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

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pyannote-audio

Neural building blocks for speaker diarization:

  • speech activity detection
  • speaker change detection
  • speaker embedding
  • speaker diarization pipeline

Installation

$ conda create --name pyannote python=3.6
$ source activate pyannote
$ pip install pyannote.audio

# support Yaafe feature extraction (optional)
$ conda install -c conda-forge yaafe

Citation

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

  • Speech activity and speaker change detection
    @inproceedings{Yin2017,
      Author = {Ruiqing Yin and Herv\'e Bredin and Claude Barras},
      Title = {{Speaker Change Detection in Broadcast TV using Bidirectional Long Short-Term Memory Networks}},
      Booktitle = {{18th Annual Conference of the International Speech Communication Association, Interspeech 2017}},
      Year = {2017},
      Month = {August},
      Address = {Stockholm, Sweden},
      Url = {https://github.com/yinruiqing/change_detection}
    }
  • Speaker embedding
    @inproceedings{Bredin2017,
        author = {Herv\'{e} Bredin},
        title = {{TristouNet: Triplet Loss for Speaker Turn Embedding}},
        booktitle = {42nd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2017},
        year = {2017},
        url = {http://arxiv.org/abs/1609.04301},
    }
  • Speaker diarization pipeline
    @inproceedings{Yin2018,
      Author = {Ruiqing Yin and Herv\'e Bredin and Claude Barras},
      Title = {{Neural Speech Turn Segmentation and Affinity Propagation for Speaker Diarization}},
      Booktitle = {{19th Annual Conference of the International Speech Communication Association, Interspeech 2018}},
      Year = {2018},
      Month = {September},
      Address = {Hyderabad, India},
    }

Tutorials

⚠️ These tutorials assumes that you installed the develop branch of pyannote.audio.
⚠️ They are most likely broken in pyannote.audio 1.x.

Documentation

Part of the API is described in this tutorial.
Other than that, there is still a lot to do (contribute?) documentation-wise...

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  • Python 61.8%
  • Jupyter Notebook 38.2%