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Scripts/steps/tandem/train_ubm.sh
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#!/bin/bash # Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0. # This trains a UBM (i.e. a mixture of Gaussians), by clustering # the Gaussians from a trained HMM/GMM system and then doing a few # iterations of UBM training. # We mostly use this for SGMM systems. # Begin configuration section. nj=4 cmd=run.pl silence_weight= # You can set it to e.g. 0.0, to weight down silence in training. stage=-2 num_gselect1=50 # first stage of Gaussian-selection num_gselect2=25 # second stage. intermediate_num_gauss=2000 num_iters=3 # 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 [ $# != 6 ]; then echo "Usage: steps/train_tandem_ubm.sh <num-gauss> <data1> <data2> <lang> <ali-dir> <exp>" echo " e.g.: steps/train_tandem_ubm.sh 400 {mfcc,bottneneck}/data/train_si84 data/lang exp/tri2b_ali_si84 exp/ubm3c" 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_gauss=$1 data1=$2 data2=$3 lang=$4 alidir=$5 dir=$6 for f in $data1/feats.scp $data2/feats.scp $lang/L.fst $alidir/ali.1.gz $alidir/final.mdl; do [ ! -f $f ] && echo "No such file $f" && exit 1; done if [ $[$num_gauss*2] -gt $intermediate_num_gauss ]; then echo "intermediate_num_gauss was too small $intermediate_num_gauss" intermediate_num_gauss=$[$num_gauss*2]; echo "setting it to $intermediate_num_gauss" fi # Set various variables. silphonelist=`cat $lang/phones/silence.csl` || exit 1; 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; splice_opts=`cat $alidir/splice_opts 2>/dev/null` # frame-splicing options. normft2=`cat $alidir/normft2 2>/dev/null` ## Set up features. 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,tandem,normft2} $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 [ ! -z "$silence_weight" ]; then weights_opt="--weights='ark,s,cs:gunzip -c $alidir/ali.JOB.gz | ali-to-post ark:- ark:- | weight-silence-post $silence_weight $silphonelist $alidir/final.mdl ark:- ark:- | post-to-weights ark:- ark:- |'" else weights_opt= fi if [ $stage -le -2 ]; then echo "$0: clustering model $alidir/final.mdl to get initial UBM" $cmd $dir/log/cluster.log \ init-ubm --intermediate-num-gauss=$intermediate_num_gauss --ubm-num-gauss=$num_gauss \ --verbose=2 --fullcov-ubm=true $alidir/final.mdl $alidir/final.occs \ $dir/0.ubm || exit 1; fi # Do initial phase of Gaussian selection and save it to disk -- later on we'll # do more Gaussian selection to further prune, as the model changes. if [ $stage -le -1 ]; then echo "$0: doing Gaussian selection" $cmd JOB=1:$nj $dir/log/gselect.JOB.log \ gmm-gselect --n=$num_gselect1 "fgmm-global-to-gmm $dir/0.ubm - |" "$feats" \ "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1; fi x=0 while [ $x -lt $num_iters ]; do echo "Pass $x" $cmd JOB=1:$nj $dir/log/acc.$x.JOB.log \ gmm-gselect --n=$num_gselect2 "--gselect=ark,s,cs:gunzip -c $dir/gselect.JOB.gz|" \ "fgmm-global-to-gmm $dir/$x.ubm - |" "$feats" ark:- \| \ fgmm-global-acc-stats $weights_opt --gselect=ark,s,cs:- $dir/$x.ubm "$feats" \ $dir/$x.JOB.acc || exit 1; lowcount_opt="--remove-low-count-gaussians=false" [ $[$x+1] -eq $num_iters ] && lowcount_opt= # Only remove low-count Gaussians # on last iter-- we can't do it earlier, or the Gaussian-selection info would # be mismatched. $cmd $dir/log/update.$x.log \ fgmm-global-est $lowcount_opt --verbose=2 $dir/$x.ubm "fgmm-global-sum-accs - $dir/$x.*.acc |" \ $dir/$[$x+1].ubm || exit 1; rm $dir/$x.*.acc $dir/$x.ubm x=$[$x+1] done rm $dir/gselect.*.gz rm $dir/final.ubm 2>/dev/null mv $dir/$x.ubm $dir/final.ubm || exit 1; |