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---
title: README
emoji: 🚀
colorFrom: yellow
colorTo: green
sdk: static
pinned: false
---
[**pyannote.audio**](https://github.com/pyannote/pyannote-audio) is a state-of-the-art open-source toolkit for speaker diarization.
Out of the box, <img src="https://avatars.githubusercontent.com/u/7559051" width="20" style="vertical-align:text-bottom;" /> `pyannote.audio` speaker diarization [pipeline v4.0](https://hf.co/pyannote/speaker-diarization-4.0) is expected to be much better than v3.1.
<img src="https://avatars.githubusercontent.com/u/162698670" width="20" style="vertical-align:text-bottom;" /> `pyannoteAI` premium models are even better (and also 2x faster). <img src="https://avatars.githubusercontent.com/u/162698670" width="20" style="vertical-align:text-bottom;" /> `labs` model is currently in private beta.
| Benchmark (last updated in 2025-08) | <a href="https://hf.co/pyannote/speaker-diarization-3.1"><img src="https://avatars.githubusercontent.com/u/7559051" width="32" /><br/>v3.1</a> | <a href="https://hf.co/pyannote/speaker-diarization-4.0"><img src="https://avatars.githubusercontent.com/u/7559051" width="32" /><br/> v4.0</a> | <a href="https://docs.pyannote.ai"><img src="https://avatars.githubusercontent.com/u/162698670" width="32" /><br/>API</a> | <a href="https://docs.pyannote.ai"><img src="https://avatars.githubusercontent.com/u/162698670" width="32" /><br/>labs</a> |
| --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------ | -------------------------------------------------| ------------------------------------------------ | --- |
| [AISHELL-4](https://arxiv.org/abs/2104.03603) | 12.2 | 11.7 | 11.8 | 11.4 |
| [AliMeeting](https://www.openslr.org/119/) (channel 1) | 24.5 | 20.3 | 16.3 | 15.2 |
| [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (IHM) | 18.8 | 17.0 | 13.2 | 12.9 |
| [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (SDM) | 22.7 | 19.9 | 15.8 | 15.6 |
| [AVA-AVD](https://arxiv.org/abs/2111.14448) | 49.7 | 44.6 | 40.7 | 37.1 |
| [CALLHOME](https://catalog.ldc.upenn.edu/LDC2001S97) ([part 2](https://github.com/BUTSpeechFIT/CALLHOME_sublists/issues/1)) | 28.5 | 26.7 | 17.6 | 16.6 |
| [DIHARD 3](https://catalog.ldc.upenn.edu/LDC2022S14) ([full](https://arxiv.org/abs/2012.01477)) | 21.4 | 20.2 | 15.7 | 14.7 |
| [Ego4D](https://arxiv.org/abs/2110.07058) (dev.) | 51.2 | 46.8 | 44.7 | 39.0 |
| [MSDWild](https://github.com/X-LANCE/MSDWILD) | 25.4 | 22.8 | 17.9 | 17.3 |
| [RAMC](https://www.openslr.org/123/) | 22.2 | 20.8 | 10.6 | 10.5 |
| [REPERE](https://www.islrn.org/resources/360-758-359-485-0/) (phase2) | 7.9 | 8.9 | 7.3 | 7.4 |
| [VoxConverse](https://github.com/joonson/voxconverse) (v0.3) | 11.2 | 11.2 | 9.0 | 8.5 |
__[Diarization error rate](http://pyannote.github.io/pyannote-metrics/reference.html#diarization) (in %, the lower, the better)__
| Benchmark (last updated in 2025-08) | <img src="https://avatars.githubusercontent.com/u/7559051" width="32" /> | <a href="https://docs.pyannote.ai"><img src="https://avatars.githubusercontent.com/u/162698670" width="32" /></a> | Speed up
| -------------- | ----------- | ----------- | ------ |
| [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (IHM), ~1h files | 31s per hour of audio | 14s per hour of audio | 2.2x faster
| [DIHARD 3](https://catalog.ldc.upenn.edu/LDC2022S14) ([full](https://arxiv.org/abs/2012.01477)), ~5min files | 37s per hour of audio | 14s per hour of audio | 2.6x faster
__Processing speed on a NVIDIA H100 80GB HBM3__
Training was made possible thanks to [GENCI](https://www.genci.fr/) on the [**Jean Zay**](http://www.idris.fr/eng/jean-zay/) supercomputer.
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