run_tdnn.sh
4.17 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
#!/bin/bash
# this is the standard "tdnn" system, built in nnet3; it's what we use to
# call multi-splice.
# Results (2 epochs):
# Number of parameters: 6056880
# %WER 15.3 | 507 17792 | 87.4 9.0 3.6 2.7 15.3 90.1 | -0.081 | exp/nnet3/tdnn_sp/decode_dev/score_10_0.5/ctm.filt.filt.sys
# %WER 13.9 | 507 17792 | 88.4 8.0 3.6 2.3 13.9 85.8 | -0.164 | exp/nnet3/tdnn_sp/decode_dev_rescore/score_10_0.5/ctm.filt.filt.sys
# %WER 13.8 | 1155 27512 | 88.5 8.7 2.7 2.3 13.8 84.2 | -0.076 | exp/nnet3/tdnn_sp/decode_test/score_10_0.0/ctm.filt.filt.sys
# %WER 12.5 | 1155 27512 | 89.6 7.7 2.6 2.1 12.5 81.5 | -0.133 | exp/nnet3/tdnn_sp/decode_test_rescore/score_10_0.0/ctm.filt.filt.sys
# 4 epochs
# %WER 14.6 | 507 17792 | 87.9 8.7 3.4 2.5 14.6 88.6 | -0.111 | exp/nnet3/tdnn/decode_dev/score_10_0.5/ctm.filt.filt.sys
# %WER 13.2 | 507 17792 | 89.4 7.7 2.9 2.6 13.2 85.0 | -0.170 | exp/nnet3/tdnn/decode_dev_rescore/score_10_0.0/ctm.filt.filt.sys
# %WER 13.5 | 1155 27512 | 88.7 8.5 2.7 2.3 13.5 83.6 | -0.110 | exp/nnet3/tdnn/decode_test/score_10_0.0/ctm.filt.filt.sys
# %WER 12.1 | 1155 27512 | 89.9 7.5 2.6 2.1 12.1 80.3 | -0.178 | exp/nnet3/tdnn/decode_test_rescore/score_10_0.0/ctm.filt.filt.sys
# At this script level we don't support not running on GPU, as it would be painfully slow.
# If you want to run without GPU you'd have to call train_tdnn.sh with --gpu false,
# --num-threads 16 and --minibatch-size 128.
stage=1
affix=
train_stage=-10
common_egs_dir=
reporting_email=
remove_egs=true
decode_iter=
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
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.
EOF
fi
dir=exp/nnet3/tdnn
dir=$dir${affix:+_$affix}
train_set=train_sp #_sp stands for speed-perturbed. This is hard-coded to speed
# pertub data.
ali_dir=exp/tri3_ali_sp
local/nnet3/run_ivector_common.sh --stage $stage --generate-alignments true || exit 1;
if [ $stage -le 9 ]; then
echo "$0: creating neural net configs";
# create the config files for nnet initialization
python steps/nnet3/tdnn/make_configs.py \
--feat-dir data/${train_set}_hires \
--ivector-dir exp/nnet3/ivectors_${train_set} \
--ali-dir $ali_dir \
--relu-dim 500 \
--splice-indexes "-1,0,1 -1,0,1,2 -3,0,3 -3,0,3 -3,0,3 -6,-3,0" \
--use-presoftmax-prior-scale true \
$dir/configs || exit 1;
fi
if [ $stage -le 10 ]; then
if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
utils/create_split_dir.pl \
/export/b{09,10,11,12}/$USER/kaldi-data/egs/tedlium-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
fi
steps/nnet3/train_dnn.py --stage=$train_stage \
--cmd="$decode_cmd" \
--feat.online-ivector-dir exp/nnet3/ivectors_${train_set} \
--feat.cmvn-opts="--norm-means=false --norm-vars=false" \
--trainer.num-epochs 2 \
--trainer.optimization.num-jobs-initial 3 \
--trainer.optimization.num-jobs-final 8 \
--trainer.optimization.initial-effective-lrate 0.0015 \
--trainer.optimization.final-effective-lrate 0.00015 \
--egs.dir "$common_egs_dir" \
--cleanup.remove-egs $remove_egs \
--cleanup.preserve-model-interval 20 \
--feat-dir=data/${train_set}_hires \
--ali-dir $ali_dir \
--lang data/lang \
--reporting.email="$reporting_email" \
--dir=$dir || exit 1;
fi
graph_dir=exp/tri3/graph
if [ $stage -le 11 ]; then
iter_opts=
if [ ! -z $decode_iter ]; then
iter_opts=" --iter $decode_iter "
fi
for decode_set in dev test; do
(
steps/nnet3/decode.sh \
--nj $(wc -l < data/$decode_set/spk2utt) --cmd "$decode_cmd" $iter_opts \
--online-ivector-dir exp/nnet3/ivectors_${decode_set} \
$graph_dir data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter} || exit 1;
steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
data/lang_test data/lang_rescore data/${decode_set}_hires \
$dir/decode_${decode_set}${decode_iter:+_$decode_iter} \
$dir/decode_${decode_set}${decode_iter:+_$decode_iter}_rescore || exit 1;
) &
done
fi
wait;