/pyannote-audio

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

Primary LanguageJupyter NotebookMIT LicenseMIT

Neural speaker diarization with pyannote.audio

pyannote.audio is an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines.

TL;DR Open In Colab

# 1. visit hf.co/pyannote/speaker-diarization and hf.co/pyannote/segmentation and accept user conditions (only if requested)
# 2. visit hf.co/settings/tokens to create an access token (only if you had to go through 1.)
# 3. instantiate pretrained speaker diarization pipeline
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization",
                                    use_auth_token="ACCESS_TOKEN_GOES_HERE")

# 4. apply pretrained pipeline
diarization = pipeline("audio.wav")

# 5. 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

Installation

Only Python 3.8+ is supported.

# install from develop branch
pip install -qq https://github.com/pyannote/pyannote-audio/archive/refs/heads/develop.zip

Documentation

Benchmark

Out of the box, pyannote.audio default speaker diarization pipeline is expected to be much better (and faster) in v2.x than in v1.1. Those numbers are diarization error rates (in %)

Dataset \ Version v1.1 v2.0 v2.1.1 (finetuned)
AISHELL-4 - 14.6 14.1 (14.5)
AliMeeting (channel 1) - - 27.4 (23.8)
AMI (IHM) 29.7 18.2 18.9 (18.5)
AMI (SDM) - 29.0 27.1 (22.2)
CALLHOME (part2) - 30.2 32.4 (29.3)
DIHARD 3 (full) 29.2 21.0 26.9 (21.9)
VoxConverse (v0.3) 21.5 12.6 11.2 (10.7)
REPERE (phase2) - 12.6 8.2 ( 8.3)
This American Life - - 20.8 (15.2)

Citations

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

@inproceedings{Bredin2020,
  Title = {{pyannote.audio: neural building blocks for speaker diarization}},
  Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe},
  Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
  Year = {2020},
}
@inproceedings{Bredin2021,
  Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}},
  Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine},
  Booktitle = {Proc. Interspeech 2021},
  Year = {2021},
}

Support

For commercial enquiries and scientific consulting, please contact me.

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

Tests rely on a set of debugging files available in test/data directory. Set PYANNOTE_DATABASE_CONFIG environment variable to test/data/database.yml before running tests:

PYANNOTE_DATABASE_CONFIG=tests/data/database.yml pytest