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https://github.com/pyannote/pyannote-audio
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
https://github.com/pyannote/pyannote-audio
overlapped-speech-detection pretrained-models pytorch speaker-change-detection speaker-diarization speaker-embedding speaker-recognition speaker-verification speech-activity-detection speech-processing voice-activity-detection
Last synced: 5 days ago
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Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
- Host: GitHub
- URL: https://github.com/pyannote/pyannote-audio
- Owner: pyannote
- License: mit
- Created: 2016-03-07T17:26:15.000Z (almost 9 years ago)
- Default Branch: develop
- Last Pushed: 2024-05-13T12:29:03.000Z (7 months ago)
- Last Synced: 2024-05-14T00:12:56.550Z (7 months ago)
- Topics: overlapped-speech-detection, pretrained-models, pytorch, speaker-change-detection, speaker-diarization, speaker-embedding, speaker-recognition, speaker-verification, speech-activity-detection, speech-processing, voice-activity-detection
- Language: Jupyter Notebook
- Homepage: http://pyannote.github.io
- Size: 250 MB
- Stars: 5,154
- Watchers: 63
- Forks: 702
- Open Issues: 84
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
Using `pyannote.audio` open-source toolkit in production?
Consider switching to [pyannoteAI](https://www.pyannote.ai) for better and faster options.
# `pyannote.audio` speaker diarization toolkit
`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.
## TL;DR
1. Install [`pyannote.audio`](https://github.com/pyannote/pyannote-audio) with `pip install pyannote.audio`
2. Accept [`pyannote/segmentation-3.0`](https://hf.co/pyannote/segmentation-3.0) user conditions
3. Accept [`pyannote/speaker-diarization-3.1`](https://hf.co/pyannote/speaker-diarization-3.1) user conditions
4. Create access token at [`hf.co/settings/tokens`](https://hf.co/settings/tokens).
```python
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained(
"pyannote/speaker-diarization-3.1",
use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE")
# send pipeline to GPU (when available)
import torch
pipeline.to(torch.device("cuda"))
# apply pretrained pipeline
diarization = pipeline("audio.wav")
# print the result
for turn, _, speaker in diarization.itertracks(yield_label=True):
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
# ...
```
## Highlights
- :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)
- :exploding_head: state-of-the-art performance (see [Benchmark](#benchmark))
- :snake: Python-first API
- :zap: multi-GPU training with [pytorch-lightning](https://pytorchlightning.ai/)
## Documentation
- [Changelog](CHANGELOG.md)
- [Frequently asked questions](FAQ.md)
- Models
- Available tasks explained
- [Applying a pretrained model](tutorials/applying_a_model.ipynb)
- [Training, fine-tuning, and transfer learning](tutorials/training_a_model.ipynb)
- Pipelines
- Available pipelines explained
- [Applying a pretrained pipeline](tutorials/applying_a_pipeline.ipynb)
- [Adapting a pretrained pipeline to your own data](tutorials/adapting_pretrained_pipeline.ipynb)
- [Training a pipeline](tutorials/voice_activity_detection.ipynb)
- Contributing
- [Adding a new model](tutorials/add_your_own_model.ipynb)
- [Adding a new task](tutorials/add_your_own_task.ipynb)
- Adding a new pipeline
- Sharing pretrained models and pipelines
- Blog
- 2022-12-02 > ["How I reached 1st place at Ego4D 2022, 1st place at Albayzin 2022, and 6th place at VoxSRC 2022 speaker diarization challenges"](tutorials/adapting_pretrained_pipeline.ipynb)
- 2022-10-23 > ["One speaker segmentation model to rule them all"](https://herve.niderb.fr/fastpages/2022/10/23/One-speaker-segmentation-model-to-rule-them-all)
- 2021-08-05 > ["Streaming voice activity detection with pyannote.audio"](https://herve.niderb.fr/fastpages/2021/08/05/Streaming-voice-activity-detection-with-pyannote.html)
- Videos
- [Introduction to speaker diarization](https://umotion.univ-lemans.fr/video/9513-speech-segmentation-and-speaker-diarization/) / JSALT 2023 summer school / 90 min
- [Speaker segmentation model](https://www.youtube.com/watch?v=wDH2rvkjymY) / Interspeech 2021 / 3 min
- [First release of pyannote.audio](https://www.youtube.com/watch?v=37R_R82lfwA) / ICASSP 2020 / 8 min
- Community contributions (not maintained by the core team)
- 2024-04-05 > [Offline speaker diarization (speaker-diarization-3.1)](tutorials/community/offline_usage_speaker_diarization.ipynb) by [Simon Ottenhaus](https://github.com/simonottenhauskenbun)
- 2024-09-24 > [Evaluating `pyannote` pretrained speech separation pipelines](tutorials/community/eval_separation_pipeline.ipynb) by [Clément Pagés](https://github.com/)
## Benchmark
Out of the box, `pyannote.audio` speaker diarization [pipeline](https://hf.co/pyannote/speaker-diarization-3.1) v3.1 is expected to be much better (and faster) than v2.x.
Those numbers are diarization error rates (in %):
| 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) |
| --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------ | ------------------------------------------------------ | ------------------------------------------------ |
| [AISHELL-4](https://arxiv.org/abs/2104.03603) | 14.1 | 12.2 | 11.9 |
| [AliMeeting](https://www.openslr.org/119/) (channel 1) | 27.4 | 24.4 | 22.5 |
| [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (IHM) | 18.9 | 18.8 | 16.6 |
| [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (SDM) | 27.1 | 22.4 | 20.9 |
| [AVA-AVD](https://arxiv.org/abs/2111.14448) | 66.3 | 50.0 | 39.8 |
| [CALLHOME](https://catalog.ldc.upenn.edu/LDC2001S97) ([part 2](https://github.com/BUTSpeechFIT/CALLHOME_sublists/issues/1)) | 31.6 | 28.4 | 22.2 |
| [DIHARD 3](https://catalog.ldc.upenn.edu/LDC2022S14) ([full](https://arxiv.org/abs/2012.01477)) | 26.9 | 21.7 | 17.2 |
| [Earnings21](https://github.com/revdotcom/speech-datasets) | 17.0 | 9.4 | 9.0 |
| [Ego4D](https://arxiv.org/abs/2110.07058) (dev.) | 61.5 | 51.2 | 43.8 |
| [MSDWild](https://github.com/X-LANCE/MSDWILD) | 32.8 | 25.3 | 19.8 |
| [RAMC](https://www.openslr.org/123/) | 22.5 | 22.2 | 18.4 |
| [REPERE](https://www.islrn.org/resources/360-758-359-485-0/) (phase2) | 8.2 | 7.8 | 7.6 |
| [VoxConverse](https://github.com/joonson/voxconverse) (v0.3) | 11.2 | 11.3 | 9.4 |
[Diarization error rate](http://pyannote.github.io/pyannote-metrics/reference.html#diarization) (in %)
## Citations
If you use `pyannote.audio` please use the following citations:
```bibtex
@inproceedings{Plaquet23,
author={Alexis Plaquet and Hervé Bredin},
title={{Powerset multi-class cross entropy loss for neural speaker diarization}},
year=2023,
booktitle={Proc. INTERSPEECH 2023},
}
```
```bibtex
@inproceedings{Bredin23,
author={Hervé Bredin},
title={{pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe}},
year=2023,
booktitle={Proc. INTERSPEECH 2023},
}
```
## Development
The commands below will setup pre-commit hooks and packages needed for developing the `pyannote.audio` library.
```bash
pip install -e .[dev,testing]
pre-commit install
```
## Test
```bash
pytest
```