train_mmi_sgmm.sh
6.46 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
#!/bin/bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
# MMI training (or optionally boosted MMI, if you give the --boost option),
# for SGMMs. 4 iterations (by default) of Extended Baum-Welch update.
#
# Begin configuration section.
cmd=run.pl
num_iters=4
boost=0.0
cancel=true # if true, cancel num and den counts on each frame.
acwt=0.1
stage=0
update_opts=
transform_dir=
# End configuration section
echo "$0 $@" # Print the command line for logging
[ -f ./path.sh ] && . ./path.sh; # source the path.
. parse_options.sh || exit 1;
if [ $# -ne 5 ]; then
echo "Usage: steps/train_mmi_sgmm.sh <data> <lang> <ali> <denlats> <exp>"
echo " e.g.: steps/train_mmi_sgmm.sh data/train_si84 data/lang exp/tri2b_ali_si84 exp/tri2b_denlats_si84 exp/tri2b_mmi"
echo "Main options (for others, see top of script file)"
echo " --boost <boost-weight> # (e.g. 0.1), for boosted MMI. (default 0)"
echo " --cancel (true|false) # cancel stats (true by default)"
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
echo " --config <config-file> # config containing options"
echo " --stage <stage> # stage to do partial re-run from."
echo " --transform-dir <transform-dir> # directory to find fMLLR transforms."
exit 1;
fi
data=$1
lang=$2
alidir=$3
denlatdir=$4
dir=$5
mkdir -p $dir/log
for f in $data/feats.scp $alidir/{tree,final.mdl,ali.1.gz} $denlatdir/lat.1.gz; do
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
done
nj=`cat $alidir/num_jobs` || exit 1;
[ "$nj" -ne "`cat $denlatdir/num_jobs`" ] && \
echo "$alidir and $denlatdir have different num-jobs" && exit 1;
sdata=$data/split$nj
splice_opts=`cat $alidir/splice_opts 2>/dev/null`
mkdir -p $dir/log
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
cp $alidir/splice_opts $dir 2>/dev/null
echo $nj > $dir/num_jobs
cp $alidir/tree $dir
cp $alidir/final.mdl $dir/0.mdl
silphonelist=`cat $lang/phones/silence.csl` || exit 1;
# Set up features
if [ -f $alidir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
echo "$0: feature type is $feat_type"
case $feat_type in
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $alidir/final.mat ark:- ark:- |"
cp $alidir/final.mat $dir
;;
*) echo "Invalid feature type $feat_type" && exit 1;
esac
if [ ! -z "$transform_dir" ]; then
echo "$0: using transforms from $transform_dir"
[ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" \
&& exit 1;
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |"
else
echo "$0: no fMLLR transforms."
fi
if [ -f $alidir/vecs.1 ]; then
echo "$0: using speaker vectors from $alidir"
spkvecs_opt="--spk-vecs=ark:$alidir/vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk"
else
echo "$0: no speaker vectors."
spkvecs_opt=
fi
if [ -f $alidir/gselect.1.gz ]; then
echo "$0: using Gaussian-selection info from $alidir"
gselect_opt="--gselect=ark,s,cs:gunzip -c $alidir/gselect.JOB.gz|"
else
echo "$0: error: no Gaussian-selection info found" && exit 1;
fi
lats="ark:gunzip -c $denlatdir/lat.JOB.gz|"
if [[ "$boost" != "0.0" && "$boost" != 0 ]]; then
lats="$lats lattice-boost-ali --b=$boost --silence-phones=$silphonelist $alidir/final.mdl ark:- 'ark,s,cs:gunzip -c $alidir/ali.JOB.gz|' ark:- |"
fi
x=0
while [ $x -lt $num_iters ]; do
echo "Iteration $x of MMI training"
# Note: the num and den states are accumulated at the same time, so we
# can cancel them per frame.
if [ $stage -le $x ]; then
$cmd JOB=1:$nj $dir/log/acc.$x.JOB.log \
sgmm-rescore-lattice "$gselect_opt" $spkvecs_opt $dir/$x.mdl "$lats" "$feats" ark:- \| \
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
sum-post --merge=$cancel --scale1=-1 \
ark:- "ark,s,cs:gunzip -c $alidir/ali.JOB.gz | ali-to-post ark:- ark:- |" ark:- \| \
sgmm-acc-stats2 "$gselect_opt" $spkvecs_opt $dir/$x.mdl "$feats" ark,s,cs:- \
$dir/num_acc.$x.JOB.acc $dir/den_acc.$x.JOB.acc || exit 1;
n=`echo $dir/{num,den}_acc.$x.*.acc | wc -w`;
[ "$n" -ne $[$nj*2] ] && \
echo "Wrong number of MMI accumulators $n versus 2*$nj" && exit 1;
$cmd $dir/log/den_acc_sum.$x.log \
sgmm-sum-accs $dir/den_acc.$x.acc $dir/den_acc.$x.*.acc || exit 1;
rm $dir/den_acc.$x.*.acc
$cmd $dir/log/num_acc_sum.$x.log \
sgmm-sum-accs $dir/num_acc.$x.acc $dir/num_acc.$x.*.acc || exit 1;
rm $dir/num_acc.$x.*.acc
$cmd $dir/log/update.$x.log \
sgmm-est-ebw $update_opts $dir/$x.mdl $dir/num_acc.$x.acc $dir/den_acc.$x.acc $dir/$[$x+1].mdl || exit 1;
fi
# Some diagnostics: the objective function progress and auxiliary-function
# improvement. Note: this code is same as in train_mmi.sh
tail -n 50 $dir/log/acc.$x.*.log | perl -e '$acwt=shift @ARGV; while(<STDIN>) { if(m/gmm-acc-stats2.+Overall weighted acoustic likelihood per frame was (\S+) over (\S+) frames/) { $tot_aclike += $1*$2; $tot_frames1 += $2; } if(m|lattice-to-post.+Overall average log-like/frame is (\S+) over (\S+) frames. Average acoustic like/frame is (\S+)|) { $tot_den_lat_like += $1*$2; $tot_frames2 += $2; $tot_den_aclike += $3*$2; } } if (abs($tot_frames1 - $tot_frames2) > 0.01*($tot_frames1 + $tot_frames2)) { print STDERR "Frame-counts disagree $tot_frames1 versus $tot_frames2\n"; } $tot_den_lat_like /= $tot_frames2; $tot_den_aclike /= $tot_frames2; $tot_aclike *= ($acwt / $tot_frames1); $num_like = $tot_aclike + $tot_den_aclike; $per_frame_objf = $num_like - $tot_den_lat_like; print "$per_frame_objf $tot_frames1\n"; ' $acwt > $dir/tmpf
objf=`cat $dir/tmpf | awk '{print $1}'`;
nf=`cat $dir/tmpf | awk '{print $2}'`;
rm $dir/tmpf
impr=`grep -w Overall $dir/log/update.$x.log | awk '{x += $10*$12;} END{print x;}'`
impr=`perl -e "print ($impr*$acwt/$nf);"` # We multiply by acwt, and divide by $nf which is the "real" number of frames.
echo "Iteration $x: objf was $objf, MMI auxf change was $impr" | tee $dir/objf.$x.log
x=$[$x+1]
done
echo "MMI training finished"
rm $dir/final.mdl 2>/dev/null
ln -s $x.mdl $dir/final.mdl
exit 0;