change-detection

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Copyright (c) 2017-2018 CNRS

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AUTHORS Ruiqing Yin Hervé Bredin - http://herve.niderb.fr

Speaker change detection with pyannote.audio

In this tutorial, you will learn how to train, validate, and apply a speaker change detector based on MFCCs and LSTMs, using pyannote-change-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 speaker change 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-change-detection relies on a configuration file defining the experimental setup:

$ cat tutorials/change-detection/config.yml

# train the network for speaker change detection
# see pyannote.audio.labeling.tasks for more details
task:
   name: SpeakerChangeDetection
   params:
      duration: 3.2     # sub-sequence duration
      per_epoch: 36000  # 10 hours of audio per epoch
      collar: 0.200     # up-sampling collar
      batch_size: 32    # number of sub-sequences per batch
      parallel: 4       # number of background batch generators

# use precomputed features (see feature extraction tutorial)
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: [32, 20]
     bidirectional: True
     linear: [40, 10]

# use cyclic learning rate scheduler
scheduler:
   name: CyclicScheduler
   params:
       learning_rate: auto

Training

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

$ export EXPERIMENT_DIR=tutorials/change-detection
$ pyannote-change-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-change-detection validate --purity=0.8 ${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 change scores in /path/to/scd:

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

We can then use these raw scores to perform actual speaker change 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/scd')

# peak detection
>>> from pyannote.audio.signal import Peak
# alpha / min_duration 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
>>> peak = Peak(alpha=0.5, min_duration=1.0, log_scale=True)

# evaluation metric
>>> from pyannote.metrics.diarization import DiarizationPurityCoverageFMeasure
>>> metric = DiarizationPurityCoverageFMeasure()

# 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 change scores as pyannote.core.SlidingWindowFeature
...    scd_scores = precomputed(test_file)
...
...    # binarize scores to obtain speech regions as pyannote.core.Timeline
...    hypothesis = peak.apply(scd_scores, dimension=1)
...
...    # evaluate speech activity detection
...    metric(reference, hypothesis.to_annotation(), uem=uem)

>>> purity, coverage, fmeasure = metric.compute_metrics()
>>> print(f'Purity = {100*purity:.1f}% / Coverage = {100*coverage:.1f}%')

More options

For more options, see:

$ pyannote-change-detection --help

That's all folks!