align_si.sh.svn-base
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#!/bin/bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
# Korbinian Riedhammer
# Apache 2.0
# Computes training alignments using a model with delta or
# LDA+MLLT features.
# If you supply the "--use-graphs true" option, it will use the training
# graphs from the source directory (where the model is). In this
# case the number of jobs must match with the source directory.
# Begin configuration section.
nj=4
cmd=run.pl
use_graphs=false
# Begin configuration.
scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
beam=10
retry_beam=40
boost_silence=1.0 # Factor by which to boost silence during alignment.
# 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_si.sh <data1-dir> <data2-dir> <lang-dir> <src-dir> <align-dir>"
echo "e.g.: steps/tandem/align_si.sh {mfcc,bottleneck}/data/train data/lang exp/tri1 exp/tri1_ali"
echo "main options (for others, see top of script file)"
echo " --config <config-file> # config containing options"
echo " --nj <nj> # number of parallel jobs"
echo " --use-graphs true # use graphs in src-dir"
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
exit 1;
fi
data1=$1
data2=$2
lang=$3
srcdir=$4
dir=$5
oov=`cat $lang/oov.int` || exit 1;
mkdir -p $dir/log
echo $nj > $dir/num_jobs
# Set up the features
sdata1=$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;
cp $srcdir/final.occs $dir;
# Get some info on the feature types
splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
normft2=`cat $srcdir/normft2 2>/dev/null` || exit 1;
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
# for lda-type features, we need to copy both the lda (for baseft) and mllt
# transformation (for the pasted features)
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/{tandem,splice_opts,normft2} $dir 2>/dev/null
echo "$0: aligning data in $data using model from $srcdir, putting alignments in $dir"
mdl="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $dir/final.mdl - |"
if $use_graphs; then
[ $nj != "`cat $srcdir/num_jobs`" ] && echo "$0: mismatch in num-jobs" && exit 1;
[ ! -f $srcdir/fsts.1.gz ] && echo "$0: no such file $srcdir/fsts.1.gz" && exit 1;
$cmd JOB=1:$nj $dir/log/align.JOB.log \
gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$mdl" \
"ark:gunzip -c $srcdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
else
tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata1/JOB/text|";
# We could just use gmm-align in the next line, but it's less efficient as it compiles the
# training graphs one by one.
$cmd JOB=1:$nj $dir/log/align.JOB.log \
compile-train-graphs $dir/tree $dir/final.mdl $lang/L.fst "$tra" ark:- \| \
gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$mdl" ark:- \
"$feats" "ark,t:|gzip -c >$dir/ali.JOB.gz" || exit 1;
fi
echo "$0: done aligning data."