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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A free, local desktop app to extract subtitles (SRT) from video and translate them into any language — unlimited use, no signup, no cloud.
| Date | Stars |
|---|---|
| 2026-07-24 | 540 |
| 2026-07-25 | 547 |
| 2026-07-28 | 554 |
| 2026-07-30 | 555 |
| 2026-08-06 | 555 |
Today
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Momentum
35.0
growth rate 0.00%/day
# WhisperSubTranslate English | [한국어](./docs/README.ko.md) | [日本語](./docs/README.ja.md) | [中文](./docs/README.zh.md) | [Polski](./docs/README.pl.md) Turn any video into multilingual subtitles, locally. Drop in a video, generate an SRT with whisper.cpp, then translate it offline with the bundled Hy-MT2 model or with free/paid online engines. > This app creates new subtitles from your video's audio (speech to text). It does not extract embedded subtitle tracks or read on-screen text (no OCR). ## Preview <p align="center"> <img src="assets/hero/hero.png" alt="WhisperSubTranslate main UI" width="100%"> </p> ## Features - 100% local speech to text. Your video never leaves your machine, no account, no upload. - Offline translation with the bundled Hy-MT2 model, or online engines (MyMemory, DeepL, OpenAI, Gemini) with your own keys. - Automatic model download. No Python, no manual setup. - Sync repair models (large-v2 Sync and Sync Lite) for videos where normal models drift out of sync. - Queue, live progress, and local-only job history. ## Getting started ### Users Download the latest portable archive from [Releases](https://github.com/Blue-B/WhisperSubTranslate/releases), extract it, and run `WhisperSubTranslate.exe`. Extraction runs fully offline on your PC. Translation is optional. ### Developers ```bash npm install npm start ``` - Node.js >= 20.19 or >= 22.12 (Electron 42 toolchain) - whisper.cpp is downloaded during `npm install` (CUDA build on Windows, ~700MB) - FFmpeg is included via npm; the selected GGML model downloads on first use ### Linux ```bash sudo apt install cmake build-essential git ffmpeg # Ubuntu/Debian npm install # whisper.cpp is built from source npm start ``` For CUDA acceleration, install the NVIDIA CUDA Toolkit before `npm install`. Manual whisper.cpp build steps are in [CONTRIBUTING.md](CONTRIBUTING.md). ### Build (Windows) ```bash npm run build-win # artifacts are emitted to dist2/ ``` ## Translation engines Translate subtitles fully offline with the bundled Tencent Hy-MT2 model, or route to free/paid online engines using your own API keys. | Engine | Offline | API key | Cost | Notes | | --- | :---: | :---: | --- | --- | | Hy-MT2 1.8B (local, default) | Yes | No | Free | ~1.13GB, VRAM 2GB / RAM 4GB, on-device | | Hy-MT2 7B (local) | Yes | No | Free | ~6.16GB, VRAM 8GB / RAM 12GB, larger model | | MyMemory | No | No | Free | ~50K chars/day per IP | | DeepL | No | Yes | Free 500K/month | Deterministic output | | OpenAI GPT-5.4 mini | No | Yes | Paid | Context-aware | | OpenAI GPT-5.4 nano | No | Yes | Paid | Cheaper tier | | Gemini 3 Flash | No | Yes | Free / low-cost | Recommended low-cost route ([get key](https://aistudio.google.com/app/apikey)) | The local Hy-MT2 engine is the only option that needs no API key, no network, and no per-use cost, so your dialogue never leaves your machine. ### Translation quality (offline engine) WhisperSubTranslate ships Tencent's Hy-MT2 models (1.8B default, 7B optional). Tencent's official evaluation shows the Hy-MT2 family competing with leading commercial translation APIs, and ahead of several of them on some benchmarks.  Source: official benchmarks from Tencent: [Hy-MT2 repository](https://github.com/Tencent-Hunyuan/Hy-MT2), [technical report](https://arxiv.org/pdf/2605.22064), [models on HuggingFace](https://huggingface.co/tencent/Hy-MT2-1.8B). The chart is redrawn from Tencent's official Figure 1, with bundled-model (1.8B/7B) numbers checked against the paper tables. These figures measure the underlying model on standard machine translation benchmarks (WildMTBench, WMT25, FLORES-200, etc.), not a WhisperSubTranslate-specific benchmark. For long videos (1hr+), MyMemory's daily limit can cause slowdowns. Use Gemini, DeepL, or a configured GPT model instead. ## Speech recognition models Models download on demand into
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:80075c7c6e73e369, topic:whisper, readme:speech recognition, readme:speech-to-text