pipeline

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AUTHOR
Hervé Bredin - http://herve.niderb.fr

Speaker diarization pipeline with pyannote.audio

In this tutorial, you will learn how to optimize a speaker diarization pipeline using pyannote-pipeline command line tool.

Table of contents

• Citation
• Configuration
• Training
• Validation
• Application
• More options

Citation

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If you use pyannote-audio for speaker diarization, please cite the following paper:

@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},
}

Configuration

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To ensure reproducibility, pyannote-pipeline relies on a configuration file defining the experimental setup:

$ cat tutorials/pipeline/config.yml

pipeline:
   name: Yin2018
   params:
      sad: tutorials/pipeline/sad
      scd: tutorials/pipeline/scd
      emb: tutorials/pipeline/emb
      metric: angular

sampler:
   name: CMAES

This configuration file assumes that you have already been through the other tutorials and applied

• speech activity detection (into tutorials/pipeline/sad)
• speaker change detection (into tutorials/pipeline/scd)
• speaker embedding (into tutorials/pipeline/emb)

Training

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The following command will run hyper-parameter optimization on the development subset of the AMI database:

$ export EXPERIMENT_DIR=tutorials/pipeline
$ pyannote-pipeline train --forever ${EXPERIMENT_DIR} AMI.SpeakerDiarization.MixHeadset

This will create a bunch of files in TRAIN_DIR (defined below). One can follow along the training process using tensorboard.

$ tensorboard --logdir=${EXPERIMENT_DIR}

One can run this command on several machines in parallel to speed up the hyper-parameter search.

Application

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The optimized pipeline can then be applied on all files of the AMI database:

$ export TRAIN_DIR=${EXPERIMENT_DIR}/train/AMI.SpeakerDiarization.MixHeadset.train
$ pyannote-pipeline apply ${TRAIN_DIR}/params.yml AMI.SpeakerDiarization.MixHeadset /path/to/pipeline/output

More options

For more options, see:

$ pyannote-pipeline --help

That's all folks!