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Neural building blocks for speaker diarization

Project description

Neural speaker diarization with 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 and accept user conditions (only if requested)
# 2. visit to create an access token (only if you had to go through 1.)
# 3. instantiate pretrained speaker diarization pipeline
from import Pipeline
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization",

# 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
# ...

What's new in 2.x?

For version 2.x of, I decided to rewrite almost everything from scratch. Highlights of this release are:


Only Python 3.8+ is officially supported (though it might work with Python 3.7)

conda create -n pyannote python=3.8
conda activate pyannote

# pytorch 1.11 is required for speechbrain compatibility
# (see
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 -c pytorch

pip install -qq


Frequently asked questions

How does one capitalize and pronounce the name of this awesome library?

📝 Written in lower case: (or pyannote if you are lazy). Not PyAnnote nor PyAnnotate (sic). 📢 Pronounced like the french verb pianoter. pi like in piano, not py like in python. 🎹 pianoter means to play the piano (hence the logo 🤯).

Pretrained pipelines do not produce good results on my data. What can I do?

  1. Annotate dozens of conversations manually and separate them into development and test subsets in pyannote.database.
  2. Optimize the hyper-parameters of the pretained pipeline using the development set. If performance is still not good enough, go to step 3.
  3. Annotate hundreds of conversations manually and set them up as training subset in pyannote.database.
  4. Fine-tune the models (on which the pipeline relies) using the training set.
  5. Optimize the hyper-parameters of the pipeline using the fine-tuned models using the development set. If performance is still not good enough, go back to step 3.


Out of the box, 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)


If you use please use the following citations:

  Title = {{ 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},
  Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}},
  Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine},
  Booktitle = {Proc. Interspeech 2021},
  Year = {2021},


For commercial enquiries and scientific consulting, please contact me.


The commands below will setup pre-commit hooks and packages needed for developing the 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

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