run_ivector_common.sh
5.12 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
#!/bin/bash
set -euo pipefail
# 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="devtest test"
gmm=tri3b
nnet3_affix=
. ./cmd.sh
. ./path.sh
. utils/parse_options.sh
gmm_dir=exp/${gmm}
ali_dir=exp/${gmm}_ali_${train_set}_sp
for f in data/${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
# perturb data to get alignments
# nnet will be trained by high resolution data
# _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 "$0: making mfcc features for low-resolution speed-perturbed data"
steps/make_mfcc.sh \
--cmd "$train_cmd" \
--nj 10 \
data/${train_set}_sp
steps/compute_cmvn_stats.sh \
data/${train_set}_sp
utils/fix_data_dir.sh \
data/${train_set}_sp
fi
if [ $stage -le 2 ]; then
echo "$0: aligning with the perturbed low-resolution data"
steps/align_fmllr.sh \
--nj 20 \
--cmd "$train_cmd" \
data/${train_set}_sp \
data/lang \
$gmm_dir \
$ali_dir
fi
if [ $stage -le 3 ]; then
# Create high-resolution MFCC features (with 40 cepstra instead of 13).
echo "$0: creating high-resolution MFCC features"
mfccdir=data/${train_set}_sp_hires/data
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
for datadir in ${train_set}_sp ${test_sets}; do
steps/make_mfcc.sh \
--nj 10 \
--mfcc-config conf/mfcc_hires.conf \
--cmd "$train_cmd" \
data/${datadir}_hires
steps/compute_cmvn_stats.sh \
data/${datadir}_hires
utils/fix_data_dir.sh \
data/${datadir}_hires
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/${train_set}_sp_hires/utt2spk)
num_utts=$[$num_utts_total/4]
utils/data/subset_data_dir.sh \
data/${train_set}_sp_hires \
$num_utts \
${temp_data_root}/${train_set}_sp_hires_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_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 20 \
--num-frames 700000 \
--num-threads 8 \
${temp_data_root}/${train_set}_sp_hires_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 the speed-perturbed data .
# 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/${train_set}_sp_hires \
exp/nnet3${nnet3_affix}/diag_ubm \
exp/nnet3${nnet3_affix}/extractor
fi
# combine and train system on short segments.
# extract iVectors on speed-perturbed training data
# With --utts-per-spk-max 2, script pairs utterances into twos.
# Treats each pair as one speaker.
# Gives more diversity in iVectors.
# Extracted online.
# note: extract ivectors from max2 data
# Why is max2 not encoded in ivectordir name?
# valid for non-max2 data
# utterance list is the same.
# having a larger number of speakers is helpful for generalization, and to
# handle per-utterance decoding well (iVector starts at zero).
if [ $stage -le 6 ]; then
ivectordir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
temp_data_root=${ivectordir}
utils/data/modify_speaker_info.sh \
--utts-per-spk-max 2 \
data/${train_set}_sp_hires \
${temp_data_root}/${train_set}_sp_hires_max2
steps/online/nnet2/extract_ivectors_online.sh \
--cmd "$train_cmd" \
--nj 20 \
${temp_data_root}/${train_set}_sp_hires_max2 \
exp/nnet3${nnet3_affix}/extractor \
$ivectordir
fi
# Also extract iVectors for test data.
# No need for speed perturbation (sp).
if [ $stage -le 7 ]; then
for data in $test_sets; do
steps/online/nnet2/extract_ivectors_online.sh \
--cmd "$train_cmd" \
--nj 1 \
data/${data}_hires \
exp/nnet3${nnet3_affix}/extractor \
exp/nnet3${nnet3_affix}/ivectors_${data}_hires
done
fi
exit 0