#!/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 " 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 # (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 ) # how to run jobs." echo " --config # config containing options" echo " --stage # stage to do partial re-run from." echo " --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() { 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;