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--- |
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title: DiariZen Speaker Diarization |
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emoji: 🎙️ |
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colorFrom: blue |
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colorTo: purple |
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sdk: gradio |
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sdk_version: 4.44.1 |
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app_file: app.py |
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suggested_hardware: t4-small |
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pinned: false |
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license: mit |
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--- |
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# 🎙️ DiariZen Speaker Diarization |
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High-performance speaker diarization using DiariZen from BUT-FIT. |
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## Features |
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- **3 Models Available**: WavLM Large (recommended), WavLM Base (faster), WavLM Large MLC (multilingual) |
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- **Simple Interface**: Upload audio → Select model → Run → Download RTTM |
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- **High Performance**: Substantially outperforms Pyannote v3.1 |
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- **GPU Accelerated**: Uses Hugging Face Spaces GPU |
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## Performance |
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DiariZen achieves state-of-the-art results: |
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- **AMI-SDM**: 13.9% DER (vs 22.4% Pyannote v3.1) |
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- **VoxConverse**: 9.1% DER (vs 11.3% Pyannote v3.1) |
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- **AISHELL-4**: 10.1% DER (vs 12.2% Pyannote v3.1) |
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## Usage |
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1. Upload audio file or record |
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2. Select diarization model |
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3. Click "Run Diarization" |
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4. View results and download RTTM file |
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## Technical Details |
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This Space uses a custom Dockerfile to install DiariZen with all its dependencies: |
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- PyTorch 2.1.1 with CUDA 12.1 |
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- DiariZen toolkit with git submodules |
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- Bundled pyannote-audio (custom version) |
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- FFmpeg for audio processing |
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## Citation |
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```bibtex |
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@inproceedings{diariZen2024, |
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title={DiariZen: A toolkit for speaker diarization}, |
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author={Han, Ivo and Landini, Federico and Burget, Lukáš and Černocký, Jan}, |
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booktitle={INTERSPEECH}, |
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year={2024} |
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} |
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``` |
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## Source |
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- **DiariZen**: https://github.com/BUTSpeechFIT/DiariZen |
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- **License**: MIT (Code) | Research/Non-commercial (Models) |
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