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--- |
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title: VTT with Diarization |
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emoji: 🎙️ |
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colorFrom: purple |
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colorTo: indigo |
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sdk: gradio |
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sdk_version: 5.49.1 |
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app_file: app.py |
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pinned: false |
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license: mit |
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--- |
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# Voice-to-Text with Speaker Diarization |
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Powered by **faster-whisper** and **pyannote.audio** running locally on this Space. |
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## Features |
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- 🎯 **High-quality transcription** using faster-whisper (2-4x faster than OpenAI Whisper) |
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- 👥 **Speaker diarization** with pyannote.audio 3.1 |
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- 🌍 **Multi-language support** (Arabic, English, French, German, Spanish, Russian, Chinese, etc.) |
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- ⚙️ **Configurable parameters** (beam size, best_of, model size) |
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- 🔧 **Optimized for Arabic customer service calls** with specialized prompts |
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## Usage |
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1. Upload an audio file (mp3, wav, m4a, flac, etc.) |
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2. Select language (or leave blank for auto-detect) |
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3. Enable speaker diarization if needed (requires HF_TOKEN) |
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4. Adjust quality parameters if desired |
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5. Click "Transcribe" |
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## Configuration |
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Set these in Space Settings → Variables: |
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- `WHISPER_MODEL_SIZE`: Model size (`tiny`, `base`, `small`, `medium`, `large-v3`) - default: `small` |
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- `WHISPER_DEVICE`: Device (`cpu` or `cuda`) - default: `cpu` |
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- `WHISPER_COMPUTE_TYPE`: Compute type (`int8`, `int16`, `float32`) - default: `int8` |
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- `DEFAULT_LANGUAGE`: Default language code - default: `ar` (Arabic) |
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- `WHISPER_BEAM_SIZE`: Beam search size (1-10) - default: `5` |
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- `WHISPER_BEST_OF`: Best of candidates (1-10) - default: `5` |
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### Secrets (required for diarization): |
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- `HF_TOKEN`: Your Hugging Face token with access to `pyannote/speaker-diarization-3.1` |
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## Model Information |
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### Whisper Models |
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| Model | Size | RAM | Quality | Speed | |
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|-------|------|-----|---------|-------| |
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| tiny | 75MB | 1GB | ⭐⭐ | Very Fast | |
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| base | 150MB | 1GB | ⭐⭐⭐ | Fast | |
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| small | 500MB | 2GB | ⭐⭐⭐⭐ | Moderate | |
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| medium | 1.5GB | 5GB | ⭐⭐⭐⭐⭐ | Slow | |
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| large-v3 | 3GB | 10GB | ⭐⭐⭐⭐⭐⭐ | Very Slow | |
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### First Run |
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- First transcription will download the selected Whisper model automatically |
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- Diarization downloads ~700MB on first use (cached afterward) |
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- Models are stored in the Space's persistent storage |
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## Technical Details |
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- Uses the same model loading approach as the Django backend |
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- faster-whisper automatically downloads models from Hugging Face |
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- Diarization pipeline is downloaded locally to avoid repeated API calls |
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- All processing happens on this Space (no external inference APIs) |
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## Credits |
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- [faster-whisper](https://github.com/guillaumekln/faster-whisper) by Guillaume Klein |
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- [pyannote.audio](https://github.com/pyannote/pyannote-audio) by Hervé Bredin |
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- Original Django backend by IZI Techs |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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