English | 한국어 | 日本語 | 中文 | Polski
A fast, local desktop app for turning video into subtitles (SRT) and translating them into the language you need. Powered by whisper.cpp for extraction and optional online engines for translation.
Important: This app creates new SRT subtitles from your video's audio using whisper.cpp. It does not extract existing embedded subtitle tracks or on‑screen text (no OCR).
Subtitle extraction runs 100% locally — your video never leaves your machine. No cloud uploads, no accounts, no credit cards. Create accurate SRT offline; translation requires internet connection (free MyMemory, or your own DeepL/OpenAI API keys).
| Need | What you get |
|---|---|
| Privacy & control | 100% local STT; no cloud uploads |
| Zero signup | No account, no credit card, no personal data |
| Unlimited use | No app‑level daily/monthly limits |
| Understand foreign videos | Extract + translate SRT in one run |
| Avoid setup pain | Auto model download; no Python required |
| Clear feedback | Queue, smooth progress, ETA |
Note: When using online translation engines, provider‑side limits may apply (e.g., MyMemory quota). The app itself does not impose usage caps.
- Download the latest portable archive from Releases:
WhisperSubTranslate-v1.3.1-win-x64.zip - Open the extracted folder and run
WhisperSubTranslate.exe
That's it — extraction runs fully offline on your PC. Translation is optional (free MyMemory is pre‑wired; DeepL/OpenAI require your own API keys).
npm install
npm start- whisper-cpp is automatically downloaded during
npm install(~700MB CUDA version) - FFmpeg is automatically included via npm package
- First run will download the selected GGML model into
_models/when missing
If auto-download fails, manually download from whisper.cpp releases and extract to
whisper-cpp/folder.
npm run build-winArtifacts are emitted to dist2/.
| Area | Details |
|---|---|
| Runtime | Electron, Node.js, JavaScript |
| Packaging | electron‑builder |
| Networking | axios |
| Speech‑to‑text | whisper.cpp (GGML models) |
| Translation (optional) | DeepL API, OpenAI (GPT-5-nano), Gemini, MyMemory |
| Engine | Cost | API key | Limits / Notes |
|---|---|---|---|
| MyMemory | Free | No | ~50K chars/day per IP |
| DeepL | Free 500K/month | Yes | Paid tiers available |
| GPT-5-nano (OpenAI) | Paid | Yes | $0.05 input / $0.40 output per 1M tokens |
| Gemini 3 Flash | Free/Paid | Yes | Free: 250 subs/day (~20-30min), Paid: unlimited (Get key) |
API keys and preferences are saved locally on your PC under app.getPath('userData') with basic encoding to prevent casual exposure. The configuration file is never uploaded to Git or included in builds.
| Data | Location |
|---|---|
| Settings & API Keys | %APPDATA%\whispersubtranslate\translation-config-encrypted.json |
| Error Logs | %APPDATA%\whispersubtranslate\logs\translation-errors.log |
| Models | _models/ (in app folder) |
Korean, English, Japanese, Chinese, Polish (5 languages)
Korean (ko), English (en), Japanese (ja), Chinese (zh), Spanish (es), French (fr), German (de), Italian (it), Portuguese (pt), Russian (ru), Hungarian (hu), Arabic (ar), Polish (pl)
whisper.cpp supports 100+ languages including all major world languages (English, Spanish, French, German, Italian, Portuguese, Russian, Chinese, Japanese, Korean, Arabic, Hindi, Turkish, and many more).
Models are stored under _models/ and auto‑downloaded on demand. Choose a size that fits your machine; larger models are slower but may be more accurate. CUDA is used when available; otherwise CPU runs by default.
| Model | Size | VRAM | Speed | Quality |
|---|---|---|---|---|
| tiny | ~75MB | ~1GB | Fastest | Basic |
| base | ~142MB | ~1GB | Fast | Good |
| small | ~466MB | ~2GB | Medium | Better |
| medium | ~1.5GB | ~4GB | Slow | Great |
| large-v3 | ~3GB | ~5GB | Slowest | Best |
| large-v3-turbo ⭐ | ~809MB | ~4GB | Fast | Excellent |
Note: VRAM requirements are for whisper.cpp with GGML optimization, which is significantly lower than PyTorch Whisper (~10GB for large). Tested: large-v3 works on 6GB VRAM GPU.
Trunk-based development: keep a single main as the trunk; work in short‑lived branches and merge fast via PR.
| Branch | Purpose | Rule |
|---|---|---|
| main | Always releasable | Tag releases, e.g. v1.0.0 |
| feature/* | Small, focused work | Branch from main, merge via PR into main |
Want to add a new language? See the Translation Guide.
Use one branch type for everything (features, fixes, docs):
| Pattern | Use for |
|---|---|
feature/<scope>-<short-desc> |
All changes |
Recommended values: i18n, ui, translation, whisper, model, download, queue, progress, ipc, main, renderer, updater, config, build, logging, perf, docs, readme
Examples:
feature/i18n-api-modal
feature/ui-progress-smoothing
feature/translation-deepl-test
feature/main-disable-devtools
Use prefixes like feat:, fix:, docs:, refactor:, chore:, perf:, build:.
feat: add DeepL connection test
fix: localize target language note
| Topic | Guideline |
|---|---|
| I18N | Don't inline UI/log strings. Add them to I18N tables and reference by key |
| UX | Keep progress/ETA/queue states consistent; avoid regressions |
| Scope | Prefer small, focused changes with clear function names |
| Multi‑language UI | Update ko/en/ja/zh together when adding UI |
| Scenario | Verify |
|---|---|
| Extraction only | Start/stop flows, progress/ETA behavior |
| Extraction + translation | End‑to‑end result and final SRT naming |
| Model download | Missing model path; cancel/stop mid‑download |
| I18N switch | Target‑language label, API modal texts update correctly |
| Translation engines | MyMemory (no key), DeepL/OpenAI (with keys) |
| Build | npm run build-win completes |
| Item | Expectation |
|---|---|
| Description | Clear explanation of changes |
| UI impact | Screenshots for visual changes |
| Testing | Steps to reproduce/verify |
| Assets | No large binaries in Git; screenshots under docs/ |
If this project saves you time or helps you publish better subtitles, supporting it directly accelerates development:
- Your support helps: bug fixes, model download reliability, UI polish, new translation options, and Windows build/testing.
- Transparency: I don't sell data; funds go to development time, infra for release builds, and test credits for translation APIs.
- One‑time sponsors are credited in README and release notes (opt‑out available).
- Monthly sponsors ($3/mo via GitHub Sponsors, auto‑billing) also get best‑effort priority triage for "Sponsor Request" issues.
- whisper.cpp is developed by Georgi Gerganov: ggml-org/whisper.cpp
- FFmpeg: ffmpeg.org
GPL-3.0. External APIs/services (DeepL, OpenAI, etc.) require compliance with their own terms.