Project: pyannote-audio

Neural building blocks for speaker diarization

Project Details

Latest version
3.1.1
Home Page
https://github.com/pyannote/pyannote-audio
PyPI Page
https://pypi.org/project/pyannote-audio/

Project Popularity

PageRank
0.010935000935237825
Number of downloads
123522

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pyannote.audio speaker diarization toolkit

pyannote.audio is an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it comes with state-of-the-art pretrained models and pipelines, that can be further finetuned to your own data for even better performance.

TL;DR

  1. Install pyannote.audio with pip install pyannote.audio
  2. Accept pyannote/segmentation-3.0 user conditions
  3. Accept pyannote/speaker-diarization-3.1 user conditions
  4. Create access token at hf.co/settings/tokens.
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

Documentation

Benchmark

Out of the box, pyannote.audio speaker diarization pipeline v3.1 is expected to be much better (and faster) than v2.x. Those numbers are diarization error rates (in %):

Benchmark v2.1 v3.1 Premium
AISHELL-4 14.1 12.3 11.9
AliMeeting (channel 1) 27.4 24.5 22.5
AMI (IHM) 18.9 18.8 16.6
AMI (SDM) 27.1 22.6 20.9
AVA-AVD 66.3 50.0 39.8
CALLHOME (part 2) 31.6 28.4 22.2
DIHARD 3 (full) 26.9 21.4 17.2
Ego4D (dev.) 61.5 51.2 43.8
MSDWild 32.8 25.4 19.8
REPERE (phase2) 8.2 7.8 7.6
VoxConverse (v0.3) 11.2 11.2 9.4

Diarization error rate (in %)

Citations

If you use pyannote.audio please use the following citations:

@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},
}
@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.

pip install -e .[dev,testing]
pre-commit install

Test

pytest