run_ivector_common.sh
4.61 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
#!/bin/bash
#set -e
# this script is based on local/multicondition/run_nnet2_common.sh
# minor corrections were made to dir names for nnet3
stage=1
snrs="20:10:15:5:0"
foreground_snrs="20:10:15:5:0"
background_snrs="20:10:15:5:0"
num_data_reps=3
base_rirs="simulated"
set -e
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
# check if the required tools are present
local/multi_condition/check_version.sh || exit 1;
mkdir -p exp/nnet3
if [ $stage -le 1 ]; then
# Download the package that includes the real RIRs, simulated RIRs, isotropic noises and point-source noises
wget --no-check-certificate http://www.openslr.org/resources/28/rirs_noises.zip
unzip rirs_noises.zip
rvb_opts=()
if [ "$base_rirs" == "simulated" ]; then
# This is the config for the system using simulated RIRs and point-source noises
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")
rvb_opts+=(--noise-set-parameters RIRS_NOISES/pointsource_noises/noise_list)
else
# This is the config for the JHU ASpIRE submission system
rvb_opts+=(--rir-set-parameters "1.0, RIRS_NOISES/real_rirs_isotropic_noises/rir_list")
rvb_opts+=(--noise-set-parameters RIRS_NOISES/real_rirs_isotropic_noises/noise_list)
fi
# corrupt the fisher data to generate multi-condition data
# for data_dir in train dev test; do
for data_dir in train dev test; do
if [ "$data_dir" == "train" ]; then
num_reps=$num_data_reps
else
num_reps=1
fi
python steps/data/reverberate_data_dir.py \
"${rvb_opts[@]}" \
--prefix "rev" \
--foreground-snrs $foreground_snrs \
--background-snrs $background_snrs \
--speech-rvb-probability 1 \
--pointsource-noise-addition-probability 1 \
--isotropic-noise-addition-probability 1 \
--num-replications $num_reps \
--max-noises-per-minute 1 \
--source-sampling-rate 8000 \
data/${data_dir} data/${data_dir}_rvb
done
fi
if [ $stage -le 2 ]; then
mfccdir=mfcc_reverb
if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $mfccdir/storage ]; then
date=$(date +'%m_%d_%H_%M')
utils/create_split_dir.pl /export/b0{1,2,3,4}/$USER/kaldi-data/mfcc/aspire-$date/s5/$mfccdir/storage $mfccdir/storage
fi
for data_dir in train_rvb dev_rvb test_rvb dev_aspire dev test ; do
utils/copy_data_dir.sh data/$data_dir data/${data_dir}_hires
steps/make_mfcc.sh --nj 70 --mfcc-config conf/mfcc_hires.conf \
--cmd "$train_cmd" data/${data_dir}_hires \
exp/make_reverb_hires/${data_dir} $mfccdir || exit 1;
steps/compute_cmvn_stats.sh data/${data_dir}_hires exp/make_reverb_hires/${data_dir} $mfccdir || exit 1;
utils/fix_data_dir.sh data/${data_dir}_hires
utils/validate_data_dir.sh data/${data_dir}_hires
done
utils/subset_data_dir.sh data/train_rvb_hires 100000 data/train_rvb_hires_100k
utils/subset_data_dir.sh data/train_rvb_hires 30000 data/train_rvb_hires_30k
fi
if [ $stage -le 3 ]; then
steps/online/nnet2/get_pca_transform.sh --cmd "$train_cmd" \
--splice-opts "--left-context=3 --right-context=3" \
--max-utts 30000 --subsample 2 \
data/train_rvb_hires exp/nnet3/pca_transform
fi
if [ $stage -le 4 ]; then
# To train a diagonal UBM we don't need very much data, so use the smallest
# subset.
steps/online/nnet2/train_diag_ubm.sh --cmd "$train_cmd" --nj 30 --num-frames 400000 \
data/train_rvb_hires_30k 512 exp/nnet3/pca_transform \
exp/nnet3/diag_ubm
fi
if [ $stage -le 5 ]; then
# iVector extractors can in general be sensitive to the amount of data, but
# this one has a fairly small dim (defaults to 100) so we don't use all of it,
# we use just the 100k subset (about one sixteenth of the data).
steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj 10 \
data/train_rvb_hires_100k exp/nnet3/diag_ubm \
exp/nnet3/extractor || exit 1;
fi
if [ $stage -le 6 ]; then
ivectordir=exp/nnet3/ivectors_train_rvb
if [[ $(hostname -f) == *.clsp.jhu.edu ]]; then # this shows how you can split across multiple file-systems.
utils/create_split_dir.pl /export/b0{1,2,3,4}/$USER/kaldi-data/ivectors/aspire/s5/$ivectordir/storage $ivectordir/storage
fi
# having a larger number of speakers is helpful for generalization, and to
# handle per-utterance decoding well (iVector starts at zero).
steps/online/nnet2/copy_data_dir.sh --utts-per-spk-max 2 \
data/train_rvb_hires data/train_rvb_hires_max2
steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 60 \
data/train_rvb_hires_max2 exp/nnet3/extractor $ivectordir || exit 1;
fi