speech_activity_detection

The MIT License (MIT)

Copyright (c) 2019 CNRS

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

Speech activity detection pipeline with pyannote.audio

Training a model for speech activity detection is not enough to get actual speech activity detection results. One has to also tune detection thresholds (and other optional pipeline hyper-parameters).

In this tutorial, you will learn how to optimize a speech activity detection 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 speech activity detection, 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/pipelines/speech_activity_detection/config.yml

pipeline:
   name: pyannote.audio.pipeline.speech_activity_detection.SpeechActivityDetection
   params:
      scores: /path/to/precomputed/sad

This configuration file assumes that you have already been through the speech actitity detection (model) tutorial and applied it into /path/to/precomputed/sad.

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/pipelines/speech_activity_detection
$ pyannote-pipeline train --forever ${EXPERIMENT_DIR} AMI.SpeakerDiarization.MixHeadset

This will create a bunch of files in TRAIN_DIR (defined below). One can run this command on several machines in parallel to speed up the hyper-parameter search.

$ export TRAIN_DIR=${EXPERIMENT_DIR}/train/AMI.SpeakerDiarization.MixHeadset.development
$ cat ${TRAIN_DIR}/params.yml

min_duration_off: 0.6857137236312955
min_duration_on: 0.3225952679776678
offset: 0.9436397097473367
onset: 0.704966228813754
pad_offset: 0.08311274833799132
pad_onset: 0.06505433882746965

See pyannote.audio.pipeline.speech_activity_detection.SpeechActivityDetection docstring for details about these hyper-parameters.

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.development
$ 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!