speaker_change_detection

The MIT License (MIT)

Copyright (c) 2017-2019 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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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
• Databases
• 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}
}

Databases

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

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

$ cat ~/.pyannote/database.yml
Databases:
  AMI: /path/to/ami/amicorpus/*/audio/{uri}.wav
  MUSAN: /path/to/musan/{uri}.wav

Have a look at pyannote.database documentation to learn how to use other datasets.

Configuration

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

$ cat tutorials/models/speaker_change_detection/config.yml

task:
   name: SpeakerChangeDetection
   params:
      duration: 2.0      # sequences are 2s long
      collar: 0.100      # upsampling collar = 100ms
      non_speech: False  # do not try to detect non-speech/speaker changes
      batch_size: 64     # 64 sequences per batch
      per_epoch: 1       # one epoch = 1 day of audio

data_augmentation:
   name: AddNoise                                   # add noise on-the-fly
   params:
      snr_min: 10                                   # using random signal-to-noise
      snr_max: 20                                   # ratio between 10 and 20 dBs
      collection: MUSAN.Collection.BackgroundNoise  # use background noise from MUSAN
                                                    # (needs pyannote.db.musan)
feature_extraction:
   name: LibrosaMFCC      # use MFCC from librosa
   params:
      e: False            # do not use energy
      De: True            # use energy 1st derivative
      DDe: True           # use energy 2nd derivative
      coefs: 19           # use 19 MFCC coefficients
      D: True             # use coefficients 1st derivative
      DD: True            # use coefficients 2nd derivative
      duration: 0.025     # extract MFCC from 25ms windows
      step: 0.010         # extract MFCC every 10ms
      sample_rate: 16000  # convert to 16KHz first (if needed)

architecture:
   name: StackedRNN
   params:
      instance_normalize: True  # normalize sequences
      rnn: LSTM                 # use LSTM (could be GRU)
      recurrent: [128, 128]     # two layers with 128 hidden states
      bidirectional: True       # bidirectional LSTMs
      linear: [32, 32]          # add two linear layers at the end 

scheduler:
   name: CyclicScheduler        # use cyclic learning rate (LR) scheduler
   params:
      learning_rate: auto       # automatically guess LR upper bound
      epochs_per_cycle: 14      # 14 epochs per cycle

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/models/speaker_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

In practice, it is tuning a simple speaker change detection pipeline (pyannote.audio.pipeline.speaker_change_detection.SpeakerChangeDetection) after each epoch and stores the best hyper-parameter configuration on disk:

$ cat ${TRAIN_DIR}/validate/AMI.SpeakerDiarization.MixHeadset/params.yml

epoch: 870
params:
  alpha: 0.17578125
  min_duration: 0.0

One can also use tensorboard to follow the validation process.

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/precomputed/scd:

$ pyannote-change-detection apply ${TRAIN_DIR}/weights/0870.pt AMI.SpeakerDiarization.MixHeadset /path/to/precomputed/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/precomputed/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=0.17, min_duration=0.0, 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!