mk_aslf_lda_mllt.sh
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#!/bin/bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
# Korbinian Riedhammer
# Decoding script that does fMLLR. This can be on top of delta+delta-delta, or
# LDA+MLLT features.
# There are 3 models involved potentially in this script,
# and for a standard, speaker-independent system they will all be the same.
# The "alignment model" is for the 1st-pass decoding and to get the
# Gaussian-level alignments for the "adaptation model" the first time we
# do fMLLR. The "adaptation model" is used to estimate fMLLR transforms
# and to generate state-level lattices. The lattices are then rescored
# with the "final model".
#
# The following table explains where we get these 3 models from.
# Note: $srcdir is one level up from the decoding directory.
#
# Model Default source:
#
# "alignment model" $srcdir/final.alimdl --alignment-model <model>
# (or $srcdir/final.mdl if alimdl absent)
# "adaptation model" $srcdir/final.mdl --adapt-model <model>
# "final model" $srcdir/final.mdl --final-model <model>
# Begin configuration section
alignment_model=
adapt_model=
final_model=
transform_dir=
stage=0
acwt=0.083333 # Acoustic weight used in getting fMLLR transforms, and also in
# lattice generation.
max_active=7000
beam=13.0
lattice_beam=6.0
nj=4
silence_weight=0.01
cmd=run.pl
si_dir=
fmllr_update_type=full
# 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 [ $# != 4 ]; then
echo "Usage: steps/tandem/mk_aslf_lda_mllt.sh [options] <graph-dir> <data1-dir> <data2-dir> <decode-dir>"
echo " e.g.: steps/tandem/mk_aslf_lda_mllt.sh exp/tri2b/graph {mfcc,bottleneck}/data/test_dev93 exp/tri2b/decode_dev93"
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 " --cmd <cmd> # Command to run in parallel with"
echo " --adapt-model <adapt-mdl> # Model to compute transforms with"
echo " --alignment-model <ali-mdl> # Model to get Gaussian-level alignments for"
echo " # 1st pass of transform computation."
echo " --final-model <finald-mdl> # Model to finally decode with"
echo " --si-dir <speaker-indep-decoding-dir> # use this to skip 1st pass of decoding"
echo " # Caution-- must be with same tree"
echo " --acwt <acoustic-weight> # default 0.08333 ... used to get posteriors"
exit 1;
fi
graphdir=$1
data1=$2
data2=$3
dir=`echo $4 | sed 's:/$::g'` # remove any trailing slash.
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
mkdir -p $dir/log`
sdata1=$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;
echo $nj > $dir/num_jobs
# Some checks. Note: we don't need $srcdir/tree but we expect
# it should exist, given the current structure of the scripts.
for f in $graphdir/HCLG.fst $data1/feats.scp $data2/feats.scp $srcdir/tree; do
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
done
## Some checks, and setting of defaults for variables.
[ "$nj" -ne "`cat $dir/num_jobs`" ] && echo "Mismatch in #jobs with si-dir" && exit 1;
[ -z "$adapt_model" ] && adapt_model=$srcdir/final.mdl
[ -z "$final_model" ] && final_model=$srcdir/final.mdl
for f in $adapt_model $final_model; do
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
done
##
# 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"
;;
*) 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 $srcdir/lda.mat ark:- ark:- |"
fi
# set up feature stream 2; this are usually bottleneck or posterior features,
# which may be normalized if desired
feats2="scp:$sdata2/JOB/feats.scp"
if [ "$normft2" == "true" ]; then
echo "Using cmvn for feats2"
feats2="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata2/JOB/utt2spk scp:$sdata2/JOB/cmvn.scp $feats2 ark:- |"
fi
# assemble tandem features
sifeats="ark,s,cs:paste-feats '$feats1' '$feats2' ark:- |"
# add transformation, if applicable
if [ "$feat_type" == "lda" ]; then
sifeats="$sifeats transform-feats $srcdir/final.mat ark:- ark:- |"
fi
if [ -e $dir/trans.1. ]; then
echo "Using fMLLR transforms in $dir"
feats="$sifeats transform-feats --utt2spk=ark:$sdata1/JOB/utt2spk ark:$dir/trans.JOB ark:- ark:- |"
elif [ ! -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 "$0: #jobs mismatch with transform-dir." && exit 1;
feats="$sifeats 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 in test time."
fi
# Rescore the state-level lattices with the final adapted features, and the final model
# (which by default is $srcdir/final.mdl, but which may be specified on the command line,
# useful in case of discriminatively trained systems).
# At this point we prune and determinize the lattices and write them out, ready for
# language model rescoring.
echo "Rescoring lattices, converting to slf"
mkdir -p $dir/slf
$cmd JOB=1:$nj $dir/log/rescore.slf.JOB.log \
lattice-align-words $graphdir/phones/word_boundary.int $final_model "ark:gunzip -c $dir/lat.JOB.gz |" ark:- \| \
gmm-rescore-lattice $final_model ark:- "$feats" ark,t:- \| \
utils/int2sym.pl -f 3 $graphdir/words.txt \| \
utils/convert_slf.pl - $dir/slf
exit 0;