speech-activity-detection

The MIT License (MIT)

Copyright (c) 2017-2018 CNRS

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

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

Speech activity detection with pyannote.audio

In this tutorial, you will learn how to train, validate, and apply a speech activity detector based on MFCCs and LSTMs, using pyannote-speech-detection command line tool.

Table of contents

• Citation
• AMI database
• 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{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 = {{Interspeech 2017, 18th Annual Conference of the International Speech Communication Association}},
  Year = {2017},
  Month = {August},
  Address = {Stockholm, Sweden},
  Url = {https://github.com/yinruiqing/change_detection}
}

AMI database

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$ source activate pyannote
$ pip install pyannote.db.odessa.ami

This tutorial relies on the AMI database. We first need to tell pyannote where the audio files are located:

$ cat ~/.pyannote/db.yml | grep AMI
AMI: /path/to/ami/amicorpus/*/audio/{uri}.wav

If you want to use a different database, you might need to create your own pyannote.database plugin. See github.com/pyannote/pyannote-db-template for details on how to do so. You might also use pip search pyannote to browse existing plugins.

Configuration

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

$ cat tutorials/speech-activity-detection/config.yml

# train the network for speech activity detection
# see pyannote.audio.labeling.tasks for more details
task:
   name: SpeechActivityDetection
   params:
      duration: 3.2
      parallel: 4

# use precomputed features (from feature extraction tutorials)
feature_extraction:
   name: Precomputed
   params:
      root_dir: tutorials/feature-extraction

# use the StackedRNN architecture.
# see pyannote.audio.labeling.models for more details
architecture:
   name: StackedRNN
   params:
     rnn: LSTM
     recurrent: [16, 16]
     bidirectional: True

# use cyclic learning rate scheduler
scheduler:
   name: CyclicScheduler

Training

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The following command will train the network using the training set of AMI database for 1000 epochs:

$ export EXPERIMENT_DIR=tutorials/speech-activity-detection
$ pyannote-speech-detection train --gpu --to=1000 ${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}

Validation

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To get a quick idea of how the network is doing during training, one can use the validate mode. It can (should!) be run in parallel to training and evaluates the model epoch after epoch. One can use tensorboard to follow the validation process.

$ export TRAIN_DIR=${EXPERIMENT_DIR}/train/AMI.SpeakerDiarization.MixHeadset.train
$ pyannote-speech-detection validate ${TRAIN_DIR} AMI.SpeakerDiarization.MixHeadset

Application

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Now that we know how the model is doing, we can apply it on all files of the AMI database and store raw SAD scores in /path/to/sad:

$ pyannote-speech-detection apply ${TRAIN_DIR}/weights/0050.pt AMI.SpeakerDiarization.MixHeadset /path/to/sad

We can then use these raw scores to perform actual speech activity detection, and pyannote.metrics to evaluate the result:

# AMI protocol
>>> from pyannote.database import get_protocol
>>> protocol = get_protocol('AMI.SpeakerDiarization.MixHeadset')

# precomputed scores
>>> from pyannote.audio.features import Precomputed
>>> precomputed = Precomputed('/path/to/sad')

# scores binarizer
>>> from pyannote.audio.signal import Binarize
# onset / offset are tunable parameters (and should be tuned for better performance)
# we use log_scale = True because of the final log-softmax in the StackedRNN model
>>> binarize = Binarize(onset=0.5, offset=0.5, log_scale=True)

# evaluation metric
>>> from pyannote.metrics.detection import DetectionErrorRate
>>> metric = DetectionErrorRate()

# loop on test files
>>> from pyannote.database import get_annotated
>>> for test_file in protocol.test():
...    # load reference annotation
...    reference = test_file['annotation']
...    uem = get_annotated(test_file)
...
...    # load precomputed SAD scores as pyannote.core.SlidingWindowFeature
...    sad_scores = precomputed(test_file)
...
...    # binarize scores to obtain speech regions as pyannote.core.Timeline
...    speech_regions = binarize.apply(sad_scores, dimension=1)
...
...    # evaluate speech activity detection
...    metric(reference, speech_regions.to_annotation(), uem=uem)

>>> print(f'Detection error rate = {100*abs(metric):.1f}%')

More options

For more options, see:

$ pyannote-speech-detection --help

That's all folks!