Neural building blocks for speaker diarization:

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

# create a conda environment with Python 3.6 or later
$ conda create --name pyannote python=3.6
$ source activate pyannote

# install pytorch following official instructions from https://pytorch.org/

# install from source in the "develop" branch
$ git clone https://github.com/pyannote/pyannote-audio.git
$ cd pyannote-audio
$ git checkout develop
$ pip install .

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},
  }

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

• Feature extraction

• Models

  • Training LSTM-based speech activity detection
  • Training LSTM-based speaker change detection
  • Training LSTM-based overlapped speech detection
  • Training LSTM-based speaker embedding

• Pipelines

  • Tuning speech activity detection pipeline
  • Tuning speaker diarization pipeline

• In-house datasets

  • Applying pre-trained models on your own data
  • Training models on your own data

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