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Scripts/steps/tandem/.svn/text-base/train_sgmm.sh.svn-base
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#!/bin/bash # Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0. # Korbinian Riedhammer # SGMM training, with speaker vectors. This script would normally be called on # top of fMLLR features obtained from a conventional system, but it also works # on top of any type of speaker-independent features (based on # deltas+delta-deltas or LDA+MLLT). For more info on SGMMs, see the paper "The # subspace Gaussian mixture model--A structured model for speech recognition". # (Computer Speech and Language, 2011). # Begin configuration section. nj=4 cmd=run.pl stage=-6 context_opts= # e.g. set it to "--context-width=5 --central-position=2" for a # quinphone system. scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1" num_iters=25 # Total number of iterations num_iters_alimdl=3 # Number of iterations for estimating alignment model. max_iter_inc=15 # Last iter to increase #substates on. realign_iters="5 10 15"; # Iters to realign on. spkvec_iters="5 8 12 17" # Iters to estimate speaker vectors on. increase_dim_iters="6 8"; # Iters on which to increase phn dim and/or spk dim; # rarely necessary, and if it is, only the 1st will normally be necessary. rand_prune=0.1 # Randomized-pruning parameter for posteriors, to speed up training. phn_dim= # You can use this to set the phonetic subspace dim. [default: feat-dim+1] spk_dim= # You can use this to set the speaker subspace dim. [default: feat-dim] power=0.2 # Exponent for number of gaussians according to occurrence counts beam=8 retry_beam=40 cluster_thresh=-1 # for build-tree control final bottom-up clustering of leaves normft2=true # End configuration section. echo "$0 $@" # Print the command line for logging if [ -f path.sh ]; then . ./path.sh; fi . parse_options.sh || exit 1; if [ $# != 8 ]; then echo "Usage: steps/tandem/train_sgmm.sh <num-leaves> <num-substates> <data1> <data2> <lang> <ali-dir> <ubm> <exp-dir>" echo " e.g.: steps/tandem/train_sgmm.sh 3500 10000 {mfcc,bottleneck},data/train_si84 data/lang \\" echo " exp/tri3b_ali_si84 exp/ubm4a/final.ubm exp/sgmm4a" echo "main options (for others, see top of script file)" echo " --config <config-file> # config containing options" echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs." echo " --silence-weight <sil-weight> # weight for silence (e.g. 0.5 or 0.0)" echo " --num-iters <#iters> # Number of iterations of E-M" exit 1; fi num_leaves=$1 totsubstates=$2 data1=$3 data2=$4 lang=$5 alidir=$6 ubm=$7 dir=$8 # Check some files. for f in $data1/feats.scp $data2/feats.scp $lang/L.fst $alidir/ali.1.gz $alidir/final.mdl $ubm; do [ ! -f $f ] && echo "$0: no such file $f" && exit 1; done # Set some variables. oov=`cat $lang/oov.int` silphonelist=`cat $lang/phones/silence.csl` numsubstates=$num_leaves # Initial #-substates. incsubstates=$[($totsubstates-$numsubstates)/$max_iter_inc] # per-iter increment for #substates feat_dim=`gmm-info $alidir/final.mdl 2>/dev/null | awk '/feature dimension/{print $NF}'` || exit 1; [ $feat_dim -eq $feat_dim ] || exit 1; # make sure it's numeric. [ -z $phn_dim ] && phn_dim=$[$feat_dim+1] [ -z $spk_dim ] && spk_dim=$feat_dim nj=`cat $alidir/num_jobs` || 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; spkvecs_opt= # Empty option for now, until we estimate the speaker vectors. gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.JOB.gz|" ## Set up features. splice_opts=`cat $alidir/splice_opts 2>/dev/null` # frame-splicing options. normft2=`cat $alidir/normft2 2>/dev/null` if [ -f $alidir/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 $alidir/{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 $alidir/{splice_opts,normft2,tandem} $dir 2>/dev/null if [ -f $alidir/trans.1 ]; then echo "$0: using transforms from $alidir" feats="$feats transform-feats --utt2spk=ark:$sdata1/JOB/utt2spk ark,s,cs:$alidir/trans.JOB ark:- ark:- |" fi ## if [ $stage -le -6 ]; then echo "$0: accumulating tree stats" $cmd JOB=1:$nj $dir/log/acc_tree.JOB.log \ acc-tree-stats --ci-phones=$ciphonelist $alidir/final.mdl "$feats" \ "ark:gunzip -c $alidir/ali.JOB.gz|" $dir/JOB.treeacc || exit 1; [ "`ls $dir/*.treeacc | wc -w`" -ne "$nj" ] && echo "$0: Wrong #tree-stats" && exit 1; sum-tree-stats $dir/treeacc $dir/*.treeacc 2>$dir/log/sum_tree_acc.log || exit 1; rm $dir/*.treeacc fi if [ $stage -le -5 ]; then echo "$0: Getting questions for tree clustering." # preparing questions, roots file... cluster-phones $dir/treeacc $lang/phones/sets.int $dir/questions.int 2> $dir/log/questions.log || exit 1; cat $lang/phones/extra_questions.int >> $dir/questions.int compile-questions $lang/topo $dir/questions.int $dir/questions.qst 2>$dir/log/compile_questions.log || exit 1; echo "$0: Building the tree" $cmd $dir/log/build_tree.log \ build-tree --verbose=1 --max-leaves=$num_leaves \ --cluster-thresh=$cluster_thresh $dir/treeacc $lang/phones/roots.int \ $dir/questions.qst $lang/topo $dir/tree || exit 1; fi if [ $stage -le -4 ]; then echo "$0: Initializing the model" # Note: if phn_dim > feat_dim+1 or spk_dim > feat_dim, these dims # will be truncated on initialization. $cmd $dir/log/init_sgmm.log \ sgmm-init --phn-space-dim=$phn_dim --spk-space-dim=$spk_dim $lang/topo \ $dir/tree $ubm $dir/0.mdl || exit 1; fi if [ $stage -le -3 ]; then echo "$0: doing Gaussian selection" $cmd JOB=1:$nj $dir/log/gselect.JOB.log \ sgmm-gselect $dir/0.mdl "$feats" \ "ark,t:|gzip -c >$dir/gselect.JOB.gz" || exit 1; fi if [ $stage -le -2 ]; then echo "$0: compiling training graphs" text="ark: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/0.mdl $lang/L.fst \ "$text" "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1; fi if [ $stage -le -1 ]; then echo "$0: Converting alignments" $cmd JOB=1:$nj $dir/log/convert_ali.JOB.log \ convert-ali $alidir/final.mdl $dir/0.mdl $dir/tree "ark:gunzip -c $alidir/ali.JOB.gz|" \ "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; fi x=0 while [ $x -lt $num_iters ]; do echo "$0: training pass $x ... " if echo $realign_iters | grep -w $x >/dev/null && [ $stage -le $x ]; then echo "$0: re-aligning data" $cmd JOB=1:$nj $dir/log/align.$x.JOB.log \ sgmm-align-compiled $spkvecs_opt $scale_opts "$gselect_opt" \ --utt2spk=ark:$sdata1/JOB/utt2spk --beam=$beam --retry-beam=$retry_beam \ $dir/$x.mdl "ark:gunzip -c $dir/fsts.JOB.gz|" "$feats" \ "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; fi if [ $spk_dim -gt 0 ] && echo $spkvec_iters | grep -w $x >/dev/null; then if [ $stage -le $x ]; then $cmd JOB=1:$nj $dir/log/spkvecs.$x.JOB.log \ ali-to-post "ark:gunzip -c $dir/ali.JOB.gz|" ark:- \| \ weight-silence-post 0.01 $silphonelist $dir/$x.mdl ark:- ark:- \| \ sgmm-est-spkvecs --rand-prune=$rand_prune --spk2utt=ark:$sdata1/JOB/spk2utt \ $spkvecs_opt "$gselect_opt" $dir/$x.mdl "$feats" ark,s,cs:- \ ark:$dir/tmp_vecs.JOB '&&' mv $dir/tmp_vecs.JOB $dir/vecs.JOB || exit 1; fi spkvecs_opt[$n]="--spk-vecs=ark:$dir/vecs.JOB" fi if [ $x -eq 0 ]; then flags=vwcSt # on the first iteration, don't update projections M or N elif [ $spk_dim -gt 0 -a $[$x%2] -eq 1 -a $x -ge `echo $spkvec_iters | awk '{print $1}'` ]; then # Update N if we have speaker-vector space and x is odd, # and we've already updated the speaker vectors... flags=vNwcSt else # otherwise update M. flags=vMwcSt fi if [ $stage -le $x ]; then $cmd JOB=1:$nj $dir/log/acc.$x.JOB.log \ sgmm-acc-stats $spkvecs_opt --utt2spk=ark:$sdata1/JOB/utt2spk \ --update-flags=$flags "$gselect_opt" --rand-prune=$rand_prune \ $dir/$x.mdl "$feats" "ark,s,cs:gunzip -c $dir/ali.JOB.gz | ali-to-post ark:- ark:-|" \ $dir/$x.JOB.acc || exit 1; fi # The next option is needed if the user specifies a phone or speaker sub-space # dimension that's higher than the "normal" one. increase_dim_opts= if echo $increase_dim_iters | grep -w $x >/dev/null; then increase_dim_opts="--increase-phn-dim=$phn_dim --increase-spk-dim=$spk_dim" # Note: the command below might have a null effect on some iterations. if [ $spk_dim -gt $feat_dim ]; then cmd JOB=1:$nj $dir/log/copy_vecs.$x.JOB.log \ copy-vector --print-args=false --change-dim=$spk_dim \ ark:$dir/vecs.JOB ark:$dir/vecs_tmp.$JOB '&&' \ mv $dir/vecs_tmp.JOB $dir/vecs.JOB || exit 1; fi fi if [ $stage -le $x ]; then $cmd $dir/log/update.$x.log \ sgmm-est --update-flags=$flags --split-substates=$numsubstates $increase_dim_opts \ --power=$power --write-occs=$dir/$[$x+1].occs $dir/$x.mdl "sgmm-sum-accs - $dir/$x.*.acc|" \ $dir/$[$x+1].mdl || exit 1; rm $dir/$x.mdl $dir/$x.*.acc $dir/$x.occs 2>/dev/null fi if [ $x -lt $max_iter_inc ]; then numsubstates=$[$numsubstates+$incsubstates] fi x=$[$x+1]; done rm $dir/final.mdl $dir/final.occs 2>/dev/null ln -s $x.mdl $dir/final.mdl ln -s $x.occs $dir/final.occs if [ $spk_dim -gt 0 ]; then # We need to create an "alignment model" that's been trained # without the speaker vectors, to do the first-pass decoding with. # in test time. # We do this for a few iters, in this recipe. final_mdl=$dir/$x.mdl cur_alimdl=$dir/$x.mdl while [ $x -lt $[$num_iters+$num_iters_alimdl] ]; do echo "$0: building alignment model (pass $x)" if [ $x -eq $num_iters ]; then # 1st pass of building alimdl. flags=MwcS # don't update v the first time. Note-- we never update transitions. # they wouldn't change anyway as we use the same alignment as previously. else flags=vMwcS fi if [ $stage -le $x ]; then $cmd JOB=1:$nj $dir/log/acc_ali.$x.JOB.log \ ali-to-post "ark:gunzip -c $dir/ali.JOB.gz|" ark:- \| \ sgmm-post-to-gpost $spkvecs_opt "$gselect_opt" \ --utt2spk=ark:$sdata1/JOB/utt2spk $final_mdl "$feats" ark,s,cs:- ark:- \| \ sgmm-acc-stats-gpost --rand-prune=$rand_prune --update-flags=$flags \ $cur_alimdl "$feats" ark,s,cs:- $dir/$x.JOB.aliacc || exit 1; $cmd $dir/log/update_ali.$x.log \ sgmm-est --update-flags=$flags --remove-speaker-space=true --power=$power $cur_alimdl \ "sgmm-sum-accs - $dir/$x.*.aliacc|" $dir/$[$x+1].alimdl || exit 1; rm $dir/$x.*.aliacc || exit 1; [ $x -gt $num_iters ] && rm $dir/$x.alimdl fi cur_alimdl=$dir/$[$x+1].alimdl x=$[$x+1] done rm $dir/final.alimdl 2>/dev/null ln -s $x.alimdl $dir/final.alimdl fi utils/summarize_warnings.pl $dir/log echo Done |