#!/bin/bash # Copyright 2012 Johns Hopkins University (Author: Daniel Povey) # Korbinian Riedhammer # Apache 2.0 # Computes training alignments and (if needed) speaker-vectors, given an # SGMM system. If the system is built on top of SAT, you should supply # transforms with the --transform-dir option. # If you supply the --use-graphs option, it will use the training # graphs from the source directory. # Begin configuration section. stage=0 nj=4 cmd=run.pl use_graphs=false # use graphs from srcdir use_gselect=false # use gselect info from srcdir [regardless, we use # Gaussian-selection info, we might have to compute it though.] gselect=15 # Number of Gaussian-selection indices for SGMMs. # Begin configuration. scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1" beam=10 retry_beam=40 transform_dir= # directory to find fMLLR transforms in. # End configuration options. echo "$0 $@" # Print the command line for logging [ -f path.sh ] && . ./path.sh # source the path. . parse_options.sh || exit 1; if [ $# != 5 ]; then echo "usage: steps/tandem/align_sgmm2.sh " echo "e.g.: steps/tandem/align_sgmm2.sh --transform-dir exp/tri3b {mfcc,bottleneck}/data/train data/lang \\" echo " exp/sgmm4a exp/sgmm5a_ali" echo "main options (for others, see top of script file)" echo " --config # config containing options" echo " --nj # number of parallel jobs" echo " --use-graphs true # use graphs in src-dir" echo " --transform-dir # directory to find fMLLR transforms" echo " --cmd (utils/run.pl|utils/queue.pl ) # how to run jobs." exit 1; fi data1=$1 data2=$2 lang=$3 srcdir=$4 dir=$5 oov=`cat $lang/oov.int` || exit 1; silphonelist=`cat $lang/phones/silence.csl` || exit 1; mkdir -p $dir/log echo $nj > $dir/num_jobs sdata1=$data1/split$nj sdata2=$data2/split$nj [[ -d $sdata1 && $data1/feats.scp -ot $sdata1 ]] || split_data.sh $data1 $nj || exit 1; [[ -d $sdata2 && $data2/feats.scp -ot $sdata2 ]] || split_data.sh $data2 $nj || exit 1; cp $srcdir/{tree,final.mdl} $dir || exit 1; [ -f $srcdir/final.alimdl ] && cp $srcdir/final.alimdl $dir cp $srcdir/final.occs $dir; ## Set up features. splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options. normft2=`cat $srcdir/normft2 2>/dev/null` if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi case $feat_type in delta) echo "$0: feature type is $feat_type" ;; lda) echo "$0: feature type is $feat_type" cp $srcdir/{lda,final}.mat $dir/ || exit 1; ;; *) echo "$0: invalid feature type $feat_type" && exit 1; esac # set up feature stream 1; this are usually spectral features, so we will add # deltas or splice them feats1="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata1/JOB/utt2spk scp:$sdata1/JOB/cmvn.scp scp:$sdata1/JOB/feats.scp ark:- |" if [ "$feat_type" == "delta" ]; then feats1="$feats1 add-deltas ark:- ark:- |" elif [ "$feat_type" == "lda" ]; then feats1="$feats1 splice-feats $splice_opts ark:- ark:- | transform-feats $dir/lda.mat ark:- ark:- |" fi # set up feature stream 2; this are usually bottleneck or posterior features, # which may be normalized if desired feats2="scp:$sdata2/JOB/feats.scp" if [ "$normft2" == "true" ]; then feats2="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata2/JOB/utt2spk scp:$sdata2/JOB/cmvn.scp $feats2 ark:- |" fi # assemble tandem features feats="ark,s,cs:paste-feats '$feats1' '$feats2' ark:- |" # add transformation, if applicable if [ "$feat_type" == "lda" ]; then feats="$feats transform-feats $dir/final.mat ark:- ark:- |" fi # splicing/normalization options cp $srcdir/{splice_opts,normft2,tandem} $dir 2>/dev/null if [ ! -z "$transform_dir" ]; then echo "$0: using transforms from $transform_dir" [ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" && exit 1; [ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \ && echo "$0: #jobs mismatch with transform-dir." && exit 1; feats="$feats transform-feats --utt2spk=ark:$sdata1/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |" elif grep 'transform-feats --utt2spk' $srcdir/log/acc.0.1.log 2>/dev/null; then echo "$0: **WARNING**: you seem to be using an SGMM system trained with transforms," echo " but you are not providing the --transform-dir option during alignment." fi ## ## Set up model and alignment model. mdl=$srcdir/final.mdl if [ -f $srcdir/final.alimdl ]; then alimdl=$srcdir/final.alimdl else alimdl=$srcdir/final.mdl fi [ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1; ## Work out where we're getting the graphs from. if $use_graphs; then [ "$nj" != "`cat $srcdir/num_jobs`" ] && \ echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1; [ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1; graphdir=$srcdir ln.pl $srcdir/fsts.*.gz $dir else graphdir=$dir if [ $stage -le 0 ]; then echo "$0: compiling training graphs" tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata1/JOB/text|"; $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \ compile-train-graphs $dir/tree $dir/final.mdl $lang/L.fst "$tra" \ "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1; fi fi ## Work out where we're getting the Gaussian-selection info from if $use_gselect; then [ "$nj" != "`cat $srcdir/num_jobs`" ] && \ echo "$0: you specified --use-gselect true, but #jobs mismatch." && exit 1; [ ! -f $srcdir/gselect.1.gz ] && echo "No gselect info in $srcdir" && exit 1; graphdir=$srcdir gselect_opt="--gselect=ark:gunzip -c $srcdir/gselect.JOB.gz|" ln.pl $srcdir/gselect.*.gz $dir else graphdir=$dir if [ $stage -le 1 ]; then echo "$0: computing Gaussian-selection info" # Note: doesn't matter whether we use $alimdl or $mdl, they will # have the same gselect info. $cmd JOB=1:$nj $dir/log/gselect.JOB.log \ sgmm2-gselect --full-gmm-nbest=$gselect $alimdl \ "$feats" "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1; fi gselect_opt="--gselect=ark:gunzip -c $dir/gselect.JOB.gz|" fi if [ $alimdl == $mdl ]; then # Speaker-independent decoding-- just one pass. Not normal. T=`sgmm2-info $mdl | grep 'speaker vector space' | awk '{print $NF}'` || exit 1; [ "$T" -ne 0 ] && echo "No alignment model, yet speaker vector space nonempty" && exit 1; if [ $stage -le 2 ]; then echo "$0: aligning data in $data using model $mdl (no speaker-vectors)" $cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \ sgmm2-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam $alimdl \ "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; fi echo "$0: done aligning data." exit 0; fi # Continue with system with speaker vectors. if [ $stage -le 2 ]; then echo "$0: aligning data in $data using model $alimdl" $cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \ sgmm2-align-compiled $scale_opts "$gselect_opt" --beam=$beam --retry-beam=$retry_beam $alimdl \ "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1; fi if [ $stage -le 3 ]; then echo "$0: computing speaker vectors (1st pass)" $cmd JOB=1:$nj $dir/log/spk_vecs1.JOB.log \ ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \ weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \ sgmm2-post-to-gpost "$gselect_opt" $alimdl "$feats" ark:- ark:- \| \ sgmm2-est-spkvecs-gpost --spk2utt=ark:$sdata1/JOB/spk2utt \ $mdl "$feats" ark,s,cs:- ark:$dir/pre_vecs.JOB || exit 1; fi if [ $stage -le 4 ]; then echo "$0: computing speaker vectors (2nd pass)" $cmd JOB=1:$nj $dir/log/spk_vecs2.JOB.log \ ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \ weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \ sgmm2-est-spkvecs --spk2utt=ark:$sdata1/JOB/spk2utt "$gselect_opt" \ --spk-vecs=ark:$dir/pre_vecs.JOB $mdl "$feats" ark,s,cs:- ark:$dir/vecs.JOB || exit 1; rm $dir/pre_vecs.* fi if [ $stage -le 5 ]; then echo "$0: doing final alignment." $cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \ sgmm2-align-compiled $scale_opts "$gselect_opt" --beam=$beam --retry-beam=$retry_beam \ --utt2spk=ark:$sdata1/JOB/utt2spk --spk-vecs=ark:$dir/vecs.JOB \ $mdl "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; fi rm $dir/pre_ali.*.gz echo "$0: done aligning data." utils/summarize_warnings.pl $dir/log exit 0;