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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://www.pyannote.ai). |
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| Benchmark | [v2.1](https://hf.co/pyannote/speaker-diarization-2.1) | [v3.1](https://hf.co/pyannote/speaker-diarization-3.1) | [pyannoteAI](https://www.pyannote.ai) | |
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| ---------------------- | ------ | ------ | --------- | |
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| [AISHELL-4](https://arxiv.org/abs/2104.03603) | 14.1 | 12.2 | 11.2 | |
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| [AliMeeting](https://www.openslr.org/119/) (channel 1) | 27.4 | 24.4 | 19.3 | |
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| [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (IHM) | 18.9 | 18.8 | 15.8 | |
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| [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (SDM) | 27.1 | 22.4 | 19.3 | |
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| [AVA-AVD](https://arxiv.org/abs/2111.14448) | 66.3 | 50.0 | 44.8 | |
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| [CALLHOME](https://catalog.ldc.upenn.edu/LDC2001S97) ([part 2](https://github.com/BUTSpeechFIT/CALLHOME_sublists/issues/1)) | 31.6 | 28.4 | 19.8 | |
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| [DIHARD 3](https://catalog.ldc.upenn.edu/LDC2022S14) ([full](https://arxiv.org/abs/2012.01477)) | 26.9 | 21.7 | 16.8 | |
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| [Earnings21](https://github.com/revdotcom/speech-datasets) | 17.0 | 9.4 | 9.1 | |
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| [Ego4D](https://arxiv.org/abs/2110.07058) (dev.) | 61.5 | 51.2 | 44.0 | |
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| [MSDWild](https://github.com/X-LANCE/MSDWILD) | 32.8 | 25.3 | 19.8 | |
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| [RAMC](https://www.openslr.org/123/) | 22.5 | 22.2 | 11.1 | |
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| [REPERE](https://www.islrn.org/resources/360-758-359-485-0/) (phase2) | 8.2 | 7.8 | 7.6 | |
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| [VoxConverse](https://github.com/joonson/voxconverse) (v0.3) | 11.2 | 11.3 | 9.8 | |
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[Diarization error rate](http://pyannote.github.io/pyannote-metrics/reference.html#diarization) (in %) |
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Using high-end NVIDIA hardware, |
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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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* On-premise [pyannoteAI](https://www.pyannote.ai) takes less than 30s to process 1h of audio |
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