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Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
| Date | Stars |
|---|---|
| 2026-07-24 | 10329 |
| 2026-07-25 | 10334 |
| 2026-07-28 | 10334 |
| 2026-07-30 | 10334 |
| 2026-08-06 | 10334 |
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<p align="center">
<a href="https://pyannote.ai/" target="blank"><img src="https://avatars.githubusercontent.com/u/162698670" width="64" /></a>
</p>
<div align="center">
<h1><code>pyannote</code> speaker diarization toolkit</h1>
</div>
`pyannote.audio` is an open-source toolkit written in Python for speaker diarization. Based on [PyTorch](https://pytorch.org) machine learning framework, it comes with state-of-the-art [pretrained models and pipelines](https://hf.co/pyannote), that can be further finetuned to your own data for even better performance.
<p align="center">
<a href="https://www.youtube.com/watch?v=37R_R82lfwA"><img src="https://img.youtube.com/vi/37R_R82lfwA/0.jpg"></a>
</p>
## Highlights
- :exploding_head: state-of-the-art performance (see [Benchmark](#benchmark))
- :hugs: pretrained [pipelines](https://hf.co/models?other=pyannote-audio-pipeline) (and [models](https://hf.co/models?other=pyannote-audio-model)) on [:hugs: model hub](https://huggingface.co/pyannote)
- :rocket: built-in support for [pyannoteAI](https://pyannote.ai) premium speaker diarization
- :snake: Python-first API
- :zap: multi-GPU training with [pytorch-lightning](https://pytorchlightning.ai/)
## `community-1` open-source speaker diarization
1. Make sure [`ffmpeg`](https://ffmpeg.org/) is installed on your machine (needed by [`torchcodec`](https://docs.pytorch.org/torchcodec/) audio decoding library)
2. Install with [`uv`](https://docs.astral.sh/uv/)`add pyannote.audio` (recommended) or `pip install pyannote.audio`
3. Accept [`pyannote/speaker-diarization-community-1`](https://hf.co/pyannote/speaker-diarization-community-1) user conditions
4. Create Huggingface access token at [`hf.co/settings/tokens`](https://hf.co/settings/tokens)
```python
import torch
from pyannote.audio import Pipeline
from pyannote.audio.pipelines.utils.hook import ProgressHook
# Community-1 open-source speaker diarization pipeline
pipeline = Pipeline.from_pretrained(
"pyannote/speaker-diarization-community-1",
token="HUGGINGFACE_ACCESS_TOKEN")
# send pipeline to GPU (when available)
pipeline.to(torch.device("cuda"))
# apply pretrained pipeline (with optional progress hook)
with ProgressHook() as hook:
output = pipeline("audio.wav", hook=hook) # runs locally
# print the result
for turn, speaker in output.speaker_diarization:
print(f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}")
# start=0.2s stop=1.5s speaker_0
# start=1.8s stop=3.9s speaker_1
# start=4.2s stop=5.7s speaker_0
# ...
```
## `precision-2` premium speaker diarization
1. Create pyannoteAI API key at [`dashboard.pyannote.ai`](https://dashboard.pyannote.ai)
2. Enjoy free credits!
```python
from pyannote.audio import Pipeline
# Precision-2 premium speaker diarization service
pipeline = Pipeline.from_pretrained(
"pyannote/speaker-diarization-precision-2", token="PYANNOTEAI_API_KEY")
output = pipeline("audio.wav") # runs on pyannoteAI servers
# print the result
for turn, speaker in output.speaker_diarization:
print(f"start={turn.start:.1f}s stop={turn.end:.1f}s {speaker}")
# start=0.2s stop=1.6s SPEAKER_00
# start=1.8s stop=4.0s SPEAKER_01
# start=4.2s stop=5.6s SPEAKER_00
# ...
```
Visit [`docs.pyannote.ai`](https://docs.pyannote.ai) to learn about other pyannoteAI features (voiceprinting, confidence scores, ...)
## Benchmark
| Benchmark (last updated in 2025-09) | <a href="https://hf.co/pyannote/speaker-diarization-3.1">`legacy` (3.1)</a>| <a href="https://hf.co/pyannote/speaker-diarization-community-1">`community-1`</a> | <a href="https://docs.pyannote.ai">`precision-2`</a> |
| --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------ | -------------------------------------------------| ------------------------------------------------ |
| [AISHELL-4](https://arxiv.org/abs/2104.03603) Excerpt of 12,421 characters
Read on GitHub
Hervé BREDIN · @pyannoteai and CNRS · France
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Marie-Philippe Gill · Girl Knows Tech · United States
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Julien Chaumond · @huggingface · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0da3359c36aacd38, topic:pytorch