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Neural building blocks for speaker diarization

Project description

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:

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:

segmentation

Open In Colab

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.

$ pip install pyannote.audio==1.1

Documentation and tutorials

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},
}

Project details


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