run_ivector_common.sh
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#!/bin/bash
set -euo pipefail
# This script is modified based on mini_librispeech/s5/local/nnet3/run_ivector_common.sh
# This script is called from local/nnet3/run_tdnn.sh and
# local/chain/run_tdnn.sh (and may eventually be called by more
# scripts). It contains the common feature preparation and
# iVector-related parts of the script. See those scripts for examples
# of usage.
stage=0
train_set=train
test_sets="test eval"
gmm=tri5a
nnet3_affix=
. ./cmd.sh
. ./path.sh
. utils/parse_options.sh
gmm_dir=exp/${gmm}
ali_dir=exp/${gmm}_sp_ali
for f in data/${train_set}/feats.scp ${gmm_dir}/final.mdl; do
if [ ! -f $f ]; then
echo "$0: expected file $f to exist"
exit 1
fi
done
if [ $stage -le 1 ]; then
# Although the nnet will be trained by high resolution data, we still have to
# perturb the normal data to get the alignment _sp stands for speed-perturbed
echo "$0: preparing directory for low-resolution speed-perturbed data (for alignment)"
utils/data/perturb_data_dir_speed_3way.sh data/${train_set} data/${train_set}_sp
echo "$0: making MFCC features for low-resolution speed-perturbed data"
steps/make_mfcc_pitch.sh --cmd "$train_cmd" --nj 70 data/${train_set}_sp \
exp/make_mfcc/${train_set}_sp mfcc_perturbed || exit 1;
steps/compute_cmvn_stats.sh data/${train_set}_sp \
exp/make_mfcc/${train_set}_sp mfcc_perturbed || exit 1;
utils/fix_data_dir.sh data/${train_set}_sp
fi
if [ $stage -le 2 ]; then
echo "$0: aligning with the perturbed low-resolution data"
steps/align_fmllr.sh --nj 30 --cmd "$train_cmd" \
data/${train_set}_sp data/lang $gmm_dir $ali_dir || exit 1
fi
if [ $stage -le 3 ]; then
# Create high-resolution MFCC features (with 40 cepstra instead of 13).
# this shows how you can split across multiple file-systems.
echo "$0: creating high-resolution MFCC features"
mfccdir=mfcc_perturbed_hires
for datadir in ${train_set}_sp ${test_sets}; do
utils/copy_data_dir.sh data/$datadir data/${datadir}_hires
done
# do volume-perturbation on the training data prior to extracting hires
# features; this helps make trained nnets more invariant to test data volume.
utils/data/perturb_data_dir_volume.sh data/${train_set}_sp_hires || exit 1;
for datadir in ${train_set}_sp ${test_sets}; do
steps/make_mfcc_pitch.sh --nj 10 --mfcc-config conf/mfcc_hires.conf \
--cmd "$train_cmd" data/${datadir}_hires exp/make_hires/$datadir $mfccdir || exit 1;
steps/compute_cmvn_stats.sh data/${datadir}_hires exp/make_hires/$datadir $mfccdir || exit 1;
utils/fix_data_dir.sh data/${datadir}_hires || exit 1;
# create MFCC data dir without pitch to extract iVector
utils/data/limit_feature_dim.sh 0:39 data/${datadir}_hires data/${datadir}_hires_nopitch || exit 1;
steps/compute_cmvn_stats.sh data/${datadir}_hires_nopitch exp/make_hires/$datadir $mfccdir || exit 1;
done
fi
if [ $stage -le 4 ]; then
echo "$0: computing a subset of data to train the diagonal UBM."
# We'll use about a quarter of the data.
mkdir -p exp/nnet3${nnet3_affix}/diag_ubm
temp_data_root=exp/nnet3${nnet3_affix}/diag_ubm
num_utts_total=$(wc -l <data/${train_set}_sp_hires_nopitch/utt2spk)
num_utts=$[$num_utts_total/4]
utils/data/subset_data_dir.sh data/${train_set}_sp_hires_nopitch \
$num_utts ${temp_data_root}/${train_set}_sp_hires_nopitch_subset
echo "$0: computing a PCA transform from the hires data."
steps/online/nnet2/get_pca_transform.sh --cmd "$train_cmd" \
--splice-opts "--left-context=3 --right-context=3" \
--max-utts 10000 --subsample 2 \
${temp_data_root}/${train_set}_sp_hires_nopitch_subset \
exp/nnet3${nnet3_affix}/pca_transform
echo "$0: training the diagonal UBM."
# Use 512 Gaussians in the UBM.
steps/online/nnet2/train_diag_ubm.sh --cmd "$train_cmd" --nj 30 \
--num-frames 700000 \
${temp_data_root}/${train_set}_sp_hires_nopitch_subset 512 \
exp/nnet3${nnet3_affix}/pca_transform exp/nnet3${nnet3_affix}/diag_ubm
fi
if [ $stage -le 5 ]; then
# Train the iVector extractor. Use all of the speed-perturbed data since iVector extractors
# can be sensitive to the amount of data. The script defaults to an iVector dimension of
# 100.
echo "$0: training the iVector extractor"
steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj 10 \
data/${train_set}_sp_hires_nopitch exp/nnet3${nnet3_affix}/diag_ubm \
exp/nnet3${nnet3_affix}/extractor || exit 1;
fi
if [ $stage -le 6 ]; then
# We extract iVectors on the speed-perturbed training data after combining
# short segments, which will be what we train the system on. With
# --utts-per-spk-max 2, the script pairs the utterances into twos, and treats
# each of these pairs as one speaker; this gives more diversity in iVectors..
# Note that these are extracted 'online'.
# note, we don't encode the 'max2' in the name of the ivectordir even though
# that's the data we extract the ivectors from, as it's still going to be
# valid for the non-'max2' data, the utterance list is the same.
ivectordir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp
# having a larger number of speakers is helpful for generalization, and to
# handle per-utterance decoding well (iVector starts at zero).
temp_data_root=${ivectordir}
utils/data/modify_speaker_info.sh --utts-per-spk-max 2 \
data/${train_set}_sp_hires_nopitch ${temp_data_root}/${train_set}_sp_hires_nopitch_max2
steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 30 \
${temp_data_root}/${train_set}_sp_hires_nopitch_max2 \
exp/nnet3${nnet3_affix}/extractor $ivectordir
# Also extract iVectors for the test data, but in this case we don't need the speed
# perturbation (sp).
for data in $test_sets; do
steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 8 \
data/${data}_hires_nopitch exp/nnet3${nnet3_affix}/extractor \
exp/nnet3${nnet3_affix}/ivectors_${data}
done
fi
exit 0