speaker_embedding

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Copyright (c) 2017-2019 CNRS

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

Neural speech turn embedding with pyannote.audio

In this tutorial, you will learn how to train a speech turn embedding using pyannote-speaker-embedding 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 (or audio) neural embedding, please cite the following paper:

@inproceedings{Bredin2017,
    author = {Herv\'{e} Bredin},
    title = {{TristouNet: Triplet Loss for Speaker Turn Embedding}},
    booktitle = {42nd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2017},
    year = {2017},
    url = {http://arxiv.org/abs/1609.04301},
}

Databases

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

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

$ cat ~/.pyannote/database.yml
Databases:
  VoxCeleb: /path/to/voxceleb1/*/wav/{uri}.wav
  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-speaker-embedding relies on a configuration file defining the experimental setup:

$ cat tutorials/models/speaker_embedding/config.yml

feature_extraction:
   name: LibrosaMFCC
   params:
      e: False
      De: True
      DDe: True
      coefs: 19
      D: True
      DD: True
      duration: 0.025
      step: 0.010
      sample_rate: 16000

data_augmentation:
   name: AddNoise
   params:
     snr_min: 10
     snr_max: 20
     collection: MUSAN.Collection.BackgroundNoise

architecture:
   name: ClopiNet
   params:
     instance_normalize: True
     rnn: LSTM
     recurrent: [256, 256, 256]
     linear: [256]
     bidirectional: True
     pooling: sum
     batch_normalize: True
     normalize: True
     
approach:
   name: TripletLoss
   params:
     metric: cosine
     clamp: sigmoid
     margin: 0.0
     min_duration: 0.500
     max_duration: 1.500
     sampling: all
     per_fold: 20
     per_label: 3
     per_epoch: 1
     label_min_duration: 60

scheduler:
   name: CyclicScheduler
   params:
      epochs_per_cycle: 14

Training

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The following command will train the network using VoxCeleb1 for 1000 epochs (one epoch = one day of audio)

$ export EXPERIMENT_DIR=tutorials/models/speaker_embedding
$ pyannote-speaker-embedding train --gpu --to=1000 ${EXPERIMENT_DIR} VoxCeleb.SpeakerVerification.VoxCeleb1

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/VoxCeleb.SpeakerVerification.VoxCeleb1.train
$ pyannote-speaker-embedding validate --subset=test ${TRAIN_DIR} VoxCeleb.SpeakerDiarization.VoxCeleb1

This model reaches approximately 7% EER on VoxCeleb1.

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 store embeddings in /path/to/precomputed/emb:

$ pyannote-speaker-embedding apply ${TRAIN_DIR}/weights/2000.pt AMI.SpeakerDiarization.MixHeadset /path/to/precomputed/emb

We can then use these extracted embeddings like this:

# first test file of AMI protocol
>>> from pyannote.database import get_protocol
>>> protocol = get_protocol('AMI.SpeakerDiarization.MixHeadset')
>>> test_file = next(protocol.test())

# precomputed embeddings as pyannote.core.SlidingWindowFeature
>>> from pyannote.audio.features import Precomputed
>>> precomputed = Precomputed('/path/to/precomputed/emb')
>>> embeddings = precomputed(test_file)

# iterate over all embeddings
>>> for window, embedding in embeddings:
...     print(window)
...     print(embedding)
...     break

# extract embedding from a specific segment
>>> from pyannote.core import Segment
>>> fX = embeddings.crop(Segment(10, 20))
>>> print(fX.shape)

More options

For more options, see:

$ pyannote-speaker-embedding --help

That's all folks!