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

pyannote-audio | neural building blocks for speaker diarization

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:

pyannote.audio also comes with pretrained models covering a wide range of domains for voice activity detection, speaker change detection, overlapped speech detection, and speaker embedding:

Installation

pyannote.audio only supports Python 3.7 (or later) on Linux and macOS. It might work on Windows but there is no garantee that it does, nor any plan to add official support for Windows.

The instructions below assume that pytorch has been installed using the instructions from https://pytorch.org.

Until a proper release of pyannote.audio is available on PyPI, it must be installed from source using the develop branch of the official repository:

$ git clone https://github.com/pyannote/pyannote-audio.git
$ cd pyannote-audio
$ git checkout develop
$ pip install .

Documentation and tutorials

• Use pretrained models and pipelines
  • Apply pretrained pipelines on your own data
  • Apply pretrained models on your own data
• Prepare your own data
  • Annotate your own data semi-automatically with Prodigy
  • Prepare your own dataset for training
• Train models on your own data
  • Speech activity detection
  • Speaker change detection
  • Overlapped speech detection
  • Speaker embedding
  • Fine-tune pretrained models to your own data
• Tune pipelines on your own data
  • Speech activity detection pipeline
  • Speaker diarization pipeline

Until a proper documentation is released, note that part of the API is described in this tutorial.

Citation

If you use pyannote.audio please use the following citation

@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},
  Address = {Barcelona, Spain},
  Month = {May},
  Year = {2020},
}