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--- |
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title: README |
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emoji: 🚀 |
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colorFrom: yellow |
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colorTo: green |
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sdk: static |
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pinned: false |
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--- |
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[**pyannote.audio**](https://github.com/pyannote/pyannote-audio) is an open-source toolkit for speaker diarization. |
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Pretrained pipelines reach state-of-the-art performance on most academic benchmarks and are used [in production by dozens of companies](https://herve.niderb.fr/consulting.html). |
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| Benchmark | v1.1 | [v2.1](https://hf.co/pyannote/speaker-diarization-2.1) | [v3.1](https://hf.co/pyannote/speaker-diarization-3.1) | [Premium](https://forms.gle/eKhn7H2zTa68sMMx8) | |
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| ---------------------- | ---- | ------ | ------ | --------- | |
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| AISHELL-4 | - | 14.1 | 12.2 | 11.9 | |
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| AliMeeting (channel 1) | - | 27.4 | 24.4 | 22.5 | |
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| AMI (IHM) | 29.7 | 18.9 | 18.8 | 16.6 | |
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| AMI (SDM) | - | 27.1 | 22.4 | 20.9 | |
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| AVA-AVD | - | - | 50.0 | 39.8 | |
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| DIHARD 3 (full) | 29.2 | 26.9 | 21.7 | 17.2 | |
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| Ego4D (dev.) | - | 61.5 | 51.2 | 43.8 |
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| MSDWild | - | - | 25.3 | 19.8 | |
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| REPERE (phase2) | - | 8.2 | 7.8 | 7.6 | |
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| VoxConverse (v0.3) | 21.5 | 11.2 | 11.3 | 9.4 | |
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[Diarization error rate](http://pyannote.github.io/pyannote-metrics/reference.html#diarization) (in %) |
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Using one Nvidia Tesla V100 SXM2 GPU and one Intel Cascade Lake 6248 CPU, |
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* [v2.1](https://hf.co/pyannote/speaker-diarization-2.1) takes around 1m30s to process 1h of audio |
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* [v3.1](https://hf.co/pyannote/speaker-diarization-3.1) takes around 1m20s to process 1h of audio |
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* [Premium](https://forms.gle/eKhn7H2zTa68sMMx8) takes less than 1m00s to process 1h of audio |
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