After the quick introduction to Kaldi, we’ll move on to an example. I’m mostly reading about and working on speaker verficiation, rather than ASR so far, and I’ll run a x-vector speaker verifciation example.

Speaker Verification Pipeline

Go to the voxceleb folder, read the README, and go to v2. v1 uses GMM-UBM, i-vector and PLDA method. v2 uses DNN speaker embeddings (x-vector), which is currently SOTA, reason why we’ll run it.

What’s in it

The v2 folder contains several folders and files:

README.txt  cmd.sh  conf  local  path.sh  run.sh  sid  steps  utils

Here is the organisation of a typical Kaldi egs directory, as well illustrated in this Kaldi tutorial.

image

These folders contain:

  • scripts ready to launch, such as run.sh that launches the whole example and path.sh which makes sure that there is a proper configuration file
  • cmd.sh, a script to specify the type of computation you’re choosing
  • conf which is a folder that contains configuration settings for MFCC feature extraction and energy-based voice activity detection (VAD), e.g setting the threshold under which you don’t detect a voice
  • local which contains code to setup the dataset in the correct format and shape the features for the x-vector pipeline
  • sid is really important and contains code to compute the VAD, extract the i-vector, the x-vector, training the UBM…
  • steps contains lower level scripts such as feature extraction, functions to re-format the training data and other utilities

The easy way out is to launch run.sh. This is the high-level script that runs everything mentioned. Rather than running it, we’ll break it down into several steps.

The data

The example does not come with data, you need to place it in the repository. TODO

We are now going to break down the run.sh script. This script was written by Daniel Garcia-Romero, Daniel Povey, David Snyder and Ewald Enzinger (Johns Hopkins University). I am either adding comments above the code, or in-between the lines.

Set the paths

The first step is to set the correct path to the training files. The Musan data are used for data augmentation in the X-vector generation.

. ./cmd.sh
. ./path.sh
set -e
mfccdir=`pwd`/mfcc
vaddir=`pwd`/mfcc

# The trials file is downloaded by local/make_voxceleb1_v2.pl.
voxceleb1_trials=data/voxceleb1_test/trials
voxceleb1_root=/export/corpora/VoxCeleb1
voxceleb2_root=/export/corpora/VoxCeleb2
nnet_dir=exp/xvector_nnet_1a
musan_root=/export/corpora/JHU/musan

Build the datasets

We must now format the training and testing data:

stage=0

# Control if it is the first stage
if [ $stage -le 0 ]; then
  # Apply the formating to the train and test data
  local/make_voxceleb2.pl $voxceleb2_root dev data/voxceleb2_train
  local/make_voxceleb2.pl $voxceleb2_root test data/voxceleb2_test
  # The evaluation set becomes Voxceleb1 test data
  local/make_voxceleb1_v2.pl $voxceleb1_root dev data/voxceleb1_train
  local/make_voxceleb1_v2.pl $voxceleb1_root test data/voxceleb1_test
  # We'll train on all of VoxCeleb2, plus the training portion of VoxCeleb1.
  # This should give 7,323 speakers and 1,276,888 utterances.
  # We combine the datasets
  utils/combine_data.sh data/train data/voxceleb2_train data/voxceleb2_test data/voxceleb1_train
fi

Build the features

We now have a training set ready with 7323 speakers and 1.276 million utterances. What we should do is extract the features for the whole training set. The process is in 2 steps. We extract features for the train and test set, and compute the voice activity detection decision.

if [ $stage -le 1 ]; then
  # Make MFCCs and compute the energy-based VAD for each dataset
  for name in train voxceleb1_test; do
  	# Compute the MFCC
    steps/make_mfcc.sh --write-utt2num-frames true --mfcc-config conf/mfcc.conf --nj 40 --cmd "$train_cmd" \
      data/${name} exp/make_mfcc $mfccdir
    utils/fix_data_dir.sh data/${name}
    # Compute the VAD
    sid/compute_vad_decision.sh --nj 40 --cmd "$train_cmd" \
      data/${name} exp/make_vad $vaddir
    utils/fix_data_dir.sh data/${name}
  done
fi

Data augmentation

X-vector is based on a robust embedding, and the major guarantee for the robustness is the data augmentation process. We can augment the initial dataset using several techniques:

  • add reverberation to the speech
  • add background music
  • add background noise
  • add babble noise

Note, there are plenty of ways to augment data. One could also speed up the sammple, combine all augmentations on a single utternace…

if [ $stage -le 2 ]; then
  frame_shift=0.01
  awk -v frame_shift=$frame_shift '{print $1, $2*frame_shift;}' data/train/utt2num_frames > data/train/reco2dur

  # Download the package that includes the real RIRs, simulated RIRs, isotropic noises and point-source noises
  if [ ! -d "RIRS_NOISES" ]; then
    wget --no-check-certificate http://www.openslr.org/resources/28/rirs_noises.zip
    unzip rirs_noises.zip
  fi

  # Make a version with reverberated speech
  rvb_opts=()
  rvb_opts+=(--rir-set-parameters "0.5, RIRS_NOISES/simulated_rirs/smallroom/rir_list")
  rvb_opts+=(--rir-set-parameters "0.5, RIRS_NOISES/simulated_rirs/mediumroom/rir_list")

  # Make a reverberated version of the VoxCeleb2 list. No additive noise.
  steps/data/reverberate_data_dir.py \
    "${rvb_opts[@]}" \
    --speech-rvb-probability 1 \
    --pointsource-noise-addition-probability 0 \
    --isotropic-noise-addition-probability 0 \
    --num-replications 1 \
    --source-sampling-rate 16000 \
    data/train data/train_reverb
  cp data/train/vad.scp data/train_reverb/
  utils/copy_data_dir.sh --utt-suffix "-reverb" data/train_reverb data/train_reverb.new
  rm -rf data/train_reverb
  mv data/train_reverb.new data/train_reverb

  # Prepare the MUSAN corpus, which consists of music, speech, and noise
  # suitable for augmentation.
  steps/data/make_musan.sh --sampling-rate 16000 $musan_root data

  # Get the duration of the MUSAN recordings.  This will be used by the
  # script augment_data_dir.py.
  for name in speech noise music; do
    utils/data/get_utt2dur.sh data/musan_${name}
    mv data/musan_${name}/utt2dur data/musan_${name}/reco2dur
  done

  # Augment with musan_noise
  steps/data/augment_data_dir.py --utt-suffix "noise" --fg-interval 1 --fg-snrs "15:10:5:0" --fg-noise-dir "data/musan_noise" data/train data/train_noise
  # Augment with musan_music
  steps/data/augment_data_dir.py --utt-suffix "music" --bg-snrs "15:10:8:5" --num-bg-noises "1" --bg-noise-dir "data/musan_music" data/train data/train_music
  # Augment with musan_speech
  steps/data/augment_data_dir.py --utt-suffix "babble" --bg-snrs "20:17:15:13" --num-bg-noises "3:4:5:6:7" --bg-noise-dir "data/musan_speech" data/train data/train_babble

  # Combine reverb, noise, music, and babble into one directory.
  utils/combine_data.sh data/train_aug data/train_reverb data/train_noise data/train_music data/train_babble
fi

Alright, this long script was useful to augment the dataset. We now must compute the MFCC features on the augmented dataset. This will approximately double the training set size. Note that this process is really different from what data augmentation in Computer Vision would look like. Indeed, in CV, we usually way more than double the number of training data, and apply the augmentation on-the-fly.

if [ $stage -le 3 ]; then
  # Take a random subset of the augmentations
  utils/subset_data_dir.sh data/train_aug 1000000 data/train_aug_1m
  utils/fix_data_dir.sh data/train_aug_1m

  # Make MFCCs for the augmented data.  Note that we do not compute a new
  # vad.scp file here.  Instead, we use the vad.scp from the clean version of
  # the list.
  steps/make_mfcc.sh --mfcc-config conf/mfcc.conf --nj 40 --cmd "$train_cmd" \
    data/train_aug_1m exp/make_mfcc $mfccdir

  # Combine the clean and augmented VoxCeleb2 list.  This is now roughly
  # double the size of the original clean list.
  utils/combine_data.sh data/train_combined data/train_aug_1m data/train
fi

Normalization

We then apply a cepstral mean and variance normalization (CMVN) on the features, as it is required as an input for the x-vector.

# Now we prepare the features to generate examples for xvector training.
if [ $stage -le 4 ]; then
  # This script applies CMVN and removes nonspeech frames.  Note that this is somewhat
  # wasteful, as it roughly doubles the amount of training data on disk.  After
  # creating training examples, this can be removed.
  local/nnet3/xvector/prepare_feats_for_egs.sh --nj 40 --cmd "$train_cmd" \
    data/train_combined data/train_combined_no_sil exp/train_combined_no_sil
  utils/fix_data_dir.sh data/train_combined_no_sil
fi

Filter training data

Among training data, you’ll encounter some that are:

  • too short once silence has been removed (under 5s.)
  • don’t have enough utterance per speaker (under 8)

We need to remove those training data.

if [ $stage -le 5 ]; then
  # Now, we need to remove features that are too short after removing silence
  # frames.  We want atleast 5s (500 frames) per utterance.
  min_len=400
  mv data/train_combined_no_sil/utt2num_frames data/train_combined_no_sil/utt2num_frames.bak
  awk -v min_len=${min_len} '$2 > min_len {print $1, $2}' data/train_combined_no_sil/utt2num_frames.bak > data/train_combined_no_sil/utt2num_frames
  utils/filter_scp.pl data/train_combined_no_sil/utt2num_frames data/train_combined_no_sil/utt2spk > data/train_combined_no_sil/utt2spk.new
  mv data/train_combined_no_sil/utt2spk.new data/train_combined_no_sil/utt2spk
  utils/fix_data_dir.sh data/train_combined_no_sil

  # We also want several utterances per speaker. Now we'll throw out speakers
  # with fewer than 8 utterances.
  min_num_utts=8
  awk '{print $1, NF-1}' data/train_combined_no_sil/spk2utt > data/train_combined_no_sil/spk2num
  awk -v min_num_utts=${min_num_utts} '$2 >= min_num_utts {print $1, $2}' data/train_combined_no_sil/spk2num | utils/filter_scp.pl - data/train_combined_no_sil/spk2utt > data/train_combined_no_sil/spk2utt.new
  mv data/train_combined_no_sil/spk2utt.new data/train_combined_no_sil/spk2utt
  utils/spk2utt_to_utt2spk.pl data/train_combined_no_sil/spk2utt > data/train_combined_no_sil/utt2spk

  utils/filter_scp.pl data/train_combined_no_sil/utt2spk data/train_combined_no_sil/utt2num_frames > data/train_combined_no_sil/utt2num_frames.new
  mv data/train_combined_no_sil/utt2num_frames.new data/train_combined_no_sil/utt2num_frames

  # Now we're ready to create training examples.
  utils/fix_data_dir.sh data/train_combined_no_sil
fi

Training the DNN

Steps 6 to 8 are grouped in the training of the DNN. The aim of the training is to learn an embedding.

# Stages 6 through 8 are handled in run_xvector.sh
local/nnet3/xvector/run_xvector.sh --stage $stage --train-stage -1 \
  --data data/train_combined_no_sil --nnet-dir $nnet_dir \
  --egs-dir $nnet_dir/egs

Extract the x-vector

By extracting the last hidden layer, before the prediction, we have an embedding called an x-vector, representing the information extacted from the voice of the speaker. We run the script extract_xvectors.sh to extract it in the training and test set.

if [ $stage -le 9 ]; then
  # Extract x-vectors for centering, LDA, and PLDA training.
  sid/nnet3/xvector/extract_xvectors.sh --cmd "$train_cmd --mem 4G" --nj 80 \
    $nnet_dir data/train \
    $nnet_dir/xvectors_train

  # Extract x-vectors used in the evaluation.
  sid/nnet3/xvector/extract_xvectors.sh --cmd "$train_cmd --mem 4G" --nj 40 \
    $nnet_dir data/voxceleb1_test \
    $nnet_dir/xvectors_voxceleb1_test
fi

Mean vector, LDA and PLDA

We then compute the mean vector to center the evaluation x-vectors. We then reduce the dimension of the x-vectors to 200 using LDA. Finally, the PLDA acts as a classifier. We train it here.

if [ $stage -le 10 ]; then
  # Compute the mean vector for centering the evaluation xvectors.
  $train_cmd $nnet_dir/xvectors_train/log/compute_mean.log \
    ivector-mean scp:$nnet_dir/xvectors_train/xvector.scp \
    $nnet_dir/xvectors_train/mean.vec || exit 1;

  # This script uses LDA to decrease the dimensionality prior to PLDA.
  lda_dim=200
  $train_cmd $nnet_dir/xvectors_train/log/lda.log \
    ivector-compute-lda --total-covariance-factor=0.0 --dim=$lda_dim \
    "ark:ivector-subtract-global-mean scp:$nnet_dir/xvectors_train/xvector.scp ark:- |" \
    ark:data/train/utt2spk $nnet_dir/xvectors_train/transform.mat || exit 1;

  # Train the PLDA model.
  $train_cmd $nnet_dir/xvectors_train/log/plda.log \
    ivector-compute-plda ark:data/train/spk2utt \
    "ark:ivector-subtract-global-mean scp:$nnet_dir/xvectors_train/xvector.scp ark:- | transform-vec $nnet_dir/xvectors_train/transform.mat ark:- ark:- | ivector-normalize-length ark:-  ark:- |" \
    $nnet_dir/xvectors_train/plda || exit 1;
fi

Make predictions and assess performance

All models are trained, we can now make predictions on the test set of Voxceleb1. This script will also generate and display performance metrics (Equal Error Rate and min DCF).

if [ $stage -le 11 ]; then
  $train_cmd exp/scores/log/voxceleb1_test_scoring.log \
    ivector-plda-scoring --normalize-length=true \
    "ivector-copy-plda --smoothing=0.0 $nnet_dir/xvectors_train/plda - |" \
    "ark:ivector-subtract-global-mean $nnet_dir/xvectors_train/mean.vec scp:$nnet_dir/xvectors_voxceleb1_test/xvector.scp ark:- | transform-vec $nnet_dir/xvectors_train/transform.mat ark:- ark:- | ivector-normalize-length ark:- ark:- |" \
    "ark:ivector-subtract-global-mean $nnet_dir/xvectors_train/mean.vec scp:$nnet_dir/xvectors_voxceleb1_test/xvector.scp ark:- | transform-vec $nnet_dir/xvectors_train/transform.mat ark:- ark:- | ivector-normalize-length ark:- ark:- |" \
    "cat '$voxceleb1_trials' | cut -d\  --fields=1,2 |" exp/scores_voxceleb1_test || exit 1;
fi

if [ $stage -le 12 ]; then
  eer=`compute-eer <(local/prepare_for_eer.py $voxceleb1_trials exp/scores_voxceleb1_test) 2> /dev/null`
  mindcf1=`sid/compute_min_dcf.py --p-target 0.01 exp/scores_voxceleb1_test $voxceleb1_trials 2> /dev/null`
  mindcf2=`sid/compute_min_dcf.py --p-target 0.001 exp/scores_voxceleb1_test $voxceleb1_trials 2> /dev/null`
  echo "EER: $eer%"
  echo "minDCF(p-target=0.01): $mindcf1"
  echo "minDCF(p-target=0.001): $mindcf2"
  # EER: 3.128%
  # minDCF(p-target=0.01): 0.3258
  # minDCF(p-target=0.001): 0.5003
fi

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