Skip to content

Latest commit

 

History

History
204 lines (165 loc) · 6.89 KB

README.md

File metadata and controls

204 lines (165 loc) · 6.89 KB
Raw

The MIT License (MIT)

Copyright (c) 2017-2020 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:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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

End-to-end speech activity detection with pyannote.audio

This tutorial assumes that you have already followed the data preparation tutorial, and teaches how to train, validate, and apply a speech activity detection neural network on the AMI dataset using pyannote-audio command line tool. In particular, this should reproduce the result reported in second line of Table 1 of this introductory paper.

Table of contents

Citation

(↑up to table of contents)

If you use pyannote-audio for speech activity detection, please cite the following papers:

@inproceedings{Bredin2020,
  Title = {{pyannote.audio: neural building blocks for speaker diarization}},
  Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe},
  Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
  Address = {Barcelona, Spain},
  Month = {May},
  Year = {2020},
}
@inproceedings{Lavechin2020,
  Title = {{End-to-end Domain-Adversarial Voice Activity Detection}},
  Author = {{Lavechin}, Marvin and {Gill}, Marie-Philippe and {Bousbib}, Ruben and {Bredin}, Herv{\'e} and {Garcia-Perera}, Leibny Paola},
  Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
  Address = {Barcelona, Spain},
  Month = {May},
  Year = {2020},
}

Configuration

(↑up to table of contents)

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

$ export EXP_DIR=tutorials/models/speech_activity_detection
$ cat ${EXP_DIR}/config.yml
# A speech activity detection model is trained.
# Here, training relies on 2s-long audio chunks,
# batches of 64 audio chunks, and saves model to
# disk every one (1) day worth of audio.
task:
   name: SpeechActivityDetection
   params:
      duration: 2.0
      batch_size: 64
      per_epoch: 1

# Data augmentation is applied during training.
# Here, it consists in additive noise from the
# MUSAN database, with random signal-to-noise
# ratio between 5 and 20 dB
data_augmentation:
   name: AddNoise
   params:
      snr_min: 5
      snr_max: 20
      collection: MUSAN.Collection.BackgroundNoise

# Since we are training an end-to-end model, the
# feature extraction step simply returns the raw
# waveform.
feature_extraction:
   name: RawAudio
   params:
      sample_rate: 16000

# We use the PyanNet architecture in Figure 2 of
# pyannote.audio introductory paper. More details
# about the architecture and its parameters can be
# found directly in PyanNet docstring.
architecture:
   name: pyannote.audio.models.PyanNet
   params:
      rnn:
         unit: LSTM
         hidden_size: 128
         num_layers: 2
         bidirectional: True
      ff:
         hidden_size: [128, 128]

# We use a constant learning rate of 1e-2
scheduler:
   name: ConstantScheduler
   params:
      learning_rate: 0.01

Training

(↑up to table of contents)

The following command will train the network using the training subset of AMI database for 200 epochs:

$ pyannote-audio sad train --subset=train --to=200 --parallel=4 ${EXP_DIR} AMI.SpeakerDiarization.MixHeadset

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

$ tensorboard --logdir=${EXP_DIR}

tensorboard screenshot

Validation

(↑up to table of contents)

To get a quick idea of how the network is doing on the development set, one can use the validate mode.

$ export TRN_DIR=${EXP_DIR}/train/AMI.SpeakerDiarization.MixHeadset.train
$ pyannote-audio sad validate --subset=development --from=10 --to=200 --every=10 ${TRN_DIR} AMI.SpeakerDiarization.MixHeadset

It can be run while the model is still training and evaluates the model every 10 epochs. This will create a bunch of files in VAL_DIR (defined below).

In practice, it tunes a simple speech activity detection pipeline every 10 epochs and stores the best hyper-parameter configuration on disk (i.e. the one that maximizes detection f-score):

$ export VAL_DIR=${TRN_DIR}/validate_detection_fscore/AMI.SpeakerDiarization.MixHeadset.development
$ cat ${VAL_DIR}/params.yml
detection_fscore: 0.9713032559713921
epoch: 140
params:
  min_duration_off: 0.1
  min_duration_on: 0.1
  offset: 0.48012924491559134
  onset: 0.48012924491559134
  pad_offset: 0.0
  pad_onset: 0.0

See pyannote.audio.pipeline.speech_activity_detection.SpeechActivityDetection for details on the role of each parameter.

Like for training, one can also use tensorboard to follow the validation process:

$ tensorboard --logdir=${EXP_DIR}

tensorboard screenshot

Application

(↑up to table of contents)

Now that we know how the model is doing, we can apply it on test files of the AMI database:

$ pyannote-audio sad apply --subset=test ${VAL_DIR} AMI.SpeakerDiarization.MixHeadset

Raw model output and speech activity detection results will be dumped into the following directory: ${VAL_DIR}/apply/{BEST_EPOCH}.

More options

For more options, see:

$ pyannote-audio --help

That's all folks!