run_nnet2_ms_disc.sh
6.22 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
#!/bin/bash
# This script does discriminative training on top of the online, multi-splice
# system trained in run_nnet2_ms.sh.
# note: this relies on having a cluster that has plenty of CPUs as well as GPUs,
# since the lattice generation runs in about real-time, so takes of the order of
# 1000 hours of CPU time.
#
# Note: rather than using any features we have dumped on disk, this script
# regenerates them from the wav data three times-- when we do lattice
# generation, numerator alignment and discriminative training. This made the
# script easier to write and more generic, because we don't have to know where
# the features and the iVectors are, but of course it's a little inefficient.
# The time taken is dominated by the lattice generation anyway, so this isn't
# a huge deal.
. ./cmd.sh
stage=0
train_stage=-10
use_gpu=true
srcdir=exp/nnet2_online/nnet_ms_a
criterion=smbr
drop_frames=false # only matters for MMI anyway.
effective_lrate=0.000005
num_jobs_nnet=6
train_stage=-10 # can be used to start training in the middle.
decode_start_epoch=0 # can be used to avoid decoding all epochs, e.g. if we decided to run more.
num_epochs=4
cleanup=false # run with --cleanup true --stage 6 to clean up (remove large things like denlats,
# alignments and degs).
set -e
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
if $use_gpu; then
if ! cuda-compiled; then
cat <<EOF && exit 1
This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
If you want to use GPUs (and have them), go to src/, and configure and make on a machine
where "nvcc" is installed. Otherwise, call this script with --use-gpu false
EOF
fi
parallel_opts="--gpu 1"
num_threads=1
else
# Use 4 nnet jobs just like run_4d_gpu.sh so the results should be
# almost the same, but this may be a little bit slow.
num_threads=16
parallel_opts="--num-threads $num_threads"
fi
if [ ! -f ${srcdir}_online/final.mdl ]; then
echo "$0: expected ${srcdir}_online/final.mdl to exist; first run run_nnet2_ms.sh."
exit 1;
fi
if [ $stage -le 1 ]; then
nj=50 # this doesn't really affect anything strongly, except the num-jobs for one of
# the phases of get_egs_discriminative2.sh below.
num_threads_denlats=6
subsplit=40 # number of jobs that run per job (but 2 run at a time, so total jobs is 80, giving
# max total slots = 80 * 6 = 480.
steps/nnet2/make_denlats.sh --cmd "$decode_cmd --mem 1G --num-threads $num_threads_denlats" \
--online-ivector-dir exp/nnet2_online/ivectors_train_hires \
--nj $nj --sub-split $subsplit --num-threads "$num_threads_denlats" --config conf/decode.config \
data/train_hires data/lang $srcdir ${srcdir}_denlats || exit 1;
# the command below is a more generic, but slower, way to do it.
#steps/online/nnet2/make_denlats.sh --cmd "$decode_cmd --mem 1G --num-threads $num_threads_denlats" \
# --nj $nj --sub-split $subsplit --num-threads "$num_threads_denlats" --config conf/decode.config \
# data/train_hires data/lang ${srcdir}_online ${srcdir}_denlats || exit 1;
fi
if [ $stage -le 2 ]; then
# hardcode no-GPU for alignment, although you could use GPU [you wouldn't
# get excellent GPU utilization though.]
nj=350 # have a high number of jobs because this could take a while, and we might
# have some stragglers.
use_gpu=no
gpu_opts=
steps/nnet2/align.sh --cmd "$decode_cmd $gpu_opts" --use-gpu "$use_gpu" \
--online-ivector-dir exp/nnet2_online/ivectors_train_hires \
--nj $nj data/train_hires data/lang $srcdir ${srcdir}_ali || exit 1;
# the command below is a more generic, but slower, way to do it.
# steps/online/nnet2/align.sh --cmd "$decode_cmd $gpu_opts" --use-gpu "$use_gpu" \
# --nj $nj data/train_hires data/lang ${srcdir}_online ${srcdir}_ali || exit 1;
fi
if [ $stage -le 3 ]; then
if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d ${srcdir}_degs/storage ]; then
utils/create_split_dir.pl \
/export/b0{1,2,5,6}/$USER/kaldi-data/egs/tedlium-$(date +'%m_%d_%H_%M')/s5/${srcdir}_degs/storage ${srcdir}_degs/storage
fi
# have a higher maximum num-jobs if
if [ -d ${srcdir}_degs/storage ]; then max_jobs=10; else max_jobs=5; fi
steps/nnet2/get_egs_discriminative2.sh \
--cmd "$decode_cmd --max-jobs-run $max_jobs" \
--online-ivector-dir exp/nnet2_online/ivectors_train_hires \
--criterion $criterion --drop-frames $drop_frames \
data/train_hires data/lang ${srcdir}{_ali,_denlats,/final.mdl,_degs} || exit 1;
# the command below is a more generic, but slower, way to do it.
#steps/online/nnet2/get_egs_discriminative2.sh \
# --cmd "$decode_cmd --max-jobs-run $max_jobs" \
# --criterion $criterion --drop-frames $drop_frames \
# data/train_hires data/lang ${srcdir}{_ali,_denlats,_online,_degs} || exit 1;
fi
if [ $stage -le 4 ]; then
steps/nnet2/train_discriminative2.sh --cmd "$decode_cmd $parallel_opts" \
--stage $train_stage \
--effective-lrate $effective_lrate \
--criterion $criterion --drop-frames $drop_frames \
--num-epochs $num_epochs \
--num-jobs-nnet 6 --num-threads $num_threads \
${srcdir}_degs ${srcdir}_${criterion}_${effective_lrate} || exit 1;
fi
if [ $stage -le 5 ]; then
dir=${srcdir}_${criterion}_${effective_lrate}
ln -sf $(utils/make_absolute.sh ${srcdir}_online/conf) $dir/conf # so it acts like an online-decoding directory
for epoch in $(seq $decode_start_epoch $num_epochs); do
for decode_set in dev test; do
(
num_jobs=`cat data/${decode_set}_hires/utt2spk|cut -d' ' -f2|sort -u|wc -l`
decode_dir=$dir/decode_epoch${epoch}_${decode_set}
steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj $num_jobs \
--iter epoch$epoch exp/tri3/graph data/${decode_set}_hires $decode_dir || exit 1
steps/lmrescore_const_arpa.sh data/lang_test data/lang_rescore data/${decode_set}_hires $decode_dir $decode_dir.rescore || exit 1
) &
done
done
wait
for dir in $dir/decode*; do grep Sum $dir/score_*/*.sys | utils/best_wer.sh; done
fi
if [ $stage -le 6 ] && $cleanup; then
# if you run with "--cleanup true --stage 6" you can clean up.
rm ${srcdir}_denlats/lat.*.gz || true
rm ${srcdir}_ali/ali.*.gz || true
steps/nnet2/remove_egs.sh ${srcdir}_degs || true
fi
exit 0;