run_dnn.sh
4.53 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
#!/bin/bash
# Copyright 2012-2014 Brno University of Technology (Author: Karel Vesely)
# Apache 2.0
# This example script trains a DNN on top of fMLLR features.
# The training is done in 3 stages,
#
# 1) RBM pre-training:
# in this unsupervised stage we train stack of RBMs,
# a good starting point for frame cross-entropy trainig.
# 2) frame cross-entropy training:
# the objective is to classify frames to correct pdfs.
# 3) sequence-training optimizing sMBR:
# the objective is to emphasize state-sequences with better
# frame accuracy w.r.t. reference alignment.
. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
## This relates to the queue.
. ./path.sh ## Source the tools/utils (import the queue.pl)
# Config:
gmmdir=exp/tri4b
data_fmllr=data-fmllr-tri4b
stage=0 # resume training with --stage=N
# End of config.
. utils/parse_options.sh || exit 1;
#
if [ $stage -le 0 ]; then
# Store fMLLR features, so we can train on them easily,
# test_dev93
dir=$data_fmllr/test_dev93
steps/nnet/make_fmllr_feats.sh --nj 10 --cmd "$train_cmd" \
--transform-dir $gmmdir/decode_bd_tgpr_dev93 \
$dir data/test_dev93 $gmmdir $dir/log $dir/data || exit 1
# test_eval92
dir=$data_fmllr/test_eval92
steps/nnet/make_fmllr_feats.sh --nj 8 --cmd "$train_cmd" \
--transform-dir $gmmdir/decode_bd_tgpr_eval92 \
$dir data/test_eval92 $gmmdir $dir/log $dir/data || exit 1
# train
dir=$data_fmllr/train_si284
steps/nnet/make_fmllr_feats.sh --nj 10 --cmd "$train_cmd" \
--transform-dir ${gmmdir}_ali_si284 \
$dir data/train_si284 $gmmdir $dir/log $dir/data || exit 1
# split the data : 90% train 10% cross-validation (held-out)
utils/subset_data_dir_tr_cv.sh $dir ${dir}_tr90 ${dir}_cv10 || exit 1
fi
if [ $stage -le 1 ]; then
# Pre-train DBN, i.e. a stack of RBMs
dir=exp/dnn5b_pretrain-dbn
(tail --pid=$$ -F $dir/log/pretrain_dbn.log 2>/dev/null)& # forward log
$cuda_cmd $dir/log/pretrain_dbn.log \
steps/nnet/pretrain_dbn.sh --rbm-iter 3 $data_fmllr/train_si284 $dir || exit 1;
fi
if [ $stage -le 2 ]; then
# Train the DNN optimizing per-frame cross-entropy.
dir=exp/dnn5b_pretrain-dbn_dnn
ali=${gmmdir}_ali_si284
feature_transform=exp/dnn5b_pretrain-dbn/final.feature_transform
dbn=exp/dnn5b_pretrain-dbn/6.dbn
(tail --pid=$$ -F $dir/log/train_nnet.log 2>/dev/null)& # forward log
# Train
$cuda_cmd $dir/log/train_nnet.log \
steps/nnet/train.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 \
$data_fmllr/train_si284_tr90 $data_fmllr/train_si284_cv10 data/lang $ali $ali $dir || exit 1;
# Decode (reuse HCLG graph)
steps/nnet/decode.sh --nj 10 --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt 0.1 \
$gmmdir/graph_bd_tgpr $data_fmllr/test_dev93 $dir/decode_bd_tgpr_dev93 || exit 1;
steps/nnet/decode.sh --nj 8 --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt 0.1 \
$gmmdir/graph_bd_tgpr $data_fmllr/test_eval92 $dir/decode_bd_tgpr_eval92 || exit 1;
fi
# Sequence training using sMBR criterion, we do Stochastic-GD
# with per-utterance updates. We use usually good acwt 0.1
# Lattices are re-generated after 1st epoch, to get faster convergence.
dir=exp/dnn5b_pretrain-dbn_dnn_smbr
srcdir=exp/dnn5b_pretrain-dbn_dnn
acwt=0.1
if [ $stage -le 3 ]; then
# First we generate lattices and alignments:
steps/nnet/align.sh --nj 100 --cmd "$train_cmd" \
$data_fmllr/train_si284 data/lang $srcdir ${srcdir}_ali || exit 1;
steps/nnet/make_denlats.sh --nj 100 --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt $acwt \
$data_fmllr/train_si284 data/lang $srcdir ${srcdir}_denlats || exit 1;
fi
if [ $stage -le 4 ]; then
# Re-train the DNN by 1 iteration of sMBR
steps/nnet/train_mpe.sh --cmd "$cuda_cmd" --num-iters 5 --acwt $acwt --do-smbr true \
$data_fmllr/train_si284 data/lang $srcdir ${srcdir}_ali ${srcdir}_denlats $dir || exit 1
# Decode (reuse HCLG graph)
for ITER in 5 4 3 1; do
steps/nnet/decode.sh --nj 10 --cmd "$decode_cmd" --config conf/decode_dnn.config \
--nnet $dir/${ITER}.nnet --acwt $acwt \
$gmmdir/graph_bd_tgpr $data_fmllr/test_dev93 $dir/decode_bd_tgpr_dev93_it${ITER} || exit 1;
steps/nnet/decode.sh --nj 8 --cmd "$decode_cmd" --config conf/decode_dnn.config \
--nnet $dir/${ITER}.nnet --acwt $acwt \
$gmmdir/graph_bd_tgpr $data_fmllr/test_eval92 $dir/decode_bd_tgpr_eval92_it${ITER} || exit 1;
done
fi
# Getting results [see RESULTS file]
# for x in exp/*/decode*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; done