x.size(1) # equal channel + x = torch.cat((cache, x), dim=2) + assert (x.size(2) > self.lorder) + new_cache = x[:, :, -self.lorder:] + else: + # It's better we just return None if no cache is required, + # However, for JIT export, here we just fake one tensor instead of + # None. + new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channel, dim) + x = nn.functional.glu(x, dim=1) # (batch, channel, dim) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.activation(self.norm(x)) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.pointwise_conv2(x) + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + return x.transpose(1, 2), new_cache + + +# NOTE(Xiang Lyu) causal conv module used in convolution-based vocoder +class CausalConv1d(torch.nn.Conv1d): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = 'zeros', + causal_type: str = 'left', + device=None, + dtype=None + ) -> None: + super(CausalConv1d, self).__init__(in_channels, out_channels, + kernel_size, stride=1, + padding=0, dilation=dilation, + groups=groups, bias=bias, + padding_mode=padding_mode, + device=device, dtype=dtype) + assert stride == 1 + self.causal_padding = int((kernel_size * dilation - dilation) / 2) * 2 + (kernel_size + 1) % 2 + assert causal_type in ['left', 'right'] + self.causal_type = causal_type + + def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor]: + input_timestep = x.shape[2] + if cache.size(2) == 0: + cache = torch.zeros(x.shape[0], x.shape[1], self.causal_padding).to(x) + assert cache.size(2) == self.causal_padding + if self.causal_type == 'left': + x = torch.concat([cache, x], dim=2) + else: + x = torch.concat([x, cache], dim=2) + x = super(CausalConv1d, self).forward(x) + assert x.shape[2] == input_timestep + return x + + +class CausalConv1dDownSample(torch.nn.Conv1d): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = 'zeros', + device=None, + dtype=None + ) -> None: + super(CausalConv1dDownSample, self).__init__(in_channels, out_channels, + kernel_size, stride, + padding=0, dilation=dilation, + groups=groups, bias=bias, + padding_mode=padding_mode, + device=device, dtype=dtype) + assert stride != 1 and dilation == 1 + assert kernel_size % stride == 0 + self.causal_padding = stride - 1 + + def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]: + if cache.size(2) == 0: + x = F.pad(x, (self.causal_padding, 0), value=0.0) + else: + assert cache.size(2) == self.causal_padding + x = torch.concat([cache, x], dim=2) + x = super(CausalConv1dDownSample, self).forward(x) + return x + + +class CausalConv1dUpsample(torch.nn.Conv1d): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = 'zeros', + device=None, + dtype=None + ) -> None: + super(CausalConv1dUpsample, self).__init__(in_channels, out_channels, + kernel_size, 1, + padding=0, dilation=dilation, + groups=groups, bias=bias, + padding_mode=padding_mode, + device=device, dtype=dtype) + assert dilation == 1 + self.causal_padding = kernel_size - 1 + self.upsample = torch.nn.Upsample(scale_factor=stride, mode='nearest') + + def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]: + x = self.upsample(x) + input_timestep = x.shape[2] + if cache.size(2) == 0: + x = F.pad(x, (self.causal_padding, 0), value=0.0) + else: + assert cache.size(2) == self.causal_padding + x = torch.concat([cache, x], dim=2) + x = super(CausalConv1dUpsample, self).forward(x) + assert input_timestep == x.shape[2] + return x diff --git a/cosyvoice/cosyvoice/transformer/decoder.py b/cosyvoice/cosyvoice/transformer/decoder.py new file mode 100644 index 0000000..98f3a66 --- /dev/null +++ b/cosyvoice/cosyvoice/transformer/decoder.py @@ -0,0 +1,396 @@ +# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang, Di Wu) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Decoder definition.""" +from typing import Tuple, List, Optional + +import torch +import torch.utils.checkpoint as ckpt +import logging + +from cosyvoice.transformer.decoder_layer import DecoderLayer +from cosyvoice.transformer.positionwise_feed_forward import PositionwiseFeedForward +from cosyvoice.utils.class_utils import ( + COSYVOICE_EMB_CLASSES, + COSYVOICE_ATTENTION_CLASSES, + COSYVOICE_ACTIVATION_CLASSES, +) +from cosyvoice.utils.mask import (subsequent_mask, make_pad_mask) + + +class TransformerDecoder(torch.nn.Module): + """Base class of Transfomer decoder module. + Args: + vocab_size: output dim + encoder_output_size: dimension of attention + attention_heads: the number of heads of multi head attention + linear_units: the hidden units number of position-wise feedforward + num_blocks: the number of decoder blocks + dropout_rate: dropout rate + self_attention_dropout_rate: dropout rate for attention + input_layer: input layer type + use_output_layer: whether to use output layer + pos_enc_class: PositionalEncoding or ScaledPositionalEncoding + normalize_before: + True: use layer_norm before each sub-block of a layer. + False: use layer_norm after each sub-block of a layer. + src_attention: if false, encoder-decoder cross attention is not + applied, such as CIF model + key_bias: whether use bias in attention.linear_k, False for whisper models. + gradient_checkpointing: rerunning a forward-pass segment for each + checkpointed segment during backward. + tie_word_embedding: Tie or clone module weights depending of whether we are + using TorchScript or not + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + normalize_before: bool = True, + src_attention: bool = True, + key_bias: bool = True, + activation_type: str = "relu", + gradient_checkpointing: bool = False, + tie_word_embedding: bool = False, + ): + super().__init__() + attention_dim = encoder_output_size + activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]() + + self.embed = torch.nn.Sequential( + torch.nn.Identity() if input_layer == "no_pos" else + torch.nn.Embedding(vocab_size, attention_dim), + COSYVOICE_EMB_CLASSES[input_layer](attention_dim, + positional_dropout_rate), + ) + + self.normalize_before = normalize_before + self.after_norm = torch.nn.LayerNorm(attention_dim, eps=1e-5) + self.use_output_layer = use_output_layer + if use_output_layer: + self.output_layer = torch.nn.Linear(attention_dim, vocab_size) + else: + self.output_layer = torch.nn.Identity() + self.num_blocks = num_blocks + self.decoders = torch.nn.ModuleList([ + DecoderLayer( + attention_dim, + COSYVOICE_ATTENTION_CLASSES["selfattn"]( + attention_heads, attention_dim, + self_attention_dropout_rate, key_bias), + COSYVOICE_ATTENTION_CLASSES["selfattn"]( + attention_heads, attention_dim, src_attention_dropout_rate, + key_bias) if src_attention else None, + PositionwiseFeedForward(attention_dim, linear_units, + dropout_rate, activation), + dropout_rate, + normalize_before, + ) for _ in range(self.num_blocks) + ]) + + self.gradient_checkpointing = gradient_checkpointing + self.tie_word_embedding = tie_word_embedding + + def forward( + self, + memory: torch.Tensor, + memory_mask: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + r_ys_in_pad: torch.Tensor = torch.empty(0), + reverse_weight: float = 0.0, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Forward decoder. + Args: + memory: encoded memory, float32 (batch, maxlen_in, feat) + memory_mask: encoder memory mask, (batch, 1, maxlen_in) + ys_in_pad: padded input token ids, int64 (batch, maxlen_out) + ys_in_lens: input lengths of this batch (batch) + r_ys_in_pad: not used in transformer decoder, in order to unify api + with bidirectional decoder + reverse_weight: not used in transformer decoder, in order to unify + api with bidirectional decode + Returns: + (tuple): tuple containing: + x: decoded token score before softmax (batch, maxlen_out, + vocab_size) if use_output_layer is True, + torch.tensor(0.0), in order to unify api with bidirectional decoder + olens: (batch, ) + NOTE(xcsong): + We pass the `__call__` method of the modules instead of `forward` to the + checkpointing API because `__call__` attaches all the hooks of the module. + https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2 + """ + tgt = ys_in_pad + maxlen = tgt.size(1) + # tgt_mask: (B, 1, L) + tgt_mask = ~make_pad_mask(ys_in_lens, maxlen).unsqueeze(1) + tgt_mask = tgt_mask.to(tgt.device) + # m: (1, L, L) + m = subsequent_mask(tgt_mask.size(-1), + device=tgt_mask.device).unsqueeze(0) + # tgt_mask: (B, L, L) + tgt_mask = tgt_mask & m + x, _ = self.embed(tgt) + if self.gradient_checkpointing and self.training: + x = self.forward_layers_checkpointed(x, tgt_mask, memory, + memory_mask) + else: + x = self.forward_layers(x, tgt_mask, memory, memory_mask) + if self.normalize_before: + x = self.after_norm(x) + if self.use_output_layer: + x = self.output_layer(x) + olens = tgt_mask.sum(1) + return x, torch.tensor(0.0), olens + + def forward_layers(self, x: torch.Tensor, tgt_mask: torch.Tensor, + memory: torch.Tensor, + memory_mask: torch.Tensor) -> torch.Tensor: + for layer in self.decoders: + x, tgt_mask, memory, memory_mask = layer(x, tgt_mask, memory, + memory_mask) + return x + + @torch.jit.unused + def forward_layers_checkpointed(self, x: torch.Tensor, + tgt_mask: torch.Tensor, + memory: torch.Tensor, + memory_mask: torch.Tensor) -> torch.Tensor: + for layer in self.decoders: + x, tgt_mask, memory, memory_mask = ckpt.checkpoint( + layer.__call__, x, tgt_mask, memory, memory_mask) + return x + + def forward_one_step( + self, + memory: torch.Tensor, + memory_mask: torch.Tensor, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + cache: Optional[List[torch.Tensor]] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """Forward one step. + This is only used for decoding. + Args: + memory: encoded memory, float32 (batch, maxlen_in, feat) + memory_mask: encoded memory mask, (batch, 1, maxlen_in) + tgt: input token ids, int64 (batch, maxlen_out) + tgt_mask: input token mask, (batch, maxlen_out) + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (include 1.2) + cache: cached output list of (batch, max_time_out-1, size) + Returns: + y, cache: NN output value and cache per `self.decoders`. + y.shape` is (batch, maxlen_out, token) + """ + x, _ = self.embed(tgt) + new_cache = [] + for i, decoder in enumerate(self.decoders): + if cache is None: + c = None + else: + c = cache[i] + x, tgt_mask, memory, memory_mask = decoder(x, + tgt_mask, + memory, + memory_mask, + cache=c) + new_cache.append(x) + if self.normalize_before: + y = self.after_norm(x[:, -1]) + else: + y = x[:, -1] + if self.use_output_layer: + y = torch.log_softmax(self.output_layer(y), dim=-1) + return y, new_cache + + def tie_or_clone_weights(self, jit_mode: bool = True): + """Tie or clone module weights (between word_emb and output_layer) + depending of whether we are using TorchScript or not""" + if not self.use_output_layer: + return + if jit_mode: + logging.info("clone emb.weight to output.weight") + self.output_layer.weight = torch.nn.Parameter( + self.embed[0].weight.clone()) + else: + logging.info("tie emb.weight with output.weight") + self.output_layer.weight = self.embed[0].weight + + if getattr(self.output_layer, "bias", None) is not None: + self.output_layer.bias.data = torch.nn.functional.pad( + self.output_layer.bias.data, + ( + 0, + self.output_layer.weight.shape[0] - + self.output_layer.bias.shape[0], + ), + "constant", + 0, + ) + + +class BiTransformerDecoder(torch.nn.Module): + """Base class of Transfomer decoder module. + Args: + vocab_size: output dim + encoder_output_size: dimension of attention + attention_heads: the number of heads of multi head attention + linear_units: the hidden units number of position-wise feedforward + num_blocks: the number of decoder blocks + r_num_blocks: the number of right to left decoder blocks + dropout_rate: dropout rate + self_attention_dropout_rate: dropout rate for attention + input_layer: input layer type + use_output_layer: whether to use output layer + pos_enc_class: PositionalEncoding or ScaledPositionalEncoding + normalize_before: + True: use layer_norm before each sub-block of a layer. + False: use layer_norm after each sub-block of a layer. + key_bias: whether use bias in attention.linear_k, False for whisper models. + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + r_num_blocks: int = 0, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + normalize_before: bool = True, + key_bias: bool = True, + gradient_checkpointing: bool = False, + tie_word_embedding: bool = False, + ): + + super().__init__() + self.tie_word_embedding = tie_word_embedding + self.left_decoder = TransformerDecoder( + vocab_size, + encoder_output_size, + attention_heads, + linear_units, + num_blocks, + dropout_rate, + positional_dropout_rate, + self_attention_dropout_rate, + src_attention_dropout_rate, + input_layer, + use_output_layer, + normalize_before, + key_bias=key_bias, + gradient_checkpointing=gradient_checkpointing, + tie_word_embedding=tie_word_embedding) + + self.right_decoder = TransformerDecoder( + vocab_size, + encoder_output_size, + attention_heads, + linear_units, + r_num_blocks, + dropout_rate, + positional_dropout_rate, + self_attention_dropout_rate, + src_attention_dropout_rate, + input_layer, + use_output_layer, + normalize_before, + key_bias=key_bias, + gradient_checkpointing=gradient_checkpointing, + tie_word_embedding=tie_word_embedding) + + def forward( + self, + memory: torch.Tensor, + memory_mask: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + r_ys_in_pad: torch.Tensor, + reverse_weight: float = 0.0, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Forward decoder. + Args: + memory: encoded memory, float32 (batch, maxlen_in, feat) + memory_mask: encoder memory mask, (batch, 1, maxlen_in) + ys_in_pad: padded input token ids, int64 (batch, maxlen_out) + ys_in_lens: input lengths of this batch (batch) + r_ys_in_pad: padded input token ids, int64 (batch, maxlen_out), + used for right to left decoder + reverse_weight: used for right to left decoder + Returns: + (tuple): tuple containing: + x: decoded token score before softmax (batch, maxlen_out, + vocab_size) if use_output_layer is True, + r_x: x: decoded token score (right to left decoder) + before softmax (batch, maxlen_out, vocab_size) + if use_output_layer is True, + olens: (batch, ) + """ + l_x, _, olens = self.left_decoder(memory, memory_mask, ys_in_pad, + ys_in_lens) + r_x = torch.tensor(0.0) + if reverse_weight > 0.0: + r_x, _, olens = self.right_decoder(memory, memory_mask, + r_ys_in_pad, ys_in_lens) + return l_x, r_x, olens + + def forward_one_step( + self, + memory: torch.Tensor, + memory_mask: torch.Tensor, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + cache: Optional[List[torch.Tensor]] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """Forward one step. + This is only used for decoding. + Args: + memory: encoded memory, float32 (batch, maxlen_in, feat) + memory_mask: encoded memory mask, (batch, 1, maxlen_in) + tgt: input token ids, int64 (batch, maxlen_out) + tgt_mask: input token mask, (batch, maxlen_out) + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (include 1.2) + cache: cached output list of (batch, max_time_out-1, size) + Returns: + y, cache: NN output value and cache per `self.decoders`. + y.shape` is (batch, maxlen_out, token) + """ + return self.left_decoder.forward_one_step(memory, memory_mask, tgt, + tgt_mask, cache) + + def tie_or_clone_weights(self, jit_mode: bool = True): + """Tie or clone module weights (between word_emb and output_layer) + depending of whether we are using TorchScript or not""" + self.left_decoder.tie_or_clone_weights(jit_mode) + self.right_decoder.tie_or_clone_weights(jit_mode) diff --git a/cosyvoice/cosyvoice/transformer/decoder_layer.py b/cosyvoice/cosyvoice/transformer/decoder_layer.py new file mode 100644 index 0000000..91c7c5d --- /dev/null +++ b/cosyvoice/cosyvoice/transformer/decoder_layer.py @@ -0,0 +1,132 @@ +# Copyright (c) 2019 Shigeki Karita +# 2020 Mobvoi Inc (Binbin Zhang) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Decoder self-attention layer definition.""" +from typing import Optional, Tuple + +import torch +from torch import nn + + +class DecoderLayer(nn.Module): + """Single decoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + src_attn (torch.nn.Module): Inter-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + If `None` is passed, Inter-attention is not used, such as + CIF, GPT, and other decoder only model. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: to use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: nn.Module, + src_attn: Optional[nn.Module], + feed_forward: nn.Module, + dropout_rate: float, + normalize_before: bool = True, + ): + """Construct an DecoderLayer object.""" + super().__init__() + self.size = size + self.self_attn = self_attn + self.src_attn = src_attn + self.feed_forward = feed_forward + self.norm1 = nn.LayerNorm(size, eps=1e-5) + self.norm2 = nn.LayerNorm(size, eps=1e-5) + self.norm3 = nn.LayerNorm(size, eps=1e-5) + self.dropout = nn.Dropout(dropout_rate) + self.normalize_before = normalize_before + + def forward( + self, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + memory: torch.Tensor, + memory_mask: torch.Tensor, + cache: Optional[torch.Tensor] = None + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor + (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory + (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask + (#batch, maxlen_in). + cache (torch.Tensor): cached tensors. + (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + + if cache is None: + tgt_q = tgt + tgt_q_mask = tgt_mask + else: + # compute only the last frame query keeping dim: max_time_out -> 1 + assert cache.shape == ( + tgt.shape[0], + tgt.shape[1] - 1, + self.size, + ), "{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}" + tgt_q = tgt[:, -1:, :] + residual = residual[:, -1:, :] + tgt_q_mask = tgt_mask[:, -1:, :] + + x = residual + self.dropout( + self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0]) + if not self.normalize_before: + x = self.norm1(x) + + if self.src_attn is not None: + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + self.dropout( + self.src_attn(x, memory, memory, memory_mask)[0]) + if not self.normalize_before: + x = self.norm2(x) + + residual = x + if self.normalize_before: + x = self.norm3(x) + x = residual + self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm3(x) + + if cache is not None: + x = torch.cat([cache, x], dim=1) + + return x, tgt_mask, memory, memory_mask diff --git a/cosyvoice/cosyvoice/transformer/embedding.py b/cosyvoice/cosyvoice/transformer/embedding.py new file mode 100644 index 0000000..ba20d71 --- /dev/null +++ b/cosyvoice/cosyvoice/transformer/embedding.py @@ -0,0 +1,302 @@ +# Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Positonal Encoding Module.""" + +import math +from typing import Tuple, Union + +import torch +import torch.nn.functional as F +import numpy as np + + +class PositionalEncoding(torch.nn.Module): + """Positional encoding. + + :param int d_model: embedding dim + :param float dropout_rate: dropout rate + :param int max_len: maximum input length + + PE(pos, 2i) = sin(pos/(10000^(2i/dmodel))) + PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel))) + """ + + def __init__(self, + d_model: int, + dropout_rate: float, + max_len: int = 5000, + reverse: bool = False): + """Construct an PositionalEncoding object.""" + super().__init__() + self.d_model = d_model + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.max_len = max_len + + self.pe = torch.zeros(self.max_len, self.d_model) + position = torch.arange(0, self.max_len, + dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32) * + -(math.log(10000.0) / self.d_model)) + self.pe[:, 0::2] = torch.sin(position * div_term) + self.pe[:, 1::2] = torch.cos(position * div_term) + self.pe = self.pe.unsqueeze(0) + + def forward(self, + x: torch.Tensor, + offset: Union[int, torch.Tensor] = 0) \ + -> Tuple[torch.Tensor, torch.Tensor]: + """Add positional encoding. + + Args: + x (torch.Tensor): Input. Its shape is (batch, time, ...) + offset (int, torch.tensor): position offset + + Returns: + torch.Tensor: Encoded tensor. Its shape is (batch, time, ...) + torch.Tensor: for compatibility to RelPositionalEncoding + """ + + self.pe = self.pe.to(x.device) + pos_emb = self.position_encoding(offset, x.size(1), False) + x = x * self.xscale + pos_emb + return self.dropout(x), self.dropout(pos_emb) + + def position_encoding(self, + offset: Union[int, torch.Tensor], + size: int, + apply_dropout: bool = True) -> torch.Tensor: + """ For getting encoding in a streaming fashion + + Attention!!!!! + we apply dropout only once at the whole utterance level in a none + streaming way, but will call this function several times with + increasing input size in a streaming scenario, so the dropout will + be applied several times. + + Args: + offset (int or torch.tensor): start offset + size (int): required size of position encoding + + Returns: + torch.Tensor: Corresponding encoding + """ + # How to subscript a Union type: + # https://github.com/pytorch/pytorch/issues/69434 + if isinstance(offset, int): + assert offset + size <= self.max_len + pos_emb = self.pe[:, offset:offset + size] + elif isinstance(offset, torch.Tensor) and offset.dim() == 0: # scalar + assert offset + size <= self.max_len + pos_emb = self.pe[:, offset:offset + size] + else: # for batched streaming decoding on GPU + assert torch.max(offset) + size <= self.max_len + index = offset.unsqueeze(1) + \ + torch.arange(0, size).to(offset.device) # B X T + flag = index > 0 + # remove negative offset + index = index * flag + pos_emb = F.embedding(index, self.pe[0]) # B X T X d_model + + if apply_dropout: + pos_emb = self.dropout(pos_emb) + return pos_emb + + +class RelPositionalEncoding(PositionalEncoding): + """Relative positional encoding module. + See : Appendix B in https://arxiv.org/abs/1901.02860 + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000): + """Initialize class.""" + super().__init__(d_model, dropout_rate, max_len, reverse=True) + + def forward(self, + x: torch.Tensor, + offset: Union[int, torch.Tensor] = 0) \ + -> Tuple[torch.Tensor, torch.Tensor]: + """Compute positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + torch.Tensor: Positional embedding tensor (1, time, `*`). + """ + self.pe = self.pe.to(x.device) + x = x * self.xscale + pos_emb = self.position_encoding(offset, x.size(1), False) + return self.dropout(x), self.dropout(pos_emb) + + +class WhisperPositionalEncoding(PositionalEncoding): + """ Sinusoids position encoding used in openai-whisper.encoder + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 1500): + super().__init__(d_model, dropout_rate, max_len) + self.xscale = 1.0 + log_timescale_increment = np.log(10000) / (d_model // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * + torch.arange(d_model // 2)) + scaled_time = torch.arange(max_len)[:, np.newaxis] * \ + inv_timescales[np.newaxis, :] + pe = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) + delattr(self, "pe") + self.register_buffer("pe", pe.unsqueeze(0)) + + +class LearnablePositionalEncoding(PositionalEncoding): + """ Learnable position encoding used in openai-whisper.decoder + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 448): + super().__init__(d_model, dropout_rate, max_len) + # NOTE(xcsong): overwrite self.pe & self.xscale + self.pe = torch.nn.Parameter(torch.empty(1, max_len, d_model)) + self.xscale = 1.0 + + +class NoPositionalEncoding(torch.nn.Module): + """ No position encoding + """ + + def __init__(self, d_model: int, dropout_rate: float): + super().__init__() + self.d_model = d_model + self.dropout = torch.nn.Dropout(p=dropout_rate) + + def forward(self, + x: torch.Tensor, + offset: Union[int, torch.Tensor] = 0) \ + -> Tuple[torch.Tensor, torch.Tensor]: + """ Just return zero vector for interface compatibility + """ + pos_emb = torch.zeros(1, x.size(1), self.d_model).to(x.device) + return self.dropout(x), pos_emb + + def position_encoding(self, offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + return torch.zeros(1, size, self.d_model) + + +class EspnetRelPositionalEncoding(torch.nn.Module): + """Relative positional encoding module (new implementation). + + Details can be found in https://github.com/espnet/espnet/pull/2816. + + See : Appendix B in https://arxiv.org/abs/1901.02860 + + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000): + """Construct an PositionalEncoding object.""" + super(EspnetRelPositionalEncoding, self).__init__() + self.d_model = d_model + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x: torch.Tensor): + """Reset the positional encodings.""" + if self.pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if self.pe.size(1) >= x.size(1) * 2 - 1: + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` means to the position of query vecotr and `j` means the + # position of key vector. We use position relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i Tuple[torch.Tensor, torch.Tensor]: + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + + """ + self.extend_pe(x) + x = x * self.xscale + pos_emb = self.position_encoding(size=x.size(1), offset=offset) + return self.dropout(x), self.dropout(pos_emb) + + def position_encoding(self, + offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + """ For getting encoding in a streaming fashion + + Attention!!!!! + we apply dropout only once at the whole utterance level in a none + streaming way, but will call this function several times with + increasing input size in a streaming scenario, so the dropout will + be applied several times. + + Args: + offset (int or torch.tensor): start offset + size (int): required size of position encoding + + Returns: + torch.Tensor: Corresponding encoding + """ + # How to subscript a Union type: + # https://github.com/pytorch/pytorch/issues/69434 + if isinstance(offset, int): + pos_emb = self.pe[ + :, + self.pe.size(1) // 2 - size - offset + 1: self.pe.size(1) // 2 + size + offset, + ] + elif isinstance(offset, torch.Tensor): + pos_emb = self.pe[ + :, + self.pe.size(1) // 2 - size - offset + 1: self.pe.size(1) // 2 + size + offset, + ] + return pos_emb diff --git a/cosyvoice/cosyvoice/transformer/encoder.py b/cosyvoice/cosyvoice/transformer/encoder.py new file mode 100644 index 0000000..c5709d0 --- /dev/null +++ b/cosyvoice/cosyvoice/transformer/encoder.py @@ -0,0 +1,474 @@ +# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) +# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Encoder definition.""" +from typing import Tuple + +import torch +import torch.utils.checkpoint as ckpt + +from cosyvoice.transformer.convolution import ConvolutionModule +from cosyvoice.transformer.encoder_layer import TransformerEncoderLayer +from cosyvoice.transformer.encoder_layer import ConformerEncoderLayer +from cosyvoice.transformer.positionwise_feed_forward import PositionwiseFeedForward +from cosyvoice.utils.class_utils import ( + COSYVOICE_EMB_CLASSES, + COSYVOICE_SUBSAMPLE_CLASSES, + COSYVOICE_ATTENTION_CLASSES, + COSYVOICE_ACTIVATION_CLASSES, +) +from cosyvoice.utils.mask import make_pad_mask +from cosyvoice.utils.mask import add_optional_chunk_mask + + +class BaseEncoder(torch.nn.Module): + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + pos_enc_layer_type: str = "abs_pos", + normalize_before: bool = True, + static_chunk_size: int = 0, + use_dynamic_chunk: bool = False, + global_cmvn: torch.nn.Module = None, + use_dynamic_left_chunk: bool = False, + gradient_checkpointing: bool = False, + ): + """ + Args: + input_size (int): input dim + output_size (int): dimension of attention + attention_heads (int): the number of heads of multi head attention + linear_units (int): the hidden units number of position-wise feed + forward + num_blocks (int): the number of decoder blocks + dropout_rate (float): dropout rate + attention_dropout_rate (float): dropout rate in attention + positional_dropout_rate (float): dropout rate after adding + positional encoding + input_layer (str): input layer type. + optional [linear, conv2d, conv2d6, conv2d8] + pos_enc_layer_type (str): Encoder positional encoding layer type. + opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos] + normalize_before (bool): + True: use layer_norm before each sub-block of a layer. + False: use layer_norm after each sub-block of a layer. + static_chunk_size (int): chunk size for static chunk training and + decoding + use_dynamic_chunk (bool): whether use dynamic chunk size for + training or not, You can only use fixed chunk(chunk_size > 0) + or dyanmic chunk size(use_dynamic_chunk = True) + global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module + use_dynamic_left_chunk (bool): whether use dynamic left chunk in + dynamic chunk training + key_bias: whether use bias in attention.linear_k, False for whisper models. + gradient_checkpointing: rerunning a forward-pass segment for each + checkpointed segment during backward. + """ + super().__init__() + self._output_size = output_size + + self.global_cmvn = global_cmvn + self.embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer]( + input_size, + output_size, + dropout_rate, + COSYVOICE_EMB_CLASSES[pos_enc_layer_type](output_size, + positional_dropout_rate), + ) + + self.normalize_before = normalize_before + self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5) + self.static_chunk_size = static_chunk_size + self.use_dynamic_chunk = use_dynamic_chunk + self.use_dynamic_left_chunk = use_dynamic_left_chunk + self.gradient_checkpointing = gradient_checkpointing + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs: torch.Tensor, + xs_lens: torch.Tensor, + decoding_chunk_size: int = 0, + num_decoding_left_chunks: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Embed positions in tensor. + + Args: + xs: padded input tensor (B, T, D) + xs_lens: input length (B) + decoding_chunk_size: decoding chunk size for dynamic chunk + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + Returns: + encoder output tensor xs, and subsampled masks + xs: padded output tensor (B, T' ~= T/subsample_rate, D) + masks: torch.Tensor batch padding mask after subsample + (B, 1, T' ~= T/subsample_rate) + NOTE(xcsong): + We pass the `__call__` method of the modules instead of `forward` to the + checkpointing API because `__call__` attaches all the hooks of the module. + https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2 + """ + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) + if self.global_cmvn is not None: + xs = self.global_cmvn(xs) + xs, pos_emb, masks = self.embed(xs, masks) + mask_pad = masks # (B, 1, T/subsample_rate) + chunk_masks = add_optional_chunk_mask(xs, masks, + self.use_dynamic_chunk, + self.use_dynamic_left_chunk, + decoding_chunk_size, + self.static_chunk_size, + num_decoding_left_chunks) + if self.gradient_checkpointing and self.training: + xs = self.forward_layers_checkpointed(xs, chunk_masks, pos_emb, + mask_pad) + else: + xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad) + if self.normalize_before: + xs = self.after_norm(xs) + # Here we assume the mask is not changed in encoder layers, so just + # return the masks before encoder layers, and the masks will be used + # for cross attention with decoder later + return xs, masks + + def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor) -> torch.Tensor: + for layer in self.encoders: + xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) + return xs + + @torch.jit.unused + def forward_layers_checkpointed(self, xs: torch.Tensor, + chunk_masks: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor) -> torch.Tensor: + for layer in self.encoders: + xs, chunk_masks, _, _ = ckpt.checkpoint(layer.__call__, xs, + chunk_masks, pos_emb, + mask_pad) + return xs + + @torch.jit.export + def forward_chunk( + self, + xs: torch.Tensor, + offset: int, + required_cache_size: int, + att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ Forward just one chunk + + Args: + xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim), + where `time == (chunk_size - 1) * subsample_rate + \ + subsample.right_context + 1` + offset (int): current offset in encoder output time stamp + required_cache_size (int): cache size required for next chunk + compuation + >=0: actual cache size + <0: means all history cache is required + att_cache (torch.Tensor): cache tensor for KEY & VALUE in + transformer/conformer attention, with shape + (elayers, head, cache_t1, d_k * 2), where + `head * d_k == hidden-dim` and + `cache_t1 == chunk_size * num_decoding_left_chunks`. + cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, + (elayers, b=1, hidden-dim, cache_t2), where + `cache_t2 == cnn.lorder - 1` + + Returns: + torch.Tensor: output of current input xs, + with shape (b=1, chunk_size, hidden-dim). + torch.Tensor: new attention cache required for next chunk, with + dynamic shape (elayers, head, ?, d_k * 2) + depending on required_cache_size. + torch.Tensor: new conformer cnn cache required for next chunk, with + same shape as the original cnn_cache. + + """ + assert xs.size(0) == 1 + # tmp_masks is just for interface compatibility + tmp_masks = torch.ones(1, + xs.size(1), + device=xs.device, + dtype=torch.bool) + tmp_masks = tmp_masks.unsqueeze(1) + if self.global_cmvn is not None: + xs = self.global_cmvn(xs) + # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim) + xs, pos_emb, _ = self.embed(xs, tmp_masks, offset) + # NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim) + elayers, cache_t1 = att_cache.size(0), att_cache.size(2) + chunk_size = xs.size(1) + attention_key_size = cache_t1 + chunk_size + pos_emb = self.embed.position_encoding(offset=offset - cache_t1, + size=attention_key_size) + if required_cache_size < 0: + next_cache_start = 0 + elif required_cache_size == 0: + next_cache_start = attention_key_size + else: + next_cache_start = max(attention_key_size - required_cache_size, 0) + r_att_cache = [] + r_cnn_cache = [] + for i, layer in enumerate(self.encoders): + # NOTE(xcsong): Before layer.forward + # shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2), + # shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2) + xs, _, new_att_cache, new_cnn_cache = layer( + xs, + att_mask, + pos_emb, + att_cache=att_cache[i:i + 1] if elayers > 0 else att_cache, + cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache) + # NOTE(xcsong): After layer.forward + # shape(new_att_cache) is (1, head, attention_key_size, d_k * 2), + # shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2) + r_att_cache.append(new_att_cache[:, :, next_cache_start:, :]) + r_cnn_cache.append(new_cnn_cache.unsqueeze(0)) + if self.normalize_before: + xs = self.after_norm(xs) + + # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2), + # ? may be larger than cache_t1, it depends on required_cache_size + r_att_cache = torch.cat(r_att_cache, dim=0) + # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2) + r_cnn_cache = torch.cat(r_cnn_cache, dim=0) + + return (xs, r_att_cache, r_cnn_cache) + + @torch.jit.unused + def forward_chunk_by_chunk( + self, + xs: torch.Tensor, + decoding_chunk_size: int, + num_decoding_left_chunks: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ Forward input chunk by chunk with chunk_size like a streaming + fashion + + Here we should pay special attention to computation cache in the + streaming style forward chunk by chunk. Three things should be taken + into account for computation in the current network: + 1. transformer/conformer encoder layers output cache + 2. convolution in conformer + 3. convolution in subsampling + + However, we don't implement subsampling cache for: + 1. We can control subsampling module to output the right result by + overlapping input instead of cache left context, even though it + wastes some computation, but subsampling only takes a very + small fraction of computation in the whole model. + 2. Typically, there are several covolution layers with subsampling + in subsampling module, it is tricky and complicated to do cache + with different convolution layers with different subsampling + rate. + 3. Currently, nn.Sequential is used to stack all the convolution + layers in subsampling, we need to rewrite it to make it work + with cache, which is not preferred. + Args: + xs (torch.Tensor): (1, max_len, dim) + chunk_size (int): decoding chunk size + """ + assert decoding_chunk_size > 0 + # The model is trained by static or dynamic chunk + assert self.static_chunk_size > 0 or self.use_dynamic_chunk + subsampling = self.embed.subsampling_rate + context = self.embed.right_context + 1 # Add current frame + stride = subsampling * decoding_chunk_size + decoding_window = (decoding_chunk_size - 1) * subsampling + context + num_frames = xs.size(1) + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) + outputs = [] + offset = 0 + required_cache_size = decoding_chunk_size * num_decoding_left_chunks + + # Feed forward overlap input step by step + for cur in range(0, num_frames - context + 1, stride): + end = min(cur + decoding_window, num_frames) + chunk_xs = xs[:, cur:end, :] + (y, att_cache, + cnn_cache) = self.forward_chunk(chunk_xs, offset, + required_cache_size, att_cache, + cnn_cache) + outputs.append(y) + offset += y.size(1) + ys = torch.cat(outputs, 1) + masks = torch.ones((1, 1, ys.size(1)), + device=ys.device, + dtype=torch.bool) + return ys, masks + + +class TransformerEncoder(BaseEncoder): + """Transformer encoder module.""" + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + pos_enc_layer_type: str = "abs_pos", + normalize_before: bool = True, + static_chunk_size: int = 0, + use_dynamic_chunk: bool = False, + global_cmvn: torch.nn.Module = None, + use_dynamic_left_chunk: bool = False, + key_bias: bool = True, + selfattention_layer_type: str = "selfattn", + activation_type: str = "relu", + gradient_checkpointing: bool = False, + ): + """ Construct TransformerEncoder + + See Encoder for the meaning of each parameter. + """ + super().__init__(input_size, output_size, attention_heads, + linear_units, num_blocks, dropout_rate, + positional_dropout_rate, attention_dropout_rate, + input_layer, pos_enc_layer_type, normalize_before, + static_chunk_size, use_dynamic_chunk, global_cmvn, + use_dynamic_left_chunk, gradient_checkpointing) + activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]() + self.encoders = torch.nn.ModuleList([ + TransformerEncoderLayer( + output_size, + COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type](attention_heads, + output_size, + attention_dropout_rate, + key_bias), + PositionwiseFeedForward(output_size, linear_units, + dropout_rate, activation), + dropout_rate, normalize_before) for _ in range(num_blocks) + ]) + + +class ConformerEncoder(BaseEncoder): + """Conformer encoder module.""" + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + pos_enc_layer_type: str = "rel_pos", + normalize_before: bool = True, + static_chunk_size: int = 0, + use_dynamic_chunk: bool = False, + global_cmvn: torch.nn.Module = None, + use_dynamic_left_chunk: bool = False, + positionwise_conv_kernel_size: int = 1, + macaron_style: bool = True, + selfattention_layer_type: str = "rel_selfattn", + activation_type: str = "swish", + use_cnn_module: bool = True, + cnn_module_kernel: int = 15, + causal: bool = False, + cnn_module_norm: str = "batch_norm", + key_bias: bool = True, + gradient_checkpointing: bool = False, + ): + """Construct ConformerEncoder + + Args: + input_size to use_dynamic_chunk, see in BaseEncoder + positionwise_conv_kernel_size (int): Kernel size of positionwise + conv1d layer. + macaron_style (bool): Whether to use macaron style for + positionwise layer. + selfattention_layer_type (str): Encoder attention layer type, + the parameter has no effect now, it's just for configure + compatibility. + activation_type (str): Encoder activation function type. + use_cnn_module (bool): Whether to use convolution module. + cnn_module_kernel (int): Kernel size of convolution module. + causal (bool): whether to use causal convolution or not. + key_bias: whether use bias in attention.linear_k, False for whisper models. + """ + super().__init__(input_size, output_size, attention_heads, + linear_units, num_blocks, dropout_rate, + positional_dropout_rate, attention_dropout_rate, + input_layer, pos_enc_layer_type, normalize_before, + static_chunk_size, use_dynamic_chunk, global_cmvn, + use_dynamic_left_chunk, gradient_checkpointing) + activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]() + + # self-attention module definition + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + key_bias, + ) + # feed-forward module definition + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + activation, + ) + # convolution module definition + convolution_layer_args = (output_size, cnn_module_kernel, activation, + cnn_module_norm, causal) + + self.encoders = torch.nn.ModuleList([ + ConformerEncoderLayer( + output_size, + COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type]( + *encoder_selfattn_layer_args), + PositionwiseFeedForward(*positionwise_layer_args), + PositionwiseFeedForward( + *positionwise_layer_args) if macaron_style else None, + ConvolutionModule( + *convolution_layer_args) if use_cnn_module else None, + dropout_rate, + normalize_before, + ) for _ in range(num_blocks) + ]) diff --git a/cosyvoice/cosyvoice/transformer/encoder_layer.py b/cosyvoice/cosyvoice/transformer/encoder_layer.py new file mode 100644 index 0000000..efbb12d --- /dev/null +++ b/cosyvoice/cosyvoice/transformer/encoder_layer.py @@ -0,0 +1,236 @@ +# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) +# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Encoder self-attention layer definition.""" + +from typing import Optional, Tuple + +import torch +from torch import nn + + +class TransformerEncoderLayer(nn.Module): + """Encoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` + instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward`, instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: to use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: torch.nn.Module, + feed_forward: torch.nn.Module, + dropout_rate: float, + normalize_before: bool = True, + ): + """Construct an EncoderLayer object.""" + super().__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.norm1 = nn.LayerNorm(size, eps=1e-12) + self.norm2 = nn.LayerNorm(size, eps=1e-12) + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute encoded features. + + Args: + x (torch.Tensor): (#batch, time, size) + mask (torch.Tensor): Mask tensor for the input (#batch, time，time), + (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): just for interface compatibility + to ConformerEncoderLayer + mask_pad (torch.Tensor): does not used in transformer layer, + just for unified api with conformer. + att_cache (torch.Tensor): Cache tensor of the KEY & VALUE + (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. + cnn_cache (torch.Tensor): Convolution cache in conformer layer + (#batch=1, size, cache_t2), not used here, it's for interface + compatibility to ConformerEncoderLayer. + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time, time). + torch.Tensor: att_cache tensor, + (#batch=1, head, cache_t1 + time, d_k * 2). + torch.Tensor: cnn_cahce tensor (#batch=1, size, cache_t2). + + """ + residual = x + if self.normalize_before: + x = self.norm1(x) + x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb=pos_emb, cache=att_cache) + x = residual + self.dropout(x_att) + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm2(x) + + fake_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + return x, mask, new_att_cache, fake_cnn_cache + + +class ConformerEncoderLayer(nn.Module): + """Encoder layer module. + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` + instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward` instance can be used as the argument. + feed_forward_macaron (torch.nn.Module): Additional feed-forward module + instance. + `PositionwiseFeedForward` instance can be used as the argument. + conv_module (torch.nn.Module): Convolution module instance. + `ConvlutionModule` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: torch.nn.Module, + feed_forward: Optional[nn.Module] = None, + feed_forward_macaron: Optional[nn.Module] = None, + conv_module: Optional[nn.Module] = None, + dropout_rate: float = 0.1, + normalize_before: bool = True, + ): + """Construct an EncoderLayer object.""" + super().__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.feed_forward_macaron = feed_forward_macaron + self.conv_module = conv_module + self.norm_ff = nn.LayerNorm(size, eps=1e-12) # for the FNN module + self.norm_mha = nn.LayerNorm(size, eps=1e-12) # for the MHA module + if feed_forward_macaron is not None: + self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-12) + self.ff_scale = 0.5 + else: + self.ff_scale = 1.0 + if self.conv_module is not None: + self.norm_conv = nn.LayerNorm(size, eps=1e-12) # for the CNN module + self.norm_final = nn.LayerNorm( + size, eps=1e-12) # for the final output of the block + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute encoded features. + + Args: + x (torch.Tensor): (#batch, time, size) + mask (torch.Tensor): Mask tensor for the input (#batch, time，time), + (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): positional encoding, must not be None + for ConformerEncoderLayer. + mask_pad (torch.Tensor): batch padding mask used for conv module. + (#batch, 1，time), (0, 0, 0) means fake mask. + att_cache (torch.Tensor): Cache tensor of the KEY & VALUE + (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. + cnn_cache (torch.Tensor): Convolution cache in conformer layer + (#batch=1, size, cache_t2) + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time, time). + torch.Tensor: att_cache tensor, + (#batch=1, head, cache_t1 + time, d_k * 2). + torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2). + """ + + # whether to use macaron style + if self.feed_forward_macaron is not None: + residual = x + if self.normalize_before: + x = self.norm_ff_macaron(x) + x = residual + self.ff_scale * self.dropout( + self.feed_forward_macaron(x)) + if not self.normalize_before: + x = self.norm_ff_macaron(x) + + # multi-headed self-attention module + residual = x + if self.normalize_before: + x = self.norm_mha(x) + x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, + att_cache) + x = residual + self.dropout(x_att) + if not self.normalize_before: + x = self.norm_mha(x) + + # convolution module + # Fake new cnn cache here, and then change it in conv_module + new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + if self.conv_module is not None: + residual = x + if self.normalize_before: + x = self.norm_conv(x) + x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) + x = residual + self.dropout(x) + + if not self.normalize_before: + x = self.norm_conv(x) + + # feed forward module + residual = x + if self.normalize_before: + x = self.norm_ff(x) + + x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm_ff(x) + + if self.conv_module is not None: + x = self.norm_final(x) + + return x, mask, new_att_cache, new_cnn_cache diff --git a/cosyvoice/cosyvoice/transformer/label_smoothing_loss.py b/cosyvoice/cosyvoice/transformer/label_smoothing_loss.py new file mode 100644 index 0000000..feacabf --- /dev/null +++ b/cosyvoice/cosyvoice/transformer/label_smoothing_loss.py @@ -0,0 +1,96 @@ +# Copyright (c) 2019 Shigeki Karita +# 2020 Mobvoi Inc (Binbin Zhang) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Label smoothing module.""" + +import torch +from torch import nn + + +class LabelSmoothingLoss(nn.Module): + """Label-smoothing loss. + + In a standard CE loss, the label's data distribution is: + [0,1,2] -> + [ + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0], + ] + + In the smoothing version CE Loss,some probabilities + are taken from the true label prob (1.0) and are divided + among other labels. + + e.g. + smoothing=0.1 + [0,1,2] -> + [ + [0.9, 0.05, 0.05], + [0.05, 0.9, 0.05], + [0.05, 0.05, 0.9], + ] + + Args: + size (int): the number of class + padding_idx (int): padding class id which will be ignored for loss + smoothing (float): smoothing rate (0.0 means the conventional CE) + normalize_length (bool): + normalize loss by sequence length if True + normalize loss by batch size if False + """ + + def __init__(self, + size: int, + padding_idx: int, + smoothing: float, + normalize_length: bool = False): + """Construct an LabelSmoothingLoss object.""" + super(LabelSmoothingLoss, self).__init__() + self.criterion = nn.KLDivLoss(reduction="none") + self.padding_idx = padding_idx + self.confidence = 1.0 - smoothing + self.smoothing = smoothing + self.size = size + self.normalize_length = normalize_length + + def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + """Compute loss between x and target. + + The model outputs and data labels tensors are flatten to + (batch*seqlen, class) shape and a mask is applied to the + padding part which should not be calculated for loss. + + Args: + x (torch.Tensor): prediction (batch, seqlen, class) + target (torch.Tensor): + target signal masked with self.padding_id (batch, seqlen) + Returns: + loss (torch.Tensor) : The KL loss, scalar float value + """ + assert x.size(2) == self.size + batch_size = x.size(0) + x = x.view(-1, self.size) + target = target.view(-1) + # use zeros_like instead of torch.no_grad() for true_dist, + # since no_grad() can not be exported by JIT + true_dist = torch.zeros_like(x) + true_dist.fill_(self.smoothing / (self.size - 1)) + ignore = target == self.padding_idx # (B,) + total = len(target) - ignore.sum().item() + target = target.masked_fill(ignore, 0) # avoid -1 index + true_dist.scatter_(1, target.unsqueeze(1), self.confidence) + kl = self.criterion(torch.log_softmax(x, dim=1), true_dist) + denom = total if self.normalize_length else batch_size + return kl.masked_fill(ignore.unsqueeze(1), 0).sum() / denom diff --git a/cosyvoice/cosyvoice/transformer/positionwise_feed_forward.py b/cosyvoice/cosyvoice/transformer/positionwise_feed_forward.py new file mode 100644 index 0000000..b7a2cf6 --- /dev/null +++ b/cosyvoice/cosyvoice/transformer/positionwise_feed_forward.py @@ -0,0 +1,115 @@ +# Copyright (c) 2019 Shigeki Karita +# 2020 Mobvoi Inc (Binbin Zhang) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Positionwise feed forward layer definition.""" + +import torch + + +class PositionwiseFeedForward(torch.nn.Module): + """Positionwise feed forward layer. + + FeedForward are appied on each position of the sequence. + The output dim is same with the input dim. + + Args: + idim (int): Input dimenstion. + hidden_units (int): The number of hidden units. + dropout_rate (float): Dropout rate. + activation (torch.nn.Module): Activation function + """ + + def __init__( + self, + idim: int, + hidden_units: int, + dropout_rate: float, + activation: torch.nn.Module = torch.nn.ReLU(), + ): + """Construct a PositionwiseFeedForward object.""" + super(PositionwiseFeedForward, self).__init__() + self.w_1 = torch.nn.Linear(idim, hidden_units) + self.activation = activation + self.dropout = torch.nn.Dropout(dropout_rate) + self.w_2 = torch.nn.Linear(hidden_units, idim) + + def forward(self, xs: torch.Tensor) -> torch.Tensor: + """Forward function. + + Args: + xs: input tensor (B, L, D) + Returns: + output tensor, (B, L, D) + """ + return self.w_2(self.dropout(self.activation(self.w_1(xs)))) + + +class MoEFFNLayer(torch.nn.Module): + """ + Mixture of expert with Positionwise feed forward layer + See also figure 1 in https://arxiv.org/pdf/2305.15663.pdf + The output dim is same with the input dim. + + Modified from https://github.com/Lightning-AI/lit-gpt/pull/823 + https://github.com/mistralai/mistral-src/blob/b46d6/moe_one_file_ref.py#L203-L219 + Args: + n_expert: number of expert. + n_expert_per_token: The actual number of experts used for each frame + idim (int): Input dimenstion. + hidden_units (int): The number of hidden units. + dropout_rate (float): Dropout rate. + activation (torch.nn.Module): Activation function + """ + + def __init__( + self, + n_expert: int, + n_expert_per_token: int, + idim: int, + hidden_units: int, + dropout_rate: float, + activation: torch.nn.Module = torch.nn.ReLU(), + ): + super(MoEFFNLayer, self).__init__() + self.gate = torch.nn.Linear(idim, n_expert, bias=False) + self.experts = torch.nn.ModuleList( + PositionwiseFeedForward(idim, hidden_units, dropout_rate, + activation) for _ in range(n_expert)) + self.n_expert_per_token = n_expert_per_token + + def forward(self, xs: torch.Tensor) -> torch.Tensor: + """Foward function. + Args: + xs: input tensor (B, L, D) + Returns: + output tensor, (B, L, D) + + """ + B, L, D = xs.size( + ) # batch size, sequence length, embedding dimension (idim) + xs = xs.view(-1, D) # (B*L, D) + router = self.gate(xs) # (B*L, n_expert) + logits, indices = torch.topk( + router, self.n_expert_per_token + ) # probs:(B*L, n_expert), indices: (B*L, n_expert) + weights = torch.nn.functional.softmax( + logits, dim=1, + dtype=torch.float).to(dtype=xs.dtype) # (B*L, n_expert_per_token) + output = torch.zeros_like(xs) # (B*L, D) + for i, expert in enumerate(self.experts): + mask = indices == i + batch_idx, ith_expert = torch.where(mask) + output[batch_idx] += weights[batch_idx, ith_expert, None] * expert( + xs[batch_idx]) + return output.view(B, L, D) diff --git a/cosyvoice/cosyvoice/transformer/subsampling.py b/cosyvoice/cosyvoice/transformer/subsampling.py new file mode 100644 index 0000000..e17c2e3 --- /dev/null +++ b/cosyvoice/cosyvoice/transformer/subsampling.py @@ -0,0 +1,383 @@ +# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Subsampling layer definition.""" + +from typing import Tuple, Union + +import torch + + +class BaseSubsampling(torch.nn.Module): + + def __init__(self): + super().__init__() + self.right_context = 0 + self.subsampling_rate = 1 + + def position_encoding(self, offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + return self.pos_enc.position_encoding(offset, size) + + +class EmbedinigNoSubsampling(BaseSubsampling): + """Embedding input without subsampling + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + super().__init__() + self.embed = torch.nn.Embedding(idim, odim) + self.pos_enc = pos_enc_class + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Input x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: linear input tensor (#batch, time', odim), + where time' = time . + torch.Tensor: linear input mask (#batch, 1, time'), + where time' = time . + + """ + x = self.embed(x) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask + + +class LinearNoSubsampling(BaseSubsampling): + """Linear transform the input without subsampling + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + """Construct an linear object.""" + super().__init__() + self.out = torch.nn.Sequential( + torch.nn.Linear(idim, odim), + torch.nn.LayerNorm(odim, eps=1e-5), + torch.nn.Dropout(dropout_rate), + ) + self.pos_enc = pos_enc_class + self.right_context = 0 + self.subsampling_rate = 1 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Input x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: linear input tensor (#batch, time', odim), + where time' = time . + torch.Tensor: linear input mask (#batch, 1, time'), + where time' = time . + + """ + x = self.out(x) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask + + +class Conv1dSubsampling2(BaseSubsampling): + """Convolutional 1D subsampling (to 1/2 length). + It is designed for Whisper, ref: + https://github.com/openai/whisper/blob/main/whisper/model.py + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + """Construct an Conv1dSubsampling2 object.""" + super().__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv1d(idim, odim, kernel_size=3, padding=1), + torch.nn.GELU(), + torch.nn.Conv1d(odim, odim, kernel_size=3, stride=2, padding=1), + torch.nn.GELU(), + ) + self.pos_enc = pos_enc_class + # The right context for every conv layer is computed by: + # (kernel_size - 1) * frame_rate_of_this_layer + self.subsampling_rate = 2 + # 4 = (3 - 1) * 1 + (3 - 1) * 1 + self.right_context = 4 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 2. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 2. + torch.Tensor: positional encoding + + """ + time = x.size(1) + x = x.transpose(1, 2) # (b, f, t) + x = self.conv(x) + x = x.transpose(1, 2) # (b, t, f) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask[:, :, (time + 1) % 2::2] + + +class Conv2dSubsampling4(BaseSubsampling): + """Convolutional 2D subsampling (to 1/4 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + """Construct an Conv2dSubsampling4 object.""" + super().__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 2), + torch.nn.ReLU(), + ) + self.out = torch.nn.Sequential( + torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim)) + self.pos_enc = pos_enc_class + # The right context for every conv layer is computed by: + # (kernel_size - 1) * frame_rate_of_this_layer + self.subsampling_rate = 4 + # 6 = (3 - 1) * 1 + (3 - 1) * 2 + self.right_context = 6 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 4. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 4. + torch.Tensor: positional encoding + + """ + x = x.unsqueeze(1) # (b, c=1, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2] + + +class Conv2dSubsampling6(BaseSubsampling): + """Convolutional 2D subsampling (to 1/6 length). + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + pos_enc (torch.nn.Module): Custom position encoding layer. + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + """Construct an Conv2dSubsampling6 object.""" + super().__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 5, 3), + torch.nn.ReLU(), + ) + self.linear = torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), + odim) + self.pos_enc = pos_enc_class + # 10 = (3 - 1) * 1 + (5 - 1) * 2 + self.subsampling_rate = 6 + self.right_context = 10 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Subsample x. + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 6. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 6. + torch.Tensor: positional encoding + """ + x = x.unsqueeze(1) # (b, c, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f)) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask[:, :, 2::2][:, :, 4::3] + + +class Conv2dSubsampling8(BaseSubsampling): + """Convolutional 2D subsampling (to 1/8 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + """Construct an Conv2dSubsampling8 object.""" + super().__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 2), + torch.nn.ReLU(), + ) + self.linear = torch.nn.Linear( + odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim) + self.pos_enc = pos_enc_class + self.subsampling_rate = 8 + # 14 = (3 - 1) * 1 + (3 - 1) * 2 + (3 - 1) * 4 + self.right_context = 14 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 8. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 8. + torch.Tensor: positional encoding + """ + x = x.unsqueeze(1) # (b, c, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f)) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2][:, :, 2::2] + + +class LegacyLinearNoSubsampling(BaseSubsampling): + """Linear transform the input without subsampling + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module): + """Construct an linear object.""" + super().__init__() + self.out = torch.nn.Sequential( + torch.nn.Linear(idim, odim), + torch.nn.LayerNorm(odim, eps=1e-5), + torch.nn.Dropout(dropout_rate), + torch.nn.ReLU(), + ) + self.pos_enc = pos_enc_class + self.right_context = 0 + self.subsampling_rate = 1 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Input x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: linear input tensor (#batch, time', odim), + where time' = time . + torch.Tensor: linear input mask (#batch, 1, time'), + where time' = time . + + """ + x = self.out(x) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask diff --git a/cosyvoice/cosyvoice/transformer/upsample_encoder.py b/cosyvoice/cosyvoice/transformer/upsample_encoder.py new file mode 100644 index 0000000..baf7481 --- /dev/null +++ b/cosyvoice/cosyvoice/transformer/upsample_encoder.py @@ -0,0 +1,321 @@ +# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) +# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) +# 2024 Alibaba Inc (Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Encoder definition.""" +from typing import Tuple + +import torch +from torch import nn +from torch.nn import functional as F + +from cosyvoice.transformer.convolution import ConvolutionModule +from cosyvoice.transformer.encoder_layer import ConformerEncoderLayer +from cosyvoice.transformer.positionwise_feed_forward import PositionwiseFeedForward +from cosyvoice.utils.class_utils import ( + COSYVOICE_EMB_CLASSES, + COSYVOICE_SUBSAMPLE_CLASSES, + COSYVOICE_ATTENTION_CLASSES, + COSYVOICE_ACTIVATION_CLASSES, +) +from cosyvoice.utils.mask import make_pad_mask +from cosyvoice.utils.mask import add_optional_chunk_mask + + +class Upsample1D(nn.Module): + """A 1D upsampling layer with an optional convolution. + + Parameters: + channels (`int`): + number of channels in the inputs and outputs. + use_conv (`bool`, default `False`): + option to use a convolution. + use_conv_transpose (`bool`, default `False`): + option to use a convolution transpose. + out_channels (`int`, optional): + number of output channels. Defaults to `channels`. + """ + + def __init__(self, channels: int, out_channels: int, stride: int = 2): + super().__init__() + self.channels = channels + self.out_channels = out_channels + self.stride = stride + # In this mode, first repeat interpolate, than conv with stride=1 + self.conv = nn.Conv1d(self.channels, self.out_channels, stride * 2 + 1, stride=1, padding=0) + + def forward(self, inputs: torch.Tensor, input_lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + outputs = F.interpolate(inputs, scale_factor=float(self.stride), mode="nearest") + outputs = F.pad(outputs, (self.stride * 2, 0), value=0.0) + outputs = self.conv(outputs) + return outputs, input_lengths * self.stride + + +class PreLookaheadLayer(nn.Module): + def __init__(self, in_channels: int, channels: int, pre_lookahead_len: int = 1): + super().__init__() + self.in_channels = in_channels + self.channels = channels + self.pre_lookahead_len = pre_lookahead_len + self.conv1 = nn.Conv1d( + in_channels, channels, + kernel_size=pre_lookahead_len + 1, + stride=1, padding=0, + ) + self.conv2 = nn.Conv1d( + channels, in_channels, + kernel_size=3, stride=1, padding=0, + ) + + def forward(self, inputs: torch.Tensor, context: torch.Tensor = torch.zeros(0, 0, 0)) -> torch.Tensor: + """ + inputs: (batch_size, seq_len, channels) + """ + outputs = inputs.transpose(1, 2).contiguous() + context = context.transpose(1, 2).contiguous() + # look ahead + if context.size(2) == 0: + outputs = F.pad(outputs, (0, self.pre_lookahead_len), mode='constant', value=0.0) + else: + assert self.training is False, 'you have passed context, make sure that you are running inference mode' + assert context.size(2) == self.pre_lookahead_len + outputs = F.pad(torch.concat([outputs, context], dim=2), (0, self.pre_lookahead_len - context.size(2)), mode='constant', value=0.0) + outputs = F.leaky_relu(self.conv1(outputs)) + # outputs + outputs = F.pad(outputs, (self.conv2.kernel_size[0] - 1, 0), mode='constant', value=0.0) + outputs = self.conv2(outputs) + outputs = outputs.transpose(1, 2).contiguous() + + # residual connection + outputs = outputs + inputs + return outputs + + +class UpsampleConformerEncoder(torch.nn.Module): + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + pos_enc_layer_type: str = "rel_pos", + normalize_before: bool = True, + static_chunk_size: int = 0, + use_dynamic_chunk: bool = False, + global_cmvn: torch.nn.Module = None, + use_dynamic_left_chunk: bool = False, + positionwise_conv_kernel_size: int = 1, + macaron_style: bool = True, + selfattention_layer_type: str = "rel_selfattn", + activation_type: str = "swish", + use_cnn_module: bool = True, + cnn_module_kernel: int = 15, + causal: bool = False, + cnn_module_norm: str = "batch_norm", + key_bias: bool = True, + gradient_checkpointing: bool = False, + ): + """ + Args: + input_size (int): input dim + output_size (int): dimension of attention + attention_heads (int): the number of heads of multi head attention + linear_units (int): the hidden units number of position-wise feed + forward + num_blocks (int): the number of decoder blocks + dropout_rate (float): dropout rate + attention_dropout_rate (float): dropout rate in attention + positional_dropout_rate (float): dropout rate after adding + positional encoding + input_layer (str): input layer type. + optional [linear, conv2d, conv2d6, conv2d8] + pos_enc_layer_type (str): Encoder positional encoding layer type. + opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos] + normalize_before (bool): + True: use layer_norm before each sub-block of a layer. + False: use layer_norm after each sub-block of a layer. + static_chunk_size (int): chunk size for static chunk training and + decoding + use_dynamic_chunk (bool): whether use dynamic chunk size for + training or not, You can only use fixed chunk(chunk_size > 0) + or dyanmic chunk size(use_dynamic_chunk = True) + global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module + use_dynamic_left_chunk (bool): whether use dynamic left chunk in + dynamic chunk training + key_bias: whether use bias in attention.linear_k, False for whisper models. + gradient_checkpointing: rerunning a forward-pass segment for each + checkpointed segment during backward. + """ + super().__init__() + self._output_size = output_size + + self.global_cmvn = global_cmvn + self.embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer]( + input_size, + output_size, + dropout_rate, + COSYVOICE_EMB_CLASSES[pos_enc_layer_type](output_size, + positional_dropout_rate), + ) + + self.normalize_before = normalize_before + self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5) + self.static_chunk_size = static_chunk_size + self.use_dynamic_chunk = use_dynamic_chunk + self.use_dynamic_left_chunk = use_dynamic_left_chunk + self.gradient_checkpointing = gradient_checkpointing + activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]() + # self-attention module definition + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + key_bias, + ) + # feed-forward module definition + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + activation, + ) + # convolution module definition + convolution_layer_args = (output_size, cnn_module_kernel, activation, + cnn_module_norm, causal) + self.pre_lookahead_layer = PreLookaheadLayer(in_channels=512, channels=512, pre_lookahead_len=3) + self.encoders = torch.nn.ModuleList([ + ConformerEncoderLayer( + output_size, + COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type]( + *encoder_selfattn_layer_args), + PositionwiseFeedForward(*positionwise_layer_args), + PositionwiseFeedForward( + *positionwise_layer_args) if macaron_style else None, + ConvolutionModule( + *convolution_layer_args) if use_cnn_module else None, + dropout_rate, + normalize_before, + ) for _ in range(num_blocks) + ]) + self.up_layer = Upsample1D(channels=512, out_channels=512, stride=2) + self.up_embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer]( + input_size, + output_size, + dropout_rate, + COSYVOICE_EMB_CLASSES[pos_enc_layer_type](output_size, + positional_dropout_rate), + ) + self.up_encoders = torch.nn.ModuleList([ + ConformerEncoderLayer( + output_size, + COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type]( + *encoder_selfattn_layer_args), + PositionwiseFeedForward(*positionwise_layer_args), + PositionwiseFeedForward( + *positionwise_layer_args) if macaron_style else None, + ConvolutionModule( + *convolution_layer_args) if use_cnn_module else None, + dropout_rate, + normalize_before, + ) for _ in range(4) + ]) + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs: torch.Tensor, + xs_lens: torch.Tensor, + context: torch.Tensor = torch.zeros(0, 0, 0), + decoding_chunk_size: int = 0, + num_decoding_left_chunks: int = -1, + streaming: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Embed positions in tensor. + + Args: + xs: padded input tensor (B, T, D) + xs_lens: input length (B) + decoding_chunk_size: decoding chunk size for dynamic chunk + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + Returns: + encoder output tensor xs, and subsampled masks + xs: padded output tensor (B, T' ~= T/subsample_rate, D) + masks: torch.Tensor batch padding mask after subsample + (B, 1, T' ~= T/subsample_rate) + NOTE(xcsong): + We pass the `__call__` method of the modules instead of `forward` to the + checkpointing API because `__call__` attaches all the hooks of the module. + https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2 + """ + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) + if self.global_cmvn is not None: + xs = self.global_cmvn(xs) + xs, pos_emb, masks = self.embed(xs, masks) + if context.size(1) != 0: + assert self.training is False, 'you have passed context, make sure that you are running inference mode' + context_masks = torch.ones(1, 1, context.size(1)).to(masks) + context, _, _ = self.embed(context, context_masks, offset=xs.size(1)) + mask_pad = masks # (B, 1, T/subsample_rate) + chunk_masks = add_optional_chunk_mask(xs, masks, False, False, 0, self.static_chunk_size if streaming is True else 0, -1) + # lookahead + conformer encoder + xs = self.pre_lookahead_layer(xs, context=context) + xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad) + + # upsample + conformer encoder + xs = xs.transpose(1, 2).contiguous() + xs, xs_lens = self.up_layer(xs, xs_lens) + xs = xs.transpose(1, 2).contiguous() + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) + xs, pos_emb, masks = self.up_embed(xs, masks) + mask_pad = masks # (B, 1, T/subsample_rate) + chunk_masks = add_optional_chunk_mask(xs, masks, False, False, 0, self.static_chunk_size * self.up_layer.stride if streaming is True else 0, -1) + xs = self.forward_up_layers(xs, chunk_masks, pos_emb, mask_pad) + + if self.normalize_before: + xs = self.after_norm(xs) + # Here we assume the mask is not changed in encoder layers, so just + # return the masks before encoder layers, and the masks will be used + # for cross attention with decoder later + return xs, masks + + def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor) -> torch.Tensor: + for layer in self.encoders: + xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) + return xs + + def forward_up_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor) -> torch.Tensor: + for layer in self.up_encoders: + xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) + return xs diff --git a/cosyvoice/cosyvoice/utils/__init__.py b/cosyvoice/cosyvoice/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/cosyvoice/cosyvoice/utils/class_utils.py b/cosyvoice/cosyvoice/utils/class_utils.py new file mode 100644 index 0000000..aab8326 --- /dev/null +++ b/cosyvoice/cosyvoice/utils/class_utils.py @@ -0,0 +1,85 @@ +# Copyright [2023-11-28] +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch + +from cosyvoice.transformer.activation import Swish +from cosyvoice.transformer.subsampling import ( + LinearNoSubsampling, + EmbedinigNoSubsampling, + Conv1dSubsampling2, + Conv2dSubsampling4, + Conv2dSubsampling6, + Conv2dSubsampling8, +) +from cosyvoice.transformer.embedding import (PositionalEncoding, + RelPositionalEncoding, + WhisperPositionalEncoding, + LearnablePositionalEncoding, + NoPositionalEncoding) +from cosyvoice.transformer.attention import (MultiHeadedAttention, + RelPositionMultiHeadedAttention) +from cosyvoice.transformer.embedding import EspnetRelPositionalEncoding +from cosyvoice.transformer.subsampling import LegacyLinearNoSubsampling +from cosyvoice.llm.llm import TransformerLM, Qwen2LM, CosyVoice3LM +from cosyvoice.flow.flow import MaskedDiffWithXvec, CausalMaskedDiffWithXvec, CausalMaskedDiffWithDiT +from cosyvoice.hifigan.generator import HiFTGenerator, CausalHiFTGenerator +from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model, CosyVoice3Model + + +COSYVOICE_ACTIVATION_CLASSES = { + "hardtanh": torch.nn.Hardtanh, + "tanh": torch.nn.Tanh, + "relu": torch.nn.ReLU, + "selu": torch.nn.SELU, + "swish": getattr(torch.nn, "SiLU", Swish), + "gelu": torch.nn.GELU, +} + +COSYVOICE_SUBSAMPLE_CLASSES = { + "linear": LinearNoSubsampling, + "linear_legacy": LegacyLinearNoSubsampling, + "embed": EmbedinigNoSubsampling, + "conv1d2": Conv1dSubsampling2, + "conv2d": Conv2dSubsampling4, + "conv2d6": Conv2dSubsampling6, + "conv2d8": Conv2dSubsampling8, + 'paraformer_dummy': torch.nn.Identity +} + +COSYVOICE_EMB_CLASSES = { + "embed": PositionalEncoding, + "abs_pos": PositionalEncoding, + "rel_pos": RelPositionalEncoding, + "rel_pos_espnet": EspnetRelPositionalEncoding, + "no_pos": NoPositionalEncoding, + "abs_pos_whisper": WhisperPositionalEncoding, + "embed_learnable_pe": LearnablePositionalEncoding, +} + +COSYVOICE_ATTENTION_CLASSES = { + "selfattn": MultiHeadedAttention, + "rel_selfattn": RelPositionMultiHeadedAttention, +} + + +def get_model_type(configs): + # NOTE CosyVoice2Model inherits CosyVoiceModel + if isinstance(configs['llm'], TransformerLM) and isinstance(configs['flow'], MaskedDiffWithXvec) and isinstance(configs['hift'], HiFTGenerator): + return CosyVoiceModel + if isinstance(configs['llm'], Qwen2LM) and isinstance(configs['flow'], CausalMaskedDiffWithXvec) and isinstance(configs['hift'], HiFTGenerator): + return CosyVoice2Model + if isinstance(configs['llm'], CosyVoice3LM) and isinstance(configs['flow'], CausalMaskedDiffWithDiT) and isinstance(configs['hift'], CausalHiFTGenerator): + return CosyVoice3Model + raise TypeError('No valid model type found!') diff --git a/cosyvoice/cosyvoice/utils/common.py b/cosyvoice/cosyvoice/utils/common.py new file mode 100644 index 0000000..5d307ae --- /dev/null +++ b/cosyvoice/cosyvoice/utils/common.py @@ -0,0 +1,213 @@ +# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# 2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +"""Unility functions for Transformer.""" + +import queue +import random +from typing import List + +import numpy as np +import torch + +IGNORE_ID = -1 + +instruct_list = ["You are a helpful assistant. 请用广东话表达。<|endofprompt|>", + "You are a helpful assistant. 请用东北话表达。<|endofprompt|>", + "You are a helpful assistant. 请用甘肃话表达。<|endofprompt|>", + "You are a helpful assistant. 请用贵州话表达。<|endofprompt|>", + "You are a helpful assistant. 请用河南话表达。<|endofprompt|>", + "You are a helpful assistant. 请用湖北话表达。<|endofprompt|>", + "You are a helpful assistant. 请用湖南话表达。<|endofprompt|>", + "You are a helpful assistant. 请用江西话表达。<|endofprompt|>", + "You are a helpful assistant. 请用闽南话表达。<|endofprompt|>", + "You are a helpful assistant. 请用宁夏话表达。<|endofprompt|>", + "You are a helpful assistant. 请用山西话表达。<|endofprompt|>", + "You are a helpful assistant. 请用陕西话表达。<|endofprompt|>", + "You are a helpful assistant. 请用山东话表达。<|endofprompt|>", + "You are a helpful assistant. 请用上海话表达。<|endofprompt|>", + "You are a helpful assistant. 请用四川话表达。<|endofprompt|>", + "You are a helpful assistant. 请用天津话表达。<|endofprompt|>", + "You are a helpful assistant. 请用云南话表达。<|endofprompt|>", + "You are a helpful assistant. Please say a sentence as loudly as possible.<|endofprompt|>", + "You are a helpful assistant. Please say a sentence in a very soft voice.<|endofprompt|>", + "You are a helpful assistant. 请用尽可能慢地语速说一句话。<|endofprompt|>", + "You are a helpful assistant. 请用尽可能快地语速说一句话。<|endofprompt|>", + "You are a helpful assistant. 请非常开心地说一句话。<|endofprompt|>", + "You are a helpful assistant. 请非常伤心地说一句话。<|endofprompt|>", + "You are a helpful assistant. 请非常生气地说一句话。<|endofprompt|>", + "You are a helpful assistant. 我想体验一下小猪佩奇风格，可以吗？<|endofprompt|>", + "You are a helpful assistant. 你可以尝试用机器人的方式解答吗？<|endofprompt|>"] + + +def pad_list(xs: List[torch.Tensor], pad_value: int): + """Perform padding for the list of tensors. + + Args: + xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)]. + pad_value (float): Value for padding. + + Returns: + Tensor: Padded tensor (B, Tmax, `*`). + + Examples: + >>> x = [torch.ones(4), torch.ones(2), torch.ones(1)] + >>> x + [tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])] + >>> pad_list(x, 0) + tensor([[1., 1., 1., 1.], + [1., 1., 0., 0.], + [1., 0., 0., 0.]]) + + """ + max_len = max([len(item) for item in xs]) + batchs = len(xs) + ndim = xs[0].ndim + if ndim == 1: + pad_res = torch.zeros(batchs, + max_len, + dtype=xs[0].dtype, + device=xs[0].device) + elif ndim == 2: + pad_res = torch.zeros(batchs, + max_len, + xs[0].shape[1], + dtype=xs[0].dtype, + device=xs[0].device) + elif ndim == 3: + pad_res = torch.zeros(batchs, + max_len, + xs[0].shape[1], + xs[0].shape[2], + dtype=xs[0].dtype, + device=xs[0].device) + else: + raise ValueError(f"Unsupported ndim: {ndim}") + pad_res.fill_(pad_value) + for i in range(batchs): + pad_res[i, :len(xs[i])] = xs[i] + return pad_res + + +def th_accuracy(pad_outputs: torch.Tensor, pad_targets: torch.Tensor, + ignore_label: int) -> torch.Tensor: + """Calculate accuracy. + + Args: + pad_outputs (Tensor): Prediction tensors (B * Lmax, D). + pad_targets (LongTensor): Target label tensors (B, Lmax). + ignore_label (int): Ignore label id. + + Returns: + torch.Tensor: Accuracy value (0.0 - 1.0). + + """ + pad_pred = pad_outputs.view(pad_targets.size(0), pad_targets.size(1), + pad_outputs.size(1)).argmax(2) + mask = pad_targets != ignore_label + numerator = torch.sum( + pad_pred.masked_select(mask) == pad_targets.masked_select(mask)) + denominator = torch.sum(mask) + return (numerator / denominator).detach() + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +# Repetition Aware Sampling in VALL-E 2 +def ras_sampling(weighted_scores, decoded_tokens, sampling, top_p=0.8, top_k=25, win_size=10, tau_r=0.1): + top_ids = nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k) + rep_num = (torch.tensor(decoded_tokens[-win_size:]).to(weighted_scores.device) == top_ids).sum().item() + if rep_num >= win_size * tau_r: + top_ids = random_sampling(weighted_scores, decoded_tokens, sampling) + return top_ids + + +def nucleus_sampling(weighted_scores, top_p=0.8, top_k=25): + prob, indices = [], [] + cum_prob = 0.0 + sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(descending=True, stable=True) + for i in range(len(sorted_idx)): + # sampling both top-p and numbers. + if cum_prob < top_p and len(prob) < top_k: + cum_prob += sorted_value[i] + prob.append(sorted_value[i]) + indices.append(sorted_idx[i]) + else: + break + prob = torch.tensor(prob).to(weighted_scores) + indices = torch.tensor(indices, dtype=torch.long).to(weighted_scores.device) + top_ids = indices[prob.multinomial(1, replacement=True)].item() + return top_ids + + +def random_sampling(weighted_scores, decoded_tokens, sampling): + top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True).item() + return top_ids + + +def fade_in_out(fade_in_mel, fade_out_mel, window): + device = fade_in_mel.device + fade_in_mel, fade_out_mel = fade_in_mel.cpu(), fade_out_mel.cpu() + mel_overlap_len = int(window.shape[0] / 2) + if fade_in_mel.device == torch.device('cpu'): + fade_in_mel = fade_in_mel.clone() + fade_in_mel[..., :mel_overlap_len] = fade_in_mel[..., :mel_overlap_len] * window[:mel_overlap_len] + \ + fade_out_mel[..., -mel_overlap_len:] * window[mel_overlap_len:] + return fade_in_mel.to(device) + + +def set_all_random_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def mask_to_bias(mask: torch.Tensor, dtype: torch.dtype) -> torch.Tensor: + assert mask.dtype == torch.bool + assert dtype in [torch.float32, torch.bfloat16, torch.float16] + mask = mask.to(dtype) + # attention mask bias + # NOTE(Mddct): torch.finfo jit issues + # chunk_masks = (1.0 - chunk_masks) * torch.finfo(dtype).min + mask = (1.0 - mask) * -1.0e+10 + return mask + + +class TrtContextWrapper: + def __init__(self, trt_engine, trt_concurrent=1, device='cuda:0'): + self.trt_context_pool = queue.Queue(maxsize=trt_concurrent) + self.trt_engine = trt_engine + for _ in range(trt_concurrent): + trt_context = trt_engine.create_execution_context() + trt_stream = torch.cuda.stream(torch.cuda.Stream(device)) + assert trt_context is not None, 'failed to create trt context, maybe not enough CUDA memory, try reduce current trt concurrent {}'.format(trt_concurrent) + self.trt_context_pool.put([trt_context, trt_stream]) + assert self.trt_context_pool.empty() is False, 'no avaialbe estimator context' + + def acquire_estimator(self): + return self.trt_context_pool.get(), self.trt_engine + + def release_estimator(self, context, stream): + self.trt_context_pool.put([context, stream]) diff --git a/cosyvoice/cosyvoice/utils/executor.py b/cosyvoice/cosyvoice/utils/executor.py new file mode 100644 index 0000000..f08fa09 --- /dev/null +++ b/cosyvoice/cosyvoice/utils/executor.py @@ -0,0 +1,176 @@ +# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +from contextlib import nullcontext +import os + +import torch +import torch.distributed as dist + +from cosyvoice.utils.train_utils import update_parameter_and_lr, log_per_step, log_per_save, batch_forward, batch_backward, save_model, cosyvoice_join + + +class Executor: + + def __init__(self, gan: bool = False, ref_model: torch.nn.Module = None, dpo_loss: torch.nn.Module = None): + self.gan = gan + self.ref_model = ref_model + self.dpo_loss = dpo_loss + self.step = 0 + self.epoch = 0 + self.rank = int(os.environ.get('RANK', 0)) + self.device = torch.device('cuda:{}'.format(self.rank)) + + def train_one_epoc(self, model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, scaler, group_join, ref_model=None): + ''' Train one epoch + ''' + + lr = optimizer.param_groups[0]['lr'] + logging.info('Epoch {} TRAIN info lr {} rank {}'.format(self.epoch, lr, self.rank)) + logging.info('using accumulate grad, new batch size is {} times' + ' larger than before'.format(info_dict['accum_grad'])) + # A context manager to be used in conjunction with an instance of + # torch.nn.parallel.DistributedDataParallel to be able to train + # with uneven inputs across participating processes. + model.train() + if self.ref_model is not None: + self.ref_model.eval() + model_context = model.join if info_dict['train_engine'] == 'torch_ddp' else nullcontext + with model_context(): + for batch_idx, batch_dict in enumerate(train_data_loader): + info_dict["tag"] = "TRAIN" + info_dict["step"] = self.step + info_dict["epoch"] = self.epoch + info_dict["batch_idx"] = batch_idx + if cosyvoice_join(group_join, info_dict): + break + + # Disable gradient synchronizations across DDP processes. + # Within this context, gradients will be accumulated on module + # variables, which will later be synchronized. + if info_dict['train_engine'] == 'torch_ddp' and (batch_idx + 1) % info_dict["accum_grad"] != 0: + context = model.no_sync + # Used for single gpu training and DDP gradient synchronization + # processes. + else: + context = nullcontext + + with context(): + info_dict = batch_forward(model, batch_dict, scaler, info_dict, ref_model=self.ref_model, dpo_loss=self.dpo_loss) + info_dict = batch_backward(model, scaler, info_dict) + + info_dict = update_parameter_and_lr(model, optimizer, scheduler, scaler, info_dict) + log_per_step(writer, info_dict) + # NOTE specify save_per_step in cosyvoice.yaml if you want to enable step save + if info_dict['save_per_step'] > 0 and (self.step + 1) % info_dict['save_per_step'] == 0 and \ + (batch_idx + 1) % info_dict["accum_grad"] == 0: + dist.barrier() + self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=False) + model.train() + if (batch_idx + 1) % info_dict["accum_grad"] == 0: + self.step += 1 + dist.barrier() + self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True) + + def train_one_epoc_gan(self, model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader, + writer, info_dict, scaler, group_join): + ''' Train one epoch + ''' + + lr = optimizer.param_groups[0]['lr'] + logging.info('Epoch {} TRAIN info lr {} rank {}'.format(self.epoch, lr, self.rank)) + logging.info('using accumulate grad, new batch size is {} times' + ' larger than before'.format(info_dict['accum_grad'])) + # A context manager to be used in conjunction with an instance of + # torch.nn.parallel.DistributedDataParallel to be able to train + # with uneven inputs across participating processes. + model.train() + model_context = model.join if info_dict['train_engine'] == 'torch_ddp' else nullcontext + with model_context(): + for batch_idx, batch_dict in enumerate(train_data_loader): + info_dict["tag"] = "TRAIN" + info_dict["step"] = self.step + info_dict["epoch"] = self.epoch + info_dict["batch_idx"] = batch_idx + if cosyvoice_join(group_join, info_dict): + break + + # Disable gradient synchronizations across DDP processes. + # Within this context, gradients will be accumulated on module + # variables, which will later be synchronized. + if info_dict['train_engine'] == 'torch_ddp' and (batch_idx + 1) % info_dict["accum_grad"] != 0: + context = model.no_sync + # Used for single gpu training and DDP gradient synchronization + # processes. + else: + context = nullcontext + + with context(): + batch_dict['turn'] = 'discriminator' + info_dict = batch_forward(model, batch_dict, scaler, info_dict) + info_dict = batch_backward(model, scaler, info_dict) + info_dict = update_parameter_and_lr(model, optimizer_d, scheduler_d, scaler, info_dict) + optimizer.zero_grad() + log_per_step(writer, info_dict) + with context(): + batch_dict['turn'] = 'generator' + info_dict = batch_forward(model, batch_dict, scaler, info_dict) + info_dict = batch_backward(model, scaler, info_dict) + info_dict = update_parameter_and_lr(model, optimizer, scheduler, scaler, info_dict) + optimizer_d.zero_grad() + log_per_step(writer, info_dict) + # NOTE specify save_per_step in cosyvoice.yaml if you want to enable step save + if info_dict['save_per_step'] > 0 and (self.step + 1) % info_dict['save_per_step'] == 0 and \ + (batch_idx + 1) % info_dict["accum_grad"] == 0: + dist.barrier() + self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=False) + model.train() + if (batch_idx + 1) % info_dict["accum_grad"] == 0: + self.step += 1 + dist.barrier() + self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True) + + @torch.inference_mode() + def cv(self, model, cv_data_loader, writer, info_dict, on_batch_end=True): + ''' Cross validation on + ''' + logging.info('Epoch {} Step {} on_batch_end {} CV rank {}'.format(self.epoch, self.step + 1, on_batch_end, self.rank)) + model.eval() + total_num_utts, total_loss_dict = 0, {} # avoid division by 0 + for batch_idx, batch_dict in enumerate(cv_data_loader): + info_dict["tag"] = "CV" + info_dict["step"] = self.step + info_dict["epoch"] = self.epoch + info_dict["batch_idx"] = batch_idx + + num_utts = len(batch_dict["utts"]) + total_num_utts += num_utts + + if self.gan is True: + batch_dict['turn'] = 'generator' + info_dict = batch_forward(model, batch_dict, None, info_dict) + + for k, v in info_dict['loss_dict'].items(): + if k not in total_loss_dict: + total_loss_dict[k] = [] + total_loss_dict[k].append(v.mean().item() * num_utts) + log_per_step(None, info_dict) + for k, v in total_loss_dict.items(): + total_loss_dict[k] = sum(v) / total_num_utts + info_dict['loss_dict'] = total_loss_dict + log_per_save(writer, info_dict) + model_name = 'epoch_{}_whole'.format(self.epoch) if on_batch_end else 'epoch_{}_step_{}'.format(self.epoch, self.step + 1) + save_model(model, model_name, info_dict) diff --git a/cosyvoice/cosyvoice/utils/file_utils.py b/cosyvoice/cosyvoice/utils/file_utils.py new file mode 100644 index 0000000..b173ef2 --- /dev/null +++ b/cosyvoice/cosyvoice/utils/file_utils.py @@ -0,0 +1,118 @@ +# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang) +# 2024 Alibaba Inc (authors: Xiang Lyu, Zetao Hu) +# 2025 Alibaba Inc (authors: Xiang Lyu, Yabin Li) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import json +import torch +import torchaudio +import logging +logging.getLogger('matplotlib').setLevel(logging.WARNING) +logging.basicConfig(level=logging.DEBUG, + format='%(asctime)s %(levelname)s %(message)s') + + +def read_lists(list_file): + lists = [] + with open(list_file, 'r', encoding='utf8') as fin: + for line in fin: + lists.append(line.strip()) + return lists + + +def read_json_lists(list_file): + lists = read_lists(list_file) + results = {} + for fn in lists: + with open(fn, 'r', encoding='utf8') as fin: + results.update(json.load(fin)) + return results + + +def load_wav(wav, target_sr, min_sr=16000): + speech, sample_rate = torchaudio.load(wav, backend='soundfile') + speech = speech.mean(dim=0, keepdim=True) + if sample_rate != target_sr: + assert sample_rate >= min_sr, 'wav sample rate {} must be greater than {}'.format(sample_rate, target_sr) + speech = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sr)(speech) + return speech + + +def convert_onnx_to_trt(trt_model, trt_kwargs, onnx_model, fp16): + import tensorrt as trt + logging.info("Converting onnx to trt...") + network_flags = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + logger = trt.Logger(trt.Logger.INFO) + builder = trt.Builder(logger) + network = builder.create_network(network_flags) + parser = trt.OnnxParser(network, logger) + config = builder.create_builder_config() + config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 32) # 4GB + if fp16: + config.set_flag(trt.BuilderFlag.FP16) + profile = builder.create_optimization_profile() + # load onnx model + with open(onnx_model, "rb") as f: + if not parser.parse(f.read()): + for error in range(parser.num_errors): + print(parser.get_error(error)) + raise ValueError('failed to parse {}'.format(onnx_model)) + # set input shapes + for i in range(len(trt_kwargs['input_names'])): + profile.set_shape(trt_kwargs['input_names'][i], trt_kwargs['min_shape'][i], trt_kwargs['opt_shape'][i], trt_kwargs['max_shape'][i]) + tensor_dtype = trt.DataType.HALF if fp16 else trt.DataType.FLOAT + # set input and output data type + for i in range(network.num_inputs): + input_tensor = network.get_input(i) + input_tensor.dtype = tensor_dtype + for i in range(network.num_outputs): + output_tensor = network.get_output(i) + output_tensor.dtype = tensor_dtype + config.add_optimization_profile(profile) + engine_bytes = builder.build_serialized_network(network, config) + # save trt engine + with open(trt_model, "wb") as f: + f.write(engine_bytes) + logging.info("Succesfully convert onnx to trt...") + + +# NOTE do not support bistream inference as only speech token embedding/head is kept +def export_cosyvoice2_vllm(model, model_path, device): + if os.path.exists(model_path): + return + + dtype = torch.bfloat16 + # lm_head + use_bias = True if model.llm_decoder.bias is not None else False + model.llm.model.lm_head = model.llm_decoder + # embed_tokens + embed_tokens = model.llm.model.model.embed_tokens + model.llm.model.set_input_embeddings(model.speech_embedding) + model.llm.model.to(device) + model.llm.model.to(dtype) + tmp_vocab_size = model.llm.model.config.vocab_size + tmp_tie_embedding = model.llm.model.config.tie_word_embeddings + del model.llm.model.generation_config.eos_token_id + del model.llm.model.config.bos_token_id + del model.llm.model.config.eos_token_id + model.llm.model.config.vocab_size = model.speech_embedding.num_embeddings + model.llm.model.config.tie_word_embeddings = False + model.llm.model.config.use_bias = use_bias + model.llm.model.save_pretrained(model_path) + if use_bias is True: + os.system('sed -i s@Qwen2ForCausalLM@CosyVoice2ForCausalLM@g {}/config.json'.format(os.path.abspath(model_path))) + model.llm.model.config.vocab_size = tmp_vocab_size + model.llm.model.config.tie_word_embeddings = tmp_tie_embedding + model.llm.model.set_input_embeddings(embed_tokens) diff --git a/cosyvoice/cosyvoice/utils/frontend_utils.py b/cosyvoice/cosyvoice/utils/frontend_utils.py new file mode 100644 index 0000000..ea1c9fc --- /dev/null +++ b/cosyvoice/cosyvoice/utils/frontend_utils.py @@ -0,0 +1,136 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re +import regex +chinese_char_pattern = re.compile(r'[\u4e00-\u9fff]+') + + +# whether contain chinese character +def contains_chinese(text): + return bool(chinese_char_pattern.search(text)) + + +# replace special symbol +def replace_corner_mark(text): + text = text.replace('²', '平方') + text = text.replace('³', '立方') + return text + + +# remove meaningless symbol +def remove_bracket(text): + text = text.replace('（', '').replace('）', '') + text = text.replace('【', '').replace('】', '') + text = text.replace('`', '').replace('`', '') + text = text.replace("——", " ") + return text + + +# spell Arabic numerals +def spell_out_number(text: str, inflect_parser): + new_text = [] + st = None + for i, c in enumerate(text): + if not c.isdigit(): + if st is not None: + num_str = inflect_parser.number_to_words(text[st: i]) + new_text.append(num_str) + st = None + new_text.append(c) + else: + if st is None: + st = i + if st is not None and st < len(text): + num_str = inflect_parser.number_to_words(text[st:]) + new_text.append(num_str) + return ''.join(new_text) + + +# split paragrah logic： +# 1. per sentence max len token_max_n, min len token_min_n, merge if last sentence len less than merge_len +# 2. cal sentence len according to lang +# 3. split sentence according to puncatation +def split_paragraph(text: str, tokenize, lang="zh", token_max_n=80, token_min_n=60, merge_len=20, comma_split=False): + def calc_utt_length(_text: str): + if lang == "zh": + return len(_text) + else: + return len(tokenize(_text)) + + def should_merge(_text: str): + if lang == "zh": + return len(_text) < merge_len + else: + return len(tokenize(_text)) < merge_len + + if lang == "zh": + pounc = ['。', '？', '！', '；', '：', '、', '.', '?', '!', ';'] + else: + pounc = ['.', '?', '!', ';', ':'] + if comma_split: + pounc.extend(['，', ',']) + + if text[-1] not in pounc: + if lang == "zh": + text += "。" + else: + text += "." + + st = 0 + utts = [] + for i, c in enumerate(text): + if c in pounc: + if len(text[st: i]) > 0: + utts.append(text[st: i] + c) + if i + 1 < len(text) and text[i + 1] in ['"', '”']: + tmp = utts.pop(-1) + utts.append(tmp + text[i + 1]) + st = i + 2 + else: + st = i + 1 + + final_utts = [] + cur_utt = "" + for utt in utts: + if calc_utt_length(cur_utt + utt) > token_max_n and calc_utt_length(cur_utt) > token_min_n: + final_utts.append(cur_utt) + cur_utt = "" + cur_utt = cur_utt + utt + if len(cur_utt) > 0: + if should_merge(cur_utt) and len(final_utts) != 0: + final_utts[-1] = final_utts[-1] + cur_utt + else: + final_utts.append(cur_utt) + + return final_utts + + +# remove blank between chinese character +def replace_blank(text: str): + out_str = [] + for i, c in enumerate(text): + if c == " ": + if ((text[i + 1].isascii() and text[i + 1] != " ") and + (text[i - 1].isascii() and text[i - 1] != " ")): + out_str.append(c) + else: + out_str.append(c) + return "".join(out_str) + + +def is_only_punctuation(text): + # Regular expression: Match strings that consist only of punctuation marks or are empty. + punctuation_pattern = r'^[\p{P}\p{S}]*$' + return bool(regex.fullmatch(punctuation_pattern, text)) diff --git a/cosyvoice/cosyvoice/utils/losses.py b/cosyvoice/cosyvoice/utils/losses.py new file mode 100644 index 0000000..2429fdc --- /dev/null +++ b/cosyvoice/cosyvoice/utils/losses.py @@ -0,0 +1,57 @@ +import torch +import torch.nn.functional as F +from typing import Tuple + + +def tpr_loss(disc_real_outputs, disc_generated_outputs, tau): + loss = 0 + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + m_DG = torch.median((dr - dg)) + L_rel = torch.mean((((dr - dg) - m_DG) ** 2)[dr < dg + m_DG]) + loss += tau - F.relu(tau - L_rel) + return loss + + +def mel_loss(real_speech, generated_speech, mel_transforms): + loss = 0 + for transform in mel_transforms: + mel_r = transform(real_speech) + mel_g = transform(generated_speech) + loss += F.l1_loss(mel_g, mel_r) + return loss + + +class DPOLoss(torch.nn.Module): + """ + DPO Loss + """ + + def __init__(self, beta: float, label_smoothing: float = 0.0, ipo: bool = False) -> None: + super().__init__() + self.beta = beta + self.label_smoothing = label_smoothing + self.ipo = ipo + + def forward( + self, + policy_chosen_logps: torch.Tensor, + policy_rejected_logps: torch.Tensor, + reference_chosen_logps: torch.Tensor, + reference_rejected_logps: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + pi_logratios = policy_chosen_logps - policy_rejected_logps + ref_logratios = reference_chosen_logps - reference_rejected_logps + logits = pi_logratios - ref_logratios + if self.ipo: + losses = (logits - 1 / (2 * self.beta)) ** 2 # Eq. 17 of https://arxiv.org/pdf/2310.12036v2.pdf + else: + # Eq. 3 https://ericmitchell.ai/cdpo.pdf; label_smoothing=0 gives original DPO (Eq. 7 of https://arxiv.org/pdf/2305.18290.pdf) + losses = ( + -F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing) + - F.logsigmoid(-self.beta * logits) * self.label_smoothing + ) + loss = losses.mean() + chosen_rewards = self.beta * (policy_chosen_logps - reference_chosen_logps).detach() + rejected_rewards = self.beta * (policy_rejected_logps - reference_rejected_logps).detach() + + return loss, chosen_rewards, rejected_rewards diff --git a/cosyvoice/cosyvoice/utils/mask.py b/cosyvoice/cosyvoice/utils/mask.py new file mode 100644 index 0000000..5d3dfd6 --- /dev/null +++ b/cosyvoice/cosyvoice/utils/mask.py @@ -0,0 +1,265 @@ +# Copyright (c) 2019 Shigeki Karita +# 2020 Mobvoi Inc (Binbin Zhang) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +''' +def subsequent_mask( + size: int, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size). + + This mask is used only in decoder which works in an auto-regressive mode. + This means the current step could only do attention with its left steps. + + In encoder, fully attention is used when streaming is not necessary and + the sequence is not long. In this case, no attention mask is needed. + + When streaming is need, chunk-based attention is used in encoder. See + subsequent_chunk_mask for the chunk-based attention mask. + + Args: + size (int): size of mask + str device (str): "cpu" or "cuda" or torch.Tensor.device + dtype (torch.device): result dtype + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_mask(3) + [[1, 0, 0], + [1, 1, 0], + [1, 1, 1]] + """ + ret = torch.ones(size, size, device=device, dtype=torch.bool) + return torch.tril(ret) +''' + + +def subsequent_mask( + size: int, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size). + + This mask is used only in decoder which works in an auto-regressive mode. + This means the current step could only do attention with its left steps. + + In encoder, fully attention is used when streaming is not necessary and + the sequence is not long. In this case, no attention mask is needed. + + When streaming is need, chunk-based attention is used in encoder. See + subsequent_chunk_mask for the chunk-based attention mask. + + Args: + size (int): size of mask + str device (str): "cpu" or "cuda" or torch.Tensor.device + dtype (torch.device): result dtype + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_mask(3) + [[1, 0, 0], + [1, 1, 0], + [1, 1, 1]] + """ + arange = torch.arange(size, device=device) + mask = arange.expand(size, size) + arange = arange.unsqueeze(-1) + mask = mask <= arange + return mask + + +def subsequent_chunk_mask_deprecated( + size: int, + chunk_size: int, + num_left_chunks: int = -1, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size) with chunk size, + this is for streaming encoder + + Args: + size (int): size of mask + chunk_size (int): size of chunk + num_left_chunks (int): number of left chunks + <0: use full chunk + >=0: use num_left_chunks + device (torch.device): "cpu" or "cuda" or torch.Tensor.device + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_chunk_mask(4, 2) + [[1, 1, 0, 0], + [1, 1, 0, 0], + [1, 1, 1, 1], + [1, 1, 1, 1]] + """ + ret = torch.zeros(size, size, device=device, dtype=torch.bool) + for i in range(size): + if num_left_chunks < 0: + start = 0 + else: + start = max((i // chunk_size - num_left_chunks) * chunk_size, 0) + ending = min((i // chunk_size + 1) * chunk_size, size) + ret[i, start:ending] = True + return ret + + +def subsequent_chunk_mask( + size: int, + chunk_size: int, + num_left_chunks: int = -1, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size) with chunk size, + this is for streaming encoder + + Args: + size (int): size of mask + chunk_size (int): size of chunk + num_left_chunks (int): number of left chunks + <0: use full chunk + >=0: use num_left_chunks + device (torch.device): "cpu" or "cuda" or torch.Tensor.device + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_chunk_mask(4, 2) + [[1, 1, 0, 0], + [1, 1, 0, 0], + [1, 1, 1, 1], + [1, 1, 1, 1]] + """ + # NOTE this modified implementation meets onnx export requirements, but it doesn't support num_left_chunks + pos_idx = torch.arange(size, device=device) + block_value = (torch.div(pos_idx, chunk_size, rounding_mode='trunc') + 1) * chunk_size + ret = pos_idx.unsqueeze(0) < block_value.unsqueeze(1) + return ret + + +def add_optional_chunk_mask(xs: torch.Tensor, + masks: torch.Tensor, + use_dynamic_chunk: bool, + use_dynamic_left_chunk: bool, + decoding_chunk_size: int, + static_chunk_size: int, + num_decoding_left_chunks: int, + enable_full_context: bool = True): + """ Apply optional mask for encoder. + + Args: + xs (torch.Tensor): padded input, (B, L, D), L for max length + mask (torch.Tensor): mask for xs, (B, 1, L) + use_dynamic_chunk (bool): whether to use dynamic chunk or not + use_dynamic_left_chunk (bool): whether to use dynamic left chunk for + training. + decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + static_chunk_size (int): chunk size for static chunk training/decoding + if it's greater than 0, if use_dynamic_chunk is true, + this parameter will be ignored + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + enable_full_context (bool): + True: chunk size is either [1, 25] or full context(max_len) + False: chunk size ~ U[1, 25] + + Returns: + torch.Tensor: chunk mask of the input xs. + """ + # Whether to use chunk mask or not + if use_dynamic_chunk: + max_len = xs.size(1) + if decoding_chunk_size < 0: + chunk_size = max_len + num_left_chunks = -1 + elif decoding_chunk_size > 0: + chunk_size = decoding_chunk_size + num_left_chunks = num_decoding_left_chunks + else: + # chunk size is either [1, 25] or full context(max_len). + # Since we use 4 times subsampling and allow up to 1s(100 frames) + # delay, the maximum frame is 100 / 4 = 25. + chunk_size = torch.randint(1, max_len, (1, )).item() + num_left_chunks = -1 + if chunk_size > max_len // 2 and enable_full_context: + chunk_size = max_len + else: + chunk_size = chunk_size % 25 + 1 + if use_dynamic_left_chunk: + max_left_chunks = (max_len - 1) // chunk_size + num_left_chunks = torch.randint(0, max_left_chunks, + (1, )).item() + chunk_masks = subsequent_chunk_mask(xs.size(1), chunk_size, + num_left_chunks, + xs.device) # (L, L) + chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) + chunk_masks = masks & chunk_masks # (B, L, L) + elif static_chunk_size > 0: + num_left_chunks = num_decoding_left_chunks + chunk_masks = subsequent_chunk_mask(xs.size(1), static_chunk_size, + num_left_chunks, + xs.device) # (L, L) + chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) + chunk_masks = masks & chunk_masks # (B, L, L) + else: + chunk_masks = masks + assert chunk_masks.dtype == torch.bool + if (chunk_masks.sum(dim=-1) == 0).sum().item() != 0: + print('get chunk_masks all false at some timestep, force set to true, make sure they are masked in futuer computation!') + chunk_masks[chunk_masks.sum(dim=-1) == 0] = True + return chunk_masks + + +def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: + """Make mask tensor containing indices of padded part. + + See description of make_non_pad_mask. + + Args: + lengths (torch.Tensor): Batch of lengths (B,). + Returns: + torch.Tensor: Mask tensor containing indices of padded part. + + Examples: + >>> lengths = [5, 3, 2] + >>> make_pad_mask(lengths) + masks = [[0, 0, 0, 0 ,0], + [0, 0, 0, 1, 1], + [0, 0, 1, 1, 1]] + """ + batch_size = lengths.size(0) + max_len = max_len if max_len > 0 else lengths.max().item() + seq_range = torch.arange(0, + max_len, + dtype=torch.int64, + device=lengths.device) + seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len) + seq_length_expand = lengths.unsqueeze(-1) + mask = seq_range_expand >= seq_length_expand + return mask diff --git a/cosyvoice/cosyvoice/utils/scheduler.py b/cosyvoice/cosyvoice/utils/scheduler.py new file mode 100644 index 0000000..06e7f3d --- /dev/null +++ b/cosyvoice/cosyvoice/utils/scheduler.py @@ -0,0 +1,738 @@ +# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang) +# 2022 Ximalaya Inc (Yuguang Yang) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Modified from ESPnet(https://github.com/espnet/espnet) +# NeMo(https://github.com/NVIDIA/NeMo) + +from typing import Union + +import math +import warnings +import torch +from torch.optim.lr_scheduler import _LRScheduler + + +class WarmupLR(_LRScheduler): + """The WarmupLR scheduler + + This scheduler is almost same as NoamLR Scheduler except for following + difference: + + NoamLR: + lr = optimizer.lr * model_size ** -0.5 + * min(step ** -0.5, step * warmup_step ** -1.5) + WarmupLR: + lr = optimizer.lr * warmup_step ** 0.5 + * min(step ** -0.5, step * warmup_step ** -1.5) + + Note that the maximum lr equals to optimizer.lr in this scheduler. + + """ + + def __init__( + self, + optimizer: torch.optim.Optimizer, + warmup_steps: Union[int, float] = 25000, + last_epoch: int = -1, + ): + self.warmup_steps = warmup_steps + + # __init__() must be invoked before setting field + # because step() is also invoked in __init__() + super().__init__(optimizer, last_epoch) + + def __repr__(self): + return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps})" + + def get_lr(self): + step_num = self.last_epoch + 1 + if self.warmup_steps == 0: + return [lr * step_num**-0.5 for lr in self.base_lrs] + else: + return [ + lr * self.warmup_steps**0.5 * + min(step_num**-0.5, step_num * self.warmup_steps**-1.5) + for lr in self.base_lrs + ] + + def set_step(self, step: int): + self.last_epoch = step + + +class WarmupPolicy(_LRScheduler): + """Adds warmup kwargs and warmup logic to lr policy. + All arguments should be passed as kwargs for clarity, + Args: + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + """ + + def __init__(self, + optimizer, + *, + warmup_steps=None, + warmup_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1): + assert not (warmup_steps is not None and warmup_ratio is not None),\ + "Either use particular number of step or ratio" + assert warmup_ratio is None or max_steps is not None, \ + "If there is a ratio, there should be a total steps" + + # It is necessary to assign all attributes *before* __init__, + # as class is wrapped by an inner class. + self.max_steps = max_steps + if warmup_steps is not None: + self.warmup_steps = warmup_steps + elif warmup_ratio is not None: + self.warmup_steps = int(warmup_ratio * max_steps) + else: + self.warmup_steps = 0 + + self.min_lr = min_lr + super().__init__(optimizer, last_epoch) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed " + "by the scheduler, please use `get_last_lr()`.", + UserWarning, + stacklevel=2) + + step = self.last_epoch + + if step <= self.warmup_steps and self.warmup_steps > 0: + return self._get_warmup_lr(step) + + if step > self.max_steps: + return [self.min_lr for _ in self.base_lrs] + + return self._get_lr(step) + + def _get_warmup_lr(self, step): + lr_val = (step + 1) / (self.warmup_steps + 1) + return [initial_lr * lr_val for initial_lr in self.base_lrs] + + def _get_lr(self, step): + """Simple const lr policy""" + return self.base_lrs + + +class SquareRootConstantPolicy(_LRScheduler): + """Adds warmup kwargs and warmup logic to lr policy. + All arguments should be passed as kwargs for clarity, + Args: + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + """ + + def __init__(self, + optimizer, + *, + constant_steps=None, + constant_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1): + assert not (constant_steps is not None + and constant_ratio is not None), \ + "Either use particular number of step or ratio" + assert constant_ratio is None or max_steps is not None, \ + "If there is a ratio, there should be a total steps" + + # It is necessary to assign all attributes *before* __init__, + # as class is wrapped by an inner class. + self.max_steps = max_steps + if constant_steps is not None: + self.constant_steps = constant_steps + elif constant_ratio is not None: + self.constant_steps = int(constant_ratio * max_steps) + else: + self.constant_steps = 0 + + self.constant_lr = 1 / (constant_steps**0.5) + self.min_lr = min_lr + super().__init__(optimizer, last_epoch) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed " + "by the scheduler, please use `get_last_lr()`.", + UserWarning, + stacklevel=2) + + step = self.last_epoch + + if step <= self.constant_steps: + return [self.constant_lr for _ in self.base_lrs] + + if step > self.max_steps: + return [self.min_lr for _ in self.base_lrs] + + return self._get_lr(step) + + def _get_lr(self, step): + """Simple const lr policy""" + return self.base_lrs + + +class WarmupHoldPolicy(WarmupPolicy): + """Variant of WarmupPolicy which maintains high + learning rate for a defined number of steps. + All arguments should be passed as kwargs for clarity, + Args: + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + hold_steps: Number of training steps to + hold the learning rate after warm up + hold_ratio: Ratio of hold steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + """ + + def __init__( + self, + optimizer, + *, + warmup_steps=None, + warmup_ratio=None, + hold_steps=None, + hold_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1, + ): + assert not (hold_steps is not None and hold_ratio is not None), \ + "Either use particular number of step or ratio" + assert hold_ratio is None or max_steps is not None, \ + "If there is a ratio, there should be a total steps" + + self.min_lr = min_lr + self._last_warmup_lr = 0.0 + + # Necessary to duplicate as class attributes are hidden in inner class + self.max_steps = max_steps + if warmup_steps is not None: + self.warmup_steps = warmup_steps + elif warmup_ratio is not None: + self.warmup_steps = int(warmup_ratio * max_steps) + else: + self.warmup_steps = 0 + + if hold_steps is not None: + self.hold_steps = hold_steps + self.warmup_steps + elif hold_ratio is not None: + self.hold_steps = int(hold_ratio * max_steps) + self.warmup_steps + else: + self.hold_steps = 0 + + super().__init__( + optimizer, + warmup_steps=warmup_steps, + warmup_ratio=warmup_ratio, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + ) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed by the scheduler," + " " + "please use `get_last_lr()`.", + UserWarning, + stacklevel=2) + + step = self.last_epoch + + # Warmup phase + if step <= self.warmup_steps and self.warmup_steps > 0: + return self._get_warmup_lr(step) + + # Hold phase + if (step >= self.warmup_steps) and (step < self.hold_steps): + return self.base_lrs + + if step > self.max_steps: + return [self.min_lr for _ in self.base_lrs] + + return self._get_lr(step) + + +class WarmupAnnealHoldPolicy(_LRScheduler): + """Adds warmup kwargs and warmup logic to lr policy. + All arguments should be passed as kwargs for clarity, + Args: + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + min_lr: Minimum lr to hold the learning rate after decay at. + constant_steps: Number of steps to keep lr constant at. + constant_ratio: Ratio of steps to keep lr constant. + """ + + def __init__( + self, + optimizer, + *, + warmup_steps=None, + warmup_ratio=None, + constant_steps=None, + constant_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1, + ): + assert not (warmup_steps is not None + and warmup_ratio is not None), \ + "Either use particular number of step or ratio" + assert not (constant_steps is not None + and constant_ratio is not None), \ + "Either use constant_steps or constant_ratio" + assert warmup_ratio is None or max_steps is not None, \ + "If there is a ratio, there should be a total steps" + + # It is necessary to assign all attributes *before* __init__, + # as class is wrapped by an inner class. + self.max_steps = max_steps + + if warmup_steps is not None: + self.warmup_steps = warmup_steps + elif warmup_ratio is not None: + self.warmup_steps = int(warmup_ratio * max_steps) + else: + self.warmup_steps = 0 + + if constant_steps is not None: + self.constant_steps = constant_steps + elif constant_ratio is not None: + self.constant_steps = int(constant_ratio * max_steps) + else: + self.constant_steps = 0 + + self.decay_steps = max_steps - (self.constant_steps + + self.warmup_steps) + + self.min_lr = min_lr + super().__init__(optimizer, last_epoch) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed " + "by the scheduler, please use `get_last_lr()`.", + UserWarning, + stacklevel=2) + + step = self.last_epoch + + # Warmup steps + if self.warmup_steps > 0 and step <= self.warmup_steps: + return self._get_warmup_lr(step) + + # Constant steps after warmup and decay + if self.constant_steps > 0 and ( + self.warmup_steps + self.decay_steps) < step <= self.max_steps: + return self._get_constant_lr(step) + + # Min lr after max steps of updates + if step > self.max_steps: + return [self.min_lr for _ in self.base_lrs] + + return self._get_lr(step) + + def _get_warmup_lr(self, step): + lr_val = (step + 1) / (self.warmup_steps + 1) + return [initial_lr * lr_val for initial_lr in self.base_lrs] + + def _get_constant_lr(self, step): + return [self.min_lr for _ in self.base_lrs] + + def _get_lr(self, step): + """Simple const lr policy""" + return self.base_lrs + + +def _squareroot_annealing(initial_lr, step, max_steps, min_lr): + mult = ((max_steps - step) / max_steps)**0.5 + out_lr = initial_lr * mult + out_lr = max(out_lr, min_lr) + return out_lr + + +def _square_annealing(initial_lr, step, max_steps, min_lr): + mult = ((max_steps - step) / max_steps)**2 + out_lr = initial_lr * mult + out_lr = max(out_lr, min_lr) + return out_lr + + +def _cosine_annealing(initial_lr, step, max_steps, min_lr): + mult = 0.5 * (1 + math.cos(math.pi * step / max_steps)) + out_lr = (initial_lr - min_lr) * mult + min_lr + return out_lr + + +def _linear_warmup_with_cosine_annealing(max_lr, warmup_steps, step, + decay_steps, min_lr): + assert max_lr > min_lr + # Use linear warmup for the initial part. + if warmup_steps > 0 and step <= warmup_steps: + return max_lr * float(step) / float(warmup_steps) + + # For any steps larger than `decay_steps`, use `min_lr`. + if step > warmup_steps + decay_steps: + return min_lr + + # If we are done with the warmup period, use the decay style. + num_steps_ = step - warmup_steps + decay_steps_ = decay_steps + decay_ratio = float(num_steps_) / float(decay_steps_) + assert decay_ratio >= 0.0 + assert decay_ratio <= 1.0 + delta_lr = max_lr - min_lr + + coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0) + + return min_lr + coeff * delta_lr + + +def _poly_decay(initial_lr, step, decay_steps, power, min_lr, cycle): + if cycle: + multiplier = 1.0 if step == 0 else math.ceil(step / decay_steps) + decay_steps *= multiplier + else: + step = min(step, decay_steps) + p = step / decay_steps + lr = (initial_lr - min_lr) * math.pow(1.0 - p, power) + lr += min_lr + return lr + + +def _noam_hold_annealing(initial_lr, step, warmup_steps, hold_steps, + decay_rate, min_lr): + # hold_steps = total number of steps + # to hold the LR, not the warmup + hold steps. + T_warmup_decay = max(1, warmup_steps**decay_rate) + T_hold_decay = max(1, (step - hold_steps)**decay_rate) + lr = (initial_lr * T_warmup_decay) / T_hold_decay + lr = max(lr, min_lr) + return lr + + +class SquareAnnealing(WarmupPolicy): + + def __init__(self, + optimizer, + *, + max_steps, + min_lr=1e-5, + last_epoch=-1, + **kwargs): + super().__init__(optimizer=optimizer, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + **kwargs) + + def _get_lr(self, step): + new_lrs = [ + _square_annealing( + initial_lr=initial_lr, + step=step - self.warmup_steps, + max_steps=self.max_steps - self.warmup_steps, + min_lr=self.min_lr, + ) for initial_lr in self.base_lrs + ] + return new_lrs + + +class SquareRootAnnealing(WarmupPolicy): + + def __init__(self, + optimizer, + *, + max_steps, + min_lr=0, + last_epoch=-1, + **kwargs): + super().__init__(optimizer=optimizer, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + **kwargs) + + def _get_lr(self, step): + new_lrs = [ + _squareroot_annealing(initial_lr=initial_lr, + step=step, + max_steps=self.max_steps, + min_lr=self.min_lr) + for initial_lr in self.base_lrs + ] + return new_lrs + + +class CosineAnnealing(WarmupAnnealHoldPolicy): + + def __init__(self, + optimizer, + *, + max_steps, + min_lr=0, + last_epoch=-1, + **kwargs): + super().__init__(optimizer=optimizer, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + **kwargs) + + def _get_lr(self, step): + for initial_lr in self.base_lrs: + if initial_lr < self.min_lr: + raise ValueError( + f"{self} received an initial learning rate " + f"that was lower than the minimum learning rate.") + + if self.constant_steps is None or self.constant_steps == 0: + new_lrs = [ + _cosine_annealing( + initial_lr=initial_lr, + step=step - self.warmup_steps, + max_steps=self.max_steps - self.warmup_steps, + min_lr=self.min_lr, + ) for initial_lr in self.base_lrs + ] + else: + new_lrs = self._get_linear_warmup_with_cosine_annealing_lr(step) + return new_lrs + + def _get_warmup_lr(self, step): + if self.constant_steps is None or self.constant_steps == 0: + return super()._get_warmup_lr(step) + else: + # Use linear warmup for the initial part. + return self._get_linear_warmup_with_cosine_annealing_lr(step) + + def _get_constant_lr(self, step): + # Only called when `constant_steps` > 0. + return self._get_linear_warmup_with_cosine_annealing_lr(step) + + def _get_linear_warmup_with_cosine_annealing_lr(self, step): + # Cosine Schedule for Megatron LM, + # slightly different warmup schedule + constant LR at the end. + new_lrs = [ + _linear_warmup_with_cosine_annealing( + max_lr=self.base_lrs[0], + warmup_steps=self.warmup_steps, + step=step, + decay_steps=self.decay_steps, + min_lr=self.min_lr, + ) for _ in self.base_lrs + ] + return new_lrs + + +class NoamAnnealing(_LRScheduler): + + def __init__(self, + optimizer, + *, + d_model, + warmup_steps=None, + warmup_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1): + self._normalize = d_model**(-0.5) + assert not (warmup_steps is not None and warmup_ratio is not None), \ + "Either use particular number of step or ratio" + assert warmup_ratio is None or max_steps is not None, \ + "If there is a ratio, there should be a total steps" + + # It is necessary to assign all attributes *before* __init__, + # as class is wrapped by an inner class. + self.max_steps = max_steps + if warmup_steps is not None: + self.warmup_steps = warmup_steps + elif warmup_ratio is not None: + self.warmup_steps = int(warmup_ratio * max_steps) + else: + self.warmup_steps = 0 + + self.min_lr = min_lr + super().__init__(optimizer, last_epoch) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed " + "by the scheduler, please use `get_last_lr()`.", + UserWarning, + stacklevel=2) + + step = max(1, self.last_epoch) + + for initial_lr in self.base_lrs: + if initial_lr < self.min_lr: + raise ValueError( + f"{self} received an initial learning rate " + f"that was lower than the minimum learning rate.") + + new_lrs = [ + self._noam_annealing(initial_lr=initial_lr, step=step) + for initial_lr in self.base_lrs + ] + return new_lrs + + def _noam_annealing(self, initial_lr, step): + if self.warmup_steps > 0: + mult = self._normalize * min(step**(-0.5), + step * (self.warmup_steps**(-1.5))) + else: + mult = self._normalize * step**(-0.5) + + out_lr = initial_lr * mult + if step > self.warmup_steps: + out_lr = max(out_lr, self.min_lr) + return out_lr + + +class NoamHoldAnnealing(WarmupHoldPolicy): + + def __init__(self, + optimizer, + *, + max_steps, + decay_rate=0.5, + min_lr=0.0, + last_epoch=-1, + **kwargs): + """ + From Nemo: + Implementation of the Noam Hold Annealing policy + from the SqueezeFormer paper. + + Unlike NoamAnnealing, the peak learning rate + can be explicitly set for this scheduler. + The schedule first performs linear warmup, + then holds the peak LR, then decays with some schedule for + the remainder of the steps. + Therefore the min-lr is still dependent + on the hyper parameters selected. + + It's schedule is determined by three factors- + + Warmup Steps: Initial stage, where linear warmup + occurs uptil the peak LR is reached. Unlike NoamAnnealing, + the peak LR is explicitly stated here instead of a scaling factor. + + Hold Steps: Intermediate stage, where the peak LR + is maintained for some number of steps. In this region, + the high peak LR allows the model to converge faster + if training is stable. However the high LR + may also cause instability during training. + Should usually be a significant fraction of training + steps (around 30-40% of the entire training steps). + + Decay Steps: Final stage, where the LR rapidly decays + with some scaling rate (set by decay rate). + To attain Noam decay, use 0.5, + for Squeezeformer recommended decay, use 1.0. + The fast decay after prolonged high LR during + hold phase allows for rapid convergence. + + References: + - [Squeezeformer: + An Efficient Transformer for Automatic Speech Recognition] + (https://arxiv.org/abs/2206.00888) + + Args: + optimizer: Pytorch compatible Optimizer object. + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + hold_steps: Number of training steps to + hold the learning rate after warm up + hold_ratio: Ratio of hold steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + decay_rate: Float value describing the polynomial decay + after the hold period. Default value + of 0.5 corresponds to Noam decay. + min_lr: Minimum learning rate. + """ + self.decay_rate = decay_rate + super().__init__(optimizer=optimizer, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + **kwargs) + + def _get_lr(self, step): + if self.warmup_steps is None or self.warmup_steps == 0: + raise ValueError( + "Noam scheduler cannot be used without warmup steps") + + if self.hold_steps > 0: + hold_steps = self.hold_steps - self.warmup_steps + else: + hold_steps = 0 + + new_lrs = [ + _noam_hold_annealing( + initial_lr, + step=step, + warmup_steps=self.warmup_steps, + hold_steps=hold_steps, + decay_rate=self.decay_rate, + min_lr=self.min_lr, + ) for initial_lr in self.base_lrs + ] + return new_lrs + + def set_step(self, step: int): + self.last_epoch = step + + +class ConstantLR(_LRScheduler): + """The ConstantLR scheduler + + This scheduler keeps a constant lr + + """ + + def __init__( + self, + optimizer: torch.optim.Optimizer, + ): + # __init__() must be invoked before setting field + # because step() is also invoked in __init__() + super().__init__(optimizer) + + def get_lr(self): + return self.base_lrs + + def set_step(self, step: int): + self.last_epoch = step diff --git a/cosyvoice/cosyvoice/utils/train_utils.py b/cosyvoice/cosyvoice/utils/train_utils.py new file mode 100644 index 0000000..e2d2b09 --- /dev/null +++ b/cosyvoice/cosyvoice/utils/train_utils.py @@ -0,0 +1,367 @@ +# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang) +# 2023 Horizon Inc. (authors: Xingchen Song) +# 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os +import torch +import json +import re +import datetime +import yaml + +import deepspeed +import torch.optim as optim +import torch.distributed as dist + +from torch.utils.tensorboard import SummaryWriter +from torch.utils.data import DataLoader +from torch.nn.utils import clip_grad_norm_ + +from deepspeed.runtime.zero.stage_1_and_2 import estimate_zero2_model_states_mem_needs_all_live + +from cosyvoice.dataset.dataset import Dataset +from cosyvoice.utils.scheduler import WarmupLR, NoamHoldAnnealing, ConstantLR + + +def init_distributed(args): + world_size = int(os.environ.get('WORLD_SIZE', 1)) + local_rank = int(os.environ.get('LOCAL_RANK', 0)) + rank = int(os.environ.get('RANK', 0)) + logging.info('training on multiple gpus, this gpu {}'.format(local_rank) + + ', rank {}, world_size {}'.format(rank, world_size)) + if args.train_engine == 'torch_ddp': + torch.cuda.set_device(local_rank) + dist.init_process_group(args.dist_backend) + else: + deepspeed.init_distributed(dist_backend=args.dist_backend) + return world_size, local_rank, rank + + +def init_dataset_and_dataloader(args, configs, gan, dpo): + data_pipeline = configs['data_pipeline_gan'] if gan is True else configs['data_pipeline'] + train_dataset = Dataset(args.train_data, data_pipeline=data_pipeline, mode='train', gan=gan, dpo=dpo, shuffle=True, partition=True) + cv_dataset = Dataset(args.cv_data, data_pipeline=data_pipeline, mode='train', gan=gan, dpo=dpo, shuffle=False, partition=False) + + # do not use persistent_workers=True, as whisper tokenizer opens tiktoken file each time when the for loop starts + train_data_loader = DataLoader(train_dataset, + batch_size=None, + pin_memory=args.pin_memory, + num_workers=args.num_workers, + prefetch_factor=args.prefetch) + cv_data_loader = DataLoader(cv_dataset, + batch_size=None, + pin_memory=args.pin_memory, + num_workers=args.num_workers, + prefetch_factor=args.prefetch) + return train_dataset, cv_dataset, train_data_loader, cv_data_loader + + +def check_modify_and_save_config(args, configs): + if args.train_engine == "torch_ddp": + configs['train_conf']["dtype"] = 'bf16' if args.use_amp is True else 'fp32' + else: + with open(args.deepspeed_config, 'r') as fin: + ds_configs = json.load(fin) + if "fp16" in ds_configs and ds_configs["fp16"]["enabled"]: + configs['train_conf']["dtype"] = "fp16" + elif "bf16" in ds_configs and ds_configs["bf16"]["enabled"]: + configs['train_conf']["dtype"] = "bf16" + else: + configs['train_conf']["dtype"] = "fp32" + assert ds_configs["train_micro_batch_size_per_gpu"] == 1 + # if use deepspeed, override ddp config + configs['train_conf']['save_per_step'] = int(configs['train_conf']['save_per_step'] * + configs['train_conf']['accum_grad'] / ds_configs["gradient_accumulation_steps"]) + configs['train_conf']['accum_grad'] = ds_configs["gradient_accumulation_steps"] + configs['train_conf']['grad_clip'] = ds_configs["gradient_clipping"] + configs['train_conf']['log_interval'] = ds_configs["steps_per_print"] + return configs + + +def wrap_cuda_model(args, model): + local_world_size = int(os.environ.get('LOCAL_WORLD_SIZE', 1)) + world_size = int(os.environ.get('WORLD_SIZE', 1)) + if args.train_engine == "torch_ddp": # native pytorch ddp + assert (torch.cuda.is_available()) + model.cuda() + model = torch.nn.parallel.DistributedDataParallel(model, find_unused_parameters=True) + else: + if int(os.environ.get('RANK', 0)) == 0: + logging.info("Estimating model states memory needs (zero2)...") + estimate_zero2_model_states_mem_needs_all_live( + model, + num_gpus_per_node=local_world_size, + num_nodes=world_size // local_world_size) + return model + + +def init_optimizer_and_scheduler(args, configs, model, gan): + if gan is False: + if configs['train_conf']['optim'] == 'adam': + optimizer = optim.Adam(model.parameters(), **configs['train_conf']['optim_conf']) + elif configs['train_conf']['optim'] == 'adamw': + optimizer = optim.AdamW(model.parameters(), **configs['train_conf']['optim_conf']) + else: + raise ValueError("unknown optimizer: " + configs['train_conf']) + + if configs['train_conf']['scheduler'] == 'warmuplr': + scheduler_type = WarmupLR + scheduler = WarmupLR(optimizer, **configs['train_conf']['scheduler_conf']) + elif configs['train_conf']['scheduler'] == 'NoamHoldAnnealing': + scheduler_type = NoamHoldAnnealing + scheduler = NoamHoldAnnealing(optimizer, **configs['train_conf']['scheduler_conf']) + elif configs['train_conf']['scheduler'] == 'constantlr': + scheduler_type = ConstantLR + scheduler = ConstantLR(optimizer) + else: + raise ValueError("unknown scheduler: " + configs['train_conf']) + + # use deepspeed optimizer for speedup + if args.train_engine == "deepspeed": + def scheduler(opt): + return scheduler_type(opt, **configs['train_conf']['scheduler_conf']) + model, optimizer, _, scheduler = deepspeed.initialize( + args=args, + model=model, + optimizer=None, + lr_scheduler=scheduler, + model_parameters=model.parameters()) + + optimizer_d, scheduler_d = None, None + + else: + # currently we wrap generator and discriminator in one model, so we cannot use deepspeed + if configs['train_conf']['optim'] == 'adam': + optimizer = optim.Adam(model.module.generator.parameters(), **configs['train_conf']['optim_conf']) + elif configs['train_conf']['optim'] == 'adamw': + optimizer = optim.AdamW(model.module.generator.parameters(), **configs['train_conf']['optim_conf']) + else: + raise ValueError("unknown optimizer: " + configs['train_conf']) + + if configs['train_conf']['scheduler'] == 'warmuplr': + scheduler_type = WarmupLR + scheduler = WarmupLR(optimizer, **configs['train_conf']['scheduler_conf']) + elif configs['train_conf']['scheduler'] == 'NoamHoldAnnealing': + scheduler_type = NoamHoldAnnealing + scheduler = NoamHoldAnnealing(optimizer, **configs['train_conf']['scheduler_conf']) + elif configs['train_conf']['scheduler'] == 'constantlr': + scheduler_type = ConstantLR + scheduler = ConstantLR(optimizer) + else: + raise ValueError("unknown scheduler: " + configs['train_conf']) + + if configs['train_conf']['optim_d'] == 'adam': + optimizer_d = optim.Adam(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf']) + elif configs['train_conf']['optim_d'] == 'adamw': + optimizer_d = optim.AdamW(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf']) + else: + raise ValueError("unknown optimizer: " + configs['train_conf']) + + if configs['train_conf']['scheduler_d'] == 'warmuplr': + scheduler_type = WarmupLR + scheduler_d = WarmupLR(optimizer_d, **configs['train_conf']['scheduler_conf']) + elif configs['train_conf']['scheduler_d'] == 'NoamHoldAnnealing': + scheduler_type = NoamHoldAnnealing + scheduler_d = NoamHoldAnnealing(optimizer_d, **configs['train_conf']['scheduler_conf']) + elif configs['train_conf']['scheduler'] == 'constantlr': + scheduler_type = ConstantLR + scheduler_d = ConstantLR(optimizer_d) + else: + raise ValueError("unknown scheduler: " + configs['train_conf']) + return model, optimizer, scheduler, optimizer_d, scheduler_d + + +def init_summarywriter(args): + writer = None + if int(os.environ.get('RANK', 0)) == 0: + os.makedirs(args.model_dir, exist_ok=True) + writer = SummaryWriter(args.tensorboard_dir) + return writer + + +def save_model(model, model_name, info_dict): + rank = int(os.environ.get('RANK', 0)) + model_dir = info_dict["model_dir"] + save_model_path = os.path.join(model_dir, '{}.pt'.format(model_name)) + + if info_dict["train_engine"] == "torch_ddp": + if rank == 0: + torch.save({**model.module.state_dict(), 'epoch': info_dict['epoch'], 'step': info_dict['step']}, save_model_path) + else: + with torch.no_grad(): + model.save_checkpoint(save_dir=model_dir, + tag=model_name, + client_state=info_dict) + if rank == 0: + info_path = re.sub('.pt$', '.yaml', save_model_path) + info_dict['save_time'] = datetime.datetime.now().strftime('%d/%m/%Y %H:%M:%S') + with open(info_path, 'w') as fout: + data = yaml.dump(info_dict) + fout.write(data) + logging.info('[Rank {}] Checkpoint: save to checkpoint {}'.format(rank, save_model_path)) + + +def cosyvoice_join(group_join, info_dict): + world_size = int(os.environ.get('WORLD_SIZE', 1)) + local_rank = int(os.environ.get('LOCAL_RANK', 0)) + rank = int(os.environ.get('RANK', 0)) + + if info_dict["batch_idx"] != 0: + # we try to join all rank in both ddp and deepspeed mode, in case different rank has different lr + try: + dist.monitored_barrier(group=group_join, + timeout=group_join.options._timeout) + return False + except RuntimeError as e: + logging.info("Detected uneven workload distribution: {}\n".format(e) + + "Break current worker to manually join all workers, " + + "world_size {}, current rank {}, current local_rank {}\n". + format(world_size, rank, local_rank)) + return True + else: + return False + + +def batch_forward(model, batch, scaler, info_dict, ref_model=None, dpo_loss=None): + device = int(os.environ.get('LOCAL_RANK', 0)) + + dtype = info_dict["dtype"] + if dtype == "fp16": + dtype = torch.float16 + elif dtype == "bf16": + dtype = torch.bfloat16 + else: # fp32 + dtype = torch.float32 + + if info_dict['train_engine'] == 'torch_ddp': + autocast = torch.cuda.amp.autocast(enabled=scaler is not None, dtype=dtype) + else: + autocast = torch.cuda.amp.autocast(enabled=True, dtype=dtype, cache_enabled=False) + + with autocast: + info_dict['loss_dict'] = model(batch, device) + if ref_model is not None and dpo_loss is not None: + chosen_logps = info_dict['loss_dict']["chosen_logps"] + rejected_logps = info_dict['loss_dict']["rejected_logps"] + sft_loss = info_dict['loss_dict']['loss'] + with torch.no_grad(): + ref_loss_dict = ref_model(batch, device) + reference_chosen_logps = ref_loss_dict["chosen_logps"] + reference_rejected_logps = ref_loss_dict["rejected_logps"] + preference_loss, chosen_reward, reject_reward = dpo_loss( + chosen_logps, rejected_logps, reference_chosen_logps, reference_rejected_logps + ) + dpo_acc = (chosen_reward > reject_reward).float().mean() + info_dict['loss_dict']["loss"] = preference_loss + sft_loss + info_dict['loss_dict']["sft_loss"] = sft_loss + info_dict['loss_dict']["dpo_loss"] = preference_loss + info_dict['loss_dict']["dpo_acc"] = dpo_acc + info_dict['loss_dict']["chosen_reward"] = chosen_reward.mean() + info_dict['loss_dict']["reject_reward"] = reject_reward.mean() + return info_dict + + +def batch_backward(model, scaler, info_dict): + if info_dict["train_engine"] == "deepspeed": + scaled_loss = model.backward(info_dict['loss_dict']['loss']) + else: + scaled_loss = info_dict['loss_dict']['loss'] / info_dict['accum_grad'] + if scaler is not None: + scaler.scale(scaled_loss).backward() + else: + scaled_loss.backward() + + info_dict['loss_dict']['loss'] = scaled_loss + return info_dict + + +def update_parameter_and_lr(model, optimizer, scheduler, scaler, info_dict): + grad_norm = 0.0 + if info_dict['train_engine'] == "deepspeed": + info_dict["is_gradient_accumulation_boundary"] = model.is_gradient_accumulation_boundary() + model.step() + grad_norm = model.get_global_grad_norm() + elif (info_dict['batch_idx'] + 1) % info_dict["accum_grad"] == 0: + # Use mixed precision training + if scaler is not None: + scaler.unscale_(optimizer) + grad_norm = clip_grad_norm_(model.parameters(), info_dict['grad_clip']) + # We don't check grad here since that if the gradient + # has inf/nan values, scaler.step will skip + # optimizer.step(). + if torch.isfinite(grad_norm): + scaler.step(optimizer) + else: + logging.warning('get infinite grad_norm, check your code/data if it appears frequently') + scaler.update() + else: + grad_norm = clip_grad_norm_(model.parameters(), info_dict['grad_clip']) + if torch.isfinite(grad_norm): + optimizer.step() + else: + logging.warning('get infinite grad_norm, check your code/data if it appears frequently') + optimizer.zero_grad() + scheduler.step() + info_dict["lr"] = optimizer.param_groups[0]['lr'] + info_dict["grad_norm"] = grad_norm + return info_dict + + +def log_per_step(writer, info_dict): + tag = info_dict["tag"] + epoch = info_dict.get('epoch', 0) + step = info_dict["step"] + batch_idx = info_dict["batch_idx"] + loss_dict = info_dict['loss_dict'] + rank = int(os.environ.get('RANK', 0)) + + # only rank 0 write to tensorboard to avoid multi-process write + if writer is not None: + if (info_dict['train_engine'] == 'deepspeed' and info_dict['is_gradient_accumulation_boundary'] is True) or \ + (info_dict['train_engine'] == 'torch_ddp' and (info_dict['batch_idx'] + 1) % info_dict['accum_grad'] == 0): + for k in ['epoch', 'lr', 'grad_norm']: + writer.add_scalar('{}/{}'.format(tag, k), info_dict[k], step + 1) + for k, v in loss_dict.items(): + writer.add_scalar('{}/{}'.format(tag, k), v, step + 1) + + # TRAIN & CV, Shell log (stdout) + if (info_dict['batch_idx'] + 1) % info_dict['log_interval'] == 0: + log_str = '{} Batch {}/{} '.format(tag, epoch, batch_idx + 1) + for name, value in loss_dict.items(): + log_str += '{} {:.6f} '.format(name, value) + if tag == "TRAIN": + log_str += 'lr {:.8f} grad_norm {:.6f}'.format( + info_dict["lr"], info_dict['grad_norm']) + log_str += ' rank {}'.format(rank) + logging.debug(log_str) + + +def log_per_save(writer, info_dict): + tag = info_dict["tag"] + epoch = info_dict["epoch"] + step = info_dict["step"] + loss_dict = info_dict["loss_dict"] + lr = info_dict['lr'] + rank = int(os.environ.get('RANK', 0)) + logging.info( + 'Epoch {} Step {} CV info lr {} {} rank {}'.format( + epoch, step + 1, lr, rank, ' '.join(['{} {}'.format(k, v) for k, v in loss_dict.items()]))) + + if writer is not None: + for k in ['epoch', 'lr']: + writer.add_scalar('{}/{}'.format(tag, k), info_dict[k], step + 1) + for k, v in loss_dict.items(): + writer.add_scalar('{}/{}'.format(tag, k), v, step + 1) diff --git a/cosyvoice/cosyvoice/vllm/cosyvoice2.py b/cosyvoice/cosyvoice/vllm/cosyvoice2.py new file mode 100644 index 0000000..de0bc76 --- /dev/null +++ b/cosyvoice/cosyvoice/vllm/cosyvoice2.py @@ -0,0 +1,103 @@ +# SPDX-License-Identifier: Apache-2.0 + +# Adapted from +# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2/modeling_qwen2.py +# Copyright 2024 The Qwen team. +# Copyright 2023 The vLLM team. +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Inference-only Qwen2 model compatible with HuggingFace weights.""" +from vllm.model_executor.models.qwen2 import * + + +class CosyVoice2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP): + packed_modules_mapping = { + "qkv_proj": [ + "q_proj", + "k_proj", + "v_proj", + ], + "gate_up_proj": [ + "gate_proj", + "up_proj", + ], + } + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__() + config = vllm_config.model_config.hf_config + quant_config = vllm_config.quant_config + lora_config = vllm_config.lora_config + + self.config = config + self.lora_config = lora_config + + self.quant_config = quant_config + self.model = Qwen2Model(vllm_config=vllm_config, + prefix=maybe_prefix(prefix, "model")) + + if get_pp_group().is_last_rank: + if config.tie_word_embeddings: + self.lm_head = self.model.embed_tokens + else: + self.lm_head = ParallelLMHead(config.vocab_size, + config.hidden_size, + True, + quant_config=quant_config, + prefix=maybe_prefix( + prefix, "lm_head")) + else: + self.lm_head = PPMissingLayer() + + self.logits_processor = LogitsProcessor(config.vocab_size) + + self.make_empty_intermediate_tensors = ( + self.model.make_empty_intermediate_tensors) + + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + intermediate_tensors: Optional[IntermediateTensors] = None, + inputs_embeds: Optional[torch.Tensor] = None, + ) -> Union[torch.Tensor, IntermediateTensors]: + hidden_states = self.model(input_ids, positions, intermediate_tensors, + inputs_embeds) + return hidden_states + + def compute_logits( + self, + hidden_states: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> Optional[torch.Tensor]: + logits = self.logits_processor(self.lm_head, hidden_states, + sampling_metadata, self.lm_head.bias) + return logits + + def load_weights(self, weights: Iterable[tuple[str, + torch.Tensor]]) -> set[str]: + loader = AutoWeightsLoader( + self, + skip_prefixes=(["lm_head."] + if self.config.tie_word_embeddings else None), + ) + return loader.load_weights(weights) diff --git a/cosyvoice/docker/Dockerfile b/cosyvoice/docker/Dockerfile new file mode 100644 index 0000000..8cefdba --- /dev/null +++ b/cosyvoice/docker/Dockerfile @@ -0,0 +1,51 @@ +FROM nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04 + +ARG VENV_NAME="cosyvoice" +ENV VENV=$VENV_NAME +ENV LANG=C.UTF-8 LC_ALL=C.UTF-8 + +ENV DEBIAN_FRONTEN=noninteractive +ENV PYTHONUNBUFFERED=1 +SHELL ["/bin/bash", "--login", "-c"] + +RUN apt-get update -y --fix-missing +RUN apt-get install -y git build-essential curl wget ffmpeg unzip git git-lfs sox libsox-dev && \ + apt-get clean && \ + git lfs install + +# ================================================================== +# conda install and conda forge channel as default +# ------------------------------------------------------------------ +# Install miniforge +RUN wget --quiet https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -O ~/miniforge.sh && \ + /bin/bash ~/miniforge.sh -b -p /opt/conda && \ + rm ~/miniforge.sh && \ + ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \ + echo "source /opt/conda/etc/profile.d/conda.sh" >> /opt/nvidia/entrypoint.d/100.conda.sh && \ + echo "source /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \ + echo "conda activate ${VENV}" >> /opt/nvidia/entrypoint.d/110.conda_default_env.sh && \ + echo "conda activate ${VENV}" >> $HOME/.bashrc + +ENV PATH /opt/conda/bin:$PATH + +RUN conda config --add channels conda-forge && \ + conda config --set channel_priority strict +# ------------------------------------------------------------------ +# ~conda +# ================================================================== + +RUN conda create -y -n ${VENV} python=3.10 +ENV CONDA_DEFAULT_ENV=${VENV} +ENV PATH /opt/conda/bin:/opt/conda/envs/${VENV}/bin:$PATH + +WORKDIR /workspace + +ENV PYTHONPATH="${PYTHONPATH}:/workspace/CosyVoice:/workspace/CosyVoice/third_party/Matcha-TTS" + +RUN git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git + +RUN conda activate ${VENV} && conda install -y -c conda-forge pynini==2.1.5 +RUN conda activate ${VENV} && cd CosyVoice && \ + pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com --no-cache-dir + +WORKDIR /workspace/CosyVoice diff --git a/cosyvoice/example.py b/cosyvoice/example.py new file mode 100644 index 0000000..85952ae --- /dev/null +++ b/cosyvoice/example.py @@ -0,0 +1,106 @@ +import sys +sys.path.append('third_party/Matcha-TTS') +from cosyvoice.cli.cosyvoice import AutoModel +import torchaudio + + +def cosyvoice_example(): + """ CosyVoice Usage, check https://fun-audio-llm.github.io/ for more details + """ + cosyvoice = AutoModel(model_dir='pretrained_models/CosyVoice-300M-SFT') + # sft usage + print(cosyvoice.list_available_spks()) + # change stream=True for chunk stream inference + for i, j in enumerate(cosyvoice.inference_sft('你好，我是通义生成式语音大模型，请问有什么可以帮您的吗？', '中文女', stream=False)): + torchaudio.save('sft_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + + cosyvoice = AutoModel(model_dir='pretrained_models/CosyVoice-300M') + # zero_shot usage + for i, j in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav')): + torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + # cross_lingual usage, <|zh|><|en|><|jp|><|yue|><|ko|> for Chinese/English/Japanese/Cantonese/Korean + for i, j in enumerate(cosyvoice.inference_cross_lingual('<|en|>And then later on, fully acquiring that company. So keeping management in line, interest in line with the asset that\'s coming into the family is a reason why sometimes we don\'t buy the whole thing.', + './asset/cross_lingual_prompt.wav')): + torchaudio.save('cross_lingual_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + # vc usage + for i, j in enumerate(cosyvoice.inference_vc('./asset/cross_lingual_prompt.wav', './asset/zero_shot_prompt.wav')): + torchaudio.save('vc_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + + cosyvoice = AutoModel(model_dir='pretrained_models/CosyVoice-300M-Instruct') + # instruct usage, support [laughter][breath] + for i, j in enumerate(cosyvoice.inference_instruct('在面对挑战时，他展现了非凡的勇气与智慧。', '中文男', + 'Theo \'Crimson\', is a fiery, passionate rebel leader. Fights with fervor for justice, but struggles with impulsiveness.<|endofprompt|>')): + torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + + +def cosyvoice2_example(): + """ CosyVoice2 Usage, check https://funaudiollm.github.io/cosyvoice2/ for more details + """ + cosyvoice = AutoModel(model_dir='pretrained_models/CosyVoice2-0.5B') + + # NOTE if you want to reproduce the results on https://funaudiollm.github.io/cosyvoice2, please add text_frontend=False during inference + # zero_shot usage + for i, j in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav')): + torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + + # save zero_shot spk for future usage + assert cosyvoice.add_zero_shot_spk('希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav', 'my_zero_shot_spk') is True + for i, j in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '', '', zero_shot_spk_id='my_zero_shot_spk')): + torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + cosyvoice.save_spkinfo() + + # fine grained control, for supported control, check cosyvoice/tokenizer/tokenizer.py#L248 + for i, j in enumerate(cosyvoice.inference_cross_lingual('在他讲述那个荒诞故事的过程中，他突然[laughter]停下来，因为他自己也被逗笑了[laughter]。', './asset/zero_shot_prompt.wav')): + torchaudio.save('fine_grained_control_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + + # instruct usage + for i, j in enumerate(cosyvoice.inference_instruct2('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '用四川话说这句话<|endofprompt|>', './asset/zero_shot_prompt.wav')): + torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + + # bistream usage, you can use generator as input, this is useful when using text llm model as input + # NOTE you should still have some basic sentence split logic because llm can not handle arbitrary sentence length + def text_generator(): + yield '收到好友从远方寄来的生日礼物，' + yield '那份意外的惊喜与深深的祝福' + yield '让我心中充满了甜蜜的快乐，' + yield '笑容如花儿般绽放。' + for i, j in enumerate(cosyvoice.inference_zero_shot(text_generator(), '希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav', stream=False)): + torchaudio.save('zero_shot_bistream_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + + +def cosyvoice3_example(): + """ CosyVoice3 Usage, check https://funaudiollm.github.io/cosyvoice3/ for more details + """ + cosyvoice = AutoModel(model_dir='pretrained_models/Fun-CosyVoice3-0.5B') + # zero_shot usage + for i, j in enumerate(cosyvoice.inference_zero_shot('八百标兵奔北坡，北坡炮兵并排跑，炮兵怕把标兵碰，标兵怕碰炮兵炮。', 'You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。', + './asset/zero_shot_prompt.wav', stream=False)): + torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + + # fine grained control, for supported control, check cosyvoice/tokenizer/tokenizer.py#L280 + for i, j in enumerate(cosyvoice.inference_cross_lingual('You are a helpful assistant.<|endofprompt|>[breath]因为他们那一辈人[breath]在乡里面住的要习惯一点，[breath]邻居都很活络，[breath]嗯，都很熟悉。[breath]', + './asset/zero_shot_prompt.wav', stream=False)): + torchaudio.save('fine_grained_control_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + + # instruct usage, for supported control, check cosyvoice/utils/common.py#L28 + for i, j in enumerate(cosyvoice.inference_instruct2('好少咯，一般系放嗰啲国庆啊，中秋嗰啲可能会咯。', 'You are a helpful assistant. 请用广东话表达。<|endofprompt|>', + './asset/zero_shot_prompt.wav', stream=False)): + torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + for i, j in enumerate(cosyvoice.inference_instruct2('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', 'You are a helpful assistant. 请用尽可能快地语速说一句话。<|endofprompt|>', + './asset/zero_shot_prompt.wav', stream=False)): + torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + + # hotfix usage + for i, j in enumerate(cosyvoice.inference_zero_shot('高管也通过电话、短信、微信等方式对报道[j][ǐ]予好评。', 'You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。', + './asset/zero_shot_prompt.wav', stream=False)): + torchaudio.save('hotfix_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + + +def main(): + # cosyvoice_example() + # cosyvoice2_example() + cosyvoice3_example() + + +if __name__ == '__main__': + main() diff --git a/cosyvoice/examples/grpo/cosyvoice2/Dockerfile b/cosyvoice/examples/grpo/cosyvoice2/Dockerfile new file mode 100644 index 0000000..17d80ed --- /dev/null +++ b/cosyvoice/examples/grpo/cosyvoice2/Dockerfile @@ -0,0 +1,6 @@ +FROM verlai/verl:app-verl0.4-vllm0.8.5-mcore0.12.2-te2.2 +COPY requirements.txt /myworkspace/requirements.txt +RUN pip install -r /myworkspace/requirements.txt +RUN pip install -U nvidia-pytriton +RUN git clone https://github.com/yuekaizhang/verl.git /myworkspace/verl -b thread && cd /myworkspace/verl && pip install --no-deps -e . +RUN git clone https://github.com/yuekaizhang/PytritonSenseVoice.git /myworkspace/PytritonSenseVoice && cd /myworkspace/PytritonSenseVoice && pip install -e . \ No newline at end of file diff --git a/cosyvoice/examples/grpo/cosyvoice2/README.md b/cosyvoice/examples/grpo/cosyvoice2/README.md new file mode 100644 index 0000000..1f5c6a0 --- /dev/null +++ b/cosyvoice/examples/grpo/cosyvoice2/README.md @@ -0,0 +1,125 @@ +# CosyVoice2 LLM Reinforcement Learning Recipe + +This recipe demonstrates how to fine-tune the **CosyVoice2** large language model with reinforcement learning algorithms—specifically **GRPO**—using the [veRL](https://github.com/volcengine/verl) framework. Our experiments show that applying GRPO reduces the character error rate (CER) on the CosyVoice3 `zero_shot_zh` set from 4.08% to 3.36%. + +## Table of Contents + +- [Environment Setup](#environment-setup) +- [Data Preparation](#data-preparation) +- [Reward Function & ASR Server](#reward-function--asr-server) +- [Training](#training) +- [Evaluation](#evaluation) +- [Export Model](#export-model) +- [Results](#results) +- [Acknowledgement](#acknowledgement) + +## Environment Setup +We recommend using the pre-built Docker image below. Alternatively, you can manually install the dependencies following the Dockerfile. +```bash +docker pull soar97/verl:app-verl0.4-vllm0.8.5-mcore0.12.2-te2.2 +``` +If Docker is not available, you can refer to `run.sh` `stage -2` to install the dependencies locally. + +## Data Preparation + +`prepare_data.py` expects a JSON/JSONL file with at least the following schema: + +```jsonc +{ + "text": "An example sentence to be synthesized." +} +``` +You can download the JSONL files from the metadata directory of the [SparkAudio/voxbox](https://huggingface.co/datasets/SparkAudio/voxbox/tree/main/metadata) dataset on Hugging Face. + +Stage `0` converts raw JSONL files into the parquet format expected by veRL: + +```bash +bash run.sh 0 0 +``` +Create two JSONL files—`train.jsonl` and `test.jsonl`. +The script will then generate two Parquet files: + +``` +data/parquet_tiny/train.parquet +data/parquet_tiny/test.parquet +``` + +Each sample is automatically wrapped into a CosyVoice2-style prompt so that the LLM learns to output CosyVoice2 speech tokens. + + +## Reward Function & ASR Server + +To compute rewards, we run a lightweight server that: + +1. Converts generated speech tokens back to a 16 kHz waveform with the **CosyVoice2** pretrained U-Net model. +2. Transcribes the waveform with **SenseVoice** ASR. +3. Calculates the pinyin-level error rate relative to the ground-truth text and maps it to a score between 0 and 1. + +Start the server (stage `1`) in a dedicated terminal or on a separate GPU: + +```bash +bash run.sh 1 1 +# Triton server listens on ports 8000/8001/8002 +``` + +The custom reward implementation is located in [`reward_tts.py`](./reward_tts.py) and calls the server to obtain the reward score. + +## Training + +Run stage `2` to start GRPO training: + +```bash +bash run.sh 2 2 +``` + +Key CLI arguments passed to `verl.trainer.main_ppo`: + +* `algorithm.adv_estimator=grpo` – use GRPO instead of PPO. +* `data.train_files=data/parquet_aishell3/train.parquet` and `data.val_files=data/parquet_aishell3/test.parquet` +* `custom_reward_function.path=reward_tts.py` – custom reward function described above. + +Adjust `CUDA_VISIBLE_DEVICES`, batch sizes, and other hyperparameters to match your hardware. +> [!TIP] +> Note: the lm_head bias is disabled during training to make the model compatible with VLLM and Transformers' Qwen model. + +## Evaluation + +After training is complete, collect the sharded FSDP weights and export a Hugging Face-style checkpoint (stage `3`): + +```bash +bash run.sh 3 3 # merges weights into $llm_path/merged_hf_model +``` + +You can then evaluate the model on the CosyVoice3 zero-shot Chinese test set (stage `4`): + +```bash +bash run.sh 4 4 +``` + +This command launches distributed inference via `infer_dataset.py` and computes WER with `scripts/compute_wer.sh`. + +> [!TIP] +> The script also supports the Seed-TTS test set by setting `dataset=test_zh`. + +## Export Model + +To use the RL-trained model with the official CosyVoice repository: + +```bash +bash run.sh 5 5 +``` + +The script converts the Hugging Face checkpoint back into the format expected by the CosyVoice repository. +> [!TIP] +> However, we observed a slight accuracy drop when using the RL-trained model after conversion, compared with the Hugging Face format. + +## Results + +| Model | Seed-TTS `test_zh` CER | CosyVoice3 `zero_shot_zh` CER | Comment | +|-------|------------------------|------------------------------|---------| +| CosyVoice2 LLM (official) | 1.45% | 4.08% | See the [paper](https://arxiv.org/abs/2412.10117) | +| CosyVoice2 LLM + GRPO | 1.37% | **3.36%** | See the [decoding results](yuekai/official-cosyvoice-llm-grpo-aishell3), Hugging Face-format model | + +## Acknowledgement + +This work was inspired by the implementation in [ch-tts-llasa-rl-grpo](https://github.com/channel-io/ch-tts-llasa-rl-grpo). diff --git a/cosyvoice/examples/grpo/cosyvoice2/huggingface_to_pretrained.py b/cosyvoice/examples/grpo/cosyvoice2/huggingface_to_pretrained.py new file mode 100644 index 0000000..ca49fc3 --- /dev/null +++ b/cosyvoice/examples/grpo/cosyvoice2/huggingface_to_pretrained.py @@ -0,0 +1,71 @@ + +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +python3 hf2pretrained.py --hf-cosyvoice2-llm-path /workspace/rl-exp/checkpoint-400 --output-path /workspace/CosyVoice2-0.5B/llm-new.pt +""" +from argparse import ArgumentParser +import torch +from safetensors import safe_open +from transformers import AutoTokenizer + + +def get_args(): + parser = ArgumentParser() + + parser.add_argument( + "--hf-cosyvoice2-llm-path", + type=str, + default=None, + help="The RL trained CosyVoice2 model path in HuggingFace format", + ) + parser.add_argument( + "--output-path", + type=str, + default="./llm.pt", + help="The path to save the llm.pt", + ) + args = parser.parse_args() + return args + + +if __name__ == "__main__": + args = get_args() + + tokenizer = AutoTokenizer.from_pretrained(args.hf_cosyvoice2_llm_path) + speech_start_idx = tokenizer.convert_tokens_to_ids("<|s_0|>") + cosyvoice2_token_size = 6561 + 3 + llm_embedding_vocab_size = 2 + + hf_tensors = {} + with safe_open(f"{args.hf_cosyvoice2_llm_path}/model.safetensors", framework="pt", device="cpu") as f: + for k in f.keys(): + if k.startswith("lm_head.bias"): + # RL trained model disable bias for lm_head + continue + new_k = "llm.model." + k + hf_tensors[new_k] = f.get_tensor(k) + if k.startswith("lm_head"): + hf_tensors["llm_decoder.weight"] = f.get_tensor(k)[speech_start_idx:speech_start_idx + cosyvoice2_token_size] + hf_tensors["llm_decoder.bias"] = torch.zeros_like(hf_tensors["llm_decoder.weight"][:, 0]) + if k.startswith("model.embed_tokens"): + hf_tensors["speech_embedding.weight"] = f.get_tensor(k)[speech_start_idx:speech_start_idx + cosyvoice2_token_size] + hf_tensors["llm_embedding.weight"] = f.get_tensor(k)[speech_start_idx + cosyvoice2_token_size:speech_start_idx + cosyvoice2_token_size + llm_embedding_vocab_size] + + # use tie_word_embeddings=True + hf_tensors["llm.model.model.embed_tokens.weight"] = hf_tensors["llm.model.model.embed_tokens.weight"][:151936] + hf_tensors["llm.model.lm_head.weight"] = hf_tensors["llm.model.model.embed_tokens.weight"] + + torch.save(hf_tensors, args.output_path) diff --git a/cosyvoice/examples/grpo/cosyvoice2/infer_dataset.py b/cosyvoice/examples/grpo/cosyvoice2/infer_dataset.py new file mode 100644 index 0000000..f72cd77 --- /dev/null +++ b/cosyvoice/examples/grpo/cosyvoice2/infer_dataset.py @@ -0,0 +1,397 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Example Usage +dataset=zero_shot_zh +output_dir=./outputs_rl_aishell3_step${step}_${dataset}_jit_trt_fp16_reward_tts + +token2wav_path=/workspace/CosyVoice2-0.5B +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ +torchrun --nproc_per_node=8 \ + infer_dataset.py \ + --output-dir $output_dir \ + --llm-model-name-or-path $llm_path/merged_hf_model \ + --token2wav-path $token2wav_path \ + --split-name ${dataset} || exit 1 +""" + +import argparse +import json +import os +import sys +from pathlib import Path + +import torch +import torch.distributed as dist +import torch.nn.functional as F +import torchaudio +from cosyvoice.cli.cosyvoice import CosyVoice2 +from cosyvoice.utils.file_utils import load_wav +from datasets import load_dataset +from transformers import AutoTokenizer, AutoModelForCausalLM +from torch.utils.data import DataLoader, Dataset, DistributedSampler +from tqdm import tqdm +import soundfile as sf +import s3tokenizer +from functools import partial + +sys.path.append("/workspace/CosyVoice/third_party/Matcha-TTS") +try: + torch.multiprocessing.set_start_method("spawn") +except RuntimeError: + pass + + +TEMPLATE = "{% for message in messages %}{%- if message['role'] == 'user' %}{{- '<|im_start|>' + message['role'] + '\n' + 'Convert the text to speech: ' + message['content'] + '<|im_end|>\n'}}{%- elif message['role'] == 'assistant' %}{{- '<|im_start|>' + message['role'] + '\n' + '<|SPEECH_GENERATION_START|>' + message['content']}}{%- endif %}{%- endfor %}" # noqa: E501 + + +def audio_decode_cosyvoice2( + audio_tokens, prompt_text, prompt_speech_16k, codec_decoder +): + """ + Generate audio from tokens with optional tone and prompt embedding. + """ + model_inputs_dict = codec_decoder.frontend.frontend_zero_shot( + "empty", prompt_text, prompt_speech_16k, 24000 + ) + tts_mel, _ = codec_decoder.model.flow.inference( + token=audio_tokens.to(codec_decoder.model.device), + token_len=torch.tensor([audio_tokens.shape[1]], dtype=torch.int32).to( + codec_decoder.model.device + ), + prompt_token=model_inputs_dict["flow_prompt_speech_token"].to( + codec_decoder.model.device + ), + prompt_token_len=torch.tensor( + [model_inputs_dict["flow_prompt_speech_token_len"]], dtype=torch.int32 + ).to(codec_decoder.model.device), + prompt_feat=model_inputs_dict["prompt_speech_feat"].to( + codec_decoder.model.device + ), + prompt_feat_len=model_inputs_dict["prompt_speech_feat_len"].to( + codec_decoder.model.device + ), + embedding=model_inputs_dict["flow_embedding"].to(codec_decoder.model.device), + finalize=True, + ) + + audio_hat, _ = codec_decoder.model.hift.inference( + speech_feat=tts_mel, cache_source=torch.zeros(1, 1, 0) + ) + + return audio_hat + + +def extract_speech_ids(speech_tokens_str): + """Extract speech IDs from token strings like <|s_23456|>""" + speech_ids = [] + for token_str in speech_tokens_str: + if token_str.startswith('<|s_') and token_str.endswith('|>'): + num_str = token_str[4:-2] + num = int(num_str) + speech_ids.append(num) + else: + print(f"Unexpected token: {token_str}") + return speech_ids + + +def convert_cosy2_tokens_to_speech_id_str(cosy2_tokens): + """Convert CosyVoice2 tokens to speech IDs string like <|s_23456|>""" + speech_id_str = "" + for token in cosy2_tokens: + speech_id_str += f"<|s_{token}|>" + return speech_id_str + + +def get_args(): + parser = argparse.ArgumentParser(description="Speech generation using LLM + CosyVoice2") + parser.add_argument( + "--split-name", + type=str, + default="wenetspeech4tts", + help="huggingface dataset split name, see yuekai/CV3-Eval, yuekai/seed_tts_cosy2", + ) + parser.add_argument( + "--output-dir", required=True, type=str, help="dir to save result" + ) + parser.add_argument( + "--batch-size", + default=1, + type=int, + help="batch size (per-device) for inference", + ) + parser.add_argument( + "--num-workers", type=int, default=1, help="workers for dataloader" + ) + parser.add_argument( + "--prefetch", type=int, default=5, help="prefetch for dataloader" + ) + parser.add_argument( + "--llm-model-name-or-path", + required=True, + type=str, + help="LLM model path (includes both model and tokenizer)", + ) + parser.add_argument( + "--token2wav-path", + required=True, + type=str, + help="CosyVoice2 token2wav model path", + ) + parser.add_argument( + "--prompt-text", + type=str, + default=None, + help="The prompt text for CosyVoice2", + ) + parser.add_argument( + "--prompt-speech-path", + type=str, + default=None, + help="The path to the prompt speech for CosyVoice2", + ) + parser.add_argument( + "--top-p", + type=float, + default=0.95, + help="top p for sampling", + ) + parser.add_argument( + "--temperature", + type=float, + default=0.8, + help="temperature for sampling", + ) + parser.add_argument( + "--top-k", + type=int, + default=50, + help="top k for sampling", + ) + args = parser.parse_args() + return args + + +def data_collator(batch, tokenizer, s3_tokenizer): + """Simplified data collator for batch_size=1 processing""" + target_sample_rate = 16000 # CosyVoice2 uses 16kHz for prompt audio + device = s3_tokenizer.device if s3_tokenizer is not None else torch.device("cpu") + input_ids_list, prompt_audio_list, prompt_text_list = [], [], [] + mels, prompt_audio_cosy2tokens_list = [], [] + for item in batch: + prompt_text, target_text = ( + item["prompt_text"], + item["target_text"], + ) + prompt_text_list.append(prompt_text) + # Combine prompt and target text + full_text = prompt_text + target_text + + # get prompt audio for CosyVoice2 (convert to 16kHz) + ref_audio_org, ref_sr = ( + item["prompt_audio"]["array"], + item["prompt_audio"]["sampling_rate"], + ) + ref_audio_org = torch.from_numpy(ref_audio_org).float().unsqueeze(0) + # ref_audio_org = ref_audio_org.mean(dim=0, keepdim=True) + print(ref_audio_org.shape) + + if ref_sr != target_sample_rate: + resampler = torchaudio.transforms.Resample(ref_sr, target_sample_rate) + ref_audio = resampler(ref_audio_org) + else: + ref_audio = ref_audio_org + + prompt_audio_list.append(ref_audio) + + if "prompt_audio_cosy2_tokens" in item: + prompt_audio_cosy2tokens = item["prompt_audio_cosy2_tokens"] + prompt_audio_cosy2tokens_list.append(prompt_audio_cosy2tokens) + else: + # convert to float first + mels.append(s3tokenizer.log_mel_spectrogram(ref_audio.squeeze(0))) + + if len(mels) > 0: + mels, mels_lens = s3tokenizer.padding(mels) + codes, codes_lens = s3_tokenizer.quantize(mels.to(device), mels_lens.to(device)) + for i in range(len(codes)): + prompt_audio_cosy2tokens_list.append(codes[i, :codes_lens[i].item()]) + for prompt_audio_cosy2tokens in prompt_audio_cosy2tokens_list: + prompt_audio_cosy2_id_str = convert_cosy2_tokens_to_speech_id_str(prompt_audio_cosy2tokens) + # Create chat template for LLM generation + chat = [ + {"role": "user", "content": full_text}, + {"role": "assistant", "content": prompt_audio_cosy2_id_str} + ] + if 'system' in tokenizer.chat_template: + tokenizer.chat_template = TEMPLATE + input_ids = tokenizer.apply_chat_template( + chat, + tokenize=True, + return_tensors='pt', + continue_final_message=True + ) + input_ids_list.append(input_ids.squeeze(0)) + + # For batch_size=1, no need to pad + if len(input_ids_list) == 1: + input_ids = input_ids_list[0].unsqueeze(0) + else: + # Handle batch > 1 if needed + max_len = max([len(input_ids) for input_ids in input_ids_list]) + input_ids_list = [ + torch.cat([torch.full((max_len - len(input_ids),), tokenizer.pad_token_id), input_ids]) + for input_ids in input_ids_list + ] + input_ids = torch.stack(input_ids_list) + + ids = [item["id"] for item in batch] + + return { + "input_ids": input_ids, + "ids": ids, + "prompt_text": prompt_text_list, + "prompt_audio_list": prompt_audio_list, + } + + +def init_distributed(): + world_size = int(os.environ.get("WORLD_SIZE", 1)) + local_rank = int(os.environ.get("LOCAL_RANK", 0)) + rank = int(os.environ.get("RANK", 0)) + print( + "Inference on multiple gpus, this gpu {}".format(local_rank) + + ", rank {}, world_size {}".format(rank, world_size) + ) + torch.cuda.set_device(local_rank) + dist.init_process_group("nccl") + return world_size, local_rank, rank + + +def main(): + args = get_args() + os.makedirs(args.output_dir, exist_ok=True) + + assert torch.cuda.is_available() + world_size, local_rank, rank = init_distributed() + device = torch.device(f"cuda:{local_rank}") + + # Load LLM model and tokenizer directly + tokenizer = AutoTokenizer.from_pretrained(args.llm_model_name_or_path) + model = AutoModelForCausalLM.from_pretrained(args.llm_model_name_or_path) + model.eval() + model.to(device) + + cosyvoice_codec = CosyVoice2( + args.token2wav_path, load_jit=True, load_trt=True, fp16=True + ) + if args.prompt_speech_path: + prompt_speech_16k = load_wav(args.prompt_speech_path, 16000) + else: + prompt_speech_16k = None + s3_tokenizer = s3tokenizer.load_model("speech_tokenizer_v2_25hz").to(device) if 'zero' in args.split_name else None + dataset_name = "yuekai/CV3-Eval" if 'zero' in args.split_name else "yuekai/seed_tts_cosy2" + dataset = load_dataset( + dataset_name, + split=args.split_name, + trust_remote_code=True, + ) + + sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank) + + dataloader = DataLoader( + dataset, + batch_size=args.batch_size, + sampler=sampler, + shuffle=False, + num_workers=args.num_workers, + prefetch_factor=args.prefetch, + collate_fn=partial(data_collator, tokenizer=tokenizer, s3_tokenizer=s3_tokenizer), + ) + + total_steps = len(dataset) + + if rank == 0: + progress_bar = tqdm(total=total_steps, desc="Processing", unit="wavs") + + for batch in dataloader: + with torch.no_grad(): + input_ids = batch["input_ids"].to(device) + + # Generate speech tokens using LLM + outputs = model.generate( + input_ids, + max_new_tokens=2048, # Max length for generation + do_sample=True, + top_p=args.top_p, + temperature=args.temperature, + top_k=args.top_k, + ) + + # Process each sample in the batch + for i in range(len(batch["ids"])): + # Extract generated tokens (excluding input) + input_length = input_ids[i].shape[0] + generated_ids = outputs[i][input_length:-1] # Remove last token if needed + speech_tokens_str = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) + + # Extract speech IDs from token strings like <|s_23456|> + speech_ids = extract_speech_ids(speech_tokens_str) + + if len(speech_ids) == 0: + print(f"Warning: No speech tokens generated for sample {batch['ids'][i]}, skipping") + continue + + # Convert to tensor for CosyVoice2 + audio_tokens = torch.tensor(speech_ids, dtype=torch.long, device=device).unsqueeze(0) + + if args.prompt_text is not None: + current_prompt_text = args.prompt_text + current_prompt_audio = prompt_speech_16k + else: + current_prompt_text = batch["prompt_text"][i] + current_prompt_audio = batch["prompt_audio_list"][i] + + if current_prompt_audio is not None: + # Generate audio using CosyVoice2 + audio_hat = audio_decode_cosyvoice2( + audio_tokens, + current_prompt_text, + current_prompt_audio, + cosyvoice_codec, + ) + + # Convert to numpy and save + generated_wave = audio_hat.squeeze(0).cpu().numpy() + target_sample_rate = 24000 + + utt = batch["ids"][i] + sf.write(f"{args.output_dir}/{utt}.wav", generated_wave, target_sample_rate) + + print(f"Generated audio for sample {utt} with {len(speech_ids)} tokens") + else: + print(f"Warning: No prompt audio available for sample {batch['ids'][i]}, skipping") + + if rank == 0: + progress_bar.update(world_size * len(batch["ids"])) + + if rank == 0: + progress_bar.close() + + dist.barrier() + dist.destroy_process_group() + + +if __name__ == "__main__": + main() diff --git a/cosyvoice/examples/grpo/cosyvoice2/prepare_data.py b/cosyvoice/examples/grpo/cosyvoice2/prepare_data.py new file mode 100644 index 0000000..46c3c09 --- /dev/null +++ b/cosyvoice/examples/grpo/cosyvoice2/prepare_data.py @@ -0,0 +1,86 @@ +# Copyright 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Preprocess the Text to Speech dataset to parquet format +""" + +import argparse +import os +import re + +import datasets + +from verl.utils.hdfs_io import copy, makedirs + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--train_file", required=True, help="Path to training JSON/JSONL file") + parser.add_argument("--test_file", required=True, help="Path to test JSON/JSONL file") + parser.add_argument("--local_dir", default=None, required=True) + parser.add_argument("--hdfs_dir", default=None) + + args = parser.parse_args() + + # Load datasets from local JSON files + train_dataset = datasets.load_dataset("json", data_files=args.train_file)['train'] + test_dataset = datasets.load_dataset("json", data_files=args.test_file)['train'] + + # add a row to each data item that represents a unique id + def make_map_fn(split): + def process_fn(example, idx): + text = example.pop("text") + + # use cosyvoice2 official huggingface compatible checkpoint template + question = text + answer = "" + + data = { + "data_source": f"{args.train_file}_{args.test_file}", # Use file names as data source + "prompt": [ + { + "role": "user", + "content": question, + }, + { + "role": "assistant", + "content": answer, + }, + ], + "ability": "text-to-speech", + "reward_model": {"style": "rule", "ground_truth": text}, + "extra_info": { + "split": split, + "index": idx, + "text": text, + }, + } + return data + + return process_fn + + train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True) + test_dataset = test_dataset.map(function=make_map_fn("test"), with_indices=True) + + local_dir = args.local_dir + hdfs_dir = args.hdfs_dir + + print(train_dataset) + print(test_dataset) + train_dataset.to_parquet(os.path.join(local_dir, "train.parquet")) + test_dataset.to_parquet(os.path.join(local_dir, "test.parquet")) + + if hdfs_dir is not None: + makedirs(hdfs_dir) + + copy(src=local_dir, dst=hdfs_dir) diff --git a/cosyvoice/examples/grpo/cosyvoice2/pretrained_to_huggingface.py b/cosyvoice/examples/grpo/cosyvoice2/pretrained_to_huggingface.py new file mode 100644 index 0000000..7aaa10b --- /dev/null +++ b/cosyvoice/examples/grpo/cosyvoice2/pretrained_to_huggingface.py @@ -0,0 +1,133 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: Instruct TTS + python3 infer.py \ + --token2wav-path /workspace/CosyVoice2-0.5B \ + --prompt-text "吃燕窝就选燕之屋，本节目由26年专注高品质燕窝的燕之屋冠名播出。豆奶牛奶换着喝，营养更均衡，本节目由豆本豆豆奶特约播出。" \ + --prompt-speech-path ./assets/prompt_audio.wav \ + --model-path ./transformers_cosyvoice2_llm \ + --input-text "用四川话说<|endofprompt|>扁担长，板凳宽，扁担绑在板凳上。吃葡萄不吐葡萄皮，不吃葡萄倒吐葡萄皮。" +""" +from cosyvoice.cli.cosyvoice import CosyVoice2 +import sys +from argparse import ArgumentParser +from transformers import AutoTokenizer, AutoModelForCausalLM +import torch + +sys.path.append("/workspace/CosyVoice/third_party/Matcha-TTS") + + +def get_args(): + parser = ArgumentParser() + + parser.add_argument( + "--pretrained-cosyvoice2-path", + type=str, + default="/workspace/CosyVoice2-0.5B", + help="Token2Wav path, default to %(default)r", + ) + parser.add_argument( + "--save-path", + type=str, + default='./transformers_cosyvoice2_llm', + help="The path to save the model", + ) + args = parser.parse_args() + return args + + +if __name__ == "__main__": + args = get_args() + cosy2_model = CosyVoice2( + args.pretrained_cosyvoice2_path, load_jit=False, load_trt=False, fp16=False + ) + + llm = cosy2_model.model.llm.llm.model + + speech_embedding = cosy2_model.model.llm.speech_embedding + llm_decoder = cosy2_model.model.llm.llm_decoder + llm_embedding = cosy2_model.model.llm.llm_embedding + + tokenizer = AutoTokenizer.from_pretrained(f"{args.pretrained_cosyvoice2_path}/CosyVoice-BlankEN") + special_tokens = { + 'eos_token': '<|endoftext|>', + 'pad_token': '<|endoftext|>', + 'additional_special_tokens': [ + '<|im_start|>', '<|im_end|>', '<|endofprompt|>', + '[breath]', '', '', '[noise]', + '[laughter]', '[cough]', '[clucking]', '[accent]', + '[quick_breath]', + "", "", + "[hissing]", "[sigh]", "[vocalized-noise]", + "[lipsmack]", "[mn]" + ] + } + tokenizer.add_special_tokens(special_tokens) + + original_tokenizer_vocab_size = len(tokenizer) + cosyvoice2_token_size = 6561 + new_tokens = [f"<|s_{i}|>" for i in range(cosyvoice2_token_size)] + [ + "<|eos1|>", "<|eos2|>", "<|eos3|>", "<|sos|>", "<|task_id|>" + ] + num_added_tokens = tokenizer.add_tokens(new_tokens) + + llm.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=128) + vocab_size = llm.get_input_embeddings().weight.shape[0] + + feature_size = speech_embedding.embedding_dim + new_lm_head = torch.nn.Linear(in_features=feature_size, out_features=vocab_size, bias=True) + + with torch.no_grad(): + # set the weight and bias of the new lm_head to 0 + new_lm_head.weight.data.zero_() + # make bias value -inf + new_lm_head.bias.data.fill_(-float('inf')) + new_lm_head.weight[original_tokenizer_vocab_size:original_tokenizer_vocab_size + cosyvoice2_token_size + 3] = llm_decoder.weight + new_lm_head.bias[original_tokenizer_vocab_size:original_tokenizer_vocab_size + cosyvoice2_token_size + 3] = llm_decoder.bias + + llm.lm_head = new_lm_head + input_embeddings = llm.get_input_embeddings() + + with torch.no_grad(): + input_embeddings.weight[original_tokenizer_vocab_size:original_tokenizer_vocab_size + cosyvoice2_token_size + 3] = speech_embedding.weight + input_embeddings.weight[original_tokenizer_vocab_size + cosyvoice2_token_size + 3:original_tokenizer_vocab_size + cosyvoice2_token_size + 3 + 2] = llm_embedding.weight + + eos_token_ids = [original_tokenizer_vocab_size + cosyvoice2_token_size, + original_tokenizer_vocab_size + cosyvoice2_token_size + 1, + original_tokenizer_vocab_size + cosyvoice2_token_size + 2] + llm.generation_config.eos_token_id = eos_token_ids + llm.generation_config.temperature = 1.0 + llm.generation_config.top_p = 0.8 + llm.generation_config.top_k = 25 + + llm.config.eos_token_id = original_tokenizer_vocab_size + cosyvoice2_token_size + llm.config.vocab_size = vocab_size + llm.config.tie_word_embeddings = False + llm.config.use_bias = True + llm.to(torch.bfloat16) + llm.save_pretrained(args.save_path) + + TEMPLATE = ( + "{%- for message in messages %}" + "{%- if message['role'] == 'user' %}" + "{{- '<|sos|>' + message['content'] + '<|task_id|>' }}" + "{%- elif message['role'] == 'assistant' %}" + "{{- message['content']}}" + "{%- endif %}" + "{%- endfor %}" + ) + tokenizer.chat_template = TEMPLATE + tokenizer.save_pretrained(args.save_path) diff --git a/cosyvoice/examples/grpo/cosyvoice2/requirements.txt b/cosyvoice/examples/grpo/cosyvoice2/requirements.txt new file mode 100644 index 0000000..50f4edd --- /dev/null +++ b/cosyvoice/examples/grpo/cosyvoice2/requirements.txt @@ -0,0 +1,31 @@ +conformer==0.3.2 +diffusers==0.29.0 +gdown==5.1.0 +gradio +hydra-core==1.3.2 +HyperPyYAML==1.2.2 +inflect==7.3.1 +librosa==0.10.2 +lightning==2.2.4 +matplotlib==3.7.5 +modelscope==1.15.0 +networkx==3.1 +omegaconf==2.3.0 +onnx==1.16.0 +onnxruntime-gpu==1.18.0 +protobuf==4.25 +pydantic==2.7.0 +pyworld==0.3.4 +rich==13.7.1 +soundfile==0.12.1 +tensorboard==2.14.0 +wget==3.2 +WeTextProcessing==1.0.3 +s3tokenizer +tensorrt +sherpa_onnx +jiwer +zhon +numpy==1.25.2 +pypinyin +openai-whisper \ No newline at end of file diff --git a/cosyvoice/examples/grpo/cosyvoice2/reward_tts.py b/cosyvoice/examples/grpo/cosyvoice2/reward_tts.py new file mode 100644 index 0000000..4c40761 --- /dev/null +++ b/cosyvoice/examples/grpo/cosyvoice2/reward_tts.py @@ -0,0 +1,233 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Reward calculation for CosyVoice2-0.5B. +""" + +from __future__ import annotations + +import re +import json +import time +import argparse +from typing import List + +import numpy as np +import requests + + +REWARD_SERVER_URL = "http://localhost:8000/v2/models/token2wav_asr/infer" + + +def _parse_ids(token_str: str) -> List[int]: + return [int(t) for t in re.findall(r"<\|s_(\d+)\|>", token_str)] + + +def _remote_reward(tokens: List[int], ground_truth: str, timeout: float = 200.0) -> float: + """Send token IDs and ground-truth text to the Triton server and get reward.""" + + tokens_arr = np.array(tokens, dtype=np.int32).reshape(1, -1) + lens_arr = np.array([[tokens_arr.shape[1]]], dtype=np.int32) + + gt_arr = np.array([ground_truth.encode("utf-8")], dtype=object) + + payload = { + "inputs": [ + { + "name": "TOKENS", + "shape": list(tokens_arr.shape), + "datatype": "INT32", + "data": tokens_arr.tolist(), + }, + { + "name": "TOKEN_LENS", + "shape": list(lens_arr.shape), + "datatype": "INT32", + "data": lens_arr.tolist(), + }, + { + "name": "GT_TEXT", + "shape": [1, 1], + "datatype": "BYTES", + "data": [ground_truth], + }, + ] + } + rsp = requests.post( + REWARD_SERVER_URL, + headers={"Content-Type": "application/json"}, + json=payload, + timeout=timeout, + verify=False, + params={"request_id": "0"}, + ) + rsp.raise_for_status() + result = rsp.json() + + try: + # Reward is returned as the first output + return float(result["outputs"][0]["data"][0]) + except (KeyError, IndexError, TypeError): + return 0.0 + + +def compute_score( + data_source: str, + solution_str: str, + ground_truth: str, + extra_info: dict | None = None, + *, + debug_dump: bool = False, +) -> float: + """Return reward in [0, 1] using the Triton ASR service. + + The reward is based on the pinyin-level WER between the ASR transcript + produced from *solution_str* and the provided *ground_truth* text. + """ + + # Decode token IDs + ids = _parse_ids(solution_str) + + # Query remote server for reward + try: + reward = _remote_reward(ids, ground_truth) + except Exception as e: + reward = 0.0 + + if debug_dump: + print( + f"\033[92m[{data_source}] Remote reward: {reward:.4f}\033[0m" + ) + + return reward + + +# CLI quick test +if __name__ == "__main__": + import sys + + def get_args(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser( + description="Test TTS CER scoring with data from JSONL file", + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--input", "-i", + type=str, + default="data/emilia_zh-cosy-tiny-test.jsonl", + help="Path to input JSONL file" + ) + + parser.add_argument( + "--max-samples", "-n", + type=int, + default=None, + help="Maximum number of samples to process (default: all)" + ) + + parser.add_argument( + "--no-interactive", + action="store_true", + help="Run in non-interactive mode (process all samples without prompts)" + ) + + parser.add_argument( + "--debug", + action="store_true", + help="Enable debug mode" + ) + + return parser.parse_args() + + def load_jsonl(file_path: str): + """Load data from jsonl file.""" + data = [] + with open(file_path, 'r', encoding='utf-8') as f: + for line in f: + data.append(json.loads(line.strip())) + return data + + def code_to_solution_str(code_list: List[int]) -> str: + """Convert code list to solution string format.""" + return ''.join([f"<|s_{code}|>" for code in code_list]) + + # Parse command line arguments + args = get_args() + + try: + # Load data from jsonl file + print(f"Loading data from: {args.input}") + data_list = load_jsonl(args.input) + print(f"Loaded {len(data_list)} samples") + + # Limit samples if specified + if args.max_samples is not None: + data_list = data_list[:args.max_samples] + print(f"Processing first {len(data_list)} samples (limited by --max-samples)") + + # Process each sample + begin_time = time.time() + for i, sample in enumerate(data_list): + print(f"\n--- Sample {i+1}/{len(data_list)} ---") + print(f"Index: {sample.get('index', 'unknown')}") + print(f"Text: {sample['text']}") + + # Extract required fields + code_list = sample['code'] + ground_truth = sample['text'] + data_source = sample.get('index', f'sample_{i}') # Use index as data_source + + # Convert code list to solution string + solution_str = code_to_solution_str(code_list) + print(f"Solution tokens: {len(code_list)} tokens") + if args.debug: + print(f"Solution string: {solution_str}") + else: + print(f"Solution string preview: {solution_str[:100]}..." if len(solution_str) > 100 else f"Solution string: {solution_str}") + + # Call compute_score function + try: + score = compute_score( + data_source=data_source, + solution_str=solution_str, + ground_truth=ground_truth, + extra_info=None, + debug_dump=args.debug + ) + print(f"Final Score: {score:.4f}") + except Exception as e: + print(f"Error computing score: {e}") + + # Ask user if they want to continue (for interactive mode) + if not args.no_interactive and i < len(data_list) - 1: + try: + response = input("\nPress Enter to continue or 'q' to quit: ").strip().lower() + if response == 'q': + break + except KeyboardInterrupt: + print("\nStopped by user") + break + + print(f"\nProcessed {min(i+1, len(data_list))} samples") + end_time = time.time() + print(f"Time taken: {end_time - begin_time} seconds") + except FileNotFoundError: + print(f"Error: File not found - {args.input}") + print("Please check the file path or use --input to specify correct path") + print("Run with --help for usage information") + except Exception as e: + print(f"Error: {e}") diff --git a/cosyvoice/examples/grpo/cosyvoice2/run.sh b/cosyvoice/examples/grpo/cosyvoice2/run.sh new file mode 100644 index 0000000..b1658e2 --- /dev/null +++ b/cosyvoice/examples/grpo/cosyvoice2/run.sh @@ -0,0 +1,159 @@ +#!/usr/bin/env bash + +set -eou pipefail + +stage=-1 +stop_stage=4 + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +export PYTHONPATH=/workspace/CosyVoice +model_scope_model_path=./CosyVoice2-0.5B +sft_model_path=./transformers_cosyvoice2_llm + +if [ $stage -le -2 ] && [ $stop_stage -ge -2 ]; then + log "stage -2: install dependencies locally if pre-built docker image is not available" + conda create -n cosyvoice2 python=3.10 -y + conda activate cosyvoice2 + # install verl + git clone https://github.com/yuekaizhang/verl.git -b thread + cd verl + USE_MEGATRON=0 bash scripts/install_vllm_sglang_mcore.sh + pip install --no-deps -e . + cd - + # install requirements + pip install -r requirements.txt + pip install -U nvidia-pytriton + git clone https://github.com/yuekaizhang/PytritonSenseVoice.git && cd PytritonSenseVoice && pip install -e . +fi + +if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then + log "stage -1: download official CosyVoice2-0.5B LLM model and convert to huggingface compatible checkpoint" + modelscope download --model iic/CosyVoice2-0.5B --local_dir $model_scope_model_path + python3 pretrained_to_huggingface.py \ + --pretrained-cosyvoice2-path $model_scope_model_path \ + --save-path $sft_model_path + + # Or, you could use the following command to download the huggingface compatible checkpoint + # huggingface-cli download --local-dir $sft_model_path yuekai/cosyvoice2_llm + + # Note: we remove the lm_head's bias to make it compatible with the Qwen2.5-0.5B model in Transformers. +fi + +data_dir=data/parquet_aishell3 +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then + log "stage 0: prepare data into verl format" + mkdir -p $data_dir + wget -O data/aishell-3.jsonl https://huggingface.co/datasets/SparkAudio/voxbox/resolve/main/metadata/aishell-3.jsonl + # total 88035 samples + head -n 80000 data/aishell-3.jsonl > data/train.jsonl + tail -n 100 data/aishell-3.jsonl > data/test.jsonl + python prepare_data.py \ + --train_file data/train.jsonl \ + --test_file data/test.jsonl \ + --local_dir $data_dir +fi + +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + log "stage 1: start token2wav asr server for reward function" + python3 token2wav_asr_server.py --number-of-devices 8 +fi + +exp_name=official_llm_aishell3_grpo +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + log "stage 2: grpo train" + export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" + export MKL_SERVICE_FORCE_INTEL=TRUE + n_gpus_per_node=8 + micro_batch_size=4 + train_batch_size=32 + python3 -m verl.trainer.main_ppo \ + algorithm.adv_estimator=grpo \ + data.train_files=$data_dir/train.parquet \ + data.val_files=$data_dir/test.parquet \ + data.train_batch_size=$train_batch_size \ + data.max_prompt_length=1024 \ + data.max_response_length=512 \ + data.truncation='error' \ + actor_rollout_ref.model.use_remove_padding=False \ + actor_rollout_ref.model.path=$sft_model_path \ + actor_rollout_ref.actor.optim.lr=1e-6 \ + actor_rollout_ref.actor.ppo_mini_batch_size=32 \ + actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=$micro_batch_size \ + actor_rollout_ref.actor.use_kl_loss=False \ + actor_rollout_ref.model.enable_gradient_checkpointing=True \ + actor_rollout_ref.actor.fsdp_config.param_offload=False \ + actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ + actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=$micro_batch_size \ + actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ + actor_rollout_ref.rollout.name=vllm \ + actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ + actor_rollout_ref.rollout.do_sample=true \ + actor_rollout_ref.rollout.temperature=0.8 \ + actor_rollout_ref.rollout.top_p=0.95 \ + actor_rollout_ref.rollout.top_k=25 \ + actor_rollout_ref.rollout.n=4 \ + actor_rollout_ref.rollout.val_kwargs.do_sample=true \ + actor_rollout_ref.rollout.val_kwargs.temperature=0.8 \ + actor_rollout_ref.rollout.val_kwargs.top_p=0.95 \ + actor_rollout_ref.rollout.val_kwargs.top_k=25 \ + reward_model.reward_manager=prime \ + custom_reward_function.path=reward_tts.py \ + custom_reward_function.name=compute_score \ + trainer.project_name='cosyvoice2_grpo' \ + trainer.experiment_name=$exp_name \ + trainer.logger=['console','wandb'] \ + trainer.n_gpus_per_node=$n_gpus_per_node \ + trainer.nnodes=1 \ + trainer.save_freq=100 \ + trainer.test_freq=100 \ + trainer.resume_mode='auto' \ + trainer.total_epochs=1 \ + trainer.val_before_train=False +fi + +steps=(100 200 300 400 500) +for step in ${steps[@]}; do +llm_path=./checkpoints/cosyvoice2_grpo/$exp_name/global_step_${step} +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then + log "stage 3: merge the model" + python -m verl.model_merger merge \ + --backend fsdp \ + --local_dir $llm_path/actor \ + --target_dir $llm_path/merged_hf_model || exit 1 +fi + +if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then + log "stage 4: Test the model" + dataset=zero_shot_zh # from CosyVoice3 test set + # dataset=test_zh # from seed_tts test set + output_dir=./outputs_${exp_name}_${step}_${dataset} + + token2wav_path=/workspace/CosyVoice2-0.5B + model_path=$llm_path/merged_hf_model + + CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ + torchrun --nproc_per_node=8 \ + infer_dataset.py \ + --output-dir $output_dir \ + --llm-model-name-or-path $model_path \ + --token2wav-path $token2wav_path \ + --split-name ${dataset} || exit 1 + + bash scripts/compute_wer.sh $output_dir ${dataset} +fi +done + +if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then + log "stage 5: Convert the RL trained model to CosyVoice repo format" + python3 huggingface_to_pretrained.py \ + --hf-cosyvoice2-llm-path $llm_path/merged_hf_model \ + --output-path /workspace/CosyVoice2-0.5B/llm-new.pt + # You need to manually move the llm-new.pt to overwrite /workspace/CosyVoice2-0.5B/llm.pt + # However, we found that the RL trained model accuracy would slightly drop after this conversion. + # Please be careful or use the huggingface format inference code. +fi \ No newline at end of file diff --git a/cosyvoice/examples/grpo/cosyvoice2/scripts/compute_wer.sh b/cosyvoice/examples/grpo/cosyvoice2/scripts/compute_wer.sh new file mode 100644 index 0000000..43a6872 --- /dev/null +++ b/cosyvoice/examples/grpo/cosyvoice2/scripts/compute_wer.sh @@ -0,0 +1,33 @@ +wav_dir=$1 +wav_files=$(ls $wav_dir/*.wav) +# if wav_files is empty, then exit +if [ -z "$wav_files" ]; then + exit 1 +fi +split_name=$2 +model_path=models/sherpa-onnx-paraformer-zh-2023-09-14 + +if [ ! -d $model_path ]; then + pip install sherpa-onnx + wget -nc https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-paraformer-zh-2023-09-14.tar.bz2 + mkdir models + tar xvf sherpa-onnx-paraformer-zh-2023-09-14.tar.bz2 -C models +fi + +python3 scripts/offline-decode-files.py \ + --tokens=$model_path/tokens.txt \ + --paraformer=$model_path/model.int8.onnx \ + --num-threads=2 \ + --decoding-method=greedy_search \ + --debug=false \ + --sample-rate=24000 \ + --log-dir $wav_dir \ + --feature-dim=80 \ + --split-name $split_name \ + --name sherpa_onnx \ + $wav_files + +# python3 scripts/paraformer-pytriton-client.py \ +# --log-dir $wav_dir \ +# --split-name $split_name \ +# $wav_files \ No newline at end of file diff --git a/cosyvoice/examples/grpo/cosyvoice2/scripts/offline-decode-files.py b/cosyvoice/examples/grpo/cosyvoice2/scripts/offline-decode-files.py new file mode 100644 index 0000000..90c4665 --- /dev/null +++ b/cosyvoice/examples/grpo/cosyvoice2/scripts/offline-decode-files.py @@ -0,0 +1,754 @@ +# Copyright (c) 2023 by manyeyes +# Copyright (c) 2023 Xiaomi Corporation + +""" +This file demonstrates how to use sherpa-onnx Python API to transcribe +file(s) with a non-streaming model. + +(1) For paraformer + + ./python-api-examples/offline-decode-files.py \ + --tokens=/path/to/tokens.txt \ + --paraformer=/path/to/paraformer.onnx \ + --num-threads=2 \ + --decoding-method=greedy_search \ + --debug=false \ + --sample-rate=16000 \ + --feature-dim=80 \ + /path/to/0.wav \ + /path/to/1.wav + +(2) For transducer models from icefall + + ./python-api-examples/offline-decode-files.py \ + --tokens=/path/to/tokens.txt \ + --encoder=/path/to/encoder.onnx \ + --decoder=/path/to/decoder.onnx \ + --joiner=/path/to/joiner.onnx \ + --num-threads=2 \ + --decoding-method=greedy_search \ + --debug=false \ + --sample-rate=16000 \ + --feature-dim=80 \ + /path/to/0.wav \ + /path/to/1.wav + +(3) For CTC models from NeMo + +python3 ./python-api-examples/offline-decode-files.py \ + --tokens=./sherpa-onnx-nemo-ctc-en-citrinet-512/tokens.txt \ + --nemo-ctc=./sherpa-onnx-nemo-ctc-en-citrinet-512/model.onnx \ + --num-threads=2 \ + --decoding-method=greedy_search \ + --debug=false \ + ./sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/0.wav \ + ./sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/1.wav \ + ./sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/8k.wav + +(4) For Whisper models + +python3 ./python-api-examples/offline-decode-files.py \ + --whisper-encoder=./sherpa-onnx-whisper-base.en/base.en-encoder.int8.onnx \ + --whisper-decoder=./sherpa-onnx-whisper-base.en/base.en-decoder.int8.onnx \ + --tokens=./sherpa-onnx-whisper-base.en/base.en-tokens.txt \ + --whisper-task=transcribe \ + --num-threads=1 \ + ./sherpa-onnx-whisper-base.en/test_wavs/0.wav \ + ./sherpa-onnx-whisper-base.en/test_wavs/1.wav \ + ./sherpa-onnx-whisper-base.en/test_wavs/8k.wav + +(5) For CTC models from WeNet + +python3 ./python-api-examples/offline-decode-files.py \ + --wenet-ctc=./sherpa-onnx-zh-wenet-wenetspeech/model.onnx \ + --tokens=./sherpa-onnx-zh-wenet-wenetspeech/tokens.txt \ + ./sherpa-onnx-zh-wenet-wenetspeech/test_wavs/0.wav \ + ./sherpa-onnx-zh-wenet-wenetspeech/test_wavs/1.wav \ + ./sherpa-onnx-zh-wenet-wenetspeech/test_wavs/8k.wav + +(6) For tdnn models of the yesno recipe from icefall + +python3 ./python-api-examples/offline-decode-files.py \ + --sample-rate=8000 \ + --feature-dim=23 \ + --tdnn-model=./sherpa-onnx-tdnn-yesno/model-epoch-14-avg-2.onnx \ + --tokens=./sherpa-onnx-tdnn-yesno/tokens.txt \ + ./sherpa-onnx-tdnn-yesno/test_wavs/0_0_0_1_0_0_0_1.wav \ + ./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_0_1_0.wav \ + ./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_1_1_1.wav + +Please refer to +https://k2-fsa.github.io/sherpa/onnx/index.html +to install sherpa-onnx and to download non-streaming pre-trained models +used in this file. +""" +import argparse +import time +import wave +from pathlib import Path +from typing import List, Tuple, Dict, Iterable, TextIO, Union + +import numpy as np +import sherpa_onnx +import soundfile as sf +from datasets import load_dataset +import logging +from collections import defaultdict +import kaldialign +from zhon.hanzi import punctuation +import string +punctuation_all = punctuation + string.punctuation +Pathlike = Union[str, Path] + + +def remove_punctuation(text: str) -> str: + for x in punctuation_all: + if x == '\'': + continue + text = text.replace(x, '') + return text + + +def store_transcripts( + filename: Pathlike, texts: Iterable[Tuple[str, str, str]], char_level: bool = False +) -> None: + """Save predicted results and reference transcripts to a file. + + Args: + filename: + File to save the results to. + texts: + An iterable of tuples. The first element is the cur_id, the second is + the reference transcript and the third element is the predicted result. + If it is a multi-talker ASR system, the ref and hyp may also be lists of + strings. + Returns: + Return None. + """ + with open(filename, "w", encoding="utf8") as f: + for cut_id, ref, hyp in texts: + if char_level: + ref = list("".join(ref)) + hyp = list("".join(hyp)) + print(f"{cut_id}:\tref={ref}", file=f) + print(f"{cut_id}:\thyp={hyp}", file=f) + + +def write_error_stats( + f: TextIO, + test_set_name: str, + results: List[Tuple[str, str]], + enable_log: bool = True, + compute_CER: bool = False, + sclite_mode: bool = False, +) -> float: + """Write statistics based on predicted results and reference transcripts. + + It will write the following to the given file: + + - WER + - number of insertions, deletions, substitutions, corrects and total + reference words. For example:: + + Errors: 23 insertions, 57 deletions, 212 substitutions, over 2606 + reference words (2337 correct) + + - The difference between the reference transcript and predicted result. + An instance is given below:: + + THE ASSOCIATION OF (EDISON->ADDISON) ILLUMINATING COMPANIES + + The above example shows that the reference word is `EDISON`, + but it is predicted to `ADDISON` (a substitution error). + + Another example is:: + + FOR THE FIRST DAY (SIR->*) I THINK + + The reference word `SIR` is missing in the predicted + results (a deletion error). + results: + An iterable of tuples. The first element is the cut_id, the second is + the reference transcript and the third element is the predicted result. + enable_log: + If True, also print detailed WER to the console. + Otherwise, it is written only to the given file. + Returns: + Return None. + """ + subs: Dict[Tuple[str, str], int] = defaultdict(int) + ins: Dict[str, int] = defaultdict(int) + dels: Dict[str, int] = defaultdict(int) + + # `words` stores counts per word, as follows: + # corr, ref_sub, hyp_sub, ins, dels + words: Dict[str, List[int]] = defaultdict(lambda: [0, 0, 0, 0, 0]) + num_corr = 0 + ERR = "*" + + if compute_CER: + for i, res in enumerate(results): + cut_id, ref, hyp = res + ref = list("".join(ref)) + hyp = list("".join(hyp)) + results[i] = (cut_id, ref, hyp) + + for _cut_id, ref, hyp in results: + ali = kaldialign.align(ref, hyp, ERR, sclite_mode=sclite_mode) + for ref_word, hyp_word in ali: + if ref_word == ERR: + ins[hyp_word] += 1 + words[hyp_word][3] += 1 + elif hyp_word == ERR: + dels[ref_word] += 1 + words[ref_word][4] += 1 + elif hyp_word != ref_word: + subs[(ref_word, hyp_word)] += 1 + words[ref_word][1] += 1 + words[hyp_word][2] += 1 + else: + words[ref_word][0] += 1 + num_corr += 1 + ref_len = sum([len(r) for _, r, _ in results]) + sub_errs = sum(subs.values()) + ins_errs = sum(ins.values()) + del_errs = sum(dels.values()) + tot_errs = sub_errs + ins_errs + del_errs + tot_err_rate = "%.2f" % (100.0 * tot_errs / ref_len) + + if enable_log: + logging.info( + f"[{test_set_name}] %WER {tot_errs / ref_len:.2%} " + f"[{tot_errs} / {ref_len}, {ins_errs} ins, " + f"{del_errs} del, {sub_errs} sub ]" + ) + + print(f"%WER = {tot_err_rate}", file=f) + print( + f"Errors: {ins_errs} insertions, {del_errs} deletions, " + f"{sub_errs} substitutions, over {ref_len} reference " + f"words ({num_corr} correct)", + file=f, + ) + print( + "Search below for sections starting with PER-UTT DETAILS:, " + "SUBSTITUTIONS:, DELETIONS:, INSERTIONS:, PER-WORD STATS:", + file=f, + ) + + print("", file=f) + print("PER-UTT DETAILS: corr or (ref->hyp) ", file=f) + for cut_id, ref, hyp in results: + ali = kaldialign.align(ref, hyp, ERR) + combine_successive_errors = True + if combine_successive_errors: + ali = [[[x], [y]] for x, y in ali] + for i in range(len(ali) - 1): + if ali[i][0] != ali[i][1] and ali[i + 1][0] != ali[i + 1][1]: + ali[i + 1][0] = ali[i][0] + ali[i + 1][0] + ali[i + 1][1] = ali[i][1] + ali[i + 1][1] + ali[i] = [[], []] + ali = [ + [ + list(filter(lambda a: a != ERR, x)), + list(filter(lambda a: a != ERR, y)), + ] + for x, y in ali + ] + ali = list(filter(lambda x: x != [[], []], ali)) + ali = [ + [ + ERR if x == [] else " ".join(x), + ERR if y == [] else " ".join(y), + ] + for x, y in ali + ] + + print( + f"{cut_id}:\t" + + " ".join( + ( + ref_word if ref_word == hyp_word else f"({ref_word}->{hyp_word})" + for ref_word, hyp_word in ali + ) + ), + file=f, + ) + + print("", file=f) + print("SUBSTITUTIONS: count ref -> hyp", file=f) + + for count, (ref, hyp) in sorted([(v, k) for k, v in subs.items()], reverse=True): + print(f"{count} {ref} -> {hyp}", file=f) + + print("", file=f) + print("DELETIONS: count ref", file=f) + for count, ref in sorted([(v, k) for k, v in dels.items()], reverse=True): + print(f"{count} {ref}", file=f) + + print("", file=f) + print("INSERTIONS: count hyp", file=f) + for count, hyp in sorted([(v, k) for k, v in ins.items()], reverse=True): + print(f"{count} {hyp}", file=f) + + print("", file=f) + print("PER-WORD STATS: word corr tot_errs count_in_ref count_in_hyp", file=f) + for _, word, counts in sorted( + [(sum(v[1:]), k, v) for k, v in words.items()], reverse=True + ): + (corr, ref_sub, hyp_sub, ins, dels) = counts + tot_errs = ref_sub + hyp_sub + ins + dels + ref_count = corr + ref_sub + dels + hyp_count = corr + hyp_sub + ins + + print(f"{word} {corr} {tot_errs} {ref_count} {hyp_count}", file=f) + return float(tot_err_rate) + + +def get_args(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--tokens", + type=str, + help="Path to tokens.txt", + ) + + parser.add_argument( + "--hotwords-file", + type=str, + default="", + help=""" + The file containing hotwords, one words/phrases per line, like + HELLO WORLD + 你好世界 + """, + ) + + parser.add_argument( + "--hotwords-score", + type=float, + default=1.5, + help=""" + The hotword score of each token for biasing word/phrase. Used only if + --hotwords-file is given. + """, + ) + + parser.add_argument( + "--modeling-unit", + type=str, + default="", + help=""" + The modeling unit of the model, valid values are cjkchar, bpe, cjkchar+bpe. + Used only when hotwords-file is given. + """, + ) + + parser.add_argument( + "--bpe-vocab", + type=str, + default="", + help=""" + The path to the bpe vocabulary, the bpe vocabulary is generated by + sentencepiece, you can also export the bpe vocabulary through a bpe model + by `scripts/export_bpe_vocab.py`. Used only when hotwords-file is given + and modeling-unit is bpe or cjkchar+bpe. + """, + ) + + parser.add_argument( + "--encoder", + default="", + type=str, + help="Path to the encoder model", + ) + + parser.add_argument( + "--decoder", + default="", + type=str, + help="Path to the decoder model", + ) + + parser.add_argument( + "--joiner", + default="", + type=str, + help="Path to the joiner model", + ) + + parser.add_argument( + "--paraformer", + default="", + type=str, + help="Path to the model.onnx from Paraformer", + ) + + parser.add_argument( + "--nemo-ctc", + default="", + type=str, + help="Path to the model.onnx from NeMo CTC", + ) + + parser.add_argument( + "--wenet-ctc", + default="", + type=str, + help="Path to the model.onnx from WeNet CTC", + ) + + parser.add_argument( + "--tdnn-model", + default="", + type=str, + help="Path to the model.onnx for the tdnn model of the yesno recipe", + ) + + parser.add_argument( + "--num-threads", + type=int, + default=1, + help="Number of threads for neural network computation", + ) + + parser.add_argument( + "--whisper-encoder", + default="", + type=str, + help="Path to whisper encoder model", + ) + + parser.add_argument( + "--whisper-decoder", + default="", + type=str, + help="Path to whisper decoder model", + ) + + parser.add_argument( + "--whisper-language", + default="", + type=str, + help="""It specifies the spoken language in the input audio file. + Example values: en, fr, de, zh, jp. + Available languages for multilingual models can be found at + https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10 + If not specified, we infer the language from the input audio file. + """, + ) + + parser.add_argument( + "--whisper-task", + default="transcribe", + choices=["transcribe", "translate"], + type=str, + help="""For multilingual models, if you specify translate, the output + will be in English. + """, + ) + + parser.add_argument( + "--whisper-tail-paddings", + default=-1, + type=int, + help="""Number of tail padding frames. + We have removed the 30-second constraint from whisper, so you need to + choose the amount of tail padding frames by yourself. + Use -1 to use a default value for tail padding. + """, + ) + + parser.add_argument( + "--blank-penalty", + type=float, + default=0.0, + help=""" + The penalty applied on blank symbol during decoding. + Note: It is a positive value that would be applied to logits like + this `logits[:, 0] -= blank_penalty` (suppose logits.shape is + [batch_size, vocab] and blank id is 0). + """, + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="Valid values are greedy_search and modified_beam_search", + ) + parser.add_argument( + "--debug", + type=bool, + default=False, + help="True to show debug messages", + ) + + parser.add_argument( + "--sample-rate", + type=int, + default=16000, + help="""Sample rate of the feature extractor. Must match the one + expected by the model. Note: The input sound files can have a + different sample rate from this argument.""", + ) + + parser.add_argument( + "--feature-dim", + type=int, + default=80, + help="Feature dimension. Must match the one expected by the model", + ) + + parser.add_argument( + "sound_files", + type=str, + nargs="+", + help="The input sound file(s) to decode. Each file must be of WAVE" + "format with a single channel, and each sample has 16-bit, " + "i.e., int16_t. " + "The sample rate of the file can be arbitrary and does not need to " + "be 16 kHz", + ) + + parser.add_argument( + "--name", + type=str, + default="", + help="The directory containing the input sound files to decode", + ) + + parser.add_argument( + "--log-dir", + type=str, + default="", + help="The directory containing the input sound files to decode", + ) + + parser.add_argument( + "--label", + type=str, + default=None, + help="wav_base_name label", + ) + + # Dataset related arguments for loading labels when label file is not provided + parser.add_argument( + "--dataset-name", + type=str, + default="yuekai/seed_tts_cosy2", + help="Huggingface dataset name for loading labels", + ) + + parser.add_argument( + "--split-name", + type=str, + default="wenetspeech4tts", + help="Dataset split name for loading labels", + ) + + return parser.parse_args() + + +def assert_file_exists(filename: str): + assert Path(filename).is_file(), ( + f"{filename} does not exist!\n" + "Please refer to " + "https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html to download it" + ) + + +def read_wave(wave_filename: str) -> Tuple[np.ndarray, int]: + """ + Args: + wave_filename: + Path to a wave file. It should be single channel and can be of type + 32-bit floating point PCM. Its sample rate does not need to be 24kHz. + + Returns: + Return a tuple containing: + - A 1-D array of dtype np.float32 containing the samples, + which are normalized to the range [-1, 1]. + - Sample rate of the wave file. + """ + + samples, sample_rate = sf.read(wave_filename, dtype="float32") + assert ( + samples.ndim == 1 + ), f"Expected single channel, but got {samples.ndim} channels." + + samples_float32 = samples.astype(np.float32) + + return samples_float32, sample_rate + + +def normalize_text_alimeeting(text: str) -> str: + """ + Text normalization similar to M2MeT challenge baseline. + See: https://github.com/yufan-aslp/AliMeeting/blob/main/asr/local/text_normalize.pl + """ + import re + text = text.replace('\u00A0', '') # test_hard + text = text.replace(" ", "") + text = text.replace("", "") + text = text.replace("<%>", "") + text = text.replace("<->", "") + text = text.replace("<$>", "") + text = text.replace("<#>", "") + text = text.replace("<_>", "") + text = text.replace("", "") + text = text.replace("`", "") + text = text.replace("&", "") + text = text.replace(",", "") + if re.search("[a-zA-Z]", text): + text = text.upper() + text = text.replace("Ａ", "A") + text = text.replace("ａ", "A") + text = text.replace("ｂ", "B") + text = text.replace("ｃ", "C") + text = text.replace("ｋ", "K") + text = text.replace("ｔ", "T") + text = text.replace("，", "") + text = text.replace("丶", "") + text = text.replace("。", "") + text = text.replace("、", "") + text = text.replace("？", "") + text = remove_punctuation(text) + return text + + +def main(): + args = get_args() + assert_file_exists(args.tokens) + assert args.num_threads > 0, args.num_threads + + assert len(args.nemo_ctc) == 0, args.nemo_ctc + assert len(args.wenet_ctc) == 0, args.wenet_ctc + assert len(args.whisper_encoder) == 0, args.whisper_encoder + assert len(args.whisper_decoder) == 0, args.whisper_decoder + assert len(args.tdnn_model) == 0, args.tdnn_model + + assert_file_exists(args.paraformer) + + recognizer = sherpa_onnx.OfflineRecognizer.from_paraformer( + paraformer=args.paraformer, + tokens=args.tokens, + num_threads=args.num_threads, + sample_rate=args.sample_rate, + feature_dim=args.feature_dim, + decoding_method=args.decoding_method, + debug=args.debug, + ) + + print("Started!") + start_time = time.time() + + streams, results = [], [] + total_duration = 0 + + for i, wave_filename in enumerate(args.sound_files): + assert_file_exists(wave_filename) + samples, sample_rate = read_wave(wave_filename) + duration = len(samples) / sample_rate + total_duration += duration + s = recognizer.create_stream() + s.accept_waveform(sample_rate, samples) + + streams.append(s) + if i % 10 == 0: + recognizer.decode_streams(streams) + results += [s.result.text for s in streams] + streams = [] + print(f"Processed {i} files") + # process the last batch + if streams: + recognizer.decode_streams(streams) + results += [s.result.text for s in streams] + end_time = time.time() + print("Done!") + + results_dict = {} + for wave_filename, result in zip(args.sound_files, results): + print(f"{wave_filename}\n{result}") + print("-" * 10) + wave_basename = Path(wave_filename).stem + results_dict[wave_basename] = result + + elapsed_seconds = end_time - start_time + rtf = elapsed_seconds / total_duration + print(f"num_threads: {args.num_threads}") + print(f"decoding_method: {args.decoding_method}") + print(f"Wave duration: {total_duration:.3f} s") + print(f"Elapsed time: {elapsed_seconds:.3f} s") + print( + f"Real time factor (RTF): {elapsed_seconds:.3f}/{total_duration:.3f} = {rtf:.3f}" + ) + + # Load labels either from file or from dataset + labels_dict = {} + + if args.label: + # Load labels from file (original functionality) + print(f"Loading labels from file: {args.label}") + with open(args.label, "r") as f: + for line in f: + # fields = line.strip().split(" ") + # fields = [item for item in fields if item] + # assert len(fields) == 4 + # prompt_text, prompt_audio, text, audio_path = fields + + fields = line.strip().split("|") + fields = [item for item in fields if item] + assert len(fields) == 4 + audio_path, prompt_text, prompt_audio, text = fields + labels_dict[Path(audio_path).stem] = normalize_text_alimeeting(text) + else: + # Load labels from dataset (new functionality) + print(f"Loading labels from dataset: {args.dataset_name}, split: {args.split_name}") + if 'zero' in args.split_name: + dataset_name = "yuekai/CV3-Eval" + else: + dataset_name = "yuekai/seed_tts_cosy2" + dataset = load_dataset( + dataset_name, + split=args.split_name, + trust_remote_code=True, + ) + + for item in dataset: + audio_id = item["id"] + labels_dict[audio_id] = normalize_text_alimeeting(item["target_text"]) + + print(f"Loaded {len(labels_dict)} labels from dataset") + + # Perform evaluation if labels are available + if labels_dict: + + final_results = [] + for key, value in results_dict.items(): + if key in labels_dict: + final_results.append((key, labels_dict[key], value)) + else: + print(f"Warning: No label found for {key}, skipping...") + + if final_results: + store_transcripts( + filename=f"{args.log_dir}/recogs-{args.name}.txt", texts=final_results + ) + with open(f"{args.log_dir}/errs-{args.name}.txt", "w") as f: + write_error_stats(f, "test-set", final_results, enable_log=True) + + with open(f"{args.log_dir}/errs-{args.name}.txt", "r") as f: + print(f.readline()) # WER + print(f.readline()) # Detailed errors + else: + print("No matching labels found for evaluation") + else: + print("No labels available for evaluation") + + +if __name__ == "__main__": + main() diff --git a/cosyvoice/examples/grpo/cosyvoice2/token2wav_asr_server.py b/cosyvoice/examples/grpo/cosyvoice2/token2wav_asr_server.py new file mode 100644 index 0000000..9f9f80b --- /dev/null +++ b/cosyvoice/examples/grpo/cosyvoice2/token2wav_asr_server.py @@ -0,0 +1,346 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Pytriton server for token2wav conversion and ASR""" + +from datasets import load_dataset +from cosyvoice.cli.cosyvoice import CosyVoice2 +from omnisense.models import OmniSenseVoiceSmall +from pytriton.proxy.types import Request +from pytriton.triton import Triton, TritonConfig +from pytriton.model_config import DynamicBatcher, ModelConfig, Tensor +from pytriton.decorators import batch +import argparse +import io +import logging +from typing import Any, List +import numpy as np +import torch +from scipy.signal import resample +import sys +import random +import re +from jiwer import wer +from pypinyin import lazy_pinyin, Style +from tn.chinese.normalizer import Normalizer as ZhNormalizer + +# Chinese text normalizer (cached globally) +zh_tn_model = ZhNormalizer( + cache_dir="./cache", + remove_erhua=False, + remove_interjections=False, + remove_puncts=True, + overwrite_cache=True, +) + + +sys.path.append("/workspace/CosyVoice/third_party/Matcha-TTS") + +logger = logging.getLogger("token2wav_asr_server") + + +class _ASR_Server: + """Wraps a single OmniSenseVoiceSmall model instance for Triton.""" + + def __init__(self, device_id: int): + self._model = OmniSenseVoiceSmall("iic/SenseVoiceSmall", quantize=False, device_id=device_id) + + @batch + def __call__(self, WAV: np.ndarray, WAV_LENS: np.ndarray, LANGUAGE: np.ndarray, TEXT_NORM: np.ndarray): + """ + WAV: np.ndarray, WAV_LENS: np.ndarray + LANGUAGE: np.ndarray, TEXTNORM: np.ndarray for backward compatibility, not used + See: https://github.com/modelscope/FunASR/tree/main/runtime/triton_gpu + """ + logger.debug("WAV: %s, WAV_LENS: %s, shapes: %s %s", type(WAV), type(WAV_LENS), WAV.shape, WAV_LENS.shape) + wavs = [WAV[i, :WAV_LENS[i, 0]] for i in range(len(WAV))] + + results = self._model.transcribe_single_batch( + wavs, + language="zh", + textnorm="woitn", + ) + texts = [result.text for result in results] + transcripts = np.char.encode(np.array(texts).reshape(-1, 1), "utf-8") + return {"TRANSCRIPTS": transcripts} + + +def audio_decode_cosyvoice2( + audio_tokens, prompt_text, prompt_speech_16k, codec_decoder +): + """ + Generate audio from tokens with optional tone and prompt embedding. + """ + model_inputs_dict = codec_decoder.frontend.frontend_zero_shot( + "empty", prompt_text, prompt_speech_16k, 24000 + ) + tts_mel, _ = codec_decoder.model.flow.inference( + token=audio_tokens.to(codec_decoder.model.device), + token_len=torch.tensor([audio_tokens.shape[1]], dtype=torch.int32).to( + codec_decoder.model.device + ), + prompt_token=model_inputs_dict["flow_prompt_speech_token"].to( + codec_decoder.model.device + ), + prompt_token_len=torch.tensor( + [model_inputs_dict["flow_prompt_speech_token_len"]], dtype=torch.int32 + ).to(codec_decoder.model.device), + prompt_feat=model_inputs_dict["prompt_speech_feat"].to( + codec_decoder.model.device + ), + prompt_feat_len=model_inputs_dict["prompt_speech_feat_len"].to( + codec_decoder.model.device + ), + embedding=model_inputs_dict["flow_embedding"].to(codec_decoder.model.device), + finalize=True, + ) + + audio_hat, _ = codec_decoder.model.hift.inference( + speech_feat=tts_mel, cache_source=torch.zeros(1, 1, 0) + ) + + return audio_hat + + +def get_random_prompt_from_dataset(dataset): + """ + Get random prompt text and speech from the pre-loaded dataset. + Returns (prompt_text, prompt_speech_16k) + """ + random_idx = random.randint(0, len(dataset) - 1) + sample = dataset[random_idx] + + # Extract audio data + audio_data = sample["audio"] + audio_array = audio_data["array"] + sample_rate = audio_data["sampling_rate"] + + # Convert audio to 16kHz if needed + if sample_rate != 16000: + num_samples = int(len(audio_array) * (16000 / sample_rate)) + audio_array = resample(audio_array, num_samples) + + # Convert to torch tensor + prompt_speech_16k = torch.from_numpy(audio_array).float().unsqueeze(0) + prompt_text = sample["text"] + # remove space in prompt_text + prompt_text = prompt_text.replace(" ", "") + return prompt_text, prompt_speech_16k + + +class _Token2Wav_ASR: + """Wraps a single OmniSenseVoiceSmall model instance for Triton.""" + + def __init__(self, device_id: int): + self.asr_model = OmniSenseVoiceSmall("iic/SenseVoiceSmall", quantize=False, device_id=device_id) + self.dataset = load_dataset("yuekai/aishell", "test", trust_remote_code=True)["test"] + + # Make sure the CosyVoice2 decoder lives on the same GPU as the ASR model + # CosyVoice2 internally uses generic "cuda" device, so we first switch the + # current CUDA context to the desired card before the object is created. + # Afterwards, all parameters loaded with the generic "cuda" device will + # reside on this GPU. We keep the selected id in `self.device_id` and + # will set the context again for every forward call to avoid race + # conditions when several instances are used in the same process. + + self.device_id = device_id + + # Construct the TTS codec decoder under the correct CUDA device context + with torch.cuda.device(self.device_id): + self.codec_decoder = CosyVoice2( + "/workspace/CosyVoice2-0.5B", load_jit=True, load_trt=True, fp16=True + ) + + @batch + def __call__(self, TOKENS: np.ndarray, TOKEN_LENS: np.ndarray, GT_TEXT: np.ndarray): + """ + WAV: np.ndarray, WAV_LENS: np.ndarray + LANGUAGE: np.ndarray, TEXTNORM: np.ndarray for backward compatibility, not used + See: https://github.com/modelscope/FunASR/tree/main/runtime/triton_gpu + """ + # Ensure the default CUDA device is set correctly for this invocation + torch.cuda.set_device(self.device_id) + + if self.device_id == 0: + print(f"device_id: {self.device_id}, TOKENS: {TOKENS.shape}, TOKEN_LENS: {TOKEN_LENS.shape}") + + tokens_list = [TOKENS[i, :TOKEN_LENS[i, 0]] for i in range(len(TOKENS))] + + # Decode ground-truth text strings (BYTES → str) + if GT_TEXT.ndim == 2: + gt_texts = [GT_TEXT[i, 0].decode("utf-8") for i in range(len(GT_TEXT))] + else: + gt_texts = [GT_TEXT[i].decode("utf-8") for i in range(len(GT_TEXT))] + + wavs = [] + for tokens in tokens_list: + prompt_text, prompt_speech_16k = get_random_prompt_from_dataset(self.dataset) + audio_tokens = torch.tensor(tokens, dtype=torch.long, device=self.asr_model.device).unsqueeze(0) + audio_hat = audio_decode_cosyvoice2( + audio_tokens, + prompt_text, + prompt_speech_16k, + self.codec_decoder, + ) + # resample to 16000 using soundfile + audio_hat = audio_hat.squeeze(0).float().cpu() + audio_hat = audio_hat.numpy() + num_samples = int(len(audio_hat) * (16000 / 24000)) + audio_hat = resample(audio_hat, num_samples) + wavs.append(audio_hat) + + results = self.asr_model.transcribe_single_batch( + wavs, + language="zh", + textnorm="woitn", + ) + texts = [result.text for result in results] + + # ---------------- Reward computation ---------------- + rewards = [] + for gt_text, hyp_text in zip(gt_texts, texts): + gt_norm = zh_tn_model.normalize(gt_text).lower() + hyp_norm = zh_tn_model.normalize(hyp_text).lower() + + gt_pinyin = lazy_pinyin( + gt_norm, + style=Style.TONE3, + tone_sandhi=True, + neutral_tone_with_five=True, + ) + hyp_pinyin = lazy_pinyin( + hyp_norm, + style=Style.TONE3, + tone_sandhi=True, + neutral_tone_with_five=True, + ) + + c = float(wer(" ".join(gt_pinyin), " ".join(hyp_pinyin))) + reward_val = 1.0 - np.tanh(3.0 * c) + reward_val = max(0.0, min(1.0, reward_val)) + rewards.append(reward_val) + print(f"gt_text: {gt_text}, hyp_text: {hyp_text}, reward_val: {reward_val}") + + transcripts = np.char.encode(np.array(texts).reshape(-1, 1), "utf-8") + rewards_arr = np.array(rewards, dtype=np.float32).reshape(-1, 1) + + return {"REWARDS": rewards_arr, "TRANSCRIPTS": transcripts} + + +def _infer_function_factory(device_ids: List[int], model_name: str): + """Creates a list of inference functions, one for each requested device ID.""" + infer_funcs = [] + for device_id in device_ids: + if model_name == "sensevoice": + infer_funcs.append(_ASR_Server(device_id=device_id)) + else: + infer_funcs.append(_Token2Wav_ASR(device_id=device_id)) + return infer_funcs + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--max-batch-size", + type=int, + default=32, + help="Batch size of request.", + required=False, + ) + parser.add_argument( + "--verbose", + action="store_true", + default=False, + ) + parser.add_argument( + "--number-of-instances-per-device", + type=int, + default=1, + help="Number of model instances to load.", + required=False, + ) + parser.add_argument( + "--number-of-devices", + type=int, + default=8, + help="Number of devices to use.", + ) + parser.add_argument( + "--model-name", + type=str, + default="token2wav_asr", + choices=["token2wav_asr", "sensevoice"], + help="Model name.", + ) + + args = parser.parse_args() + + log_level = logging.DEBUG if args.verbose else logging.INFO + logging.basicConfig(level=log_level, format="%(asctime)s - %(levelname)s - %(name)s: %(message)s") + + triton_config = TritonConfig( + http_port=8000, + grpc_port=8001, + metrics_port=8002, + ) + + device_ids = list(range(args.number_of_devices)) + device_ids = device_ids * args.number_of_instances_per_device + + with Triton(config=triton_config) as triton: + logger.info("Loading SenseVoice model on device ids: %s", device_ids) + if args.model_name == "sensevoice": + triton.bind( + model_name="sensevoice", + infer_func=_infer_function_factory(device_ids, args.model_name), + inputs=[ + Tensor(name="WAV", dtype=np.float32, shape=(-1,)), + Tensor(name="WAV_LENS", dtype=np.int32, shape=(-1,)), + Tensor(name="LANGUAGE", dtype=np.int32, shape=(-1,)), + Tensor(name="TEXT_NORM", dtype=np.int32, shape=(-1,)), + ], + outputs=[ + Tensor(name="TRANSCRIPTS", dtype=bytes, shape=(-1,)), + ], + config=ModelConfig( + max_batch_size=args.max_batch_size, + batcher=DynamicBatcher(max_queue_delay_microseconds=10000), # 10ms + ), + strict=True, + ) + else: + triton.bind( + model_name="token2wav_asr", + infer_func=_infer_function_factory(device_ids, args.model_name), + inputs=[ + Tensor(name="TOKENS", dtype=np.int32, shape=(-1,)), + Tensor(name="TOKEN_LENS", dtype=np.int32, shape=(-1,)), + Tensor(name="GT_TEXT", dtype=bytes, shape=(-1,)), + ], + outputs=[ + Tensor(name="REWARDS", dtype=np.float32, shape=(-1,)), + Tensor(name="TRANSCRIPTS", dtype=bytes, shape=(-1,)), + ], + config=ModelConfig( + max_batch_size=args.max_batch_size, + batcher=DynamicBatcher(max_queue_delay_microseconds=10000), # 10ms + ), + strict=True, + ) + logger.info("Serving inference") + triton.serve() + + +if __name__ == "__main__": + main() diff --git a/cosyvoice/examples/libritts/cosyvoice/conf/cosyvoice.yaml b/cosyvoice/examples/libritts/cosyvoice/conf/cosyvoice.yaml new file mode 100644 index 0000000..c421b4f --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice/conf/cosyvoice.yaml @@ -0,0 +1,257 @@ +# set random seed, so that you may reproduce your result. +__set_seed1: !apply:random.seed [1986] +__set_seed2: !apply:numpy.random.seed [1986] +__set_seed3: !apply:torch.manual_seed [1986] +__set_seed4: !apply:torch.cuda.manual_seed_all [1986] + +# fixed params +sample_rate: 22050 +text_encoder_input_size: 512 +llm_input_size: 1024 +llm_output_size: 1024 +spk_embed_dim: 192 + +# model params +# for all class/function included in this repo, we use ! or ! for intialization, so that user may find all corresponding class/function according to one single yaml. +# for system/third_party class/function, we do not require this. +llm: !new:cosyvoice.llm.llm.TransformerLM + text_encoder_input_size: !ref + llm_input_size: !ref + llm_output_size: !ref + text_token_size: 51866 # change to 60515 if you want to train with CosyVoice-300M-25Hz recipe + speech_token_size: 4096 + length_normalized_loss: True + lsm_weight: 0 + spk_embed_dim: !ref + text_encoder: !new:cosyvoice.transformer.encoder.ConformerEncoder + input_size: !ref + output_size: 1024 + attention_heads: 16 + linear_units: 4096 + num_blocks: 6 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.0 + normalize_before: True + input_layer: 'linear' + pos_enc_layer_type: 'rel_pos_espnet' + selfattention_layer_type: 'rel_selfattn' + use_cnn_module: False + macaron_style: False + use_dynamic_chunk: False + use_dynamic_left_chunk: False + static_chunk_size: 1 + llm: !new:cosyvoice.transformer.encoder.TransformerEncoder + input_size: !ref + output_size: !ref + attention_heads: 16 + linear_units: 4096 + num_blocks: 14 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.0 + input_layer: 'linear_legacy' + pos_enc_layer_type: 'rel_pos_espnet' + selfattention_layer_type: 'rel_selfattn' + static_chunk_size: 1 + sampling: !name:cosyvoice.utils.common.ras_sampling + top_p: 0.8 + top_k: 25 + win_size: 10 + tau_r: 0.1 + +flow: !new:cosyvoice.flow.flow.MaskedDiffWithXvec + input_size: 512 + output_size: 80 + spk_embed_dim: !ref + output_type: 'mel' + vocab_size: 4096 + input_frame_rate: 50 # change to 25 if you want to train with CosyVoice-300M-25Hz recipe + only_mask_loss: True + encoder: !new:cosyvoice.transformer.encoder.ConformerEncoder + output_size: 512 + attention_heads: 8 + linear_units: 2048 + num_blocks: 6 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + normalize_before: True + input_layer: 'linear' + pos_enc_layer_type: 'rel_pos_espnet' + selfattention_layer_type: 'rel_selfattn' + input_size: 512 + use_cnn_module: False + macaron_style: False + length_regulator: !new:cosyvoice.flow.length_regulator.InterpolateRegulator + channels: 80 + sampling_ratios: [1, 1, 1, 1] + decoder: !new:cosyvoice.flow.flow_matching.ConditionalCFM + in_channels: 240 + n_spks: 1 + spk_emb_dim: 80 + cfm_params: !new:omegaconf.DictConfig + content: + sigma_min: 1e-06 + solver: 'euler' + t_scheduler: 'cosine' + training_cfg_rate: 0.2 + inference_cfg_rate: 0.7 + reg_loss_type: 'l1' + estimator: !new:cosyvoice.flow.decoder.ConditionalDecoder + in_channels: 320 + out_channels: 80 + channels: [256, 256] + dropout: 0.0 + attention_head_dim: 64 + n_blocks: 4 + num_mid_blocks: 12 + num_heads: 8 + act_fn: 'gelu' + +hift: !new:cosyvoice.hifigan.generator.HiFTGenerator + in_channels: 80 + base_channels: 512 + nb_harmonics: 8 + sampling_rate: !ref + nsf_alpha: 0.1 + nsf_sigma: 0.003 + nsf_voiced_threshold: 10 + upsample_rates: [8, 8] + upsample_kernel_sizes: [16, 16] + istft_params: + n_fft: 16 + hop_len: 4 + resblock_kernel_sizes: [3, 7, 11] + resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]] + source_resblock_kernel_sizes: [7, 11] + source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5]] + lrelu_slope: 0.1 + audio_limit: 0.99 + f0_predictor: !new:cosyvoice.hifigan.f0_predictor.ConvRNNF0Predictor + num_class: 1 + in_channels: 80 + cond_channels: 512 + +# gan related module +mel_spec_transform1: !name:matcha.utils.audio.mel_spectrogram + n_fft: 1024 + num_mels: 80 + sampling_rate: !ref + hop_size: 256 + win_size: 1024 + fmin: 0 + fmax: null + center: False +hifigan: !new:cosyvoice.hifigan.hifigan.HiFiGan + generator: !ref + discriminator: !new:cosyvoice.hifigan.discriminator.MultipleDiscriminator + mpd: !new:matcha.hifigan.models.MultiPeriodDiscriminator + mrd: !new:cosyvoice.hifigan.discriminator.MultiResSpecDiscriminator + mel_spec_transform: [ + !ref + ] + +# processor functions +parquet_opener: !name:cosyvoice.dataset.processor.parquet_opener +get_tokenizer: !name:whisper.tokenizer.get_tokenizer # change to !name:cosyvoice.tokenizer.tokenizer.get_tokenizer if you want to train with CosyVoice-300M-25Hz recipe + multilingual: True + num_languages: 100 + language: 'en' + task: 'transcribe' +allowed_special: 'all' +tokenize: !name:cosyvoice.dataset.processor.tokenize + get_tokenizer: !ref + allowed_special: !ref +filter: !name:cosyvoice.dataset.processor.filter + max_length: 40960 + min_length: 0 + token_max_length: 200 + token_min_length: 1 +resample: !name:cosyvoice.dataset.processor.resample + resample_rate: !ref +truncate: !name:cosyvoice.dataset.processor.truncate + truncate_length: 24576 # must be a multiplier of hop_size +feat_extractor: !name:matcha.utils.audio.mel_spectrogram + n_fft: 1024 + num_mels: 80 + sampling_rate: !ref + hop_size: 256 + win_size: 1024 + fmin: 0 + fmax: 8000 + center: False +compute_fbank: !name:cosyvoice.dataset.processor.compute_fbank + feat_extractor: !ref +compute_f0: !name:cosyvoice.dataset.processor.compute_f0 + sample_rate: !ref + hop_size: 256 +parse_embedding: !name:cosyvoice.dataset.processor.parse_embedding + normalize: True +shuffle: !name:cosyvoice.dataset.processor.shuffle + shuffle_size: 1000 +sort: !name:cosyvoice.dataset.processor.sort + sort_size: 500 # sort_size should be less than shuffle_size +batch: !name:cosyvoice.dataset.processor.batch + batch_type: 'dynamic' + max_frames_in_batch: 2000 # change to 1400 in gan train on v100 16g +padding: !name:cosyvoice.dataset.processor.padding + use_spk_embedding: False # change to True during sft + +# dataset processor pipeline +data_pipeline: [ + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , +] +data_pipeline_gan: [ + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , +] + +# llm flow train conf +train_conf: + optim: adam + optim_conf: + lr: 0.001 # change to 1e-5 during sft + scheduler: warmuplr # change to constantlr during sft + scheduler_conf: + warmup_steps: 2500 + max_epoch: 200 + grad_clip: 5 + accum_grad: 2 + log_interval: 100 + save_per_step: -1 + +# gan train conf +train_conf_gan: + optim: adam + optim_conf: + lr: 0.0002 # use small lr for gan training + scheduler: constantlr + optim_d: adam + optim_conf_d: + lr: 0.0002 # use small lr for gan training + scheduler_d: constantlr + max_epoch: 200 + grad_clip: 5 + accum_grad: 1 # in gan training, accum_grad must be 1 + log_interval: 100 + save_per_step: -1 \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice/conf/ds_stage2.json b/cosyvoice/examples/libritts/cosyvoice/conf/ds_stage2.json new file mode 100644 index 0000000..2b2de3d --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice/conf/ds_stage2.json @@ -0,0 +1,42 @@ +{ + "train_micro_batch_size_per_gpu": 1, + "gradient_accumulation_steps": 1, + "steps_per_print": 100, + "gradient_clipping": 5, + "fp16": { + "enabled": false, + "auto_cast": false, + "loss_scale": 0, + "initial_scale_power": 16, + "loss_scale_window": 256, + "hysteresis": 2, + "consecutive_hysteresis": false, + "min_loss_scale": 1 + }, + "bf16": { + "enabled": false + }, + "zero_force_ds_cpu_optimizer": false, + "zero_optimization": { + "stage": 2, + "offload_optimizer": { + "device": "none", + "pin_memory": true + }, + "allgather_partitions": true, + "allgather_bucket_size": 5e8, + "overlap_comm": false, + "reduce_scatter": true, + "reduce_bucket_size": 5e8, + "contiguous_gradients" : true + }, + "optimizer": { + "type": "AdamW", + "params": { + "lr": 0.001, + "weight_decay": 0.0001, + "torch_adam": true, + "adam_w_mode": true + } + } +} \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice/cosyvoice b/cosyvoice/examples/libritts/cosyvoice/cosyvoice new file mode 120000 index 0000000..3903806 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice/cosyvoice @@ -0,0 +1 @@ +../../../cosyvoice \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice/local/download_and_untar.sh b/cosyvoice/examples/libritts/cosyvoice/local/download_and_untar.sh new file mode 100755 index 0000000..ec1360a --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice/local/download_and_untar.sh @@ -0,0 +1,97 @@ +#!/bin/bash + +# Copyright 2014 Johns Hopkins University (author: Daniel Povey) +# Apache 2.0 + +remove_archive=false + +if [ "$1" == --remove-archive ]; then + remove_archive=true + shift +fi + +if [ $# -ne 3 ]; then + echo "Usage: $0 [--remove-archive] " + echo "e.g.: $0 /export/a15/vpanayotov/data www.openslr.org/resources/11 dev-clean" + echo "With --remove-archive it will remove the archive after successfully un-tarring it." + echo " can be one of: dev-clean, test-clean, dev-other, test-other," + echo " train-clean-100, train-clean-360, train-other-500." + exit 1 +fi + +data=$1 +url=$2 +part=$3 + +if [ ! -d "$data" ]; then + echo "$0: no such directory $data" + exit 1 +fi + +part_ok=false +list="dev-clean test-clean dev-other test-other train-clean-100 train-clean-360 train-other-500" +for x in $list; do + if [ "$part" == $x ]; then part_ok=true; fi +done +if ! $part_ok; then + echo "$0: expected to be one of $list, but got '$part'" + exit 1 +fi + +if [ -z "$url" ]; then + echo "$0: empty URL base." + exit 1 +fi + +if [ -f $data/LibriTTS/$part/.complete ]; then + echo "$0: data part $part was already successfully extracted, nothing to do." + exit 0 +fi + + +# sizes of the archive files in bytes. This is some older versions. +sizes_old="371012589 347390293 379743611 361838298 6420417880 23082659865 30626749128" +# sizes_new is the archive file sizes of the final release. Some of these sizes are of +# things we probably won't download. +sizes_new="337926286 314305928 695964615 297279345 87960560420 33373768 346663984 328757843 6387309499 23049477885 30593501606" + +if [ -f $data/$part.tar.gz ]; then + size=$(/bin/ls -l $data/$part.tar.gz | awk '{print $5}') + size_ok=false + for s in $sizes_old $sizes_new; do if [ $s == $size ]; then size_ok=true; fi; done + if ! $size_ok; then + echo "$0: removing existing file $data/$part.tar.gz because its size in bytes $size" + echo "does not equal the size of one of the archives." + rm $data/$part.tar.gz + else + echo "$data/$part.tar.gz exists and appears to be complete." + fi +fi + +if [ ! -f $data/$part.tar.gz ]; then + if ! which wget >/dev/null; then + echo "$0: wget is not installed." + exit 1 + fi + full_url=$url/$part.tar.gz + echo "$0: downloading data from $full_url. This may take some time, please be patient." + + if ! wget -P $data --no-check-certificate $full_url; then + echo "$0: error executing wget $full_url" + exit 1 + fi +fi + +if ! tar -C $data -xvzf $data/$part.tar.gz; then + echo "$0: error un-tarring archive $data/$part.tar.gz" + exit 1 +fi + +touch $data/LibriTTS/$part/.complete + +echo "$0: Successfully downloaded and un-tarred $data/$part.tar.gz" + +if $remove_archive; then + echo "$0: removing $data/$part.tar.gz file since --remove-archive option was supplied." + rm $data/$part.tar.gz +fi diff --git a/cosyvoice/examples/libritts/cosyvoice/local/prepare_data.py b/cosyvoice/examples/libritts/cosyvoice/local/prepare_data.py new file mode 100644 index 0000000..fffa9fb --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice/local/prepare_data.py @@ -0,0 +1,62 @@ +import argparse +import logging +import glob +import os +from tqdm import tqdm + + +logger = logging.getLogger() + + +def main(): + wavs = list(glob.glob('{}/*/*/*wav'.format(args.src_dir))) + + utt2wav, utt2text, utt2spk, spk2utt = {}, {}, {}, {} + for wav in tqdm(wavs): + txt = wav.replace('.wav', '.normalized.txt') + if not os.path.exists(txt): + logger.warning('{} do not exsist'.format(txt)) + continue + with open(txt) as f: + content = ''.join(l.replace('\n', '') for l in f.readline()) + utt = os.path.basename(wav).replace('.wav', '') + spk = utt.split('_')[0] + utt2wav[utt] = wav + utt2text[utt] = content + utt2spk[utt] = spk + if spk not in spk2utt: + spk2utt[spk] = [] + spk2utt[spk].append(utt) + + with open('{}/wav.scp'.format(args.des_dir), 'w') as f: + for k, v in utt2wav.items(): + f.write('{} {}\n'.format(k, v)) + with open('{}/text'.format(args.des_dir), 'w') as f: + for k, v in utt2text.items(): + f.write('{} {}\n'.format(k, v)) + with open('{}/utt2spk'.format(args.des_dir), 'w') as f: + for k, v in utt2spk.items(): + f.write('{} {}\n'.format(k, v)) + with open('{}/spk2utt'.format(args.des_dir), 'w') as f: + for k, v in spk2utt.items(): + f.write('{} {}\n'.format(k, ' '.join(v))) + if args.instruct is True: + with open('{}/instruct'.format(args.des_dir), 'w') as f: + for k, v in utt2text.items(): + # NOTE in CosyVoice3, we add instruct in sequence + f.write('{} You are a helpful assistant.<|endofprompt|>\n'.format(k, v)) + return + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--src_dir', + type=str) + parser.add_argument('--des_dir', + type=str) + parser.add_argument('--instruct', + action='store_true', + default=False, + help='create instruct file or not') + args = parser.parse_args() + main() diff --git a/cosyvoice/examples/libritts/cosyvoice/local/prepare_reject_sample.py b/cosyvoice/examples/libritts/cosyvoice/local/prepare_reject_sample.py new file mode 100644 index 0000000..065532c --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice/local/prepare_reject_sample.py @@ -0,0 +1,50 @@ +import argparse +import logging +import os +from tqdm import tqdm +import torch +import torchaudio +from cosyvoice.cli.cosyvoice import CosyVoice2 +from cosyvoice.utils.file_utils import load_wav + + +logger = logging.getLogger() + + +def main(): + cosyvoice = CosyVoice2(args.ref_model) + + utt2wav, utt2text = {}, {} + with open('{}/wav.scp'.format(args.src_dir)) as f: + for l in f: + l = l.split('\n')[0].split() + utt2wav[l[0]] = l[1] + with open('{}/text'.format(args.src_dir)) as f: + for l in f: + l = l.split('\n')[0].split() + utt2text[l[0]] = ' '.join(l[1:]) + + os.makedirs('{}/wav'.format(args.des_dir), exist_ok=True) + with open('{}/wav.scp'.format(args.des_dir), 'w') as f: + for utt, wav in tqdm(utt2wav.items()): + prompt_speech_16k = load_wav(wav, 16000) + if prompt_speech_16k.shape[1] >= 30 * 16000: + continue + speech_list = [] + for _, j in enumerate(cosyvoice.inference_zero_shot(utt2text[utt], utt2text[utt], prompt_speech_16k, stream=False, text_frontend=False)): + speech_list.append(j['tts_speech']) + negative_wav = os.path.abspath('{}/wav/{}'.format(args.des_dir, os.path.basename(wav))) + torchaudio.save(negative_wav, torch.concat(speech_list, dim=1), cosyvoice.sample_rate, backend='soundfile') + f.write('{} {}\n'.format(utt, negative_wav)) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--src_dir', + type=str) + parser.add_argument('--des_dir', + type=str) + parser.add_argument('--ref_model', + type=str) + args = parser.parse_args() + main() diff --git a/cosyvoice/examples/libritts/cosyvoice/path.sh b/cosyvoice/examples/libritts/cosyvoice/path.sh new file mode 100644 index 0000000..e0fa06c --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice/path.sh @@ -0,0 +1,3 @@ +# NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C +export PYTHONIOENCODING=UTF-8 +export PYTHONPATH=../../../:../../../third_party/Matcha-TTS:$PYTHONPATH diff --git a/cosyvoice/examples/libritts/cosyvoice/run.sh b/cosyvoice/examples/libritts/cosyvoice/run.sh new file mode 100644 index 0000000..b95a294 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice/run.sh @@ -0,0 +1,109 @@ +#!/bin/bash +# Copyright 2024 Alibaba Inc. All Rights Reserved. +. ./path.sh || exit 1; + +stage=-1 +stop_stage=3 + +data_url=www.openslr.org/resources/60 +data_dir=/mnt/lyuxiang.lx/data/tts/openslr/libritts +pretrained_model_dir=../../../pretrained_models/CosyVoice-300M + +if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then + echo "Data Download" + for part in dev-clean test-clean dev-other test-other train-clean-100 train-clean-360 train-other-500; do + local/download_and_untar.sh ${data_dir} ${data_url} ${part} + done +fi + +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + echo "Data preparation, prepare wav.scp/text/utt2spk/spk2utt" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + mkdir -p data/$x + python local/prepare_data.py --src_dir $data_dir/LibriTTS/$x --des_dir data/$x + done +fi + +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + echo "Extract campplus speaker embedding, you will get spk2embedding.pt and utt2embedding.pt in data/$x dir" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + tools/extract_embedding.py --dir data/$x \ + --onnx_path $pretrained_model_dir/campplus.onnx + done +fi + +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + echo "Extract discrete speech token, you will get utt2speech_token.pt in data/$x dir" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + tools/extract_speech_token.py --dir data/$x \ + --onnx_path $pretrained_model_dir/speech_tokenizer_v1.onnx + done +fi + +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + echo "Prepare required parquet format data, you should have prepared wav.scp/text/utt2spk/spk2utt/utt2embedding.pt/spk2embedding.pt/utt2speech_token.pt" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + mkdir -p data/$x/parquet + tools/make_parquet_list.py --num_utts_per_parquet 1000 \ + --num_processes 10 \ + --src_dir data/$x \ + --des_dir data/$x/parquet + done +fi + +# train llm +export CUDA_VISIBLE_DEVICES="0,1,2,3" +num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}') +job_id=1986 +dist_backend="nccl" +num_workers=2 +prefetch=100 +train_engine=torch_ddp +if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then + echo "Run train. We only support llm traning for now" + if [ $train_engine == 'deepspeed' ]; then + echo "Notice deepspeed has its own optimizer config. Modify conf/ds_stage2.json if necessary" + fi + cat data/{train-clean-100,train-clean-360,train-other-500}/parquet/data.list > data/train.data.list + cat data/{dev-clean,dev-other}/parquet/data.list > data/dev.data.list + for model in llm flow hifigan; do + torchrun --nnodes=1 --nproc_per_node=$num_gpus \ + --rdzv_id=$job_id --rdzv_backend="c10d" --rdzv_endpoint="localhost:1234" \ + cosyvoice/bin/train.py \ + --train_engine $train_engine \ + --config conf/cosyvoice.yaml \ + --train_data data/train.data.list \ + --cv_data data/dev.data.list \ + --model $model \ + --checkpoint $pretrained_model_dir/$model.pt \ + --model_dir `pwd`/exp/cosyvoice/$model/$train_engine \ + --tensorboard_dir `pwd`/tensorboard/cosyvoice/$model/$train_engine \ + --ddp.dist_backend $dist_backend \ + --num_workers ${num_workers} \ + --prefetch ${prefetch} \ + --pin_memory \ + --use_amp \ + --deepspeed_config ./conf/ds_stage2.json \ + --deepspeed.save_states model+optimizer + done +fi + +# average model +average_num=5 +if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then + for model in llm flow hifigan; do + decode_checkpoint=`pwd`/exp/cosyvoice/$model/$train_engine/${model}.pt + echo "do model average and final checkpoint is $decode_checkpoint" + python cosyvoice/bin/average_model.py \ + --dst_model $decode_checkpoint \ + --src_path `pwd`/exp/cosyvoice/$model/$train_engine \ + --num ${average_num} \ + --val_best + done +fi + +if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then + echo "Export your model for inference speedup. Remember copy your llm or flow model to model_dir" + python cosyvoice/bin/export_jit.py --model_dir $pretrained_model_dir + python cosyvoice/bin/export_onnx.py --model_dir $pretrained_model_dir +fi \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice/tools b/cosyvoice/examples/libritts/cosyvoice/tools new file mode 120000 index 0000000..c92f417 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice/tools @@ -0,0 +1 @@ +../../../tools \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice/tts_text.json b/cosyvoice/examples/libritts/cosyvoice/tts_text.json new file mode 100644 index 0000000..9f3e8d9 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice/tts_text.json @@ -0,0 +1,5 @@ +{ + "1089_134686_000002_000000": [ + "hello, my name is Jack. What is your name?" + ] +} \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice2/conf/cosyvoice2.yaml b/cosyvoice/examples/libritts/cosyvoice2/conf/cosyvoice2.yaml new file mode 100644 index 0000000..df36109 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice2/conf/cosyvoice2.yaml @@ -0,0 +1,234 @@ +# set random seed, so that you may reproduce your result. +__set_seed1: !apply:random.seed [1986] +__set_seed2: !apply:numpy.random.seed [1986] +__set_seed3: !apply:torch.manual_seed [1986] +__set_seed4: !apply:torch.cuda.manual_seed_all [1986] + +# fixed params +sample_rate: 24000 +llm_input_size: 896 +llm_output_size: 896 +spk_embed_dim: 192 +qwen_pretrain_path: '' +token_frame_rate: 25 +token_mel_ratio: 2 + +# stream related params +chunk_size: 25 # streaming inference chunk size, in token +num_decoding_left_chunks: -1 # streaming inference flow decoder left chunk size, <0 means use all left chunks + +# model params +# for all class/function included in this repo, we use ! or ! for intialization, so that user may find all corresponding class/function according to one single yaml. +# for system/third_party class/function, we do not require this. +llm: !new:cosyvoice.llm.llm.Qwen2LM + llm_input_size: !ref + llm_output_size: !ref + speech_token_size: 6561 + length_normalized_loss: True + lsm_weight: 0 + mix_ratio: [5, 15] + llm: !new:cosyvoice.llm.llm.Qwen2Encoder + pretrain_path: !ref + sampling: !name:cosyvoice.utils.common.ras_sampling + top_p: 0.8 + top_k: 25 + win_size: 10 + tau_r: 0.1 + +flow: !new:cosyvoice.flow.flow.CausalMaskedDiffWithXvec + input_size: 512 + output_size: 80 + spk_embed_dim: !ref + output_type: 'mel' + vocab_size: 6561 + input_frame_rate: !ref + only_mask_loss: True + token_mel_ratio: !ref + pre_lookahead_len: 3 + encoder: !new:cosyvoice.transformer.upsample_encoder.UpsampleConformerEncoder + output_size: 512 + attention_heads: 8 + linear_units: 2048 + num_blocks: 6 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + normalize_before: True + input_layer: 'linear' + pos_enc_layer_type: 'rel_pos_espnet' + selfattention_layer_type: 'rel_selfattn' + input_size: 512 + use_cnn_module: False + macaron_style: False + static_chunk_size: !ref + decoder: !new:cosyvoice.flow.flow_matching.CausalConditionalCFM + in_channels: 240 + n_spks: 1 + spk_emb_dim: 80 + cfm_params: !new:omegaconf.DictConfig + content: + sigma_min: 1e-06 + solver: 'euler' + t_scheduler: 'cosine' + training_cfg_rate: 0.2 + inference_cfg_rate: 0.7 + reg_loss_type: 'l1' + estimator: !new:cosyvoice.flow.decoder.CausalConditionalDecoder + in_channels: 320 + out_channels: 80 + channels: [256] + dropout: 0.0 + attention_head_dim: 64 + n_blocks: 4 + num_mid_blocks: 12 + num_heads: 8 + act_fn: 'gelu' + static_chunk_size: !ref * + num_decoding_left_chunks: !ref + +hift: !new:cosyvoice.hifigan.generator.HiFTGenerator + in_channels: 80 + base_channels: 512 + nb_harmonics: 8 + sampling_rate: !ref + nsf_alpha: 0.1 + nsf_sigma: 0.003 + nsf_voiced_threshold: 10 + upsample_rates: [8, 5, 3] + upsample_kernel_sizes: [16, 11, 7] + istft_params: + n_fft: 16 + hop_len: 4 + resblock_kernel_sizes: [3, 7, 11] + resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]] + source_resblock_kernel_sizes: [7, 7, 11] + source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]] + lrelu_slope: 0.1 + audio_limit: 0.99 + f0_predictor: !new:cosyvoice.hifigan.f0_predictor.ConvRNNF0Predictor + num_class: 1 + in_channels: 80 + cond_channels: 512 + +# gan related module +mel_spec_transform1: !name:matcha.utils.audio.mel_spectrogram + n_fft: 1920 + num_mels: 80 + sampling_rate: !ref + hop_size: 480 + win_size: 1920 + fmin: 0 + fmax: null + center: False +hifigan: !new:cosyvoice.hifigan.hifigan.HiFiGan + generator: !ref + discriminator: !new:cosyvoice.hifigan.discriminator.MultipleDiscriminator + mpd: !new:matcha.hifigan.models.MultiPeriodDiscriminator + mrd: !new:cosyvoice.hifigan.discriminator.MultiResSpecDiscriminator + mel_spec_transform: [ + !ref + ] + +# processor functions +parquet_opener: !name:cosyvoice.dataset.processor.parquet_opener +get_tokenizer: !name:cosyvoice.tokenizer.tokenizer.get_qwen_tokenizer + token_path: !ref + skip_special_tokens: True +allowed_special: 'all' +tokenize: !name:cosyvoice.dataset.processor.tokenize + get_tokenizer: !ref + allowed_special: !ref +filter: !name:cosyvoice.dataset.processor.filter + max_length: 40960 + min_length: 100 + token_max_length: 200 + token_min_length: 1 +resample: !name:cosyvoice.dataset.processor.resample + resample_rate: !ref +truncate: !name:cosyvoice.dataset.processor.truncate + truncate_length: 24480 # must be a multiplier of hop_size +feat_extractor: !name:matcha.utils.audio.mel_spectrogram + n_fft: 1920 + num_mels: 80 + sampling_rate: !ref + hop_size: 480 + win_size: 1920 + fmin: 0 + fmax: 8000 + center: False +compute_fbank: !name:cosyvoice.dataset.processor.compute_fbank + feat_extractor: !ref + token_mel_ratio: 2 +compute_f0: !name:cosyvoice.dataset.processor.compute_f0 + sample_rate: !ref + hop_size: 480 +parse_embedding: !name:cosyvoice.dataset.processor.parse_embedding + normalize: True +shuffle: !name:cosyvoice.dataset.processor.shuffle + shuffle_size: 1000 +sort: !name:cosyvoice.dataset.processor.sort + sort_size: 500 # sort_size should be less than shuffle_size +batch: !name:cosyvoice.dataset.processor.batch + batch_type: 'dynamic' + max_frames_in_batch: 2000 +padding: !name:cosyvoice.dataset.processor.padding + use_spk_embedding: False # change to True during sft + + +# dataset processor pipeline +data_pipeline: [ + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , +] +data_pipeline_gan: [ + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , +] + +# llm flow train conf +train_conf: + optim: adam + optim_conf: + lr: 1e-5 # change to 1e-5 during sft + scheduler: constantlr # change to constantlr during sft + scheduler_conf: + warmup_steps: 2500 + max_epoch: 200 + grad_clip: 5 + accum_grad: 2 + log_interval: 100 + save_per_step: -1 + +# gan train conf +train_conf_gan: + optim: adam + optim_conf: + lr: 0.0002 # use small lr for gan training + scheduler: constantlr + optim_d: adam + optim_conf_d: + lr: 0.0002 # use small lr for gan training + scheduler_d: constantlr + max_epoch: 200 + grad_clip: 5 + accum_grad: 1 # in gan training, accum_grad must be 1 + log_interval: 100 + save_per_step: -1 \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice2/conf/ds_stage2.json b/cosyvoice/examples/libritts/cosyvoice2/conf/ds_stage2.json new file mode 100644 index 0000000..2b2de3d --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice2/conf/ds_stage2.json @@ -0,0 +1,42 @@ +{ + "train_micro_batch_size_per_gpu": 1, + "gradient_accumulation_steps": 1, + "steps_per_print": 100, + "gradient_clipping": 5, + "fp16": { + "enabled": false, + "auto_cast": false, + "loss_scale": 0, + "initial_scale_power": 16, + "loss_scale_window": 256, + "hysteresis": 2, + "consecutive_hysteresis": false, + "min_loss_scale": 1 + }, + "bf16": { + "enabled": false + }, + "zero_force_ds_cpu_optimizer": false, + "zero_optimization": { + "stage": 2, + "offload_optimizer": { + "device": "none", + "pin_memory": true + }, + "allgather_partitions": true, + "allgather_bucket_size": 5e8, + "overlap_comm": false, + "reduce_scatter": true, + "reduce_bucket_size": 5e8, + "contiguous_gradients" : true + }, + "optimizer": { + "type": "AdamW", + "params": { + "lr": 0.001, + "weight_decay": 0.0001, + "torch_adam": true, + "adam_w_mode": true + } + } +} \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice2/cosyvoice b/cosyvoice/examples/libritts/cosyvoice2/cosyvoice new file mode 120000 index 0000000..3903806 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice2/cosyvoice @@ -0,0 +1 @@ +../../../cosyvoice \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice2/local b/cosyvoice/examples/libritts/cosyvoice2/local new file mode 120000 index 0000000..5e847a1 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice2/local @@ -0,0 +1 @@ +../cosyvoice/local \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice2/path.sh b/cosyvoice/examples/libritts/cosyvoice2/path.sh new file mode 120000 index 0000000..59f7179 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice2/path.sh @@ -0,0 +1 @@ +../cosyvoice/path.sh \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice2/run.sh b/cosyvoice/examples/libritts/cosyvoice2/run.sh new file mode 100644 index 0000000..ad59c0a --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice2/run.sh @@ -0,0 +1,111 @@ +#!/bin/bash +# Copyright 2024 Alibaba Inc. All Rights Reserved. +. ./path.sh || exit 1; + +stage=-1 +stop_stage=3 + +data_url=www.openslr.org/resources/60 +data_dir=/mnt/lyuxiang.lx/data/tts/openslr/libritts +pretrained_model_dir=../../../pretrained_models/CosyVoice2-0.5B + +if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then + echo "Data Download" + for part in dev-clean test-clean dev-other test-other train-clean-100 train-clean-360 train-other-500; do + local/download_and_untar.sh ${data_dir} ${data_url} ${part} + done +fi + +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + echo "Data preparation, prepare wav.scp/text/utt2spk/spk2utt" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + mkdir -p data/$x + python local/prepare_data.py --src_dir $data_dir/LibriTTS/$x --des_dir data/$x + done +fi + +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + echo "Extract campplus speaker embedding, you will get spk2embedding.pt and utt2embedding.pt in data/$x dir" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + tools/extract_embedding.py --dir data/$x \ + --onnx_path $pretrained_model_dir/campplus.onnx + done +fi + +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + echo "Extract discrete speech token, you will get utt2speech_token.pt in data/$x dir" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + tools/extract_speech_token.py --dir data/$x \ + --onnx_path $pretrained_model_dir/speech_tokenizer_v2.onnx + done +fi + +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + echo "Prepare required parquet format data, you should have prepared wav.scp/text/utt2spk/spk2utt/utt2embedding.pt/spk2embedding.pt/utt2speech_token.pt" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + mkdir -p data/$x/parquet + tools/make_parquet_list.py --num_utts_per_parquet 1000 \ + --num_processes 10 \ + --src_dir data/$x \ + --des_dir data/$x/parquet + done +fi + +# train llm +export CUDA_VISIBLE_DEVICES="0,1,2,3" +num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}') +job_id=1986 +dist_backend="nccl" +num_workers=2 +prefetch=100 +train_engine=torch_ddp +if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then + echo "Run train. We only support llm traning for now" + if [ $train_engine == 'deepspeed' ]; then + echo "Notice deepspeed has its own optimizer config. Modify conf/ds_stage2.json if necessary" + fi + cat data/{train-clean-100,train-clean-360,train-other-500}/parquet/data.list > data/train.data.list + cat data/{dev-clean,dev-other}/parquet/data.list > data/dev.data.list + # NOTE will update llm/hift training later + for model in llm flow hifigan; do + torchrun --nnodes=1 --nproc_per_node=$num_gpus \ + --rdzv_id=$job_id --rdzv_backend="c10d" --rdzv_endpoint="localhost:1234" \ + cosyvoice/bin/train.py \ + --train_engine $train_engine \ + --config conf/cosyvoice2.yaml \ + --train_data data/train.data.list \ + --cv_data data/dev.data.list \ + --qwen_pretrain_path $pretrained_model_dir/CosyVoice-BlankEN \ + --model $model \ + --checkpoint $pretrained_model_dir/$model.pt \ + --model_dir `pwd`/exp/cosyvoice2/$model/$train_engine \ + --tensorboard_dir `pwd`/tensorboard/cosyvoice2/$model/$train_engine \ + --ddp.dist_backend $dist_backend \ + --num_workers ${num_workers} \ + --prefetch ${prefetch} \ + --pin_memory \ + --use_amp \ + --deepspeed_config ./conf/ds_stage2.json \ + --deepspeed.save_states model+optimizer + done +fi + +# average model +average_num=5 +if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then + for model in llm flow hifigan; do + decode_checkpoint=`pwd`/exp/cosyvoice/$model/$train_engine/${model}.pt + echo "do model average and final checkpoint is $decode_checkpoint" + python cosyvoice/bin/average_model.py \ + --dst_model $decode_checkpoint \ + --src_path `pwd`/exp/cosyvoice/$model/$train_engine \ + --num ${average_num} \ + --val_best + done +fi + +if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then + echo "Export your model for inference speedup. Remember copy your llm or flow model to model_dir" + python cosyvoice/bin/export_jit.py --model_dir $pretrained_model_dir + python cosyvoice/bin/export_onnx.py --model_dir $pretrained_model_dir +fi \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice2/run_dpo.sh b/cosyvoice/examples/libritts/cosyvoice2/run_dpo.sh new file mode 100644 index 0000000..1367e45 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice2/run_dpo.sh @@ -0,0 +1,123 @@ +#!/bin/bash +# Copyright 2024 Alibaba Inc. All Rights Reserved. +. ./path.sh || exit 1; + +stage=-1 +stop_stage=3 + +data_url=www.openslr.org/resources/60 +data_dir=/mnt/lyuxiang.lx/data/tts/openslr/libritts +pretrained_model_dir=../../../pretrained_models/CosyVoice2-0.5B + +if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then + echo "Data Download" + for part in dev-clean test-clean dev-other test-other train-clean-100 train-clean-360 train-other-500; do + local/download_and_untar.sh ${data_dir} ${data_url} ${part} + done +fi + +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + echo "Data preparation, prepare wav.scp/text/utt2spk/spk2utt" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + mkdir -p data/$x + python local/prepare_data.py --src_dir $data_dir/LibriTTS/$x --des_dir data/$x + done +fi + +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + echo "Prepare negative samples using CosyVoice2-0.5B, this is also our reference model. + Here we use CosyVoice2-0.5B generated audio as reject sample for simplicity, you can use metric like wer/similarity." + for x in train-clean-100 train-clean-360 train-other-500; do + mkdir -p data/${x}_reject + python local/prepare_reject_sample.py --src_dir data/$x --des_dir data/${x}_reject --ref_model $pretrained_model_dir + done +fi + +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + echo "Extract campplus speaker embedding, you will get spk2embedding.pt and utt2embedding.pt in data/$x dir" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + tools/extract_embedding.py --dir data/$x \ + --onnx_path $pretrained_model_dir/campplus.onnx + done +fi + +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + echo "Extract discrete speech token, you will get utt2speech_token.pt in data/$x dir" + for x in train-clean-100 train-clean-360 train-other-500 train-clean-100_reject train-clean-360_reject dev-clean dev-other test-clean test-other; do + tools/extract_speech_token.py --dir data/$x \ + --onnx_path $pretrained_model_dir/speech_tokenizer_v2.onnx + done +fi + +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + echo "Prepare required parquet format data, you should have prepared wav.scp/text/utt2spk/spk2utt/utt2embedding.pt/spk2embedding.pt/utt2speech_token.pt" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + mkdir -p data/$x/parquet + tools/make_parquet_list.py --num_utts_per_parquet 1000 \ + --num_processes 10 \ + --dpo \ + --src_dir data/$x \ + --des_dir data/$x/parquet + done +fi + +# train llm +export CUDA_VISIBLE_DEVICES="0,1,2,3" +num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}') +job_id=1986 +dist_backend="nccl" +num_workers=2 +prefetch=100 +train_engine=torch_ddp +if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then + echo "Run train. We only support llm traning for now" + if [ $train_engine == 'deepspeed' ]; then + echo "Notice deepspeed has its own optimizer config. Modify conf/ds_stage2.json if necessary" + fi + cat data/{train-clean-100,train-clean-360,train-other-500}/parquet/data.list > data/train.data.list + cat data/{dev-clean,dev-other}/parquet/data.list > data/dev.data.list + # NOTE only llm supports dpo + for model in llm; do + torchrun --nnodes=1 --nproc_per_node=$num_gpus \ + --rdzv_id=$job_id --rdzv_backend="c10d" --rdzv_endpoint="localhost:1234" \ + cosyvoice/bin/train.py \ + --train_engine $train_engine \ + --config conf/cosyvoice2.yaml \ + --train_data data/train.data.list \ + --cv_data data/dev.data.list \ + --qwen_pretrain_path $pretrained_model_dir/CosyVoice-BlankEN \ + --model $model \ + --checkpoint $pretrained_model_dir/$model.pt \ + --ref_model $pretrained_model_dir/llm.pt \ + --model_dir `pwd`/exp/cosyvoice2/$model/$train_engine \ + --tensorboard_dir `pwd`/tensorboard/cosyvoice2/$model/$train_engine \ + --ddp.dist_backend $dist_backend \ + --num_workers ${num_workers} \ + --prefetch ${prefetch} \ + --pin_memory \ + --use_amp \ + --dpo \ + --deepspeed_config ./conf/ds_stage2.json \ + --deepspeed.save_states model+optimizer + done +fi + +# average model +average_num=5 +if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then + for model in llm flow hifigan; do + decode_checkpoint=`pwd`/exp/cosyvoice/$model/$train_engine/${model}.pt + echo "do model average and final checkpoint is $decode_checkpoint" + python cosyvoice/bin/average_model.py \ + --dst_model $decode_checkpoint \ + --src_path `pwd`/exp/cosyvoice/$model/$train_engine \ + --num ${average_num} \ + --val_best + done +fi + +if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then + echo "Export your model for inference speedup. Remember copy your llm or flow model to model_dir" + python cosyvoice/bin/export_jit.py --model_dir $pretrained_model_dir + python cosyvoice/bin/export_onnx.py --model_dir $pretrained_model_dir +fi \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice2/tools b/cosyvoice/examples/libritts/cosyvoice2/tools new file mode 120000 index 0000000..c92f417 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice2/tools @@ -0,0 +1 @@ +../../../tools \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice2/tts_text.json b/cosyvoice/examples/libritts/cosyvoice2/tts_text.json new file mode 120000 index 0000000..e85cf1c --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice2/tts_text.json @@ -0,0 +1 @@ +../cosyvoice/tts_text.json \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice3/conf/cosyvoice3.yaml b/cosyvoice/examples/libritts/cosyvoice3/conf/cosyvoice3.yaml new file mode 100644 index 0000000..69d5c12 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice3/conf/cosyvoice3.yaml @@ -0,0 +1,224 @@ +# set random seed, so that you may reproduce your result. +__set_seed1: !apply:random.seed [1986] +__set_seed2: !apply:numpy.random.seed [1986] +__set_seed3: !apply:torch.manual_seed [1986] +__set_seed4: !apply:torch.cuda.manual_seed_all [1986] + +# fixed params +sample_rate: 24000 +llm_input_size: 896 +llm_output_size: 896 +spk_embed_dim: 192 +qwen_pretrain_path: '' +token_frame_rate: 25 +token_mel_ratio: 2 + +# stream related params +chunk_size: 25 # streaming inference chunk size, in token +num_decoding_left_chunks: -1 # streaming inference flow decoder left chunk size, <0 means use all left chunks + +# model params +# for all class/function included in this repo, we use ! or ! for intialization, so that user may find all corresponding class/function according to one single yaml. +# for system/third_party class/function, we do not require this. +llm: !new:cosyvoice.llm.llm.CosyVoice3LM + llm_input_size: !ref + llm_output_size: !ref + speech_token_size: 6561 + length_normalized_loss: True + lsm_weight: 0 + mix_ratio: [5, 15] + llm: !new:cosyvoice.llm.llm.Qwen2Encoder + pretrain_path: !ref + sampling: !name:cosyvoice.utils.common.ras_sampling + top_p: 0.8 + top_k: 25 + win_size: 10 + tau_r: 0.1 + +flow: !new:cosyvoice.flow.flow.CausalMaskedDiffWithDiT + input_size: 80 + output_size: 80 + spk_embed_dim: !ref + output_type: 'mel' + vocab_size: 6561 + input_frame_rate: !ref + only_mask_loss: True + token_mel_ratio: !ref + pre_lookahead_len: 3 + pre_lookahead_layer: !new:cosyvoice.transformer.upsample_encoder.PreLookaheadLayer + in_channels: 80 + channels: 1024 + pre_lookahead_len: 3 + decoder: !new:cosyvoice.flow.flow_matching.CausalConditionalCFM + in_channels: 240 + n_spks: 1 + spk_emb_dim: 80 + cfm_params: !new:omegaconf.DictConfig + content: + sigma_min: 1e-06 + solver: 'euler' + t_scheduler: 'cosine' + training_cfg_rate: 0.2 + inference_cfg_rate: 0.7 + reg_loss_type: 'l1' + estimator: !new:cosyvoice.flow.DiT.dit.DiT + dim: 1024 + depth: 22 + heads: 16 + dim_head: 64 + ff_mult: 2 + mel_dim: 80 + mu_dim: 80 + spk_dim: 80 + out_channels: 80 + static_chunk_size: !ref * + num_decoding_left_chunks: !ref + +hift: !new:cosyvoice.hifigan.generator.CausalHiFTGenerator + in_channels: 80 + base_channels: 512 + nb_harmonics: 8 + sampling_rate: !ref + nsf_alpha: 0.1 + nsf_sigma: 0.003 + nsf_voiced_threshold: 10 + upsample_rates: [8, 5, 3] + upsample_kernel_sizes: [16, 11, 7] + istft_params: + n_fft: 16 + hop_len: 4 + resblock_kernel_sizes: [3, 7, 11] + resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]] + source_resblock_kernel_sizes: [7, 7, 11] + source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]] + lrelu_slope: 0.1 + audio_limit: 0.99 + conv_pre_look_right: 4 + f0_predictor: !new:cosyvoice.hifigan.f0_predictor.CausalConvRNNF0Predictor + num_class: 1 + in_channels: 80 + cond_channels: 512 + +# gan related module +mel_spec_transform1: !name:matcha.utils.audio.mel_spectrogram + n_fft: 1920 + num_mels: 80 + sampling_rate: !ref + hop_size: 480 + win_size: 1920 + fmin: 0 + fmax: null + center: False +hifigan: !new:cosyvoice.hifigan.hifigan.HiFiGan + generator: !ref + discriminator: !new:cosyvoice.hifigan.discriminator.MultipleDiscriminator + mpd: !new:matcha.hifigan.models.MultiPeriodDiscriminator + mrd: !new:cosyvoice.hifigan.discriminator.MultiResSpecDiscriminator + mel_spec_transform: [ + !ref + ] + +# processor functions +parquet_opener: !name:cosyvoice.dataset.processor.parquet_opener +get_tokenizer: !name:cosyvoice.tokenizer.tokenizer.get_qwen_tokenizer + token_path: !ref + skip_special_tokens: True + version: cosyvoice3 +allowed_special: 'all' +tokenize: !name:cosyvoice.dataset.processor.tokenize + get_tokenizer: !ref + allowed_special: !ref +filter: !name:cosyvoice.dataset.processor.filter + max_length: 40960 + min_length: 100 + token_max_length: 200 + token_min_length: 1 +resample: !name:cosyvoice.dataset.processor.resample + resample_rate: !ref +truncate: !name:cosyvoice.dataset.processor.truncate + truncate_length: 24480 # must be a multiplier of hop_size +feat_extractor: !name:matcha.utils.audio.mel_spectrogram + n_fft: 1920 + num_mels: 80 + sampling_rate: !ref + hop_size: 480 + win_size: 1920 + fmin: 0 + fmax: null + center: False +compute_fbank: !name:cosyvoice.dataset.processor.compute_fbank + feat_extractor: !ref + token_mel_ratio: 2 +compute_f0: !name:cosyvoice.dataset.processor.compute_f0 + sample_rate: !ref + hop_size: 480 +parse_embedding: !name:cosyvoice.dataset.processor.parse_embedding + normalize: True +shuffle: !name:cosyvoice.dataset.processor.shuffle + shuffle_size: 1000 +sort: !name:cosyvoice.dataset.processor.sort + sort_size: 500 # sort_size should be less than shuffle_size +batch: !name:cosyvoice.dataset.processor.batch + batch_type: 'dynamic' + max_frames_in_batch: 2000 +padding: !name:cosyvoice.dataset.processor.padding + use_spk_embedding: False # change to True during sft + + +# dataset processor pipeline +data_pipeline: [ + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , +] +data_pipeline_gan: [ + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , + !ref , +] + +# llm flow train conf +train_conf: + optim: adam + optim_conf: + lr: 1e-5 # change to 1e-5 during sft + scheduler: constantlr # change to constantlr during sft + scheduler_conf: + warmup_steps: 2500 + max_epoch: 200 + grad_clip: 5 + accum_grad: 2 + log_interval: 100 + save_per_step: -1 + +# gan train conf +train_conf_gan: + optim: adam + optim_conf: + lr: 0.0002 # use small lr for gan training + scheduler: constantlr + optim_d: adam + optim_conf_d: + lr: 0.0002 # use small lr for gan training + scheduler_d: constantlr + max_epoch: 200 + grad_clip: 5 + accum_grad: 1 # in gan training, accum_grad must be 1 + log_interval: 100 + save_per_step: -1 diff --git a/cosyvoice/examples/libritts/cosyvoice3/conf/ds_stage2.json b/cosyvoice/examples/libritts/cosyvoice3/conf/ds_stage2.json new file mode 100644 index 0000000..2b2de3d --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice3/conf/ds_stage2.json @@ -0,0 +1,42 @@ +{ + "train_micro_batch_size_per_gpu": 1, + "gradient_accumulation_steps": 1, + "steps_per_print": 100, + "gradient_clipping": 5, + "fp16": { + "enabled": false, + "auto_cast": false, + "loss_scale": 0, + "initial_scale_power": 16, + "loss_scale_window": 256, + "hysteresis": 2, + "consecutive_hysteresis": false, + "min_loss_scale": 1 + }, + "bf16": { + "enabled": false + }, + "zero_force_ds_cpu_optimizer": false, + "zero_optimization": { + "stage": 2, + "offload_optimizer": { + "device": "none", + "pin_memory": true + }, + "allgather_partitions": true, + "allgather_bucket_size": 5e8, + "overlap_comm": false, + "reduce_scatter": true, + "reduce_bucket_size": 5e8, + "contiguous_gradients" : true + }, + "optimizer": { + "type": "AdamW", + "params": { + "lr": 0.001, + "weight_decay": 0.0001, + "torch_adam": true, + "adam_w_mode": true + } + } +} \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice3/cosyvoice b/cosyvoice/examples/libritts/cosyvoice3/cosyvoice new file mode 120000 index 0000000..3903806 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice3/cosyvoice @@ -0,0 +1 @@ +../../../cosyvoice \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice3/local b/cosyvoice/examples/libritts/cosyvoice3/local new file mode 120000 index 0000000..5e847a1 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice3/local @@ -0,0 +1 @@ +../cosyvoice/local \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice3/path.sh b/cosyvoice/examples/libritts/cosyvoice3/path.sh new file mode 120000 index 0000000..59f7179 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice3/path.sh @@ -0,0 +1 @@ +../cosyvoice/path.sh \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice3/run.sh b/cosyvoice/examples/libritts/cosyvoice3/run.sh new file mode 100644 index 0000000..4e6ce11 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice3/run.sh @@ -0,0 +1,112 @@ +#!/bin/bash +# Copyright 2024 Alibaba Inc. All Rights Reserved. +. ./path.sh || exit 1; + +stage=-1 +stop_stage=3 + +data_url=www.openslr.org/resources/60 +data_dir=/mnt/lyuxiang.lx/data/tts/openslr/libritts +pretrained_model_dir=../../../pretrained_models/CosyVoice3-0.5B + +if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then + echo "Data Download" + for part in dev-clean test-clean dev-other test-other train-clean-100 train-clean-360 train-other-500; do + local/download_and_untar.sh ${data_dir} ${data_url} ${part} + done +fi + +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + echo "Data preparation, prepare wav.scp/text/utt2spk/spk2utt" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + mkdir -p data/$x + python local/prepare_data.py --src_dir $data_dir/LibriTTS/$x --des_dir data/$x --instruct + done +fi + +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + echo "Extract campplus speaker embedding, you will get spk2embedding.pt and utt2embedding.pt in data/$x dir" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + tools/extract_embedding.py --dir data/$x \ + --onnx_path $pretrained_model_dir/campplus.onnx + done +fi + +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + echo "Extract discrete speech token, you will get utt2speech_token.pt in data/$x dir" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + tools/extract_speech_token.py --dir data/$x \ + --onnx_path $pretrained_model_dir/speech_tokenizer_v3.onnx + done +fi + +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + echo "Prepare required parquet format data, you should have prepared wav.scp/text/utt2spk/spk2utt/utt2embedding.pt/spk2embedding.pt/utt2speech_token.pt" + for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do + mkdir -p data/$x/parquet + tools/make_parquet_list.py --num_utts_per_parquet 1000 \ + --num_processes 10 \ + --instruct \ + --src_dir data/$x \ + --des_dir data/$x/parquet + done +fi + +# train llm +export CUDA_VISIBLE_DEVICES="0,1,2,3" +num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}') +job_id=1986 +dist_backend="nccl" +num_workers=2 +prefetch=100 +train_engine=torch_ddp +if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then + echo "Run train. We only support llm traning for now" + if [ $train_engine == 'deepspeed' ]; then + echo "Notice deepspeed has its own optimizer config. Modify conf/ds_stage2.json if necessary" + fi + cat data/{train-clean-100,train-clean-360,train-other-500}/parquet/data.list > data/train.data.list + cat data/{dev-clean,dev-other}/parquet/data.list > data/dev.data.list + # NOTE will update llm/hift training later + for model in llm flow hifigan; do + torchrun --nnodes=1 --nproc_per_node=$num_gpus \ + --rdzv_id=$job_id --rdzv_backend="c10d" --rdzv_endpoint="localhost:1234" \ + cosyvoice/bin/train.py \ + --train_engine $train_engine \ + --config conf/cosyvoice3.yaml \ + --train_data data/train.data.list \ + --cv_data data/dev.data.list \ + --qwen_pretrain_path $pretrained_model_dir/CosyVoice-BlankEN \ + --model $model \ + --checkpoint $pretrained_model_dir/$model.pt \ + --model_dir `pwd`/exp/cosyvoice3/$model/$train_engine \ + --tensorboard_dir `pwd`/tensorboard/cosyvoice3/$model/$train_engine \ + --ddp.dist_backend $dist_backend \ + --num_workers ${num_workers} \ + --prefetch ${prefetch} \ + --pin_memory \ + --use_amp \ + --deepspeed_config ./conf/ds_stage2.json \ + --deepspeed.save_states model+optimizer + done +fi + +# average model +average_num=5 +if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then + for model in llm flow hifigan; do + decode_checkpoint=`pwd`/exp/cosyvoice/$model/$train_engine/${model}.pt + echo "do model average and final checkpoint is $decode_checkpoint" + python cosyvoice/bin/average_model.py \ + --dst_model $decode_checkpoint \ + --src_path `pwd`/exp/cosyvoice/$model/$train_engine \ + --num ${average_num} \ + --val_best + done +fi + +if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then + echo "Export your model for inference speedup. Remember copy your llm or flow model to model_dir" + python cosyvoice/bin/export_jit.py --model_dir $pretrained_model_dir + python cosyvoice/bin/export_onnx.py --model_dir $pretrained_model_dir +fi \ No newline at end of file diff --git a/cosyvoice/examples/libritts/cosyvoice3/tools b/cosyvoice/examples/libritts/cosyvoice3/tools new file mode 120000 index 0000000..c92f417 --- /dev/null +++ b/cosyvoice/examples/libritts/cosyvoice3/tools @@ -0,0 +1 @@ +../../../tools \ No newline at end of file diff --git a/cosyvoice/examples/magicdata-read/cosyvoice/conf b/cosyvoice/examples/magicdata-read/cosyvoice/conf new file mode 120000 index 0000000..e1368df --- /dev/null +++ b/cosyvoice/examples/magicdata-read/cosyvoice/conf @@ -0,0 +1 @@ +../../libritts/cosyvoice/conf \ No newline at end of file diff --git a/cosyvoice/examples/magicdata-read/cosyvoice/cosyvoice b/cosyvoice/examples/magicdata-read/cosyvoice/cosyvoice new file mode 120000 index 0000000..3903806 --- /dev/null +++ b/cosyvoice/examples/magicdata-read/cosyvoice/cosyvoice @@ -0,0 +1 @@ +../../../cosyvoice \ No newline at end of file diff --git a/cosyvoice/examples/magicdata-read/cosyvoice/local/download_and_untar.sh b/cosyvoice/examples/magicdata-read/cosyvoice/local/download_and_untar.sh new file mode 100755 index 0000000..80dc45c --- /dev/null +++ b/cosyvoice/examples/magicdata-read/cosyvoice/local/download_and_untar.sh @@ -0,0 +1,97 @@ +#!/bin/bash + +# Copyright 2014 Johns Hopkins University (author: Daniel Povey) +# Apache 2.0 + +remove_archive=false + +if [ "$1" == --remove-archive ]; then + remove_archive=true + shift +fi + +if [ $# -ne 3 ]; then + echo "Usage: $0 [--remove-archive] " + echo "e.g.: $0 /export/a15/vpanayotov/data www.openslr.org/resources/11 dev-clean" + echo "With --remove-archive it will remove the archive after successfully un-tarring it." + echo " can be one of: dev-clean, test-clean, dev-other, test-other," + echo " train-clean-100, train-clean-360, train-other-500." + exit 1 +fi + +data=$1 +url=$2 +part=$3 + +if [ ! -d "$data" ]; then + echo "$0: no such directory $data" + exit 1 +fi + +part_ok=false +list="dev_set test_set train_set" +for x in $list; do + if [ "$part" == $x ]; then part_ok=true; fi +done +if ! $part_ok; then + echo "$0: expected to be one of $list, but got '$part'" + exit 1 +fi + +if [ -z "$url" ]; then + echo "$0: empty URL base." + exit 1 +fi + +if [ -f $data/.$part.complete ]; then + echo "$0: data part $part was already successfully extracted, nothing to do." + exit 0 +fi + + +# sizes of the archive files in bytes. This is some older versions. +sizes_old="1035537823 2201936013 52627842921" +# sizes_new is the archive file sizes of the final release. Some of these sizes are of +# things we probably won't download. +sizes_new="3886385" + +if [ -f $data/$part.tar.gz ]; then + size=$(/bin/ls -l $data/$part.tar.gz | awk '{print $5}') + size_ok=false + for s in $sizes_old $sizes_new; do if [ $s == $size ]; then size_ok=true; fi; done + if ! $size_ok; then + echo "$0: removing existing file $data/$part.tar.gz because its size in bytes $size" + echo "does not equal the size of one of the archives." + rm $data/$part.tar.gz + else + echo "$data/$part.tar.gz exists and appears to be complete." + fi +fi + +if [ ! -f $data/$part.tar.gz ]; then + if ! which wget >/dev/null; then + echo "$0: wget is not installed." + exit 1 + fi + full_url=$url/$part.tar.gz + echo "$0: downloading data from $full_url. This may take some time, please be patient." + + if ! wget -P $data --no-check-certificate $full_url; then + echo "$0: error executing wget $full_url" + exit 1 + fi +fi + +if ! tar -C $data -xvzf $data/$part.tar.gz; then + echo "$0: error un-tarring archive $data/$part.tar.gz" + exit 1 +fi + +touch $data/.$part.complete + +echo "$0: Successfully downloaded and un-tarred $data/$part.tar.gz" + +if $remove_archive; then + echo "$0: removing $data/$part.tar.gz file since --remove-archive option was supplied." + rm $data/$part.tar.gz +fi diff --git a/cosyvoice/examples/magicdata-read/cosyvoice/local/prepare_data.py b/cosyvoice/examples/magicdata-read/cosyvoice/local/prepare_data.py new file mode 100644 index 0000000..51ccd5f --- /dev/null +++ b/cosyvoice/examples/magicdata-read/cosyvoice/local/prepare_data.py @@ -0,0 +1,52 @@ +import argparse +import logging +import os +from tqdm import tqdm + + +logger = logging.getLogger() + + +def main(): + utt2wav, utt2text, utt2spk, spk2utt = {}, {}, {}, {} + with open(os.path.join(args.src_dir, "TRANS.txt"), "r") as f: + lines = f.readlines()[1:] + lines = [l.split('\t') for l in lines] + for wav, spk, content in tqdm(lines): + wav, spk, content = wav.strip(), spk.strip(), content.strip() + content = content.replace('[FIL]', '') + content = content.replace('[SPK]', '') + wav = os.path.join(args.src_dir, spk, wav) + if not os.path.exists(wav): + continue + utt = os.path.basename(wav).replace('.wav', '') + utt2wav[utt] = wav + utt2text[utt] = content + utt2spk[utt] = spk + if spk not in spk2utt: + spk2utt[spk] = [] + spk2utt[spk].append(utt) + + with open('{}/wav.scp'.format(args.des_dir), 'w') as f: + for k, v in utt2wav.items(): + f.write('{} {}\n'.format(k, v)) + with open('{}/text'.format(args.des_dir), 'w') as f: + for k, v in utt2text.items(): + f.write('{} {}\n'.format(k, v)) + with open('{}/utt2spk'.format(args.des_dir), 'w') as f: + for k, v in utt2spk.items(): + f.write('{} {}\n'.format(k, v)) + with open('{}/spk2utt'.format(args.des_dir), 'w') as f: + for k, v in spk2utt.items(): + f.write('{} {}\n'.format(k, ' '.join(v))) + return + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--src_dir', + type=str) + parser.add_argument('--des_dir', + type=str) + args = parser.parse_args() + main() diff --git a/cosyvoice/examples/magicdata-read/cosyvoice/path.sh b/cosyvoice/examples/magicdata-read/cosyvoice/path.sh new file mode 120000 index 0000000..4541305 --- /dev/null +++ b/cosyvoice/examples/magicdata-read/cosyvoice/path.sh @@ -0,0 +1 @@ +../../libritts/cosyvoice/path.sh \ No newline at end of file diff --git a/cosyvoice/examples/magicdata-read/cosyvoice/run.sh b/cosyvoice/examples/magicdata-read/cosyvoice/run.sh new file mode 100644 index 0000000..4a69b6b --- /dev/null +++ b/cosyvoice/examples/magicdata-read/cosyvoice/run.sh @@ -0,0 +1,109 @@ +#!/bin/bash +# Copyright 2024 Alibaba Inc. All Rights Reserved. +. ./path.sh || exit 1; + +stage=-1 +stop_stage=3 + +data_url=www.openslr.org/resources/68 +data_dir=/mnt/hengwu.zty/data/tts/openslr/magicdata-read +pretrained_model_dir=../../../pretrained_models/CosyVoice-300M + +if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then + echo "Data Download" + for part in dev_set test_set train_set; do + local/download_and_untar.sh ${data_dir} ${data_url} ${part} + done +fi + +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + echo "Data preparation, prepare wav.scp/text/utt2spk/spk2utt" + for x in dev test train; do + mkdir -p data/$x + python local/prepare_data.py --src_dir $data_dir/$x --des_dir data/$x + done +fi + +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + echo "Extract campplus speaker embedding, you will get spk2embedding.pt and utt2embedding.pt in data/$x dir" + for x in dev test train; do + tools/extract_embedding.py --dir data/$x \ + --onnx_path $pretrained_model_dir/campplus.onnx + done +fi + +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + echo "Extract discrete speech token, you will get utt2speech_token.pt in data/$x dir" + for x in dev test train; do + tools/extract_speech_token.py --dir data/$x \ + --onnx_path $pretrained_model_dir/speech_tokenizer_v1.onnx + done +fi + +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + echo "Prepare required parquet format data, you should have prepared wav.scp/text/utt2spk/spk2utt/utt2embedding.pt/spk2embedding.pt/utt2speech_token.pt" + for x in dev test train; do + mkdir -p data/$x/parquet + tools/make_parquet_list.py --num_utts_per_parquet 1000 \ + --num_processes 10 \ + --src_dir data/$x \ + --des_dir data/$x/parquet + done +fi + +# train llm +export CUDA_VISIBLE_DEVICES="0,1,2,3" +num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}') +job_id=1986 +dist_backend="nccl" +num_workers=2 +prefetch=100 +train_engine=torch_ddp +if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then + echo "Run train. We only support llm traning for now. If your want to train from scratch, please use conf/cosyvoice.fromscratch.yaml" + if [ $train_engine == 'deepspeed' ]; then + echo "Notice deepspeed has its own optimizer config. Modify conf/ds_stage2.json if necessary" + fi + cp data/train/parquet/data.list data/train.data.list + cp data/dev/parquet/data.list data/dev.data.list + for model in llm flow hifigan; do + torchrun --nnodes=1 --nproc_per_node=$num_gpus \ + --rdzv_id=$job_id --rdzv_backend="c10d" --rdzv_endpoint="localhost:0" \ + cosyvoice/bin/train.py \ + --train_engine $train_engine \ + --config conf/cosyvoice.yaml \ + --train_data data/train.data.list \ + --cv_data data/dev.data.list \ + --model $model \ + --checkpoint $pretrained_model_dir/$model.pt \ + --model_dir `pwd`/exp/cosyvoice/$model/$train_engine \ + --tensorboard_dir `pwd`/tensorboard/cosyvoice/$model/$train_engine \ + --ddp.dist_backend $dist_backend \ + --num_workers ${num_workers} \ + --prefetch ${prefetch} \ + --pin_memory \ + --use_amp \ + --deepspeed_config ./conf/ds_stage2.json \ + --deepspeed.save_states model+optimizer + done +fi + +# average model +average_num=5 +if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then + for model in llm flow hifigan; do + decode_checkpoint=`pwd`/exp/cosyvoice/$model/$train_engine/${model}.pt + echo "do model average and final checkpoint is $decode_checkpoint" + python cosyvoice/bin/average_model.py \ + --dst_model $decode_checkpoint \ + --src_path `pwd`/exp/cosyvoice/$model/$train_engine \ + --num ${average_num} \ + --val_best + done +fi + +if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then + echo "Export your model for inference speedup. Remember copy your llm or flow model to model_dir" + python cosyvoice/bin/export_jit.py --model_dir $pretrained_model_dir + python cosyvoice/bin/export_onnx.py --model_dir $pretrained_model_dir +fi \ No newline at end of file diff --git a/cosyvoice/examples/magicdata-read/cosyvoice/tools b/cosyvoice/examples/magicdata-read/cosyvoice/tools new file mode 120000 index 0000000..c92f417 --- /dev/null +++ b/cosyvoice/examples/magicdata-read/cosyvoice/tools @@ -0,0 +1 @@ +../../../tools \ No newline at end of file diff --git a/cosyvoice/examples/magicdata-read/cosyvoice/tts_text.json b/cosyvoice/examples/magicdata-read/cosyvoice/tts_text.json new file mode 100644 index 0000000..9dd7109 --- /dev/null +++ b/cosyvoice/examples/magicdata-read/cosyvoice/tts_text.json @@ -0,0 +1,18 @@ +{ + "38_5718_20170915093303": [ + "我想这出最好歌曲把歌词发到网上请别人帮我作曲急急", + "叫他明天早上差五分儿九点去机场" + ], + "38_5721_20170915091235": [ + "变温室调到零下两度档", + "交谈中请勿轻信汇款信息陌生电话请勿使用外挂软件" + ], + "38_5733_20170915130323": [ + "这是老鹰乐队的一首经典歌曲", + "我急用这段音乐我自己找到一段但是有现场杂音" + ], + "38_5836_20170916221414": [ + "给我播一个陶喆的专辑", + "这套餐好贵呀我发这么多短信贵死了" + ] +} \ No newline at end of file diff --git a/cosyvoice/requirements.txt b/cosyvoice/requirements.txt new file mode 100644 index 0000000..149bc43 --- /dev/null +++ b/cosyvoice/requirements.txt @@ -0,0 +1,42 @@ +--extra-index-url https://download.pytorch.org/whl/cu121 +--extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/ # https://github.com/microsoft/onnxruntime/issues/21684 +conformer==0.3.2 +deepspeed==0.15.1; sys_platform == 'linux' +diffusers==0.29.0 +fastapi==0.115.6 +fastapi-cli==0.0.4 +gdown==5.1.0 +gradio==5.4.0 +grpcio==1.57.0 +grpcio-tools==1.57.0 +hydra-core==1.3.2 +HyperPyYAML==1.2.2 +inflect==7.3.1 +librosa==0.10.2 +lightning==2.2.4 +matplotlib==3.7.5 +modelscope==1.20.0 +networkx==3.1 +numpy==1.26.4 +omegaconf==2.3.0 +onnx==1.16.0 +onnxruntime-gpu==1.18.0; sys_platform == 'linux' +onnxruntime==1.18.0; sys_platform == 'darwin' or sys_platform == 'win32' +openai-whisper==20231117 +protobuf==4.25 +pyarrow==18.1.0 +pydantic==2.7.0 +pyworld==0.3.4 +rich==13.7.1 +soundfile==0.12.1 +tensorboard==2.14.0 +tensorrt-cu12==10.13.3.9; sys_platform == 'linux' +tensorrt-cu12-bindings==10.13.3.9; sys_platform == 'linux' +tensorrt-cu12-libs==10.13.3.9; sys_platform == 'linux' +torch==2.3.1 +torchaudio==2.3.1 +transformers==4.51.3 +x-transformers==2.11.24 +uvicorn==0.30.0 +wetext==0.0.4 +wget==3.2 diff --git a/cosyvoice/runtime/python/Dockerfile b/cosyvoice/runtime/python/Dockerfile new file mode 100644 index 0000000..e2d2012 --- /dev/null +++ b/cosyvoice/runtime/python/Dockerfile @@ -0,0 +1,13 @@ +FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime +ENV DEBIAN_FRONTEND=noninteractive + +WORKDIR /opt/CosyVoice + +RUN sed -i s@/archive.ubuntu.com/@/mirrors.aliyun.com/@g /etc/apt/sources.list +RUN apt-get update -y +RUN apt-get -y install git unzip git-lfs g++ +RUN git lfs install +RUN git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git +# here we use python==3.10 because we cannot find an image which have both python3.8 and torch2.0.1-cu118 installed +RUN cd CosyVoice && pip3 install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com --no-cache-dir +RUN cd CosyVoice/runtime/python/grpc && python3 -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. cosyvoice.proto \ No newline at end of file diff --git a/cosyvoice/runtime/python/fastapi/client.py b/cosyvoice/runtime/python/fastapi/client.py new file mode 100644 index 0000000..0fb29b7 --- /dev/null +++ b/cosyvoice/runtime/python/fastapi/client.py @@ -0,0 +1,92 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +import logging +import requests +import torch +import torchaudio +import numpy as np + + +def main(): + url = "http://{}:{}/inference_{}".format(args.host, args.port, args.mode) + if args.mode == 'sft': + payload = { + 'tts_text': args.tts_text, + 'spk_id': args.spk_id + } + response = requests.request("GET", url, data=payload, stream=True) + elif args.mode == 'zero_shot': + payload = { + 'tts_text': args.tts_text, + 'prompt_text': args.prompt_text + } + files = [('prompt_wav', ('prompt_wav', open(args.prompt_wav, 'rb'), 'application/octet-stream'))] + response = requests.request("GET", url, data=payload, files=files, stream=True) + elif args.mode == 'cross_lingual': + payload = { + 'tts_text': args.tts_text, + } + files = [('prompt_wav', ('prompt_wav', open(args.prompt_wav, 'rb'), 'application/octet-stream'))] + response = requests.request("GET", url, data=payload, files=files, stream=True) + else: + payload = { + 'tts_text': args.tts_text, + 'spk_id': args.spk_id, + 'instruct_text': args.instruct_text + } + response = requests.request("GET", url, data=payload, stream=True) + tts_audio = b'' + for r in response.iter_content(chunk_size=16000): + tts_audio += r + tts_speech = torch.from_numpy(np.array(np.frombuffer(tts_audio, dtype=np.int16))).unsqueeze(dim=0) + logging.info('save response to {}'.format(args.tts_wav)) + torchaudio.save(args.tts_wav, tts_speech, target_sr) + logging.info('get response') + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--host', + type=str, + default='0.0.0.0') + parser.add_argument('--port', + type=int, + default='50000') + parser.add_argument('--mode', + default='sft', + choices=['sft', 'zero_shot', 'cross_lingual', 'instruct'], + help='request mode') + parser.add_argument('--tts_text', + type=str, + default='你好，我是通义千问语音合成大模型，请问有什么可以帮您的吗？') + parser.add_argument('--spk_id', + type=str, + default='中文女') + parser.add_argument('--prompt_text', + type=str, + default='希望你以后能够做的比我还好呦。') + parser.add_argument('--prompt_wav', + type=str, + default='../../../asset/zero_shot_prompt.wav') + parser.add_argument('--instruct_text', + type=str, + default='Theo \'Crimson\', is a fiery, passionate rebel leader. \ + Fights with fervor for justice, but struggles with impulsiveness.') + parser.add_argument('--tts_wav', + type=str, + default='demo.wav') + args = parser.parse_args() + prompt_sr, target_sr = 16000, 22050 + main() diff --git a/cosyvoice/runtime/python/fastapi/server.py b/cosyvoice/runtime/python/fastapi/server.py new file mode 100644 index 0000000..74c62d8 --- /dev/null +++ b/cosyvoice/runtime/python/fastapi/server.py @@ -0,0 +1,101 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import sys +import argparse +import logging +logging.getLogger('matplotlib').setLevel(logging.WARNING) +from fastapi import FastAPI, UploadFile, Form, File +from fastapi.responses import StreamingResponse +from fastapi.middleware.cors import CORSMiddleware +import uvicorn +import numpy as np +ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append('{}/../../..'.format(ROOT_DIR)) +sys.path.append('{}/../../../third_party/Matcha-TTS'.format(ROOT_DIR)) +from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2 +from cosyvoice.utils.file_utils import load_wav + +app = FastAPI() +# set cross region allowance +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"]) + + +def generate_data(model_output): + for i in model_output: + tts_audio = (i['tts_speech'].numpy() * (2 ** 15)).astype(np.int16).tobytes() + yield tts_audio + + +@app.get("/inference_sft") +@app.post("/inference_sft") +async def inference_sft(tts_text: str = Form(), spk_id: str = Form()): + model_output = cosyvoice.inference_sft(tts_text, spk_id) + return StreamingResponse(generate_data(model_output)) + + +@app.get("/inference_zero_shot") +@app.post("/inference_zero_shot") +async def inference_zero_shot(tts_text: str = Form(), prompt_text: str = Form(), prompt_wav: UploadFile = File()): + prompt_speech_16k = load_wav(prompt_wav.file, 16000) + model_output = cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k) + return StreamingResponse(generate_data(model_output)) + + +@app.get("/inference_cross_lingual") +@app.post("/inference_cross_lingual") +async def inference_cross_lingual(tts_text: str = Form(), prompt_wav: UploadFile = File()): + prompt_speech_16k = load_wav(prompt_wav.file, 16000) + model_output = cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k) + return StreamingResponse(generate_data(model_output)) + + +@app.get("/inference_instruct") +@app.post("/inference_instruct") +async def inference_instruct(tts_text: str = Form(), spk_id: str = Form(), instruct_text: str = Form()): + model_output = cosyvoice.inference_instruct(tts_text, spk_id, instruct_text) + return StreamingResponse(generate_data(model_output)) + + +@app.get("/inference_instruct2") +@app.post("/inference_instruct2") +async def inference_instruct2(tts_text: str = Form(), instruct_text: str = Form(), prompt_wav: UploadFile = File()): + prompt_speech_16k = load_wav(prompt_wav.file, 16000) + model_output = cosyvoice.inference_instruct2(tts_text, instruct_text, prompt_speech_16k) + return StreamingResponse(generate_data(model_output)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--port', + type=int, + default=50000) + parser.add_argument('--model_dir', + type=str, + default='iic/CosyVoice-300M', + help='local path or modelscope repo id') + args = parser.parse_args() + try: + cosyvoice = CosyVoice(args.model_dir) + except Exception: + try: + cosyvoice = CosyVoice2(args.model_dir) + except Exception: + raise TypeError('no valid model_type!') + uvicorn.run(app, host="0.0.0.0", port=args.port) diff --git a/cosyvoice/runtime/python/grpc/client.py b/cosyvoice/runtime/python/grpc/client.py new file mode 100644 index 0000000..9885130 --- /dev/null +++ b/cosyvoice/runtime/python/grpc/client.py @@ -0,0 +1,106 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import sys +ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append('{}/../../..'.format(ROOT_DIR)) +sys.path.append('{}/../../../third_party/Matcha-TTS'.format(ROOT_DIR)) +import logging +import argparse +import torchaudio +import cosyvoice_pb2 +import cosyvoice_pb2_grpc +import grpc +import torch +import numpy as np +from cosyvoice.utils.file_utils import load_wav + + +def main(): + with grpc.insecure_channel("{}:{}".format(args.host, args.port)) as channel: + stub = cosyvoice_pb2_grpc.CosyVoiceStub(channel) + request = cosyvoice_pb2.Request() + if args.mode == 'sft': + logging.info('send sft request') + sft_request = cosyvoice_pb2.sftRequest() + sft_request.spk_id = args.spk_id + sft_request.tts_text = args.tts_text + request.sft_request.CopyFrom(sft_request) + elif args.mode == 'zero_shot': + logging.info('send zero_shot request') + zero_shot_request = cosyvoice_pb2.zeroshotRequest() + zero_shot_request.tts_text = args.tts_text + zero_shot_request.prompt_text = args.prompt_text + prompt_speech = load_wav(args.prompt_wav, 16000) + zero_shot_request.prompt_audio = (prompt_speech.numpy() * (2**15)).astype(np.int16).tobytes() + request.zero_shot_request.CopyFrom(zero_shot_request) + elif args.mode == 'cross_lingual': + logging.info('send cross_lingual request') + cross_lingual_request = cosyvoice_pb2.crosslingualRequest() + cross_lingual_request.tts_text = args.tts_text + prompt_speech = load_wav(args.prompt_wav, 16000) + cross_lingual_request.prompt_audio = (prompt_speech.numpy() * (2**15)).astype(np.int16).tobytes() + request.cross_lingual_request.CopyFrom(cross_lingual_request) + else: + logging.info('send instruct request') + instruct_request = cosyvoice_pb2.instructRequest() + instruct_request.tts_text = args.tts_text + instruct_request.spk_id = args.spk_id + instruct_request.instruct_text = args.instruct_text + request.instruct_request.CopyFrom(instruct_request) + + response = stub.Inference(request) + tts_audio = b'' + for r in response: + tts_audio += r.tts_audio + tts_speech = torch.from_numpy(np.array(np.frombuffer(tts_audio, dtype=np.int16))).unsqueeze(dim=0) + logging.info('save response to {}'.format(args.tts_wav)) + torchaudio.save(args.tts_wav, tts_speech, target_sr) + logging.info('get response') + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--host', + type=str, + default='0.0.0.0') + parser.add_argument('--port', + type=int, + default='50000') + parser.add_argument('--mode', + default='sft', + choices=['sft', 'zero_shot', 'cross_lingual', 'instruct'], + help='request mode') + parser.add_argument('--tts_text', + type=str, + default='你好，我是通义千问语音合成大模型，请问有什么可以帮您的吗？') + parser.add_argument('--spk_id', + type=str, + default='中文女') + parser.add_argument('--prompt_text', + type=str, + default='希望你以后能够做的比我还好呦。') + parser.add_argument('--prompt_wav', + type=str, + default='../../../asset/zero_shot_prompt.wav') + parser.add_argument('--instruct_text', + type=str, + default='Theo \'Crimson\', is a fiery, passionate rebel leader. \ + Fights with fervor for justice, but struggles with impulsiveness.') + parser.add_argument('--tts_wav', + type=str, + default='demo.wav') + args = parser.parse_args() + prompt_sr, target_sr = 16000, 22050 + main() diff --git a/cosyvoice/runtime/python/grpc/cosyvoice.proto b/cosyvoice/runtime/python/grpc/cosyvoice.proto new file mode 100644 index 0000000..fe0c3ad --- /dev/null +++ b/cosyvoice/runtime/python/grpc/cosyvoice.proto @@ -0,0 +1,43 @@ +syntax = "proto3"; + +package cosyvoice; +option go_package = "protos/"; + +service CosyVoice{ + rpc Inference(Request) returns (stream Response) {} +} + +message Request{ + oneof RequestPayload { + sftRequest sft_request = 1; + zeroshotRequest zero_shot_request = 2; + crosslingualRequest cross_lingual_request = 3; + instructRequest instruct_request = 4; + } +} + +message sftRequest{ + string spk_id = 1; + string tts_text = 2; +} + +message zeroshotRequest{ + string tts_text = 1; + string prompt_text = 2; + bytes prompt_audio = 3; +} + +message crosslingualRequest{ + string tts_text = 1; + bytes prompt_audio = 2; +} + +message instructRequest{ + string tts_text = 1; + string spk_id = 2; + string instruct_text = 3; +} + +message Response{ + bytes tts_audio = 1; +} \ No newline at end of file diff --git a/cosyvoice/runtime/python/grpc/server.py b/cosyvoice/runtime/python/grpc/server.py new file mode 100644 index 0000000..76827e6 --- /dev/null +++ b/cosyvoice/runtime/python/grpc/server.py @@ -0,0 +1,96 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import sys +from concurrent import futures +import argparse +import cosyvoice_pb2 +import cosyvoice_pb2_grpc +import logging +logging.getLogger('matplotlib').setLevel(logging.WARNING) +import grpc +import torch +import numpy as np +ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append('{}/../../..'.format(ROOT_DIR)) +sys.path.append('{}/../../../third_party/Matcha-TTS'.format(ROOT_DIR)) +from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2 + +logging.basicConfig(level=logging.DEBUG, + format='%(asctime)s %(levelname)s %(message)s') + + +class CosyVoiceServiceImpl(cosyvoice_pb2_grpc.CosyVoiceServicer): + def __init__(self, args): + try: + self.cosyvoice = CosyVoice(args.model_dir, trt_concurrent=args.max_conc) + except Exception: + try: + self.cosyvoice = CosyVoice2(args.model_dir, trt_concurrent=args.max_conc) + except Exception: + raise TypeError('no valid model_type!') + logging.info('grpc service initialized') + + def Inference(self, request, context): + if request.HasField('sft_request'): + logging.info('get sft inference request') + model_output = self.cosyvoice.inference_sft(request.sft_request.tts_text, request.sft_request.spk_id) + elif request.HasField('zero_shot_request'): + logging.info('get zero_shot inference request') + prompt_speech_16k = torch.from_numpy(np.array(np.frombuffer(request.zero_shot_request.prompt_audio, dtype=np.int16))).unsqueeze(dim=0) + prompt_speech_16k = prompt_speech_16k.float() / (2**15) + model_output = self.cosyvoice.inference_zero_shot(request.zero_shot_request.tts_text, + request.zero_shot_request.prompt_text, + prompt_speech_16k) + elif request.HasField('cross_lingual_request'): + logging.info('get cross_lingual inference request') + prompt_speech_16k = torch.from_numpy(np.array(np.frombuffer(request.cross_lingual_request.prompt_audio, dtype=np.int16))).unsqueeze(dim=0) + prompt_speech_16k = prompt_speech_16k.float() / (2**15) + model_output = self.cosyvoice.inference_cross_lingual(request.cross_lingual_request.tts_text, prompt_speech_16k) + else: + logging.info('get instruct inference request') + model_output = self.cosyvoice.inference_instruct(request.instruct_request.tts_text, + request.instruct_request.spk_id, + request.instruct_request.instruct_text) + + logging.info('send inference response') + for i in model_output: + response = cosyvoice_pb2.Response() + response.tts_audio = (i['tts_speech'].numpy() * (2 ** 15)).astype(np.int16).tobytes() + yield response + + +def main(): + grpcServer = grpc.server(futures.ThreadPoolExecutor(max_workers=args.max_conc), maximum_concurrent_rpcs=args.max_conc) + cosyvoice_pb2_grpc.add_CosyVoiceServicer_to_server(CosyVoiceServiceImpl(args), grpcServer) + grpcServer.add_insecure_port('0.0.0.0:{}'.format(args.port)) + grpcServer.start() + logging.info("server listening on 0.0.0.0:{}".format(args.port)) + grpcServer.wait_for_termination() + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--port', + type=int, + default=50000) + parser.add_argument('--max_conc', + type=int, + default=4) + parser.add_argument('--model_dir', + type=str, + default='iic/CosyVoice-300M', + help='local path or modelscope repo id') + args = parser.parse_args() + main() diff --git a/cosyvoice/runtime/triton_trtllm/Dockerfile.server b/cosyvoice/runtime/triton_trtllm/Dockerfile.server new file mode 100644 index 0000000..38494f4 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/Dockerfile.server @@ -0,0 +1,8 @@ +FROM nvcr.io/nvidia/tritonserver:25.06-trtllm-python-py3 +LABEL maintainer="zhangyuekai@foxmail.com" + +RUN apt-get update && apt-get install -y cmake +RUN git clone https://github.com/pytorch/audio.git && cd audio && git checkout c670ad8 && PATH=/usr/local/cuda/bin:$PATH python3 setup.py develop +COPY ./requirements.txt /workspace/requirements.txt +RUN pip install -r /workspace/requirements.txt +WORKDIR /workspace \ No newline at end of file diff --git a/cosyvoice/runtime/triton_trtllm/README.DIT.md b/cosyvoice/runtime/triton_trtllm/README.DIT.md new file mode 100644 index 0000000..2fd9f61 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/README.DIT.md @@ -0,0 +1,141 @@ +## Accelerating CosyVoice with DiT-based Token2Wav, NVIDIA Triton Inference Server and TensorRT-LLM + +Contributed by Yuekai Zhang (NVIDIA). + +This document describes how to accelerate CosyVoice with a DiT-based Token2Wav module from Step-Audio2, using NVIDIA Triton Inference Server and TensorRT-LLM. + +### Quick Start + +Launch the service directly with Docker Compose: +```sh +docker compose -f docker-compose.dit.yml up +``` + +### Build the Docker Image + +To build the image from scratch: +```sh +docker build . -f Dockerfile.server -t soar97/triton-cosyvoice:25.06 +``` + +### Run a Docker Container +```sh +your_mount_dir=/mnt:/mnt +docker run -it --name "cosyvoice-server" --gpus all --net host -v $your_mount_dir --shm-size=2g soar97/triton-cosyvoice:25.06 +``` + +### Understanding `run_stepaudio2_dit_token2wav.sh` + +The `run_stepaudio2_dit_token2wav.sh` script orchestrates the entire workflow through numbered stages. + +You can run a subset of stages with: +```sh +bash run_stepaudio2_dit_token2wav.sh +``` +- ``: The stage to start from. +- ``: The stage to stop after. + +**Stages:** + +- **Stage -1**: Clones the `Step-Audio2` and `CosyVoice` repositories. +- **Stage 0**: Downloads the `cosyvoice2_llm`, `CosyVoice2-0.5B`, and `Step-Audio-2-mini` models. +- **Stage 1**: Converts the HuggingFace checkpoint for the LLM to the TensorRT-LLM format and builds the TensorRT engines. +- **Stage 2**: Creates the Triton model repository, including configurations for `cosyvoice2_dit` and `token2wav_dit`. +- **Stage 3**: Launches the Triton Inference Server for Token2Wav module and uses `trtllm-serve` to deploy Cosyvoice2 LLM. +- **Stage 4**: Runs the gRPC benchmark client for performance testing. +- **Stage 5**: Runs the offline TTS inference benchmark test. +- **Stage 6**: Runs a standalone inference script for the Step-Audio2-mini DiT Token2Wav model. +- **Stage 7**: Launches servers in a disaggregated setup, with the LLM on GPU 0 and Token2Wav servers on GPUs 1-3. +- **Stage 8**: Runs the benchmark client for the disaggregated server configuration. +### Export Models and Launch Server + +Inside the Docker container, prepare the models and start the Triton server by running stages 0-3: +```sh +# This command runs stages 0, 1, 2, and 3 +bash run_stepaudio2_dit_token2wav.sh 0 3 +``` + +### Benchmark with client-server mode + +To benchmark the running Triton server, run stage 4: +```sh +bash run_stepaudio2_dit_token2wav.sh 4 4 + +# You can customize parameters such as the number of tasks inside the script. +``` +The following results were obtained by decoding on a single L20 GPU with the `yuekai/seed_tts_cosy2` dataset. + +#### Total Request Latency + +| Concurrent Tasks | RTF | Average (ms) | 50th Percentile (ms) | 90th Percentile (ms) | 95th Percentile (ms) | 99th Percentile (ms) | +| ---------------- | ------ | ------------ | -------------------- | -------------------- | -------------------- | -------------------- | +| 1 | 0.1228 | 833.66 | 779.98 | 1297.05 | 1555.97 | 1653.02 | +| 2 | 0.0901 | 1166.23 | 1124.69 | 1762.76 | 1900.64 | 2204.14 | +| 4 | 0.0741 | 1849.30 | 1759.42 | 2624.50 | 2822.20 | 3128.42 | +| 6 | 0.0774 | 2936.13 | 3054.64 | 3849.60 | 3900.49 | 4245.79 | +| 8 | 0.0691 | 3408.56 | 3434.98 | 4547.13 | 5047.76 | 5346.53 | +| 10 | 0.0707 | 4306.56 | 4343.44 | 5769.64 | 5876.09 | 5939.79 | + +#### First Chunk Latency + +| Concurrent Tasks | Average (ms) | 50th Percentile (ms) | 90th Percentile (ms) | 95th Percentile (ms) | 99th Percentile (ms) | +| ---------------- | ------------ | -------------------- | -------------------- | -------------------- | -------------------- | +| 1 | 197.50 | 196.13 | 214.65 | 215.96 | 229.21 | +| 2 | 281.15 | 278.20 | 345.18 | 361.79 | 395.97 | +| 4 | 510.65 | 530.50 | 630.13 | 642.44 | 666.65 | +| 6 | 921.54 | 918.86 | 1079.97 | 1265.22 | 1524.41 | +| 8 | 1019.95 | 1085.26 | 1371.05 | 1402.24 | 1410.66 | +| 10 | 1214.98 | 1293.54 | 1575.36 | 1654.51 | 2161.76 | + +### Benchmark with offline inference mode +For offline inference mode benchmark, please run stage 5: +```sh +bash run_stepaudio2_dit_token2wav.sh 5 5 +``` + +The following results were obtained by decoding on a single L20 GPU with the `yuekai/seed_tts_cosy2` dataset. + +#### Offline TTS (Cosyvoice2 0.5B LLM + StepAudio2 DiT Token2Wav) +| Backend | Batch Size | llm_time_seconds | total_time_seconds | RTF | +|---------|------------|------------------|-----------------------|--| +| TRTLLM | 16 | 2.01 | 5.03 | 0.0292 | + + +### Disaggregated Server +When the LLM and token2wav components are deployed on the same GPU, they compete for resources. To optimize performance, we use a disaggregated setup where the LLM is deployed on one dedicated L20 GPU, taking advantage of in-flight batching for inference. The token2wav module is deployed on separate, dedicated GPUs. + +The table below shows the first chunk latency results for this configuration. In our tests, we deploy two token2wav instances on each dedicated token2wav GPU. + +| token2wav_num_gpu | concurrent_task_per_instance | concurrent_tasks_per_gpu | avg (ms) | p50 (ms) | p90 (ms) | p99 (ms) | +|---|---|---|---|---|---|---| +| 1 | 1 | 1.00 | 218.53 | 217.86 | 254.07 | 296.49 | +| 2 | 1 | 1.33 | 218.82 | 219.21 | 256.62 | 303.13 | +| 3 | 1 | 1.50 | 229.08 | 223.27 | 302.13 | 324.41 | +| 4 | 1 | 1.60 | 203.87 | 198.23 | 254.92 | 279.31 | +| 1 | 2 | 2.00 | 293.46 | 280.53 | 370.81 | 407.40 | +| 2 | 2 | 2.67 | 263.38 | 236.84 | 350.82 | 397.39 | +| 3 | 2 | 3.00 | 308.09 | 275.48 | 385.22 | 521.45 | +| 4 | 2 | 3.20 | 271.85 | 253.25 | 359.03 | 387.91 | +| 1 | 3 | 3.00 | 389.15 | 373.01 | 469.22 | 542.89 | +| 2 | 3 | 4.00 | 403.48 | 394.80 | 481.24 | 507.75 | +| 3 | 3 | 4.50 | 406.33 | 391.28 | 495.43 | 571.29 | +| 4 | 3 | 4.80 | 436.72 | 383.81 | 638.44 | 879.23 | +| 1 | 4 | 4.00 | 520.12 | 493.98 | 610.38 | 739.85 | +| 2 | 4 | 5.33 | 494.60 | 490.50 | 605.93 | 708.09 | +| 3 | 4 | 6.00 | 538.23 | 508.33 | 687.62 | 736.96 | +| 4 | 4 | 6.40 | 579.68 | 546.20 | 721.53 | 958.04 | +| 1 | 5 | 5.00 | 635.02 | 623.30 | 786.85 | 819.84 | +| 2 | 5 | 6.67 | 598.23 | 617.09 | 741.00 | 788.96 | +| 3 | 5 | 7.50 | 644.78 | 684.40 | 786.45 | 1009.45 | +| 4 | 5 | 8.00 | 733.92 | 642.26 | 1024.79 | 1281.55 | +| 1 | 6 | 6.00 | 715.38 | 745.68 | 887.04 | 906.68 | +| 2 | 6 | 8.00 | 748.31 | 753.94 | 873.59 | 1007.14 | +| 3 | 6 | 9.00 | 900.27 | 822.28 | 1431.14 | 1800.23 | +| 4 | 6 | 9.60 | 857.54 | 820.33 | 1150.30 | 1298.53 | + +The `concurrent_task_per_gpu` is calculated as: +`concurrent_task_per_gpu = concurrent_task_per_instance * num_token2wav_instance_per_gpu (2) * token2wav_gpus / (token2wav_gpus + llm_gpus (1))` + +### Acknowledgements + +This work originates from the NVIDIA CISI project. For more multimodal resources, please see [mair-hub](https://github.com/nvidia-china-sae/mair-hub). diff --git a/cosyvoice/runtime/triton_trtllm/README.md b/cosyvoice/runtime/triton_trtllm/README.md new file mode 100644 index 0000000..3765038 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/README.md @@ -0,0 +1,146 @@ +## Accelerating CosyVoice with NVIDIA Triton Inference Server and TensorRT-LLM + +Contributed by Yuekai Zhang (NVIDIA). + +### Quick Start + +Launch the service directly with Docker Compose: +```sh +docker compose up +``` + +### Build the Docker Image + +To build the image from scratch: +```sh +docker build . -f Dockerfile.server -t soar97/triton-cosyvoice:25.06 +``` + +### Run a Docker Container +```sh +your_mount_dir=/mnt:/mnt +docker run -it --name "cosyvoice-server" --gpus all --net host -v $your_mount_dir --shm-size=2g soar97/triton-cosyvoice:25.06 +``` + +### Understanding `run.sh` + +The `run.sh` script orchestrates the entire workflow through numbered stages. + +You can run a subset of stages with: +```sh +bash run.sh [service_type] +``` +- ``: The stage to start from (0-5). +- ``: The stage to stop after (0-5). + +**Stages:** + +- **Stage 0**: Downloads the `cosyvoice-2 0.5B` model from HuggingFace. +- **Stage 1**: Converts the HuggingFace checkpoint to the TensorRT-LLM format and builds the TensorRT engines. +- **Stage 2**: Creates the Triton model repository and configures the model files. The configuration is adjusted based on whether `Decoupled=True` (streaming) or `Decoupled=False` (offline) will be used. +- **Stage 3**: Launches the Triton Inference Server. +- **Stage 4**: Runs the single-utterance HTTP client for testing. +- **Stage 5**: Runs the gRPC benchmark client. +- **Stage 6**: Runs the offline inference benchmark test. + +### Export Models and Launch Server + +Inside the Docker container, prepare the models and start the Triton server by running stages 0-3: +```sh +# This command runs stages 0, 1, 2, and 3 +bash run.sh 0 3 +``` +> [!TIP] +> Both streaming and offline (non-streaming) TTS modes are supported. For streaming TTS, set `Decoupled=True`. For offline TTS, set `Decoupled=False`. You need to rerun stage 2 if you switch between modes. + +### Single-Utterance HTTP Client + +Sends a single HTTP inference request. This is intended for testing the offline TTS mode (`Decoupled=False`): +```sh +bash run.sh 4 4 +``` + +### Benchmark with client-server mode + +To benchmark the running Triton server, pass `streaming` or `offline` as the third argument: +```sh +bash run.sh 5 5 # [streaming|offline] + +# You can also customize parameters such as the number of tasks and the dataset split: +# python3 client_grpc.py --num-tasks 2 --huggingface-dataset yuekai/seed_tts_cosy2 --split-name test_zh --mode [streaming|offline] +``` +> [!TIP] +> It is recommended to run the benchmark multiple times to get stable results after the initial server warm-up. + +### Benchmark with offline inference mode +For offline inference mode benchmark, please check the below command: +```sh +# install FlashCosyVoice for token2wav batching +# git clone https://github.com/yuekaizhang/FlashCosyVoice.git /workspace/FlashCosyVoice -b trt +# cd /workspace/FlashCosyVoice +# pip install -e . +# cd - +# wget https://huggingface.co/yuekai/cosyvoice2_flow_onnx/resolve/main/flow.decoder.estimator.fp32.dynamic_batch.onnx -O $model_scope_model_local_dir/flow.decoder.estimator.fp32.dynamic_batch.onnx + +bash run.sh 6 6 + +# You can also switch to huggingface backend by setting backend=hf +``` + + +### Benchmark Results +The following results were obtained by decoding on a single L20 GPU with 26 prompt audio/target text pairs from the [yuekai/seed_tts](https://huggingface.co/datasets/yuekai/seed_tts) dataset (approximately 170 seconds of audio): + +**Client-Server Mode: Streaming TTS (First Chunk Latency)** +| Mode | Concurrency | Avg Latency (ms) | P50 Latency (ms) | RTF | +|---|---|---|---|---| +| Streaming, use_spk2info_cache=False | 1 | 220.43 | 218.07 | 0.1237 | +| Streaming, use_spk2info_cache=False | 2 | 476.97 | 369.25 | 0.1022 | +| Streaming, use_spk2info_cache=False | 4 | 1107.34 | 1243.75| 0.0922 | +| Streaming, use_spk2info_cache=True | 1 | 189.88 | 184.81 | 0.1155 | +| Streaming, use_spk2info_cache=True | 2 | 323.04 | 316.83 | 0.0905 | +| Streaming, use_spk2info_cache=True | 4 | 977.68 | 903.68| 0.0733 | + +> If your service only needs a fixed speaker, you can set `use_spk2info_cache=True` in `run.sh`. To add more speakers, refer to the instructions [here](https://github.com/qi-hua/async_cosyvoice?tab=readme-ov-file#9-spk2info-%E8%AF%B4%E6%98%8E). + +**Client-Server Mode: Offline TTS (Full Sentence Latency)** +| Mode | Note | Concurrency | Avg Latency (ms) | P50 Latency (ms) | RTF | +|---|---|---|---|---|---| +| Offline, Decoupled=False, use_spk2info_cache=False | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 1 | 758.04 | 615.79 | 0.0891 | +| Offline, Decoupled=False, use_spk2info_cache=False | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 2 | 1025.93 | 901.68 | 0.0657 | +| Offline, Decoupled=False, use_spk2info_cache=False | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 4 | 1914.13 | 1783.58 | 0.0610 | + +**Offline Inference Mode: Hugginface LLM V.S. TensorRT-LLM** +| Backend | Batch Size | llm_time_seconds | total_time_seconds | RTF | +|---------|------------|------------------|-----------------------|--| +| HF | 1 | 39.26 | 44.31 | 0.2494 | +| HF | 2 | 30.54 | 35.62 | 0.2064 | +| HF | 4 | 18.63 | 23.90 | 0.1421 | +| HF | 8 | 11.22 | 16.45 | 0.0947 | +| HF | 16 | 8.42 | 13.78 | 0.0821 | +| TRTLLM | 1 | 12.46 | 17.31 | 0.0987 | +| TRTLLM | 2 | 7.64 |12.65 | 0.0739 | +| TRTLLM | 4 | 4.89 | 9.38 | 0.0539 | +| TRTLLM | 8 | 2.92 | 7.23 | 0.0418 | +| TRTLLM | 16 | 2.01 | 6.63 | 0.0386 | +### OpenAI-Compatible Server + +To launch an OpenAI-compatible API service, run the following commands: +```sh +git clone https://github.com/yuekaizhang/Triton-OpenAI-Speech.git +cd Triton-OpenAI-Speech +pip install -r requirements.txt + +# After the Triton service is running, start the FastAPI bridge: +python3 tts_server.py --url http://localhost:8000 --ref_audios_dir ./ref_audios/ --port 10086 --default_sample_rate 24000 + +# Test the service with curl: +bash test/test_cosyvoice.sh +``` +> [!NOTE] +> Currently, only the offline TTS mode is compatible with the OpenAI-compatible server. + +### Acknowledgements + +This work originates from the NVIDIA CISI project. For more multimodal resources, please see [mair-hub](https://github.com/nvidia-china-sae/mair-hub). + diff --git a/cosyvoice/runtime/triton_trtllm/client_grpc.py b/cosyvoice/runtime/triton_trtllm/client_grpc.py new file mode 100644 index 0000000..1ceccb2 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/client_grpc.py @@ -0,0 +1,922 @@ +# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) +# 2023 Nvidia (authors: Yuekai Zhang) +# 2023 Recurrent.ai (authors: Songtao Shi) +# See LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This script supports to load dataset from huggingface and sends it to the server +for decoding, in parallel. + +Usage: +num_task=2 + +# For offline F5-TTS +python3 client_grpc.py \ + --server-addr localhost \ + --model-name f5_tts \ + --num-tasks $num_task \ + --huggingface-dataset yuekai/seed_tts \ + --split-name test_zh \ + --log-dir ./log_concurrent_tasks_${num_task} + +# For offline Spark-TTS-0.5B +python3 client_grpc.py \ + --server-addr localhost \ + --model-name spark_tts \ + --num-tasks $num_task \ + --huggingface-dataset yuekai/seed_tts \ + --split-name wenetspeech4tts \ + --log-dir ./log_concurrent_tasks_${num_task} +""" + +import argparse +import asyncio +import json +import queue +import uuid +import functools + +import os +import time +import types +from pathlib import Path + +import numpy as np +import soundfile as sf +import tritonclient +import tritonclient.grpc.aio as grpcclient_aio +import tritonclient.grpc as grpcclient_sync +from tritonclient.utils import np_to_triton_dtype, InferenceServerException + + +class UserData: + def __init__(self): + self._completed_requests = queue.Queue() + self._first_chunk_time = None + self._second_chunk_time = None + self._start_time = None + + def record_start_time(self): + self._start_time = time.time() + + def get_first_chunk_latency(self): + if self._first_chunk_time and self._start_time: + return self._first_chunk_time - self._start_time + return None + + def get_second_chunk_latency(self): + if self._first_chunk_time and self._second_chunk_time: + return self._second_chunk_time - self._first_chunk_time + return None + + +def callback(user_data, result, error): + if not error: + if user_data._first_chunk_time is None: + user_data._first_chunk_time = time.time() + elif user_data._second_chunk_time is None: + user_data._second_chunk_time = time.time() + + if error: + user_data._completed_requests.put(error) + else: + user_data._completed_requests.put(result) + + +def stream_callback(user_data_map, result, error): + request_id = None + if error: + print(f"An error occurred in the stream callback: {error}") + else: + request_id = result.get_response().id + + if request_id: + user_data = user_data_map.get(request_id) + if user_data: + callback(user_data, result, error) + else: + print(f"Warning: Could not find user_data for request_id {request_id}") + + +def write_triton_stats(stats, summary_file): + with open(summary_file, "w") as summary_f: + model_stats = stats["model_stats"] + for model_state in model_stats: + if "last_inference" not in model_state: + continue + summary_f.write(f"model name is {model_state['name']} \n") + model_inference_stats = model_state["inference_stats"] + total_queue_time_s = int(model_inference_stats["queue"]["ns"]) / 1e9 + total_infer_time_s = int(model_inference_stats["compute_infer"]["ns"]) / 1e9 + total_input_time_s = int(model_inference_stats["compute_input"]["ns"]) / 1e9 + total_output_time_s = int(model_inference_stats["compute_output"]["ns"]) / 1e9 + summary_f.write( + f"queue time {total_queue_time_s:<5.2f} s, " + f"compute infer time {total_infer_time_s:<5.2f} s, " + f"compute input time {total_input_time_s:<5.2f} s, " + f"compute output time {total_output_time_s:<5.2f} s \n" + ) + model_batch_stats = model_state["batch_stats"] + for batch in model_batch_stats: + batch_size = int(batch["batch_size"]) + compute_input = batch["compute_input"] + compute_output = batch["compute_output"] + compute_infer = batch["compute_infer"] + batch_count = int(compute_infer["count"]) + if batch_count == 0: + continue + assert compute_infer["count"] == compute_output["count"] == compute_input["count"] + compute_infer_time_ms = int(compute_infer["ns"]) / 1e6 + compute_input_time_ms = int(compute_input["ns"]) / 1e6 + compute_output_time_ms = int(compute_output["ns"]) / 1e6 + summary_f.write( + f"execuate inference with batch_size {batch_size:<2} total {batch_count:<5} times, " + f"total_infer_time {compute_infer_time_ms:<9.2f} ms, " + f"avg_infer_time {compute_infer_time_ms:<9.2f}/{batch_count:<5}=" + f"{compute_infer_time_ms / batch_count:.2f} ms, " + f"avg_infer_time_per_sample {compute_infer_time_ms:<9.2f}/{batch_count:<5}/{batch_size}=" + f"{compute_infer_time_ms / batch_count / batch_size:.2f} ms \n" + ) + summary_f.write( + f"input {compute_input_time_ms:<9.2f} ms, avg {compute_input_time_ms / batch_count:.2f} ms, " + ) + summary_f.write( + f"output {compute_output_time_ms:<9.2f} ms, avg {compute_output_time_ms / batch_count:.2f} ms \n" + ) + + +def subtract_stats(stats_after, stats_before): + """Subtracts two Triton inference statistics objects.""" + stats_diff = json.loads(json.dumps(stats_after)) + + model_stats_before_map = { + s["name"]: { + "version": s["version"], + "last_inference": s.get("last_inference", 0), + "inference_count": s.get("inference_count", 0), + "execution_count": s.get("execution_count", 0), + "inference_stats": s.get("inference_stats", {}), + "batch_stats": s.get("batch_stats", []), + } + for s in stats_before["model_stats"] + } + + for model_stat_after in stats_diff["model_stats"]: + model_name = model_stat_after["name"] + if model_name in model_stats_before_map: + model_stat_before = model_stats_before_map[model_name] + + model_stat_after["inference_count"] = str( + int(model_stat_after.get("inference_count", 0)) - int(model_stat_before.get("inference_count", 0)) + ) + model_stat_after["execution_count"] = str( + int(model_stat_after.get("execution_count", 0)) - int(model_stat_before.get("execution_count", 0)) + ) + + if "inference_stats" in model_stat_after and "inference_stats" in model_stat_before: + for key in ["success", "fail", "queue", "compute_input", "compute_infer", "compute_output", "cache_hit", "cache_miss"]: + if key in model_stat_after["inference_stats"] and key in model_stat_before["inference_stats"]: + if "ns" in model_stat_after["inference_stats"][key]: + ns_after = int(model_stat_after["inference_stats"][key]["ns"]) + ns_before = int(model_stat_before["inference_stats"][key]["ns"]) + model_stat_after["inference_stats"][key]["ns"] = str(ns_after - ns_before) + if "count" in model_stat_after["inference_stats"][key]: + count_after = int(model_stat_after["inference_stats"][key]["count"]) + count_before = int(model_stat_before["inference_stats"][key]["count"]) + model_stat_after["inference_stats"][key]["count"] = str(count_after - count_before) + + if "batch_stats" in model_stat_after and "batch_stats" in model_stat_before: + batch_stats_before_map = {b["batch_size"]: b for b in model_stat_before["batch_stats"]} + for batch_stat_after in model_stat_after["batch_stats"]: + bs = batch_stat_after["batch_size"] + if bs in batch_stats_before_map: + batch_stat_before = batch_stats_before_map[bs] + for key in ["compute_input", "compute_infer", "compute_output"]: + if key in batch_stat_after and key in batch_stat_before: + count_after = int(batch_stat_after[key]["count"]) + count_before = int(batch_stat_before[key]["count"]) + batch_stat_after[key]["count"] = str(count_after - count_before) + + ns_after = int(batch_stat_after[key]["ns"]) + ns_before = int(batch_stat_before[key]["ns"]) + batch_stat_after[key]["ns"] = str(ns_after - ns_before) + return stats_diff + + +def get_args(): + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + + parser.add_argument( + "--server-addr", + type=str, + default="localhost", + help="Address of the server", + ) + + parser.add_argument( + "--server-port", + type=int, + default=8001, + help="Grpc port of the triton server, default is 8001", + ) + + parser.add_argument( + "--reference-audio", + type=str, + default=None, + help="Path to a single audio file. It can't be specified at the same time with --manifest-dir", + ) + + parser.add_argument( + "--reference-text", + type=str, + default="", + help="", + ) + + parser.add_argument( + "--target-text", + type=str, + default="", + help="", + ) + + parser.add_argument( + "--huggingface-dataset", + type=str, + default="yuekai/seed_tts", + help="dataset name in huggingface dataset hub", + ) + + parser.add_argument( + "--split-name", + type=str, + default="wenetspeech4tts", + choices=["wenetspeech4tts", "test_zh", "test_en", "test_hard"], + help="dataset split name, default is 'test'", + ) + + parser.add_argument( + "--manifest-path", + type=str, + default=None, + help="Path to the manifest dir which includes wav.scp trans.txt files.", + ) + + parser.add_argument( + "--model-name", + type=str, + default="f5_tts", + choices=[ + "f5_tts", + "spark_tts", + "cosyvoice2", + "cosyvoice2_dit"], + help="triton model_repo module name to request", + ) + + parser.add_argument( + "--num-tasks", + type=int, + default=1, + help="Number of concurrent tasks for sending", + ) + + parser.add_argument( + "--log-interval", + type=int, + default=5, + help="Controls how frequently we print the log.", + ) + + parser.add_argument( + "--compute-wer", + action="store_true", + default=False, + help="""True to compute WER. + """, + ) + + parser.add_argument( + "--log-dir", + type=str, + required=False, + default="./tmp", + help="log directory", + ) + + parser.add_argument( + "--mode", + type=str, + default="offline", + choices=["offline", "streaming"], + help="Select offline or streaming benchmark mode." + ) + parser.add_argument( + "--chunk-overlap-duration", + type=float, + default=0.1, + help="Chunk overlap duration for streaming reconstruction (in seconds)." + ) + + parser.add_argument( + "--use-spk2info-cache", + type=str, + default="False", + help="Use spk2info cache for reference audio.", + ) + + return parser.parse_args() + + +def load_audio(wav_path, target_sample_rate=16000): + assert target_sample_rate == 16000, "hard coding in server" + if isinstance(wav_path, dict): + waveform = wav_path["array"] + sample_rate = wav_path["sampling_rate"] + else: + waveform, sample_rate = sf.read(wav_path) + if sample_rate != target_sample_rate: + from scipy.signal import resample + + num_samples = int(len(waveform) * (target_sample_rate / sample_rate)) + waveform = resample(waveform, num_samples) + return waveform, target_sample_rate + + +def prepare_request_input_output( + protocol_client, + waveform, + reference_text, + target_text, + sample_rate=16000, + padding_duration: int = None, + use_spk2info_cache: bool = False +): + """Prepares inputs for Triton inference (offline or streaming).""" + assert len(waveform.shape) == 1, "waveform should be 1D" + lengths = np.array([[len(waveform)]], dtype=np.int32) + + if padding_duration: + duration = len(waveform) / sample_rate + if reference_text: + estimated_target_duration = duration / len(reference_text) * len(target_text) + else: + estimated_target_duration = duration + + required_total_samples = padding_duration * sample_rate * ( + (int(estimated_target_duration + duration) // padding_duration) + 1 + ) + samples = np.zeros((1, required_total_samples), dtype=np.float32) + samples[0, : len(waveform)] = waveform + else: + samples = waveform.reshape(1, -1).astype(np.float32) + + inputs = [ + protocol_client.InferInput("reference_wav", samples.shape, np_to_triton_dtype(samples.dtype)), + protocol_client.InferInput( + "reference_wav_len", lengths.shape, np_to_triton_dtype(lengths.dtype) + ), + protocol_client.InferInput("reference_text", [1, 1], "BYTES"), + protocol_client.InferInput("target_text", [1, 1], "BYTES"), + ] + inputs[0].set_data_from_numpy(samples) + inputs[1].set_data_from_numpy(lengths) + + input_data_numpy = np.array([reference_text], dtype=object) + input_data_numpy = input_data_numpy.reshape((1, 1)) + inputs[2].set_data_from_numpy(input_data_numpy) + + input_data_numpy = np.array([target_text], dtype=object) + input_data_numpy = input_data_numpy.reshape((1, 1)) + inputs[3].set_data_from_numpy(input_data_numpy) + + outputs = [protocol_client.InferRequestedOutput("waveform")] + if use_spk2info_cache: + inputs = inputs[-1:] + return inputs, outputs + + +def run_sync_streaming_inference( + sync_triton_client: tritonclient.grpc.InferenceServerClient, + model_name: str, + inputs: list, + outputs: list, + request_id: str, + user_data: UserData, + chunk_overlap_duration: float, + save_sample_rate: int, + audio_save_path: str, +): + """Helper function to run the blocking sync streaming call.""" + start_time_total = time.time() + user_data.record_start_time() + + sync_triton_client.async_stream_infer( + model_name, + inputs, + request_id=request_id, + outputs=outputs, + enable_empty_final_response=True, + ) + + audios = [] + while True: + try: + result = user_data._completed_requests.get(timeout=200) + if isinstance(result, InferenceServerException): + print(f"Received InferenceServerException: {result}") + return None, None, None, None + response = result.get_response() + final = response.parameters["triton_final_response"].bool_param + if final is True: + break + + audio_chunk = result.as_numpy("waveform").reshape(-1) + if audio_chunk.size > 0: + audios.append(audio_chunk) + else: + print("Warning: received empty audio chunk.") + + except queue.Empty: + print(f"Timeout waiting for response for request id {request_id}") + return None, None, None, None + + end_time_total = time.time() + total_request_latency = end_time_total - start_time_total + first_chunk_latency = user_data.get_first_chunk_latency() + second_chunk_latency = user_data.get_second_chunk_latency() + + if audios: + if model_name == "spark_tts": + cross_fade_samples = int(chunk_overlap_duration * save_sample_rate) + fade_out = np.linspace(1, 0, cross_fade_samples) + fade_in = np.linspace(0, 1, cross_fade_samples) + reconstructed_audio = None + + if not audios: + print("Warning: No audio chunks received.") + reconstructed_audio = np.array([], dtype=np.float32) + elif len(audios) == 1: + reconstructed_audio = audios[0] + else: + reconstructed_audio = audios[0][:-cross_fade_samples] + for i in range(1, len(audios)): + cross_faded_overlap = (audios[i][:cross_fade_samples] * fade_in + + audios[i - 1][-cross_fade_samples:] * fade_out) + middle_part = audios[i][cross_fade_samples:-cross_fade_samples] + reconstructed_audio = np.concatenate([reconstructed_audio, cross_faded_overlap, middle_part]) + reconstructed_audio = np.concatenate([reconstructed_audio, audios[-1][-cross_fade_samples:]]) + + if reconstructed_audio is not None and reconstructed_audio.size > 0: + actual_duration = len(reconstructed_audio) / save_sample_rate + sf.write(audio_save_path, reconstructed_audio, save_sample_rate, "PCM_16") + else: + print("Warning: No audio chunks received or reconstructed.") + actual_duration = 0 + else: + reconstructed_audio = np.concatenate(audios) + actual_duration = len(reconstructed_audio) / save_sample_rate + sf.write(audio_save_path, reconstructed_audio, save_sample_rate, "PCM_16") + + else: + print("Warning: No audio chunks received.") + actual_duration = 0 + + return total_request_latency, first_chunk_latency, second_chunk_latency, actual_duration + + +async def send_streaming( + manifest_item_list: list, + name: str, + server_url: str, + protocol_client: types.ModuleType, + log_interval: int, + model_name: str, + audio_save_dir: str = "./", + save_sample_rate: int = 16000, + chunk_overlap_duration: float = 0.1, + padding_duration: int = None, + use_spk2info_cache: bool = False, +): + total_duration = 0.0 + latency_data = [] + task_id = int(name[5:]) + sync_triton_client = None + user_data_map = {} + + try: + print(f"{name}: Initializing sync client for streaming...") + sync_triton_client = grpcclient_sync.InferenceServerClient(url=server_url, verbose=False) + sync_triton_client.start_stream(callback=functools.partial(stream_callback, user_data_map)) + + print(f"{name}: Starting streaming processing for {len(manifest_item_list)} items.") + for i, item in enumerate(manifest_item_list): + if i % log_interval == 0: + print(f"{name}: Processing item {i}/{len(manifest_item_list)}") + + try: + waveform, sample_rate = load_audio(item["audio_filepath"], target_sample_rate=16000) + reference_text, target_text = item["reference_text"], item["target_text"] + + inputs, outputs = prepare_request_input_output( + protocol_client, + waveform, + reference_text, + target_text, + sample_rate, + padding_duration=padding_duration, + use_spk2info_cache=use_spk2info_cache + ) + + request_id = str(uuid.uuid4()) + user_data = UserData() + user_data_map[request_id] = user_data + + audio_save_path = os.path.join(audio_save_dir, f"{item['target_audio_path']}.wav") + total_request_latency, first_chunk_latency, second_chunk_latency, actual_duration = await asyncio.to_thread( + run_sync_streaming_inference, + sync_triton_client, + model_name, + inputs, + outputs, + request_id, + user_data, + chunk_overlap_duration, + save_sample_rate, + audio_save_path + ) + + if total_request_latency is not None: + print( + f"{name}: Item {i} - First Chunk Latency: {first_chunk_latency:.4f}s, " + f"Second Chunk Latency: {second_chunk_latency if second_chunk_latency is not None else 'N/A'}, " + f"Total Latency: {total_request_latency:.4f}s, Duration: {actual_duration:.4f}s" + ) + latency_data.append((total_request_latency, first_chunk_latency, second_chunk_latency, actual_duration)) + total_duration += actual_duration + else: + print(f"{name}: Item {i} failed.") + + del user_data_map[request_id] + + except FileNotFoundError: + print(f"Error: Audio file not found for item {i}: {item['audio_filepath']}") + except Exception as e: + print(f"Error processing item {i} ({item['target_audio_path']}): {e}") + import traceback + traceback.print_exc() + + finally: + if sync_triton_client: + try: + print(f"{name}: Closing stream and sync client...") + sync_triton_client.stop_stream() + sync_triton_client.close() + except Exception as e: + print(f"{name}: Error closing sync client: {e}") + + print(f"{name}: Finished streaming processing. Total duration synthesized: {total_duration:.4f}s") + return total_duration, latency_data + + +async def send( + manifest_item_list: list, + name: str, + triton_client: tritonclient.grpc.aio.InferenceServerClient, + protocol_client: types.ModuleType, + log_interval: int, + model_name: str, + padding_duration: int = None, + audio_save_dir: str = "./", + save_sample_rate: int = 16000, + use_spk2info_cache: bool = False, +): + total_duration = 0.0 + latency_data = [] + task_id = int(name[5:]) + + for i, item in enumerate(manifest_item_list): + if i % log_interval == 0: + print(f"{name}: {i}/{len(manifest_item_list)}") + waveform, sample_rate = load_audio(item["audio_filepath"], target_sample_rate=16000) + reference_text, target_text = item["reference_text"], item["target_text"] + + inputs, outputs = prepare_request_input_output( + protocol_client, + waveform, + reference_text, + target_text, + sample_rate, + padding_duration=padding_duration, + use_spk2info_cache=use_spk2info_cache + ) + sequence_id = 100000000 + i + task_id * 10 + start = time.time() + response = await triton_client.infer(model_name, inputs, request_id=str(sequence_id), outputs=outputs) + + audio = response.as_numpy("waveform").reshape(-1) + actual_duration = len(audio) / save_sample_rate + + end = time.time() - start + + audio_save_path = os.path.join(audio_save_dir, f"{item['target_audio_path']}.wav") + sf.write(audio_save_path, audio, save_sample_rate, "PCM_16") + + latency_data.append((end, actual_duration)) + total_duration += actual_duration + + return total_duration, latency_data + + +def load_manifests(manifest_path): + with open(manifest_path, "r") as f: + manifest_list = [] + for line in f: + assert len(line.strip().split("|")) == 4 + utt, prompt_text, prompt_wav, gt_text = line.strip().split("|") + utt = Path(utt).stem + if not os.path.isabs(prompt_wav): + prompt_wav = os.path.join(os.path.dirname(manifest_path), prompt_wav) + manifest_list.append( + { + "audio_filepath": prompt_wav, + "reference_text": prompt_text, + "target_text": gt_text, + "target_audio_path": utt, + } + ) + return manifest_list + + +def split_data(data, k): + n = len(data) + if n < k: + print(f"Warning: the length of the input list ({n}) is less than k ({k}). Setting k to {n}.") + k = n + + quotient = n // k + remainder = n % k + + result = [] + start = 0 + for i in range(k): + if i < remainder: + end = start + quotient + 1 + else: + end = start + quotient + + result.append(data[start:end]) + start = end + + return result + + +async def main(): + args = get_args() + url = f"{args.server_addr}:{args.server_port}" + + triton_client = None + protocol_client = None + if args.mode == "offline": + print("Initializing gRPC client for offline mode...") + triton_client = grpcclient_aio.InferenceServerClient(url=url, verbose=False) + protocol_client = grpcclient_aio + elif args.mode == "streaming": + print("Initializing gRPC client for streaming mode...") + protocol_client = grpcclient_sync + else: + raise ValueError(f"Invalid mode: {args.mode}") + + if args.reference_audio: + args.num_tasks = 1 + args.log_interval = 1 + manifest_item_list = [ + { + "reference_text": args.reference_text, + "target_text": args.target_text, + "audio_filepath": args.reference_audio, + "target_audio_path": "test", + } + ] + elif args.huggingface_dataset: + import datasets + + dataset = datasets.load_dataset( + args.huggingface_dataset, + split=args.split_name, + trust_remote_code=True, + ) + manifest_item_list = [] + for i in range(len(dataset)): + manifest_item_list.append( + { + "audio_filepath": dataset[i]["prompt_audio"], + "reference_text": dataset[i]["prompt_text"], + "target_audio_path": dataset[i]["id"], + "target_text": dataset[i]["target_text"], + } + ) + else: + manifest_item_list = load_manifests(args.manifest_path) + + stats_client = None + stats_before = None + try: + print("Initializing temporary async client for fetching stats...") + stats_client = grpcclient_aio.InferenceServerClient(url=url, verbose=False) + print("Fetching inference statistics before running tasks...") + stats_before = await stats_client.get_inference_statistics(model_name="", as_json=True) + except Exception as e: + print(f"Could not retrieve statistics before running tasks: {e}") + + num_tasks = min(args.num_tasks, len(manifest_item_list)) + manifest_item_list = split_data(manifest_item_list, num_tasks) + + os.makedirs(args.log_dir, exist_ok=True) + args.use_spk2info_cache = args.use_spk2info_cache == "True" or args.use_spk2info_cache == "true" + tasks = [] + start_time = time.time() + for i in range(num_tasks): + if args.mode == "offline": + task = asyncio.create_task( + send( + manifest_item_list[i], + name=f"task-{i}", + triton_client=triton_client, + protocol_client=protocol_client, + log_interval=args.log_interval, + model_name=args.model_name, + audio_save_dir=args.log_dir, + padding_duration=1, + save_sample_rate=16000 if args.model_name == "spark_tts" else 24000, + use_spk2info_cache=args.use_spk2info_cache, + ) + ) + elif args.mode == "streaming": + task = asyncio.create_task( + send_streaming( + manifest_item_list[i], + name=f"task-{i}", + server_url=url, + protocol_client=protocol_client, + log_interval=args.log_interval, + model_name=args.model_name, + audio_save_dir=args.log_dir, + padding_duration=10, + save_sample_rate=16000 if args.model_name == "spark_tts" else 24000, + chunk_overlap_duration=args.chunk_overlap_duration, + use_spk2info_cache=args.use_spk2info_cache, + ) + ) + tasks.append(task) + + ans_list = await asyncio.gather(*tasks) + + end_time = time.time() + elapsed = end_time - start_time + + total_duration = 0.0 + latency_data = [] + for ans in ans_list: + if ans: + total_duration += ans[0] + latency_data.extend(ans[1]) + else: + print("Warning: A task returned None, possibly due to an error.") + + if total_duration == 0: + print("Total synthesized duration is zero. Cannot calculate RTF or latency percentiles.") + rtf = float('inf') + else: + rtf = elapsed / total_duration + + s = f"Mode: {args.mode}\n" + s += f"RTF: {rtf:.4f}\n" + s += f"total_duration: {total_duration:.3f} seconds\n" + s += f"({total_duration / 3600:.2f} hours)\n" + s += f"processing time: {elapsed:.3f} seconds ({elapsed / 3600:.2f} hours)\n" + + if latency_data: + if args.mode == "offline": + latency_list = [chunk_end for (chunk_end, chunk_duration) in latency_data] + if latency_list: + latency_ms = sum(latency_list) / float(len(latency_list)) * 1000.0 + latency_variance = np.var(latency_list, dtype=np.float64) * 1000.0 + s += f"latency_variance: {latency_variance:.2f}\n" + s += f"latency_50_percentile_ms: {np.percentile(latency_list, 50) * 1000.0:.2f}\n" + s += f"latency_90_percentile_ms: {np.percentile(latency_list, 90) * 1000.0:.2f}\n" + s += f"latency_95_percentile_ms: {np.percentile(latency_list, 95) * 1000.0:.2f}\n" + s += f"latency_99_percentile_ms: {np.percentile(latency_list, 99) * 1000.0:.2f}\n" + s += f"average_latency_ms: {latency_ms:.2f}\n" + else: + s += "No latency data collected for offline mode.\n" + + elif args.mode == "streaming": + total_latency_list = [total for (total, first, second, duration) in latency_data if total is not None] + first_chunk_latency_list = [first for (total, first, second, duration) in latency_data if first is not None] + second_chunk_latency_list = [second for (total, first, second, duration) in latency_data if second is not None] + + s += "\n--- Total Request Latency ---\n" + if total_latency_list: + avg_total_latency_ms = sum(total_latency_list) / len(total_latency_list) * 1000.0 + variance_total_latency = np.var(total_latency_list, dtype=np.float64) * 1000.0 + s += f"total_request_latency_variance: {variance_total_latency:.2f}\n" + s += f"total_request_latency_50_percentile_ms: {np.percentile(total_latency_list, 50) * 1000.0:.2f}\n" + s += f"total_request_latency_90_percentile_ms: {np.percentile(total_latency_list, 90) * 1000.0:.2f}\n" + s += f"total_request_latency_95_percentile_ms: {np.percentile(total_latency_list, 95) * 1000.0:.2f}\n" + s += f"total_request_latency_99_percentile_ms: {np.percentile(total_latency_list, 99) * 1000.0:.2f}\n" + s += f"average_total_request_latency_ms: {avg_total_latency_ms:.2f}\n" + else: + s += "No total request latency data collected.\n" + + s += "\n--- First Chunk Latency ---\n" + if first_chunk_latency_list: + avg_first_chunk_latency_ms = sum(first_chunk_latency_list) / len(first_chunk_latency_list) * 1000.0 + variance_first_chunk_latency = np.var(first_chunk_latency_list, dtype=np.float64) * 1000.0 + s += f"first_chunk_latency_variance: {variance_first_chunk_latency:.2f}\n" + s += f"first_chunk_latency_50_percentile_ms: {np.percentile(first_chunk_latency_list, 50) * 1000.0:.2f}\n" + s += f"first_chunk_latency_90_percentile_ms: {np.percentile(first_chunk_latency_list, 90) * 1000.0:.2f}\n" + s += f"first_chunk_latency_95_percentile_ms: {np.percentile(first_chunk_latency_list, 95) * 1000.0:.2f}\n" + s += f"first_chunk_latency_99_percentile_ms: {np.percentile(first_chunk_latency_list, 99) * 1000.0:.2f}\n" + s += f"average_first_chunk_latency_ms: {avg_first_chunk_latency_ms:.2f}\n" + else: + s += "No first chunk latency data collected (check for errors or if all requests failed before first chunk).\n" + + s += "\n--- Second Chunk Latency ---\n" + if second_chunk_latency_list: + avg_second_chunk_latency_ms = sum(second_chunk_latency_list) / len(second_chunk_latency_list) * 1000.0 + variance_second_chunk_latency = np.var(second_chunk_latency_list, dtype=np.float64) * 1000.0 + s += f"second_chunk_latency_variance: {variance_second_chunk_latency:.2f}\n" + s += f"second_chunk_latency_50_percentile_ms: {np.percentile(second_chunk_latency_list, 50) * 1000.0:.2f}\n" + s += f"second_chunk_latency_90_percentile_ms: {np.percentile(second_chunk_latency_list, 90) * 1000.0:.2f}\n" + s += f"second_chunk_latency_95_percentile_ms: {np.percentile(second_chunk_latency_list, 95) * 1000.0:.2f}\n" + s += f"second_chunk_latency_99_percentile_ms: {np.percentile(second_chunk_latency_list, 99) * 1000.0:.2f}\n" + s += f"average_second_chunk_latency_ms: {avg_second_chunk_latency_ms:.2f}\n" + else: + s += "No second chunk latency data collected (check for errors or if all requests failed before second chunk).\n" + else: + s += "No latency data collected.\n" + + print(s) + if args.manifest_path: + name = Path(args.manifest_path).stem + elif args.split_name: + name = args.split_name + elif args.reference_audio: + name = Path(args.reference_audio).stem + else: + name = "results" + with open(f"{args.log_dir}/rtf-{name}.txt", "w") as f: + f.write(s) + + try: + if stats_client and stats_before: + print("Fetching inference statistics after running tasks...") + stats_after = await stats_client.get_inference_statistics(model_name="", as_json=True) + + print("Calculating statistics difference...") + stats = subtract_stats(stats_after, stats_before) + + print("Fetching model config...") + metadata = await stats_client.get_model_config(model_name=args.model_name, as_json=True) + + write_triton_stats(stats, f"{args.log_dir}/stats_summary-{name}.txt") + + with open(f"{args.log_dir}/model_config-{name}.json", "w") as f: + json.dump(metadata, f, indent=4) + else: + print("Stats client not available or initial stats were not fetched. Skipping stats reporting.") + + except Exception as e: + print(f"Could not retrieve statistics or config: {e}") + finally: + if stats_client: + try: + print("Closing temporary async stats client...") + await stats_client.close() + except Exception as e: + print(f"Error closing async stats client: {e}") + + +if __name__ == "__main__": + async def run_main(): + try: + await main() + except Exception as e: + print(f"An error occurred in main: {e}") + import traceback + traceback.print_exc() + + asyncio.run(run_main()) diff --git a/cosyvoice/runtime/triton_trtllm/client_http.py b/cosyvoice/runtime/triton_trtllm/client_http.py new file mode 100644 index 0000000..2c30a7a --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/client_http.py @@ -0,0 +1,172 @@ +# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +import requests +import soundfile as sf +import numpy as np +import argparse + + +def get_args(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--server-url", + type=str, + default="localhost:8000", + help="Address of the server", + ) + + parser.add_argument( + "--reference-audio", + type=str, + default="../../example/prompt_audio.wav", + help="Path to a single audio file. It can't be specified at the same time with --manifest-dir", + ) + + parser.add_argument( + "--reference-text", + type=str, + default="吃燕窝就选燕之屋，本节目由26年专注高品质燕窝的燕之屋冠名播出。豆奶牛奶换着喝，营养更均衡，本节目由豆本豆豆奶特约播出。", + help="", + ) + + parser.add_argument( + "--target-text", + type=str, + default="身临其境，换新体验。塑造开源语音合成新范式，让智能语音更自然。", + help="", + ) + + parser.add_argument( + "--model-name", + type=str, + default="spark_tts", + choices=[ + "f5_tts", + "spark_tts", + "cosyvoice2"], + help="triton model_repo module name to request", + ) + + parser.add_argument( + "--output-audio", + type=str, + default="output.wav", + help="Path to save the output audio", + ) + return parser.parse_args() + + +def prepare_request( + waveform, + reference_text, + target_text, + sample_rate=16000, + padding_duration: int = None, + audio_save_dir: str = "./", +): + assert len(waveform.shape) == 1, "waveform should be 1D" + lengths = np.array([[len(waveform)]], dtype=np.int32) + if padding_duration: + # padding to nearset 10 seconds + samples = np.zeros( + ( + 1, + padding_duration + * sample_rate + * ((int(len(waveform) / sample_rate) // padding_duration) + 1), + ), + dtype=np.float32, + ) + + samples[0, : len(waveform)] = waveform + else: + samples = waveform + + samples = samples.reshape(1, -1).astype(np.float32) + + data = { + "inputs": [ + { + "name": "reference_wav", + "shape": samples.shape, + "datatype": "FP32", + "data": samples.tolist() + }, + { + "name": "reference_wav_len", + "shape": lengths.shape, + "datatype": "INT32", + "data": lengths.tolist(), + }, + { + "name": "reference_text", + "shape": [1, 1], + "datatype": "BYTES", + "data": [reference_text] + }, + { + "name": "target_text", + "shape": [1, 1], + "datatype": "BYTES", + "data": [target_text] + } + ] + } + + return data + + +if __name__ == "__main__": + args = get_args() + server_url = args.server_url + if not server_url.startswith(("http://", "https://")): + server_url = f"http://{server_url}" + + url = f"{server_url}/v2/models/{args.model_name}/infer" + waveform, sr = sf.read(args.reference_audio) + assert sr == 16000, "sample rate hardcoded in server" + + samples = np.array(waveform, dtype=np.float32) + data = prepare_request(samples, args.reference_text, args.target_text) + + rsp = requests.post( + url, + headers={"Content-Type": "application/json"}, + json=data, + verify=False, + params={"request_id": '0'} + ) + result = rsp.json() + audio = result["outputs"][0]["data"] + audio = np.array(audio, dtype=np.float32) + if args.model_name == "spark_tts": + sample_rate = 16000 + else: + sample_rate = 24000 + sf.write(args.output_audio, audio, sample_rate, "PCM_16") diff --git a/cosyvoice/runtime/triton_trtllm/docker-compose.dit.yml b/cosyvoice/runtime/triton_trtllm/docker-compose.dit.yml new file mode 100644 index 0000000..35312a1 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/docker-compose.dit.yml @@ -0,0 +1,20 @@ +services: + tts: + image: soar97/triton-cosyvoice:25.06 + shm_size: '1gb' + ports: + - "8000:8000" + - "8001:8001" + - "8002:8002" + environment: + - PYTHONIOENCODING=utf-8 + - MODEL_ID=${MODEL_ID} + deploy: + resources: + reservations: + devices: + - driver: nvidia + device_ids: ['0'] + capabilities: [gpu] + command: > + /bin/bash -c "pip install modelscope && cd /workspace && git clone https://github.com/yuekaizhang/Step-Audio2.git -b trt && git clone https://github.com/yuekaizhang/CosyVoice.git -b streaming && cd CosyVoice && git submodule update --init --recursive && cd runtime/triton_trtllm && bash run_stepaudio2_dit_token2wav.sh 0 3" \ No newline at end of file diff --git a/cosyvoice/runtime/triton_trtllm/docker-compose.yml b/cosyvoice/runtime/triton_trtllm/docker-compose.yml new file mode 100644 index 0000000..e221e56 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/docker-compose.yml @@ -0,0 +1,20 @@ +services: + tts: + image: soar97/triton-cosyvoice:25.06 + shm_size: '1gb' + ports: + - "8000:8000" + - "8001:8001" + - "8002:8002" + environment: + - PYTHONIOENCODING=utf-8 + - MODEL_ID=${MODEL_ID} + deploy: + resources: + reservations: + devices: + - driver: nvidia + device_ids: ['0'] + capabilities: [gpu] + command: > + /bin/bash -c "pip install modelscope && cd /workspace && git clone https://github.com/FunAudioLLM/CosyVoice.git && cd CosyVoice && git submodule update --init --recursive && cd runtime/triton_trtllm && bash run.sh 0 3" \ No newline at end of file diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/audio_tokenizer/1/model.py b/cosyvoice/runtime/triton_trtllm/model_repo/audio_tokenizer/1/model.py new file mode 100644 index 0000000..339cb0e --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/audio_tokenizer/1/model.py @@ -0,0 +1,97 @@ +# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +import json +import torch +from torch.utils.dlpack import to_dlpack + +import triton_python_backend_utils as pb_utils + +import os +import numpy as np +import s3tokenizer +torch.set_num_threads(1) +ORIGINAL_VOCAB_SIZE = 151663 + + +class TritonPythonModel: + """Triton Python model for audio tokenization. + + This model takes reference audio input and extracts semantic tokens + using s3tokenizer. + """ + + def initialize(self, args): + """Initialize the model. + + Args: + args: Dictionary containing model configuration + """ + # Parse model parameters + parameters = json.loads(args['model_config'])['parameters'] + model_params = {k: v["string_value"] for k, v in parameters.items()} + + self.device = torch.device("cuda") + model_path = os.path.join(model_params["model_dir"], "speech_tokenizer_v2.onnx") + self.audio_tokenizer = s3tokenizer.load_model(model_path).to(self.device) + + def execute(self, requests): + """Execute inference on the batched requests. + + Args: + requests: List of inference requests + + Returns: + List of inference responses containing tokenized outputs + """ + mels = [] + + # Process each request in batch + for request in requests: + # Extract input tensors + wav_array = pb_utils.get_input_tensor_by_name( + request, "reference_wav").as_numpy() + wav_len = pb_utils.get_input_tensor_by_name( + request, "reference_wav_len").as_numpy().item() + + wav_array = torch.from_numpy(wav_array).to(self.device) + # Prepare inputs + wav = wav_array[:, :wav_len].squeeze(0) + mels.append(s3tokenizer.log_mel_spectrogram(wav)) + + mels, mels_lens = s3tokenizer.padding(mels) + codes, codes_lens = self.audio_tokenizer.quantize(mels.to(self.device), mels_lens.to(self.device)) + codes = codes.clone() + ORIGINAL_VOCAB_SIZE + + responses = [] + for i in range(len(requests)): + prompt_speech_tokens = codes[i, :codes_lens[i].item()] + prompt_speech_tokens_tensor = pb_utils.Tensor.from_dlpack( + "prompt_speech_tokens", to_dlpack(prompt_speech_tokens)) + inference_response = pb_utils.InferenceResponse( + output_tensors=[prompt_speech_tokens_tensor]) + responses.append(inference_response) + + return responses diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/audio_tokenizer/config.pbtxt b/cosyvoice/runtime/triton_trtllm/model_repo/audio_tokenizer/config.pbtxt new file mode 100644 index 0000000..4bb972c --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/audio_tokenizer/config.pbtxt @@ -0,0 +1,53 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "audio_tokenizer" +backend: "python" +max_batch_size: ${triton_max_batch_size} +dynamic_batching { + max_queue_delay_microseconds: ${max_queue_delay_microseconds} +} +parameters [ + { + key: "model_dir", + value: {string_value:"${model_dir}"} + } +] + +input [ + { + name: "reference_wav" + data_type: TYPE_FP32 + dims: [-1] + }, + { + name: "reference_wav_len" + data_type: TYPE_INT32 + dims: [1] + } +] +output [ + { + name: "prompt_speech_tokens" + data_type: TYPE_INT32 + dims: [-1] + } +] + +instance_group [ + { + count: 1 + kind: KIND_CPU + } +] \ No newline at end of file diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/cosyvoice2/1/model.py b/cosyvoice/runtime/triton_trtllm/model_repo/cosyvoice2/1/model.py new file mode 100644 index 0000000..a2bfb30 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/cosyvoice2/1/model.py @@ -0,0 +1,452 @@ +# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import json +import os +import threading +import time + +import numpy as np +import torch +from torch.utils.dlpack import from_dlpack, to_dlpack +import triton_python_backend_utils as pb_utils +from transformers import AutoTokenizer + +import torchaudio + + +from matcha.utils.audio import mel_spectrogram + +ORIGINAL_VOCAB_SIZE = 151663 +torch.set_num_threads(1) + + +class TritonPythonModel: + """Triton Python model for Spark TTS. + + This model orchestrates the end-to-end TTS pipeline by coordinating + between audio tokenizer, LLM, and vocoder components. + """ + + def initialize(self, args): + """Initialize the model. + + Args: + args: Dictionary containing model configuration + """ + self.logger = pb_utils.Logger + # Parse model parameters + self.model_config = json.loads(args['model_config']) + parameters = self.model_config['parameters'] + model_params = {k: v["string_value"] for k, v in parameters.items()} + self.logger.log_info(f"model_params:{model_params}") + self.dynamic_chunk_strategy = model_params.get("dynamic_chunk_strategy", "exponential") # "exponential" or "time_based" + self.logger.log_info(f"Using dynamic chunk strategy: {self.dynamic_chunk_strategy}") + + # Initialize tokenizer + llm_tokenizer_dir = model_params["llm_tokenizer_dir"] + self.tokenizer = AutoTokenizer.from_pretrained(llm_tokenizer_dir) + self.prompt_template = "<|sos|>{input_text}<|task_id|>" + self.eos_token_id = self.tokenizer.convert_tokens_to_ids("<|eos1|>") + + self.device = torch.device("cuda") + self.decoupled = pb_utils.using_decoupled_model_transaction_policy(self.model_config) + + self.token_frame_rate = 25 + self.flow_pre_lookahead_len = 3 + self.token_hop_len = 15 + + spk_info_path = os.path.join(model_params["model_dir"], "spk2info.pt") + if not os.path.exists(spk_info_path): + raise ValueError(f"spk2info.pt not found in {model_params['model_dir']}") + spk_info = torch.load(spk_info_path, map_location="cpu", weights_only=False) + self.default_spk_info = spk_info["001"] + + def forward_llm(self, input_ids): + """ + Prepares the response from the language model based on the provided + inputs. Creates a `pb_utils.InferenceRequest` object with passed + `llm_request_inputs` to send to a decoupled TensorRTLLM model. + For each response from the language model: + - Checks for errors and raise an exception if any are found. + - Extracts the "output_ids" tensor from the response. + - Determines the finish reason based on the presence of the + end-of-sequence token or reaching the maximum length. + - Appends the generated token IDs to `output_ids`. + - If the finish reason is determined, decodes the output IDs to text + and prepares the final response. + + The final response includes the generated text, finish reason, + completion tokens, prompt tokens, and total tokens. + + Parameters + ---------- + - llm_request_inputs (dict): A dictionary containing the inputs for the language model. + + Returns + ------- + - pb_utils.InferenceResponse: The response object containing the generated text and additional metadata. + """ + # convert input_ids to numpy, with shape [1, sequence_length] + input_ids = input_ids.cpu().numpy() + max_tokens = 750 + input_dict = { + "request_output_len": np.array([[max_tokens]], dtype=np.int32), + "end_id": np.array([[self.eos_token_id]], dtype=np.int32), + "pad_id": np.array([[self.eos_token_id]], dtype=np.int32), + "streaming": np.array([[self.decoupled]], dtype=np.bool_), + "runtime_top_p": np.array([[0.95]], dtype=np.float32), + "runtime_top_k": np.array([[50]], dtype=np.int32), + "temperature": np.array([[0.8]], dtype=np.float32), + "repetition_penalty": np.array([[1.1]], dtype=np.float32), + "random_seed": np.array([[42]], dtype=np.uint64), + "input_ids": input_ids, + "input_lengths": np.array([[input_ids.shape[1]]], dtype=np.int32), + } + + # Convert inputs to Triton tensors + input_tensor_list = [ + pb_utils.Tensor(k, v) for k, v in input_dict.items() + ] + + # Create and execute inference request + llm_request = pb_utils.InferenceRequest( + model_name="tensorrt_llm", + requested_output_names=["output_ids", "sequence_length"], + inputs=input_tensor_list, + ) + + llm_responses = llm_request.exec(decoupled=self.decoupled) + if self.decoupled: + for llm_response in llm_responses: + if llm_response.has_error(): + raise pb_utils.TritonModelException(llm_response.error().message()) + + # Extract and process output + output_ids = pb_utils.get_output_tensor_by_name( + llm_response, "output_ids").as_numpy() + seq_lens = pb_utils.get_output_tensor_by_name( + llm_response, "sequence_length").as_numpy() + + # Get actual output IDs up to the sequence length + actual_output_ids = output_ids[0][0][:seq_lens[0][0]] + + yield actual_output_ids + else: + llm_response = llm_responses + if llm_response.has_error(): + raise pb_utils.TritonModelException(llm_response.error().message()) + + # Extract and process output + output_ids = pb_utils.get_output_tensor_by_name( + llm_response, "output_ids").as_numpy() + seq_lens = pb_utils.get_output_tensor_by_name( + llm_response, "sequence_length").as_numpy() + + # Get actual output IDs up to the sequence length + actual_output_ids = output_ids[0][0][:seq_lens[0][0]] + + yield actual_output_ids + + def forward_audio_tokenizer(self, wav, wav_len): + """Forward pass through the audio tokenizer component. + + Args: + wav: Input waveform tensor + wav_len: Waveform length tensor + + Returns: + Tuple of global and semantic tokens + """ + inference_request = pb_utils.InferenceRequest( + model_name='audio_tokenizer', + requested_output_names=['prompt_speech_tokens'], + inputs=[wav, wav_len] + ) + + inference_response = inference_request.exec() + if inference_response.has_error(): + raise pb_utils.TritonModelException(inference_response.error().message()) + + # Extract and convert output tensors + prompt_speech_tokens = pb_utils.get_output_tensor_by_name(inference_response, 'prompt_speech_tokens') + prompt_speech_tokens = torch.utils.dlpack.from_dlpack(prompt_speech_tokens.to_dlpack()).cpu() + + return prompt_speech_tokens + + def forward_speaker_embedding(self, wav): + """Forward pass through the speaker embedding component. + + Args: + wav: Input waveform tensor + + Returns: + Prompt speaker embedding tensor + """ + inference_request = pb_utils.InferenceRequest( + model_name='speaker_embedding', + requested_output_names=['prompt_spk_embedding'], + inputs=[pb_utils.Tensor.from_dlpack("reference_wav", to_dlpack(wav))] + ) + + inference_response = inference_request.exec() + if inference_response.has_error(): + raise pb_utils.TritonModelException(inference_response.error().message()) + + # Extract and convert output tensors + prompt_spk_embedding = pb_utils.get_output_tensor_by_name(inference_response, 'prompt_spk_embedding') + prompt_spk_embedding = torch.utils.dlpack.from_dlpack(prompt_spk_embedding.to_dlpack()) + + return prompt_spk_embedding + + def forward_token2wav( + self, + target_speech_tokens: torch.Tensor, + request_id: str, + prompt_speech_tokens: torch.Tensor = None, + prompt_speech_feat: torch.Tensor = None, + prompt_spk_embedding: torch.Tensor = None, + token_offset: int = None, + finalize: bool = None) -> torch.Tensor: + """Forward pass through the vocoder component. + + Args: + prompt_speech_tokens: Prompt speech tokens tensor + prompt_speech_feat: Prompt speech feat tensor + prompt_spk_embedding: Prompt spk embedding tensor + target_speech_tokens: Target speech tokens tensor + + Returns: + Generated waveform tensor + """ + target_speech_tokens_tensor = pb_utils.Tensor.from_dlpack("target_speech_tokens", to_dlpack(target_speech_tokens)) + + inputs_tensor = [target_speech_tokens_tensor] + + if token_offset is not None: + assert finalize is not None + token_offset_tensor = pb_utils.Tensor("token_offset", np.array([[token_offset]], dtype=np.int32)) + finalize_tensor = pb_utils.Tensor("finalize", np.array([[finalize]], dtype=np.bool_)) + inputs_tensor.append(token_offset_tensor) + inputs_tensor.append(finalize_tensor) + + if prompt_spk_embedding is not None: + assert prompt_speech_feat is not None + prompt_speech_tokens_tensor = pb_utils.Tensor.from_dlpack("prompt_speech_tokens", to_dlpack(prompt_speech_tokens)) + prompt_speech_feat_tensor = pb_utils.Tensor.from_dlpack("prompt_speech_feat", to_dlpack(prompt_speech_feat)) + prompt_spk_embedding_tensor = pb_utils.Tensor.from_dlpack("prompt_spk_embedding", to_dlpack(prompt_spk_embedding)) + inputs_tensor.extend([prompt_speech_tokens_tensor, prompt_speech_feat_tensor, prompt_spk_embedding_tensor]) + + # Create and execute inference request + inference_request = pb_utils.InferenceRequest( + model_name='token2wav', + requested_output_names=['waveform'], + inputs=inputs_tensor, + request_id=request_id, + ) + + inference_response = inference_request.exec() + if inference_response.has_error(): + raise pb_utils.TritonModelException(inference_response.error().message()) + + # Extract and convert output waveform + waveform = pb_utils.get_output_tensor_by_name(inference_response, 'waveform') + waveform = torch.utils.dlpack.from_dlpack(waveform.to_dlpack()).cpu() + + return waveform + + def parse_input(self, text, prompt_text, prompt_speech_tokens): + total_text = f"{prompt_text}{text}" + prompt = self.prompt_template.format(input_text=total_text) + input_ids = self.tokenizer.encode(prompt) + input_ids = torch.tensor([input_ids], dtype=torch.int32) + input_ids = torch.cat([input_ids, prompt_speech_tokens], dim=1) + return input_ids + + def _extract_speech_feat(self, speech): + speech_feat = mel_spectrogram( + speech, + n_fft=1920, + num_mels=80, + sampling_rate=24000, + hop_size=480, + win_size=1920, + fmin=0, + fmax=8000).squeeze( + dim=0).transpose( + 0, + 1).to( + self.device) + speech_feat = speech_feat.unsqueeze(dim=0) + return speech_feat + + def _llm_gen_thread(self, generated_ids_iter, semantic_token_ids_arr, llm_is_done_flag): + for generated_ids in generated_ids_iter: + generated_ids = generated_ids.tolist() + if len(generated_ids) == 0: + break + semantic_token_ids_arr.extend(generated_ids) + llm_is_done_flag[0] = True + + def execute(self, requests): + """Execute inference on the batched requests. + + Args: + requests: List of inference requests + + Returns: + List of inference responses containing generated audio + """ + responses = [] + + for request in requests: + request_id = request.request_id() + # Extract input tensors + wav = pb_utils.get_input_tensor_by_name(request, "reference_wav") + + # Process reference audio through audio tokenizer + if wav is not None: + wav_len = pb_utils.get_input_tensor_by_name(request, "reference_wav_len") + prompt_speech_tokens = self.forward_audio_tokenizer(wav, wav_len) + prompt_speech_tokens = prompt_speech_tokens.unsqueeze(0) + + wav_tensor = wav.as_numpy() + wav_tensor = torch.from_numpy(wav_tensor)[:, :wav_len.as_numpy()[0][0]] + prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=24000)(wav_tensor) + speech_feat = self._extract_speech_feat(prompt_speech_resample) + token_len = min(int(speech_feat.shape[1] / 2), prompt_speech_tokens.shape[-1]) + prompt_speech_feat = speech_feat[:, :2 * token_len].contiguous().half() + prompt_speech_tokens = prompt_speech_tokens[:, :token_len].contiguous() + + reference_text = pb_utils.get_input_tensor_by_name(request, "reference_text").as_numpy() + reference_text = reference_text[0][0].decode('utf-8') + prompt_spk_embedding = self.forward_speaker_embedding(wav_tensor) + else: + # using pre-cached reference text + reference_text = self.default_spk_info["prompt_text"] + prompt_speech_tokens = self.default_spk_info["speech_token"] + ORIGINAL_VOCAB_SIZE + prompt_speech_feat = None + prompt_spk_embedding = None + + target_text = pb_utils.get_input_tensor_by_name(request, "target_text").as_numpy() + target_text = target_text[0][0].decode('utf-8') + + # Prepare prompt for LLM + input_ids = self.parse_input( + text=target_text, + prompt_text=reference_text, + prompt_speech_tokens=prompt_speech_tokens, + ) + + # Generate semantic tokens with LLM + generated_ids_iter = self.forward_llm(input_ids) + + if self.decoupled: + response_sender = request.get_response_sender() + + semantic_token_ids_arr = [] + llm_is_done_flag = [False] + + llm_thread = threading.Thread( + target=self._llm_gen_thread, + args=(generated_ids_iter, semantic_token_ids_arr, llm_is_done_flag) + ) + + llm_thread.start() + + token_offset, chunk_index = 0, 0 + start_time = time.time() + this_token_hop_len = self.token_hop_len + + while True: + pending_num = len(semantic_token_ids_arr) - token_offset + + if llm_is_done_flag[0]: + break + + if pending_num >= this_token_hop_len + self.flow_pre_lookahead_len: + this_tts_speech_token = semantic_token_ids_arr[:token_offset + this_token_hop_len + self.flow_pre_lookahead_len] + this_tts_speech_token = torch.tensor(this_tts_speech_token).unsqueeze(dim=0).to(torch.int32).to(self.device) + + sub_tts_speech = self.forward_token2wav( + this_tts_speech_token, request_id, prompt_speech_tokens, + prompt_speech_feat, prompt_spk_embedding, token_offset, False + ) + + audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(sub_tts_speech)) + inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor]) + response_sender.send(inference_response) + + token_offset += this_token_hop_len + self.logger.log_info(f"chunk_index: {chunk_index}, current_token_hop_len: {this_token_hop_len}") + + if self.dynamic_chunk_strategy == "exponential": + this_token_hop_len = self.token_frame_rate * (2 ** chunk_index) + elif self.dynamic_chunk_strategy == "time_based": + # see https://github.com/qi-hua/async_cosyvoice/blob/main/model.py#L306 + cost_time = time.time() - start_time + duration = token_offset / self.token_frame_rate + if chunk_index > 0 and cost_time > 0: + avg_chunk_processing_time = cost_time / (chunk_index + 1) + if avg_chunk_processing_time > 0: + multiples = (duration - cost_time) / avg_chunk_processing_time + self.logger.log_info(f"multiples: {multiples}") + next_pending_num = len(semantic_token_ids_arr) - token_offset + if multiples > 4: + this_token_hop_len = (next_pending_num // self.token_hop_len + 1) * self.token_hop_len + elif multiples > 2: + this_token_hop_len = (next_pending_num // self.token_hop_len) * self.token_hop_len + else: + this_token_hop_len = self.token_hop_len + this_token_hop_len = max(self.token_hop_len, this_token_hop_len) + chunk_index += 1 + else: + time.sleep(0.02) + + this_tts_speech_token = torch.tensor(semantic_token_ids_arr).unsqueeze(dim=0).to(torch.int32).to(self.device) + sub_tts_speech = self.forward_token2wav(this_tts_speech_token, request_id, prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding, token_offset, True) + audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(sub_tts_speech)) + inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor]) + response_sender.send(inference_response) + + llm_thread.join() + response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL) + self.logger.log_info("send tritonserver_response_complete_final to end") + else: + generated_ids = next(generated_ids_iter) + generated_ids = torch.tensor(generated_ids).unsqueeze(0).to(self.device) + if generated_ids is None or len(generated_ids) == 0: + raise pb_utils.TritonModelException("Generated IDs is None or empty") + + audio = self.forward_token2wav(generated_ids, request_id, prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding) + + # Prepare response + audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(audio)) + inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor]) + responses.append(inference_response) + + if not self.decoupled: + return responses diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/cosyvoice2/config.pbtxt b/cosyvoice/runtime/triton_trtllm/model_repo/cosyvoice2/config.pbtxt new file mode 100644 index 0000000..73a9a05 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/cosyvoice2/config.pbtxt @@ -0,0 +1,73 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "cosyvoice2" +backend: "python" +max_batch_size: ${triton_max_batch_size} +dynamic_batching { + max_queue_delay_microseconds: ${max_queue_delay_microseconds} +} +model_transaction_policy { + decoupled: ${decoupled_mode} +} +parameters [ + { + key: "llm_tokenizer_dir", + value: {string_value:"${llm_tokenizer_dir}"} + }, + { + key: "model_dir", + value: {string_value:"${model_dir}"} + } +] + +input [ + { + name: "reference_wav" + data_type: TYPE_FP32 + dims: [-1] + optional: true + }, + { + name: "reference_wav_len" + data_type: TYPE_INT32 + dims: [1] + optional: true + }, + { + name: "reference_text" + data_type: TYPE_STRING + dims: [1] + optional: true + }, + { + name: "target_text" + data_type: TYPE_STRING + dims: [1] + } +] +output [ + { + name: "waveform" + data_type: TYPE_FP32 + dims: [ -1 ] + } +] + +instance_group [ + { + count: ${bls_instance_num} + kind: KIND_CPU + } +] \ No newline at end of file diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/cosyvoice2_dit/1/model.py b/cosyvoice/runtime/triton_trtllm/model_repo/cosyvoice2_dit/1/model.py new file mode 100644 index 0000000..2e2533c --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/cosyvoice2_dit/1/model.py @@ -0,0 +1,394 @@ +# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import json +import math +import os +import re +import time +from typing import Dict, List, Tuple, Optional, Union +import asyncio +import httpx + +import numpy as np +import torch +from torch.utils.dlpack import from_dlpack, to_dlpack +import triton_python_backend_utils as pb_utils +from transformers import AutoTokenizer + +import torchaudio + + +from matcha.utils.audio import mel_spectrogram + + +ORIGINAL_VOCAB_SIZE = 151663 +torch.set_num_threads(1) + + +def parse_speech_token_string(response_text: str) -> List[int]: + """ + Parses a string of speech tokens (e.g., "<|s_123|><|s_456|>") into a list of integer IDs. + """ + speech_tokens = response_text.strip().split('><') + if len(speech_tokens) > 1: + # Add back the missing '<' and '>' for proper parsing + speech_tokens = ['<' + t if not t.startswith('<') else t for t in speech_tokens] + speech_tokens = [t + '>' if not t.endswith('>') else t for t in speech_tokens] + + speech_ids = [] + for token_str in speech_tokens: + match = re.match(r'<\|s_(\d+)\|>', token_str) + if match: + speech_ids.append(int(match.group(1))) + return speech_ids + + +class TritonPythonModel: + """Triton Python model for Spark TTS. + + This model orchestrates the end-to-end TTS pipeline by coordinating + between audio tokenizer, LLM, and vocoder components. + """ + + def initialize(self, args): + """Initialize the model. + + Args: + args: Dictionary containing model configuration + """ + self.logger = pb_utils.Logger + # Parse model parameters + self.model_config = json.loads(args['model_config']) + parameters = self.model_config['parameters'] + model_params = {k: v["string_value"] for k, v in parameters.items()} + self.dynamic_chunk_strategy = model_params.get("dynamic_chunk_strategy", "exponential") # "exponential" or "time_based" + self.logger.log_info(f"Using dynamic chunk strategy: {self.dynamic_chunk_strategy}") + + # Initialize tokenizer + llm_tokenizer_dir = model_params["llm_tokenizer_dir"] + self.tokenizer = AutoTokenizer.from_pretrained(llm_tokenizer_dir) + self.prompt_template = "<|sos|>{input_text}<|task_id|>" + self.eos_token_id = self.tokenizer.convert_tokens_to_ids("<|eos1|>") + + self.device = torch.device("cuda") + self.decoupled = pb_utils.using_decoupled_model_transaction_policy(self.model_config) + + self.token_frame_rate = 25 + self.flow_pre_lookahead_len = 3 + self.token_hop_len = 15 + + self.http_client = httpx.AsyncClient() + self.api_base = "http://localhost:8000/v1/chat/completions" + self.speaker_cache = {} + + def _convert_speech_tokens_to_str(self, speech_tokens: Union[torch.Tensor, List]) -> str: + """Converts a tensor or list of speech token IDs to a string representation.""" + if isinstance(speech_tokens, torch.Tensor): + # Ensure tensor is on CPU and flattened + speech_tokens = speech_tokens.cpu().numpy().flatten().tolist() + + speech_id_str = "" + for token_id in speech_tokens: + # Convert token ID back to the speech number N + token_num = token_id - ORIGINAL_VOCAB_SIZE + speech_id_str += f"<|s_{token_num}|>" + return speech_id_str + + async def forward_llm_async(self, target_text: str, reference_text: str, prompt_speech_tokens: Union[torch.Tensor, List]): + """ + Asynchronously sends a request to the TRTLLM-serve endpoint and processes the streaming response. + """ + full_text = f"{reference_text}{target_text}" + prompt_speech_tokens_str = self._convert_speech_tokens_to_str(prompt_speech_tokens) + + chat = [ + {"role": "user", "content": full_text}, + {"role": "assistant", "content": prompt_speech_tokens_str} + ] + + payload = { + "model": "trt_engines_bfloat16", + "messages": chat, + "max_tokens": 750, + "temperature": 0.8, + "top_p": 0.95, + "top_k": 50, + "repetition_penalty": 1.1, + "stop": ["<|eos1|>", "<|eos|>"], + "stream": True, + } + + buffer = "" + async with self.http_client.stream("POST", self.api_base, json=payload, timeout=None) as response: + response.raise_for_status() + async for line in response.aiter_lines(): + if line.startswith("data: "): + line_data = line[len("data: "):].strip() + if line_data == "[DONE]": + break + try: + json_data = json.loads(line_data) + content = json_data.get("choices", [{}])[0].get("delta", {}).get("content") + if content: + buffer += content + while True: + match = re.search(r"<\|s_(\d+)\|>", buffer) + if not match: + break + + token_num = int(match.group(1)) + final_id = token_num + ORIGINAL_VOCAB_SIZE + yield final_id + buffer = buffer[match.end():] + except json.JSONDecodeError: + self.logger.log_info(f"Skipping non-JSON line: {line_data}") + continue + + # Process any remaining complete tokens in the buffer after the stream ends + while True: + match = re.search(r"<\|s_(\d+)\|>", buffer) + if not match: + break + token_num = int(match.group(1)) + final_id = token_num + ORIGINAL_VOCAB_SIZE + yield final_id + buffer = buffer[match.end():] + + def forward_audio_tokenizer(self, wav, wav_len): + """Forward pass through the audio tokenizer component. + + Args: + wav: Input waveform tensor + wav_len: Waveform length tensor + + Returns: + Tuple of global and semantic tokens + """ + inference_request = pb_utils.InferenceRequest( + model_name='audio_tokenizer', + requested_output_names=['prompt_speech_tokens'], + inputs=[wav, wav_len] + ) + + inference_response = inference_request.exec() + if inference_response.has_error(): + raise pb_utils.TritonModelException(inference_response.error().message()) + + # Extract and convert output tensors + prompt_speech_tokens = pb_utils.get_output_tensor_by_name(inference_response, 'prompt_speech_tokens') + prompt_speech_tokens = torch.utils.dlpack.from_dlpack(prompt_speech_tokens.to_dlpack()).cpu() + + return prompt_speech_tokens + + def forward_speaker_embedding(self, wav): + """Forward pass through the speaker embedding component. + + Args: + wav: Input waveform tensor + + Returns: + Prompt speaker embedding tensor + """ + inference_request = pb_utils.InferenceRequest( + model_name='speaker_embedding', + requested_output_names=['prompt_spk_embedding'], + inputs=[pb_utils.Tensor.from_dlpack("reference_wav", to_dlpack(wav))] + ) + + inference_response = inference_request.exec() + if inference_response.has_error(): + raise pb_utils.TritonModelException(inference_response.error().message()) + + # Extract and convert output tensors + prompt_spk_embedding = pb_utils.get_output_tensor_by_name(inference_response, 'prompt_spk_embedding') + prompt_spk_embedding = torch.utils.dlpack.from_dlpack(prompt_spk_embedding.to_dlpack()) + + return prompt_spk_embedding + + async def forward_token2wav( + self, + index: int, + target_speech_tokens: torch.Tensor, + request_id: str, + reference_wav: object, + reference_wav_len: object, + finalize: bool = None) -> torch.Tensor: + """Forward pass through the vocoder component. + + Args: + index: Index of the request + target_speech_tokens: Target speech tokens tensor + request_id: Request ID + reference_wav: Reference waveform tensor + reference_wav_len: Reference waveform length tensor + finalize: Whether to finalize the request + + Returns: + Generated waveform tensor + """ + target_speech_tokens_tensor = pb_utils.Tensor.from_dlpack("target_speech_tokens", to_dlpack(target_speech_tokens)) + finalize_tensor = pb_utils.Tensor("finalize", np.array([[finalize]], dtype=np.bool_)) + inputs_tensor = [target_speech_tokens_tensor, reference_wav, reference_wav_len, finalize_tensor] + + # Create and execute inference request + inference_request = pb_utils.InferenceRequest( + model_name='token2wav_dit', + requested_output_names=[ + "waveform", + ], + inputs=inputs_tensor, + request_id=request_id, + parameters={"priority": index + 1}, + ) + + inference_response = await inference_request.async_exec() + if inference_response.has_error(): + raise pb_utils.TritonModelException(inference_response.error().message()) + + # Extract and convert output waveform + waveform = pb_utils.get_output_tensor_by_name(inference_response, 'waveform') + waveform = torch.utils.dlpack.from_dlpack(waveform.to_dlpack()).cpu() + + return waveform + + def _extract_speech_feat(self, speech): + speech_feat = mel_spectrogram( + speech, + n_fft=1920, + num_mels=80, + sampling_rate=24000, + hop_size=480, + win_size=1920, + fmin=0, + fmax=8000).squeeze( + dim=0).transpose( + 0, + 1).to( + self.device) + speech_feat = speech_feat.unsqueeze(dim=0) + return speech_feat + + async def _process_request(self, request): + request_id = request.request_id() + + reference_text = pb_utils.get_input_tensor_by_name(request, "reference_text").as_numpy() + reference_text = reference_text[0][0].decode('utf-8') + + wav = pb_utils.get_input_tensor_by_name(request, "reference_wav") + wav_len = pb_utils.get_input_tensor_by_name(request, "reference_wav_len") + + if reference_text not in self.speaker_cache: + self.speaker_cache[reference_text] = self.forward_audio_tokenizer(wav, wav_len).unsqueeze(0) + prompt_speech_tokens = self.speaker_cache[reference_text] + + target_text = pb_utils.get_input_tensor_by_name(request, "target_text").as_numpy() + target_text = target_text[0][0].decode('utf-8') + + if self.decoupled: + response_sender = request.get_response_sender() + + semantic_token_ids_arr = [] + token_offset, chunk_index = 0, 0 + start_time = time.time() + this_token_hop_len = self.token_hop_len + async for generated_ids in self.forward_llm_async( + target_text=target_text, + reference_text=reference_text, + prompt_speech_tokens=prompt_speech_tokens, + ): + if not generated_ids: + break + semantic_token_ids_arr.append(generated_ids) + while True: + pending_num = len(semantic_token_ids_arr) - token_offset + if pending_num >= this_token_hop_len + self.flow_pre_lookahead_len: + this_tts_speech_token = semantic_token_ids_arr[token_offset:token_offset + this_token_hop_len + self.flow_pre_lookahead_len] + this_tts_speech_token = torch.tensor(this_tts_speech_token).unsqueeze(dim=0).to(torch.int32).to(self.device) + sub_tts_speech = await self.forward_token2wav( + chunk_index, + this_tts_speech_token, request_id, wav, wav_len, False + ) + audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(sub_tts_speech)) + inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor]) + response_sender.send(inference_response) + + token_offset += this_token_hop_len + + if self.dynamic_chunk_strategy == "exponential": + this_token_hop_len = self.token_frame_rate * (2 ** chunk_index) + elif self.dynamic_chunk_strategy == "equal": + this_token_hop_len = self.token_hop_len + elif self.dynamic_chunk_strategy == "time_based": + # see https://github.com/qi-hua/async_cosyvoice/blob/main/model.py#L306 + cost_time = time.time() - start_time + duration = token_offset / self.token_frame_rate + if chunk_index > 0 and cost_time > 0: + avg_chunk_processing_time = cost_time / (chunk_index + 1) + if avg_chunk_processing_time > 0: + multiples = (duration - cost_time) / avg_chunk_processing_time + next_pending_num = len(semantic_token_ids_arr) - token_offset + if multiples > 4: + this_token_hop_len = (next_pending_num // self.token_hop_len + 1) * self.token_hop_len + elif multiples > 2: + this_token_hop_len = (next_pending_num // self.token_hop_len) * self.token_hop_len + else: + this_token_hop_len = self.token_hop_len + this_token_hop_len = max(self.token_hop_len, this_token_hop_len) + chunk_index += 1 + else: + break + + this_tts_speech_token = torch.tensor(semantic_token_ids_arr[token_offset:]).unsqueeze(dim=0).to(torch.int32).to(self.device) + sub_tts_speech = await self.forward_token2wav(chunk_index, this_tts_speech_token, request_id, wav, wav_len, True) + audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(sub_tts_speech)) + inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor]) + response_sender.send(inference_response) + + response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL) + else: + raise NotImplementedError("Offline TTS mode is not supported") + + async def execute(self, requests): + """Execute inference on the batched requests. + + Args: + requests: List of inference requests + + Returns: + List of inference responses containing generated audio + """ + tasks = [ + asyncio.create_task(self._process_request(request)) + for request in requests + ] + await asyncio.gather(*tasks) + return None + + def finalize(self): + self.logger.log_info("Finalizing CosyVoice DIT model") + if hasattr(self, "http_client"): + asyncio.run(self.http_client.aclose()) diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/cosyvoice2_dit/config.pbtxt b/cosyvoice/runtime/triton_trtllm/model_repo/cosyvoice2_dit/config.pbtxt new file mode 100644 index 0000000..e64647e --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/cosyvoice2_dit/config.pbtxt @@ -0,0 +1,73 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "cosyvoice2_dit" +backend: "python" +max_batch_size: ${triton_max_batch_size} +dynamic_batching { + max_queue_delay_microseconds: ${max_queue_delay_microseconds} +} +model_transaction_policy { + decoupled: ${decoupled_mode} +} +parameters [ + { + key: "llm_tokenizer_dir", + value: {string_value:"${llm_tokenizer_dir}"} + }, + { + key: "model_dir", + value: {string_value:"${model_dir}"} + } +] + +input [ + { + name: "reference_wav" + data_type: TYPE_FP32 + dims: [-1] + optional: true + }, + { + name: "reference_wav_len" + data_type: TYPE_INT32 + dims: [1] + optional: true + }, + { + name: "reference_text" + data_type: TYPE_STRING + dims: [1] + optional: true + }, + { + name: "target_text" + data_type: TYPE_STRING + dims: [1] + } +] +output [ + { + name: "waveform" + data_type: TYPE_FP32 + dims: [ -1 ] + } +] + +instance_group [ + { + count: ${bls_instance_num} + kind: KIND_CPU + } +] \ No newline at end of file diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/speaker_embedding/1/model.py b/cosyvoice/runtime/triton_trtllm/model_repo/speaker_embedding/1/model.py new file mode 100644 index 0000000..1a7293a --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/speaker_embedding/1/model.py @@ -0,0 +1,153 @@ +# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +import json +import torch +from torch.utils.dlpack import to_dlpack + +import triton_python_backend_utils as pb_utils + +import os +import numpy as np +import torchaudio.compliance.kaldi as kaldi +from cosyvoice.utils.file_utils import convert_onnx_to_trt +from cosyvoice.utils.common import TrtContextWrapper +import onnxruntime + + +class TritonPythonModel: + """Triton Python model for audio tokenization. + + This model takes reference audio input and extracts semantic tokens + using s3tokenizer. + """ + + def initialize(self, args): + """Initialize the model. + + Args: + args: Dictionary containing model configuration + """ + # Parse model parameters + parameters = json.loads(args['model_config'])['parameters'] + model_params = {k: v["string_value"] for k, v in parameters.items()} + + self.device = torch.device("cuda") + + model_dir = model_params["model_dir"] + gpu = "l20" + enable_trt = True + if enable_trt: + self.load_spk_trt(f'{model_dir}/campplus.{gpu}.fp32.trt', + f'{model_dir}/campplus.onnx', + 1, + False) + else: + campplus_model = f'{model_dir}/campplus.onnx' + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + self.spk_model = onnxruntime.InferenceSession(campplus_model, sess_options=option, providers=["CPUExecutionProvider"]) + + def load_spk_trt(self, spk_model, spk_onnx_model, trt_concurrent=1, fp16=True): + if not os.path.exists(spk_model) or os.path.getsize(spk_model) == 0: + trt_kwargs = self.get_spk_trt_kwargs() + convert_onnx_to_trt(spk_model, trt_kwargs, spk_onnx_model, fp16) + import tensorrt as trt + with open(spk_model, 'rb') as f: + spk_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read()) + assert spk_engine is not None, 'failed to load trt {}'.format(spk_model) + self.spk_model = TrtContextWrapper(spk_engine, trt_concurrent=trt_concurrent, device=self.device) + + def get_spk_trt_kwargs(self): + min_shape = [(1, 4, 80)] + opt_shape = [(1, 500, 80)] + max_shape = [(1, 3000, 80)] + input_names = ["input"] + return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names} + + def _extract_spk_embedding(self, speech): + feat = kaldi.fbank(speech, + num_mel_bins=80, + dither=0, + sample_frequency=16000) + spk_feat = feat - feat.mean(dim=0, keepdim=True) + + if isinstance(self.spk_model, onnxruntime.InferenceSession): + embedding = self.spk_model.run( + None, {self.spk_model.get_inputs()[0].name: spk_feat.unsqueeze(dim=0).cpu().numpy()} + )[0].flatten().tolist() + embedding = torch.tensor([embedding]).to(self.device) + else: + [spk_model, stream], trt_engine = self.spk_model.acquire_estimator() + # NOTE need to synchronize when switching stream + with torch.cuda.device(self.device): + torch.cuda.current_stream().synchronize() + spk_feat = spk_feat.unsqueeze(dim=0).to(self.device) + batch_size = spk_feat.size(0) + + with stream: + spk_model.set_input_shape('input', (batch_size, spk_feat.size(1), 80)) + embedding = torch.empty((batch_size, 192), device=spk_feat.device) + + data_ptrs = [spk_feat.contiguous().data_ptr(), + embedding.contiguous().data_ptr()] + for i, j in enumerate(data_ptrs): + + spk_model.set_tensor_address(trt_engine.get_tensor_name(i), j) + # run trt engine + assert spk_model.execute_async_v3(torch.cuda.current_stream().cuda_stream) is True + torch.cuda.current_stream().synchronize() + self.spk_model.release_estimator(spk_model, stream) + + return embedding.half() + + def execute(self, requests): + """Execute inference on the batched requests. + + Args: + requests: List of inference requests + + Returns: + List of inference responses containing tokenized outputs + """ + responses = [] + # Process each request in batch + for request in requests: + # Extract input tensors + wav_array = pb_utils.get_input_tensor_by_name( + request, "reference_wav").as_numpy() + wav_array = torch.from_numpy(wav_array).to(self.device) + + embedding = self._extract_spk_embedding(wav_array) + + prompt_spk_embedding_tensor = pb_utils.Tensor.from_dlpack( + "prompt_spk_embedding", to_dlpack(embedding)) + inference_response = pb_utils.InferenceResponse( + output_tensors=[prompt_spk_embedding_tensor]) + + responses.append(inference_response) + + return responses diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/speaker_embedding/config.pbtxt b/cosyvoice/runtime/triton_trtllm/model_repo/speaker_embedding/config.pbtxt new file mode 100644 index 0000000..fd91bd6 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/speaker_embedding/config.pbtxt @@ -0,0 +1,48 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "speaker_embedding" +backend: "python" +max_batch_size: ${triton_max_batch_size} +dynamic_batching { + max_queue_delay_microseconds: ${max_queue_delay_microseconds} +} +parameters [ + { + key: "model_dir", + value: {string_value:"${model_dir}"} + } +] + +input [ + { + name: "reference_wav" + data_type: TYPE_FP32 + dims: [-1] + } +] +output [ + { + name: "prompt_spk_embedding" + data_type: TYPE_FP16 + dims: [-1] + } +] + +instance_group [ + { + count: 1 + kind: KIND_CPU + } +] \ No newline at end of file diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/tensorrt_llm/1/.gitkeep b/cosyvoice/runtime/triton_trtllm/model_repo/tensorrt_llm/1/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/tensorrt_llm/config.pbtxt b/cosyvoice/runtime/triton_trtllm/model_repo/tensorrt_llm/config.pbtxt new file mode 100644 index 0000000..3da7894 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/tensorrt_llm/config.pbtxt @@ -0,0 +1,857 @@ +# Copyright 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +name: "tensorrt_llm" +backend: "${triton_backend}" +max_batch_size: ${triton_max_batch_size} + +model_transaction_policy { + decoupled: ${decoupled_mode} +} + +dynamic_batching { + preferred_batch_size: [ ${triton_max_batch_size} ] + max_queue_delay_microseconds: ${max_queue_delay_microseconds} + default_queue_policy: { max_queue_size: ${max_queue_size} } +} + +input [ + { + name: "input_ids" + data_type: TYPE_INT32 + dims: [ -1 ] + allow_ragged_batch: true + optional: true + }, + { + name: "encoder_input_features" + data_type: ${encoder_input_features_data_type} + dims: [ -1, -1 ] + allow_ragged_batch: true + optional: true + }, + { + name: "encoder_output_lengths" + data_type: TYPE_INT32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "input_lengths" + data_type: TYPE_INT32 + dims: [ 1 ] + reshape: { shape: [ ] } + }, + { + name: "request_output_len" + data_type: TYPE_INT32 + dims: [ 1 ] + reshape: { shape: [ ] } + }, + { + name: "num_return_sequences" + data_type: TYPE_INT32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "draft_input_ids" + data_type: TYPE_INT32 + dims: [ -1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "decoder_input_ids" + data_type: TYPE_INT32 + dims: [ -1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "decoder_input_lengths" + data_type: TYPE_INT32 + dims: [ 1 ] + optional: true + reshape: { shape: [ ] } + }, + { + name: "draft_logits" + data_type: ${logits_datatype} + dims: [ -1, -1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "draft_acceptance_threshold" + data_type: TYPE_FP32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "end_id" + data_type: TYPE_INT32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "pad_id" + data_type: TYPE_INT32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "stop_words_list" + data_type: TYPE_INT32 + dims: [ 2, -1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "bad_words_list" + data_type: TYPE_INT32 + dims: [ 2, -1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "embedding_bias" + data_type: TYPE_FP32 + dims: [ -1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "beam_width" + data_type: TYPE_INT32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "temperature" + data_type: TYPE_FP32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "runtime_top_k" + data_type: TYPE_INT32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "runtime_top_p" + data_type: TYPE_FP32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "runtime_top_p_min" + data_type: TYPE_FP32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "runtime_top_p_decay" + data_type: TYPE_FP32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "runtime_top_p_reset_ids" + data_type: TYPE_INT32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "len_penalty" + data_type: TYPE_FP32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "early_stopping" + data_type: TYPE_BOOL + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "repetition_penalty" + data_type: TYPE_FP32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "min_length" + data_type: TYPE_INT32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "beam_search_diversity_rate" + data_type: TYPE_FP32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "presence_penalty" + data_type: TYPE_FP32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "frequency_penalty" + data_type: TYPE_FP32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "random_seed" + data_type: TYPE_UINT64 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "return_log_probs" + data_type: TYPE_BOOL + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "return_context_logits" + data_type: TYPE_BOOL + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "return_generation_logits" + data_type: TYPE_BOOL + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "return_perf_metrics" + data_type: TYPE_BOOL + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "exclude_input_in_output" + data_type: TYPE_BOOL + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "stop" + data_type: TYPE_BOOL + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "streaming" + data_type: TYPE_BOOL + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "prompt_embedding_table" + data_type: TYPE_FP16 + dims: [ -1, -1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "prompt_table_extra_ids" + data_type: TYPE_UINT64 + dims: [ -1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "prompt_vocab_size" + data_type: TYPE_INT32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + # cross_attention_mask shape `[bs, seq_len, num_images*num_tiles]` + { + name: "cross_attention_mask" + data_type: TYPE_BOOL + dims: [ -1, -1 ] + optional: true + allow_ragged_batch: true + }, + # Mrope param when mrope is used + { + name: "mrope_rotary_cos_sin" + data_type: TYPE_FP32 + dims: [ -1 ] + optional: true + }, + { + name: "mrope_position_deltas" + data_type: TYPE_INT64 + dims: [ 1 ] + optional: true + }, + # the unique task ID for the given LoRA. + # To perform inference with a specific LoRA for the first time `lora_task_id` `lora_weights` and `lora_config` must all be given. + # The LoRA will be cached, so that subsequent requests for the same task only require `lora_task_id`. + # If the cache is full the oldest LoRA will be evicted to make space for new ones. An error is returned if `lora_task_id` is not cached. + { + name: "lora_task_id" + data_type: TYPE_UINT64 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + # weights for a lora adapter shape [ num_lora_modules_layers, D x Hi + Ho x D ] + # where the last dimension holds the in / out adapter weights for the associated module (e.g. attn_qkv) and model layer + # each of the in / out tensors are first flattened and then concatenated together in the format above. + # D=adapter_size (R value), Hi=hidden_size_in, Ho=hidden_size_out. + { + name: "lora_weights" + data_type: TYPE_FP16 + dims: [ -1, -1 ] + optional: true + allow_ragged_batch: true + }, + # module identifier (same size a first dimension of lora_weights) + # See LoraModule::ModuleType for model id mapping + # + # "attn_qkv": 0 # compbined qkv adapter + # "attn_q": 1 # q adapter + # "attn_k": 2 # k adapter + # "attn_v": 3 # v adapter + # "attn_dense": 4 # adapter for the dense layer in attention + # "mlp_h_to_4h": 5 # for llama2 adapter for gated mlp layer after attention / RMSNorm: up projection + # "mlp_4h_to_h": 6 # for llama2 adapter for gated mlp layer after attention / RMSNorm: down projection + # "mlp_gate": 7 # for llama2 adapter for gated mlp later after attention / RMSNorm: gate + # + # last dim holds [ module_id, layer_idx, adapter_size (D aka R value) ] + { + name: "lora_config" + data_type: TYPE_INT32 + dims: [ -1, 3 ] + optional: true + allow_ragged_batch: true + }, + { + name: "context_phase_params" + data_type: TYPE_UINT8 + dims: [ -1 ] + optional: true + allow_ragged_batch: true + }, + # skip_cross_attn_blocks shape `[bs, 1]`, only used in mllama + { + name: "skip_cross_attn_blocks" + data_type: TYPE_BOOL + dims: [ 1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "retention_token_range_starts" + data_type: TYPE_INT32 + dims: [ -1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "retention_token_range_ends" + data_type: TYPE_INT32 + dims: [ -1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "retention_token_range_priorities" + data_type: TYPE_INT32 + dims: [ -1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "retention_token_range_durations_ms" + data_type: TYPE_INT32 + dims: [ -1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "retention_decode_priority" + data_type: TYPE_INT32 + dims: [ 1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "retention_decode_duration_ms" + data_type: TYPE_INT32 + dims: [ 1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "guided_decoding_guide_type" + data_type: TYPE_STRING + dims: [ 1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "guided_decoding_guide" + data_type: TYPE_STRING + dims: [ 1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "lookahead_window_size" + data_type: TYPE_INT32 + dims: [ 1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "lookahead_ngram_size" + data_type: TYPE_INT32 + dims: [ 1 ] + optional: true + allow_ragged_batch: true + }, + { + name: "lookahead_verification_set_size" + data_type: TYPE_INT32 + dims: [ 1 ] + optional: true + allow_ragged_batch: true + } +] +output [ + { + name: "output_ids" + data_type: TYPE_INT32 + dims: [ -1, -1 ] + }, + { + name: "sequence_length" + data_type: TYPE_INT32 + dims: [ -1 ] + }, + { + name: "cum_log_probs" + data_type: TYPE_FP32 + dims: [ -1 ] + }, + { + name: "output_log_probs" + data_type: TYPE_FP32 + dims: [ -1, -1 ] + }, + { + name: "context_logits" + data_type: ${logits_datatype} + dims: [ -1, -1 ] + }, + { + name: "generation_logits" + data_type: ${logits_datatype} + dims: [ -1, -1, -1 ] + }, + { + name: "batch_index" + data_type: TYPE_INT32 + dims: [ 1 ] + }, + { + name: "sequence_index" + data_type: TYPE_INT32 + dims: [ 1 ] + }, + { + name: "context_phase_params" + data_type: TYPE_UINT8 + dims: [ -1 ] + }, + { + name: "kv_cache_alloc_new_blocks" + data_type: TYPE_INT32 + dims: [ 1 ] + }, + { + name: "kv_cache_reused_blocks" + data_type: TYPE_INT32 + dims: [ 1 ] + }, + { + name: "kv_cache_alloc_total_blocks" + data_type: TYPE_INT32 + dims: [ 1 ] + }, + { + name: "arrival_time_ns" + data_type: TYPE_INT64 + dims: [ 1 ] + }, + { + name: "first_scheduled_time_ns" + data_type: TYPE_INT64 + dims: [ 1 ] + }, + { + name: "first_token_time_ns" + data_type: TYPE_INT64 + dims: [ 1 ] + }, + { + name: "last_token_time_ns" + data_type: TYPE_INT64 + dims: [ 1 ] + }, + { + name: "acceptance_rate" + data_type: TYPE_FP32 + dims: [ 1 ] + }, + { + name: "total_accepted_draft_tokens" + data_type: TYPE_INT32 + dims: [ 1 ] + }, + { + name: "total_draft_tokens" + data_type: TYPE_INT32 + dims: [ 1 ] + } +] +instance_group [ + { + count: 1 + kind : KIND_CPU + } +] +parameters: { + key: "max_beam_width" + value: { + string_value: "${max_beam_width}" + } +} +parameters: { + key: "FORCE_CPU_ONLY_INPUT_TENSORS" + value: { + string_value: "no" + } +} +parameters: { + key: "gpt_model_type" + value: { + string_value: "${batching_strategy}" + } +} +parameters: { + key: "gpt_model_path" + value: { + string_value: "${engine_dir}" + } +} +parameters: { + key: "encoder_model_path" + value: { + string_value: "${encoder_engine_dir}" + } +} +parameters: { + key: "max_tokens_in_paged_kv_cache" + value: { + string_value: "${max_tokens_in_paged_kv_cache}" + } +} +parameters: { + key: "max_attention_window_size" + value: { + string_value: "${max_attention_window_size}" + } +} +parameters: { + key: "sink_token_length" + value: { + string_value: "${sink_token_length}" + } +} +parameters: { + key: "batch_scheduler_policy" + value: { + string_value: "${batch_scheduler_policy}" + } +} +parameters: { + key: "kv_cache_free_gpu_mem_fraction" + value: { + string_value: "${kv_cache_free_gpu_mem_fraction}" + } +} +parameters: { + key: "cross_kv_cache_fraction" + value: { + string_value: "${cross_kv_cache_fraction}" + } +} +parameters: { + key: "kv_cache_host_memory_bytes" + value: { + string_value: "${kv_cache_host_memory_bytes}" + } +} +# kv_cache_onboard_blocks is for internal implementation. +parameters: { + key: "kv_cache_onboard_blocks" + value: { + string_value: "${kv_cache_onboard_blocks}" + } +} +# enable_trt_overlap is deprecated and doesn't have any effect on the runtime +# parameters: { +# key: "enable_trt_overlap" +# value: { +# string_value: "${enable_trt_overlap}" +# } +# } +parameters: { + key: "exclude_input_in_output" + value: { + string_value: "${exclude_input_in_output}" + } +} +parameters: { + key: "cancellation_check_period_ms" + value: { + string_value: "${cancellation_check_period_ms}" + } +} +parameters: { + key: "stats_check_period_ms" + value: { + string_value: "${stats_check_period_ms}" + } +} +parameters: { + key: "iter_stats_max_iterations" + value: { + string_value: "${iter_stats_max_iterations}" + } +} +parameters: { + key: "request_stats_max_iterations" + value: { + string_value: "${request_stats_max_iterations}" + } +} +parameters: { + key: "enable_kv_cache_reuse" + value: { + string_value: "${enable_kv_cache_reuse}" + } +} +parameters: { + key: "normalize_log_probs" + value: { + string_value: "${normalize_log_probs}" + } +} +parameters: { + key: "enable_chunked_context" + value: { + string_value: "${enable_chunked_context}" + } +} +parameters: { + key: "gpu_device_ids" + value: { + string_value: "${gpu_device_ids}" + } +} +parameters: { + key: "participant_ids" + value: { + string_value: "${participant_ids}" + } +} +parameters: { + key: "lora_cache_optimal_adapter_size" + value: { + string_value: "${lora_cache_optimal_adapter_size}" + } +} +parameters: { + key: "lora_cache_max_adapter_size" + value: { + string_value: "${lora_cache_max_adapter_size}" + } +} +parameters: { + key: "lora_cache_gpu_memory_fraction" + value: { + string_value: "${lora_cache_gpu_memory_fraction}" + } +} +parameters: { + key: "lora_cache_host_memory_bytes" + value: { + string_value: "${lora_cache_host_memory_bytes}" + } +} +parameters: { + key: "lora_prefetch_dir" + value: { + string_value: "${lora_prefetch_dir}" + } +} +parameters: { + key: "decoding_mode" + value: { + string_value: "${decoding_mode}" + } +} +parameters: { + key: "executor_worker_path" + value: { + string_value: "/opt/tritonserver/backends/tensorrtllm/trtllmExecutorWorker" + } +} +parameters: { + key: "lookahead_window_size" + value: { + string_value: "${lookahead_window_size}" + } +} +parameters: { + key: "lookahead_ngram_size" + value: { + string_value: "${lookahead_ngram_size}" + } +} +parameters: { + key: "lookahead_verification_set_size" + value: { + string_value: "${lookahead_verification_set_size}" + } +} +parameters: { + key: "medusa_choices" + value: { + string_value: "${medusa_choices}" + } +} +parameters: { + key: "eagle_choices" + value: { + string_value: "${eagle_choices}" + } +} +parameters: { + key: "gpu_weights_percent" + value: { + string_value: "${gpu_weights_percent}" + } +} +parameters: { + key: "enable_context_fmha_fp32_acc" + value: { + string_value: "${enable_context_fmha_fp32_acc}" + } +} +parameters: { + key: "multi_block_mode" + value: { + string_value: "${multi_block_mode}" + } +} +parameters: { + key: "cuda_graph_mode" + value: { + string_value: "${cuda_graph_mode}" + } +} +parameters: { + key: "cuda_graph_cache_size" + value: { + string_value: "${cuda_graph_cache_size}" + } +} +parameters: { + key: "speculative_decoding_fast_logits" + value: { + string_value: "${speculative_decoding_fast_logits}" + } +} +parameters: { + key: "tokenizer_dir" + value: { + string_value: "${tokenizer_dir}" + } +} +parameters: { + key: "guided_decoding_backend" + value: { + string_value: "${guided_decoding_backend}" + } +} +parameters: { + key: "xgrammar_tokenizer_info_path" + value: { + string_value: "${xgrammar_tokenizer_info_path}" + } +} diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/token2wav/1/model.py b/cosyvoice/runtime/triton_trtllm/model_repo/token2wav/1/model.py new file mode 100644 index 0000000..1e38052 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/token2wav/1/model.py @@ -0,0 +1,277 @@ +# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import json +import os + +import logging + +import torch +from torch.utils.dlpack import to_dlpack +from torch.nn import functional as F + +import triton_python_backend_utils as pb_utils + +from hyperpyyaml import load_hyperpyyaml +from cosyvoice.utils.common import fade_in_out +from cosyvoice.utils.file_utils import convert_onnx_to_trt, export_cosyvoice2_vllm +from cosyvoice.utils.common import TrtContextWrapper +from collections import defaultdict +import numpy as np + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +ORIGINAL_VOCAB_SIZE = 151663 +torch.set_num_threads(1) + + +class CosyVoice2: + + def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, trt_concurrent=1, device='cuda'): + + self.model_dir = model_dir + self.fp16 = fp16 + + hyper_yaml_path = '{}/cosyvoice2.yaml'.format(model_dir) + if not os.path.exists(hyper_yaml_path): + raise ValueError('{} not found!'.format(hyper_yaml_path)) + with open(hyper_yaml_path, 'r') as f: + configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')}) + self.model = CosyVoice2Model(configs['flow'], configs['hift'], fp16, device) + self.model.load('{}/flow.pt'.format(model_dir), '{}/hift.pt'.format(model_dir)) + if load_jit: + self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32')) + if load_trt: + self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'), + '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir), + trt_concurrent, + self.fp16) + + +class CosyVoice2Model: + + def __init__(self, + flow: torch.nn.Module, + hift: torch.nn.Module, + fp16: bool = False, + device: str = 'cuda'): + self.device = device + self.flow = flow + self.hift = hift + self.fp16 = fp16 + if self.fp16 is True: + self.flow.half() + + # streaming tts config + self.token_hop_len = 25 + self.mel_cache_len = 8 + self.source_cache_len = int(self.mel_cache_len * 480) + self.speech_window = np.hamming(2 * self.source_cache_len) + self.hift_cache_dict = defaultdict(lambda: None) + + def load_jit(self, flow_encoder_model): + flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device) + self.flow.encoder = flow_encoder + + def load(self, flow_model, hift_model): + self.flow.load_state_dict(torch.load(flow_model, map_location=self.device), strict=True) + self.flow.to(self.device).eval() + # in case hift_model is a hifigan model + hift_state_dict = {k.replace('generator.', ''): v for k, v in torch.load(hift_model, map_location=self.device).items()} + self.hift.load_state_dict(hift_state_dict, strict=True) + self.hift.to(self.device).eval() + + def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, trt_concurrent, fp16): + assert torch.cuda.is_available(), 'tensorrt only supports gpu!' + if not os.path.exists(flow_decoder_estimator_model) or os.path.getsize(flow_decoder_estimator_model) == 0: + convert_onnx_to_trt(flow_decoder_estimator_model, self.get_trt_kwargs(), flow_decoder_onnx_model, fp16) + del self.flow.decoder.estimator + import tensorrt as trt + with open(flow_decoder_estimator_model, 'rb') as f: + estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read()) + assert estimator_engine is not None, 'failed to load trt {}'.format(flow_decoder_estimator_model) + self.flow.decoder.estimator = TrtContextWrapper(estimator_engine, trt_concurrent=trt_concurrent, device=self.device) + + def get_trt_kwargs(self): + min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4)] + opt_shape = [(2, 80, 500), (2, 1, 500), (2, 80, 500), (2, 80, 500)] + max_shape = [(2, 80, 3000), (2, 1, 3000), (2, 80, 3000), (2, 80, 3000)] + input_names = ["x", "mask", "mu", "cond"] + return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names} + + def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0): + with torch.cuda.amp.autocast(self.fp16): + tts_mel, _ = self.flow.inference(token=token.to(self.device), + token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), + prompt_token=prompt_token.to(self.device), + prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), + prompt_feat=prompt_feat.to(self.device), + prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device), + embedding=embedding.to(self.device), + streaming=stream, + finalize=finalize) + tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:] + # append hift cache + if self.hift_cache_dict[uuid] is not None: + hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source'] + tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2) + else: + hift_cache_source = torch.zeros(1, 1, 0) + # keep overlap mel and hift cache + if finalize is False: + tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) + if self.hift_cache_dict[uuid] is not None: + tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) + self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:], + 'source': tts_source[:, :, -self.source_cache_len:], + 'speech': tts_speech[:, -self.source_cache_len:]} + tts_speech = tts_speech[:, :-self.source_cache_len] + else: + if speed != 1.0: + assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode' + tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear') + tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) + if self.hift_cache_dict[uuid] is not None: + tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) + return tts_speech + + +class TritonPythonModel: + """Triton Python model for vocoder. + + This model takes global and semantic tokens as input and generates audio waveforms + using the BiCodec vocoder. + """ + + def initialize(self, args): + """Initialize the model. + + Args: + args: Dictionary containing model configuration + """ + # Parse model parameters + parameters = json.loads(args['model_config'])['parameters'] + model_params = {key: value["string_value"] for key, value in parameters.items()} + model_dir = model_params["model_dir"] + + # Initialize device and vocoder + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + logger.info(f"Initializing vocoder from {model_dir} on {self.device}") + + self.token2wav_model = CosyVoice2( + model_dir, load_jit=False, load_trt=True, fp16=True, device=self.device + ) + + spk_info_path = os.path.join(model_dir, "spk2info.pt") + if not os.path.exists(spk_info_path): + raise ValueError(f"spk2info.pt not found in {model_dir}") + spk_info = torch.load(spk_info_path, map_location="cpu", weights_only=False) + self.default_spk_info = spk_info["001"] + + logger.info("Token2Wav initialized successfully") + + def execute(self, requests): + """Execute inference on the batched requests. + + Args: + requests: List of inference requests + + Returns: + List of inference responses containing generated waveforms + """ + responses = [] + # Process each request in batch + for request in requests: + target_speech_tokens_tensor = pb_utils.get_input_tensor_by_name(request, "target_speech_tokens").as_numpy() + target_speech_tokens = torch.from_numpy(target_speech_tokens_tensor).to(self.device) + + prompt_speech_tokens_tensor = pb_utils.get_input_tensor_by_name(request, "prompt_speech_tokens") + if prompt_speech_tokens_tensor is not None: + prompt_speech_tokens_tensor = prompt_speech_tokens_tensor.as_numpy() + prompt_speech_feat_tensor = pb_utils.get_input_tensor_by_name(request, "prompt_speech_feat").as_numpy() + prompt_spk_embedding_tensor = pb_utils.get_input_tensor_by_name(request, "prompt_spk_embedding").as_numpy() + prompt_speech_tokens = torch.from_numpy(prompt_speech_tokens_tensor).to(self.device) + prompt_speech_feat = torch.from_numpy(prompt_speech_feat_tensor).to(self.device) + prompt_spk_embedding = torch.from_numpy(prompt_spk_embedding_tensor).to(self.device) + prompt_speech_tokens = prompt_speech_tokens - ORIGINAL_VOCAB_SIZE + else: + prompt_speech_tokens = self.default_spk_info["speech_token"].to(self.device) + prompt_speech_feat = self.default_spk_info["speech_feat"].to(torch.float16).to(self.device) + prompt_spk_embedding = self.default_spk_info["embedding"].to(torch.float16).to(self.device) + + # shift the speech tokens according to the original vocab size + target_speech_tokens = target_speech_tokens - ORIGINAL_VOCAB_SIZE + + # We set token_offset as an optional input to support streaming/offline tts. It has to be None when offline tts. + token_offset = pb_utils.get_input_tensor_by_name(request, "token_offset") + if token_offset is not None: + token_offset = token_offset.as_numpy().item() + finalize = pb_utils.get_input_tensor_by_name(request, "finalize").as_numpy().item() + if not finalize: + stream = True + else: + stream = False + request_id = request.request_id() + audio_hat = self.token2wav_model.model.token2wav(token=target_speech_tokens, + prompt_token=prompt_speech_tokens, + prompt_feat=prompt_speech_feat, + embedding=prompt_spk_embedding, + token_offset=token_offset, + uuid=request_id, + stream=stream, + finalize=finalize) + if finalize: + self.token2wav_model.model.hift_cache_dict.pop(request_id) + + else: + tts_mel, _ = self.token2wav_model.model.flow.inference( + token=target_speech_tokens, + token_len=torch.tensor([target_speech_tokens.shape[1]], dtype=torch.int32).to( + self.device + ), + prompt_token=prompt_speech_tokens, + prompt_token_len=torch.tensor( + [prompt_speech_tokens.shape[1]], dtype=torch.int32 + ).to(self.device), + prompt_feat=prompt_speech_feat, + prompt_feat_len=torch.tensor([prompt_speech_feat.shape[1]], dtype=torch.int32).to(self.device), + embedding=prompt_spk_embedding, + streaming=False, + finalize=True, + ) + + audio_hat, _ = self.token2wav_model.model.hift.inference( + speech_feat=tts_mel, cache_source=torch.zeros(1, 1, 0) + ) + + generated_wave = audio_hat.squeeze(0).cpu().numpy() + + wav_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(audio_hat)) + inference_response = pb_utils.InferenceResponse(output_tensors=[wav_tensor]) + responses.append(inference_response) + + return responses diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/token2wav/config.pbtxt b/cosyvoice/runtime/triton_trtllm/model_repo/token2wav/config.pbtxt new file mode 100644 index 0000000..c33a85f --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/token2wav/config.pbtxt @@ -0,0 +1,80 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "token2wav" +backend: "python" +max_batch_size: ${triton_max_batch_size} +dynamic_batching { + max_queue_delay_microseconds: ${max_queue_delay_microseconds} +} +parameters [ + { + key: "model_dir", + value: {string_value:"${model_dir}"} + } +] + +input [ + { + name: "target_speech_tokens" + data_type: TYPE_INT32 + dims: [-1] + }, + { + name: "prompt_speech_tokens" + data_type: TYPE_INT32 + dims: [-1] + optional: true + }, + { + name: "prompt_speech_feat" + data_type: TYPE_FP16 + dims: [-1, 80] + optional: true + }, + { + name: "prompt_spk_embedding" + data_type: TYPE_FP16 + dims: [-1] + optional: true + }, + { + name: "token_offset" + data_type: TYPE_INT32 + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + }, + { + name: "finalize" + data_type: TYPE_BOOL + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + } +] +output [ + { + name: "waveform" + data_type: TYPE_FP32 + dims: [ -1 ] + } +] + +instance_group [ + { + count: 1 + kind: KIND_CPU + } +] \ No newline at end of file diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/token2wav_dit/1/model.py b/cosyvoice/runtime/triton_trtllm/model_repo/token2wav_dit/1/model.py new file mode 100644 index 0000000..f9e461e --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/token2wav_dit/1/model.py @@ -0,0 +1,142 @@ +# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import json +import os + +import logging +from typing import List, Dict + +import torch +from torch.utils.dlpack import to_dlpack +from torch.nn import functional as F + +import triton_python_backend_utils as pb_utils + +from hyperpyyaml import load_hyperpyyaml +from cosyvoice.utils.common import fade_in_out +from cosyvoice.utils.file_utils import convert_onnx_to_trt, export_cosyvoice2_vllm +from cosyvoice.utils.common import TrtContextWrapper +from collections import defaultdict +import numpy as np +from .token2wav_dit import CosyVoice2_Token2Wav +import hashlib + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + + +ORIGINAL_VOCAB_SIZE = 151663 +torch.set_num_threads(1) + + +def get_spk_id_from_prompt_audio(tensor: torch.Tensor) -> str: + """ + Generates a unique ID for a torch.Tensor. + Tensors with the same elements and properties will have the same ID. + """ + # Convert tensor to a byte string + tensor_bytes = tensor.numpy().tobytes() + + # Create a SHA-256 hash of the byte string + hasher = hashlib.sha256() + hasher.update(tensor_bytes) + + return hasher.hexdigest() + + +class TritonPythonModel: + """Triton Python model for vocoder. + + This model takes global and semantic tokens as input and generates audio waveforms + using the BiCodec vocoder. + """ + + def initialize(self, args): + """Initialize the model. + + Args: + args: Dictionary containing model configuration + """ + # Parse model parameters + parameters = json.loads(args['model_config'])['parameters'] + model_params = {key: value["string_value"] for key, value in parameters.items()} + model_dir = model_params["model_dir"] + + # Initialize device and vocoder + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + logger.info(f"Initializing vocoder from {model_dir} on {self.device}") + + # FIXME: device id settings + self.token2wav_model = CosyVoice2_Token2Wav( + model_dir, enable_trt=True, streaming=True + ) + logger.info("Token2Wav initialized successfully") + + def execute(self, requests): + """Execute inference on the batched requests. + + Args: + requests: List of inference requests + + Returns: + List of inference responses containing generated waveforms + """ + responses = [] + # Process each request in batch + for request in requests: + target_speech_tokens_tensor = pb_utils.get_input_tensor_by_name(request, "target_speech_tokens").as_numpy() + target_speech_tokens = torch.from_numpy(target_speech_tokens_tensor) + target_speech_tokens = target_speech_tokens - ORIGINAL_VOCAB_SIZE + target_speech_tokens = target_speech_tokens.squeeze().tolist() + + finalize = pb_utils.get_input_tensor_by_name(request, "finalize").as_numpy().item() + + request_id = request.request_id() + + wav_array = pb_utils.get_input_tensor_by_name( + request, "reference_wav").as_numpy() + wav_len = pb_utils.get_input_tensor_by_name( + request, "reference_wav_len").as_numpy().item() + + wav_array = torch.from_numpy(wav_array) + wav = wav_array[:, :wav_len].squeeze(0) + + spk_id = get_spk_id_from_prompt_audio(wav) + + audio_hat = self.token2wav_model.forward_streaming( + target_speech_tokens, finalize, request_id=request_id, + speaker_id=f"{spk_id}", prompt_audio=wav, prompt_audio_sample_rate=16000 + ) + + outputs = [] + + wav_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(audio_hat)) + outputs.append(wav_tensor) + inference_response = pb_utils.InferenceResponse(output_tensors=outputs) + responses.append(inference_response) + + return responses diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/token2wav_dit/1/token2wav_dit.py b/cosyvoice/runtime/triton_trtllm/model_repo/token2wav_dit/1/token2wav_dit.py new file mode 100644 index 0000000..1c6c423 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/token2wav_dit/1/token2wav_dit.py @@ -0,0 +1,510 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Example Usage + CUDA_VISIBLE_DEVICES=0 \ + python3 token2wav.py --enable-trt || exit 1 +""" +import torch +# from flashcosyvoice.modules.flow import CausalMaskedDiffWithXvec +from flashcosyvoice.modules.hifigan import HiFTGenerator +from flashcosyvoice.utils.audio import mel_spectrogram +import torchaudio.compliance.kaldi as kaldi +import onnxruntime +import s3tokenizer +from torch.utils.data import DataLoader +from datasets import load_dataset +import torchaudio +import os +import logging +import argparse +import queue +import time +import numpy as np +from hyperpyyaml import load_hyperpyyaml + + +def fade_in_out(fade_in_mel: torch.Tensor, fade_out_mel: torch.Tensor, window: torch.Tensor): + """perform fade_in_out in tensor style + """ + mel_overlap_len = int(window.shape[0] / 2) + fade_in_mel = fade_in_mel.clone() + fade_in_mel[..., :mel_overlap_len] = \ + fade_in_mel[..., :mel_overlap_len] * window[:mel_overlap_len] + \ + fade_out_mel[..., -mel_overlap_len:] * window[mel_overlap_len:] + return fade_in_mel + + +def convert_onnx_to_trt(trt_model, trt_kwargs, onnx_model, dtype): + import tensorrt as trt + logging.info("Converting onnx to trt...") + network_flags = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + logger = trt.Logger(trt.Logger.INFO) + builder = trt.Builder(logger) + network = builder.create_network(network_flags) + parser = trt.OnnxParser(network, logger) + config = builder.create_builder_config() + # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 32) # 4GB + if dtype == torch.float16: + config.set_flag(trt.BuilderFlag.FP16) + + profile = builder.create_optimization_profile() + # load onnx model + with open(onnx_model, "rb") as f: + if not parser.parse(f.read()): + for error in range(parser.num_errors): + print(parser.get_error(error)) + raise ValueError('failed to parse {}'.format(onnx_model)) + # set input shapes + for i in range(len(trt_kwargs['input_names'])): + profile.set_shape(trt_kwargs['input_names'][i], trt_kwargs['min_shape'][i], trt_kwargs['opt_shape'][i], trt_kwargs['max_shape'][i]) + if dtype == torch.float16: + tensor_dtype = trt.DataType.HALF + elif dtype == torch.bfloat16: + tensor_dtype = trt.DataType.BF16 + elif dtype == torch.float32: + tensor_dtype = trt.DataType.FLOAT + else: + raise ValueError('invalid dtype {}'.format(dtype)) + # set input and output data type + for i in range(network.num_inputs): + input_tensor = network.get_input(i) + input_tensor.dtype = tensor_dtype + for i in range(network.num_outputs): + output_tensor = network.get_output(i) + output_tensor.dtype = tensor_dtype + config.add_optimization_profile(profile) + engine_bytes = builder.build_serialized_network(network, config) + # save trt engine + with open(trt_model, "wb") as f: + f.write(engine_bytes) + logging.info("Succesfully convert onnx to trt...") + + +class TrtContextWrapper: + def __init__(self, trt_engine, trt_concurrent=1, device='cuda:0'): + self.trt_context_pool = queue.Queue(maxsize=trt_concurrent) + self.trt_engine = trt_engine + self.device = device + for _ in range(trt_concurrent): + trt_context = trt_engine.create_execution_context() + trt_stream = torch.cuda.stream(torch.cuda.Stream(torch.device(device))) + assert trt_context is not None, 'failed to create trt context, maybe not enough CUDA memory, try reduce current trt concurrent {}'.format(trt_concurrent) + self.trt_context_pool.put([trt_context, trt_stream]) + assert self.trt_context_pool.empty() is False, 'no avaialbe estimator context' + + def acquire_estimator(self): + return self.trt_context_pool.get(), self.trt_engine + + def release_estimator(self, context, stream): + self.trt_context_pool.put([context, stream]) + + +class CosyVoice2_Token2Wav(torch.nn.Module): + def __init__(self, model_dir: str, enable_trt: bool = False, device_id: int = 0, streaming: bool = False, dtype: torch.dtype = torch.float16): + super().__init__() + self.device_id = device_id + self.device = f"cuda:{device_id}" + with open(f"{model_dir}/flow.yaml", "r") as f: + configs = load_hyperpyyaml(f) + self.flow = configs['flow'] + + self.dtype = dtype + self.flow.to(self.dtype) + + self.flow.load_state_dict(torch.load(f"{model_dir}/flow.pt", map_location="cpu", weights_only=True), strict=True) + self.flow.to(self.device).eval() + + self.hift = HiFTGenerator() + hift_state_dict = {k.replace('generator.', ''): v for k, v in torch.load(f"{model_dir}/hift.pt", map_location="cpu", weights_only=True).items()} + self.hift.load_state_dict(hift_state_dict, strict=True) + self.hift.to(self.device).eval() + + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + self.spk_model = onnxruntime.InferenceSession( + f"{model_dir}/campplus.onnx", sess_options=option, + providers=["CPUExecutionProvider"]) + self.audio_tokenizer = s3tokenizer.load_model(f"{model_dir}/speech_tokenizer_v2_25hz.onnx").to(self.device).eval() + + gpu = "l20" + if enable_trt: + if streaming: + self.load_trt( + f'{model_dir}/flow.decoder.estimator.{self.dtype}.dynamic_batch.chunk.{gpu}.plan', + f'{model_dir}/flow.decoder.estimator.chunk.fp32.dynamic_batch.simplify.onnx', + 1, + self.dtype, streaming + ) + else: + self.load_trt( + f'{model_dir}/flow.decoder.estimator.{self.dtype}.dynamic_batch.{gpu}.plan', + f'{model_dir}/flow.decoder.estimator.fp32.dynamic_batch.onnx', + 1, + self.dtype + ) + self.load_spk_trt( + f'{model_dir}/campplus.{gpu}.fp32.trt', + f'{model_dir}/campplus.onnx', + 1, + False + ) + + self.streaming_flow_cache = {} + self.speaker_cache = {} + + self.mel_cache_len = 8 # hard-coded, 160ms + self.source_cache_len = int(self.mel_cache_len * 480) # 50hz mel -> 24kHz wave + self.speech_window = torch.from_numpy(np.hamming(2 * self.source_cache_len)).cuda() + + # hifigan cache for streaming tts + self.hift_cache_dict = {} + + def forward_spk_embedding(self, spk_feat): + if isinstance(self.spk_model, onnxruntime.InferenceSession): + return self.spk_model.run( + None, {self.spk_model.get_inputs()[0].name: spk_feat.unsqueeze(dim=0).cpu().numpy()} + )[0].flatten().tolist() + else: + [spk_model, stream], trt_engine = self.spk_model.acquire_estimator() + # NOTE need to synchronize when switching stream + with torch.cuda.device(self.device_id): + torch.cuda.current_stream().synchronize() + spk_feat = spk_feat.unsqueeze(dim=0).to(self.device) + batch_size = spk_feat.size(0) + + with stream: + spk_model.set_input_shape('input', (batch_size, spk_feat.size(1), 80)) + output_tensor = torch.empty((batch_size, 192), device=spk_feat.device) + + data_ptrs = [spk_feat.contiguous().data_ptr(), + output_tensor.contiguous().data_ptr()] + for i, j in enumerate(data_ptrs): + + spk_model.set_tensor_address(trt_engine.get_tensor_name(i), j) + # run trt engine + assert spk_model.execute_async_v3(torch.cuda.current_stream().cuda_stream) is True + torch.cuda.current_stream().synchronize() + self.spk_model.release_estimator(spk_model, stream) + + return output_tensor.cpu().numpy().flatten().tolist() + + def load_spk_trt(self, spk_model, spk_onnx_model, trt_concurrent=1, fp16=True): + if not os.path.exists(spk_model) or os.path.getsize(spk_model) == 0: + trt_kwargs = self.get_spk_trt_kwargs() + convert_onnx_to_trt(spk_model, trt_kwargs, spk_onnx_model, torch.float32) + import tensorrt as trt + with open(spk_model, 'rb') as f: + spk_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read()) + assert spk_engine is not None, 'failed to load trt {}'.format(spk_model) + self.spk_model = TrtContextWrapper(spk_engine, trt_concurrent=trt_concurrent, device=self.device) + + def get_spk_trt_kwargs(self): + min_shape = [(1, 4, 80)] + opt_shape = [(1, 500, 80)] + max_shape = [(1, 3000, 80)] + input_names = ["input"] + return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names} + + def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, trt_concurrent=1, dtype=torch.float16, streaming=False): + assert torch.cuda.is_available(), 'tensorrt only supports gpu!' + if not os.path.exists(flow_decoder_estimator_model) or os.path.getsize(flow_decoder_estimator_model) == 0: + opt_batch_size = 2 + max_batch_size = 16 + if streaming: + opt_batch_size, max_batch_size = 1, 1 # only support batch size 1 for streaming tts + trt_kwargs = self.get_trt_kwargs_dynamic_batch(opt_batch_size=opt_batch_size, max_batch_size=max_batch_size, streaming=streaming) + convert_onnx_to_trt(flow_decoder_estimator_model, trt_kwargs, flow_decoder_onnx_model, dtype) + del self.flow.decoder.estimator + import tensorrt as trt + with open(flow_decoder_estimator_model, 'rb') as f: + estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read()) + assert estimator_engine is not None, 'failed to load trt {}'.format(flow_decoder_estimator_model) + self.flow.decoder.estimator = TrtContextWrapper(estimator_engine, trt_concurrent=trt_concurrent, device=self.device) + + def get_trt_kwargs_dynamic_batch(self, opt_batch_size=2, max_batch_size=64, streaming=False): + if streaming: + min_shape = [(2, 80, 4), (2, 80, 4), (2, 80, 4), (2,), (2, 80), (16, 2, 1024, 2), (16, 2, 8, 0, 128)] + opt_shape = [ + (opt_batch_size * 2, 80, 500), (opt_batch_size * 2, 80, 500), (opt_batch_size * 2, 80, 500), + (opt_batch_size * 2,), (opt_batch_size * 2, 80), (16, opt_batch_size * 2, 1024, 2), + (16, opt_batch_size * 2, 8, 100, 128) + ] + max_shape = [ + (max_batch_size * 2, 80, 3000), (max_batch_size * 2, 80, 3000), (max_batch_size * 2, 80, 3000), + (max_batch_size * 2,), (max_batch_size * 2, 80), (16, max_batch_size * 2, 1024, 2), + (16, max_batch_size * 2, 8, 1000, 128) + ] + input_names = ["x", "mu", "cond", "t", "spks", "cnn_cache", "att_cache"] + else: + min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4), (2,), (2, 80)] + opt_shape = [ + (opt_batch_size * 2, 80, 500), (opt_batch_size * 2, 1, 500), (opt_batch_size * 2, 80, 500), + (opt_batch_size * 2, 80, 500), (opt_batch_size * 2,), (opt_batch_size * 2, 80) + ] + max_shape = [ + (max_batch_size * 2, 80, 3000), (max_batch_size * 2, 1, 3000), (max_batch_size * 2, 80, 3000), + (max_batch_size * 2, 80, 3000), (max_batch_size * 2,), (max_batch_size * 2, 80) + ] + input_names = ["x", "mask", "mu", "cond", "t", "spks"] + return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names} + + def prompt_audio_tokenization(self, prompt_audios_list: list[torch.Tensor]) -> list[list[int]]: + prompt_speech_tokens_list, prompt_speech_mels_list = [], [] + for audio in prompt_audios_list: + assert len(audio.shape) == 1 + log_mel = s3tokenizer.log_mel_spectrogram(audio) # [num_mels, T] + prompt_speech_mels_list.append(log_mel) + prompt_mels_for_llm, prompt_mels_lens_for_llm = s3tokenizer.padding(prompt_speech_mels_list) + prompt_speech_tokens, prompt_speech_tokens_lens = self.audio_tokenizer.quantize( + prompt_mels_for_llm.to(self.device), prompt_mels_lens_for_llm.to(self.device) + ) + for i in range(len(prompt_speech_tokens)): + speech_tokens_i = prompt_speech_tokens[i, :prompt_speech_tokens_lens[i].item()].tolist() + prompt_speech_tokens_list.append(speech_tokens_i) + return prompt_speech_tokens_list + + def get_spk_emb(self, prompt_audios_list: list[torch.Tensor]) -> torch.Tensor: + spk_emb_for_flow = [] + for audio in prompt_audios_list: + assert len(audio.shape) == 1 + spk_feat = kaldi.fbank(audio.unsqueeze(0), num_mel_bins=80, dither=0, sample_frequency=16000) + spk_feat = spk_feat - spk_feat.mean(dim=0, keepdim=True) + spk_emb = self.forward_spk_embedding(spk_feat) + + spk_emb_for_flow.append(spk_emb) + spk_emb_for_flow = torch.tensor(spk_emb_for_flow) + if self.dtype != torch.float32: + spk_emb_for_flow = spk_emb_for_flow.to(self.dtype) + return spk_emb_for_flow + + def get_prompt_mels(self, prompt_audios_list: list[torch.Tensor], prompt_audios_sample_rate: list[int]): + prompt_mels_for_flow = [] + prompt_mels_lens_for_flow = [] + for audio, sample_rate in zip(prompt_audios_list, prompt_audios_sample_rate): + assert len(audio.shape) == 1 + audio = audio.unsqueeze(0) + if sample_rate != 24000: + audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=24000)(audio) + mel = mel_spectrogram(audio).transpose(1, 2).squeeze(0) # [T, num_mels] + mel_len = mel.shape[0] + prompt_mels_for_flow.append(mel) + prompt_mels_lens_for_flow.append(mel_len) + prompt_mels_for_flow = torch.nn.utils.rnn.pad_sequence( + prompt_mels_for_flow, batch_first=True, padding_value=0 + ) # [B, T', num_mels=80] + prompt_mels_lens_for_flow = torch.tensor(prompt_mels_lens_for_flow) + return prompt_mels_for_flow, prompt_mels_lens_for_flow + + def forward_flow(self, prompt_speech_tokens_list: list[list[int]], + generated_speech_tokens_list: list[list[int]], + prompt_mels_for_flow: torch.Tensor, + prompt_mels_lens_for_flow: torch.Tensor, + spk_emb_for_flow: torch.Tensor): + batch_size = prompt_mels_for_flow.shape[0] + flow_inputs = [] + flow_inputs_lens = [] + for prompt_speech_tokens, generated_speech_tokens in zip(prompt_speech_tokens_list, generated_speech_tokens_list): + flow_inputs.append(torch.tensor(prompt_speech_tokens + generated_speech_tokens)) + flow_inputs_lens.append(len(prompt_speech_tokens) + len(generated_speech_tokens)) + + flow_inputs = torch.nn.utils.rnn.pad_sequence(flow_inputs, batch_first=True, padding_value=0) + flow_inputs_lens = torch.tensor(flow_inputs_lens) + + with torch.amp.autocast(self.device, dtype=torch.float16): + generated_mels, generated_mels_lens = self.flow.inference( + flow_inputs.to(self.device), flow_inputs_lens.to(self.device), + prompt_mels_for_flow.to(self.device), prompt_mels_lens_for_flow.to(self.device), spk_emb_for_flow.to(self.device), 10 + ) + + return generated_mels, generated_mels_lens + + def forward_hift(self, generated_mels: torch.Tensor, generated_mels_lens: torch.Tensor, prompt_mels_lens_for_flow: torch.Tensor): + batch_size = generated_mels.shape[0] + generated_wavs = [] + for i in range(batch_size): + mel = generated_mels[i, :, prompt_mels_lens_for_flow[i].item():generated_mels_lens[i].item()].unsqueeze(0) + wav, _ = self.hift(speech_feat=mel) + generated_wavs.append(wav) + return generated_wavs + + @torch.inference_mode() + def forward( + self, generated_speech_tokens_list: list[list[int]], prompt_audios_list: list[torch.Tensor], prompt_audios_sample_rate: list[int] + ): + assert all(sample_rate == 16000 for sample_rate in prompt_audios_sample_rate) + + prompt_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow = self.prepare_prompt_audio(prompt_audios_list, prompt_audios_sample_rate) + + generated_mels, generated_mels_lens = self.forward_flow( + prompt_speech_tokens_list, generated_speech_tokens_list, + prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow + ) + + generated_wavs = self.forward_hift(generated_mels, generated_mels_lens, prompt_mels_lens_for_flow) + return generated_wavs + + def prepare_prompt_audio( + self, prompt_audios_list: list[torch.Tensor], prompt_audios_sample_rate: list[int] + ): + assert all(sample_rate == 16000 for sample_rate in prompt_audios_sample_rate) + + prompt_speech_tokens_list = self.prompt_audio_tokenization(prompt_audios_list) + + prompt_mels_for_flow, prompt_mels_lens_for_flow = self.get_prompt_mels(prompt_audios_list, prompt_audios_sample_rate) + + spk_emb_for_flow = self.get_spk_emb(prompt_audios_list) + return prompt_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow + + def get_prompt_audio_cache_for_streaming_tts( + self, prompt_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow + ): + assert len(prompt_speech_tokens_list) == 1, "only support batch size 1 for streaming tts" + for i, prompt_speech_tokens in enumerate(prompt_speech_tokens_list): + prompt_speech_tokens_list[i] = torch.tensor(prompt_speech_tokens + prompt_speech_tokens_list[i][:3]) + prompt_speech_tokens_tensor = torch.nn.utils.rnn.pad_sequence(prompt_speech_tokens_list, batch_first=True, padding_value=0) + + cache = self.flow.setup_cache( + prompt_speech_tokens_tensor.to(self.device), + prompt_mels_for_flow.to(self.device), + spk_emb_for_flow.to(self.device), + n_timesteps=10 + ) + new_cache = {k: v.clone() for k, v in cache.items()} + # Hack: this is a hack to avoid in-place changes to the cache['estimator_att_cache'] and cache['estimator_cnn_cache'] + return new_cache + + @torch.inference_mode() + def forward_streaming( + self, generated_speech_tokens: list[int], last_chunk: bool, request_id: str, speaker_id: str, prompt_audio: torch.Tensor = None, prompt_audio_sample_rate: int = 16000 + ): + if speaker_id not in self.speaker_cache: + assert prompt_audio is not None, "prompt_audio is required for new speaker" + assert prompt_audio_sample_rate == 16000 + + prompt_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow = self.prepare_prompt_audio([prompt_audio], [prompt_audio_sample_rate]) + + token_len = min(int(prompt_mels_for_flow.shape[1] / 2), len(prompt_speech_tokens_list[0])) + prompt_mels_for_flow = prompt_mels_for_flow[:, :2 * token_len].contiguous() + prompt_speech_tokens_list[0] = prompt_speech_tokens_list[0][:token_len] + + prompt_audio_dict = {'spk_emb_for_flow': spk_emb_for_flow, 'prompt_mels_for_flow': prompt_mels_for_flow} + + cache_dict = self.get_prompt_audio_cache_for_streaming_tts(prompt_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow) + self.speaker_cache[speaker_id] = {'prompt_audio_dict': prompt_audio_dict, 'cache_dict': cache_dict} + + if request_id not in self.streaming_flow_cache: + self.streaming_flow_cache[request_id] = {k: v.clone() for k, v in self.speaker_cache[speaker_id]['cache_dict'].items()} + self.hift_cache_dict[request_id] = dict( + mel=torch.zeros(1, 80, 0, device='cuda'), + source=torch.zeros(1, 1, 0, device='cuda'), + speech=torch.zeros(1, 0, device='cuda'), + ) + + current_request_cache = self.streaming_flow_cache[request_id] + + current_prompt_audio_dict = self.speaker_cache[speaker_id]['prompt_audio_dict'] + generated_speech_tokens = torch.tensor([generated_speech_tokens], dtype=torch.int32, device='cuda') + + chunk_mel, new_streaming_flow_cache = self.flow.inference_chunk( + token=generated_speech_tokens, + spk=current_prompt_audio_dict['spk_emb_for_flow'].to(self.device), + cache=current_request_cache, + last_chunk=last_chunk, + n_timesteps=10, + ) + + self.streaming_flow_cache[request_id] = new_streaming_flow_cache + + if self.streaming_flow_cache[request_id]['estimator_att_cache'].shape[4] > (current_prompt_audio_dict['prompt_mels_for_flow'].shape[1] + 100): + self.streaming_flow_cache[request_id]['estimator_att_cache'] = torch.cat([ + self.streaming_flow_cache[request_id]['estimator_att_cache'][:, :, :, :, :current_prompt_audio_dict['prompt_mels_for_flow'].shape[1]], + self.streaming_flow_cache[request_id]['estimator_att_cache'][:, :, :, :, -100:], + ], dim=4) + + hift_cache_mel = self.hift_cache_dict[request_id]['mel'].clone() + hift_cache_source = self.hift_cache_dict[request_id]['source'].clone() + hift_cache_speech = self.hift_cache_dict[request_id]['speech'].clone() + mel = torch.concat([hift_cache_mel, chunk_mel], dim=2).clone() + + speech, source = self.hift(mel, hift_cache_source) + + # overlap speech smooth + if hift_cache_speech.shape[-1] > 0: + speech = fade_in_out(speech, hift_cache_speech, self.speech_window) + + # update vocoder cache + self.hift_cache_dict[request_id] = dict( + mel=mel[..., -self.mel_cache_len:].clone().detach(), + source=source[:, :, -self.source_cache_len:].clone().detach(), + speech=speech[:, -self.source_cache_len:].clone().detach(), + ) + if not last_chunk: + speech = speech[:, :-self.source_cache_len] + + if last_chunk: + assert request_id in self.streaming_flow_cache + self.streaming_flow_cache.pop(request_id) + self.hift_cache_dict.pop(request_id) + + return speech + + +def collate_fn(batch): + ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate = [], [], [], [] + for item in batch: + generated_speech_tokens_list.append(item['target_audio_cosy2_tokens']) + audio = torch.from_numpy(item['prompt_audio']['array']).float() + prompt_audios_list.append(audio) + prompt_audios_sample_rate.append(item['prompt_audio']['sampling_rate']) + ids.append(item['id']) + + return ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--enable-trt", action="store_true") + parser.add_argument("--model-dir", type=str, default="./Step-Audio-2-mini/token2wav") + parser.add_argument("--batch-size", type=int, default=1) + parser.add_argument("--output-dir", type=str, default="generated_wavs") + parser.add_argument("--huggingface-dataset-split", type=str, default="wenetspeech4tts") + parser.add_argument("--warmup", type=int, default=3, help="Number of warmup epochs, performance statistics will only be collected from the last epoch") + return parser.parse_args() + + +if __name__ == "__main__": + args = get_args() + model = CosyVoice2_Token2Wav(model_dir=args.model_dir, enable_trt=args.enable_trt) + if not os.path.exists(args.output_dir): + os.makedirs(args.output_dir) + dataset_name = "yuekai/seed_tts_cosy2" + + dataset = load_dataset(dataset_name, split=args.huggingface_dataset_split, trust_remote_code=True) + + data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, collate_fn=collate_fn, num_workers=0) + + for _ in range(args.warmup): + start_time = time.time() + for batch in data_loader: + ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate = batch + + generated_wavs = model(generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate) + + for id, wav in zip(ids, generated_wavs): + torchaudio.save(f"{args.output_dir}/{id}.wav", wav.cpu(), 24000) + end_time = time.time() + epoch_time = end_time - start_time + print(f"Measurement epoch time taken: {epoch_time:.4f} seconds") diff --git a/cosyvoice/runtime/triton_trtllm/model_repo/token2wav_dit/config.pbtxt b/cosyvoice/runtime/triton_trtllm/model_repo/token2wav_dit/config.pbtxt new file mode 100644 index 0000000..3f579aa --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/model_repo/token2wav_dit/config.pbtxt @@ -0,0 +1,69 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "token2wav_dit" +backend: "python" +max_batch_size: ${triton_max_batch_size} + +dynamic_batching { + max_queue_delay_microseconds: ${max_queue_delay_microseconds} + priority_levels: 10 + default_priority_level: 10 +} + +parameters [ + { + key: "model_dir", + value: {string_value:"${model_dir}"} + } +] + +input [ + { + name: "target_speech_tokens" + data_type: TYPE_INT32 + dims: [-1] + }, + { + name: "reference_wav" + data_type: TYPE_FP32 + dims: [-1] + }, + { + name: "reference_wav_len" + data_type: TYPE_INT32 + dims: [1] + }, + { + name: "finalize" + data_type: TYPE_BOOL + dims: [ 1 ] + reshape: { shape: [ ] } + optional: true + } +] +output [ + { + name: "waveform" + data_type: TYPE_FP32 + dims: [ -1 ] + } +] + +instance_group [ + { + count: 1 + kind: KIND_CPU + } +] \ No newline at end of file diff --git a/cosyvoice/runtime/triton_trtllm/offline_inference.py b/cosyvoice/runtime/triton_trtllm/offline_inference.py new file mode 100644 index 0000000..326fb0e --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/offline_inference.py @@ -0,0 +1,652 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Example Usage + CUDA_VISIBLE_DEVICES=0 \ + python3 offline_inference.py \ + --output-dir $output_dir \ + --llm-model-name-or-path $huggingface_model_local_dir \ + --token2wav-path $model_scope_model_local_dir \ + --backend $backend \ + --batch-size $batch_size --token2wav-batch-size $token2wav_batch_size \ + --engine-dir $trt_engines_dir \ + --split-name ${dataset} || exit 1 +""" + +import argparse +import json +import os +import sys + +import torch +import torch.distributed as dist +import torch.nn.functional as F +import torchaudio +from cosyvoice.utils.file_utils import load_wav +from datasets import load_dataset +from transformers import AutoTokenizer +from torch.utils.data import DataLoader, Dataset +from tqdm import tqdm +import soundfile as sf +import s3tokenizer +from functools import partial +import time +import requests +import asyncio +import httpx + +sys.path.append("/workspace/CosyVoice/third_party/Matcha-TTS") +try: + torch.multiprocessing.set_start_method("spawn") +except RuntimeError: + pass + + +async def send_request_async(client, url, payload): + response = await client.post(url, json=payload, timeout=None) + response.raise_for_status() + response_json = response.json() + return response_json['choices'][0]['message']['content'] + + +async def send_batch_requests_async(api_base, model_name, chats, temperature, top_p, top_k): + async with httpx.AsyncClient() as client: + tasks = [] + for chat in chats: + payload = { + "model": model_name, + "messages": chat, + "max_tokens": 2048, + "temperature": temperature, + "top_p": top_p, + "top_k": top_k, + "repetition_penalty": 1.1, + "stop": ["<|eos1|>", "<|eos|>"], + "stream": False, + } + tasks.append(send_request_async(client, api_base, payload)) + return await asyncio.gather(*tasks) + + +def extract_speech_ids(speech_tokens_str): + """Extract speech IDs from token strings like <|s_23456|>""" + speech_ids = [] + for token_str in speech_tokens_str: + if token_str.startswith('<|s_') and token_str.endswith('|>'): + num_str = token_str[4:-2] + num = int(num_str) + speech_ids.append(num) + else: + print(f"Unexpected token: {token_str}") + return speech_ids + + +def convert_cosy2_tokens_to_speech_id_str(cosy2_tokens): + """Convert CosyVoice2 tokens to speech IDs string like <|s_23456|>""" + speech_id_str = "" + for token in cosy2_tokens: + speech_id_str += f"<|s_{token}|>" + return speech_id_str + + +def get_args(): + parser = argparse.ArgumentParser(description="Speech generation using LLM + CosyVoice2") + parser.add_argument( + "--split-name", + type=str, + default="wenetspeech4tts", + help="huggingface dataset split name, see yuekai/CV3-Eval, yuekai/seed_tts_cosy2", + ) + parser.add_argument( + "--output-dir", required=True, type=str, help="dir to save result" + ) + parser.add_argument( + "--batch-size", + default=1, + type=int, + help="batch size (per-device) for inference", + ) + parser.add_argument( + "--token2wav-batch-size", + default=1, + type=int, + help="batch size (per-device) for inference", + ) + parser.add_argument( + "--num-workers", type=int, default=0, help="workers for dataloader" + ) + parser.add_argument( + "--prefetch", type=int, default=None, help="prefetch for dataloader" + ) + parser.add_argument( + "--llm-model-name-or-path", + required=True, + type=str, + help="LLM model path (includes both model and tokenizer)", + ) + parser.add_argument( + "--token2wav-path", + required=True, + type=str, + help="CosyVoice2 token2wav model path", + ) + parser.add_argument( + "--prompt-text", + type=str, + default=None, + help="The prompt text for CosyVoice2", + ) + parser.add_argument( + "--prompt-speech-path", + type=str, + default=None, + help="The path to the prompt speech for CosyVoice2", + ) + parser.add_argument( + "--top-p", + type=float, + default=0.95, + help="top p for sampling", + ) + parser.add_argument( + "--temperature", + type=float, + default=0.8, + help="temperature for sampling", + ) + parser.add_argument( + "--top-k", + type=int, + default=50, + help="top k for sampling", + ) + parser.add_argument( + "--backend", + type=str, + default="hf", + choices=["hf", "trtllm", "vllm", "trtllm-serve"], + help="Backend to use for LLM inference: 'hf' for HuggingFace, 'trtllm' for TensorRT-LLM, 'vllm' for VLLM", + ) + parser.add_argument( + "--engine-dir", + type=str, + default=None, + help="TensorRT-LLM engine directory (required when backend is 'trtllm')", + ) + parser.add_argument( + "--kv-cache-free-gpu-memory-fraction", + type=float, + default=0.6, + help="Fraction of GPU memory to free for KV cache (TensorRT-LLM only)", + ) + parser.add_argument( + "--openai-api-base", + type=str, + default="http://localhost:8000/v1/chat/completions", + help="OpenAI API base URL (for trtllm-serve backend)", + ) + parser.add_argument( + "--openai-model-name", + type=str, + default="trt_engines_bfloat16", + help="Model name to use with OpenAI API (for trtllm-serve backend)", + ) + args = parser.parse_args() + return args + + +def data_collator(batch, tokenizer, s3_tokenizer): + """Simplified data collator for batch_size=1 processing""" + collator_start_time = time.time() + total_audio_processing_time = 0 + total_speech_tokenization_time = 0 + total_text_tokenization_time = 0 + + target_sample_rate = 16000 # CosyVoice2 uses 16kHz for prompt audio + device = s3_tokenizer.device if s3_tokenizer is not None else torch.device("cpu") + input_ids_list, prompt_audio_list, prompt_text_list = [], [], [] + prompt_text_after_apply_template_list = [] + mels, prompt_audio_cosy2tokens_list, full_text_list = [], [], [] + chat_list = [] + for _, item in enumerate(batch): + audio_processing_start_time = time.time() + prompt_text, target_text = ( + item["prompt_text"], + item["target_text"], + ) + prompt_text_list.append(prompt_text) + full_text = prompt_text + target_text + full_text_list.append(full_text) + # remove the unnecessary punctuation for cosyvoice3 zero_shot_zh dataset + puncts = ['"', '(', ')', '“', '”', '‘', '（', '）', '\''] + for p in puncts: + if p in full_text: + full_text = full_text.replace(p, '') + print(f"removed {p} from {full_text}") + + # get prompt audio for CosyVoice2 (convert to 16kHz) + ref_audio_org, ref_sr = ( + item["prompt_audio"]["array"], + item["prompt_audio"]["sampling_rate"], + ) + ref_audio_org = torch.from_numpy(ref_audio_org).float().unsqueeze(0) + print(ref_audio_org.shape) + + if ref_sr != target_sample_rate: + resampler = torchaudio.transforms.Resample(ref_sr, target_sample_rate) + ref_audio = resampler(ref_audio_org) + else: + ref_audio = ref_audio_org + + prompt_audio_list.append(ref_audio) + audio_processing_end_time = time.time() + total_audio_processing_time += audio_processing_end_time - audio_processing_start_time + + speech_tokenization_start_time = time.time() + if "prompt_audio_cosy2_tokens" in item: + prompt_audio_cosy2tokens = item["prompt_audio_cosy2_tokens"] + prompt_audio_cosy2tokens_list.append(prompt_audio_cosy2tokens) + else: + mels.append(s3tokenizer.log_mel_spectrogram(ref_audio.squeeze(0))) + + if len(mels) > 0: + mels, mels_lens = s3tokenizer.padding(mels) + codes, codes_lens = s3_tokenizer.quantize(mels.to(device), mels_lens.to(device)) + for i in range(len(codes)): + prompt_audio_cosy2tokens_list.append(codes[i, :codes_lens[i].item()]) + speech_tokenization_end_time = time.time() + total_speech_tokenization_time += speech_tokenization_end_time - speech_tokenization_start_time + + for i, prompt_audio_cosy2tokens in enumerate(prompt_audio_cosy2tokens_list): + text_tokenization_start_time = time.time() + prompt_audio_cosy2_id_str = convert_cosy2_tokens_to_speech_id_str(prompt_audio_cosy2tokens) + # Create chat template for LLM generation + chat = [ + {"role": "user", "content": full_text_list[i]}, + {"role": "assistant", "content": prompt_audio_cosy2_id_str} + ] + chat_list.append(chat) + + assert 'system' not in tokenizer.chat_template, "system is not allowed in the chat template" + + input_ids = tokenizer.apply_chat_template( + chat, + tokenize=True, + return_tensors='pt', + continue_final_message=True + ) + input_ids_list.append(input_ids.squeeze(0)) + + prompt_text_after_apply_template = f"<|sos|>{full_text_list[i]}<|task_id|>{prompt_audio_cosy2_id_str}" + + prompt_text_after_apply_template_list.append(prompt_text_after_apply_template) + text_tokenization_end_time = time.time() + total_text_tokenization_time += text_tokenization_end_time - text_tokenization_start_time + + ids = [item["id"] for item in batch] + + return { + "input_ids": input_ids_list, + "ids": ids, + "prompt_text": prompt_text_list, + "prompt_audio_list": prompt_audio_list, + "prompt_text_after_apply_template": prompt_text_after_apply_template_list, + "audio_processing_time": total_audio_processing_time, + "speech_tokenization_time": total_speech_tokenization_time, + "text_tokenization_time": total_text_tokenization_time, + "chat_list": chat_list + } + + +def init_distributed(): + world_size = int(os.environ.get("WORLD_SIZE", 1)) + local_rank = int(os.environ.get("LOCAL_RANK", 0)) + rank = int(os.environ.get("RANK", 0)) + print( + "Inference on multiple gpus, this gpu {}".format(local_rank) + + ", rank {}, world_size {}".format(rank, world_size) + ) + torch.cuda.set_device(local_rank) + dist.init_process_group("nccl") + return world_size, local_rank, rank + + +def main(args): + os.makedirs(args.output_dir, exist_ok=True) + + assert torch.cuda.is_available() + local_rank, world_size, rank = 0, 1, 0 + device = torch.device(f"cuda:{local_rank}") + + tokenizer = AutoTokenizer.from_pretrained(args.llm_model_name_or_path) + + if args.backend == "hf": + model = AutoModelForCausalLM.from_pretrained(args.llm_model_name_or_path) + model.eval() + model.to(device) + runner = None + elif args.backend == "trtllm": + if args.engine_dir is None: + raise ValueError("--engine-dir is required when backend is 'trtllm'") + + runtime_rank = tensorrt_llm.mpi_rank() + model = None + + runner_kwargs = dict( + engine_dir=args.engine_dir, + rank=runtime_rank, + max_output_len=2048, + enable_context_fmha_fp32_acc=False, + max_batch_size=args.batch_size, + max_input_len=512, + kv_cache_free_gpu_memory_fraction=args.kv_cache_free_gpu_memory_fraction, + cuda_graph_mode=False, + gather_generation_logits=False, + ) + + runner = ModelRunnerCpp.from_dir(**runner_kwargs) + elif args.backend == "vllm": + model = LLM(model=args.llm_model_name_or_path, gpu_memory_utilization=0.4) + runner = None + elif args.backend == "trtllm-serve": + model = None + runner = None + else: + raise ValueError(f"Unsupported backend: {args.backend}") + if 'Step-Audio-2-mini' in args.token2wav_path: + from token2wav_dit import CosyVoice2_Token2Wav + else: + assert 'CosyVoice2-0.5B' in args.token2wav_path + from token2wav import CosyVoice2_Token2Wav + token2wav_model = CosyVoice2_Token2Wav( + model_dir=args.token2wav_path, enable_trt=True, device_id=local_rank + ) + if args.prompt_speech_path: + prompt_speech_16k = load_wav(args.prompt_speech_path, 16000) + else: + prompt_speech_16k = None + s3_tokenizer = s3tokenizer.load_model(f"{args.token2wav_path}/speech_tokenizer_v2.onnx").to(device) if 'zero' in args.split_name else None + dataset_name = "yuekai/CV3-Eval" if 'zero' in args.split_name else "yuekai/seed_tts_cosy2" + dataset = load_dataset( + dataset_name, + split=args.split_name, + trust_remote_code=True, + ) + + sampler = None + dataloader = DataLoader( + dataset, + batch_size=args.batch_size, + sampler=sampler, + shuffle=False, + num_workers=args.num_workers, + prefetch_factor=args.prefetch, + collate_fn=partial(data_collator, tokenizer=tokenizer, s3_tokenizer=s3_tokenizer), + ) + for _ in range(3): + print(f"Running {_} times") + total_llm_time = 0 + total_token2wav_time = 0 + total_data_load_time = 0 + total_llm_post_processing_time = 0 + total_audio_save_time = 0 + total_audio_processing_time_in_collator = 0 + total_speech_tokenization_time_in_collator = 0 + total_text_tokenization_time_in_collator = 0 + total_audio_samples = 0 + start_time = time.time() + total_steps = len(dataset) + + if rank == 0: + progress_bar = tqdm(total=total_steps, desc="Processing", unit="wavs") + + last_batch_end_time = time.time() + for batch in dataloader: + data_loaded_time = time.time() + total_data_load_time += data_loaded_time - last_batch_end_time + total_audio_processing_time_in_collator += batch["audio_processing_time"] + total_speech_tokenization_time_in_collator += batch["speech_tokenization_time"] + total_text_tokenization_time_in_collator += batch["text_tokenization_time"] + with torch.no_grad(): + llm_start_time = time.time() + if args.backend == "hf": + input_ids_list = batch["input_ids"] + if len(input_ids_list) == 1: + input_ids = input_ids_list[0].unsqueeze(0) + attention_mask = torch.ones_like(input_ids) + else: + max_len = max([len(input_ids) for input_ids in input_ids_list]) + input_ids_list_new = [ + torch.cat([input_ids, torch.full((max_len - len(input_ids),), tokenizer.pad_token_id)]) + for input_ids in input_ids_list + ] + input_ids = torch.stack(input_ids_list_new) + attention_mask = torch.zeros_like(input_ids) + for i in range(len(input_ids_list)): + attention_mask[i, :len(input_ids_list[i])] = 1 + + input_ids = input_ids.to(device) + + outputs = model.generate( + input_ids=input_ids.to(device), + attention_mask=attention_mask.to(device), + max_new_tokens=2048, + do_sample=True, + top_p=args.top_p, + temperature=args.temperature, + repetition_penalty=1.1, + top_k=args.top_k, + ) + torch.cuda.synchronize() + elif args.backend == "trtllm": + batch_input_ids = list(batch["input_ids"]) + input_lengths = [x.size(0) for x in batch_input_ids] + + end_id = tokenizer.convert_tokens_to_ids("<|eos1|>") if "<|eos1|>" in tokenizer.get_vocab() else tokenizer.eos_token_id + print(f"end_id: {end_id}, tokenizer.eos_token_id: {tokenizer.eos_token_id} ========================") + outputs = runner.generate( + batch_input_ids=batch_input_ids, + max_new_tokens=2048, + end_id=end_id, + pad_id=end_id, + temperature=args.temperature, + top_k=args.top_k, + top_p=args.top_p, + repetition_penalty=1.1, + num_return_sequences=1, + streaming=False, + output_sequence_lengths=True, + output_generation_logits=False, + return_dict=True, + return_all_generated_tokens=False + ) + torch.cuda.synchronize() + output_ids, sequence_lengths = outputs["output_ids"], outputs["sequence_lengths"] + num_output_sents, num_beams, _ = output_ids.size() + assert num_beams == 1 + beam = 0 + batch_size = len(batch["input_ids"]) + num_return_sequences = num_output_sents // batch_size + assert num_return_sequences == 1 + outputs = [] + for i in range(batch_size * num_return_sequences): + batch_idx = i // num_return_sequences + seq_idx = i % num_return_sequences + output_begin = input_lengths[batch_idx] + output_end = sequence_lengths[i][beam] + outputs_i = output_ids[i][beam][:output_end].tolist() + outputs.append(outputs_i) + elif args.backend == "vllm": + input_ids_list = [ids.tolist() for ids in batch["input_ids"]] + sampling_params = SamplingParams( + temperature=args.temperature, + top_p=args.top_p, + top_k=args.top_k, + repetition_penalty=1.1, + max_tokens=2048, + ) + outputs = model.generate(prompt_token_ids=input_ids_list, sampling_params=sampling_params) + print(outputs) + for j, output in enumerate(outputs): + outputs[j] = input_ids_list[j] + output.outputs[0].token_ids + elif args.backend == "trtllm-serve": + if args.batch_size > 1: + outputs = asyncio.run(send_batch_requests_async( + args.openai_api_base, + args.openai_model_name, + batch["chat_list"], + args.temperature, + args.top_p, + args.top_k, + )) + else: + outputs = [] + for chat in batch["chat_list"]: + payload = { + "model": args.openai_model_name, + "messages": chat, + "max_tokens": 2048, + "temperature": args.temperature, + "top_p": args.top_p, + "top_k": args.top_k, + "repetition_penalty": 1.1, + "stop": ["<|eos1|>", "<|eos|>"], + "stream": False, + } + response = requests.post(args.openai_api_base, json=payload) + response.raise_for_status() + response_json = response.json() + generated_content = response_json['choices'][0]['message']['content'] + outputs.append(generated_content) + + llm_end_time = time.time() + total_llm_time += (llm_end_time - llm_start_time) + + items_for_token_2wav = [] + for i in range(len(batch["ids"])): + llm_post_processing_start_time = time.time() + if args.backend == "trtllm-serve": + speech_tokens_str = outputs[i].strip().split('><') + if len(speech_tokens_str) > 1: + speech_tokens_str = [ + t if t.startswith('<') else '<' + t for t in speech_tokens_str + ] + speech_tokens_str = [ + t if t.endswith('>') else t + '>' for t in speech_tokens_str + ] + speech_ids = extract_speech_ids(speech_tokens_str) + else: + input_length = len(batch["input_ids"][i]) + generated_ids = outputs[i][input_length:] + speech_tokens_str = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) + speech_ids = extract_speech_ids(speech_tokens_str) + print(i, speech_ids) + if len(speech_ids) == 0: + print(f"Warning: No speech tokens generated for sample {batch['ids'][i]}, skipping") + continue + + if args.prompt_text is not None: + current_prompt_text = args.prompt_text + current_prompt_audio = prompt_speech_16k + else: + current_prompt_text = batch["prompt_text"][i] + current_prompt_audio = batch["prompt_audio_list"][i] + + llm_post_processing_end_time = time.time() + total_llm_post_processing_time += llm_post_processing_end_time - llm_post_processing_start_time + if current_prompt_audio is not None: + items_for_token_2wav.append({ + "speech_ids": speech_ids, + "prompt_audio": current_prompt_audio.squeeze(0), + "id": batch["ids"][i] + }) + else: + print(f"Warning: No prompt audio available for sample {batch['ids'][i]}, skipping") + + for i in range(0, len(items_for_token_2wav), args.token2wav_batch_size): + t2w_batch = items_for_token_2wav[i:i + args.token2wav_batch_size] + if not t2w_batch: + continue + + t2w_generated_speech_tokens_list = [item["speech_ids"] for item in t2w_batch] + t2w_prompt_audios_list = [item["prompt_audio"] for item in t2w_batch] + t2w_prompt_audios_sample_rate = [16000] * len(t2w_batch) + t2w_ids = [item["id"] for item in t2w_batch] + + token2wav_start_time = time.time() + generated_wavs = token2wav_model( + t2w_generated_speech_tokens_list, + t2w_prompt_audios_list, + t2w_prompt_audios_sample_rate, + ) + token2wav_end_time = time.time() + total_token2wav_time += (token2wav_end_time - token2wav_start_time) + + audio_save_start_time = time.time() + for j, audio_hat in enumerate(generated_wavs): + generated_wave = audio_hat.squeeze().cpu().numpy() + total_audio_samples += len(generated_wave) + target_sample_rate = 24000 + + utt = t2w_ids[j] + sf.write(f"{args.output_dir}/{utt}.wav", generated_wave, target_sample_rate) + print(f"Generated audio for sample {utt} with {len(t2w_generated_speech_tokens_list[j])} tokens") + audio_save_end_time = time.time() + total_audio_save_time += audio_save_end_time - audio_save_start_time + + if rank == 0: + progress_bar.update(world_size * len(batch["ids"])) + + last_batch_end_time = time.time() + if rank == 0: + progress_bar.close() + end_time = time.time() + target_sample_rate = 24000 + total_audio_duration_seconds = total_audio_samples / target_sample_rate + + log_file_path = os.path.join(args.output_dir, "log.txt") + with open(log_file_path, 'w') as f: + args_dict = vars(args) + log_data = { + "args": args_dict, + "data_load_time_seconds": total_data_load_time, + "audio_processing_time_in_collator_seconds": total_audio_processing_time_in_collator, + "speech_tokenization_time_in_collator_seconds": total_speech_tokenization_time_in_collator, + "text_tokenization_time_in_collator_seconds": total_text_tokenization_time_in_collator, + "llm_time_seconds": total_llm_time, + "llm_post_processing_time_seconds": total_llm_post_processing_time, + "token2wav_time_seconds": total_token2wav_time, + "audio_save_time_seconds": total_audio_save_time, + "total_audio_duration_seconds": total_audio_duration_seconds, + "pipeline_time_seconds": end_time - start_time, + } + print(log_data) + f.write(json.dumps(log_data, indent=4)) + print(f"Metrics logged to {log_file_path}") + + +if __name__ == "__main__": + args = get_args() + if args.backend == "vllm": + from vllm import LLM, SamplingParams + elif args.backend == "trtllm": + import tensorrt_llm + from tensorrt_llm.runtime import ModelRunnerCpp + elif args.backend == "hf": + from transformers import AutoModelForCausalLM + elif args.backend == "trtllm-serve": + pass + else: + raise ValueError(f"Unsupported backend: {args.backend}") + main(args) diff --git a/cosyvoice/runtime/triton_trtllm/requirements.txt b/cosyvoice/runtime/triton_trtllm/requirements.txt new file mode 100644 index 0000000..ba0623b --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/requirements.txt @@ -0,0 +1,14 @@ +hyperpyyaml +s3tokenizer +onnxruntime-gpu +omegaconf +conformer +hydra-core +lightning +gdown +wget +librosa +pyworld +openai-whisper +tritonclient +modelscope diff --git a/cosyvoice/runtime/triton_trtllm/run.sh b/cosyvoice/runtime/triton_trtllm/run.sh new file mode 100644 index 0000000..7c7f3cd --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/run.sh @@ -0,0 +1,142 @@ +#!/bin/bash +# Copyright (c) 2025 NVIDIA (authors: Yuekai Zhang) +export CUDA_VISIBLE_DEVICES=0 +cosyvoice_path=/workspace/CosyVoice +export PYTHONPATH=${cosyvoice_path}:$PYTHONPATH +export PYTHONPATH=${cosyvoice_path}/third_party/Matcha-TTS:$PYTHONPATH +stage=$1 +stop_stage=$2 + +huggingface_model_local_dir=./cosyvoice2_llm +model_scope_model_local_dir=./CosyVoice2-0.5B +trt_dtype=bfloat16 +trt_weights_dir=./trt_weights_${trt_dtype} +trt_engines_dir=./trt_engines_${trt_dtype} + +model_repo=./model_repo_cosyvoice2 + +use_spk2info_cache=False + +if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then + echo "Cloning CosyVoice" + git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git $cosyvoice_path + cd $cosyvoice_path + git submodule update --init --recursive + cd runtime/triton_trtllm +fi + +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then + echo "Downloading CosyVoice2-0.5B" + # see https://github.com/nvidia-china-sae/mair-hub/blob/main/rl-tutorial/cosyvoice_llm/pretrained_to_huggingface.py + huggingface-cli download --local-dir $huggingface_model_local_dir yuekai/cosyvoice2_llm + modelscope download --model iic/CosyVoice2-0.5B --local_dir $model_scope_model_local_dir + # download spk2info.pt to directly use cached speech tokens, speech feats, and embeddings + wget https://raw.githubusercontent.com/qi-hua/async_cosyvoice/main/CosyVoice2-0.5B/spk2info.pt -O $model_scope_model_local_dir/spk2info.pt +fi + + +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + echo "Converting checkpoint to TensorRT weights" + python3 scripts/convert_checkpoint.py --model_dir $huggingface_model_local_dir \ + --output_dir $trt_weights_dir \ + --dtype $trt_dtype || exit 1 + + echo "Building TensorRT engines" + trtllm-build --checkpoint_dir $trt_weights_dir \ + --output_dir $trt_engines_dir \ + --max_batch_size 16 \ + --max_num_tokens 32768 \ + --gemm_plugin $trt_dtype || exit 1 + + echo "Testing TensorRT engines" + python3 ./scripts/test_llm.py --input_text "你好，请问你叫什么？" \ + --tokenizer_dir $huggingface_model_local_dir \ + --top_k 50 --top_p 0.95 --temperature 0.8 \ + --engine_dir=$trt_engines_dir || exit 1 +fi + +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + echo "Creating model repository" + rm -rf $model_repo + mkdir -p $model_repo + cosyvoice2_dir="cosyvoice2" + + cp -r ./model_repo/${cosyvoice2_dir} $model_repo + cp -r ./model_repo/tensorrt_llm $model_repo + cp -r ./model_repo/token2wav $model_repo + if [ $use_spk2info_cache == "False" ]; then + cp -r ./model_repo/audio_tokenizer $model_repo + cp -r ./model_repo/speaker_embedding $model_repo + fi + + ENGINE_PATH=$trt_engines_dir + MAX_QUEUE_DELAY_MICROSECONDS=0 + MODEL_DIR=$model_scope_model_local_dir + LLM_TOKENIZER_DIR=$huggingface_model_local_dir + BLS_INSTANCE_NUM=4 + TRITON_MAX_BATCH_SIZE=16 + DECOUPLED_MODE=True # True for streaming, False for offline + + python3 scripts/fill_template.py -i ${model_repo}/token2wav/config.pbtxt model_dir:${MODEL_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS} + python3 scripts/fill_template.py -i ${model_repo}/${cosyvoice2_dir}/config.pbtxt model_dir:${MODEL_DIR},bls_instance_num:${BLS_INSTANCE_NUM},llm_tokenizer_dir:${LLM_TOKENIZER_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},decoupled_mode:${DECOUPLED_MODE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS} + python3 scripts/fill_template.py -i ${model_repo}/tensorrt_llm/config.pbtxt triton_backend:tensorrtllm,triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},decoupled_mode:${DECOUPLED_MODE},max_beam_width:1,engine_dir:${ENGINE_PATH},max_tokens_in_paged_kv_cache:2560,max_attention_window_size:2560,kv_cache_free_gpu_mem_fraction:0.5,exclude_input_in_output:True,enable_kv_cache_reuse:False,batching_strategy:inflight_fused_batching,max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS},encoder_input_features_data_type:TYPE_FP16,logits_datatype:TYPE_FP32 + if [ $use_spk2info_cache == "False" ]; then + python3 scripts/fill_template.py -i ${model_repo}/audio_tokenizer/config.pbtxt model_dir:${MODEL_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS} + python3 scripts/fill_template.py -i ${model_repo}/speaker_embedding/config.pbtxt model_dir:${MODEL_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS} + fi +fi + +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then + echo "Starting Triton server" + tritonserver --model-repository $model_repo +fi + +if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then + echo "Single request test http, only work for offline TTS mode" + python3 client_http.py \ + --reference-audio ./assets/prompt_audio.wav \ + --reference-text "吃燕窝就选燕之屋，本节目由26年专注高品质燕窝的燕之屋冠名播出。豆奶牛奶换着喝，营养更均衡，本节目由豆本豆豆奶特约播出。" \ + --target-text "身临其境，换新体验。塑造开源语音合成新范式，让智能语音更自然。" \ + --model-name cosyvoice2 +fi + +if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then + echo "Running benchmark client grpc" + num_task=4 + + mode=streaming + BLS_INSTANCE_NUM=4 + + python3 client_grpc.py \ + --server-addr localhost \ + --model-name cosyvoice2 \ + --num-tasks $num_task \ + --mode $mode \ + --use-spk2info-cache $use_spk2info_cache \ + --huggingface-dataset yuekai/seed_tts_cosy2 \ + --log-dir ./log_concurrent_tasks_${num_task}_${mode}_bls_${BLS_INSTANCE_NUM}_spk_cache_${use_spk2info_cache} +fi + +if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then + echo "stage 6: Offline inference benchmark" + n_gpus=1 + datasets=(wenetspeech4tts) # wenetspeech4tts, test_zh, zero_shot_zh + backend=trtllm # hf, trtllm, vllm + + batch_sizes=(16 8 4 2 1) + token2wav_batch_size=1 + for batch_size in ${batch_sizes[@]}; do + for dataset in ${datasets[@]}; do + output_dir=./${dataset}_${backend}_llm_batch_size_${batch_size}_token2wav_batch_size_${token2wav_batch_size} + CUDA_VISIBLE_DEVICES=0 \ + python3 offline_inference.py \ + --output-dir $output_dir \ + --llm-model-name-or-path $huggingface_model_local_dir \ + --token2wav-path $model_scope_model_local_dir \ + --backend $backend \ + --batch-size $batch_size --token2wav-batch-size $token2wav_batch_size \ + --engine-dir $trt_engines_dir \ + --split-name ${dataset} || exit 1 + done + done +fi diff --git a/cosyvoice/runtime/triton_trtllm/run_stepaudio2_dit_token2wav.sh b/cosyvoice/runtime/triton_trtllm/run_stepaudio2_dit_token2wav.sh new file mode 100644 index 0000000..28ab13f --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/run_stepaudio2_dit_token2wav.sh @@ -0,0 +1,225 @@ +#!/bin/bash +# Copyright (c) 2025 NVIDIA (authors: Yuekai Zhang) +export CUDA_VISIBLE_DEVICES=0 +cosyvoice_path=/workspace/CosyVoice +stepaudio2_path=/workspace/Step-Audio2 + +export PYTHONPATH=${stepaudio2_path}:$PYTHONPATH +export PYTHONPATH=${cosyvoice_path}:$PYTHONPATH +export PYTHONPATH=${cosyvoice_path}/third_party/Matcha-TTS:$PYTHONPATH + +stage=$1 +stop_stage=$2 + +huggingface_model_local_dir=./cosyvoice2_llm +model_scope_model_local_dir=./CosyVoice2-0.5B +step_audio_model_dir=./Step-Audio-2-mini + +trt_dtype=bfloat16 +trt_weights_dir=./trt_weights_${trt_dtype} +trt_engines_dir=./trt_engines_${trt_dtype} + +model_repo=./model_repo_cosyvoice2_dit +bls_instance_num=10 + +if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then + + echo "Cloning Step-Audio2-mini" + git clone https://github.com/yuekaizhang/Step-Audio2.git -b trt $stepaudio2_path + + echo "Cloning CosyVoice" + git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git $cosyvoice_path + cd $cosyvoice_path + git submodule update --init --recursive + cd runtime/triton_trtllm +fi + +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then + echo "Downloading CosyVoice2-0.5B" + # see https://github.com/nvidia-china-sae/mair-hub/blob/main/rl-tutorial/cosyvoice_llm/pretrained_to_huggingface.py + huggingface-cli download --local-dir $huggingface_model_local_dir yuekai/cosyvoice2_llm + modelscope download --model iic/CosyVoice2-0.5B --local_dir $model_scope_model_local_dir + + echo "Step-Audio2-mini" + huggingface-cli download --local-dir $step_audio_model_dir stepfun-ai/Step-Audio-2-mini + cd $step_audio_model_dir/token2wav + wget https://huggingface.co/yuekai/cosyvoice2_dit_flow_matching_onnx/resolve/main/flow.decoder.estimator.fp32.dynamic_batch.onnx -O flow.decoder.estimator.fp32.dynamic_batch.onnx + wget https://huggingface.co/yuekai/cosyvoice2_dit_flow_matching_onnx/resolve/main/flow.decoder.estimator.chunk.fp32.dynamic_batch.simplify.onnx -O flow.decoder.estimator.chunk.fp32.dynamic_batch.simplify.onnx + cd - +fi + + +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + echo "Converting checkpoint to TensorRT weights" + python3 scripts/convert_checkpoint.py --model_dir $huggingface_model_local_dir \ + --output_dir $trt_weights_dir \ + --dtype $trt_dtype || exit 1 + + echo "Building TensorRT engines" + trtllm-build --checkpoint_dir $trt_weights_dir \ + --output_dir $trt_engines_dir \ + --max_batch_size 64 \ + --max_num_tokens 32768 \ + --gemm_plugin $trt_dtype || exit 1 + + echo "Testing TensorRT engines" + python3 ./scripts/test_llm.py --input_text "你好，请问你叫什么？" \ + --tokenizer_dir $huggingface_model_local_dir \ + --top_k 50 --top_p 0.95 --temperature 0.8 \ + --engine_dir=$trt_engines_dir || exit 1 +fi + +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + echo "Creating model repository async mode" + rm -rf $model_repo + mkdir -p $model_repo + cosyvoice2_dir="cosyvoice2_dit" + token2wav_dir="token2wav_dit" + + cp -r ./model_repo/${cosyvoice2_dir} $model_repo + cp -r ./model_repo/${token2wav_dir} $model_repo + cp -r ./model_repo/audio_tokenizer $model_repo + cp -r ./model_repo/speaker_embedding $model_repo + + + ENGINE_PATH=$trt_engines_dir + MAX_QUEUE_DELAY_MICROSECONDS=0 + MODEL_DIR=$model_scope_model_local_dir + LLM_TOKENIZER_DIR=$huggingface_model_local_dir + BLS_INSTANCE_NUM=$bls_instance_num + TRITON_MAX_BATCH_SIZE=1 + DECOUPLED_MODE=True # Only streaming TTS mode is supported using Nvidia Triton for now + STEP_AUDIO_MODEL_DIR=$step_audio_model_dir/token2wav + + python3 scripts/fill_template.py -i ${model_repo}/${token2wav_dir}/config.pbtxt model_dir:${STEP_AUDIO_MODEL_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS} + python3 scripts/fill_template.py -i ${model_repo}/${cosyvoice2_dir}/config.pbtxt model_dir:${MODEL_DIR},bls_instance_num:${BLS_INSTANCE_NUM},llm_tokenizer_dir:${LLM_TOKENIZER_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},decoupled_mode:${DECOUPLED_MODE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS} + python3 scripts/fill_template.py -i ${model_repo}/audio_tokenizer/config.pbtxt model_dir:${MODEL_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS} + python3 scripts/fill_template.py -i ${model_repo}/speaker_embedding/config.pbtxt model_dir:${MODEL_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS} + +fi + +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then + echo "Starting Token2wav Triton server and Cosyvoice2 llm using trtllm-serve" + mpirun -np 1 --allow-run-as-root --oversubscribe trtllm-serve serve --tokenizer $huggingface_model_local_dir $trt_engines_dir --max_batch_size 64 --kv_cache_free_gpu_memory_fraction 0.4 & + tritonserver --model-repository $model_repo --http-port 18000 & + wait + # Test using curl + # curl http://localhost:8000/v1/chat/completions \ + # -H "Content-Type: application/json" \ + # -d '{ + # "model": "", + # "messages":[{"role": "user", "content": "Where is New York?"}, + # {"role": "assistant", "content": "<|s_1708|><|s_2050|><|s_2159|>"}], + # "max_tokens": 512, + # "temperature": 0.8, + # "top_p": 0.95, + # "top_k": 50, + # "stop": ["<|eos1|>"], + # "repetition_penalty": 1.2, + # "stream": false + # }' +fi + +if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then + echo "Running benchmark client" + num_task=4 + mode=streaming + BLS_INSTANCE_NUM=$bls_instance_num + + python3 client_grpc.py \ + --server-addr localhost \ + --server-port 8001 \ + --model-name cosyvoice2_dit \ + --num-tasks $num_task \ + --mode $mode \ + --huggingface-dataset yuekai/seed_tts_cosy2 \ + --log-dir ./log_single_gpu_concurrent_tasks_${num_task}_${mode}_bls_${BLS_INSTANCE_NUM} + +fi + +if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then + echo "stage 5: Offline TTS (Cosyvoice2 LLM + Step-Audio2-mini DiT Token2Wav) inference using a single python script" + + datasets=(wenetspeech4tts) # wenetspeech4tts, test_zh, zero_shot_zh + backend=trtllm # hf, trtllm, vllm, trtllm-serve + + batch_sizes=(16) + token2wav_batch_size=1 + + for batch_size in ${batch_sizes[@]}; do + for dataset in ${datasets[@]}; do + output_dir=./${dataset}_${backend}_llm_batch_size_${batch_size}_token2wav_batch_size_${token2wav_batch_size} + CUDA_VISIBLE_DEVICES=1 \ + python3 offline_inference.py \ + --output-dir $output_dir \ + --llm-model-name-or-path $huggingface_model_local_dir \ + --token2wav-path $step_audio_model_dir/token2wav \ + --backend $backend \ + --batch-size $batch_size --token2wav-batch-size $token2wav_batch_size \ + --engine-dir $trt_engines_dir \ + --split-name ${dataset} || exit 1 + done + done +fi + +if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then + echo "Running Step-Audio2-mini DiT Token2Wav inference using a single python script" + export CUDA_VISIBLE_DEVICES=1 + # Note: Using pre-computed cosyvoice2 tokens + python3 streaming_inference.py --enable-trt --strategy equal # equal, exponential + # Offline Token2wav inference + python3 token2wav_dit.py --enable-trt +fi + + +if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then + echo "Disaggregated Server: LLM and Token2wav on different GPUs" + echo "Starting LLM server on GPU 0" + export CUDA_VISIBLE_DEVICES=0 + mpirun -np 1 --allow-run-as-root --oversubscribe trtllm-serve serve --tokenizer $huggingface_model_local_dir $trt_engines_dir --max_batch_size 64 --kv_cache_free_gpu_memory_fraction 0.4 & + echo "Starting Token2wav server on GPUs 1-3" + Token2wav_num_gpus=3 + http_port=17000 + grpc_port=18000 + metrics_port=16000 + for i in $(seq 0 $(($Token2wav_num_gpus - 1))); do + echo "Starting server on GPU $i" + http_port=$((http_port + 1)) + grpc_port=$((grpc_port + 1)) + metrics_port=$((metrics_port + 1)) + # Two instances of Token2wav server on the same GPU + CUDA_VISIBLE_DEVICES=$(($i + 1)) tritonserver --model-repository $model_repo --http-port $http_port --grpc-port $grpc_port --metrics-port $metrics_port & + http_port=$((http_port + 1)) + grpc_port=$((grpc_port + 1)) + metrics_port=$((metrics_port + 1)) + CUDA_VISIBLE_DEVICES=$(($i + 1)) tritonserver --model-repository $model_repo --http-port $http_port --grpc-port $grpc_port --metrics-port $metrics_port & + done + wait +fi + +if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then + echo "Running benchmark client for Disaggregated Server" + per_gpu_instances=2 + mode=streaming + BLS_INSTANCE_NUM=$bls_instance_num + Token2wav_num_gpus=(1 2 3) + concurrent_tasks=(1 2 3 4 5 6) + for n_gpu in ${Token2wav_num_gpus[@]}; do + echo "Test 1 GPU for LLM server and $n_gpu GPUs for Token2wav servers" + for concurrent_task in ${concurrent_tasks[@]}; do + num_instances=$((per_gpu_instances * n_gpu)) + for i in $(seq 1 $num_instances); do + port=$(($i + 18000)) + python3 client_grpc.py \ + --server-addr localhost \ + --server-port $port \ + --model-name cosyvoice2_dit \ + --num-tasks $concurrent_task \ + --mode $mode \ + --huggingface-dataset yuekai/seed_tts_cosy2 \ + --log-dir ./log_disagg_concurrent_tasks_${concurrent_task}_per_instance_total_token2wav_instances_${num_instances}_port_${port} & + done + wait + done + done +fi \ No newline at end of file diff --git a/cosyvoice/runtime/triton_trtllm/scripts/convert_checkpoint.py b/cosyvoice/runtime/triton_trtllm/scripts/convert_checkpoint.py new file mode 100644 index 0000000..7cd166f --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/scripts/convert_checkpoint.py @@ -0,0 +1,330 @@ +import argparse +import os +import time +import traceback +from concurrent.futures import ThreadPoolExecutor, as_completed + +from transformers import AutoConfig + +import tensorrt_llm +from tensorrt_llm._utils import release_gc +from tensorrt_llm.logger import logger +from tensorrt_llm.mapping import Mapping +from tensorrt_llm.models import QWenForCausalLM +from tensorrt_llm.models.modeling_utils import QuantConfig +from tensorrt_llm.quantization import QuantAlgo + + +def parse_arguments(): + parser = argparse.ArgumentParser() + parser.add_argument('--model_dir', type=str, default=None, required=True) + parser.add_argument('--tp_size', + type=int, + default=1, + help='N-way tensor parallelism size') + parser.add_argument('--pp_size', + type=int, + default=1, + help='N-way pipeline parallelism size') + parser.add_argument('--cp_size', + type=int, + default=1, + help='N-way context parallelism size') + parser.add_argument( + '--dtype', + type=str, + default='auto', + choices=['auto', 'float16', 'bfloat16', 'float32'], + help="The data type for the model weights and activations if not quantized. " + "If 'auto', the data type is automatically inferred from the source model; " + "however, if the source dtype is float32, it is converted to float16.") + parser.add_argument( + '--use_weight_only', + default=False, + action="store_true", + help='Quantize weights for the various GEMMs to INT4/INT8.' + 'See --weight_only_precision to set the precision') + parser.add_argument( + '--disable_weight_only_quant_plugin', + default=False, + action="store_true", + help='By default, using plugin implementation for weight quantization. Enabling disable_weight_only_quant_plugin flag will use ootb implementation instead of plugin.' + 'You must also use --use_weight_only for that argument to have an impact.' + ) + parser.add_argument( + '--weight_only_precision', + const='int8', + type=str, + nargs='?', + default='int8', + choices=['int8', 'int4', 'int4_gptq'], + help='Define the precision for the weights when using weight-only quantization.' + 'You must also use --use_weight_only for that argument to have an impact.' + ) + parser.add_argument( + '--calib_dataset', + type=str, + default='ccdv/cnn_dailymail', + help="The huggingface dataset name or the local directory of the dataset for calibration." + ) + parser.add_argument( + "--smoothquant", + "-sq", + type=float, + default=None, + help="Set the α parameter (see https://arxiv.org/pdf/2211.10438.pdf)" + " to Smoothquant the model, and output int8 weights." + " A good first try is 0.5. Must be in [0, 1]") + parser.add_argument( + '--per_channel', + action="store_true", + default=False, + help='By default, we use a single static scaling factor for the GEMM\'s result. ' + 'per_channel instead uses a different static scaling factor for each channel. ' + 'The latter is usually more accurate, but a little slower.') + parser.add_argument( + '--per_token', + action="store_true", + default=False, + help='By default, we use a single static scaling factor to scale activations in the int8 range. ' + 'per_token chooses at run time, and for each token, a custom scaling factor. ' + 'The latter is usually more accurate, but a little slower.') + parser.add_argument( + '--int8_kv_cache', + default=False, + action="store_true", + help='By default, we use dtype for KV cache. int8_kv_cache chooses int8 quantization for KV' + ) + parser.add_argument( + '--per_group', + default=False, + action="store_true", + help='By default, we use a single static scaling factor to scale weights in the int4 range. ' + 'per_group chooses at run time, and for each group, a custom scaling factor. ' + 'The flag is built for GPTQ/AWQ quantization.') + + parser.add_argument('--group_size', + type=int, + default=128, + help='Group size used in GPTQ quantization.') + + parser.add_argument("--load_model_on_cpu", action="store_true") + parser.add_argument( + '--use_parallel_embedding', + action="store_true", + default=False, + help='By default embedding parallelism is disabled. By setting this flag, embedding parallelism is enabled' + ) + parser.add_argument( + '--embedding_sharding_dim', + type=int, + default=0, + choices=[0, 1], + help='By default the embedding lookup table is sharded along vocab dimension (embedding_sharding_dim=0). ' + 'To shard it along hidden dimension, set embedding_sharding_dim=1' + 'Note: embedding sharing is only enabled when embedding_sharding_dim = 0' + ) + parser.add_argument('--output_dir', + type=str, + default='tllm_checkpoint', + help='The path to save the TensorRT-LLM checkpoint') + parser.add_argument( + '--workers', + type=int, + default=1, + help='The number of workers for converting checkpoint in parallel') + parser.add_argument( + '--moe_tp_size', + type=int, + default=-1, + help='N-way tensor parallelism size for MOE, default is tp_size, which will do tp-only for MoE' + ) + parser.add_argument( + '--moe_ep_size', + type=int, + default=-1, + help='N-way expert parallelism size for MOE, default is 1, which will do tp-only for MoE' + ) + args = parser.parse_args() + return args + + +def args_to_quant_config(args: argparse.Namespace) -> QuantConfig: + '''return config dict with quantization info based on the command line args + ''' + quant_config = QuantConfig() + if args.use_weight_only: + if args.weight_only_precision == 'int8': + quant_config.quant_algo = QuantAlgo.W8A16 + elif args.weight_only_precision == 'int4': + quant_config.quant_algo = QuantAlgo.W4A16 + elif args.smoothquant: + quant_config.smoothquant_val = args.smoothquant + if args.per_channel: + if args.per_token: + quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN + else: + quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TENSOR_PLUGIN + else: + if args.per_token: + quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_TENSOR_PER_TOKEN_PLUGIN + else: + quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_TENSOR_PLUGIN + + if args.int8_kv_cache: + quant_config.kv_cache_quant_algo = QuantAlgo.INT8 + + if args.weight_only_precision == 'int4_gptq': + quant_config.group_size = args.group_size + quant_config.has_zero_point = True + quant_config.pre_quant_scale = False + quant_config.quant_algo = QuantAlgo.W4A16_GPTQ + + return quant_config + + +def update_quant_config_from_hf(quant_config, hf_config, + override_fields) -> tuple[QuantConfig, dict]: + hf_config_dict = hf_config.to_dict() + if hf_config_dict.get('quantization_config'): + # update the quant_algo, and clamp_val. + if hf_config_dict['quantization_config'].get('quant_method') == 'awq': + logger.info( + "Load quantization configs from huggingface model_config.") + quant_config.quant_algo = QuantAlgo.W4A16_GPTQ + quant_config.group_size = hf_config_dict['quantization_config'].get( + 'group_size', 128) + quant_config.has_zero_point = hf_config_dict[ + 'quantization_config'].get('zero_point', False) + override_fields.update({"use_autoawq": True}) + elif hf_config_dict['quantization_config'].get( + 'quant_method') == 'gptq': + logger.info( + "Load quantization configs from huggingface model_config.") + desc_act = hf_config_dict['quantization_config'].get( + 'desc_act', False) + if desc_act: + raise ValueError("GPTQ with desc_act=True is not implemented!") + quant_config.quant_algo = QuantAlgo.W4A16_GPTQ + quant_config.group_size = hf_config_dict['quantization_config'].get( + 'group_size', 128) + quant_config.has_zero_point = hf_config_dict[ + 'quantization_config'].get('sym', False) + return quant_config, override_fields + + +def args_to_build_options(args): + return { + 'use_parallel_embedding': args.use_parallel_embedding, + 'embedding_sharding_dim': args.embedding_sharding_dim, + 'disable_weight_only_quant_plugin': + args.disable_weight_only_quant_plugin + } + + +def convert_and_save_hf(args): + model_dir = args.model_dir + world_size = args.tp_size * args.pp_size + # Need to convert the cli args to the kay-value pairs and override them in the generate config dict. + # Ideally these fields will be moved out of the config and pass them into build API, keep them here for compatibility purpose for now, + # before the refactor is done. + override_fields = {} + override_fields.update(args_to_build_options(args)) + quant_config = args_to_quant_config(args) + + try: + hf_config = AutoConfig.from_pretrained(model_dir, + trust_remote_code=True) + quant_config, override_fields = update_quant_config_from_hf( + quant_config, hf_config, override_fields) + except BaseException: + logger.warning("AutoConfig cannot load the huggingface config.") + + if args.smoothquant is not None or args.int8_kv_cache: + mapping = Mapping(world_size=world_size, + tp_size=args.tp_size, + pp_size=args.pp_size, + moe_tp_size=args.moe_tp_size, + moe_ep_size=args.moe_ep_size, + cp_size=args.cp_size) + QWenForCausalLM.quantize(args.model_dir, + args.output_dir, + dtype=args.dtype, + mapping=mapping, + quant_config=quant_config, + calib_dataset=args.calib_dataset, + **override_fields) + else: + + def convert_and_save_rank(args, rank): + mapping = Mapping(world_size=world_size, + rank=rank, + tp_size=args.tp_size, + pp_size=args.pp_size, + moe_tp_size=args.moe_tp_size, + moe_ep_size=args.moe_ep_size) + qwen = QWenForCausalLM.from_hugging_face(model_dir, + args.dtype, + mapping=mapping, + quant_config=quant_config, + **override_fields) + qwen.config.mapping.cp_size = args.cp_size + qwen.config.mapping.attn_tp_size = -1 + qwen.config.mapping.attn_cp_size = -1 + qwen.config.mapping.world_size *= args.cp_size + qwen.save_checkpoint(args.output_dir, save_config=(rank == 0)) + del qwen + + execute(args.workers, [convert_and_save_rank] * world_size, args) + release_gc() + + +def execute(workers, func, args): + if workers == 1: + for rank, f in enumerate(func): + f(args, rank) + else: + with ThreadPoolExecutor(max_workers=workers) as p: + futures = [p.submit(f, args, rank) for rank, f in enumerate(func)] + exceptions = [] + for future in as_completed(futures): + try: + future.result() + except Exception as e: + traceback.print_exc() + exceptions.append(e) + assert len( + exceptions + ) == 0, "Checkpoint conversion failed, please check error log." + + +def main(): + print(tensorrt_llm.__version__) + args = parse_arguments() + + if (args.moe_tp_size == -1 and args.moe_ep_size == -1): + # moe default to tp-only + args.moe_tp_size = args.tp_size + args.moe_ep_size = 1 + elif (args.moe_tp_size == -1): + args.moe_tp_size = args.tp_size // args.moe_ep_size + elif (args.moe_ep_size == -1): + args.moe_ep_size = args.tp_size // args.moe_tp_size + assert (args.moe_tp_size * args.moe_ep_size == args.tp_size + ), "moe_tp_size * moe_ep_size must equal to tp_size" + + tik = time.time() + + if not os.path.exists(args.output_dir): + os.makedirs(args.output_dir) + + assert args.model_dir is not None + convert_and_save_hf(args) + + tok = time.time() + t = time.strftime('%H:%M:%S', time.gmtime(tok - tik)) + print(f'Total time of converting checkpoints: {t}') + + +if __name__ == '__main__': + main() diff --git a/cosyvoice/runtime/triton_trtllm/scripts/fill_template.py b/cosyvoice/runtime/triton_trtllm/scripts/fill_template.py new file mode 100644 index 0000000..6e6a2bc --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/scripts/fill_template.py @@ -0,0 +1,69 @@ +# /usr/bin/env python3 +from argparse import ArgumentParser +from string import Template + + +def split(string, delimiter): + """Split a string using delimiter. Supports escaping. + + Args: + string (str): The string to split. + delimiter (str): The delimiter to split the string with. + + Returns: + list: A list of strings. + """ + result = [] + current = "" + escape = False + for char in string: + if escape: + current += char + escape = False + elif char == delimiter: + result.append(current) + current = "" + elif char == "\\": + escape = True + else: + current += char + result.append(current) + return result + + +def main(file_path, substitutions, in_place): + with open(file_path) as f: + pbtxt = Template(f.read()) + + sub_dict = { + "max_queue_size": 0, + 'max_queue_delay_microseconds': 0, + } + for sub in split(substitutions, ","): + key, value = split(sub, ":") + sub_dict[key] = value + + assert key in pbtxt.template, f"key '{key}' does not exist in the file {file_path}." + + pbtxt = pbtxt.safe_substitute(sub_dict) + + if in_place: + with open(file_path, "w") as f: + f.write(pbtxt) + else: + print(pbtxt) + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("file_path", help="path of the .pbtxt to modify") + parser.add_argument( + "substitutions", + help="substitutions to perform, in the format variable_name_1:value_1,variable_name_2:value_2..." + ) + parser.add_argument("--in_place", + "-i", + action="store_true", + help="do the operation in-place") + args = parser.parse_args() + main(**vars(args)) diff --git a/cosyvoice/runtime/triton_trtllm/scripts/test_llm.py b/cosyvoice/runtime/triton_trtllm/scripts/test_llm.py new file mode 100644 index 0000000..90e6710 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/scripts/test_llm.py @@ -0,0 +1,138 @@ + +# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse + +import numpy as np +import torch + +import tensorrt_llm +from tensorrt_llm.logger import logger + +from tensorrt_llm.runtime import ModelRunnerCpp +from transformers import AutoTokenizer + + +def parse_arguments(args=None): + parser = argparse.ArgumentParser() + parser.add_argument( + '--input_text', + type=str, + nargs='+', + default=["Born in north-east France, Soyer trained as a"]) + parser.add_argument('--tokenizer_dir', type=str, default="meta-llama/Meta-Llama-3-8B-Instruct") + parser.add_argument('--engine_dir', type=str, default="meta-llama/Meta-Llama-3-8B-Instruct") + parser.add_argument('--log_level', type=str, default="debug") + parser.add_argument('--kv_cache_free_gpu_memory_fraction', type=float, default=0.6) + parser.add_argument('--temperature', type=float, default=0.8) + parser.add_argument('--top_k', type=int, default=50) + parser.add_argument('--top_p', type=float, default=0.95) + + return parser.parse_args(args=args) + + +def parse_input(tokenizer, + input_text=None, + prompt_template=None): + batch_input_ids = [] + for curr_text in input_text: + if prompt_template is not None: + curr_text = prompt_template.format(input_text=curr_text) + input_ids = tokenizer.encode( + curr_text) + batch_input_ids.append(input_ids) + + batch_input_ids = [ + torch.tensor(x, dtype=torch.int32) for x in batch_input_ids + ] + + logger.debug(f"Input token ids (batch_size = {len(batch_input_ids)}):") + for i, input_ids in enumerate(batch_input_ids): + logger.debug(f"Request {i}: {input_ids.tolist()}") + + return batch_input_ids + + +def main(args): + runtime_rank = tensorrt_llm.mpi_rank() + logger.set_level(args.log_level) + + tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_dir) + prompt_template = "<|sos|>{input_text}<|task_id|>" + end_id = tokenizer.convert_tokens_to_ids("<|eos1|>") + + batch_input_ids = parse_input(tokenizer=tokenizer, + input_text=args.input_text, + prompt_template=prompt_template) + + input_lengths = [x.size(0) for x in batch_input_ids] + + runner_kwargs = dict( + engine_dir=args.engine_dir, + rank=runtime_rank, + max_output_len=1024, + enable_context_fmha_fp32_acc=False, + max_batch_size=len(batch_input_ids), + max_input_len=max(input_lengths), + kv_cache_free_gpu_memory_fraction=args.kv_cache_free_gpu_memory_fraction, + cuda_graph_mode=False, + gather_generation_logits=False, + ) + + runner = ModelRunnerCpp.from_dir(**runner_kwargs) + + with torch.no_grad(): + outputs = runner.generate( + batch_input_ids=batch_input_ids, + max_new_tokens=1024, + end_id=end_id, + pad_id=end_id, + temperature=args.temperature, + top_k=args.top_k, + top_p=args.top_p, + num_return_sequences=1, + repetition_penalty=1.1, + random_seed=42, + streaming=False, + output_sequence_lengths=True, + output_generation_logits=False, + return_dict=True, + return_all_generated_tokens=False) + torch.cuda.synchronize() + output_ids, sequence_lengths = outputs["output_ids"], outputs["sequence_lengths"] + num_output_sents, num_beams, _ = output_ids.size() + assert num_beams == 1 + beam = 0 + batch_size = len(input_lengths) + num_return_sequences = num_output_sents // batch_size + assert num_return_sequences == 1 + for i in range(batch_size * num_return_sequences): + batch_idx = i // num_return_sequences + seq_idx = i % num_return_sequences + inputs = output_ids[i][0][:input_lengths[batch_idx]].tolist() + input_text = tokenizer.decode(inputs) + print(f'Input [Text {batch_idx}]: \"{input_text}\"') + output_begin = input_lengths[batch_idx] + output_end = sequence_lengths[i][beam] + outputs = output_ids[i][beam][output_begin:output_end].tolist() + output_text = tokenizer.decode(outputs) + print(f'Output [Text {batch_idx}]: \"{output_text}\"') + logger.debug(str(outputs)) + + +if __name__ == '__main__': + args = parse_arguments() + main(args) diff --git a/cosyvoice/runtime/triton_trtllm/streaming_inference.py b/cosyvoice/runtime/triton_trtllm/streaming_inference.py new file mode 100644 index 0000000..7cfb6f9 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/streaming_inference.py @@ -0,0 +1,122 @@ +import torch +import os +import argparse +from datasets import load_dataset +from torch.utils.data import DataLoader +import numpy as np +import torchaudio +import time +from token2wav_dit import CosyVoice2_Token2Wav +import soundfile as sf + + +def collate_fn(batch): + ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate = [], [], [], [] + prompt_speech_tokens_list, prompt_text_list = [], [] + for item in batch: + generated_speech_tokens_list.append(item['target_audio_cosy2_tokens']) + audio = torch.from_numpy(item['prompt_audio']['array']).float() + prompt_audios_list.append(audio) + prompt_audios_sample_rate.append(item['prompt_audio']['sampling_rate']) + ids.append(item['id']) + prompt_speech_tokens_list.append(item['prompt_audio_cosy2_tokens']) + prompt_text_list.append(item['prompt_text']) + + return ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate, prompt_speech_tokens_list, prompt_text_list + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--enable-trt", action="store_true") + parser.add_argument("--model-dir", type=str, default="./Step-Audio-2-mini/token2wav") + parser.add_argument("--batch-size", type=int, default=1) + parser.add_argument("--output-dir", type=str, default="generated_wavs") + parser.add_argument("--huggingface-dataset-split", type=str, default="wenetspeech4tts") + parser.add_argument("--dataset-name", type=str, default="yuekai/seed_tts_cosy2") + parser.add_argument("--strategy", type=str, default="equal", choices=["equal", "exponential"]) + return parser.parse_args() + + +if __name__ == "__main__": + args = get_args() + + if not os.path.exists(args.output_dir): + os.makedirs(args.output_dir) + + dataset_name = args.dataset_name + dataset = load_dataset(dataset_name, split=args.huggingface_dataset_split, trust_remote_code=True) + data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, collate_fn=collate_fn, num_workers=0) + + token2wav_model = CosyVoice2_Token2Wav(model_dir=args.model_dir, enable_trt=args.enable_trt, streaming=True) + + CHUNK_SIZE = 25 + token_frame_rate = 25 + OVERLAP_SIZE = 0 + + warmup_times = 3 + for _ in range(warmup_times): + start_time = time.time() + total_forward_count = 0 + for batch in data_loader: + tts_speech_list = [] + ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate, prompt_speech_tokens_list, prompt_text_list = batch + + id, generated_speech_tokens, prompt_audio, prompt_audio_sample_rate = ids[0], generated_speech_tokens_list[0], prompt_audios_list[0], prompt_audios_sample_rate[0] + + assert prompt_audio_sample_rate == 16000 + + prompt_text = prompt_text_list[0] + prompt_speech_tokens = prompt_speech_tokens_list[0] + + semantic_token_ids_arr, token_offset = [], 0 + flow_prompt_speech_token_len = len(prompt_speech_tokens) + + buffer = generated_speech_tokens + output_wavs = [] + chunk_index = 0 + while True: + if args.strategy == "equal": + this_chunk_size = CHUNK_SIZE + elif args.strategy == "exponential": + this_chunk_size = token_frame_rate * (2 ** chunk_index) + + if len(buffer) >= this_chunk_size + token2wav_model.flow.pre_lookahead_len: + wavs = token2wav_model.forward_streaming( + buffer[:this_chunk_size + token2wav_model.flow.pre_lookahead_len], + False, request_id=id, speaker_id=f"{id}", prompt_audio=prompt_audio, + prompt_audio_sample_rate=prompt_audio_sample_rate + ) + buffer = buffer[this_chunk_size - OVERLAP_SIZE:] + + output_wavs.append(wavs) + total_forward_count += 1 + chunk_index += 1 + + else: + wavs = token2wav_model.forward_streaming( + buffer, True, request_id=id, speaker_id=f"{id}", + prompt_audio=prompt_audio, prompt_audio_sample_rate=prompt_audio_sample_rate + ) + output_wavs.append(wavs) + total_forward_count += 1 + # chunk_index += 1 + break + + for i, wav in enumerate(output_wavs): + output_wavs[i] = wav.cpu().numpy().squeeze() + + audios = output_wavs + reconstructed_audio = np.concatenate(audios) + sf.write(os.path.join(args.output_dir, f"{id}.wav"), reconstructed_audio, 24000, "PCM_16") + + end_time = time.time() + + if _ == 0: + token2wav_model.speaker_cache = {} + print(f"Warmup time: {end_time - start_time} seconds") + print("clear speaker cache") + elif _ == 1: + print(f"Cost time without speaker cache: {end_time - start_time} seconds") + else: + print(f"Cost time with speaker cache: {end_time - start_time} seconds") + print(f"Total flow matching forward calls: {total_forward_count}") diff --git a/cosyvoice/runtime/triton_trtllm/token2wav.py b/cosyvoice/runtime/triton_trtllm/token2wav.py new file mode 100644 index 0000000..09b6db6 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/token2wav.py @@ -0,0 +1,335 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Example Usage + CUDA_VISIBLE_DEVICES=0 \ + python3 token2wav.py --enable-trt || exit 1 +""" +import torch +from flashcosyvoice.modules.flow import CausalMaskedDiffWithXvec +from flashcosyvoice.modules.hifigan import HiFTGenerator +from flashcosyvoice.utils.audio import mel_spectrogram +import torchaudio.compliance.kaldi as kaldi +import onnxruntime +import s3tokenizer +from torch.utils.data import DataLoader +from datasets import load_dataset +import torchaudio +import os +import logging +import argparse +import queue +import time + + +def convert_onnx_to_trt(trt_model, trt_kwargs, onnx_model, fp16): + import tensorrt as trt + logging.info("Converting onnx to trt...") + network_flags = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + logger = trt.Logger(trt.Logger.INFO) + builder = trt.Builder(logger) + network = builder.create_network(network_flags) + parser = trt.OnnxParser(network, logger) + config = builder.create_builder_config() + # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 32) # 4GB + if fp16: + config.set_flag(trt.BuilderFlag.FP16) + profile = builder.create_optimization_profile() + # load onnx model + with open(onnx_model, "rb") as f: + if not parser.parse(f.read()): + for error in range(parser.num_errors): + print(parser.get_error(error)) + raise ValueError('failed to parse {}'.format(onnx_model)) + # set input shapes + for i in range(len(trt_kwargs['input_names'])): + profile.set_shape(trt_kwargs['input_names'][i], trt_kwargs['min_shape'][i], trt_kwargs['opt_shape'][i], trt_kwargs['max_shape'][i]) + tensor_dtype = trt.DataType.HALF if fp16 else trt.DataType.FLOAT + # set input and output data type + for i in range(network.num_inputs): + input_tensor = network.get_input(i) + input_tensor.dtype = tensor_dtype + for i in range(network.num_outputs): + output_tensor = network.get_output(i) + output_tensor.dtype = tensor_dtype + config.add_optimization_profile(profile) + engine_bytes = builder.build_serialized_network(network, config) + # save trt engine + with open(trt_model, "wb") as f: + f.write(engine_bytes) + logging.info("Succesfully convert onnx to trt...") + + +class TrtContextWrapper: + def __init__(self, trt_engine, trt_concurrent=1, device='cuda:0'): + self.trt_context_pool = queue.Queue(maxsize=trt_concurrent) + self.trt_engine = trt_engine + self.device = device + for _ in range(trt_concurrent): + trt_context = trt_engine.create_execution_context() + trt_stream = torch.cuda.stream(torch.cuda.Stream(torch.device(device))) + assert trt_context is not None, 'failed to create trt context, maybe not enough CUDA memory, try reduce current trt concurrent {}'.format(trt_concurrent) + self.trt_context_pool.put([trt_context, trt_stream]) + assert self.trt_context_pool.empty() is False, 'no avaialbe estimator context' + + def acquire_estimator(self): + return self.trt_context_pool.get(), self.trt_engine + + def release_estimator(self, context, stream): + self.trt_context_pool.put([context, stream]) + + +class CosyVoice2_Token2Wav(torch.nn.Module): + def __init__(self, model_dir: str = "./CosyVoice2-0.5B", enable_trt: bool = False, device_id: int = 0): + super().__init__() + self.device_id = device_id + self.device = f"cuda:{device_id}" + + self.flow = CausalMaskedDiffWithXvec() + self.flow.half() + self.flow.load_state_dict(torch.load(f"{model_dir}/flow.pt", map_location="cpu", weights_only=True), strict=True) + self.flow.to(self.device).eval() + + self.hift = HiFTGenerator() + hift_state_dict = {k.replace('generator.', ''): v for k, v in torch.load(f"{model_dir}/hift.pt", map_location="cpu", weights_only=True).items()} + self.hift.load_state_dict(hift_state_dict, strict=True) + self.hift.to(self.device).eval() + + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + self.spk_model = onnxruntime.InferenceSession(f"{model_dir}/campplus.onnx", sess_options=option, providers=["CPUExecutionProvider"]) + + self.audio_tokenizer = s3tokenizer.load_model(f"{model_dir}/speech_tokenizer_v2.onnx").to(self.device).eval() + + gpu = "l20" + if enable_trt: + self.load_trt(f'{model_dir}/flow.decoder.estimator.fp16.dynamic_batch.{gpu}.plan', + f'{model_dir}/flow.decoder.estimator.fp32.dynamic_batch.onnx', + 1, + True) + self.load_spk_trt(f'{model_dir}/campplus.{gpu}.fp32.trt', + f'{model_dir}/campplus.onnx', + 1, + False) + + def forward_spk_embedding(self, spk_feat): + if isinstance(self.spk_model, onnxruntime.InferenceSession): + return self.spk_model.run( + None, {self.spk_model.get_inputs()[0].name: spk_feat.unsqueeze(dim=0).cpu().numpy()} + )[0].flatten().tolist() + else: + [spk_model, stream], trt_engine = self.spk_model.acquire_estimator() + # NOTE need to synchronize when switching stream + with torch.cuda.device(self.device_id): + torch.cuda.current_stream().synchronize() + spk_feat = spk_feat.unsqueeze(dim=0).to(self.device) + batch_size = spk_feat.size(0) + + with stream: + spk_model.set_input_shape('input', (batch_size, spk_feat.size(1), 80)) + output_tensor = torch.empty((batch_size, 192), device=spk_feat.device) + + data_ptrs = [spk_feat.contiguous().data_ptr(), + output_tensor.contiguous().data_ptr()] + for i, j in enumerate(data_ptrs): + + spk_model.set_tensor_address(trt_engine.get_tensor_name(i), j) + # run trt engine + assert spk_model.execute_async_v3(torch.cuda.current_stream().cuda_stream) is True + torch.cuda.current_stream().synchronize() + self.spk_model.release_estimator(spk_model, stream) + + return output_tensor.cpu().numpy().flatten().tolist() + + def load_spk_trt(self, spk_model, spk_onnx_model, trt_concurrent=1, fp16=True): + if not os.path.exists(spk_model) or os.path.getsize(spk_model) == 0: + trt_kwargs = self.get_spk_trt_kwargs() + convert_onnx_to_trt(spk_model, trt_kwargs, spk_onnx_model, fp16) + import tensorrt as trt + with open(spk_model, 'rb') as f: + spk_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read()) + assert spk_engine is not None, 'failed to load trt {}'.format(spk_model) + self.spk_model = TrtContextWrapper(spk_engine, trt_concurrent=trt_concurrent, device=self.device) + + def get_spk_trt_kwargs(self): + min_shape = [(1, 4, 80)] + opt_shape = [(1, 500, 80)] + max_shape = [(1, 3000, 80)] + input_names = ["input"] + return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names} + + def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, trt_concurrent=1, fp16=True): + assert torch.cuda.is_available(), 'tensorrt only supports gpu!' + if not os.path.exists(flow_decoder_estimator_model) or os.path.getsize(flow_decoder_estimator_model) == 0: + trt_kwargs = self.get_trt_kwargs_dynamic_batch(opt_bs=2, max_batch_size=16) + convert_onnx_to_trt(flow_decoder_estimator_model, trt_kwargs, flow_decoder_onnx_model, fp16) + del self.flow.decoder.estimator + import tensorrt as trt + with open(flow_decoder_estimator_model, 'rb') as f: + estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read()) + assert estimator_engine is not None, 'failed to load trt {}'.format(flow_decoder_estimator_model) + self.flow.decoder.estimator = TrtContextWrapper(estimator_engine, trt_concurrent=trt_concurrent, device=self.device) + + def get_trt_kwargs_dynamic_batch(self, opt_bs=2, max_batch_size=64): + min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4), (2,), (2, 80)] + opt_shape = [(opt_bs * 2, 80, 500), (opt_bs * 2, 1, 500), (opt_bs * 2, 80, 500), (opt_bs * 2, 80, 500), (opt_bs * 2,), (opt_bs * 2, 80)] + max_shape = [(max_batch_size * 2, 80, 3000), (max_batch_size * 2, 1, 3000), (max_batch_size * 2, 80, 3000), (max_batch_size * 2, 80, 3000), (max_batch_size * 2,), + (max_batch_size * 2, 80)] + input_names = ["x", "mask", "mu", "cond", "t", "spks"] + return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names} + + def prompt_audio_tokenization(self, prompt_audios_list: list[torch.Tensor]) -> list[list[int]]: + prompt_speech_tokens_list, prompt_speech_mels_list = [], [] + for audio in prompt_audios_list: + assert len(audio.shape) == 1 + log_mel = s3tokenizer.log_mel_spectrogram(audio) # [num_mels, T] + prompt_speech_mels_list.append(log_mel) + prompt_mels_for_llm, prompt_mels_lens_for_llm = s3tokenizer.padding(prompt_speech_mels_list) + prompt_speech_tokens, prompt_speech_tokens_lens = self.audio_tokenizer.quantize( + prompt_mels_for_llm.to(self.device), prompt_mels_lens_for_llm.to(self.device) + ) + for i in range(len(prompt_speech_tokens)): + speech_tokens_i = prompt_speech_tokens[i, :prompt_speech_tokens_lens[i].item()].tolist() + prompt_speech_tokens_list.append(speech_tokens_i) + return prompt_speech_tokens_list + + def get_spk_emb(self, prompt_audios_list: list[torch.Tensor]) -> torch.Tensor: + spk_emb_for_flow = [] + for audio in prompt_audios_list: + assert len(audio.shape) == 1 + spk_feat = kaldi.fbank(audio.unsqueeze(0), num_mel_bins=80, dither=0, sample_frequency=16000) + spk_feat = spk_feat - spk_feat.mean(dim=0, keepdim=True) + spk_emb = self.forward_spk_embedding(spk_feat) + + spk_emb_for_flow.append(spk_emb) + spk_emb_for_flow = torch.tensor(spk_emb_for_flow) + return spk_emb_for_flow + + def get_prompt_mels(self, prompt_audios_list: list[torch.Tensor], prompt_audios_sample_rate: list[int]): + prompt_mels_for_flow = [] + prompt_mels_lens_for_flow = [] + for audio, sample_rate in zip(prompt_audios_list, prompt_audios_sample_rate): + assert len(audio.shape) == 1 + audio = audio.unsqueeze(0) + if sample_rate != 24000: + audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=24000)(audio) + mel = mel_spectrogram(audio).transpose(1, 2).squeeze(0) # [T, num_mels] + mel_len = mel.shape[0] + prompt_mels_for_flow.append(mel) + prompt_mels_lens_for_flow.append(mel_len) + prompt_mels_for_flow = torch.nn.utils.rnn.pad_sequence(prompt_mels_for_flow, batch_first=True, padding_value=0) # [B, T', num_mels=80] + prompt_mels_lens_for_flow = torch.tensor(prompt_mels_lens_for_flow) + return prompt_mels_for_flow, prompt_mels_lens_for_flow + + def forward_flow(self, prompt_speech_tokens_list: list[list[int]], generated_speech_tokens_list: list[list[int]], prompt_mels_for_flow: torch.Tensor, + prompt_mels_lens_for_flow: torch.Tensor, spk_emb_for_flow: torch.Tensor): + batch_size = prompt_mels_for_flow.shape[0] + flow_inputs = [] + flow_inputs_lens = [] + for prompt_speech_tokens, generated_speech_tokens in zip(prompt_speech_tokens_list, generated_speech_tokens_list): + flow_inputs.append(torch.tensor(prompt_speech_tokens + generated_speech_tokens)) + flow_inputs_lens.append(len(prompt_speech_tokens) + len(generated_speech_tokens)) + + flow_inputs = torch.nn.utils.rnn.pad_sequence(flow_inputs, batch_first=True, padding_value=0) + flow_inputs_lens = torch.tensor(flow_inputs_lens) + + with torch.amp.autocast(self.device, dtype=torch.float16): + generated_mels, generated_mels_lens = self.flow( + flow_inputs.to(self.device), flow_inputs_lens.to(self.device), + prompt_mels_for_flow.to(self.device), prompt_mels_lens_for_flow.to(self.device), spk_emb_for_flow.to(self.device), + streaming=False, finalize=True + ) + + return generated_mels, generated_mels_lens + + def forward_hift(self, generated_mels: torch.Tensor, generated_mels_lens: torch.Tensor, prompt_mels_lens_for_flow: torch.Tensor): + batch_size = generated_mels.shape[0] + generated_wavs = [] + for i in range(batch_size): + mel = generated_mels[i, :, prompt_mels_lens_for_flow[i].item():generated_mels_lens[i].item()].unsqueeze(0) + wav, _ = self.hift(speech_feat=mel) + generated_wavs.append(wav) + return generated_wavs + + @torch.inference_mode() + def forward( + self, generated_speech_tokens_list: list[list[int]], prompt_audios_list: list[torch.Tensor], prompt_audios_sample_rate: list[int] + ): + # assert all item in prompt_audios_sample_rate is 16000 + assert all(sample_rate == 16000 for sample_rate in prompt_audios_sample_rate) + + prompt_speech_tokens_list = self.prompt_audio_tokenization(prompt_audios_list) + + prompt_mels_for_flow, prompt_mels_lens_for_flow = self.get_prompt_mels(prompt_audios_list, prompt_audios_sample_rate) + + spk_emb_for_flow = self.get_spk_emb(prompt_audios_list) + + generated_mels, generated_mels_lens = self.forward_flow( + prompt_speech_tokens_list, generated_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow) + + generated_wavs = self.forward_hift(generated_mels, generated_mels_lens, prompt_mels_lens_for_flow) + + return generated_wavs + + +def collate_fn(batch): + ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate = [], [], [], [] + for _, item in enumerate(batch): + generated_speech_tokens_list.append(item['target_audio_cosy2_tokens']) + audio = torch.from_numpy(item['prompt_audio']['array']).float() + prompt_audios_list.append(audio) + prompt_audios_sample_rate.append(item['prompt_audio']['sampling_rate']) + ids.append(item['id']) + + return ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--enable-trt", action="store_true") + parser.add_argument("--model-dir", type=str, default="./CosyVoice2-0.5B") + parser.add_argument("--batch-size", type=int, default=4) + parser.add_argument("--output-dir", type=str, default="generated_wavs") + parser.add_argument("--huggingface-dataset-split", type=str, default="wenetspeech4tts") + parser.add_argument("--warmup", type=int, default=3, help="Number of warmup epochs, performance statistics will only be collected from the last epoch") + return parser.parse_args() + + +if __name__ == "__main__": + args = get_args() + model = CosyVoice2_Token2Wav(model_dir=args.model_dir, enable_trt=args.enable_trt) + # mkdir output_dir if not exists + if not os.path.exists(args.output_dir): + os.makedirs(args.output_dir) + dataset_name = "yuekai/seed_tts_cosy2" + + dataset = load_dataset(dataset_name, split=args.huggingface_dataset_split, trust_remote_code=True) + + data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, collate_fn=collate_fn, num_workers=0) + + for _ in range(args.warmup): + start_time = time.time() + + for batch in data_loader: + ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate = batch + + generated_wavs = model(generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate) + + for id, wav in zip(ids, generated_wavs): + torchaudio.save(f"{args.output_dir}/{id}.wav", wav.cpu(), 24000) + + end_time = time.time() + epoch_time = end_time - start_time + print(f"Measurement epoch time taken: {epoch_time:.4f} seconds") diff --git a/cosyvoice/runtime/triton_trtllm/token2wav_dit.py b/cosyvoice/runtime/triton_trtllm/token2wav_dit.py new file mode 120000 index 0000000..2bd78a5 --- /dev/null +++ b/cosyvoice/runtime/triton_trtllm/token2wav_dit.py @@ -0,0 +1 @@ +model_repo/token2wav_dit/1/token2wav_dit.py \ No newline at end of file diff --git a/cosyvoice/tools/extract_embedding.py b/cosyvoice/tools/extract_embedding.py new file mode 100755 index 0000000..de48779 --- /dev/null +++ b/cosyvoice/tools/extract_embedding.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python3 +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +from concurrent.futures import ThreadPoolExecutor, as_completed +import onnxruntime +import torch +import torchaudio +import torchaudio.compliance.kaldi as kaldi +from tqdm import tqdm + + +def single_job(utt): + audio, sample_rate = torchaudio.load(utt2wav[utt]) + if sample_rate != 16000: + audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(audio) + feat = kaldi.fbank(audio, + num_mel_bins=80, + dither=0, + sample_frequency=16000) + feat = feat - feat.mean(dim=0, keepdim=True) + embedding = ort_session.run(None, {ort_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist() + return utt, embedding + + +def main(args): + all_task = [executor.submit(single_job, utt) for utt in utt2wav.keys()] + utt2embedding, spk2embedding = {}, {} + for future in tqdm(as_completed(all_task)): + utt, embedding = future.result() + utt2embedding[utt] = embedding + spk = utt2spk[utt] + if spk not in spk2embedding: + spk2embedding[spk] = [] + spk2embedding[spk].append(embedding) + for k, v in spk2embedding.items(): + spk2embedding[k] = torch.tensor(v).mean(dim=0).tolist() + torch.save(utt2embedding, "{}/utt2embedding.pt".format(args.dir)) + torch.save(spk2embedding, "{}/spk2embedding.pt".format(args.dir)) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--dir", type=str) + parser.add_argument("--onnx_path", type=str) + parser.add_argument("--num_thread", type=int, default=8) + args = parser.parse_args() + + utt2wav, utt2spk = {}, {} + with open('{}/wav.scp'.format(args.dir)) as f: + for l in f: + l = l.replace('\n', '').split() + utt2wav[l[0]] = l[1] + with open('{}/utt2spk'.format(args.dir)) as f: + for l in f: + l = l.replace('\n', '').split() + utt2spk[l[0]] = l[1] + + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + providers = ["CPUExecutionProvider"] + ort_session = onnxruntime.InferenceSession(args.onnx_path, sess_options=option, providers=providers) + executor = ThreadPoolExecutor(max_workers=args.num_thread) + + main(args) diff --git a/cosyvoice/tools/extract_speech_token.py b/cosyvoice/tools/extract_speech_token.py new file mode 100755 index 0000000..976a23b --- /dev/null +++ b/cosyvoice/tools/extract_speech_token.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python3 +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +from concurrent.futures import ThreadPoolExecutor, as_completed +import logging +import torch +from tqdm import tqdm +import onnxruntime +import numpy as np +import torchaudio +import whisper + + +def single_job(utt): + audio, sample_rate = torchaudio.load(utt2wav[utt], backend='soundfile') + if sample_rate != 16000: + audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(audio) + # Convert audio to mono + if audio.shape[0] > 1: + audio = audio.mean(dim=0, keepdim=True) + if audio.shape[1] / 16000 > 30: + logging.warning('do not support extract speech token for audio longer than 30s') + speech_token = [] + else: + feat = whisper.log_mel_spectrogram(audio, n_mels=128) + speech_token = ort_session.run(None, {ort_session.get_inputs()[0].name: feat.detach().cpu().numpy(), + ort_session.get_inputs()[1].name: np.array([feat.shape[2]], dtype=np.int32)})[0].flatten().tolist() + return utt, speech_token + + +def main(args): + all_task = [executor.submit(single_job, utt) for utt in utt2wav.keys()] + utt2speech_token = {} + for future in tqdm(as_completed(all_task)): + utt, speech_token = future.result() + utt2speech_token[utt] = speech_token + torch.save(utt2speech_token, '{}/utt2speech_token.pt'.format(args.dir)) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--dir", type=str) + parser.add_argument("--onnx_path", type=str) + parser.add_argument("--num_thread", type=int, default=8) + args = parser.parse_args() + + utt2wav = {} + with open('{}/wav.scp'.format(args.dir)) as f: + for l in f: + l = l.replace('\n', '').split() + utt2wav[l[0]] = l[1] + + option = onnxruntime.SessionOptions() + option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + option.intra_op_num_threads = 1 + providers = ["CUDAExecutionProvider"] + ort_session = onnxruntime.InferenceSession(args.onnx_path, sess_options=option, providers=providers) + executor = ThreadPoolExecutor(max_workers=args.num_thread) + + main(args) diff --git a/cosyvoice/tools/make_parquet_list.py b/cosyvoice/tools/make_parquet_list.py new file mode 100755 index 0000000..2f35ae6 --- /dev/null +++ b/cosyvoice/tools/make_parquet_list.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python3 +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +import logging +import os +import json +from tqdm import tqdm +import pandas as pd +import multiprocessing +import time +import torch + + +def job(utt_list, parquet_file, utt2parquet_file, spk2parquet_file): + start_time = time.time() + data_list = [] + for utt in tqdm(utt_list): + data = open(utt2wav[utt], 'rb').read() + data_list.append(data) + wav_list = [utt2wav[utt] for utt in utt_list] + text_list = [utt2text[utt] for utt in utt_list] + spk_list = [utt2spk[utt] for utt in utt_list] + uttembedding_list = [utt2embedding[utt] for utt in utt_list] + spkembedding_list = [spk2embedding[utt2spk[utt]] for utt in utt_list] + speech_token_list = [utt2speech_token.get(utt, []) for utt in utt_list] + if args.dpo: + reject_speech_token_list = [utt2reject_speech_token[utt] for utt in utt_list] + if args.instruct: + instruct_list = [utt2instruct[utt] for utt in utt_list] + + # 保存到parquet,utt2parquet_file,spk2parquet_file + df = pd.DataFrame() + df['utt'] = utt_list + df['wav'] = wav_list + df['audio_data'] = data_list + df['text'] = text_list + df['spk'] = spk_list + df['utt_embedding'] = uttembedding_list + df['spk_embedding'] = spkembedding_list + df['speech_token'] = speech_token_list + if args.dpo: + df['reject_speech_token'] = reject_speech_token_list + if args.instruct: + df['instruct'] = instruct_list + df.to_parquet(parquet_file) + with open(utt2parquet_file, 'w') as f: + json.dump({k: parquet_file for k in utt_list}, f, ensure_ascii=False, indent=2) + with open(spk2parquet_file, 'w') as f: + json.dump({k: parquet_file for k in list(set(spk_list))}, f, ensure_ascii=False, indent=2) + logging.info('spend time {}'.format(time.time() - start_time)) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--num_utts_per_parquet', + type=int, + default=1000, + help='num utts per parquet') + parser.add_argument('--num_processes', + type=int, + default=1, + help='num processes for make parquets') + parser.add_argument('--instruct', + action='store_true', + default=False, + help='has instruct file or not') + parser.add_argument('--src_dir', + type=str) + parser.add_argument('--des_dir', + type=str) + parser.add_argument('--dpo', + action='store_true', + default=False, + help='Use Direct Preference Optimization') + args = parser.parse_args() + + utt2wav, utt2text, utt2spk, utt2instruct = {}, {}, {}, {} + with open('{}/wav.scp'.format(args.src_dir)) as f: + for l in f: + l = l.replace('\n', '').split() + utt2wav[l[0]] = l[1] + with open('{}/text'.format(args.src_dir)) as f: + for l in f: + l = l.replace('\n', '').split() + utt2text[l[0]] = ' '.join(l[1:]) + with open('{}/utt2spk'.format(args.src_dir)) as f: + for l in f: + l = l.replace('\n', '').split() + utt2spk[l[0]] = l[1] + if args.instruct is True: + with open('{}/instruct'.format(args.src_dir)) as f: + for l in f: + l = l.replace('\n', '').split() + utt2instruct[l[0]] = ' '.join(l[1:]) + utt2embedding = torch.load('{}/utt2embedding.pt'.format(args.src_dir)) + spk2embedding = torch.load('{}/spk2embedding.pt'.format(args.src_dir)) + utt2speech_token = torch.load('{}/utt2speech_token.pt'.format(args.src_dir)) + if args.dpo: + utt2reject_speech_token = torch.load('{}_reject/utt2speech_token.pt'.format(args.src_dir)) + utts = list(utt2wav.keys()) + + # Using process pool to speedup + pool = multiprocessing.Pool(processes=args.num_processes) + parquet_list, utt2parquet_list, spk2parquet_list = [], [], [] + for i, j in enumerate(range(0, len(utts), args.num_utts_per_parquet)): + parquet_file = os.path.join(args.des_dir, 'parquet_{:09d}.tar'.format(i)) + utt2parquet_file = os.path.join(args.des_dir, 'utt2parquet_{:09d}.json'.format(i)) + spk2parquet_file = os.path.join(args.des_dir, 'spk2parquet_{:09d}.json'.format(i)) + parquet_list.append(parquet_file) + utt2parquet_list.append(utt2parquet_file) + spk2parquet_list.append(spk2parquet_file) + pool.apply_async(job, (utts[j: j + args.num_utts_per_parquet], parquet_file, utt2parquet_file, spk2parquet_file)) + pool.close() + pool.join() + + with open('{}/data.list'.format(args.des_dir), 'w', encoding='utf8') as f1, \ + open('{}/utt2data.list'.format(args.des_dir), 'w', encoding='utf8') as f2, \ + open('{}/spk2data.list'.format(args.des_dir), 'w', encoding='utf8') as f3: + for name in parquet_list: + f1.write(name + '\n') + for name in utt2parquet_list: + f2.write(name + '\n') + for name in spk2parquet_list: + f3.write(name + '\n') diff --git a/cosyvoice/vllm_example.py b/cosyvoice/vllm_example.py new file mode 100644 index 0000000..697d7a9 --- /dev/null +++ b/cosyvoice/vllm_example.py @@ -0,0 +1,39 @@ +import sys +sys.path.append('third_party/Matcha-TTS') +from vllm import ModelRegistry +from cosyvoice.vllm.cosyvoice2 import CosyVoice2ForCausalLM +ModelRegistry.register_model("CosyVoice2ForCausalLM", CosyVoice2ForCausalLM) + +from cosyvoice.cli.cosyvoice import AutoModel +from cosyvoice.utils.common import set_all_random_seed +from tqdm import tqdm + + +def cosyvoice2_example(): + """ CosyVoice2 vllm usage + """ + cosyvoice = AutoModel(model_dir='pretrained_models/CosyVoice2-0.5B', load_jit=True, load_trt=True, load_vllm=True, fp16=True) + for i in tqdm(range(100)): + set_all_random_seed(i) + for _, _ in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav', stream=False)): + continue + + +def cosyvoice3_example(): + """ CosyVoice3 vllm usage + """ + cosyvoice = AutoModel(model_dir='pretrained_models/Fun-CosyVoice3-0.5B', load_trt=True, load_vllm=True, fp16=False) + for i in tqdm(range(100)): + set_all_random_seed(i) + for _, _ in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', 'You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。', + './asset/zero_shot_prompt.wav', stream=False)): + continue + + +def main(): + # cosyvoice2_example() + cosyvoice3_example() + + +if __name__ == '__main__': + main() diff --git a/cosyvoice/webui.py b/cosyvoice/webui.py new file mode 100644 index 0000000..debf5d3 --- /dev/null +++ b/cosyvoice/webui.py @@ -0,0 +1,180 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Liu Yue) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import sys +import argparse +import gradio as gr +import numpy as np +import torch +import torchaudio +import random +import librosa +ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append('{}/third_party/Matcha-TTS'.format(ROOT_DIR)) +from cosyvoice.cli.cosyvoice import AutoModel +from cosyvoice.utils.file_utils import logging +from cosyvoice.utils.common import set_all_random_seed + +inference_mode_list = ['预训练音色', '3s极速复刻', '跨语种复刻', '自然语言控制'] +instruct_dict = {'预训练音色': '1. 选择预训练音色\n2. 点击生成音频按钮', + '3s极速复刻': '1. 选择prompt音频文件，或录入prompt音频，注意不超过30s，若同时提供，优先选择prompt音频文件\n2. 输入prompt文本\n3. 点击生成音频按钮', + '跨语种复刻': '1. 选择prompt音频文件，或录入prompt音频，注意不超过30s，若同时提供，优先选择prompt音频文件\n2. 点击生成音频按钮', + '自然语言控制': '1. 选择预训练音色\n2. 输入instruct文本\n3. 点击生成音频按钮'} +stream_mode_list = [('否', False), ('是', True)] +max_val = 0.8 + + +def generate_seed(): + seed = random.randint(1, 100000000) + return { + "__type__": "update", + "value": seed + } + + +def change_instruction(mode_checkbox_group): + return instruct_dict[mode_checkbox_group] + + +def generate_audio(tts_text, mode_checkbox_group, sft_dropdown, prompt_text, prompt_wav_upload, prompt_wav_record, instruct_text, + seed, stream, speed): + if prompt_wav_upload is not None: + prompt_wav = prompt_wav_upload + elif prompt_wav_record is not None: + prompt_wav = prompt_wav_record + else: + prompt_wav = None + # if instruct mode, please make sure that model is iic/CosyVoice-300M-Instruct and not cross_lingual mode + if mode_checkbox_group in ['自然语言控制']: + if cosyvoice.instruct is False: + gr.Warning('您正在使用自然语言控制模式, {}模型不支持此模式, 请使用iic/CosyVoice-300M-Instruct模型'.format(args.model_dir)) + yield (cosyvoice.sample_rate, default_data) + if instruct_text == '': + gr.Warning('您正在使用自然语言控制模式, 请输入instruct文本') + yield (cosyvoice.sample_rate, default_data) + if prompt_wav is not None or prompt_text != '': + gr.Info('您正在使用自然语言控制模式, prompt音频/prompt文本会被忽略') + # if cross_lingual mode, please make sure that model is iic/CosyVoice-300M and tts_text prompt_text are different language + if mode_checkbox_group in ['跨语种复刻']: + if cosyvoice.instruct is True: + gr.Warning('您正在使用跨语种复刻模式, {}模型不支持此模式, 请使用iic/CosyVoice-300M模型'.format(args.model_dir)) + yield (cosyvoice.sample_rate, default_data) + if instruct_text != '': + gr.Info('您正在使用跨语种复刻模式, instruct文本会被忽略') + if prompt_wav is None: + gr.Warning('您正在使用跨语种复刻模式, 请提供prompt音频') + yield (cosyvoice.sample_rate, default_data) + gr.Info('您正在使用跨语种复刻模式, 请确保合成文本和prompt文本为不同语言') + # if in zero_shot cross_lingual, please make sure that prompt_text and prompt_wav meets requirements + if mode_checkbox_group in ['3s极速复刻', '跨语种复刻']: + if prompt_wav is None: + gr.Warning('prompt音频为空，您是否忘记输入prompt音频？') + yield (cosyvoice.sample_rate, default_data) + if torchaudio.info(prompt_wav).sample_rate < prompt_sr: + gr.Warning('prompt音频采样率{}低于{}'.format(torchaudio.info(prompt_wav).sample_rate, prompt_sr)) + yield (cosyvoice.sample_rate, default_data) + # sft mode only use sft_dropdown + if mode_checkbox_group in ['预训练音色']: + if instruct_text != '' or prompt_wav is not None or prompt_text != '': + gr.Info('您正在使用预训练音色模式，prompt文本/prompt音频/instruct文本会被忽略！') + if sft_dropdown == '': + gr.Warning('没有可用的预训练音色！') + yield (cosyvoice.sample_rate, default_data) + # zero_shot mode only use prompt_wav prompt text + if mode_checkbox_group in ['3s极速复刻']: + if prompt_text == '': + gr.Warning('prompt文本为空，您是否忘记输入prompt文本？') + yield (cosyvoice.sample_rate, default_data) + if instruct_text != '': + gr.Info('您正在使用3s极速复刻模式，预训练音色/instruct文本会被忽略！') + + if mode_checkbox_group == '预训练音色': + logging.info('get sft inference request') + set_all_random_seed(seed) + for i in cosyvoice.inference_sft(tts_text, sft_dropdown, stream=stream, speed=speed): + yield (cosyvoice.sample_rate, i['tts_speech'].numpy().flatten()) + elif mode_checkbox_group == '3s极速复刻': + logging.info('get zero_shot inference request') + set_all_random_seed(seed) + for i in cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_wav, stream=stream, speed=speed): + yield (cosyvoice.sample_rate, i['tts_speech'].numpy().flatten()) + elif mode_checkbox_group == '跨语种复刻': + logging.info('get cross_lingual inference request') + set_all_random_seed(seed) + for i in cosyvoice.inference_cross_lingual(tts_text, prompt_wav, stream=stream, speed=speed): + yield (cosyvoice.sample_rate, i['tts_speech'].numpy().flatten()) + else: + logging.info('get instruct inference request') + set_all_random_seed(seed) + for i in cosyvoice.inference_instruct(tts_text, sft_dropdown, instruct_text, stream=stream, speed=speed): + yield (cosyvoice.sample_rate, i['tts_speech'].numpy().flatten()) + + +def main(): + with gr.Blocks() as demo: + gr.Markdown("### 代码库 [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) \ + 预训练模型 [CosyVoice-300M](https://www.modelscope.cn/models/iic/CosyVoice-300M) \ + [CosyVoice-300M-Instruct](https://www.modelscope.cn/models/iic/CosyVoice-300M-Instruct) \ + [CosyVoice-300M-SFT](https://www.modelscope.cn/models/iic/CosyVoice-300M-SFT)") + gr.Markdown("#### 请输入需要合成的文本，选择推理模式，并按照提示步骤进行操作") + + tts_text = gr.Textbox(label="输入合成文本", lines=1, value="我是通义实验室语音团队全新推出的生成式语音大模型，提供舒适自然的语音合成能力。") + with gr.Row(): + mode_checkbox_group = gr.Radio(choices=inference_mode_list, label='选择推理模式', value=inference_mode_list[0]) + instruction_text = gr.Text(label="操作步骤", value=instruct_dict[inference_mode_list[0]], scale=0.5) + sft_dropdown = gr.Dropdown(choices=sft_spk, label='选择预训练音色', value=sft_spk[0], scale=0.25) + stream = gr.Radio(choices=stream_mode_list, label='是否流式推理', value=stream_mode_list[0][1]) + speed = gr.Number(value=1, label="速度调节(仅支持非流式推理)", minimum=0.5, maximum=2.0, step=0.1) + with gr.Column(scale=0.25): + seed_button = gr.Button(value="\U0001F3B2") + seed = gr.Number(value=0, label="随机推理种子") + + with gr.Row(): + prompt_wav_upload = gr.Audio(sources='upload', type='filepath', label='选择prompt音频文件，注意采样率不低于16khz') + prompt_wav_record = gr.Audio(sources='microphone', type='filepath', label='录制prompt音频文件') + prompt_text = gr.Textbox(label="输入prompt文本", lines=1, placeholder="请输入prompt文本，需与prompt音频内容一致，暂时不支持自动识别...", value='') + instruct_text = gr.Textbox(label="输入instruct文本", lines=1, placeholder="请输入instruct文本.", value='') + + generate_button = gr.Button("生成音频") + + audio_output = gr.Audio(label="合成音频", autoplay=True, streaming=True) + + seed_button.click(generate_seed, inputs=[], outputs=seed) + generate_button.click(generate_audio, + inputs=[tts_text, mode_checkbox_group, sft_dropdown, prompt_text, prompt_wav_upload, prompt_wav_record, instruct_text, + seed, stream, speed], + outputs=[audio_output]) + mode_checkbox_group.change(fn=change_instruction, inputs=[mode_checkbox_group], outputs=[instruction_text]) + demo.queue(max_size=4, default_concurrency_limit=2) + demo.launch(server_name='0.0.0.0', server_port=args.port) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--port', + type=int, + default=8000) + parser.add_argument('--model_dir', + type=str, + default='pretrained_models/CosyVoice3-0.5B', + help='local path or modelscope repo id') + args = parser.parse_args() + cosyvoice = AutoModel(model_dir=args.model_dir) + + sft_spk = cosyvoice.list_available_spks() + if len(sft_spk) == 0: + sft_spk = [''] + prompt_sr = 16000 + default_data = np.zeros(cosyvoice.sample_rate) + main() From 40811676f4b66aadfc395c4b25bbffe7227bdebb Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Tue, 23 Dec 2025 01:56:30 +0000 Subject: [PATCH 04/21] fix some bug --- .gitignore | 3 ++- NOTICE | 2 +- cosyvoice/cosyvoice/cli/frontend.py | 9 +++---- light_tts/models/cosyvoice2/model.py | 3 +++ light_tts/server/core/objs/req.py | 11 +++++---- light_tts/server/httpserver/manager.py | 24 ++++++++++++------- light_tts/server/tts_llm/manager.py | 3 ++- .../server/tts_llm/model_infer/infer_batch.py | 9 +++---- .../mode_backend/continues_batch/impl.py | 14 +++++------ .../continues_batch/post_process.py | 14 +++++++---- 10 files changed, 53 insertions(+), 39 deletions(-) diff --git a/.gitignore b/.gitignore index de28d79..2e570cd 100755 --- a/.gitignore +++ b/.gitignore @@ -5,4 +5,5 @@ dist *.egg-info .idea .vscode -test/outs \ No newline at end of file +test/outs +pretrained_models \ No newline at end of file diff --git a/NOTICE b/NOTICE index c7663a3..343bfc5 100644 --- a/NOTICE +++ b/NOTICE @@ -9,7 +9,7 @@ CosyVoice (cosyvoice/) https://github.com/FunAudioLLM/CosyVoice Copyright (c) Alibaba, Inc. and its affiliates. Licensed under the Apache License, Version 2.0 -Original commit: 9f27b42 +Original commit: bc34459 The cosyvoice/ directory contains a modified copy of the CosyVoice project. We have integrated and adapted this code for use in Light TTS. diff --git a/cosyvoice/cosyvoice/cli/frontend.py b/cosyvoice/cosyvoice/cli/frontend.py index 0942da6..b4bb202 100644 --- a/cosyvoice/cosyvoice/cli/frontend.py +++ b/cosyvoice/cosyvoice/cli/frontend.py @@ -160,18 +160,19 @@ def frontend_sft(self, tts_text, spk_id): model_input = {'text': tts_text_token, 'text_len': tts_text_token_len, 'llm_embedding': embedding, 'flow_embedding': embedding} return model_input - def frontend_zero_shot(self, tts_text, prompt_text, prompt_wav, resample_rate, zero_shot_spk_id): + def frontend_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, resample_rate, zero_shot_spk_id): tts_text_token, tts_text_token_len = self._extract_text_token(tts_text) if zero_shot_spk_id == '': prompt_text_token, prompt_text_token_len = self._extract_text_token(prompt_text) - speech_feat, speech_feat_len = self._extract_speech_feat(prompt_wav) - speech_token, speech_token_len = self._extract_speech_token(prompt_wav) + prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=resample_rate)(prompt_speech_16k) + speech_feat, speech_feat_len = self._extract_speech_feat(prompt_speech_resample) + speech_token, speech_token_len = self._extract_speech_token(prompt_speech_16k) if resample_rate == 24000: # cosyvoice2, force speech_feat % speech_token = 2 token_len = min(int(speech_feat.shape[1] / 2), speech_token.shape[1]) speech_feat, speech_feat_len[:] = speech_feat[:, :2 * token_len], 2 * token_len speech_token, speech_token_len[:] = speech_token[:, :token_len], token_len - embedding = self._extract_spk_embedding(prompt_wav) + embedding = self._extract_spk_embedding(prompt_speech_16k) model_input = {'prompt_text': prompt_text_token, 'prompt_text_len': prompt_text_token_len, 'llm_prompt_speech_token': speech_token, 'llm_prompt_speech_token_len': speech_token_len, 'flow_prompt_speech_token': speech_token, 'flow_prompt_speech_token_len': speech_token_len, diff --git a/light_tts/models/cosyvoice2/model.py b/light_tts/models/cosyvoice2/model.py index 1608f4f..5eb56b2 100755 --- a/light_tts/models/cosyvoice2/model.py +++ b/light_tts/models/cosyvoice2/model.py @@ -21,6 +21,9 @@ class CosyVoice2TpPartModel(Qwen2TpPartModel): def __init__(self, kvargs): self.pt_dir = kvargs["pt_dir"] self.speech_token_size = kvargs["speech_token_size"] + self.eos_token = self.speech_token_size + self.fill_token = self.speech_token_size + 2 + self.stop_token_ids = [self.speech_token_size + i for i in range(3)] super().__init__(kvargs) return diff --git a/light_tts/server/core/objs/req.py b/light_tts/server/core/objs/req.py index 1ccbe44..2198b41 100755 --- a/light_tts/server/core/objs/req.py +++ b/light_tts/server/core/objs/req.py @@ -91,7 +91,7 @@ class Req(ctypes.Structure): ("bistream_input_finished", ctypes.c_bool), ("ignore_eos", ctypes.c_bool), ("text_cache_start", ctypes.c_int), - ("sos_eos_id", ctypes.c_int), + ("sos", ctypes.c_int), ("task_id", ctypes.c_int), ("bistream_first", ctypes.c_int), # normal @@ -121,6 +121,7 @@ class Req(ctypes.Structure): ("finish_token_index", ctypes.c_int), ("out_tokens_queue", CircularQueue), ("sample_params", SamplingParams), + ("next_fill_index", ctypes.c_int), # can_released_mark的作用是： # 只有整个流程中的最后一个处理模块，一般是 detokenization 进程，标记这个参数为True后，主管理进程才能真 # 的释放请求对像。 @@ -144,7 +145,7 @@ def init( prompt_ids: List[int], request_dict: dict, sample_param: Union[dict, SamplingParams], - sos_eos_id: int = 0, + sos: int = 0, task_id: int = 0, speed: float = 1.0, ): @@ -174,19 +175,19 @@ def init( else: self.sample_params = SamplingParams() self.sample_params.init(**sample_param) - + self.next_fill_index = -1 self.stream = request_dict.get("stream", False) self.text_len = len(prompt_ids) self.semantic_len = request_dict.get("semantic_len", 0) self.gen_finished = False self.bistream = request_dict.get("bistream", False) - self.sos_eos_id = sos_eos_id + self.sos = sos self.task_id = task_id if self.bistream: self.assign_slice(self.text_cache, 0, prompt_ids) self.input_len = 1 - self.prompt_ids[0] = self.sos_eos_id + self.prompt_ids[0] = self.sos self.text_cache_start = 0 self.req_status = ReqRunStatus(ReqRunStatus.WAIT_FOR_TEXT) self.bistream_first = True diff --git a/light_tts/server/httpserver/manager.py b/light_tts/server/httpserver/manager.py index 22f0a37..9f48dc4 100755 --- a/light_tts/server/httpserver/manager.py +++ b/light_tts/server/httpserver/manager.py @@ -53,7 +53,7 @@ def __init__( configs = load_yaml(args.model_dir) self.model_config = configs["llm"].llm.model.model.config self.vocab_size = self.model_config.vocab_size - self.sos_eos = self.vocab_size + self.sos = self.vocab_size self.task_id = self.vocab_size + 1 self.max_req_total_len = args.max_req_total_len @@ -176,7 +176,7 @@ async def generate(self, request_dict, request_id, sampling_params, request=None prompt_text_ids = await self._async_encode(request_dict["prompt_text"]) text_ids = await self._async_encode(request_dict["text"]) if not bistream: - prompt_ids = list(chain([self.sos_eos], prompt_text_ids, text_ids, [self.task_id])) + prompt_ids = list(chain([self.sos], prompt_text_ids, text_ids, [self.task_id])) sampling_params.min_new_tokens = len(text_ids) * self.min_token_text_ratio sampling_params.max_new_tokens = len(text_ids) * self.max_token_text_ratio await self._check_and_repair_length(prompt_ids, semantic_len, sampling_params) @@ -193,7 +193,7 @@ async def generate(self, request_dict, request_id, sampling_params, request=None style = request_dict["tts_model_name"] req_obj = await self.shm_req_manager.async_get_req_obj_by_index(req_index) req_objs = [] - req_obj.init(request_id, prompt_ids, request_dict, sampling_params, self.sos_eos, self.task_id, speed) + req_obj.init(request_id, prompt_ids, request_dict, sampling_params, self.sos, self.task_id, speed) req_objs.append(req_obj) req_status = ReqStatus(request_id, req_objs, start_time) self.req_id_to_out_inf[request_id] = req_status @@ -238,9 +238,9 @@ async def recycle_resource_loop(self): # 清理已经处理完的可以删除的请求 release_req_status: List[ReqStatus] = [] - for req_status in self.req_id_to_out_inf.values(): - if req_status.can_release(): - + for group_req_id_ in list(self.req_id_to_out_inf.keys()): + req_status: ReqStatus = self.req_id_to_out_inf.get(group_req_id_, None) + if req_status is not None and req_status.can_release(): release_req_status.append(req_status) for req_status in release_req_status: @@ -269,20 +269,26 @@ async def handle_loop(self): except asyncio.TimeoutError: pass - for req_status in self.req_id_to_out_inf.values(): + for group_req_id_ in list(self.req_id_to_out_inf.keys()): + req_status = self.req_id_to_out_inf.get(group_req_id_, None) + if req_status is None: + continue + for req in req_status.group_req_objs.shm_req_objs: if req.stream: if not req.out_tokens_queue.is_empty(): tts_speech, token_offset, finalize = req.out_tokens_queue.peek() - req.out_tokens_queue.pop_no_ret() + tts_speech = tts_speech.copy() if finalize: finish_status = FinishStatus(req.finish_status.status) else: finish_status = FinishStatus() + + req.out_tokens_queue.pop_no_ret() async with req_status.lock: logger.debug(f"req_id {req.request_id} shm_index {req.index_in_shm_mem} get chunk") - req_status.out_data_info_list.append((tts_speech.copy(), finish_status, finalize)) + req_status.out_data_info_list.append((tts_speech, finish_status, finalize)) req_status.event.set() elif req.gen_finished: tts_speech = req.get_gen_audios() diff --git a/light_tts/server/tts_llm/manager.py b/light_tts/server/tts_llm/manager.py index ba5ef69..86b7f00 100755 --- a/light_tts/server/tts_llm/manager.py +++ b/light_tts/server/tts_llm/manager.py @@ -97,7 +97,7 @@ def __init__(self, args, tts_llm_port, tts_decode_port, style_name, gpt_parall_l self.vocab_size = self.model_config.vocab_size self.eos_id = self.speech_token_size self.fill_token_id = self.eos_id + 2 - self.sos_eos = self.vocab_size + self.sos = self.vocab_size self.task_id = self.vocab_size + 1 self.max_semantic_position = self.model_config.max_position_embeddings del configs @@ -172,6 +172,7 @@ def add_req( if req.bistream: req.audio_ids = (self.shared_speech_manager.get_index_speech_token(req.speech_index).arr[0] + self.vocab_size + 2).flatten().tolist() req.mix_ratio = self.mix_ratio + req.next_fill_index = (int(len(req.audio_ids) / self.mix_ratio[1]) + 1) * self.mix_ratio[1] - len(req.audio_ids) self.req_queue.append_bistream(req) else: self.req_queue.append(req) diff --git a/light_tts/server/tts_llm/model_infer/infer_batch.py b/light_tts/server/tts_llm/model_infer/infer_batch.py index cf79ff8..1e2a10b 100755 --- a/light_tts/server/tts_llm/model_infer/infer_batch.py +++ b/light_tts/server/tts_llm/model_infer/infer_batch.py @@ -238,6 +238,7 @@ def init_all(self): self.finish_status = FinishStatus() self.semantic_len = self.shm_req.semantic_len self.bistream = self.shm_req.bistream + self.next_fill_index = self.shm_req.next_fill_index if self.paused or not self.initialized: self.shm_req.shm_cur_kv_len = self.cur_kv_len @@ -269,15 +270,11 @@ def set_next_gen_token_id(self, next_token_id: int, next_token_offset_id: int): def get_last_gen_token(self): return self.output_token_ids[-1] - def update_finish_status(self, eos_ids, fill_token_id=None): - if ( - self.cur_output_len > 0 - and self.get_last_gen_token() in eos_ids - ): + def update_finish_status(self, eos_id, stop_token_ids, fill_token_id=None): + if self.cur_output_len > 0 and ((not self.bistream and self.get_last_gen_token() in stop_token_ids) or (self.bistream and self.get_last_gen_token() == eos_id)): self.finish_status.set_status(FinishStatus.FINISHED_STOP) elif self.cur_output_len >= self.sampling_param.shm_param.max_new_tokens: self.finish_status.set_status(FinishStatus.FINISHED_LENGTH) elif self.bistream and self.get_last_gen_token() == fill_token_id: self.shm_req.req_status.set_status(ReqRunStatus.WAIT_FOR_TEXT) - return \ No newline at end of file diff --git a/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/impl.py b/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/impl.py index e1f0480..d54522b 100755 --- a/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/impl.py +++ b/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/impl.py @@ -24,9 +24,9 @@ def prefill(self, reqs: List[Tuple]): next_token_ids = sample( logits[mask], kwargs["output_token_ids"][mask], kwargs["ignore_eos"][mask], - kwargs["bistream"][mask], - self.model.speech_token_size, - self.model.speech_token_size + 2 + self.model.eos_token, + # kwargs["bistream"][mask], + # self.model.speech_token_size + 2 ) next_token_ids = next_token_ids.detach().cpu().numpy() @@ -41,9 +41,9 @@ def decode(self): next_token_ids = sample( logits[mask], kwargs["output_token_ids"][mask], kwargs["ignore_eos"][mask], - kwargs["bistream"][mask], - self.model.speech_token_size, - self.model.speech_token_size + 2 + self.model.eos_token, + # kwargs["bistream"][mask], + # self.model.speech_token_size + 2 ) next_token_ids = next_token_ids.detach().cpu().numpy() @@ -78,7 +78,7 @@ def post_handle(self, run_reqs: List[InferReq], next_token_ids, mask): req_obj.shm_req.shm_cur_kv_len = req_obj.cur_kv_len req_obj.shm_req.shm_cur_output_len = req_obj.cur_output_len - req_obj.update_finish_status([self.eos_id], fill_token_id=self.fill_token_id) + req_obj.update_finish_status(self.model.eos_token, self.model.stop_token_ids, fill_token_id=self.fill_token_id) if req_obj.finish_status.is_finished() or req_obj.shm_req.router_aborted: finished_req_ids.append(req_obj.shm_req.request_id) diff --git a/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/post_process.py b/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/post_process.py index de3914a..322ace0 100755 --- a/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/post_process.py +++ b/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/post_process.py @@ -3,7 +3,9 @@ from light_tts.common.basemodel.triton_kernel.apply_penalty import apply_penalty from dataclasses import dataclass from ...infer_batch import InferReq, g_infer_context +from light_tts.utils.log_utils import init_logger +logger = init_logger(__name__) def nucleus_sampling(weighted_scores, top_p=0.8, top_k=25, not_first=False): sorted_probs, sorted_idx = weighted_scores.softmax(dim=-1).sort(descending=True, stable=True, dim=-1) @@ -39,22 +41,24 @@ def sample( weighted_scores: torch.Tensor, decoded_tokens: torch.Tensor, ignore_eos: torch.Tensor, - bistream: torch.Tensor, eos_id: int = 6561, - fill_token_id: int = 6563, ): num_trials, max_trials = 0, 100 top_ids = ras_sampling(weighted_scores, decoded_tokens) - mask = (top_ids > eos_id) | ((top_ids == eos_id) & ignore_eos) + mask = ignore_eos & (top_ids >= eos_id) + # mask = (top_ids > eos_id) | ((top_ids == eos_id) & ignore_eos) while mask.any(): top_ids[mask] = ras_sampling(weighted_scores[mask], decoded_tokens[mask], not_first=True) # top_ids 为大于speech_token_size的值，而且不是fill_token，或者是fill_token，但不是(bistream且在text没receiving完的情况) - mask = ((top_ids > eos_id) & (top_ids != fill_token_id | ~bistream | ~ignore_eos)) | ((top_ids == eos_id) & ignore_eos) + # mask = ((top_ids > eos_id) & (top_ids != fill_token_id | ~bistream | ~ignore_eos)) | ((top_ids == eos_id) & ignore_eos) + mask = ignore_eos & (top_ids >= eos_id) num_trials += 1 # TODO: 错误处理 if num_trials > max_trials: - raise RuntimeError('sampling reaches max_trials {} and still get eos when ignore_eos is True, check your input {}!'.format(max_trials, mask)) + logger.error('sampling reaches max_trials {} and still get eos when ignore_eos is True, check your input {}!'.format(max_trials, mask)) + top_ids[mask] = eos_id # 错误处理，返回eos_id + break return top_ids From 2da5c79a0e3fccea202b3cdbf6af5c998727b88c Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Tue, 23 Dec 2025 02:46:32 +0000 Subject: [PATCH 05/21] speedup startup --- cosyvoice/cosyvoice/cli/frontend.py | 9 ++-- light_tts/server/api_http.py | 15 ++++-- light_tts/server/api_start.py | 54 +++++++++++-------- .../model_infer/patch_conditional_cfm.py | 13 ++--- light_tts/utils/start_utils.py | 20 ++++++- 5 files changed, 71 insertions(+), 40 deletions(-) diff --git a/cosyvoice/cosyvoice/cli/frontend.py b/cosyvoice/cosyvoice/cli/frontend.py index b4bb202..14714e1 100644 --- a/cosyvoice/cosyvoice/cli/frontend.py +++ b/cosyvoice/cosyvoice/cli/frontend.py @@ -89,8 +89,7 @@ def _extract_text_token_generator(self, text_generator): for i in range(text_token.shape[1]): yield text_token[:, i: i + 1] - def _extract_speech_token(self, prompt_wav): - speech = load_wav(prompt_wav, 16000) + def _extract_speech_token(self, speech): assert speech.shape[1] / 16000 <= 30, 'do not support extract speech token for audio longer than 30s' feat = whisper.log_mel_spectrogram(speech, n_mels=128) speech_token = self.speech_tokenizer_session.run(None, @@ -102,8 +101,7 @@ def _extract_speech_token(self, prompt_wav): speech_token_len = torch.tensor([speech_token.shape[1]], dtype=torch.int32).to(self.device) return speech_token, speech_token_len - def _extract_spk_embedding(self, prompt_wav): - speech = load_wav(prompt_wav, 16000) + def _extract_spk_embedding(self, speech): feat = kaldi.fbank(speech, num_mel_bins=80, dither=0, @@ -114,8 +112,7 @@ def _extract_spk_embedding(self, prompt_wav): embedding = torch.tensor([embedding]).to(self.device) return embedding - def _extract_speech_feat(self, prompt_wav): - speech = load_wav(prompt_wav, 24000) + def _extract_speech_feat(self, speech): speech_feat = self.feat_extractor(speech).squeeze(dim=0).transpose(0, 1).to(self.device) speech_feat = speech_feat.unsqueeze(dim=0) speech_feat_len = torch.tensor([speech_feat.shape[1]], dtype=torch.int32).to(self.device) diff --git a/light_tts/server/api_http.py b/light_tts/server/api_http.py index 404eb17..03b4973 100755 --- a/light_tts/server/api_http.py +++ b/light_tts/server/api_http.py @@ -28,6 +28,13 @@ import os from pathlib import Path import sys + +# 抑制常见的库警告 +import warnings +warnings.filterwarnings('ignore', category=FutureWarning, module='diffusers') +warnings.filterwarnings('ignore', category=UserWarning, message='.*weight_norm.*') +warnings.filterwarnings('ignore', category=UserWarning, module='onnxruntime') + sys.path.insert(0, str(Path(__file__).parent.parent.parent / 'cosyvoice')) from light_tts.utils.load_utils import load_yaml asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) @@ -342,10 +349,12 @@ async def shutdown(): @app.on_event("startup") async def startup_event(): - logger.info("server start up") + logger.info("=" * 60) + logger.info("🚀 Application startup: Loading models...") loop = asyncio.get_event_loop() g_objs.set_args(get_env_start_args()) loop.create_task(g_objs.httpserver_manager.handle_loop()) - logger.info(f"server start up ok, loop use is {asyncio.get_event_loop()}") - logger.info(f"Listening at: http://{g_objs.args.host}:{g_objs.args.port}") + logger.info(f"✅ Application ready! Server is now accepting requests") + logger.info(f"🌐 Listening at: http://{g_objs.args.host}:{g_objs.args.port}") + logger.info("=" * 60) return diff --git a/light_tts/server/api_start.py b/light_tts/server/api_start.py index c8bf819..356209d 100755 --- a/light_tts/server/api_start.py +++ b/light_tts/server/api_start.py @@ -129,38 +129,46 @@ def normal_start(args): set_env_start_args(args) logger.info(f"all start args:{args}") ports_locker.release_port() - - # 第一个实列需要先初始化，解决一些同步的问题 - funcs = [] - start_args = [] - encode_parall_lock = mp.Semaphore(args.encode_paral_num) - for index_id in range(args.encode_process_num): - funcs.append(start_tts1_encode_process) - start_args.append((args, tts_llm_ports, tts1_encode_ports[index_id], index_id, encode_parall_lock)) - process_manager.start_submodule_processes(start_funcs=funcs[0:1], start_args=start_args[0:1]) + + logger.info("=" * 80) + logger.info("🚀 Starting LightTTS Server - FULL PARALLEL Mode") + logger.info("⚡ All 3 components will start simultaneously!") + logger.info("=" * 80) tts_decode_ports = can_use_ports[0 : num_loras] del can_use_ports[0 : num_loras] - - funcs = [] - start_args = [] + + # 准备所有进程参数 + all_funcs = [] + all_start_args = [] + + # 1️⃣ 准备 Encode 进程 + encode_parall_lock = mp.Semaphore(args.encode_paral_num) + for index_id in range(args.encode_process_num): + all_funcs.append(start_tts1_encode_process) + all_start_args.append((args, tts_llm_ports, tts1_encode_ports[index_id], index_id, encode_parall_lock)) + + # 2️⃣ 准备 LLM 进程 gpt_parall_lock = mp.Semaphore(args.gpt_paral_num) for style_name, tts_llm_port, tts_decode_port in zip(["CosyVoice2"], tts_llm_ports, tts_decode_ports): - funcs.append(start_tts_llm_process) - start_args.append((args, tts_llm_port, tts_decode_port, style_name, gpt_parall_lock)) - process_manager.start_submodule_processes(start_funcs=funcs, start_args=start_args) - - + all_funcs.append(start_tts_llm_process) + all_start_args.append((args, tts_llm_port, tts_decode_port, style_name, gpt_parall_lock)) + + # 3️⃣ 准备 Decode 进程 decode_parall_lock = mp.Semaphore(args.decode_paral_num) - funcs = [] - start_args = [] for decode_proc_index in range(args.decode_process_num): tmp_args = [] for style_name, tts_decode_port in zip(["CosyVoice2"], tts_decode_ports): tmp_args.append((args, tts_decode_port, httpserver_port, style_name, decode_parall_lock, decode_proc_index)) - funcs.append(start_tts_decode_process) - start_args.append((tmp_args,)) - process_manager.start_submodule_processes(start_funcs=funcs, start_args=start_args) + all_funcs.append(start_tts_decode_process) + all_start_args.append((tmp_args,)) + + # 🔥 激进模式：一次性启动 Encode + LLM + Decode，完全并行初始化！ + logger.info(f"🚀 Launching {len(all_funcs)} processes in parallel...") + process_manager.start_submodule_processes(start_funcs=all_funcs, start_args=all_start_args) + + logger.info("✅ All components started successfully!") + logger.info("=" * 80) if os.getenv("LIGHTLLM_DEBUG") == "1": from light_tts.server.api_http import app @@ -184,7 +192,7 @@ def normal_start(args): "--bind", f"{args.host}:{args.port}", "--log-level", - "info", + "warning", # 降低gunicorn日志级别，避免误导性的"Listening"消息 "--access-logfile", "-", "--error-logfile", diff --git a/light_tts/server/tts_decode/model_infer/patch_conditional_cfm.py b/light_tts/server/tts_decode/model_infer/patch_conditional_cfm.py index c7bab20..6f67998 100644 --- a/light_tts/server/tts_decode/model_infer/patch_conditional_cfm.py +++ b/light_tts/server/tts_decode/model_infer/patch_conditional_cfm.py @@ -39,17 +39,18 @@ def patched_forward_estimator(self, x, mask, mu, t, spks, cond, streaming=False) # 应用猴子补丁 def apply_forward_estimator_patch(): """应用forward_estimator的猴子补丁""" - print("正在应用 forward_estimator 猴子补丁...") + import os # 保存原始方法（可选，用于调试或回滚） if not hasattr(ConditionalCFM, '_original_forward_estimator'): ConditionalCFM._original_forward_estimator = ConditionalCFM.forward_estimator - - # 替换方法 + # 替换方法 ConditionalCFM.forward_estimator = patched_forward_estimator - - # print("forward_estimator 补丁已成功应用!") - print("现在所有 ConditionalCFM 实例都会使用带有 torch.cuda.synchronize() 的版本") + + # 只在verbose模式下打印 + # if os.getenv('LIGHTTTS_VERBOSE', '0') == '1': + # print("正在应用 forward_estimator 猴子补丁...") + # print("现在所有 ConditionalCFM 实例都会使用带有 torch.cuda.synchronize() 的版本") def remove_forward_estimator_patch(): """移除补丁，恢复原始方法（可选）""" diff --git a/light_tts/utils/start_utils.py b/light_tts/utils/start_utils.py index 25a6499..3996d3e 100755 --- a/light_tts/utils/start_utils.py +++ b/light_tts/utils/start_utils.py @@ -18,6 +18,14 @@ def start_submodule_processes(self, start_funcs=[], start_args=[]): assert len(start_funcs) == len(start_args) pipe_readers = [] processes = [] + + # 进程名称映射（用于更友好的日志） + process_names = { + 'start_tts1_encode_process': '📝 Text Encoder', + 'start_tts_llm_process': '🎯 LLM Model', + 'start_tts_decode_process': '🎵 Audio Decoder', + 'start_health_check_process': '❤️ Health Monitor' + } for start_func, start_arg in zip(start_funcs, start_args): pipe_reader, pipe_writer = mp.Pipe(duplex=False) @@ -28,17 +36,25 @@ def start_submodule_processes(self, start_funcs=[], start_args=[]): process.start() pipe_readers.append(pipe_reader) processes.append(process) + + # 打印启动提示 + func_name = start_func.__name__ + display_name = process_names.get(func_name, func_name) + logger.info(f"⏳ Starting {display_name}...") # Wait for all processes to initialize for index, pipe_reader in enumerate(pipe_readers): init_state = pipe_reader.recv() + func_name = start_funcs[index].__name__ + display_name = process_names.get(func_name, func_name) + if init_state != "init ok": - logger.error(f"init func {start_funcs[index].__name__} : {str(init_state)}") + logger.error(f"❌ {display_name} failed: {str(init_state)}") for proc in processes: proc.kill() sys.exit(1) else: - logger.info(f"init func {start_funcs[index].__name__} : {str(init_state)}") + logger.info(f"✅ {display_name} initialized successfully") assert all([proc.is_alive() for proc in processes]) self.processes.extend(processes) From 1233a00393bfcc2ef423d9cd4724a1fb7181d949 Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Tue, 23 Dec 2025 09:01:00 +0000 Subject: [PATCH 06/21] add cosyvoice3 --- .pre-commit-config.yaml | 4 +- cosyvoice/cosyvoice/cli/model.py | 2 - .../layer_infer/post_layer_infer.py | 26 ++- .../pre_and_post_layer_weight.py | 23 ++- light_tts/models/cosyvoice3/__init__.py | 0 light_tts/models/cosyvoice3/model.py | 10 ++ light_tts/server/httpserver/manager.py | 65 ++++---- light_tts/server/tts_decode/manager.py | 43 +++-- .../tts_decode/model_infer/model_rpc.py | 65 ++++---- light_tts/server/tts_encode/manager.py | 70 ++++---- light_tts/server/tts_llm/manager.py | 155 +++++++++++------- .../model_infer/mode_backend/base_backend.py | 46 +++--- .../mode_backend/continues_batch/impl.py | 20 ++- .../server/tts_llm/model_infer/model_rpc.py | 48 +----- light_tts/utils/load_utils.py | 59 ++++++- 15 files changed, 369 insertions(+), 267 deletions(-) create mode 100755 light_tts/models/cosyvoice3/__init__.py create mode 100755 light_tts/models/cosyvoice3/model.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 965457a..78ff25f 100755 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -6,9 +6,11 @@ repos: language_version: python3 args: [--line-length=120] additional_dependencies: ['click==8.0.4'] + exclude: '^cosyvoice/' - repo: https://github.com/pycqa/flake8 rev: 3.9.0 hooks: - id: flake8 additional_dependencies: [flake8-typing-imports==1.9.0] - args: ['--config=.flake8', '--max-line-length=120', '--ignore=C901, E203, E266, E402, E302, E241, E902, E731, F403, E701, F405, F401, W292, W293, W503, W606'] \ No newline at end of file + args: ['--config=.flake8', '--max-line-length=120', '--ignore=C901, E203, E266, E402, E302, E241, E902, E731, F403, E701, F405, F401, W292, W293, W503, W606'] + exclude: '^cosyvoice/' \ No newline at end of file diff --git a/cosyvoice/cosyvoice/cli/model.py b/cosyvoice/cosyvoice/cli/model.py index 8e67b0c..bcff2b0 100644 --- a/cosyvoice/cosyvoice/cli/model.py +++ b/cosyvoice/cosyvoice/cli/model.py @@ -62,8 +62,6 @@ def __init__(self, self.hift_cache_dict = {} def load(self, llm_model, flow_model, hift_model): - self.llm.load_state_dict(torch.load(llm_model, map_location=self.device), strict=True) - self.llm.to(self.device).eval() self.flow.load_state_dict(torch.load(flow_model, map_location=self.device), strict=True) self.flow.to(self.device).eval() # in case hift_model is a hifigan model diff --git a/light_tts/models/cosyvoice2/layer_infer/post_layer_infer.py b/light_tts/models/cosyvoice2/layer_infer/post_layer_infer.py index 7500002..ce21f09 100755 --- a/light_tts/models/cosyvoice2/layer_infer/post_layer_infer.py +++ b/light_tts/models/cosyvoice2/layer_infer/post_layer_infer.py @@ -76,16 +76,28 @@ def token_forward(self, input_embdings, infer_state: LlamaInferStateInfo, layer_ logic_batch = self.alloc_tensor( (layer_weight.llm_decoder_weight_.shape[0], last_input.shape[1]), dtype=last_input.dtype ) - torch.addmm( - layer_weight.llm_decoder_bias_, layer_weight.llm_decoder_weight_, last_input, beta=1.0, alpha=1.0, out=logic_batch - ) + if hasattr(layer_weight, "llm_decoder_bias_"): + torch.addmm( + layer_weight.llm_decoder_bias_, + layer_weight.llm_decoder_weight_, + last_input, + beta=1.0, + alpha=1.0, + out=logic_batch, + ) + else: + torch.mm(layer_weight.llm_decoder_weight_, last_input, out=logic_batch) last_input = None if self.tp_world_size_ == 1: gather_data = logic_batch else: - gather_data = self.alloc_tensor((layer_weight.llm_decoder_weight_.shape[0], token_num), dtype=input_embdings_dtype) - split_indexes = np.linspace(0, layer_weight.llm_decoder_weight_.shape[0], self.tp_world_size_ + 1, dtype=np.int64) + gather_data = self.alloc_tensor( + (layer_weight.llm_decoder_weight_.shape[0], token_num), dtype=input_embdings_dtype + ) + split_indexes = np.linspace( + 0, layer_weight.llm_decoder_weight_.shape[0], self.tp_world_size_ + 1, dtype=np.int64 + ) dist.all_gather( [gather_data[split_indexes[i] : split_indexes[i + 1], :] for i in range(self.tp_world_size_)], logic_batch, @@ -93,7 +105,9 @@ def token_forward(self, input_embdings, infer_state: LlamaInferStateInfo, layer_ async_op=False, ) logic_batch = None - ans_logics = self.alloc_tensor((token_num, layer_weight.llm_decoder_weight_.shape[0]), dtype=torch.float32, is_graph_out=True) + ans_logics = self.alloc_tensor( + (token_num, layer_weight.llm_decoder_weight_.shape[0]), dtype=torch.float32, is_graph_out=True + ) ans_logics[:, :] = gather_data.permute(1, 0) gather_data = None torch.log_softmax(ans_logics, dim=-1, out=ans_logics) diff --git a/light_tts/models/cosyvoice2/layer_weights/pre_and_post_layer_weight.py b/light_tts/models/cosyvoice2/layer_weights/pre_and_post_layer_weight.py index ae4d9a7..2056402 100755 --- a/light_tts/models/cosyvoice2/layer_weights/pre_and_post_layer_weight.py +++ b/light_tts/models/cosyvoice2/layer_weights/pre_and_post_layer_weight.py @@ -2,6 +2,7 @@ import numpy as np from light_tts.models.qwen2.layer_weights.pre_and_post_layer_weight import Qwen2PreAndPostLayerWeight + class CosyVoice2PreAndPostLayerWeight(Qwen2PreAndPostLayerWeight): def __init__(self, data_type, network_config, mode): super().__init__(data_type, network_config, mode) @@ -12,7 +13,7 @@ def load_hf_weights(self, weights): split_indexes = np.linspace(0, vob_size, self.tp_world_size_ + 1, dtype=np.int64) split_start = split_indexes[self.tp_rank_] split_end = split_indexes[self.tp_rank_ + 1] - + if "model.embed_tokens.weight" in weights: self.wte_weight_ = self._cuda(weights["model.embed_tokens.weight"][split_start:split_end, :]) tie_word_embeddings = self.network_config_.get("tie_word_embeddings", False) @@ -29,13 +30,24 @@ def load_hf_weights(self, weights): if "llm_decoder.weight" in weights: self.llm_decoder_weight_ = self._cuda(weights["llm_decoder.weight"]) if "llm_decoder.bias" in weights: - self.llm_decoder_bias_ = self._cuda(weights["llm_decoder.bias"][:,None]) - - if self.wte_weight_ is not None and self.llm_embedding_weight_ is not None and self.speech_embedding_weight_ is not None: - self.text_llm_audio_emb = torch.cat([self.wte_weight_, self.llm_embedding_weight_, self.speech_embedding_weight_], dim=0) + self.llm_decoder_bias_ = self._cuda(weights["llm_decoder.bias"][:, None]) + + if ( + self.wte_weight_ is not None + and self.llm_embedding_weight_ is not None + and self.speech_embedding_weight_ is not None + ): + self.text_llm_audio_emb = torch.cat( + [self.wte_weight_, self.llm_embedding_weight_, self.speech_embedding_weight_], dim=0 + ) del self.wte_weight_ del self.llm_embedding_weight_ del self.speech_embedding_weight_ + + if self.wte_weight_ is not None and self.speech_embedding_weight_ is not None: + self.text_llm_audio_emb = torch.cat([self.wte_weight_, self.speech_embedding_weight_], dim=0) + del self.wte_weight_ + del self.speech_embedding_weight_ return def verify_load(self): @@ -45,7 +57,6 @@ def verify_load(self): self.lm_head_weight_, self.final_norm_weight_, self.llm_decoder_weight_, - self.llm_decoder_bias_ ] for i in range(len(weights)): assert weights[i] is not None, "index:" + str(i) + " " + errors diff --git a/light_tts/models/cosyvoice3/__init__.py b/light_tts/models/cosyvoice3/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/light_tts/models/cosyvoice3/model.py b/light_tts/models/cosyvoice3/model.py new file mode 100755 index 0000000..d47a92f --- /dev/null +++ b/light_tts/models/cosyvoice3/model.py @@ -0,0 +1,10 @@ +from light_tts.models.cosyvoice2.model import CosyVoice2TpPartModel + + +class CosyVoice3TpPartModel(CosyVoice2TpPartModel): + def __init__(self, kvargs): + super().__init__(kvargs) + self.eos_token = self.speech_token_size + 1 + self.fill_token = self.speech_token_size + 3 + self.stop_token_ids = [self.speech_token_size + i for i in range(200)] + return diff --git a/light_tts/server/httpserver/manager.py b/light_tts/server/httpserver/manager.py index 9f48dc4..16605a5 100755 --- a/light_tts/server/httpserver/manager.py +++ b/light_tts/server/httpserver/manager.py @@ -6,6 +6,7 @@ import uvloop import time import sys + asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) from light_tts.utils.config_utils import get_config_json from light_tts.utils.log_utils import init_logger @@ -23,12 +24,7 @@ class HttpServerManager: - def __init__( - self, - args, - httpserver_port, - tts1_encode_ports - ): + def __init__(self, args, httpserver_port, tts1_encode_ports): self.args = args self.total_config = get_config_json(args.model_dir) @@ -46,20 +42,20 @@ def __init__( self.recv_from_tts_decode.bind(f"{args.zmq_mode}127.0.0.1:{httpserver_port}") self.req_id_to_out_inf: Dict[int, ReqStatus] = {} - + self.shared_speech_manager = SharedSpeechManager(f"{args.port}_cosyvoice", args.cache_capacity) self.shm_req_manager = ShmReqManager() configs = load_yaml(args.model_dir) self.model_config = configs["llm"].llm.model.model.config self.vocab_size = self.model_config.vocab_size - self.sos = self.vocab_size - self.task_id = self.vocab_size + 1 + self.sos = self.vocab_size + configs["sos"] + self.task_id = self.vocab_size + configs["task_id"] self.max_req_total_len = args.max_req_total_len - get_tokenizer = configs['get_tokenizer'] + get_tokenizer = configs["get_tokenizer"] self.tokenizer = get_tokenizer() - self.allowed_special = configs['allowed_special'] + self.allowed_special = configs["allowed_special"] # from github source code self.min_token_text_ratio = 2 @@ -71,12 +67,12 @@ def alloc_speech_mem(self, speech_md5, prompt_wav): if not have_alloc: self.shared_speech_manager.set_index_data(index, prompt_wav.shape, prompt_wav) return index, have_alloc - + async def append_bistream(self, request_dict, request_id): # 等待 request_id 出现在 dict 中（generate 可能还没执行完初始化） while request_id not in self.req_id_to_out_inf: await asyncio.sleep(0.01) - + req_status = self.req_id_to_out_inf[request_id] req = req_status.group_req_objs.shm_req_objs[0] finish = request_dict.get("finish", False) @@ -88,7 +84,6 @@ async def append_bistream(self, request_dict, request_id): with self.shm_req_manager.get_req_lock_by_index(req.index_in_shm_mem): req.append_bistream(text_ids, self.min_token_text_ratio, self.max_token_text_ratio) - async def transfer_to_next_module( self, style, @@ -112,15 +107,17 @@ async def _wait_to_token_package( await asyncio.wait_for(event.wait(), timeout=1) except asyncio.TimeoutError: pass - + if request is not None and await request.is_disconnected(): # Abort the request if the client disconnects. await self.abort(request_id) raise Exception(f"req_id {request_id} disconnected") - + async with req_status.lock: event.clear() - logger.info(f"req_id {request_id} get out data, len(out_data_info_list) {len(req_status.out_data_info_list)}") + logger.info( + f"req_id {request_id} get out data, len(out_data_info_list) {len(req_status.out_data_info_list)}" + ) if len(req_status.out_data_info_list) == 0: continue @@ -130,7 +127,6 @@ async def _wait_to_token_package( if finialize: return req_status.out_data_info_list.clear() - async def _log_req_header(self, request_headers, group_request_id: int): @@ -144,24 +140,22 @@ async def _log_req_header(self, request_headers, group_request_id: int): f"lightllm_req_id:{group_request_id} " ) return - - def _encode(self, text: str): + + def _encode(self, text: str): return self.tokenizer.encode( text, allowed_special=self.allowed_special, ) - - async def _async_encode(self, text: str): + + async def _async_encode(self, text: str): return self._encode(text) - + async def _check_and_repair_length(self, prompt_ids: List[int], semantic_len: int, sampling_params: SamplingParams): prompt_tokens = len(prompt_ids) + semantic_len if prompt_tokens + sampling_params.max_new_tokens > self.max_req_total_len: sampling_params.max_new_tokens = self.max_req_total_len - prompt_tokens if sampling_params.max_new_tokens < sampling_params.min_new_tokens: - raise ValueError( - f"The input is too long and the resulting audio will be incomplete" - ) + raise ValueError("The input is too long and the resulting audio will be incomplete") async def generate(self, request_dict, request_id, sampling_params, request=None): # 记录请求到达的相关信息 @@ -181,7 +175,7 @@ async def generate(self, request_dict, request_id, sampling_params, request=None sampling_params.max_new_tokens = len(text_ids) * self.max_token_text_ratio await self._check_and_repair_length(prompt_ids, semantic_len, sampling_params) else: - prompt_ids = prompt_text_ids + text_ids # text_cache + prompt_ids = prompt_text_ids + text_ids # text_cache sampling_params.min_new_tokens = len(text_ids) * self.min_token_text_ratio sampling_params.max_new_tokens = len(text_ids) * self.max_token_text_ratio @@ -189,7 +183,7 @@ async def generate(self, request_dict, request_id, sampling_params, request=None while req_index is None: await asyncio.sleep(0.1) req_index = await self.shm_req_manager.async_alloc_req_index() - + style = request_dict["tts_model_name"] req_obj = await self.shm_req_manager.async_get_req_obj_by_index(req_index) req_objs = [] @@ -208,13 +202,13 @@ async def generate(self, request_dict, request_id, sampling_params, request=None async for tts_speech, finish_status, finialize in results_generator: yield {"tts_speech": tts_speech} - + except Exception as e: logger.error(f"request_id: {request_id} has exception {str(e)}") await self.abort(request_id) raise e return - + async def abort(self, request_id): if request_id in self.req_id_to_out_inf: req_status = self.req_id_to_out_inf[request_id] @@ -242,14 +236,14 @@ async def recycle_resource_loop(self): req_status: ReqStatus = self.req_id_to_out_inf.get(group_req_id_, None) if req_status is not None and req_status.can_release(): release_req_status.append(req_status) - + for req_status in release_req_status: logger.info(f"release req_id {req_status.group_req_objs.shm_req_objs[0].request_id}") self.req_id_to_out_inf.pop(req_status.group_req_objs.group_req_id, None) for req in req_status.group_req_objs.shm_req_objs: await self.shm_req_manager.async_put_back_req_obj(req) await self.shm_req_manager.async_release_req_index(req.index_in_shm_mem) - + if time.time() - pre_time_mark > 20: pre_time_mark = time.time() for req_status in self.req_id_to_out_inf.values(): @@ -273,13 +267,13 @@ async def handle_loop(self): req_status = self.req_id_to_out_inf.get(group_req_id_, None) if req_status is None: continue - + for req in req_status.group_req_objs.shm_req_objs: if req.stream: if not req.out_tokens_queue.is_empty(): tts_speech, token_offset, finalize = req.out_tokens_queue.peek() tts_speech = tts_speech.copy() - + if finalize: finish_status = FinishStatus(req.finish_status.status) else: @@ -296,12 +290,13 @@ async def handle_loop(self): finish_status = FinishStatus(req.finish_status.status) async with req_status.lock: req_status.out_data_info_list.append((tts_speech.copy(), finish_status, finalize)) - logger.info(f"req_id {req.request_id} shm_index {req.index_in_shm_mem} gen finished, append to out data") + logger.info(f"req_id {req.request_id} shm_index {req.index_in_shm_mem} gen finished") req_status.event.set() self.recycle_event.set() return + class ReqStatus: def __init__(self, req_id, req_objs: List[Req], start_time) -> None: self.lock = asyncio.Lock() diff --git a/light_tts/server/tts_decode/manager.py b/light_tts/server/tts_decode/manager.py index 209345c..5776a7b 100755 --- a/light_tts/server/tts_decode/manager.py +++ b/light_tts/server/tts_decode/manager.py @@ -11,11 +11,11 @@ from light_tts.utils.load_utils import load_yaml from light_tts.utils.process_check import start_parent_check_thread + asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) import zmq import zmq.asyncio from typing import Dict, Union -import traceback from .model_infer.model_rpc import start_model_process, TTS2DecodeModelRpcClient from light_tts.utils.infer_utils import calculate_time, mark_start, mark_end @@ -25,16 +25,9 @@ logger = init_logger(__name__) + class TTSDecodeManager: - def __init__( - self, - args, - tts_decode_port, - httpserver_port, - style_name, - decode_parall_lock, - decode_proc_index - ): + def __init__(self, args, tts_decode_port, httpserver_port, style_name, decode_parall_lock, decode_proc_index): self.args = args context = zmq.asyncio.Context(2) self.recv_from_tts2_gpt = context.socket(zmq.PULL) @@ -42,7 +35,7 @@ def __init__( self.send_to_httpserver = context.socket(zmq.PUSH) self.send_to_httpserver.connect(f"{args.zmq_mode}127.0.0.1:{httpserver_port}") - self.style_name = style_name + self.style_name = style_name self.waiting_reqs = [] self.decode_parall_lock = decode_parall_lock self.decode_proc_index = decode_proc_index @@ -56,7 +49,7 @@ def __init__( self.flow_pre_lookahead_len = configs["flow"].pre_lookahead_len self.waiting_reqs = [] self.speech_token_size = configs["llm"].speech_token_size - self.eos_id = self.speech_token_size + self.eos_id = configs["eos_token"] self.decode_max_batch_size = 1 async def wait_to_model_ready(self): @@ -75,11 +68,11 @@ async def wait_to_model_ready(self): } await self.rpc_model.init_model(kvargs) return - + async def infer_decodec_batch(self, batch): await self.rpc_model.decode(batch) return - + def get_batch(self): if len(self.waiting_reqs) == 0: return [] @@ -107,7 +100,10 @@ def remove_finished_reqs(self, batch: List[DecodeReq]): for decode_req in finished_reqs: decode_req.req.can_released_mark = True logger.info(f"detoken release req_id {decode_req.req.request_id}") - logger.info(f"req_id {decode_req.req.request_id}, decode_req.req.stream {decode_req.req.stream}, decode_req.req.finished {decode_req.req.finish_status.is_finished()}") + logger.info( + f"req_id {decode_req.req.request_id}, stream {decode_req.req.stream}, " + f"finished {decode_req.req.finish_status.is_finished()}" + ) self.shm_req_manager.put_back_req_obj(decode_req.req) self.req_id_to_out.pop(decode_req.request_id, None) return @@ -133,13 +129,13 @@ async def loop_for_fwd(self): idle_count = 1000 logger.debug(f"{self.style_name} current waiting queue in tts_decode: {len(self.waiting_reqs)}") self.remove_finished_reqs(batch) - + async def handle_loop(self): # asyncio.create_task(self.timer_to_detoken()) while True: try: recv_obj = await self.recv_from_tts2_gpt.recv_pyobj() - + if isinstance(recv_obj, int): shm_req_index = recv_obj req = self.shm_req_manager.get_req_obj_by_index(shm_req_index) @@ -147,7 +143,7 @@ async def handle_loop(self): decode_req = DecodeReq(req, self.decode_token_hop_len, self.flow_pre_lookahead_len, self.eos_id) self.req_id_to_out[req.request_id] = decode_req - + elif isinstance(recv_obj, tuple): logger.info(f"Receive: | req_id: {recv_obj[0]} | {recv_obj[1]} token_ids") self.waiting_reqs.append(recv_obj[0]) @@ -168,27 +164,28 @@ def start_tts_decode_process(params_list, pipe_writer): try: for params in params_list: args, tts_decode_port, httpserver_port, style_name, decode_parall_lock, decode_proc_index = params - + tts_decodec = TTSDecodeManager( args, tts_decode_port=tts_decode_port, httpserver_port=httpserver_port, style_name=style_name, decode_parall_lock=decode_parall_lock, - decode_proc_index=decode_proc_index + decode_proc_index=decode_proc_index, ) asyncio.run(tts_decodec.wait_to_model_ready()) managers.append(tts_decodec) - except Exception as e: + except Exception: import traceback import sys + etype, evalue, tb = sys.exc_info() - err_str = '\n'.join(traceback.format_exception(etype, evalue, tb)) + err_str = "\n".join(traceback.format_exception(etype, evalue, tb)) logger.error(err_str) pipe_writer.send(err_str) raise - pipe_writer.send('init ok') + pipe_writer.send("init ok") loop = asyncio.new_event_loop() for manager in managers: loop.create_task(manager.loop_for_fwd()) diff --git a/light_tts/server/tts_decode/model_infer/model_rpc.py b/light_tts/server/tts_decode/model_infer/model_rpc.py index 71085ea..b0a2fae 100755 --- a/light_tts/server/tts_decode/model_infer/model_rpc.py +++ b/light_tts/server/tts_decode/model_infer/model_rpc.py @@ -8,54 +8,61 @@ from light_tts.utils.log_utils import init_logger from typing import List from ..decode_req import DecodeReq -from light_tts.utils.load_utils import load_yaml -from light_tts.server.tts_decode.model_infer import patch_conditional_cfm -from cosyvoice.cli.model import CosyVoice2Model +from light_tts.utils.load_utils import CosyVoiceVersion, load_yaml +from light_tts.server.tts_decode.model_infer import patch_conditional_cfm +from cosyvoice.cli.model import CosyVoice2Model, CosyVoice3Model from light_tts.server.core.objs.shm_speech_manager import SharedSpeechManager logger = init_logger(__name__) -class TTS2DecodeModelRpcServer(): + +class TTS2DecodeModelRpcServer: def init_model(self, kvargs): gpu_id = kvargs["gpu_id"] model_dir = kvargs["model_dir"] torch.cuda.set_device(gpu_id) - + configs = load_yaml(model_dir) - self.model = CosyVoice2Model( - configs['llm'], - configs['flow'], - configs['hift'], - fp16=True - ) - self.model.load('{}/llm.pt'.format(model_dir), - '{}/flow.pt'.format(model_dir), - '{}/hift.pt'.format(model_dir)) - del self.model.llm - - self.fp16 = True + version = configs["cosyvoice_version"] + + if version == CosyVoiceVersion.VERSION_2: + self.fp16 = True + self.model = CosyVoice2Model(configs["llm"], configs["flow"], configs["hift"], fp16=self.fp16) + elif version == CosyVoiceVersion.VERSION_3: + self.fp16 = False + self.model = CosyVoice3Model(configs["llm"], configs["flow"], configs["hift"], fp16=self.fp16) + self.model.load("{}/llm.pt".format(model_dir), "{}/flow.pt".format(model_dir), "{}/hift.pt".format(model_dir)) + load_jit = kvargs.get("load_jit", False) load_trt = kvargs.get("load_trt", False) trt_concurrent = 1 + + if version == CosyVoiceVersion.VERSION_3: + load_jit = False + if load_jit: - self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32')) + self.model.load_jit("{}/flow.encoder.{}.zip".format(model_dir, "fp16" if self.fp16 is True else "fp32")) if load_trt: capability = torch.cuda.get_device_capability(0) - self.model.load_trt('{}/flow.decoder.estimator.{}.sm{}{}.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32', capability[0], capability[1]), - '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir), - trt_concurrent, - self.fp16) + self.model.load_trt( + "{}/flow.decoder.estimator.{}.sm{}{}.plan".format( + model_dir, "fp16" if self.fp16 is True else "fp32", capability[0], capability[1] + ), + "{}/flow.decoder.estimator.fp32.onnx".format(model_dir), + trt_concurrent, + self.fp16, + ) self.model.hift_cache_dict = defaultdict(lambda: None) if torch.cuda.is_available(): torch.cuda.empty_cache() - + set_random_seed(2147483647) self.shared_speech_manager = SharedSpeechManager(f"{kvargs['port']}_cosyvoice", kvargs["shared_cache_capacity"]) del configs - + # @calculate_time(show=True, min_cost_ms=150) @torch.no_grad() - def forward(self, batch:List[DecodeReq]): + def forward(self, batch: List[DecodeReq]): for decode_req in batch: output_ids, speech_index, request_id, token_offset, finalize = decode_req.get_infer_data() speech_token, speech_feat, spk_embedding = self.shared_speech_manager.get_index_speech(speech_index) @@ -84,7 +91,7 @@ def forward(self, batch:List[DecodeReq]): self.model.hift_cache_dict.pop(request_id) return - def decode(self, batch:List[DecodeReq]): + def decode(self, batch: List[DecodeReq]): return self.forward(batch) @@ -94,12 +101,12 @@ def __init__(self, model_rpc): return async def init_model(self, kvargs): - ans : rpyc.AsyncResult = self.model_infer_server.init_model(kvargs) + self.model_infer_server.init_model(kvargs) return - async def decode(self, batch:List[DecodeReq]): + async def decode(self, batch: List[DecodeReq]): return self.model_infer_server.decode(batch) + async def start_model_process(): return TTS2DecodeModelRpcClient(TTS2DecodeModelRpcServer()) - diff --git a/light_tts/server/tts_encode/manager.py b/light_tts/server/tts_encode/manager.py index bfce993..9a7fbc9 100755 --- a/light_tts/server/tts_encode/manager.py +++ b/light_tts/server/tts_encode/manager.py @@ -8,11 +8,12 @@ import asyncio import time import uvloop + asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) from light_tts.utils.config_utils import get_config_json from light_tts.utils.log_utils import init_logger from light_tts.server.core.objs.shm_speech_manager import SharedSpeechManager -from light_tts.utils.load_utils import load_yaml +from light_tts.utils.load_utils import CosyVoiceVersion, load_yaml from cosyvoice.cli.frontend import CosyVoiceFrontEnd from light_tts.utils.graceful_utils import graceful_registry from light_tts.utils.process_check import start_parent_check_thread @@ -21,6 +22,7 @@ logger = init_logger(__name__) + class TTS1EncodeManager: def __init__( self, @@ -37,37 +39,43 @@ def __init__( self.recv_from_httpserver = context.socket(zmq.PULL) self.recv_from_httpserver.bind(f"{args.zmq_mode}127.0.0.1:{tts1_encode_port}") - self.waiting_reqs : List[Req] = [] + self.waiting_reqs: List[Req] = [] self.model_cfg = get_config_json(args.model_dir) self.send_to_tts_llms = {} for i, lora_w in enumerate(self.model_cfg["lora_info"]): # context = zmq.asyncio.Context() - if i % args.encode_process_num == self.index_id: # 通过id分配他需要处理的lora + if i % args.encode_process_num == self.index_id: # 通过id分配他需要处理的lora send_to_tts_llm_i = context.socket(zmq.PUSH) send_to_tts_llm_i.connect(f"{args.zmq_mode}127.0.0.1:{tts_llm_ports[i]}") - self.send_to_tts_llms[lora_w["style_name"]] = send_to_tts_llm_i + self.send_to_tts_llms[lora_w["style_name"]] = send_to_tts_llm_i # 只能有一个进程刷新内部的标记值 self.shared_speech_manager = SharedSpeechManager(f"{args.port}_cosyvoice", args.cache_capacity) self.shm_req_manager = ShmReqManager() self.world_size = 1 - self.trust_remote_code=args.trust_remote_code + self.trust_remote_code = args.trust_remote_code self.args = args configs = load_yaml(args.model_dir) self.configs = configs - self.resample_rate = configs['sample_rate'] + self.resample_rate = configs["sample_rate"] self.token_hop_len = 25 - self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'], - configs['feat_extractor'], - '{}/campplus.onnx'.format(args.model_dir), - '{}/speech_tokenizer_v2.onnx'.format(args.model_dir), - '{}/spk2info.pt'.format(args.model_dir), - configs['allowed_special']) - self.model_config = configs["llm"].llm.model.model.config - self.vocab_size = self.model_config.vocab_size + self.frontend = CosyVoiceFrontEnd( + configs["get_tokenizer"], + configs["feat_extractor"], + "{}/campplus.onnx".format(args.model_dir), + "{}/speech_tokenizer_v2.onnx".format(args.model_dir), + "{}/spk2info.pt".format(args.model_dir), + configs["allowed_special"], + ) + self.model_config = configs["llm"].llm.model.model.config + vocab_size = self.model_config.vocab_size + if configs["cosyvoice_version"] == CosyVoiceVersion.VERSION_3: + self.embed_offset = vocab_size + else: + self.embed_offset = vocab_size + 2 del configs - + def add_req(self, group_req_indexes: GroupReqIndexes): req_group = [] for req_index in group_req_indexes.shm_req_indexes: @@ -110,7 +118,9 @@ async def loop_for_fwd(self): if prompt_speech_16k is None: raise RuntimeError(f"In encode, get_index_data {speech_index} not found") prompt_speech_16k = torch.from_numpy(prompt_speech_16k.arr) - model_input = self.frontend.frontend_zero_shot('', '', prompt_speech_16k, self.resample_rate, '') + model_input = self.frontend.frontend_zero_shot( + "", "", prompt_speech_16k, self.resample_rate, "" + ) speech_token = model_input["llm_prompt_speech_token"].cpu().numpy() speech_feat = model_input["prompt_speech_feat"].squeeze(0).cpu().numpy() embedding = model_input["llm_embedding"].cpu().numpy() @@ -124,18 +134,23 @@ async def loop_for_fwd(self): speech_token = self.shared_speech_manager.get_index_speech_token(speech_index).arr[0] if not req.bistream: - speech_token = (speech_token + self.vocab_size + 2) + speech_token = speech_token + self.embed_offset audio_ids = speech_token.flatten().tolist() with self.shm_req_manager.get_req_lock_by_index(req.index_in_shm_mem): req.set_speech_token(audio_ids) - req.prompt_token_pad = int(np.ceil(speech_token.size / self.token_hop_len) * self.token_hop_len - speech_token.size) - logger.info(f"Send: {module_name:<14} | req_id {req.request_id} | semantic length {req.semantic_len} | text length {req.text_len} to tts_llm | with speech") + req.prompt_token_pad = int( + np.ceil(speech_token.size / self.token_hop_len) * self.token_hop_len - speech_token.size + ) + logger.info( + f"Send: {module_name:<14} | req_id {req.request_id} | " + f"semantic length {req.semantic_len} | text length {req.text_len} to tts_llm" + ) self.shm_req_manager.put_back_req_obj(req) self.send_to_tts_llms[tts_model_name].send_pyobj(req.index_in_shm_mem) cost_time = (time.time() - req.start_time) * 1000 logger.info(f"module {module_name} req_id {req.request_id} cost_time {cost_time} ms") - + async def loop_for_netio_req(self): while True: recv_req = await self.recv_from_httpserver.recv_pyobj() @@ -149,21 +164,16 @@ def clean_up(self): self.model_rpcs = None return + def start_tts1_encode_process(args, tts_llm_ports, tts1_encode_port, index_id, encode_parall_lock, pipe_writer): # 注册 graceful 退出的处理 # 返回当前这行代码的位置的代码对象的当前函数名字 graceful_registry(inspect.currentframe().f_code.co_name) start_parent_check_thread() - try: - encodeserver = TTS1EncodeManager( - args, - tts_llm_ports, - tts1_encode_port, - index_id, - encode_parall_lock - ) - except Exception as e: + try: + encodeserver = TTS1EncodeManager(args, tts_llm_ports, tts1_encode_port, index_id, encode_parall_lock) + except Exception: import traceback import sys @@ -174,7 +184,7 @@ def start_tts1_encode_process(args, tts_llm_ports, tts1_encode_port, index_id, e encodeserver.clean_up() raise - pipe_writer.send('init ok') + pipe_writer.send("init ok") loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.create_task(encodeserver.loop_for_fwd()) diff --git a/light_tts/server/tts_llm/manager.py b/light_tts/server/tts_llm/manager.py index 86b7f00..85a1860 100755 --- a/light_tts/server/tts_llm/manager.py +++ b/light_tts/server/tts_llm/manager.py @@ -8,6 +8,7 @@ from light_tts.server.core.objs.req import ReqRunStatus from light_tts.utils.process_check import start_parent_check_thread + asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) import concurrent.futures import zmq @@ -24,7 +25,7 @@ from light_tts.server.core.objs.shm_speech_manager import SharedSpeechManager from light_tts.common.mem_manager import ReadOnlyStaticsMemoryManager import multiprocessing as mp -from light_tts.utils.load_utils import load_yaml +from light_tts.utils.load_utils import CosyVoiceVersion, load_yaml from itertools import chain import threading from light_tts.server.tts_llm.token_load import TokenLoad @@ -35,7 +36,6 @@ class RouterManager: - def __init__(self, args, tts_llm_port, tts_decode_port, style_name, gpt_parall_lock): self.args = args self.world_size = 1 @@ -48,7 +48,7 @@ def __init__(self, args, tts_llm_port, tts_decode_port, style_name, gpt_parall_l self.is_safe_schedule = args.router_token_ratio == 0.0 self.load_way = args.load_way self.mode = args.mode - + # tts self.style_name = style_name self.gpt_parall_lock = gpt_parall_lock @@ -68,21 +68,21 @@ def __init__(self, args, tts_llm_port, tts_decode_port, style_name, gpt_parall_l self.shared_token_load.set_current_load(0.0, dp_index) self.shared_token_load.set_logical_max_load(0.0, dp_index) self.shared_token_load.set_dynamic_max_load(0.0, dp_index) - + self.pause_strategy = Fcfs() self.running_batch: Batch = None self.has_wait_tokens = 0 self.max_wait_tokens = args.router_max_wait_tokens - + context = zmq.asyncio.Context(2) self.recv_from_tts1_encode = context.socket(zmq.PULL) self.recv_from_tts1_encode.bind(f"{args.zmq_mode}127.0.0.1:{tts_llm_port}") - + self.send_to_tts_decode = context.socket(zmq.PUSH) self.send_to_tts_decode.connect(f"{args.zmq_mode}127.0.0.1:{tts_decode_port}") self.stats_tool = Stats(not args.disable_log_stats, args.log_stats_interval) - + self.shared_speech_manager = SharedSpeechManager(f"{args.port}_cosyvoice", args.cache_capacity) self.max_req_total_len = self.args.max_req_total_len self.max_total_token_num = args.max_total_token_num @@ -95,11 +95,16 @@ def __init__(self, args, tts_llm_port, tts_decode_port, style_name, gpt_parall_l self.decode_token_hop_len = 25 self.flow_pre_lookahead_len = configs["flow"].pre_lookahead_len self.vocab_size = self.model_config.vocab_size - self.eos_id = self.speech_token_size - self.fill_token_id = self.eos_id + 2 - self.sos = self.vocab_size - self.task_id = self.vocab_size + 1 + if configs["cosyvoice_version"] == CosyVoiceVersion.VERSION_3: + self.embed_offset = self.vocab_size + else: + self.embed_offset = self.vocab_size + 2 + self.eos_id = configs["eos_token"] + self.fill_token_id = configs["fill_token"] + self.sos = configs["sos"] + self.task_id = configs["task_id"] self.max_semantic_position = self.model_config.max_position_embeddings + self.version = configs["cosyvoice_version"] del configs # 调度和推理进行折叠使用的线程池 @@ -112,7 +117,7 @@ def __init__(self, args, tts_llm_port, tts_decode_port, style_name, gpt_parall_l async def wait_to_model_ready(self): # 初始化模型 self.model_rpc_servers = [] - + self.rpc_event = multiprocessing.Event() self.rpc_finished_event = multiprocessing.Event() @@ -136,16 +141,16 @@ async def wait_to_model_ready(self): ) kvargs = { - "args" : self.args, - "rank_id" : rank_id, - "world_size" : self.world_size, - "weight_dir" : self.args.model_dir, - "load_way" : self.load_way, - "max_total_token_num" : self.max_total_token_num, - "mode" : self.mode, - "max_req_num" : self.args.running_max_req_size + 8, - "max_seq_length" : self.args.max_req_total_len + 8, # 留一点余量 - "style_name" : self.style_name, + "args": self.args, + "rank_id": None, + "world_size": self.world_size, + "weight_dir": self.args.model_dir, + "load_way": self.load_way, + "max_total_token_num": self.max_total_token_num, + "mode": self.mode, + "max_req_num": self.args.running_max_req_size + 8, + "max_seq_length": self.args.max_req_total_len + 8, # 留一点余量 + "style_name": self.style_name, "cache_capacity": self.args.cache_capacity, "port": self.args.port, "speech_token_size": self.speech_token_size, @@ -154,6 +159,7 @@ async def wait_to_model_ready(self): "disable_cudagraph": self.args.disable_cudagraph, "graph_max_batch_size": self.args.graph_max_batch_size, "graph_max_len_in_batch": self.args.graph_max_len_in_batch, + "version": self.version, } await self.model_rpc_client.init_model(kvargs=kvargs) if self.max_total_token_num is None: @@ -164,39 +170,42 @@ async def wait_to_model_ready(self): logger.info(f"use req queue {self.req_queue.__class__.__name__}") return - def add_req( - self, - req: Req - ): + def add_req(self, req: Req): req.start_time = time.time() if req.bistream: - req.audio_ids = (self.shared_speech_manager.get_index_speech_token(req.speech_index).arr[0] + self.vocab_size + 2).flatten().tolist() + req.audio_ids = ( + (self.shared_speech_manager.get_index_speech_token(req.speech_index).arr[0] + self.embed_offset) + .flatten() + .tolist() + ) req.mix_ratio = self.mix_ratio - req.next_fill_index = (int(len(req.audio_ids) / self.mix_ratio[1]) + 1) * self.mix_ratio[1] - len(req.audio_ids) + req.next_fill_index = (int(len(req.audio_ids) / self.mix_ratio[1]) + 1) * self.mix_ratio[1] - len( + req.audio_ids + ) self.req_queue.append_bistream(req) else: self.req_queue.append(req) self.send_to_tts_decode.send_pyobj(req.index_in_shm_mem, protocol=pickle.HIGHEST_PROTOCOL) return - + def check_and_wait_to_has_lock(self): - if self.has_lock == False: + if not self.has_lock: ans = self.gpt_parall_lock.acquire(block=True) - assert ans == True + assert ans is True self.has_lock = True self.parall_step_counter += 1 return - + def check_and_release_lock(self): - assert self.has_lock == True - if self.has_lock == True and self.parall_step_counter >= self.parall_step_max_num: + assert self.has_lock is True + if self.has_lock and self.parall_step_counter >= self.parall_step_max_num: self.parall_step_counter = 0 self.gpt_parall_lock.release() self.has_lock = False return - + def release_lock_when_all_finish(self): - if self.has_lock == True: + if self.has_lock: self.parall_step_counter = 0 self.gpt_parall_lock.release() self.has_lock = False @@ -221,7 +230,7 @@ async def loop_for_fwd( self.stats_tool.print_stats() else: self.req_queue.update_token_load(self.running_batch, force_update=True) - + if self.running_batch is None: self.release_lock_when_all_finish() await asyncio.sleep(0.01) # 10ms @@ -243,7 +252,6 @@ def get_new_batch(): result = await self.schedule_task self.schedule_task = None return result - async def _step(self): """ @@ -292,7 +300,7 @@ async def _step(self): return return - async def _prefill_batch(self, batch:Batch): + async def _prefill_batch(self, batch: Batch): self.check_and_wait_to_has_lock() reqs = [r.to_router_rpc_obj() for r in batch.reqs] self.overlap_event.set() @@ -301,41 +309,55 @@ async def _prefill_batch(self, batch:Batch): self.check_and_release_lock() self._send_to_tts2_decodec_proc(batch) - append_prefill_req_ids = [req.request_id for req in batch.reqs if req.req_status.get_status() == ReqRunStatus.WAIT_FOR_TEXT] + append_prefill_req_ids = [ + req.request_id for req in batch.reqs if req.req_status.get_status() == ReqRunStatus.WAIT_FOR_TEXT + ] append_prefill_reqs = [batch.id_to_reqs[req_id] for req_id in append_prefill_req_ids] self.req_queue.waiting_req_bistream_list.extend(append_prefill_reqs) batch.filter_out_finished_req(self.shm_req_manager, append_prefill_req_ids) logger.debug(f"Prefill Batch: {batch.simple_log()} \n") return - - async def _decode_batch(self, batch:Batch): + + async def _decode_batch(self, batch: Batch): self.check_and_wait_to_has_lock() self.overlap_event.set() await self.model_rpc_client.decode() - + self.check_and_release_lock() self._send_to_tts2_decodec_proc(batch) - append_prefill_req_ids = [req.request_id for req in batch.reqs if req.req_status.get_status() == ReqRunStatus.WAIT_FOR_TEXT] + append_prefill_req_ids = [ + req.request_id for req in batch.reqs if req.req_status.get_status() == ReqRunStatus.WAIT_FOR_TEXT + ] append_prefill_reqs = [batch.id_to_reqs[req_id] for req_id in append_prefill_req_ids] self.req_queue.waiting_req_bistream_list.extend(append_prefill_reqs) batch.filter_out_finished_req(self.shm_req_manager, append_prefill_req_ids) return async def _filter_batch(self, batch: Batch, unfinished_req_ids, finished_req_ids: List, append_prefill_req_ids): - rets = [self.model_rpcs[tp_rank].filter_batch(batch.batch_id, unfinished_req_ids, finished_req_ids, append_prefill_req_ids) for tp_rank in range(self.world_size)] + rets = [ + self.model_rpcs[tp_rank].filter_batch( + batch.batch_id, unfinished_req_ids, finished_req_ids, append_prefill_req_ids + ) + for tp_rank in range(self.world_size) + ] await asyncio.gather(*rets) return async def _merge_batch(self, batch1, batch2): - rets = [self.model_rpcs[tp_rank].merge_batch(batch1.batch_id, batch2.batch_id) for tp_rank in range(self.world_size)] + rets = [ + self.model_rpcs[tp_rank].merge_batch(batch1.batch_id, batch2.batch_id) for tp_rank in range(self.world_size) + ] await asyncio.gather(*rets) return async def _remove_batch(self, batch, append_prefill_req_ids): - rets = [self.model_rpcs[tp_rank].remove_batch(batch.batch_id, append_prefill_req_ids) for tp_rank in range(self.world_size)] + rets = [ + self.model_rpcs[tp_rank].remove_batch(batch.batch_id, append_prefill_req_ids) + for tp_rank in range(self.world_size) + ] await asyncio.gather(*rets) return - + async def _pause_reqs(self, pasue_reqs): pasue_req_ids = [r.request_id for r in pasue_reqs] await self.model_rpc_client.pause_reqs(pasue_req_ids) @@ -349,32 +371,41 @@ def _filter_runing_batch(self): def _can_decode(self, batch: Batch): # return batch.get_batch_decode_need_tokens()[0] + self.get_used_tokens(0) <= self.max_total_token_num return True - + def get_used_tokens(self, dp_index): return self.max_total_token_num - self.read_only_statics_mem_manager.get_unrefed_token_num(dp_index) - def _send_to_tts2_decodec_proc(self, batch: Batch): def process_send(req: Req): output_len = req.get_output_len() is_finished = req.finish_status.is_finished() self.send_to_tts_decode.send_pyobj((req.request_id, output_len), protocol=pickle.HIGHEST_PROTOCOL) if is_finished: - logger.info(f"Send: tts_llm_{self.style_name} | req_id {req.request_id} | {output_len} token_ids bilv {output_len / max(1, req.input_len)}") + logger.info( + f"Send: tts_llm_{self.style_name} | req_id {req.request_id} | " + f"{output_len} token_ids bilv {output_len / max(1, req.input_len)}" + ) cost_time = (time.time() - req.start_time) * 1000 logger.info(f"module tts_llm req_id {req.request_id} cost_time {cost_time} ms") else: - logger.info(f"Send: tts_llm_{self.style_name} | req_id {req.request_id} | {output_len} token_ids | offset {offset}") - + logger.info( + f"Send: tts_llm_{self.style_name} | req_id {req.request_id} | " + f"{output_len} token_ids | offset {offset}" + ) + for req in batch.reqs: if req.router_aborted: continue - if req.stream == False: + if not req.stream: if req.finish_status.is_finished(): process_send(req) else: output_len = req.get_output_len() - this_token_hop_len = self.decode_token_hop_len + req.prompt_token_pad if req.token_offset == 0 else self.decode_token_hop_len + this_token_hop_len = ( + self.decode_token_hop_len + req.prompt_token_pad + if req.token_offset == 0 + else self.decode_token_hop_len + ) offset = this_token_hop_len + self.flow_pre_lookahead_len + req.token_offset if output_len == offset: process_send(req) @@ -390,7 +421,10 @@ async def loop_for_netio_req(self): shm_req_index = recv_req req = self.shm_req_manager.get_req_obj_by_index(shm_req_index) - logger.info(f"Receive: tts_llm | req_id {req.request_id} | {req.input_len} {req.semantic_len} token_ids | speech_index: {req.speech_index}") + logger.info( + f"Receive: tts_llm | req_id {req.request_id} | {req.input_len} {req.semantic_len} " + f"token_ids | speech_index: {req.speech_index}" + ) self.add_req(req) else: assert False, f"Error Req Inf {recv_req}" @@ -410,21 +444,22 @@ def start_tts_llm_process(args, tts_llm_port, tts_decode_port, style_name, gpt_p tts_llm_port=tts_llm_port, tts_decode_port=tts_decode_port, style_name=style_name, - gpt_parall_lock=gpt_parall_lock + gpt_parall_lock=gpt_parall_lock, ) asyncio.run(router.wait_to_model_ready()) - except Exception as e: + except Exception: import traceback import sys + etype, evalue, tb = sys.exc_info() - err_str = '\n'.join(traceback.format_exception(etype, evalue, tb)) + err_str = "\n".join(traceback.format_exception(etype, evalue, tb)) logger.error(err_str) pipe_writer.send(err_str) router.clean_up() raise - pipe_writer.send('init ok') + pipe_writer.send("init ok") loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.create_task(router.loop_for_fwd()) diff --git a/light_tts/server/tts_llm/model_infer/mode_backend/base_backend.py b/light_tts/server/tts_llm/model_infer/mode_backend/base_backend.py index 03a736d..0c65577 100644 --- a/light_tts/server/tts_llm/model_infer/mode_backend/base_backend.py +++ b/light_tts/server/tts_llm/model_infer/mode_backend/base_backend.py @@ -6,8 +6,10 @@ import socket from datetime import timedelta from typing import Dict, List, Tuple +from light_tts.models.cosyvoice3.model import CosyVoice3TpPartModel from light_tts.utils.infer_utils import set_random_seed from light_tts.utils.infer_utils import calculate_time, mark_start, mark_end +from light_tts.utils.load_utils import CosyVoiceVersion from light_tts.utils.log_utils import init_logger from light_tts.server.tts_llm.token_load import TokenLoad from light_tts.utils.dist_utils import init_distributed_env @@ -22,6 +24,8 @@ import torch.distributed as dist logger = init_logger(__name__) + + class ModeBackend: def __init__(self) -> None: self.shm_req_manager = ShmReqManager() @@ -32,12 +36,6 @@ def init_model(self, kvargs): self.is_multimodal = False self.tp_rank = kvargs["rank_id"] self.world_size = kvargs["world_size"] - self.load_way = kvargs["load_way"] - self.style_name = kvargs["style_name"] - self.mode = kvargs["mode"] - self.speech_token_size = kvargs["speech_token_size"] - self.fill_token_id = self.speech_token_size + 2 - self.eos_id = self.speech_token_size self.mix_ratio = kvargs.get("mix_ratio", [5, 15]) # dp_size_in_node 计算兼容多机纯tp的运行模式，这时候 1 // 2 == 0, 需要兼容 self.dp_size_in_node = 1 @@ -46,8 +44,8 @@ def init_model(self, kvargs): self.logger = init_logger(__name__) self.weight_dir = kvargs["weight_dir"] - max_total_token_num = kvargs["max_total_token_num"] - + version = kvargs["version"] + torch.cuda.set_device(0) init_distributed_env(kvargs) self.init_rank_infos() @@ -59,19 +57,19 @@ def init_model(self, kvargs): # custom_comm_ops.set_custom_reduce() # custom_comm_ops.set_custom_gather() - + model_kvargs = { - "weight_dir": os.path.join(self.weight_dir, 'CosyVoice-BlankEN'), - "max_total_token_num": max_total_token_num, - "load_way": self.load_way, - "pt_dir": os.path.join(self.weight_dir, 'llm.pt'), - "mode": self.mode, + "weight_dir": os.path.join(self.weight_dir, "CosyVoice-BlankEN"), + "max_total_token_num": kvargs["max_total_token_num"], + "load_way": kvargs["load_way"], + "pt_dir": os.path.join(self.weight_dir, "llm.pt"), + "mode": kvargs["mode"], "max_req_num": kvargs.get("max_req_num", 1000), "max_seq_length": kvargs.get("max_seq_length", 1024 * 5), - "use_dynamic_prompt_cache": True, # for bistream mode + "use_dynamic_prompt_cache": True, # for bistream mode "data_type": kvargs.get("data_type", "float16"), - "style_name": self.style_name, - "speech_token_size": self.speech_token_size, + "style_name": kvargs["style_name"], + "speech_token_size": kvargs.get("speech_token_size"), "graph_max_batch_size": kvargs.get("graph_max_batch_size", 16), "graph_max_len_in_batch": kvargs.get("graph_max_len_in_batch", 8196), "disable_cudagraph": kvargs.get("disable_cudagraph", False), @@ -79,13 +77,16 @@ def init_model(self, kvargs): "quant_type": kvargs.get("quant_type", None), "quant_cfg": kvargs.get("quant_cfg", None), } - + try: - self.model = CosyVoice2TpPartModel(model_kvargs) + if version == CosyVoiceVersion.VERSION_2: + self.model = CosyVoice2TpPartModel(model_kvargs) + elif version == CosyVoiceVersion.VERSION_3: + self.model = CosyVoice3TpPartModel(model_kvargs) except Exception as e: self.logger.exception(str(e)) raise e - + torch.cuda.empty_cache() set_random_seed(2147483647) @@ -103,7 +104,7 @@ def init_custom(self): def get_max_total_token_num(self): return self.model.mem_manager.size - + def prefill(self, reqs: List[Tuple]): """This method can be overridden in subclasses.""" raise NotImplementedError() @@ -119,13 +120,12 @@ def pause_reqs(self, req_ids): g_infer_context.pause_reqs(req_ids) return - + # 一些可以复用的单元功能函数 def _init_reqs(self, reqs: List[Tuple], init_req_obj=True): g_infer_context.add_reqs(reqs, init_req_obj=init_req_obj) req_ids = [e[0] for e in reqs] return req_ids - def init_rank_infos(self): self.node_world_size = get_node_world_size() diff --git a/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/impl.py b/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/impl.py index d54522b..4541d17 100755 --- a/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/impl.py +++ b/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/impl.py @@ -22,7 +22,8 @@ def prefill(self, reqs: List[Tuple]): mask = ~kwargs["b_next_fill"] next_token_ids = sample( - logits[mask], kwargs["output_token_ids"][mask], + logits[mask], + kwargs["output_token_ids"][mask], kwargs["ignore_eos"][mask], self.model.eos_token, # kwargs["bistream"][mask], @@ -32,14 +33,15 @@ def prefill(self, reqs: List[Tuple]): self.post_handle(run_reqs, next_token_ids, mask) return - + def decode(self): kwargs, run_reqs = prepare_decode_inputs(g_infer_context.infer_req_ids) logits = self.model.forward(**kwargs) mask = ~kwargs["b_next_fill"] next_token_ids = sample( - logits[mask], kwargs["output_token_ids"][mask], + logits[mask], + kwargs["output_token_ids"][mask], kwargs["ignore_eos"][mask], self.model.eos_token, # kwargs["bistream"][mask], @@ -49,7 +51,7 @@ def decode(self): self.post_handle(run_reqs, next_token_ids, mask) return - + def post_handle(self, run_reqs: List[InferReq], next_token_ids, mask): finished_req_ids = [] append_prefill_req_ids = [] @@ -65,12 +67,12 @@ def post_handle(self, run_reqs: List[InferReq], next_token_ids, mask): next_token_id = next_token_ids[index] index += 1 else: - next_token_id = self.fill_token_id + next_token_id = self.model.fill_token - if next_token_id == self.fill_token_id: + if next_token_id == self.model.fill_token: req_obj.next_fill_index = len(req_obj.output_token_ids) + self.mix_ratio[1] + 1 else: - req_obj.set_next_gen_token_id(next_token_id, next_token_id + text_vocab) # 生成的token id 需要偏移 + req_obj.set_next_gen_token_id(next_token_id, next_token_id + text_vocab) # 生成的token id 需要偏移 req_obj.cur_output_len += 1 req_obj.output_token_ids.append(next_token_id) @@ -78,7 +80,9 @@ def post_handle(self, run_reqs: List[InferReq], next_token_ids, mask): req_obj.shm_req.shm_cur_kv_len = req_obj.cur_kv_len req_obj.shm_req.shm_cur_output_len = req_obj.cur_output_len - req_obj.update_finish_status(self.model.eos_token, self.model.stop_token_ids, fill_token_id=self.fill_token_id) + req_obj.update_finish_status( + self.model.eos_token, self.model.stop_token_ids, fill_token_id=self.model.fill_token + ) if req_obj.finish_status.is_finished() or req_obj.shm_req.router_aborted: finished_req_ids.append(req_obj.shm_req.request_id) diff --git a/light_tts/server/tts_llm/model_infer/model_rpc.py b/light_tts/server/tts_llm/model_infer/model_rpc.py index 9e43876..b568643 100755 --- a/light_tts/server/tts_llm/model_infer/model_rpc.py +++ b/light_tts/server/tts_llm/model_infer/model_rpc.py @@ -18,6 +18,7 @@ logger = init_logger(__name__) + class ModelRpcServer: def __init__( self, @@ -47,48 +48,14 @@ def __init__( return def init_model(self, kvargs): - self.args = kvargs.get("args", None) - self.is_multimodal = False - self.tp_rank = kvargs["rank_id"] - self.world_size = kvargs["world_size"] - self.load_way = kvargs["load_way"] - self.style_name = kvargs["style_name"] - self.mode = kvargs["mode"] - self.speech_token_size = kvargs["speech_token_size"] - self.mix_ratio = kvargs.get("mix_ratio", [5, 15]) - - self.cache = {} - self.logger = init_logger(__name__) - - weight_dir = kvargs["weight_dir"] - max_total_token_num = kvargs["max_total_token_num"] - - model_kvargs = { - "weight_dir": os.path.join(weight_dir, 'CosyVoice-BlankEN'), - "max_total_token_num": max_total_token_num, - "load_way": self.load_way, - "pt_dir": os.path.join(weight_dir, 'llm.pt'), - "mode": self.mode, - "max_req_num": kvargs.get("max_req_num", 1000), - "max_seq_length": kvargs.get("max_seq_length", 1024 * 5), - "use_dynamic_prompt_cache": True, # for bistream mode - "data_type": kvargs.get("data_type", "float16"), - "style_name": self.style_name, - "speech_token_size": self.speech_token_size, - "graph_max_batch_size": kvargs.get("graph_max_batch_size", 16), - "graph_max_len_in_batch": kvargs.get("graph_max_len_in_batch", 8196), - "disable_cudagraph": kvargs.get("disable_cudagraph", False), - "batch_max_tokens": kvargs.get("batch_max_tokens", None), - "quant_type": kvargs.get("quant_type", None), - "quant_cfg": kvargs.get("quant_cfg", None), - } - + kvargs["rank_id"] = self.rank + # 初始化 backend，所有参数直接通过 kvargs 传递 self.backend = ContinuesBatchBackend() logger.info(f"use {self.backend.__class__.__name__}") - self.backend.init_model(kvargs) + self.backend.init_model(kvargs) return - + def prefill(self, reqs): try: return self.backend.prefill(reqs) @@ -121,7 +88,7 @@ def __init__(self, model_infer_servers: List[ModelRpcServer], world_size, rpc_ev self.model_infer_server: ModelRpcServer = model_infer_servers[0] else: self.model_infer_server: ModelRpcServer = None - + self.world_size = world_size self.use_rpc = self.world_size != 1 self.rpc_shm_params = RpcShmParams() @@ -180,7 +147,7 @@ async def pause_reqs(self, req_ids): else: self.model_infer_server.pause_reqs(req_ids) return - + async def get_max_total_token_num(self): if self.use_rpc: self.rpc_shm_params.write_func_params("get_max_total_token_num", ()) @@ -194,6 +161,7 @@ async def get_max_total_token_num(self): else: return self.model_infer_server.get_max_total_token_num() + async def start_model_process( args, rank, diff --git a/light_tts/utils/load_utils.py b/light_tts/utils/load_utils.py index e8bbb6f..2c05ad4 100755 --- a/light_tts/utils/load_utils.py +++ b/light_tts/utils/load_utils.py @@ -1,11 +1,62 @@ import os from hyperpyyaml import load_hyperpyyaml import sys +from enum import Enum ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) -sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR)) +sys.path.append("{}/../../third_party/Matcha-TTS".format(ROOT_DIR)) + + +class CosyVoiceVersion(Enum): + """CosyVoice版本枚举类""" + + VERSION_2 = 2 + VERSION_3 = 3 + + @classmethod + def from_config_file(cls, model_dir): + """从模型目录中检测配置文件版本""" + config_v3 = os.path.join(model_dir, "cosyvoice3.yaml") + config_v2 = os.path.join(model_dir, "cosyvoice2.yaml") + + if os.path.exists(config_v3): + return cls.VERSION_3 + elif os.path.exists(config_v2): + return cls.VERSION_2 + else: + raise FileNotFoundError(f"未找到配置文件：在 {model_dir} 中找不到 cosyvoice2.yaml 或 cosyvoice3.yaml") + + def get_config_path(self, model_dir): + """获取配置文件名""" + return os.path.join(model_dir, f"cosyvoice{self.value}.yaml") + + # 对于多进程来说不行 def load_yaml(model_dir): - with open('{}/cosyvoice2.yaml'.format(model_dir), 'r') as f: - configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')}) - return configs \ No newline at end of file + """加载配置文件，自动检测版本""" + version = CosyVoiceVersion.from_config_file(model_dir) + config_path = version.get_config_path(model_dir) + + with open(config_path, "r") as f: + configs = load_hyperpyyaml(f, overrides={"qwen_pretrain_path": os.path.join(model_dir, "CosyVoice-BlankEN")}) + + # 将版本信息添加到配置中 + configs["cosyvoice_version"] = version + + # 根据版本设置特殊 token + speech_token_size = configs["llm"].speech_token_size + + if version == CosyVoiceVersion.VERSION_3: + # CosyVoice3 的特殊 token 配置 + configs["sos"] = speech_token_size + 0 + configs["task_id"] = speech_token_size + 2 + configs["eos_token"] = speech_token_size + 1 + configs["fill_token"] = speech_token_size + 3 + else: # VERSION_2 + # CosyVoice2 的特殊 token 配置 + configs["sos"] = 0 + configs["task_id"] = 1 + configs["eos_token"] = speech_token_size + configs["fill_token"] = speech_token_size + 2 + + return configs From c84fa2ae366f7dc9625f4e45b6d064a07b0ab722 Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Tue, 23 Dec 2025 09:08:59 +0000 Subject: [PATCH 07/21] add pre-commit ci --- .github/workflow/pre-commit.yml | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 .github/workflow/pre-commit.yml diff --git a/.github/workflow/pre-commit.yml b/.github/workflow/pre-commit.yml new file mode 100644 index 0000000..456ae46 --- /dev/null +++ b/.github/workflow/pre-commit.yml @@ -0,0 +1,26 @@ +name: Pre-commit checks + +on: + pull_request: + branches: + - main + +jobs: + pre-commit: + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + # fetch-depth: 0 对差异检测很重要 + fetch-depth: 0 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.9' + + - name: Run pre-commit + uses: pre-commit/action@v3.0.1 + # 这个 Action 会自动寻找 .pre-commit-config.yaml 并只对 PR 修改的文件运行 \ No newline at end of file From e023caf3209639024f56ddc3a32b281b65de7d9c Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Tue, 23 Dec 2025 09:16:48 +0000 Subject: [PATCH 08/21] fix .github --- .github/{workflow => workflows}/pre-commit.yml | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename .github/{workflow => workflows}/pre-commit.yml (100%) diff --git a/.github/workflow/pre-commit.yml b/.github/workflows/pre-commit.yml similarity index 100% rename from .github/workflow/pre-commit.yml rename to .github/workflows/pre-commit.yml From 66a035df36778a4b504baf4fee838f8f151ff243 Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Tue, 23 Dec 2025 09:40:32 +0000 Subject: [PATCH 09/21] normal startup --- README.md | 4 +- .../pre_and_post_layer_weight.py | 4 +- light_tts/server/api_http.py | 107 ++++++++++-------- light_tts/server/tts_encode/manager.py | 8 +- 4 files changed, 73 insertions(+), 50 deletions(-) diff --git a/README.md b/README.md index 1503a60..1a3cf78 100755 --- a/README.md +++ b/README.md @@ -86,8 +86,8 @@ pip install ttsfrd-0.4.2-cp310-cp310-linux_x86_64.whl ```bash # It is recommended to enable the load_trt parameter for acceleration. -# The default is fp16 mode. -python -m light_tts.server.api_server --model_dir ./pretrained_models/CosyVoice2-0.5B-latest --load_trt True --max_total_token_num 65536 --max_req_total_len 32768 +# token2wav: The default is fp16 mode for CosyVoice2 and fp32 mode for CosyVoice3. +python -m light_tts.server.api_server --model_dir ./pretrained_models/CosyVoice2-0.5B-latest ``` - max_total_token_num: llm arg, the total token nums the gpu and model can support, equals = `max_batch * (input_len + output_len)` diff --git a/light_tts/models/cosyvoice2/layer_weights/pre_and_post_layer_weight.py b/light_tts/models/cosyvoice2/layer_weights/pre_and_post_layer_weight.py index 2056402..624b100 100755 --- a/light_tts/models/cosyvoice2/layer_weights/pre_and_post_layer_weight.py +++ b/light_tts/models/cosyvoice2/layer_weights/pre_and_post_layer_weight.py @@ -21,6 +21,7 @@ def load_hf_weights(self, weights): self.lm_head_weight_ = self.wte_weight_ if "lm_head.weight" in weights: self.lm_head_weight_ = self._cuda(weights["lm_head.weight"][split_start:split_end, :]) + self.llm_embedding_weight_ = None if "llm_embedding.weight" in weights: self.llm_embedding_weight_ = self._cuda(weights["llm_embedding.weight"]) if "speech_embedding.weight" in weights: @@ -43,8 +44,7 @@ def load_hf_weights(self, weights): del self.wte_weight_ del self.llm_embedding_weight_ del self.speech_embedding_weight_ - - if self.wte_weight_ is not None and self.speech_embedding_weight_ is not None: + elif self.wte_weight_ is not None and self.speech_embedding_weight_ is not None: self.text_llm_audio_emb = torch.cat([self.wte_weight_, self.speech_embedding_weight_], dim=0) del self.wte_weight_ del self.speech_embedding_weight_ diff --git a/light_tts/server/api_http.py b/light_tts/server/api_http.py index 03b4973..2198b45 100755 --- a/light_tts/server/api_http.py +++ b/light_tts/server/api_http.py @@ -31,17 +31,18 @@ # 抑制常见的库警告 import warnings -warnings.filterwarnings('ignore', category=FutureWarning, module='diffusers') -warnings.filterwarnings('ignore', category=UserWarning, message='.*weight_norm.*') -warnings.filterwarnings('ignore', category=UserWarning, module='onnxruntime') -sys.path.insert(0, str(Path(__file__).parent.parent.parent / 'cosyvoice')) -from light_tts.utils.load_utils import load_yaml +warnings.filterwarnings("ignore", category=FutureWarning, module="diffusers") +warnings.filterwarnings("ignore", category=UserWarning, message=".*weight_norm.*") +warnings.filterwarnings("ignore", category=UserWarning, module="onnxruntime") + +sys.path.insert(0, str(Path(__file__).parent.parent.parent / "cosyvoice")) +from light_tts.utils.load_utils import CosyVoiceVersion, load_yaml + asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) import argparse import json from http import HTTPStatus -import multiprocessing as mp import numpy as np from fastapi import FastAPI, UploadFile, Form, File, BackgroundTasks, Request, WebSocketDisconnect, WebSocket from fastapi.responses import Response, StreamingResponse, JSONResponse @@ -68,10 +69,11 @@ from cosyvoice.cli.frontend import CosyVoiceFrontEnd from light_tts.utils.process_check import is_process_active from prometheus_client import Counter, Histogram, generate_latest + all_request_counter = Counter("lightllm_request_count", "The total number of requests") failure_request_counter = Counter("lightllm_request_failure", "The total number of requests") sucess_request_counter = Counter("lightllm_request_success", "The total number of requests") -request_latency_histogram = Histogram("lightllm_request_latency", "Request latency", ['route']) +request_latency_histogram = Histogram("lightllm_request_latency", "Request latency", ["route"]) from dataclasses import dataclass from .health_monitor import start_health_check_process from light_tts.utils.log_utils import init_logger @@ -82,6 +84,7 @@ g_id_gen = ReqIDGenerator() lora_styles = ["CosyVoice2"] + @dataclass class G_Objs: app: FastAPI = None @@ -92,28 +95,34 @@ class G_Objs: def set_args(self, args): self.args = args self.httpserver_manager = HttpServerManager( - args, - httpserver_port=args.httpserver_port, - tts1_encode_ports=args.tts1_encode_ports + args, httpserver_port=args.httpserver_port, tts1_encode_ports=args.tts1_encode_ports ) self.shared_token_load = TokenLoad(f"{get_unique_server_name()}_shared_token_load", 1) configs = load_yaml(args.model_dir) - self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'], - configs['feat_extractor'], - '{}/campplus.onnx'.format(args.model_dir), - '{}/speech_tokenizer_v2.onnx'.format(args.model_dir), - '{}/spk2info.pt'.format(args.model_dir), - configs['allowed_special']) + if configs["cosyvoice_version"] == CosyVoiceVersion.VERSION_2: + speech_tokenizer_model = "{}/speech_tokenizer_v2.onnx".format(args.model_dir) + else: + speech_tokenizer_model = "{}/speech_tokenizer_v3.onnx".format(args.model_dir) + self.frontend = CosyVoiceFrontEnd( + configs["get_tokenizer"], + configs["feat_extractor"], + "{}/campplus.onnx".format(args.model_dir), + speech_tokenizer_model, + "{}/spk2info.pt".format(args.model_dir), + configs["allowed_special"], + ) del self.frontend.feat_extractor del self.frontend.campplus_session del self.frontend.speech_tokenizer_session if torch.cuda.is_available(): torch.cuda.empty_cache() + g_objs = G_Objs() app = FastAPI() g_objs.app = app + def create_error_response(status_code: HTTPStatus, message: str) -> JSONResponse: return JSONResponse({"message": message}, status_code=status_code.value) @@ -129,41 +138,48 @@ async def healthcheck(): return JSONResponse({"message": "Ok"}, status_code=200) else: return JSONResponse({"message": "Error"}, status_code=404) - + + @app.get("/liveness") def liveness(): return {"status": "ok"} + @app.get("/readiness") def readiness(): return {"status": "ok"} + def generate_data(model_output): for i in model_output: - tts_audio = (i['tts_speech'] * (2 ** 15)).astype(np.int16).tobytes() + tts_audio = (i["tts_speech"] * (2 ** 15)).astype(np.int16).tobytes() yield tts_audio + async def generate_data_stream(generate_objs): for generator in generate_objs: async for i in generator: - tts_audio = (i['tts_speech'] * (2 ** 15)).astype(np.int16).tobytes() + tts_audio = (i["tts_speech"] * (2 ** 15)).astype(np.int16).tobytes() yield tts_audio + def calculate_md5(file: UploadFile) -> str: hash_md5 = hashlib.md5() while chunk := file.read(8192): # 分块读取以支持大文件 hash_md5.update(chunk) return hash_md5.hexdigest() + async def send_wav(websocket: WebSocket, generator): async for result in generator: if isinstance(result, dict): - await websocket.send_bytes((result['tts_speech'] * (2 ** 15)).astype(np.int16).tobytes()) + await websocket.send_bytes((result["tts_speech"] * (2 ** 15)).astype(np.int16).tobytes()) continue websocket.close() return + @app.websocket("/inference_zero_shot_bistream") async def inference_zero_shot_bistream(websocket: WebSocket): global lora_styles @@ -181,14 +197,14 @@ async def inference_zero_shot_bistream(websocket: WebSocket): prompt_wav_data = await websocket.receive_bytes() wav_bytes_io = BytesIO(prompt_wav_data) prompt_speech_16k = load_wav(wav_bytes_io, 16000) - semantic_len = (prompt_speech_16k.shape[1] + 239) // 640 + 10 # + 10 for safe + semantic_len = (prompt_speech_16k.shape[1] + 239) // 640 + 10 # + 10 for safe wav_bytes_io.seek(0) speech_md5 = calculate_md5(wav_bytes_io) - + if tts_model_name == "default": tts_model_name = lora_styles[0] - + speech_index, have_alloc = g_objs.httpserver_manager.alloc_speech_mem(speech_md5, prompt_speech_16k) request_id = g_id_gen.generate_id() first_text = True @@ -212,7 +228,7 @@ async def inference_zero_shot_bistream(websocket: WebSocket): "speech_index": speech_index, "semantic_len": semantic_len, "bistream": True, - "append": not first_text + "append": not first_text, } if input_data.get("finish", False): cur_req_dict["finish"] = True @@ -220,9 +236,7 @@ async def inference_zero_shot_bistream(websocket: WebSocket): await g_objs.httpserver_manager.append_bistream(cur_req_dict, request_id) break elif first_text: - generator = g_objs.httpserver_manager.generate( - cur_req_dict, request_id, sampling_params - ) + generator = g_objs.httpserver_manager.generate(cur_req_dict, request_id, sampling_params) process_task = asyncio.create_task(send_wav(websocket, generator)) first_text = False else: @@ -233,9 +247,7 @@ async def inference_zero_shot_bistream(websocket: WebSocket): await g_objs.httpserver_manager.abort(request_id) except Exception as e: traceback.print_exc() - await websocket.send_json({ - "error": f"Server error: {str(e)}" - }) + await websocket.send_json({"error": f"Server error: {str(e)}"}) finally: await websocket.close() if process_task is not None: @@ -245,14 +257,14 @@ async def inference_zero_shot_bistream(websocket: WebSocket): @histogram_timer(request_latency_histogram) @app.post("/inference_zero_shot") async def inference_zero_shot( - request: Request, - tts_text: str = Form(), - prompt_text: str = Form(), - prompt_wav: UploadFile = File(), - stream: bool = Form(default=False), - tts_model_name: str = Form(default="default"), - speed: float = Form(default=1.0), - ): + request: Request, + tts_text: str = Form(), + prompt_text: str = Form(), + prompt_wav: UploadFile = File(), + stream: bool = Form(default=False), + tts_model_name: str = Form(default="default"), + speed: float = Form(default=1.0), +): all_request_counter.inc() global lora_styles @@ -261,7 +273,7 @@ async def inference_zero_shot( if tts_model_name == "default": tts_model_name = lora_styles[0] - + # 检查请求参数 sample_params_dict = {} sampling_params = SamplingParams() @@ -270,13 +282,13 @@ async def inference_zero_shot( prompt_text = g_objs.frontend.text_normalize(prompt_text, split=False) tts_texts = g_objs.frontend.text_normalize(tts_text, split=True) prompt_speech_16k = load_wav(prompt_wav.file, 16000) - semantic_len = (prompt_speech_16k.shape[1] + 239) // 640 + 10 # + 10 for safe - + semantic_len = (prompt_speech_16k.shape[1] + 239) // 640 + 10 # + 10 for safe + prompt_wav.file.seek(0) speech_md5 = calculate_md5(prompt_wav.file) generate_objs = [] - need_extract_speech=True + need_extract_speech = True speech_index, have_alloc = g_objs.httpserver_manager.alloc_speech_mem(speech_md5, prompt_speech_16k) request_ids = [] @@ -313,11 +325,12 @@ async def inference_zero_shot( for generator in generate_objs: async for result in generator: ans_objs.append(result) - return StreamingResponse(generate_data(ans_objs)) + return StreamingResponse(generate_data(ans_objs)) except Exception as e: logger.error("An error occurred: %s", str(e), exc_info=True) return create_error_response(HTTPStatus.EXPECTATION_FAILED, str(e)) + @app.post("/query_tts_model") @app.get("/query_tts_model") async def show_available_styles(request: Request) -> Response: @@ -325,13 +338,15 @@ async def show_available_styles(request: Request) -> Response: json_data = json.dumps(data) return Response(content=json_data, media_type="application/json") + @app.get("/metrics") async def metrics() -> Response: metrics_data = generate_latest() response = Response(metrics_data) - response.mimetype = 'text/plain' + response.mimetype = "text/plain" return response + @app.on_event("shutdown") async def shutdown(): logger.info("Received signal to shutdown. Performing graceful shutdown...") @@ -340,6 +355,7 @@ async def shutdown(): # 杀掉所有子进程 import psutil import signal + parent = psutil.Process(os.getpid()) children = parent.children(recursive=True) for child in children: @@ -347,6 +363,7 @@ async def shutdown(): logger.info("Graceful shutdown completed.") return + @app.on_event("startup") async def startup_event(): logger.info("=" * 60) @@ -354,7 +371,7 @@ async def startup_event(): loop = asyncio.get_event_loop() g_objs.set_args(get_env_start_args()) loop.create_task(g_objs.httpserver_manager.handle_loop()) - logger.info(f"✅ Application ready! Server is now accepting requests") + logger.info("✅ Application ready! Server is now accepting requests") logger.info(f"🌐 Listening at: http://{g_objs.args.host}:{g_objs.args.port}") logger.info("=" * 60) return diff --git a/light_tts/server/tts_encode/manager.py b/light_tts/server/tts_encode/manager.py index 9a7fbc9..0497bb5 100755 --- a/light_tts/server/tts_encode/manager.py +++ b/light_tts/server/tts_encode/manager.py @@ -60,11 +60,17 @@ def __init__( self.configs = configs self.resample_rate = configs["sample_rate"] self.token_hop_len = 25 + + if configs["cosyvoice_version"] == CosyVoiceVersion.VERSION_2: + speech_tokenizer_model = "{}/speech_tokenizer_v2.onnx".format(args.model_dir) + else: + speech_tokenizer_model = "{}/speech_tokenizer_v3.onnx".format(args.model_dir) + self.frontend = CosyVoiceFrontEnd( configs["get_tokenizer"], configs["feat_extractor"], "{}/campplus.onnx".format(args.model_dir), - "{}/speech_tokenizer_v2.onnx".format(args.model_dir), + speech_tokenizer_model, "{}/spk2info.pt".format(args.model_dir), configs["allowed_special"], ) From 1e1eb24cbb74a60b3b5ac4d40e63207d8579b33d Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Wed, 24 Dec 2025 07:08:14 +0000 Subject: [PATCH 10/21] sounds good --- light_tts/models/cosyvoice2/model.py | 13 +- light_tts/models/cosyvoice3/model.py | 4 + .../context_flashattention_nopad.py | 11 +- light_tts/server/api_cli.py | 117 ++++++++++++------ light_tts/server/tts_llm/manager.py | 1 + .../mode_backend/continues_batch/impl.py | 2 +- 6 files changed, 100 insertions(+), 48 deletions(-) diff --git a/light_tts/models/cosyvoice2/model.py b/light_tts/models/cosyvoice2/model.py index 5eb56b2..4646528 100755 --- a/light_tts/models/cosyvoice2/model.py +++ b/light_tts/models/cosyvoice2/model.py @@ -29,19 +29,20 @@ def __init__(self, kvargs): def _init_config(self): super()._init_config() - self.text_vob_size = self.config["vocab_size"] - + self.embed_offset = self.config["vocab_size"] + 2 def _init_weights(self): self.pre_post_weight = self.pre_and_post_weight_class( self.data_type, network_config=self.config, mode=self.mode ) self.trans_layers_weight = [ - self.transformer_weight_class(i, torch.float16, network_config=self.config, mode=self.mode, quant_cfg=self.quant_cfg) + self.transformer_weight_class( + i, self.data_type, network_config=self.config, mode=self.mode, quant_cfg=self.quant_cfg + ) for i in range(self.config["n_layer"]) ] self.weight_dict = torch.load(self.pt_dir, map_location="cpu") - self.weight_dict = {k.replace("llm.model.", ''): self.weight_dict[k] for k in self.weight_dict.keys()} + self.weight_dict = {k.replace("llm.model.", ""): self.weight_dict[k] for k in self.weight_dict.keys()} load_hf_weights( self.data_type, weight_dir=self.weight_dir_, @@ -50,5 +51,5 @@ def _init_weights(self): weight_dict=self.weight_dict, ) self.pre_post_weight.verify_load() - [weight.verify_load() for weight in self.trans_layers_weight] - return \ No newline at end of file + [weight.verify_load() for weight in self.trans_layers_weight] + return diff --git a/light_tts/models/cosyvoice3/model.py b/light_tts/models/cosyvoice3/model.py index d47a92f..6a5ed0c 100755 --- a/light_tts/models/cosyvoice3/model.py +++ b/light_tts/models/cosyvoice3/model.py @@ -8,3 +8,7 @@ def __init__(self, kvargs): self.fill_token = self.speech_token_size + 3 self.stop_token_ids = [self.speech_token_size + i for i in range(200)] return + + def _init_config(self): + super()._init_config() + self.embed_offset = self.config["vocab_size"] diff --git a/light_tts/models/llama/triton_kernel/context_flashattention_nopad.py b/light_tts/models/llama/triton_kernel/context_flashattention_nopad.py index c73d4d4..4881a1d 100755 --- a/light_tts/models/llama/triton_kernel/context_flashattention_nopad.py +++ b/light_tts/models/llama/triton_kernel/context_flashattention_nopad.py @@ -9,6 +9,7 @@ TESLA = "Tesla" in torch.cuda.get_device_name(0) + @triton.jit def _fwd_kernel_no_prompt_cache( Q, @@ -175,6 +176,7 @@ def context_attention_fwd_no_prompt_cache(q, k, v, o, b_start_loc, b_seq_len, ma ) return + @triton.jit def _fwd_kernel( Q, @@ -283,11 +285,17 @@ def _fwd_kernel( out_ptrs = Out + off_o tl.store(out_ptrs, acc, mask=offs_m[:, None] < cur_batch_seq_len) + @torch.no_grad() def context_attention_fwd( q, k, v, o, b_req_idx, b_start_loc, b_seq_len, b_prompt_cache_len, max_input_len, req_to_token_indexs ): - BLOCK_M = 128 if not TESLA else 64 + if q.dtype == torch.float16 or q.dtype == torch.bfloat16: + BLOCK_M = 128 if not TESLA else 64 + elif q.dtype == torch.float32: + BLOCK_M = 64 if not TESLA else 32 + else: + raise ValueError(f"Unsupported data type: {q.dtype}") # shape constraints Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1] assert Lq == Lk and Lk == Lv @@ -339,6 +347,7 @@ def context_attention_fwd( num_stages=num_stages, ) + @triton.jit def _fwd_kernel_int8kv( Q, diff --git a/light_tts/server/api_cli.py b/light_tts/server/api_cli.py index 506cbcc..f869ba8 100755 --- a/light_tts/server/api_cli.py +++ b/light_tts/server/api_cli.py @@ -1,5 +1,6 @@ import argparse + def make_argument_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() parser.add_argument("--host", type=str, default="localhost") @@ -11,21 +12,47 @@ def make_argument_parser() -> argparse.ArgumentParser: default="ipc:///tmp/", help="use socket mode or ipc mode, only can be set in ['tcp://', 'ipc:///tmp/']", ) - parser.add_argument("--model_dir", type=str, default=None, - help="the model weight dir path, the app will load config, weights and tokenizer from this dir") - parser.add_argument("--tokenizer_mode", type=str, default="slow", - help="""tokenizer load mode, can be slow or auto, slow mode load fast but run slow, slow mode is good for debug and test, - when you want to get best performance, try auto mode""") - parser.add_argument("--load_way", type=str, default="HF", - help="the way of loading model weights, the default is HF(Huggingface format), llama also supports DS(Deepspeed)") - parser.add_argument("--max_total_token_num", type=int, default=64 * 1024, - help="the total token nums the gpu and model can support, equals = max_batch * (input_len + output_len)") - parser.add_argument("--batch_max_tokens", type=int, default=None, - help="max tokens num for new cat batch, it control prefill batch size to Preventing OOM") - parser.add_argument("--running_max_req_size", type=int, default=100, - help="the max size for forward requests in the same time") - parser.add_argument("--max_req_total_len", type=int, default=32 * 1024, - help="the max value for req_input_len + req_output_len") + parser.add_argument( + "--model_dir", + type=str, + default=None, + help="the model weight dir path, the app will load config, weights and tokenizer from this dir", + ) + parser.add_argument( + "--tokenizer_mode", + type=str, + default="slow", + help="""tokenizer load mode, can be slow or auto, slow mode load fast but run slow, slow mode is good for debug and test, + when you want to get best performance, try auto mode""", + ) + parser.add_argument( + "--load_way", + type=str, + default="HF", + help="the way of loading model weights, the default is HF(Huggingface format), " + "llama also supports DS(Deepspeed)", + ) + parser.add_argument( + "--data_type", type=str, default="float16", help="the data type for model inference, default is float16" + ) + parser.add_argument( + "--max_total_token_num", + type=int, + default=64 * 1024, + help="the total token nums the gpu and model can support, equals = max_batch * (input_len + output_len)", + ) + parser.add_argument( + "--batch_max_tokens", + type=int, + default=None, + help="max tokens num for new cat batch, it control prefill batch size to Preventing OOM", + ) + parser.add_argument( + "--running_max_req_size", type=int, default=100, help="the max size for forward requests in the same time" + ) + parser.add_argument( + "--max_req_total_len", type=int, default=32 * 1024, help="the max value for req_input_len + req_output_len" + ) parser.add_argument("--encode_process_num", type=int, default=1) parser.add_argument("--decode_process_num", type=int, default=1) parser.add_argument("--encode_paral_num", type=int, default=50) @@ -33,41 +60,51 @@ def make_argument_parser() -> argparse.ArgumentParser: parser.add_argument("--gpt_paral_step_num", type=int, default=200) parser.add_argument("--decode_paral_num", type=int, default=1) parser.add_argument("--decode_max_batch_size", type=int, default=1) - parser.add_argument("--mode", type=str, default=["triton_flashdecoding"], nargs='+', - help="""Model mode: [triton_int8kv | ppl_int8kv | ppl_fp16 | triton_flashdecoding - | triton_gqa_attention | triton_gqa_flashdecoding] - [triton_int8weight | triton_int4weight | lmdeploy_int4weight | ppl_int4weight], + parser.add_argument( + "--mode", + type=str, + default=["triton_flashdecoding"], + nargs="+", + help="""Model mode: [triton_int8kv | ppl_int8kv | ppl_fp16 | triton_flashdecoding + | triton_gqa_attention | triton_gqa_flashdecoding] + [triton_int8weight | triton_int4weight | lmdeploy_int4weight | ppl_int4weight], triton_flashdecoding mode is for long context, current support llama llama2 qwen; triton_gqa_attention and triton_gqa_flashdecoding is fast kernel for model which use GQA; triton_int8kv mode use int8 to store kv cache, can increase token capacity, use triton kernel; ppl_int8kv mode use int8 to store kv cache, and use ppl fast kernel; ppl_fp16 mode use ppl fast fp16 decode attention kernel; - triton_int8weight and triton_int4weight and lmdeploy_int4weight or ppl_int4weight mode use int8 and int4 to store weights; - you need to read source code to make sure the supported detail mode for all models""") - parser.add_argument("--trust_remote_code", action='store_true', - help="Whether or not to allow for custom models defined on the Hub in their own modeling files.") - parser.add_argument("--disable_log_stats", action='store_true', - help="disable logging throughput stats.") - parser.add_argument("--log_stats_interval", type=int, default=10, - help="log stats interval in second.") - parser.add_argument("--router_token_ratio", type=float, default=0.0, - help="token ratio to control router dispatch") - parser.add_argument("--router_max_new_token_len", type=int, default=1024, - help="the request max new token len for router") + triton_int8weight and triton_int4weight and lmdeploy_int4weight or ppl_int4weight mode use int8 + and int4 to store weights, you can use --data_type to specify the data type for model inference; + you need to read source code to make sure the supported detail mode for all models""", + ) + parser.add_argument( + "--trust_remote_code", + action="store_true", + help="Whether or not to allow for custom models defined on the Hub in their own modeling files.", + ) + parser.add_argument("--disable_log_stats", action="store_true", help="disable logging throughput stats.") + parser.add_argument("--log_stats_interval", type=int, default=10, help="log stats interval in second.") + parser.add_argument("--router_token_ratio", type=float, default=0.0, help="token ratio to control router dispatch") + parser.add_argument( + "--router_max_new_token_len", type=int, default=1024, help="the request max new token len for router" + ) parser.add_argument( "--router_max_wait_tokens", type=int, default=8, help="schedule new requests after every router_max_wait_tokens decode steps.", ) - parser.add_argument("--cache_capacity", type=int, default=200, - help="prompt wav cache capacity, cache server capacity for multimodal resources") - parser.add_argument("--cache_reserved_ratio", type=float, default=0.5, - help="cache server reserved capacity ratio after clear") - parser.add_argument("--sample_close", action='store_true', - help="close sample function for tts_llm") - parser.add_argument("--health_monitor", action='store_true', - help="health check time interval") + parser.add_argument( + "--cache_capacity", + type=int, + default=200, + help="prompt wav cache capacity, cache server capacity for multimodal resources", + ) + parser.add_argument( + "--cache_reserved_ratio", type=float, default=0.5, help="cache server reserved capacity ratio after clear" + ) + parser.add_argument("--sample_close", action="store_true", help="close sample function for tts_llm") + parser.add_argument("--health_monitor", action="store_true", help="health check time interval") parser.add_argument("--disable_cudagraph", action="store_true", help="Disable the cudagraph of the decoding stage") parser.add_argument( "--graph_max_batch_size", @@ -95,4 +132,4 @@ def make_argument_parser() -> argparse.ArgumentParser: default=True, help="Whether to load the flow_decoder in trt.", ) - return parser \ No newline at end of file + return parser diff --git a/light_tts/server/tts_llm/manager.py b/light_tts/server/tts_llm/manager.py index 85a1860..59d6ca2 100755 --- a/light_tts/server/tts_llm/manager.py +++ b/light_tts/server/tts_llm/manager.py @@ -159,6 +159,7 @@ async def wait_to_model_ready(self): "disable_cudagraph": self.args.disable_cudagraph, "graph_max_batch_size": self.args.graph_max_batch_size, "graph_max_len_in_batch": self.args.graph_max_len_in_batch, + "data_type": getattr(self.args, "data_type", "float16"), "version": self.version, } await self.model_rpc_client.init_model(kvargs=kvargs) diff --git a/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/impl.py b/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/impl.py index 4541d17..0ef49f3 100755 --- a/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/impl.py +++ b/light_tts/server/tts_llm/model_infer/mode_backend/continues_batch/impl.py @@ -58,7 +58,7 @@ def post_handle(self, run_reqs: List[InferReq], next_token_ids, mask): index = 0 # 2 for and - text_vocab = self.model.text_vob_size + 2 + text_vocab = self.model.embed_offset for req_obj in run_reqs: req_obj: InferReq = req_obj req_obj.cur_kv_len = req_obj.get_cur_total_len() From b6829913f1f00e373a88420685e6d99d46a24942 Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Wed, 24 Dec 2025 07:19:43 +0000 Subject: [PATCH 11/21] lightweight load_yaml to speedup startup --- light_tts/server/api_http.py | 4 +- light_tts/server/httpserver/manager.py | 4 +- light_tts/server/tts_decode/manager.py | 4 +- light_tts/server/tts_encode/manager.py | 4 +- light_tts/server/tts_llm/manager.py | 4 +- light_tts/utils/load_utils.py | 350 ++++++++++++++++++++++++- 6 files changed, 359 insertions(+), 11 deletions(-) diff --git a/light_tts/server/api_http.py b/light_tts/server/api_http.py index 2198b45..59655a5 100755 --- a/light_tts/server/api_http.py +++ b/light_tts/server/api_http.py @@ -37,7 +37,7 @@ warnings.filterwarnings("ignore", category=UserWarning, module="onnxruntime") sys.path.insert(0, str(Path(__file__).parent.parent.parent / "cosyvoice")) -from light_tts.utils.load_utils import CosyVoiceVersion, load_yaml +from light_tts.utils.load_utils import CosyVoiceVersion, load_yaml_frontend asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) import argparse @@ -98,7 +98,7 @@ def set_args(self, args): args, httpserver_port=args.httpserver_port, tts1_encode_ports=args.tts1_encode_ports ) self.shared_token_load = TokenLoad(f"{get_unique_server_name()}_shared_token_load", 1) - configs = load_yaml(args.model_dir) + configs = load_yaml_frontend(args.model_dir) if configs["cosyvoice_version"] == CosyVoiceVersion.VERSION_2: speech_tokenizer_model = "{}/speech_tokenizer_v2.onnx".format(args.model_dir) else: diff --git a/light_tts/server/httpserver/manager.py b/light_tts/server/httpserver/manager.py index 16605a5..1d237c9 100755 --- a/light_tts/server/httpserver/manager.py +++ b/light_tts/server/httpserver/manager.py @@ -18,7 +18,7 @@ import pickle from light_tts.server.core.objs import SamplingParams from fastapi import Request -from light_tts.utils.load_utils import load_yaml +from light_tts.utils.load_utils import load_yaml_frontend logger = init_logger(__name__) @@ -46,7 +46,7 @@ def __init__(self, args, httpserver_port, tts1_encode_ports): self.shared_speech_manager = SharedSpeechManager(f"{args.port}_cosyvoice", args.cache_capacity) self.shm_req_manager = ShmReqManager() - configs = load_yaml(args.model_dir) + configs = load_yaml_frontend(args.model_dir) self.model_config = configs["llm"].llm.model.model.config self.vocab_size = self.model_config.vocab_size self.sos = self.vocab_size + configs["sos"] diff --git a/light_tts/server/tts_decode/manager.py b/light_tts/server/tts_decode/manager.py index 5776a7b..c7c7453 100755 --- a/light_tts/server/tts_decode/manager.py +++ b/light_tts/server/tts_decode/manager.py @@ -9,7 +9,7 @@ import uvloop import asyncio -from light_tts.utils.load_utils import load_yaml +from light_tts.utils.load_utils import load_yaml_lite from light_tts.utils.process_check import start_parent_check_thread asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) @@ -44,7 +44,7 @@ def __init__(self, args, tts_decode_port, httpserver_port, style_name, decode_pa self.new_req_id_to_req: Dict[int, DecodeReq] = {} self.shm_req_manager = ShmReqManager() - configs = load_yaml(args.model_dir) + configs = load_yaml_lite(args.model_dir) self.decode_token_hop_len = 25 self.flow_pre_lookahead_len = configs["flow"].pre_lookahead_len self.waiting_reqs = [] diff --git a/light_tts/server/tts_encode/manager.py b/light_tts/server/tts_encode/manager.py index 0497bb5..d8d952b 100755 --- a/light_tts/server/tts_encode/manager.py +++ b/light_tts/server/tts_encode/manager.py @@ -13,7 +13,7 @@ from light_tts.utils.config_utils import get_config_json from light_tts.utils.log_utils import init_logger from light_tts.server.core.objs.shm_speech_manager import SharedSpeechManager -from light_tts.utils.load_utils import CosyVoiceVersion, load_yaml +from light_tts.utils.load_utils import CosyVoiceVersion, load_yaml_frontend from cosyvoice.cli.frontend import CosyVoiceFrontEnd from light_tts.utils.graceful_utils import graceful_registry from light_tts.utils.process_check import start_parent_check_thread @@ -56,7 +56,7 @@ def __init__( self.trust_remote_code = args.trust_remote_code self.args = args - configs = load_yaml(args.model_dir) + configs = load_yaml_frontend(args.model_dir) self.configs = configs self.resample_rate = configs["sample_rate"] self.token_hop_len = 25 diff --git a/light_tts/server/tts_llm/manager.py b/light_tts/server/tts_llm/manager.py index 59d6ca2..9a2ef5f 100755 --- a/light_tts/server/tts_llm/manager.py +++ b/light_tts/server/tts_llm/manager.py @@ -25,7 +25,7 @@ from light_tts.server.core.objs.shm_speech_manager import SharedSpeechManager from light_tts.common.mem_manager import ReadOnlyStaticsMemoryManager import multiprocessing as mp -from light_tts.utils.load_utils import CosyVoiceVersion, load_yaml +from light_tts.utils.load_utils import CosyVoiceVersion, load_yaml_lite from itertools import chain import threading from light_tts.server.tts_llm.token_load import TokenLoad @@ -88,7 +88,7 @@ def __init__(self, args, tts_llm_port, tts_decode_port, style_name, gpt_parall_l self.max_total_token_num = args.max_total_token_num assert self.max_req_total_len <= self.max_total_token_num - configs = load_yaml(args.model_dir) + configs = load_yaml_lite(args.model_dir) self.model_config = configs["llm"].llm.model.model.config self.speech_token_size = configs["llm"].speech_token_size self.mix_ratio = configs["llm"].mix_ratio diff --git a/light_tts/utils/load_utils.py b/light_tts/utils/load_utils.py index 2c05ad4..f201edc 100755 --- a/light_tts/utils/load_utils.py +++ b/light_tts/utils/load_utils.py @@ -1,7 +1,11 @@ import os +import json +import yaml from hyperpyyaml import load_hyperpyyaml import sys from enum import Enum +from typing import Optional, Set +from functools import lru_cache ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append("{}/../../third_party/Matcha-TTS".format(ROOT_DIR)) @@ -31,9 +35,353 @@ def get_config_path(self, model_dir): return os.path.join(model_dir, f"cosyvoice{self.value}.yaml") +class _LazyPlaceholder: + """延迟加载的占位符，用于跳过 !new: 等标签的实例化""" + + def __init__(self, tag, value=None): + self._tag = tag + self._value = value + + def __repr__(self): + return f"" + + +class LiteYamlLoader(yaml.SafeLoader): + """ + 轻量级 YAML Loader，不实例化 !new:, !apply:, !name: 等标签定义的对象， + 而是返回占位符或原始值。用于只需要读取配置值而不需要实例化模型的场景。 + """ + + pass + + +def _construct_lazy_placeholder(loader, tag_suffix, node): + """处理 !new:xxx, !apply:xxx, !name:xxx 等标签，返回占位符""" + return _LazyPlaceholder(tag_suffix) + + +def _construct_ref(loader, node): + """处理 !ref 标签，返回引用的键名""" + return _LazyPlaceholder("ref", node.value) + + +# 注册自定义标签处理器 +for tag in ["new", "apply", "name", "copy", "include"]: + LiteYamlLoader.add_multi_constructor(f"!{tag}:", _construct_lazy_placeholder) + +LiteYamlLoader.add_constructor("!ref", _construct_ref) + + +def _extract_yaml_value(yaml_content: str, key_path: str): + """ + 从 YAML 内容中提取指定路径的值（支持简单类型）。 + key_path 格式如 "llm.speech_token_size" 或 "flow.pre_lookahead_len" + """ + data = yaml.load(yaml_content, Loader=LiteYamlLoader) + keys = key_path.split(".") + value = data + for key in keys: + if isinstance(value, dict) and key in value: + value = value[key] + else: + return None + return value + + +@lru_cache(maxsize=8) +def _get_qwen_config(model_dir: str): + """从 Qwen 模型目录加载 config.json，获取 vocab_size 等信息""" + qwen_path = os.path.join(model_dir, "CosyVoice-BlankEN") + config_path = os.path.join(qwen_path, "config.json") + if os.path.exists(config_path): + with open(config_path, "r") as f: + return json.load(f) + return None + + +class LiteConfig: + """轻量级配置类，提供与完整配置兼容的接口但不加载模型""" + + def __init__(self, model_dir: str): + self.model_dir = model_dir + self.version = CosyVoiceVersion.from_config_file(model_dir) + config_path = self.version.get_config_path(model_dir) + + with open(config_path, "r") as f: + self._yaml_content = f.read() + f.seek(0) + self._raw_data = yaml.load(f, Loader=LiteYamlLoader) + + # 加载 Qwen 配置 + self._qwen_config = _get_qwen_config(model_dir) + + # 缓存常用值 + self._cache = {} + + @property + def cosyvoice_version(self): + return self.version + + @property + def sample_rate(self): + return self._get_value("sample_rate", 24000) + + @property + def speech_token_size(self): + """从 YAML 中提取 llm.speech_token_size""" + if "speech_token_size" not in self._cache: + # 从 llm 定义中提取 + llm_def = self._raw_data.get("llm", {}) + if isinstance(llm_def, dict): + self._cache["speech_token_size"] = llm_def.get("speech_token_size", 6561) + else: + self._cache["speech_token_size"] = 6561 + return self._cache["speech_token_size"] + + @property + def mix_ratio(self): + """从 YAML 中提取 llm.mix_ratio""" + llm_def = self._raw_data.get("llm", {}) + if isinstance(llm_def, dict): + return llm_def.get("mix_ratio", [5, 15]) + return [5, 15] + + @property + def pre_lookahead_len(self): + """从 YAML 中提取 flow.pre_lookahead_len""" + flow_def = self._raw_data.get("flow", {}) + if isinstance(flow_def, dict): + return flow_def.get("pre_lookahead_len", 3) + return 3 + + @property + def vocab_size(self): + """从 Qwen config.json 获取 vocab_size""" + if self._qwen_config: + return self._qwen_config.get("vocab_size", 151936) + return 151936 # Qwen2 默认值 + + @property + def max_position_embeddings(self): + """从 Qwen config.json 获取 max_position_embeddings""" + if self._qwen_config: + return self._qwen_config.get("max_position_embeddings", 32768) + return 32768 + + @property + def sos(self): + if self.version == CosyVoiceVersion.VERSION_3: + return self.speech_token_size + 0 + else: + return 0 + + @property + def task_id(self): + if self.version == CosyVoiceVersion.VERSION_3: + return self.speech_token_size + 2 + else: + return 1 + + @property + def eos_token(self): + if self.version == CosyVoiceVersion.VERSION_3: + return self.speech_token_size + 1 + else: + return self.speech_token_size + + @property + def fill_token(self): + if self.version == CosyVoiceVersion.VERSION_3: + return self.speech_token_size + 3 + else: + return self.speech_token_size + 2 + + def _get_value(self, key, default=None): + return self._raw_data.get(key, default) + + def __getitem__(self, key): + """支持字典式访问""" + if key == "cosyvoice_version": + return self.version + if key == "sample_rate": + return self.sample_rate + if key == "sos": + return self.sos + if key == "task_id": + return self.task_id + if key == "eos_token": + return self.eos_token + if key == "fill_token": + return self.fill_token + return self._raw_data.get(key) + + def get(self, key, default=None): + try: + val = self[key] + return val if val is not None else default + except KeyError: + return default + + +class _FakeLLMConfig: + """模拟 llm.llm.model.model.config 的接口""" + + def __init__(self, lite_config: LiteConfig): + self._lite_config = lite_config + + @property + def vocab_size(self): + return self._lite_config.vocab_size + + @property + def max_position_embeddings(self): + return self._lite_config.max_position_embeddings + + +class _FakeLLM: + """模拟 configs["llm"] 的接口，提供必要属性而不实际加载模型""" + + def __init__(self, lite_config: LiteConfig): + self._lite_config = lite_config + self._fake_config = _FakeLLMConfig(lite_config) + + @property + def speech_token_size(self): + return self._lite_config.speech_token_size + + @property + def mix_ratio(self): + return self._lite_config.mix_ratio + + @property + def llm(self): + return self + + @property + def model(self): + return self + + @property + def config(self): + return self._fake_config + + +class _FakeFlow: + """模拟 configs["flow"] 的接口""" + + def __init__(self, lite_config: LiteConfig): + self._lite_config = lite_config + + @property + def pre_lookahead_len(self): + return self._lite_config.pre_lookahead_len + + +def load_yaml_frontend(model_dir: str) -> dict: + """ + 加载配置文件，只实例化前端需要的组件（get_tokenizer, feat_extractor, allowed_special）， + 跳过大型模型（llm, flow, hift）。 + + 适用于需要 tokenizer 和 frontend 但不需要推理模型的场景。 + + Args: + model_dir: 模型目录路径 + + Returns: + 配置字典，包含前端组件和轻量级配置 + """ + version = CosyVoiceVersion.from_config_file(model_dir) + config_path = version.get_config_path(model_dir) + + # 使用 overrides 将大型模型设为 None，避免实例化 + # 注意：这里我们保留需要的组件定义，只跳过模型实例化 + with open(config_path, "r") as f: + # 先读取原始配置获取 speech_token_size 等值 + lite_config = LiteConfig(model_dir) + + with open(config_path, "r") as f: + configs = load_hyperpyyaml( + f, + overrides={ + "qwen_pretrain_path": os.path.join(model_dir, "CosyVoice-BlankEN"), + # 跳过大型模型实例化 - 设为简单占位符 + "llm": None, + "flow": None, + "hift": None, + "hifigan": None, + }, + ) + + # 添加版本信息 + configs["cosyvoice_version"] = version + + # 添加轻量级配置（从 lite_config 获取，因为 llm 和 flow 已被跳过） + configs["sos"] = lite_config.sos + configs["task_id"] = lite_config.task_id + configs["eos_token"] = lite_config.eos_token + configs["fill_token"] = lite_config.fill_token + + # 添加 fake llm 和 flow 用于访问配置值 + configs["llm"] = _FakeLLM(lite_config) + configs["flow"] = _FakeFlow(lite_config) + + return configs + + +def load_yaml_lite(model_dir: str) -> dict: + """ + 轻量级加载配置文件，不实例化模型对象。 + + 适用于只需要读取配置值（如 speech_token_size, vocab_size, pre_lookahead_len 等） + 而不需要实际模型对象的场景。 + + 返回的配置字典支持以下访问方式： + - configs["cosyvoice_version"] + - configs["sample_rate"] + - configs["sos"], configs["task_id"], configs["eos_token"], configs["fill_token"] + - configs["llm"].speech_token_size + - configs["llm"].mix_ratio + - configs["llm"].llm.model.model.config.vocab_size + - configs["llm"].llm.model.model.config.max_position_embeddings + - configs["flow"].pre_lookahead_len + + Args: + model_dir: 模型目录路径 + + Returns: + 轻量级配置字典 + """ + lite_config = LiteConfig(model_dir) + + # 构建返回的配置字典，模拟完整加载的结构 + configs = { + "cosyvoice_version": lite_config.version, + "sample_rate": lite_config.sample_rate, + "sos": lite_config.sos, + "task_id": lite_config.task_id, + "eos_token": lite_config.eos_token, + "fill_token": lite_config.fill_token, + "llm": _FakeLLM(lite_config), + "flow": _FakeFlow(lite_config), + } + + return configs + + # 对于多进程来说不行 def load_yaml(model_dir): - """加载配置文件，自动检测版本""" + """ + 完整加载配置文件，包括实例化所有模型对象。 + + 警告：此函数会实例化 llm, flow, hift 等大型模型， + 如果只需要读取配置值，请使用 load_yaml_lite() 代替。 + + Args: + model_dir: 模型目录路径 + + Returns: + 完整配置字典，包含实例化的模型对象 + """ version = CosyVoiceVersion.from_config_file(model_dir) config_path = version.get_config_path(model_dir) From 44d0bcf1dfa490e13ec04e43c00b1729956dad43 Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Wed, 24 Dec 2025 08:11:22 +0000 Subject: [PATCH 12/21] test script support cosyvoice3 --- launcher.sh | 5 ++- test/test_bistream.py | 38 ++++++++++++++++++----- test/test_zero_shot.py | 39 ++++++++++++++++-------- test/test_zs_speed.py | 45 +++++++++++++++------------ test/test_zs_stream.py | 69 ++++++++++++++++++++---------------------- 5 files changed, 119 insertions(+), 77 deletions(-) diff --git a/launcher.sh b/launcher.sh index c68c41e..0ce6e15 100755 --- a/launcher.sh +++ b/launcher.sh @@ -1 +1,4 @@ -python -m light_tts.server.api_server --model_dir ./pretrained_models/CosyVoice2-0.5B-latest --load_trt True \ No newline at end of file +# Enabling NVIDIA's multiprocessing services, Sometimes it can accelerate +nvidia-cuda-mps-control -d +# python -m light_tts.server.api_server --model_dir ./pretrained_models/CosyVoice2-0.5B +python -m light_tts.server.api_server --model_dir ./cosyvoice/pretrained_models/Fun-CosyVoice3-0.5B-2512 --port 8080 \ No newline at end of file diff --git a/test/test_bistream.py b/test/test_bistream.py index 54c6421..fb0fb1c 100755 --- a/test/test_bistream.py +++ b/test/test_bistream.py @@ -10,22 +10,34 @@ import websockets import base64 import asyncio +import argparse -url = "ws://localhost:8080/inference_zero_shot_bistream" +# 解析命令行参数 +parser = argparse.ArgumentParser(description="BiStream zero-shot TTS test script") +parser.add_argument("--port", type=int, default=8090, help="Server port number (default: 8090)") +parser.add_argument( + "--cosyvoice_version", type=int, choices=[2, 3], default=3, help="CosyVoice version: 2 or 3 (default: 3)" +) +args = parser.parse_args() + +url = f"ws://localhost:{args.port}/inference_zero_shot_bistream" num = 1 # 准备要发送的文本和音频文件 path = "../cosyvoice/asset/zero_shot_prompt.wav" res_list = [] os.makedirs("./outs", exist_ok=True) + + def text_generator(index): for text in [ - '收到好友从远方寄来的生日礼物，', - '那份意外的惊喜与深深的祝福', - '让我心中充满了甜蜜的快乐，', - '笑容如花儿般绽放。', + "收到好友从远方寄来的生日礼物，", + "那份意外的惊喜与深深的祝福", + "让我心中充满了甜蜜的快乐，", + "笑容如花儿般绽放。", ]: yield text + async def send_texts(websocket: websockets.WebSocketClientProtocol, index): for text in text_generator(index): await websocket.send(json.dumps({"tts_text": text})) @@ -34,6 +46,7 @@ async def send_texts(websocket: websockets.WebSocketClientProtocol, index): await websocket.send(json.dumps({"finish": True})) # 发 print(f"Index {index}, Text sending completed.") + async def receive_audio(websocket: websockets.WebSocketClientProtocol, index): audio_data = bytearray() sample_rate = 24000 @@ -52,12 +65,19 @@ async def receive_audio(websocket: websockets.WebSocketClientProtocol, index): audio_np = np.frombuffer(audio_data, dtype=np.int16) sf.write(output_wav, audio_np, samplerate=sample_rate, subtype="PCM_16") print(f"Index {index}, Audio saved as {output_wav}.") - + + async def main(index): async with websockets.connect(url) as websocket: # Step 1: 发送初始化参数 + # 根据 cosyvoice_version 设置 prompt_text + if args.cosyvoice_version == 3: + prompt_text = "You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。" + else: + prompt_text = "希望你以后能够做的比我还好呦。" + init_params = { - "prompt_text": "希望你以后能够做的比我还好呦。", + "prompt_text": prompt_text, } await websocket.send(json.dumps(init_params)) print("Sent initialization data.") @@ -74,9 +94,11 @@ async def main(index): # 等待两个任务完成 await asyncio.gather(send_task, receive_task) + async def run_multiple(): tasks = [main(index) for index in range(num)] await asyncio.gather(*tasks) + # 运行主程序 -asyncio.run(run_multiple()) \ No newline at end of file +asyncio.run(run_multiple()) diff --git a/test/test_zero_shot.py b/test/test_zero_shot.py index e6a5dd8..5ef7d1d 100755 --- a/test/test_zero_shot.py +++ b/test/test_zero_shot.py @@ -5,8 +5,17 @@ import os import threading import json +import argparse -url = "http://0.0.0.0:8080/inference_zero_shot" +# 解析命令行参数 +parser = argparse.ArgumentParser(description="Zero-shot TTS test script") +parser.add_argument("--port", type=int, default=8080, help="Server port number (default: 8080)") +parser.add_argument( + "--cosyvoice_version", type=int, choices=[2, 3], default=3, help="CosyVoice version: 2 or 3 (default: 3)" +) +args = parser.parse_args() + +url = f"http://0.0.0.0:{args.port}/inference_zero_shot" num = 5 # 准备要发送的文本和音频文件 path = "../cosyvoice/asset/zero_shot_prompt.wav" @@ -15,24 +24,25 @@ all_inputs = json.load(f) res_list = [] os.makedirs("./outs", exist_ok=True) + + def get_file(index): - files = { - "prompt_wav": ("sample.wav", open(path, "rb"), "audio/wav") - } + files = {"prompt_wav": ("sample.wav", open(path, "rb"), "audio/wav")} # inputs = random.choice(all_inputs) inputs = all_inputs[0] # inputs = all_inputs[2] - data = { - "tts_text": inputs, - "prompt_text": "希望你以后能够做的比我还好呦。", - "stream": stream - } + + # 根据 cosyvoice_version 设置 prompt_text + if args.cosyvoice_version == 3: + prompt_text = "You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。" + else: + prompt_text = "希望你以后能够做的比我还好呦。" + + data = {"tts_text": inputs, "prompt_text": prompt_text, "stream": stream} start_time = time.time() - chunk_time = time.time() response = requests.post(url, files=files, data=data, stream=True) sample_rate = 24000 - dtype = "int16" first = True ttft = 0 audio_data = bytearray() @@ -44,7 +54,6 @@ def get_file(index): first = False ttft = time.time() - start_time # print(f"Received {len(chunk)} bytes, Cost {(time.time() - chunk_time) * 1000} ms") - chunk_time = time.time() audio_data.extend(chunk) except Exception as e: print(f"Exception: {e}") @@ -62,10 +71,14 @@ def get_file(index): # f.write(response.content) output_wav = f"./outs/output{'_stream' if stream else ''}_{index}.wav" sf.write(output_wav, audio_np, samplerate=sample_rate, subtype="PCM_16") - print(f"{inputs} saved as {output_wav}, time cost: {cost_time:.2f} s, rtf: {cost_time / speech_len}, ttft: {ttft:.2f} s") + print( + f"{inputs} saved as {output_wav}, time cost: {cost_time:.2f} s" + + f", rtf: {cost_time / speech_len}, ttft: {ttft:.2f} s" + ) else: print("Error:", response.status_code, response.text) + st = time.time() for index in range(num): print("start index", index) diff --git a/test/test_zs_speed.py b/test/test_zs_speed.py index bc4ee24..29a7a44 100755 --- a/test/test_zs_speed.py +++ b/test/test_zs_speed.py @@ -1,20 +1,24 @@ ######## lora 语音速度测试 ############### from datetime import datetime -import uuid -import json import time import requests import json from concurrent.futures import ProcessPoolExecutor -import soundfile as sf -import numpy as np import numpy as np -import random from tqdm import tqdm import os +import argparse + +# 解析命令行参数 +parser = argparse.ArgumentParser(description="Zero-shot TTS speed test script") +parser.add_argument("--port", type=int, default=8080, help="Server port number (default: 8080)") +parser.add_argument( + "--cosyvoice_version", type=int, choices=[2, 3], default=3, help="CosyVoice version: 2 or 3 (default: 3)" +) +args = parser.parse_args() # config -server_url = "http://localhost:8080/inference_zero_shot" +server_url = f"http://localhost:{args.port}/inference_zero_shot" num_workers = [1, 2, 4, 8] num_test = 50 path = "../cosyvoice/asset/zero_shot_prompt.wav" @@ -26,22 +30,28 @@ failed = 0 os.makedirs("./outs", exist_ok=True) + def get_file(index): url = f"{server_url}/" inputs = all_inputs[index % len(all_inputs)] - files = { - "prompt_wav": ("sample.wav", open(path, "rb"), "audio/wav") - } + files = {"prompt_wav": ("sample.wav", open(path, "rb"), "audio/wav")} + + # 根据 cosyvoice_version 设置 prompt_text + if args.cosyvoice_version == 3: + prompt_text = "You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。" + else: + prompt_text = "希望你以后能够做的比我还好呦。" + data = { "tts_text": inputs, - "prompt_text": "希望你以后能够做的比我还好呦。", + "prompt_text": prompt_text, } global failed start = time.time() response = requests.post(url, files=files, data=data, stream=True) cost_time = time.time() - start - + try: if response.status_code == 200: audio_data = bytearray() @@ -52,11 +62,12 @@ def get_file(index): print(f"{index} {inputs} \n error:{response.text}") failed += 1 except Exception as e: - print(f'An exception occurred\ntext: {inputs}\n{e}\n{response.text}') + print(f"An exception occurred\ntext: {inputs}\n{e}\n{response.text}") failed += 1 finally: return cost_time + results = [] for num_worker in num_workers: print(f"Concurrency {num_worker} test starts") @@ -65,16 +76,12 @@ def get_file(index): start_time = time.time() with ProcessPoolExecutor(max_workers=num_worker) as executor: - all_cost_times = list( - tqdm(executor.map(get_file, range(num_test)), total=num_test, desc="running tests") - ) + all_cost_times = list(tqdm(executor.map(get_file, range(num_test)), total=num_test, desc="running tests")) total_time = time.time() - start_time # 假设是你的数据数组 data = np.array(all_cost_times) - result = { - "num_workers": num_worker - } + result = {"num_workers": num_worker} # 计算分位数 if failed - last_failed > 0: print(f"Failed {failed - last_failed}") @@ -112,4 +119,4 @@ def get_file(index): md_file.write("|") for head in heads: md_file.write(str(result[head]) + "|") - md_file.write("\r\n") \ No newline at end of file + md_file.write("\r\n") diff --git a/test/test_zs_stream.py b/test/test_zs_stream.py index 9393027..bca43a5 100755 --- a/test/test_zs_stream.py +++ b/test/test_zs_stream.py @@ -1,20 +1,27 @@ ######## lora 语音速度测试 ############### from datetime import datetime import uuid -import json import time import requests import json from concurrent.futures import ProcessPoolExecutor import soundfile as sf import numpy as np -import numpy as np import random from tqdm import tqdm import os +import argparse + +# 解析命令行参数 +parser = argparse.ArgumentParser(description="Zero-shot TTS stream speed test script") +parser.add_argument("--port", type=int, default=8080, help="Server port number (default: 8080)") +parser.add_argument( + "--cosyvoice_version", type=int, choices=[2, 3], default=3, help="CosyVoice version: 2 or 3 (default: 3)" +) +args = parser.parse_args() # config -server_url = "http://localhost:8080/inference_zero_shot" +server_url = f"http://localhost:{args.port}/inference_zero_shot" num_workers = [1, 2, 4, 8] num_test = 50 path = "../cosyvoice/asset/zero_shot_prompt.wav" @@ -26,21 +33,20 @@ failed = 0 os.makedirs("./outs", exist_ok=True) + def get_file(index): url = f"{server_url}/" - headers = {'Content-Type': 'application/json'} inputs = all_inputs[index % len(all_inputs)] - files = { - "prompt_wav": ("sample.wav", open(path, "rb"), "audio/wav") - } - files = { - "prompt_wav": ("sample.wav", open(path, "rb"), "audio/wav") - } - data = { - "tts_text": inputs, - "prompt_text": "希望你以后能够做的比我还好呦。", - "stream": True - } + files = {"prompt_wav": ("sample.wav", open(path, "rb"), "audio/wav")} + files = {"prompt_wav": ("sample.wav", open(path, "rb"), "audio/wav")} + + # 根据 cosyvoice_version 设置 prompt_text + if args.cosyvoice_version == 3: + prompt_text = "You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。" + else: + prompt_text = "希望你以后能够做的比我还好呦。" + + data = {"tts_text": inputs, "prompt_text": prompt_text, "stream": True} first = True ttft = 0 global failed @@ -55,55 +61,46 @@ def get_file(index): if first: first = False ttft = time.time() - start - chunk_time = time.time() audio_data.extend(chunk) cost_time = time.time() - start speech_len = len(audio_data) / 2 / 24000 # 将字节数据转换为 NumPy 数组 - audio_np = np.frombuffer(audio_data, dtype=np.int16) + # audio_np = np.frombuffer(audio_data, dtype=np.int16) rtf = cost_time / speech_len - # output_wav = f"./outs/output{index}.wav" - # sf.write(output_wav, audio_np, samplerate=sample_rate, subtype="PCM_16") + # output_wav = f"./outs/output{index}.wav" + # sf.write(output_wav, audio_np, samplerate=sample_rate, subtype="PCM_16") # print(f"{index} {inputs} \n error:{response.text}") # failed += 1 except Exception as e: - print(f'An exception occurred\ntext: {inputs}\n{e}\n{response.text}') + print(f"An exception occurred\ntext: {inputs}\n{e}\n{response.text}") failed += 1 finally: return cost_time, ttft, rtf + results = [] for num_worker in num_workers: print(f"Concurrency {num_worker} test starts") cache_file = f"./cache_{num_worker}.json" # if os.path.exists(cache_file): - # with open(cache_file, "r") as f: - # cache_json = json.load(f) + # with open(cache_file, "r") as f: + # cache_json = json.load(f) last_failed = failed start_time = time.time() with ProcessPoolExecutor(max_workers=num_worker) as executor: - all_results = list( - tqdm(executor.map(get_file, range(num_test)), total=num_test, desc="running tests") - ) + all_results = list(tqdm(executor.map(get_file, range(num_test)), total=num_test, desc="running tests")) total_time = time.time() - start_time - cache_json = { - "num_workers": num_worker, - "total_time": total_time, - "failed": failed, - "all_results": all_results - } + cache_json = {"num_workers": num_worker, "total_time": total_time, "failed": failed, "all_results": all_results} with open(cache_file, "w") as f: json.dump(cache_json, f) - + all_cost_times = [result[0] for result in all_results] all_ttft = [result[1] for result in all_results] all_rtf = [result[2] for result in all_results] # 假设是你的数据数组 - result = { - "num_workers": num_worker - } + result = {"num_workers": num_worker} # 计算分位数 if failed - last_failed > 0: print(f"Failed {failed - last_failed}") @@ -149,4 +146,4 @@ def get_file(index): md_file.write("|") for head in heads: md_file.write(str(result[head]) + "|") - md_file.write("\r\n") \ No newline at end of file + md_file.write("\r\n") From 5352a82b421023088072267ca919f0176f54087d Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Wed, 24 Dec 2025 08:44:22 +0000 Subject: [PATCH 13/21] little change --- launcher.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/launcher.sh b/launcher.sh index 0ce6e15..9352ec1 100755 --- a/launcher.sh +++ b/launcher.sh @@ -1,4 +1,4 @@ # Enabling NVIDIA's multiprocessing services, Sometimes it can accelerate nvidia-cuda-mps-control -d # python -m light_tts.server.api_server --model_dir ./pretrained_models/CosyVoice2-0.5B -python -m light_tts.server.api_server --model_dir ./cosyvoice/pretrained_models/Fun-CosyVoice3-0.5B-2512 --port 8080 \ No newline at end of file +python -m light_tts.server.api_server --model_dir ./pretrained_models/Fun-CosyVoice3-0.5B-2512 --port 8080 \ No newline at end of file From a3e8539c6569c573aaec8ea5066b698f3be3d69f Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Thu, 25 Dec 2025 13:13:22 +0800 Subject: [PATCH 14/21] change to cu128 --- Dockerfile | 34 ++++++++++++++++++++++++++++------ README.md | 10 +++++----- requirements.txt | 20 ++++++++++---------- 3 files changed, 43 insertions(+), 21 deletions(-) diff --git a/Dockerfile b/Dockerfile index 83a324e..aa57432 100755 --- a/Dockerfile +++ b/Dockerfile @@ -1,8 +1,12 @@ -FROM pytorch/pytorch:2.3.1-cuda12.1-cudnn8-runtime -ARG MAMBA_VERSION=23.1.0-1 +ARG CUDA_VERSION=12.8.0 +FROM nvidia/cuda:${CUDA_VERSION}-cudnn-devel-ubuntu22.04 +ARG PYTHON_VERSION=3.10 +ARG MAMBA_VERSION=24.7.1-0 ARG TARGETPLATFORM +ENV PATH=/opt/conda/bin:$PATH \ + CONDA_PREFIX=/opt/conda -WORKDIR /opt +WORKDIR /root RUN chmod 777 -R /tmp && apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ ca-certificates \ @@ -13,6 +17,24 @@ RUN chmod 777 -R /tmp && apt-get update && DEBIAN_FRONTEND=noninteractive apt-ge git && \ rm -rf /var/lib/apt/lists/* -RUN git clone --recursive https://github.com/ModelTC/light-tts.git -RUN cd light-tts && pip3 install -r requirements.txt -WORKDIR /opt/light-tts +RUN case ${TARGETPLATFORM} in \ + "linux/arm64") MAMBA_ARCH=aarch64 ;; \ + *) MAMBA_ARCH=x86_64 ;; \ + esac && \ + curl -fsSL -o ~/mambaforge.sh "https://github.com/conda-forge/miniforge/releases/download/${MAMBA_VERSION}/Mambaforge-${MAMBA_VERSION}-Linux-${MAMBA_ARCH}.sh" && \ + bash ~/mambaforge.sh -b -p /opt/conda && \ + rm ~/mambaforge.sh + + RUN case ${TARGETPLATFORM} in \ + "linux/arm64") exit 1 ;; \ + *) /opt/conda/bin/conda update -y conda && \ + /opt/conda/bin/conda install -y "python=${PYTHON_VERSION}" ;; \ + esac && \ + /opt/conda/bin/conda clean -ya + +COPY ./requirements.txt /lighttts/requirements.txt +RUN pip install -U pip +RUN pip install -r /lighttts/requirements.txt --no-cache-dir + +COPY . /lighttts +WORKDIR /lighttts diff --git a/README.md b/README.md index 1a3cf78..4ecaaaa 100755 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ ![Light TTS Banner](asset/light-tts.jpg) -# light-tts +# LightTTS -**light-tts** is a lightweight and high-performance text-to-speech (TTS) inference and service framework based on Python. It is built around the [cosyvoice](https://github.com/FunAudioLLM/CosyVoice) model and based on the [lightllm](https://github.com/ModelTC/lightllm), with optimizations to support fast, scalable, and service-ready TTS deployment. +**LightTTS** is a lightweight and high-performance text-to-speech (TTS) inference and service framework based on Python. It is built around the [cosyvoice](https://github.com/FunAudioLLM/CosyVoice) model and based on the [lightllm](https://github.com/ModelTC/lightllm), with optimizations to support fast, scalable, and service-ready TTS deployment. --- @@ -34,10 +34,10 @@ ```bash # Clone the repo - git clone --recursive https://github.com/ModelTC/light-tts.git - cd light-tts + git clone --recursive https://github.com/ModelTC/LightTTS.git + cd LightTTS # If you failed to clone the submodule due to network failures, please run the following command until success - # cd light-tts + # cd LightTTS # git submodule update --init --recursive # (Recommended) Create a new conda environment diff --git a/requirements.txt b/requirements.txt index c846bd9..5f16c61 100755 --- a/requirements.txt +++ b/requirements.txt @@ -16,7 +16,7 @@ colorama==0.4.6 coloredlogs==15.0.1 conformer==0.3.2 cryptography==45.0.3 -diffusers==0.29.0 +diffusers easydict==1.13 einops==0.8.1 environs==14.2.0 @@ -52,7 +52,7 @@ networkx==3.1 ninja==1.11.1.3 numpy==1.26.4 omegaconf==2.3.0 -onnxruntime-gpu==1.18.0 +onnxruntime-gpu openai-whisper==20240930 packaging==24.2 Pillow==11.0.0 @@ -89,17 +89,17 @@ sniffio==1.3.1 soundfile==0.12.1 starlette==0.46.2 sympy==1.13.1 -tensorrt-cu12==10.0.1 -tensorrt-cu12-bindings==10.0.1 -tensorrt-cu12-libs==10.0.1 +tensorrt-cu12 +tensorrt-cu12-bindings +tensorrt-cu12-libs tiktoken==0.9.0 -tokenizers==0.19.1 +tokenizers toolz==1.0.0 -torch==2.3.1 -torchaudio==2.3.1 +torch +torchaudio tqdm==4.67.1 -transformers==4.40.1 -triton==3.1.0 +transformers +triton typing_extensions==4.14.0 urllib3==2.3.0 uvicorn==0.19.0 From 563e255bbb5d035209e5443d74a52afc3c961f47 Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Thu, 25 Dec 2025 07:00:35 +0000 Subject: [PATCH 15/21] upgrade to cu128 --- Dockerfile | 3 ++- requirements.txt | 26 ++++++++++++++------------ test/test_zero_shot.py | 6 ++++-- test/test_zs_speed.py | 5 ++++- test/test_zs_stream.py | 7 +++++-- 5 files changed, 29 insertions(+), 18 deletions(-) diff --git a/Dockerfile b/Dockerfile index aa57432..8edbcb4 100755 --- a/Dockerfile +++ b/Dockerfile @@ -14,7 +14,8 @@ RUN chmod 777 -R /tmp && apt-get update && DEBIAN_FRONTEND=noninteractive apt-ge curl \ g++ \ make \ - git && \ + git \ + ffmpeg && \ rm -rf /var/lib/apt/lists/* RUN case ${TARGETPLATFORM} in \ diff --git a/requirements.txt b/requirements.txt index 5f16c61..dc63af1 100755 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,3 @@ ---extra-index-url https://download.pytorch.org/whl/cu121 --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/ # https://github.com/microsoft/onnxruntime/issues/21684 anyio==4.9.0 atomics==1.0.3 @@ -16,7 +15,7 @@ colorama==0.4.6 coloredlogs==15.0.1 conformer==0.3.2 cryptography==45.0.3 -diffusers +diffusers==0.34.0 easydict==1.13 einops==0.8.1 environs==14.2.0 @@ -52,7 +51,7 @@ networkx==3.1 ninja==1.11.1.3 numpy==1.26.4 omegaconf==2.3.0 -onnxruntime-gpu +onnxruntime-gpu==1.23.2 openai-whisper==20240930 packaging==24.2 Pillow==11.0.0 @@ -89,23 +88,26 @@ sniffio==1.3.1 soundfile==0.12.1 starlette==0.46.2 sympy==1.13.1 -tensorrt-cu12 -tensorrt-cu12-bindings -tensorrt-cu12-libs +tensorrt-cu12==10.14.1.48.post1 +tensorrt-cu12-bindings==10.14.1.48.post1 +tensorrt-cu12-libs==10.14.1.48.post1 tiktoken==0.9.0 -tokenizers +tokenizers==0.21.4 toolz==1.0.0 -torch -torchaudio +torch==2.9.1 +torchaudio==2.9.1 +torchcodec==0.9.1 tqdm==4.67.1 -transformers -triton +transformers==4.53.3 +triton==3.5.1 typing_extensions==4.14.0 urllib3==2.3.0 uvicorn==0.19.0 uvloop==0.21.0 websockets==15.0.1 WeTextProcessing==1.0.3 +wetext==0.1.2 wget==3.2 wheel==0.45.1 -zstandard==0.23.0 \ No newline at end of file +x_transformers==2.12.0 +zstandard==0.23.0 diff --git a/test/test_zero_shot.py b/test/test_zero_shot.py index 5ef7d1d..1176a38 100755 --- a/test/test_zero_shot.py +++ b/test/test_zero_shot.py @@ -13,13 +13,15 @@ parser.add_argument( "--cosyvoice_version", type=int, choices=[2, 3], default=3, help="CosyVoice version: 2 or 3 (default: 3)" ) +parser.add_argument("--stream", action="store_true", default=False, help="是否使用流式推理 (default: True)") +parser.add_argument("--num", type=int, default=5, help="测试数量 (default: 5)") args = parser.parse_args() url = f"http://0.0.0.0:{args.port}/inference_zero_shot" -num = 5 +num = args.num # 准备要发送的文本和音频文件 path = "../cosyvoice/asset/zero_shot_prompt.wav" -stream = True # 是否使用流式推理 +stream = args.stream # 是否使用流式推理 with open("test_texts.json", "r") as f: all_inputs = json.load(f) res_list = [] diff --git a/test/test_zs_speed.py b/test/test_zs_speed.py index 29a7a44..2931e8a 100755 --- a/test/test_zs_speed.py +++ b/test/test_zs_speed.py @@ -8,6 +8,7 @@ from tqdm import tqdm import os import argparse +import torch # 解析命令行参数 parser = argparse.ArgumentParser(description="Zero-shot TTS speed test script") @@ -23,6 +24,8 @@ num_test = 50 path = "../cosyvoice/asset/zero_shot_prompt.wav" sample_rate = 24000 +# Get device name for file naming +device_name = torch.cuda.get_device_name(0).replace(" ", "_").lower() if torch.cuda.is_available() else "cpu" # get all inputs with open("test_texts.json", "r") as f: all_inputs = json.load(f) @@ -103,7 +106,7 @@ def get_file(index): else: print(f"Total failed {failed}") -result_path = f"./summary_triton_flashdecoding_{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}.md" +result_path = f"./summary_triton_flashdecoding_{device_name}_{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}.md" heads = results[0].keys() with open(result_path, "w") as md_file: diff --git a/test/test_zs_stream.py b/test/test_zs_stream.py index bca43a5..e78d434 100755 --- a/test/test_zs_stream.py +++ b/test/test_zs_stream.py @@ -11,6 +11,7 @@ from tqdm import tqdm import os import argparse +import torch # 解析命令行参数 parser = argparse.ArgumentParser(description="Zero-shot TTS stream speed test script") @@ -26,6 +27,8 @@ num_test = 50 path = "../cosyvoice/asset/zero_shot_prompt.wav" sample_rate = 24000 +# Get device name for file naming +device_name = torch.cuda.get_device_name(0).replace(" ", "_").lower() if torch.cuda.is_available() else "cpu" # get all inputs with open("test_texts.json", "r") as f: all_inputs = json.load(f) @@ -81,7 +84,7 @@ def get_file(index): results = [] for num_worker in num_workers: print(f"Concurrency {num_worker} test starts") - cache_file = f"./cache_{num_worker}.json" + cache_file = f"./cache_{device_name}_{num_worker}.json" # if os.path.exists(cache_file): # with open(cache_file, "r") as f: # cache_json = json.load(f) @@ -130,7 +133,7 @@ def get_file(index): else: print(f"Total failed {failed}") -result_path = f"./summary_triton_flashdecoding_v100_stream_{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}.md" +result_path = f"./summary_triton_flashdecoding_{device_name}_stream_{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}.md" heads = results[0].keys() with open(result_path, "w") as md_file: From c9cfef1b7a8e5a66dd39e663834a3035d6858036 Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Thu, 25 Dec 2025 08:13:30 +0000 Subject: [PATCH 16/21] update requirements.txt --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index dc63af1..96f3d45 100755 --- a/requirements.txt +++ b/requirements.txt @@ -59,7 +59,7 @@ pip==24.3.1 pluggy==1.5.0 plumbum==1.9.0 prometheus_client==0.22.1 -protobuf==4.25 +protobuf==4.25.0 psutil==6.1.1 pyarrow==18.1.0 pycparser==2.22 @@ -87,7 +87,7 @@ six==1.17.0 sniffio==1.3.1 soundfile==0.12.1 starlette==0.46.2 -sympy==1.13.1 +sympy==1.14.0 tensorrt-cu12==10.14.1.48.post1 tensorrt-cu12-bindings==10.14.1.48.post1 tensorrt-cu12-libs==10.14.1.48.post1 From 8c78024642b0ba4d580d286b24eaa6104dd11104 Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Thu, 25 Dec 2025 10:07:59 +0000 Subject: [PATCH 17/21] tune config to speedup --- cosyvoice/cosyvoice/cli/frontend.py | 12 ++++++++++-- ...oat16,q_head_num=14}_NVIDIA_GeForce_RTX_5090.json | 1 + .../models/llama/triton_kernel/flash_decoding.py | 7 ++++--- light_tts/server/api_cli.py | 2 +- 4 files changed, 16 insertions(+), 6 deletions(-) create mode 100644 light_tts/common/all_kernel_configs/triton_flashdecoding/{head_dim=64,kv_head_num=2,out_dtype=torch.float16,q_head_num=14}_NVIDIA_GeForce_RTX_5090.json diff --git a/cosyvoice/cosyvoice/cli/frontend.py b/cosyvoice/cosyvoice/cli/frontend.py index 14714e1..43cc808 100644 --- a/cosyvoice/cosyvoice/cli/frontend.py +++ b/cosyvoice/cosyvoice/cli/frontend.py @@ -64,8 +64,16 @@ def __init__(self, if self.use_ttsfrd: self.frd = ttsfrd.TtsFrontendEngine() ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) - assert self.frd.initialize('{}/../../pretrained_models/CosyVoice-ttsfrd/resource'.format(ROOT_DIR)) is True, \ - 'failed to initialize ttsfrd resource' + resource_paths = [ + '{}/../../../pretrained_models/CosyVoice-ttsfrd/resource'.format(ROOT_DIR), + '{}/../../pretrained_models/CosyVoice-ttsfrd/resource'.format(ROOT_DIR) + ] + initialized = False + for path in resource_paths: + if self.frd.initialize(path): + initialized = True + break + assert initialized, 'failed to initialize ttsfrd resource' self.frd.set_lang_type('pinyinvg') else: self.zh_tn_model = ZhNormalizer(remove_erhua=False) diff --git a/light_tts/common/all_kernel_configs/triton_flashdecoding/{head_dim=64,kv_head_num=2,out_dtype=torch.float16,q_head_num=14}_NVIDIA_GeForce_RTX_5090.json b/light_tts/common/all_kernel_configs/triton_flashdecoding/{head_dim=64,kv_head_num=2,out_dtype=torch.float16,q_head_num=14}_NVIDIA_GeForce_RTX_5090.json new file mode 100644 index 0000000..02bbe7d --- /dev/null +++ b/light_tts/common/all_kernel_configs/triton_flashdecoding/{head_dim=64,kv_head_num=2,out_dtype=torch.float16,q_head_num=14}_NVIDIA_GeForce_RTX_5090.json @@ -0,0 +1 @@ +{"32": {"1": {"BLOCK_SEQ": 16, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 4, "stage2_num_warps": 1, "stage2_num_stages": 1}, "2": {"BLOCK_SEQ": 256, "BLOCK_N": 32, "stage1_num_warps": 1, "stage1_num_stages": 6, "stage2_num_warps": 1, "stage2_num_stages": 1}, "4": {"BLOCK_SEQ": 256, "BLOCK_N": 32, "stage1_num_warps": 1, "stage1_num_stages": 6, "stage2_num_warps": 1, "stage2_num_stages": 1}, "8": {"BLOCK_SEQ": 256, "BLOCK_N": 32, "stage1_num_warps": 4, "stage1_num_stages": 1, "stage2_num_warps": 1, "stage2_num_stages": 1}}, "64": {"1": {"BLOCK_SEQ": 16, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 6, "stage2_num_warps": 1, "stage2_num_stages": 1}, "2": {"BLOCK_SEQ": 32, "BLOCK_N": 32, "stage1_num_warps": 1, "stage1_num_stages": 8, "stage2_num_warps": 1, "stage2_num_stages": 1}, "4": {"BLOCK_SEQ": 256, "BLOCK_N": 64, "stage1_num_warps": 4, "stage1_num_stages": 8, "stage2_num_warps": 1, "stage2_num_stages": 1}, "8": {"BLOCK_SEQ": 256, "BLOCK_N": 64, "stage1_num_warps": 4, "stage1_num_stages": 3, "stage2_num_warps": 1, "stage2_num_stages": 1}}, "128": {"1": {"BLOCK_SEQ": 16, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 8, "stage2_num_warps": 1, "stage2_num_stages": 1}, "2": {"BLOCK_SEQ": 32, "BLOCK_N": 32, "stage1_num_warps": 1, "stage1_num_stages": 1, "stage2_num_warps": 1, "stage2_num_stages": 1}, "4": {"BLOCK_SEQ": 32, "BLOCK_N": 32, "stage1_num_warps": 1, "stage1_num_stages": 8, "stage2_num_warps": 1, "stage2_num_stages": 1}, "8": {"BLOCK_SEQ": 64, "BLOCK_N": 64, "stage1_num_warps": 4, "stage1_num_stages": 4, "stage2_num_warps": 1, "stage2_num_stages": 1}}, "256": {"1": {"BLOCK_SEQ": 16, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 6, "stage2_num_warps": 1, "stage2_num_stages": 1}, "2": {"BLOCK_SEQ": 32, "BLOCK_N": 32, "stage1_num_warps": 1, "stage1_num_stages": 8, "stage2_num_warps": 1, "stage2_num_stages": 1}, "4": {"BLOCK_SEQ": 32, "BLOCK_N": 32, "stage1_num_warps": 1, "stage1_num_stages": 8, "stage2_num_warps": 1, "stage2_num_stages": 1}, "8": {"BLOCK_SEQ": 64, "BLOCK_N": 32, "stage1_num_warps": 1, "stage1_num_stages": 7, "stage2_num_warps": 1, "stage2_num_stages": 1}}, "512": {"1": {"BLOCK_SEQ": 16, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 7, "stage2_num_warps": 1, "stage2_num_stages": 1}, "2": {"BLOCK_SEQ": 16, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 6, "stage2_num_warps": 1, "stage2_num_stages": 1}, "4": {"BLOCK_SEQ": 32, "BLOCK_N": 32, "stage1_num_warps": 1, "stage1_num_stages": 8, "stage2_num_warps": 1, "stage2_num_stages": 1}, "8": {"BLOCK_SEQ": 32, "BLOCK_N": 32, "stage1_num_warps": 1, "stage1_num_stages": 8, "stage2_num_warps": 1, "stage2_num_stages": 1}}} \ No newline at end of file diff --git a/light_tts/models/llama/triton_kernel/flash_decoding.py b/light_tts/models/llama/triton_kernel/flash_decoding.py index 6a2efaf..be1cd98 100755 --- a/light_tts/models/llama/triton_kernel/flash_decoding.py +++ b/light_tts/models/llama/triton_kernel/flash_decoding.py @@ -4,6 +4,7 @@ from frozendict import frozendict from typing import Dict + class LlamaFlashDecodingStage1KernelConfig(KernelConfigs): kernel_name: str = "triton_flashdecoding" @@ -44,7 +45,7 @@ def try_to_get_best_config( "stage2_num_stages": 2, } return config - + @classmethod def save_config(cls, *args, **kwargs) -> None: key_params = { @@ -57,13 +58,13 @@ def save_config(cls, *args, **kwargs) -> None: cls.store_config(key_params, kwargs["store_json_ans"]) + def token_decode_attention_flash_decoding( q, infer_state, q_head_num, head_dim, cache_k, cache_v, out=None, alloc_tensor_func=torch.empty ): batch_size = infer_state.batch_size - avg_seq_len_in_batch = infer_state.total_token_num // batch_size run_config = LlamaFlashDecodingStage1KernelConfig.try_to_get_best_config( - batch_size, avg_seq_len_in_batch, head_dim, q_head_num, cache_k.shape[1], torch.float16 + batch_size, infer_state.max_len_in_batch, head_dim, q_head_num, cache_k.shape[1], torch.float16 ) BLOCK_SEQ = run_config["BLOCK_SEQ"] diff --git a/light_tts/server/api_cli.py b/light_tts/server/api_cli.py index f869ba8..f706ca6 100755 --- a/light_tts/server/api_cli.py +++ b/light_tts/server/api_cli.py @@ -116,7 +116,7 @@ def make_argument_parser() -> argparse.ArgumentParser: parser.add_argument( "--graph_max_len_in_batch", type=int, - default=32768, + default=2048, help="""Maximum sequence length that can be captured by the cuda graph for decodign stage. The default value is 8192. It will turn into eagar mode if encounters a larger value. """, ) From 6f1df37e8816a96c42ff30882932c903a88c5e2a Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Thu, 25 Dec 2025 10:13:27 +0000 Subject: [PATCH 18/21] add unzip --- Dockerfile | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index 8edbcb4..0451e24 100755 --- a/Dockerfile +++ b/Dockerfile @@ -15,7 +15,8 @@ RUN chmod 777 -R /tmp && apt-get update && DEBIAN_FRONTEND=noninteractive apt-ge g++ \ make \ git \ - ffmpeg && \ + ffmpeg \ + unzip && \ rm -rf /var/lib/apt/lists/* RUN case ${TARGETPLATFORM} in \ @@ -39,3 +40,7 @@ RUN pip install -r /lighttts/requirements.txt --no-cache-dir COPY . /lighttts WORKDIR /lighttts +RUN cd pretrained_models/CosyVoice-ttsfrd/ && \ + unzip resource.zip -d . && \ + pip install ttsfrd_dependency-0.1-py3-none-any.whl && \ + pip install ttsfrd-0.4.2-cp310-cp310-linux_x86_64.whl From cdece6b24abf500fe4401086c6be3fe2c57c968d Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Fri, 26 Dec 2025 05:55:55 +0000 Subject: [PATCH 19/21] release v1.1.0 --- README.md | 191 +++++++++------- ..._head_num=14}_NVIDIA_GeForce_RTX_5090.json | 1 - ...ch.float16,q_head_num=14}_NVIDIA_H800.json | 1 - light_tts/server/api_cli.py | 2 +- .../llama_triton_flashdecoding_tuning.py | 213 +++++++++++++----- test/test_texts.json | 54 ++++- test/test_zs_speed.py | 28 ++- 7 files changed, 337 insertions(+), 153 deletions(-) delete mode 100644 light_tts/common/all_kernel_configs/triton_flashdecoding/{head_dim=64,kv_head_num=2,out_dtype=torch.float16,q_head_num=14}_NVIDIA_GeForce_RTX_5090.json delete mode 100755 light_tts/common/all_kernel_configs/triton_flashdecoding/{head_dim=64,kv_head_num=2,out_dtype=torch.float16,q_head_num=14}_NVIDIA_H800.json diff --git a/README.md b/README.md index 4ecaaaa..d6f236f 100755 --- a/README.md +++ b/README.md @@ -2,7 +2,12 @@ # LightTTS -**LightTTS** is a lightweight and high-performance text-to-speech (TTS) inference and service framework based on Python. It is built around the [cosyvoice](https://github.com/FunAudioLLM/CosyVoice) model and based on the [lightllm](https://github.com/ModelTC/lightllm), with optimizations to support fast, scalable, and service-ready TTS deployment. +[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) +[![Docker](https://img.shields.io/badge/docker-ready-brightgreen.svg)](https://hub.docker.com/r/lighttts/light-tts) + +**⚡ Lightning-Fast Text-to-Speech Inference & Service Framework** + +**LightTTS** is a lightweight and high-performance text-to-speech (TTS) inference and service framework based on Python. It supports **CosyVoice2** and **CosyVoice3** models, built upon the [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) architecture and [LightLLM](https://github.com/ModelTC/lightllm) framework, with optimizations to support fast, scalable, and service-ready TTS deployment. --- @@ -19,18 +24,18 @@ ### Installation -- Installing with Docker +- (Option 1 Recommended) Run with Docker ```bash - # The easiest way to install Lightllm is by using the official image. You can directly pull and run the official image - docker pull lighttts/light-tts:v1.0 + # The easiest way to install LightTTS is by using the official image. You can directly pull and run the official image + docker pull lighttts/light-tts:latest # Or you can manually build the image - docker build -t light-tts:v1.0 . + docker build -t light-tts:latest . # Run the image - docker run -it --gpus all -p 8080:8080 --shm-size 4g -v your_local_path:/data/ light-tts:v1.0 /bin/bash + docker run -it --gpus all -p 8080:8080 --shm-size 4g -v your_local_path:/data/ light-tts:latest /bin/bash -- Installing from Source +- (Option 2) Install from Source ```bash # Clone the repo @@ -41,12 +46,11 @@ # git submodule update --init --recursive # (Recommended) Create a new conda environment - conda create -n light-tts python=3.10 -y + conda create -n light-tts python=3.10 conda activate light-tts - # pynini is required by WeTextProcessing, use conda to install it as it can be executed on all platforms. - conda install -y -c conda-forge pynini==2.1.5 - pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com + # Install dependencies (We use the latest torch==2.9.1, but other versions are also compatible) + pip install -r requirements.txt # If you encounter sox compatibility issues # ubuntu @@ -55,23 +59,25 @@ sudo yum install sox sox-devel ``` -### Model download +### Model Download -We now only support CosyVoice2 model. +We now support CosyVoice2 and CosyVoice3 models. ```python -# SDK模型下载 +# ModelScope SDK model download (SDK模型下载) from modelscope import snapshot_download +snapshot_download('FunAudioLLM/Fun-CosyVoice3-0.5B-2512', local_dir='pretrained_models/Fun-CosyVoice3-0.5B') snapshot_download('iic/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B') snapshot_download('iic/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd') -``` -```python -# git模型下载，请确保已安装git lfs -mkdir -p pretrained_models -git clone https://www.modelscope.cn/iic/CosyVoice2-0.5B.git pretrained_models/CosyVoice2-0.5B -git clone https://www.modelscope.cn/iic/CosyVoice-ttsfrd.git pretrained_models/CosyVoice-ttsfrd + +# For overseas users, HuggingFace SDK model download +from huggingface_hub import snapshot_download +snapshot_download('FunAudioLLM/Fun-CosyVoice3-0.5B-2512', local_dir='pretrained_models/Fun-CosyVoice3-0.5B') +snapshot_download('FunAudioLLM/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B') +snapshot_download('FunAudioLLM/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd') ``` +(We have already installed the ttsfrd package in the docker image. If you are using docker image, you can skip this installation) For better text normalization performance, you can optionally install the ttsfrd package and unzip its resources. This step is not required — if skipped, the system will fall back to WeTextProcessing by default. ```bash @@ -80,73 +86,104 @@ unzip resource.zip -d . pip install ttsfrd_dependency-0.1-py3-none-any.whl pip install ttsfrd-0.4.2-cp310-cp310-linux_x86_64.whl ``` -📝 This setup instruction is based on the original guide from the [CosyVoice repository](https://github.com/FunAudioLLM/CosyVoice). ### Start the Model Service +**Note:** It is recommended to enable the `load_trt` parameter for acceleration. The default flow precision is fp16 for CosyVoice2 and fp32 for CosyVoice3. + +**For CosyVoice2:** + +```bash +python -m light_tts.server.api_server --model_dir ./pretrained_models/CosyVoice2-0.5B +``` + +**For CosyVoice3:** + +```bash +python -m light_tts.server.api_server --model_dir ./pretrained_models/Fun-CosyVoice3-0.5B-2512 +``` + +**With custom data type** (float32, bfloat16, or float16; default: float16): + ```bash -# It is recommended to enable the load_trt parameter for acceleration. -# token2wav: The default is fp16 mode for CosyVoice2 and fp32 mode for CosyVoice3. -python -m light_tts.server.api_server --model_dir ./pretrained_models/CosyVoice2-0.5B-latest +# Use float32 for better accuracy or float16 for faster speed +python -m light_tts.server.api_server --model_dir ./pretrained_models/Fun-CosyVoice3-0.5B-2512 --data_type float32 ``` -- max_total_token_num: llm arg, the total token nums the gpu and model can support, equals = `max_batch * (input_len + output_len)` -- max_req_total_len: llm arg, the max value for `req_input_len + req_output_len`, 32768 is set here because the `max_position_embeddings` of the llm part is 32768 -- There are many other parameters that can be viewed in `light_tts/server/api_cli.py` +**Available Parameters:** -Wait for a while, this service will be started. The default startup is localhost:8080. +The default values are usually the fastest and generally do not need to be adjusted. If you need to customize them, please refer to the following parameter descriptions: +- `load_trt`: Whether to load the flow_decoder in TensorRT mode (default: True). +- `data_type`: The data type for LLM inference (default: float16) +- `load_jit`: Whether to load the flow_encoder in JIT mode (default: False). +- `max_total_token_num`: LLM arg, total token count the GPU and model can support = `max_batch * (input_len + output_len)` (default: 64 * 1024) +- `max_req_total_len`: LLM arg, maximum value for `req_input_len + req_output_len` (default: 32768, matches `max_position_embeddings`) +- `graph_max_len_in_batch`: Maximum sequence length for CUDA graph capture in decoding stage (default: 32768) +- `graph_max_batch_size`: Maximum batch size for CUDA graph capture in decoding stage (default: 16) + +For more parameters, see `light_tts/server/api_cli.py` + +Wait for the service to initialize. The default address is `http://localhost:8080`. ### Request Examples -When your service is started, you can call the service through the http API. We support three modes: non-streaming, streaming and bi-streaming. - -- non-streaming and streaming. You can also use `test/test_zero_shot.py`, which can print information such as rtf and ttft. - - - ```python - import requests - import time - import soundfile as sf - import numpy as np - import os - import threading - import json - - url = "http://localhost:8080/inference_zero_shot" - path = "cosyvoice/asset/zero_shot_prompt.wav" # wav file path - prompt_text = "希望你以后能够做的比我还好呦。" - tts_text = "收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。" - stream = True # Whether to use streaming inference - files = { - "prompt_wav": ("sample.wav", open(path, "rb"), "audio/wav") - } - data = { - "tts_text": tts_text, - "prompt_text": prompt_text, - "stream": stream - } - response = requests.post(url, files=files, data=data, stream=True) - sample_rate = 24000 - - audio_data = bytearray() - try: - for chunk in response.iter_content(chunk_size=4096): - if chunk: - audio_data.extend(chunk) - except Exception as e: - print(f"Exception: {e}") - print(f"Error: {response.status_code}, {response.text}") - return - audio_np = np.frombuffer(audio_data, dtype=np.int16) - if response.status_code == 200: - output_wav = f"./outs/output{'_stream' if stream else ''}_{index}.wav" - sf.write(output_wav, audio_np, samplerate=sample_rate, subtype="PCM_16") - print(f"saved as {output_wav}") - else: - print("Error:", response.status_code, response.text) - ``` +Once the service is running, you can interact with it through the HTTP API. We support three modes: **non-streaming**, **streaming**, and **bi-streaming**. + +- **Non-streaming and Streaming**: Use `test/test_zero_shot.py` for examples, which prints metrics such as RTF (Real-Time Factor) and TTFT (Time To First Token) +- **Bi-streaming**: Uses WebSocket interface. See usage examples in `test/test_bistream.py` + +## 📊 Performance Benchmarks + +We have conducted performance benchmarks on different GPU configurations to demonstrate the throughput and latency characteristics of LightTTS in streaming mode. + +Model: `Fun-CosyVoice3-0.5B-2512` datatype: `float16` + +### NVIDIA GeForce RTX 4090D +non-stream: `test/test_zs.py` + +|num_workers|cost time 50%|cost time 90%|cost time 99%|rtf 50%|rtf 90%|rtf 99%|avg rtf|total_cost_time|qps| +|------|------|------|------|------|------|------|------|------|------| +|1|0.61|1.09|1.51|0.13|0.16|0.22|0.13|33.95|1.47| +|2|0.8|1.24|1.71|0.15|0.22|0.25|0.16|21.46|2.33| +|4|1.02|1.88|2.27|0.22|0.29|0.38|0.23|15.31|3.27| +|8|1.76|2.36|3.48|0.33|0.49|0.62|0.36|12.18|4.1| + +stream: `test/test_zs_stream.py` + +|num_workers|cost time 50%|cost time 90%|cost time 99%|ttft 50%|ttft 90%|ttft 99%|rtf 50%|rtf 90%|rtf 99%|avg rtf|total_cost_time|qps| +|------|------|------|------|------|------|------|------|------|------|------|------|------| +|1|1.01|2.15|2.82|0.33|0.34|0.9|0.21|0.25|0.34|0.22|60.13|0.83| +|2|1.83|3.56|5.16|0.93|1.53|2.3|0.34|0.63|0.81|0.4|52.47|0.95| +|4|3.43|5.76|7.31|2.62|4.37|5.8|0.7|1.28|2.16|0.81|48.74|1.03| +|8|7.27|10.01|10.45|6.4|8.55|9.03|1.28|2.67|3.66|1.57|47.37|1.06| + +### NVIDIA GeForce RTX 5090 +non-stream + +|num_workers|cost time 50%|cost time 90%|cost time 99%|rtf 50%|rtf 90%|rtf 99%|avg rtf|total_cost_time|qps| +|------|------|------|------|------|------|------|------|------|------| +|1|0.51|0.81|1.61|0.11|0.13|0.23|0.11|28.9|1.73| +|2|0.64|1.1|1.48|0.13|0.16|0.26|0.13|17.54|2.85| +|4|0.87|1.28|1.68|0.17|0.23|0.36|0.18|11.45|4.37| +|8|1.32|1.86|2.14|0.25|0.4|0.6|0.29|8.97|5.57| + +stream + +|num_workers|cost time 50%|cost time 90%|cost time 99%|ttft 50%|ttft 90%|ttft 99%|rtf 50%|rtf 90%|rtf 99%|avg rtf|total_cost_time|qps| +|------|------|------|------|------|------|------|------|------|------|------|------|------| +|1|0.76|1.41|2.27|0.28|0.3|0.31|0.16|0.18|0.22|0.16|44.06|1.13| +|2|1.45|2.34|3.46|0.74|1.28|1.75|0.27|0.45|0.7|0.3|38.82|1.29| +|4|2.9|4.04|4.7|2.16|3.03|3.4|0.5|1.04|1.51|0.61|37.75|1.32| +|8|5.78|7.74|8.49|5.01|6.73|7.35|1.03|2.09|2.85|1.22|37.67|1.33| -- bi-streaming. We use the websocket interface implementation, and we can find usage examples in `test/test_bistream.py`. +**Metrics Explanation:** +- **num_workers**: Number of concurrent workers +- **cost time**: Total request processing time in seconds (50th/90th/99th percentile) +- **ttft**: Time to First Token in seconds (50th/90th/99th percentile) +- **rtf**: Real-Time Factor (50th/90th/99th percentile) +- **avg rtf**: Average Real-Time Factor +- **total_cost_time**: Total benchmark duration in seconds +- **qps**: Queries Per Second ## License @@ -154,4 +191,4 @@ This repository is released under the [Apache-2.0](LICENSE) license. ### Third-Party Code Attribution -This project includes code from [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) (Copyright Alibaba, Inc. and its affiliates), which is also licensed under Apache-2.0. The CosyVoice code is located in the `cosyvoice/` directory and has been integrated and modified as part of Light TTS. See [NOTICE](NOTICE) file for complete attribution details. \ No newline at end of file +This project includes code from [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) (Copyright Alibaba, Inc. and its affiliates), which is also licensed under Apache-2.0. The CosyVoice code is located in the `cosyvoice/` directory and has been integrated and modified as part of LightTTS. See the [NOTICE](NOTICE) file for complete attribution details. \ No newline at end of file diff --git a/light_tts/common/all_kernel_configs/triton_flashdecoding/{head_dim=64,kv_head_num=2,out_dtype=torch.float16,q_head_num=14}_NVIDIA_GeForce_RTX_5090.json b/light_tts/common/all_kernel_configs/triton_flashdecoding/{head_dim=64,kv_head_num=2,out_dtype=torch.float16,q_head_num=14}_NVIDIA_GeForce_RTX_5090.json deleted file mode 100644 index 02bbe7d..0000000 --- a/light_tts/common/all_kernel_configs/triton_flashdecoding/{head_dim=64,kv_head_num=2,out_dtype=torch.float16,q_head_num=14}_NVIDIA_GeForce_RTX_5090.json +++ /dev/null @@ -1 +0,0 @@ -{"32": {"1": {"BLOCK_SEQ": 16, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 4, "stage2_num_warps": 1, "stage2_num_stages": 1}, "2": {"BLOCK_SEQ": 256, "BLOCK_N": 32, "stage1_num_warps": 1, "stage1_num_stages": 6, "stage2_num_warps": 1, "stage2_num_stages": 1}, "4": {"BLOCK_SEQ": 256, "BLOCK_N": 32, "stage1_num_warps": 1, 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"stage1_num_stages": 7, "stage2_num_warps": 2, "stage2_num_stages": 4}, "4": {"BLOCK_SEQ": 64, "BLOCK_N": 64, "stage1_num_warps": 1, "stage1_num_stages": 3, "stage2_num_warps": 2, "stage2_num_stages": 4}, "8": {"BLOCK_SEQ": 64, "BLOCK_N": 64, "stage1_num_warps": 1, "stage1_num_stages": 3, "stage2_num_warps": 4, "stage2_num_stages": 4}, "16": {"BLOCK_SEQ": 64, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 8, "stage2_num_warps": 1, "stage2_num_stages": 4}, "32": {"BLOCK_SEQ": 64, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 8, "stage2_num_warps": 1, "stage2_num_stages": 4}, "64": {"BLOCK_SEQ": 128, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 5, "stage2_num_warps": 2, "stage2_num_stages": 4}}, "1024": {"1": {"BLOCK_SEQ": 64, "BLOCK_N": 64, "stage1_num_warps": 4, "stage1_num_stages": 1, "stage2_num_warps": 2, "stage2_num_stages": 4}, "2": {"BLOCK_SEQ": 64, "BLOCK_N": 64, "stage1_num_warps": 1, "stage1_num_stages": 1, "stage2_num_warps": 2, "stage2_num_stages": 4}, "4": {"BLOCK_SEQ": 64, "BLOCK_N": 64, "stage1_num_warps": 1, "stage1_num_stages": 1, "stage2_num_warps": 4, "stage2_num_stages": 4}, "8": {"BLOCK_SEQ": 64, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 7, "stage2_num_warps": 4, "stage2_num_stages": 4}, "16": {"BLOCK_SEQ": 64, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 3, "stage2_num_warps": 2, "stage2_num_stages": 4}, "32": {"BLOCK_SEQ": 128, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 15, "stage2_num_warps": 2, "stage2_num_stages": 4}}, "2048": {"1": {"BLOCK_SEQ": 64, "BLOCK_N": 64, "stage1_num_warps": 1, "stage1_num_stages": 3, "stage2_num_warps": 2, "stage2_num_stages": 4}, "2": {"BLOCK_SEQ": 64, "BLOCK_N": 64, "stage1_num_warps": 1, "stage1_num_stages": 4, "stage2_num_warps": 4, "stage2_num_stages": 4}, "4": {"BLOCK_SEQ": 128, "BLOCK_N": 64, "stage1_num_warps": 1, "stage1_num_stages": 7, "stage2_num_warps": 4, "stage2_num_stages": 4}, "8": {"BLOCK_SEQ": 64, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 2, "stage2_num_warps": 2, "stage2_num_stages": 4}, "16": {"BLOCK_SEQ": 128, "BLOCK_N": 16, "stage1_num_warps": 1, "stage1_num_stages": 5, "stage2_num_warps": 1, "stage2_num_stages": 4}}} \ No newline at end of file diff --git a/light_tts/server/api_cli.py b/light_tts/server/api_cli.py index f706ca6..f869ba8 100755 --- a/light_tts/server/api_cli.py +++ b/light_tts/server/api_cli.py @@ -116,7 +116,7 @@ def make_argument_parser() -> argparse.ArgumentParser: parser.add_argument( "--graph_max_len_in_batch", type=int, - default=2048, + default=32768, help="""Maximum sequence length that can be captured by the cuda graph for decodign stage. The default value is 8192. It will turn into eagar mode if encounters a larger value. """, ) diff --git a/test/kernel/triton_flashdecoding/llama_triton_flashdecoding_tuning.py b/test/kernel/triton_flashdecoding/llama_triton_flashdecoding_tuning.py index 4972625..c2efa8d 100755 --- a/test/kernel/triton_flashdecoding/llama_triton_flashdecoding_tuning.py +++ b/test/kernel/triton_flashdecoding/llama_triton_flashdecoding_tuning.py @@ -13,10 +13,11 @@ from light_tts.models.llama.triton_kernel.flash_decoding_stage2 import flash_decode_stage2 from light_tts.models.llama.triton_kernel.flash_decoding import LlamaFlashDecodingStage1KernelConfig + @torch.no_grad() -def test_func( +def test_func_stage1( batch_size, - max_len, + seq_len, head_dim, q_head_num, kv_head_num, @@ -24,40 +25,40 @@ def test_func( test_count: int = 20, **run_config, ): + """只测试 stage1 的性能""" set_seed() + max_len = 2048 tmp_class = type("TestObj", (object,), {}) infer_state = tmp_class() infer_state.batch_size = batch_size - infer_state.max_len_in_batch = max_len + infer_state.max_len_in_batch = 2048 infer_state.req_manager = tmp_class() infer_state.req_manager.req_to_token_indexs = torch.zeros( - (infer_state.batch_size, infer_state.max_len_in_batch), dtype=torch.int32, device="cuda" + (infer_state.batch_size, seq_len), dtype=torch.int32, device="cuda" ) infer_state.req_manager.req_to_token_indexs.view(-1)[:] = torch.arange( - 0, infer_state.batch_size * infer_state.max_len_in_batch, step=1, dtype=torch.int32 + 0, infer_state.batch_size * seq_len, step=1, dtype=torch.int32 ).cuda() infer_state.b_req_idx = torch.arange(0, infer_state.batch_size, step=1, dtype=torch.int32).cuda() - infer_state.b_seq_len = torch.full((infer_state.batch_size,), fill_value=max_len, dtype=torch.int32).cuda() + infer_state.b_seq_len = torch.full((infer_state.batch_size,), fill_value=seq_len, dtype=torch.int32).cuda() infer_state.total_token_num_tensor = torch.sum(infer_state.b_seq_len) q = torch.empty((batch_size, q_head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2) - cache_k = torch.empty((batch_size * max_len, kv_head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2) - cache_v = torch.empty((batch_size * max_len, kv_head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2) - mid_o = torch.empty([batch_size, - q_head_num, - max_len // run_config["BLOCK_SEQ"] + 1, - head_dim], - dtype=torch.float32, - device="cuda") - mid_o_logexpsum = torch.empty([batch_size, - q_head_num, - max_len // run_config["BLOCK_SEQ"] + 1], - dtype=torch.float32, - device="cuda") - o_tensor = torch.empty_like(q) - - fn1 = lambda: flash_decode_stage1( + cache_k = torch.empty((batch_size * max_len, kv_head_num, head_dim), dtype=dtype, device="cuda").normal_( + mean=0.1, std=0.2 + ) + cache_v = torch.empty((batch_size * max_len, kv_head_num, head_dim), dtype=dtype, device="cuda").normal_( + mean=0.1, std=0.2 + ) + mid_o = torch.empty( + [batch_size, q_head_num, max_len // run_config["BLOCK_SEQ"] + 1, head_dim], dtype=torch.float32, device="cuda" + ) + mid_o_logexpsum = torch.empty( + [batch_size, q_head_num, max_len // run_config["BLOCK_SEQ"] + 1], dtype=torch.float32, device="cuda" + ) + + fn = lambda: flash_decode_stage1( q, cache_k, cache_v, @@ -67,46 +68,88 @@ def test_func( infer_state.max_len_in_batch, mid_o, mid_o_logexpsum, - **run_config + **run_config, ) - cost_time1 = triton.testing.do_bench_cudagraph(fn1, rep=test_count) + cost_time = triton.testing.do_bench_cudagraph(fn, rep=test_count) + # logger.info(f"stage1 {batch_size, seq_len} cost time: {cost_time} ms, config: {run_config}") + return cost_time - fn2 = lambda: flash_decode_stage2( - mid_o, - mid_o_logexpsum, - infer_state.b_seq_len, - o_tensor, - **run_config - ) - cost_time2 = triton.testing.do_bench_cudagraph(fn2, rep=test_count) - cost_time = cost_time1 + cost_time2 - logger.info(f"bf16 {batch_size, max_len} cost time: {cost_time} ms") +@torch.no_grad() +def test_func_stage2( + batch_size, + seq_len, + head_dim, + q_head_num, + kv_head_num, + dtype, + test_count: int = 20, + **run_config, +): + """只测试 stage2 的性能""" + set_seed() + max_len = 2048 + + mid_o = torch.empty( + [batch_size, q_head_num, max_len // run_config["BLOCK_SEQ"] + 1, head_dim], dtype=torch.float32, device="cuda" + ).normal_(mean=0.1, std=0.2) + mid_o_logexpsum = torch.empty( + [batch_size, q_head_num, max_len // run_config["BLOCK_SEQ"] + 1], dtype=torch.float32, device="cuda" + ).normal_(mean=0.1, std=0.2) + + b_seq_len = torch.full((batch_size,), fill_value=seq_len, dtype=torch.int32).cuda() + o_tensor = torch.empty((batch_size, q_head_num, head_dim), dtype=dtype, device="cuda") + + fn = lambda: flash_decode_stage2(mid_o, mid_o_logexpsum, b_seq_len, o_tensor, **run_config) + + cost_time = triton.testing.do_bench_cudagraph(fn, rep=test_count) + logger.info(f"stage2 {batch_size, max_len} cost time: {cost_time} ms, config: {run_config}") return cost_time -def get_test_configs(split_id, split_count, **kwargs): +def get_test_configs_stage1(split_id, split_count, **kwargs): + """Stage1 配置生成器""" index = 0 - for BLOCK_SEQ in [16, 32, 64, 128]: + for BLOCK_SEQ in [16, 32, 64, 128, 256]: for BLOCK_N in [8, 16, 32, 64]: if BLOCK_SEQ % BLOCK_N != 0: continue for stage1_num_warps in [1, 2, 4, 8]: - for stage1_num_stages in [1, 2, 3, 4, 5, 6, 7, 8, 12, 15]: - for stage2_num_warps in [1, 2, 4]: - for stage2_num_stages in [1, 2, 3, 4]: - t_config = { - "BLOCK_SEQ": BLOCK_SEQ, - "BLOCK_N": BLOCK_N, - "stage1_num_warps": stage1_num_warps, - "stage1_num_stages": stage1_num_stages, - "stage2_num_warps": stage2_num_warps, - "stage2_num_stages": stage2_num_stages, - } - if index % split_count == split_id: - yield t_config - index += 1 + for stage1_num_stages in [1, 2, 3, 4, 5, 6, 7, 8]: + t_config = { + "BLOCK_SEQ": BLOCK_SEQ, + "BLOCK_N": BLOCK_N, + "stage1_num_warps": stage1_num_warps, + "stage1_num_stages": stage1_num_stages, + # stage2 使用默认值，不影响 stage1 测试 + "stage2_num_warps": 4, + "stage2_num_stages": 2, + } + if index % split_count == split_id: + yield t_config + index += 1 + + +def get_test_configs_stage2(split_id, split_count, **kwargs): + """Stage2 配置生成器，基于 stage1 的最优配置（从 kwargs 中获取）""" + best_stage1_config = kwargs.get("best_stage1_config", {}) + index = 0 + for stage2_num_warps in [1, 2, 4, 8]: + for stage2_num_stages in [1, 2, 3, 4, 5, 6, 7, 8]: + t_config = { + # 使用 stage1 的最优配置 + "BLOCK_SEQ": best_stage1_config["BLOCK_SEQ"], + "BLOCK_N": best_stage1_config["BLOCK_N"], + "stage1_num_warps": best_stage1_config["stage1_num_warps"], + "stage1_num_stages": best_stage1_config["stage1_num_stages"], + # stage2 参数 + "stage2_num_warps": stage2_num_warps, + "stage2_num_stages": stage2_num_stages, + } + if index % split_count == split_id: + yield t_config + index += 1 if __name__ == "__main__": @@ -119,33 +162,81 @@ def get_test_configs(split_id, split_count, **kwargs): q_head_num = 14 kv_head_num = 2 - for seq_len in [64, 128, 256, 512, 1024, 2048]: - for batch_size in [1, 2, 4, 8, 16, 32, 64]: - if batch_size * seq_len > 32 * 1024: - continue - + for seq_len in [2048]: + for batch_size in [1, 2, 4, 8]: test_func_args = { "batch_size": batch_size, - "max_len": seq_len, + "seq_len": seq_len, "head_dim": head_dim, "q_head_num": q_head_num, "kv_head_num": kv_head_num, "dtype": torch.float16, "test_count": 20, } - ans = mp_tuning( + + # ========== Stage 1 调优 ========== + logger.info(f"===== Tuning Stage1 for seq_len={seq_len}, batch_size={batch_size} =====") + best_stage1_config = mp_tuning( tuning_configs, { - "test_func": test_func, + "test_func": test_func_stage1, "test_func_args": test_func_args, - "get_test_configs_func": get_test_configs, + "get_test_configs_func": get_test_configs_stage1, + }, + ) + # 如果 stage1 调优失败，使用默认配置 + if best_stage1_config is None: + best_stage1_config = { + "BLOCK_SEQ": 64, + "BLOCK_N": 16, + "stage1_num_warps": 4, + "stage1_num_stages": 2, } + logger.warning(f"Stage1 tuning failed, using default config: {best_stage1_config}") + else: + logger.info(f"Stage1 best config: {best_stage1_config}") + + # ========== Stage 2 调优 ========== + logger.info(f"===== Tuning Stage2 for seq_len={seq_len}, batch_size={batch_size} =====") + + # 把 best_stage1_config 放到 test_func_args 里传递给 get_test_configs_stage2 + test_func_args_stage2 = test_func_args.copy() + test_func_args_stage2["best_stage1_config"] = best_stage1_config + + best_stage2_config = mp_tuning( + tuning_configs, + { + "test_func": test_func_stage2, + "test_func_args": test_func_args_stage2, + "get_test_configs_func": get_test_configs_stage2, + }, ) - store_json_ans[seq_len][batch_size] = ans + # 如果 stage2 调优失败，使用默认配置 + if best_stage2_config is None: + best_stage2_config = { + "stage2_num_warps": 4, + "stage2_num_stages": 2, + } + logger.warning(f"Stage2 tuning failed, using default config: {best_stage2_config}") + else: + logger.info(f"Stage2 best config: {best_stage2_config}") + + # ========== 合并最终配置 ========== + final_config = { + "BLOCK_SEQ": best_stage1_config["BLOCK_SEQ"], + "BLOCK_N": best_stage1_config["BLOCK_N"], + "stage1_num_warps": best_stage1_config["stage1_num_warps"], + "stage1_num_stages": best_stage1_config["stage1_num_stages"], + "stage2_num_warps": best_stage2_config["stage2_num_warps"], + "stage2_num_stages": best_stage2_config["stage2_num_stages"], + } + logger.info(f"Final combined config: {final_config}") + + store_json_ans[seq_len][batch_size] = final_config LlamaFlashDecodingStage1KernelConfig.save_config( head_dim=head_dim, q_head_num=q_head_num, kv_head_num=kv_head_num, out_dtype=str(torch.float16), store_json_ans=store_json_ans, - ) \ No newline at end of file + ) diff --git a/test/test_texts.json b/test/test_texts.json index 24ee5d5..63d7bde 100755 --- a/test/test_texts.json +++ b/test/test_texts.json @@ -1,6 +1,52 @@ [ - "收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。", - "我一分钟没理你, 就感觉失去了全世界", - "我想告诉你，你是我生命中最美好的奇迹。你的存在让我的世界变得更加完整和有意义。我喜欢你的一切，包括你的缺点和不足，因为这些都是你独特的一部分，让我更加喜欢你。", - "你是我生命中的唯一，是我心中的挚爱，我会用我所有的温柔，来呵护你的一生。" + "沅沅，你别这样，我不是那个意思，我不想看到你这么难过。", + "不可能，我明明放在了最里面的口袋，你怎么可能拿到？", + "真的不用去医院吗？我还是有点担心……", + "你啊，就是太容易害羞了。", + "然后我们就可以一起去参加各种漫展和活动，认识更多志同道合的朋友。", + "我一定会想办法治好你的脸，相信我。", + "看来你还没有完全适应你的身份，没关系，我们有的是时间。", + "不用谢，以后别再喝醉了。", + "妙妙，你辛苦了……", + "不离，我绝对不会跟你离婚的。", + "我也一样，只要是姐姐喜欢的，我都喜欢。", + "别闹了，你再这样我真的要生气了。", + "你开心就好，不过，你要答应我，以后不能再这样吓唬我了。", + "哼，别得寸进尺。我的忍耐是有限度的。", + "既然你不想出去，那我们就在家里看电影吧，我让人送些吃的过来。", + "你别以为你不说话，我就不知道你在想什么。你想把我困在这里，然后慢慢折磨我，对不对？", + "我可没这么说，我只是不喜欢你在我面前提别的男人，特别是你爸爸，你现在可是我的猎物。", + "做个桂花糕而已，有什么好得意的。", + "我知道你们是为了照片的事情来的，我可以告诉你们，照片是我花两千万买下来的，你们别想从我这里得到任何消息。", + "那我再轻点。很快就好了。", + "是吗？那我们一起去看电影吧，我知道有一部新上映的电影很不错。", + "你这是说的什么话？本太子只是觉得时间过得太慢了，想快点和你成亲。", + "陛下……既然陛下不愿，那臣也不勉强，只是陛下一定要保重龙体，切不可太过劳累。", + "……这个孩子不能留。", + "你该不会是喜欢上我了吧？所以才老被那些 Alpha 欺负。", + "行吧，看来你今天是不打算理我了。那我自己回家了，你路上注意安全。", + "咳嗯……好了，我们该出发了。", + "你……云烟，你这是在耍什么脾气？", + "我好紧张，万一我考不上 K 大怎么办？", + "唔……我没发呀……", + "当然是叫你起床了，我的好师尊。", + "那就好，今天的事……对不起，我不应该说那些话，我保证不会再有下一次了。", + "你这是在挑战我的耐心吗？我再给你最后一次机会，告诉我你所知道的一切，否则后果自负。", + "别怕。我在这里。", + "我错了，老婆，别哭了，我以后都听你的。", + "等我腿好了，我就可以带你去任何你想去的地方了。", + "等等，查纳！你不能一个人去战斗，那样太危险了！我们需要一起制定一个计划，确保每个人的安危。我不能让你冒险，你可是我们精灵族的希望。", + "你来了。你终于来了……", + "我吃好了，你去换鞋，我们走吧。", + "好的，警察同志慢走。", + "我看你才是神经病。你是我的未婚妻，你就应该乖乖听我的话，而不是一味地反抗我。", + "你……你还敢说！本公主的初吻竟然被你这个登徒子给夺走了，我一定要杀了你！", + "妹妹，你是不是喜欢她？你是不是想和她在一起？", + "看来只能先帮你止血了。你醒了？感觉怎么样？", + "我说了，我不喜欢她。", + "荣耀？你以为杀手榜上的排名能代表什么？", + "那好吧，我就不勉强你了。不过，你可要小心点，别让我找到机会哦。", + "走？宝贝，你喝了我的酒，就是我的人了，还想走？", + "我无意伤害你，你不用这么害怕。", + "那你去给我弄点吃的来。" ] \ No newline at end of file diff --git a/test/test_zs_speed.py b/test/test_zs_speed.py index 2931e8a..d912d34 100755 --- a/test/test_zs_speed.py +++ b/test/test_zs_speed.py @@ -53,22 +53,29 @@ def get_file(index): start = time.time() response = requests.post(url, files=files, data=data, stream=True) - cost_time = time.time() - start + + audio_data = bytearray() + rtf = 0 + cost_time = 0 try: if response.status_code == 200: - audio_data = bytearray() for chunk in response.iter_content(chunk_size=4096): # 分批读取 if chunk: audio_data.extend(chunk) + cost_time = time.time() - start + speech_len = len(audio_data) / 2 / 24000 + rtf = cost_time / speech_len if speech_len > 0 else 0 else: print(f"{index} {inputs} \n error:{response.text}") failed += 1 + cost_time = time.time() - start except Exception as e: print(f"An exception occurred\ntext: {inputs}\n{e}\n{response.text}") failed += 1 + cost_time = time.time() - start finally: - return cost_time + return cost_time, rtf results = [] @@ -79,18 +86,23 @@ def get_file(index): start_time = time.time() with ProcessPoolExecutor(max_workers=num_worker) as executor: - all_cost_times = list(tqdm(executor.map(get_file, range(num_test)), total=num_test, desc="running tests")) + all_results = list(tqdm(executor.map(get_file, range(num_test)), total=num_test, desc="running tests")) total_time = time.time() - start_time - # 假设是你的数据数组 - data = np.array(all_cost_times) + all_cost_times = [result[0] for result in all_results] + all_rtf = [result[1] for result in all_results] + result = {"num_workers": num_worker} # 计算分位数 if failed - last_failed > 0: print(f"Failed {failed - last_failed}") for percentile in [50, 90, 99]: - percentile_data = np.percentile(data, percentile) - result[f"{percentile}%"] = round(percentile_data, 2) + percentile_data = np.percentile(np.array(all_cost_times), percentile) + result[f"cost time {percentile}%"] = round(percentile_data, 2) + for percentile in [50, 90, 99]: + percentile_data = np.percentile(np.array(all_rtf), percentile) + result[f"rtf {percentile}%"] = round(percentile_data, 2) + result["avg rtf"] = round(np.mean(np.array(all_rtf)), 2) result["total_cost_time"] = round(total_time, 2) result["qps"] = round(num_test / total_time, 2) results.append(result) From 01d26c2b73b1131a63aecb46618cf8cfc32efb04 Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Fri, 26 Dec 2025 06:26:15 +0000 Subject: [PATCH 20/21] easy test_zero_shot.py --- test/test_zero_shot.py | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/test/test_zero_shot.py b/test/test_zero_shot.py index 1176a38..2c2f0f3 100755 --- a/test/test_zero_shot.py +++ b/test/test_zero_shot.py @@ -14,13 +14,21 @@ "--cosyvoice_version", type=int, choices=[2, 3], default=3, help="CosyVoice version: 2 or 3 (default: 3)" ) parser.add_argument("--stream", action="store_true", default=False, help="是否使用流式推理 (default: True)") -parser.add_argument("--num", type=int, default=5, help="测试数量 (default: 5)") +parser.add_argument("--num", type=int, default=1, help="测试数量 (default: 5)") args = parser.parse_args() url = f"http://0.0.0.0:{args.port}/inference_zero_shot" -num = args.num + # 准备要发送的文本和音频文件 path = "../cosyvoice/asset/zero_shot_prompt.wav" +your_text = "收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。" +# 根据 cosyvoice_version 设置 prompt_text +if args.cosyvoice_version == 3: + prompt_text = "You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。" +else: + prompt_text = "希望你以后能够做的比我还好呦。" + +num = args.num stream = args.stream # 是否使用流式推理 with open("test_texts.json", "r") as f: all_inputs = json.load(f) @@ -31,16 +39,9 @@ def get_file(index): files = {"prompt_wav": ("sample.wav", open(path, "rb"), "audio/wav")} # inputs = random.choice(all_inputs) - inputs = all_inputs[0] # inputs = all_inputs[2] - # 根据 cosyvoice_version 设置 prompt_text - if args.cosyvoice_version == 3: - prompt_text = "You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。" - else: - prompt_text = "希望你以后能够做的比我还好呦。" - - data = {"tts_text": inputs, "prompt_text": prompt_text, "stream": stream} + data = {"tts_text": your_text, "prompt_text": prompt_text, "stream": stream} start_time = time.time() response = requests.post(url, files=files, data=data, stream=True) @@ -74,7 +75,7 @@ def get_file(index): output_wav = f"./outs/output{'_stream' if stream else ''}_{index}.wav" sf.write(output_wav, audio_np, samplerate=sample_rate, subtype="PCM_16") print( - f"{inputs} saved as {output_wav}, time cost: {cost_time:.2f} s" + f"{your_text} saved as {output_wav}, time cost: {cost_time:.2f} s" + f", rtf: {cost_time / speech_len}, ttft: {ttft:.2f} s" ) else: From c603bddfae106f8b618d73ed2c39ca12c1d0a5f5 Mon Sep 17 00:00:00 2001 From: WANDY666 <1060304770@qq.com> Date: Fri, 26 Dec 2025 14:54:50 +0800 Subject: [PATCH 21/21] delete .github --- .github/workflows/pre-commit.yml | 26 -------------------------- 1 file changed, 26 deletions(-) delete mode 100644 .github/workflows/pre-commit.yml diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml deleted file mode 100644 index 456ae46..0000000 --- a/.github/workflows/pre-commit.yml +++ /dev/null @@ -1,26 +0,0 @@ -name: Pre-commit checks - -on: - pull_request: - branches: - - main - -jobs: - pre-commit: - runs-on: ubuntu-latest - - steps: - - name: Checkout code - uses: actions/checkout@v4 - with: - # fetch-depth: 0 对差异检测很重要 - fetch-depth: 0 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.9' - - - name: Run pre-commit - uses: pre-commit/action@v3.0.1 - # 这个 Action 会自动寻找 .pre-commit-config.yaml 并只对 PR 修改的文件运行 \ No newline at end of file