Skip to content

Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

License

Notifications You must be signed in to change notification settings

pyannote/pyannote-audio

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Using pyannote.audio open-source toolkit in production? Consider switching to pyannoteAI 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 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 pyannoteAI
AISHELL-4 14.1 12.2 11.9
AliMeeting (channel 1) 27.4 24.4 22.5
AMI (IHM) 18.9 18.8 16.6
AMI (SDM) 27.1 22.4 20.9
AVA-AVD 66.3 50.0 39.8
CALLHOME (part 2) 31.6 28.4 22.2
DIHARD 3 (full) 26.9 21.7 17.2
Earnings21 17.0 9.4 9.0
Ego4D (dev.) 61.5 51.2 43.8
MSDWild 32.8 25.3 19.8
RAMC 22.5 22.2 18.4
REPERE (phase2) 8.2 7.8 7.6
VoxConverse (v0.3) 11.2 11.3 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