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#!/bin/bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0.
# This script does decoding with an SGMM system, with speaker vectors.
# If the SGMM system was
# built on top of fMLLR transforms from a conventional system, you should
# provide the --transform-dir option.
# Begin configuration section.
stage=1
alignment_model=
transform_dir= # dir to find fMLLR transforms.
nj=4 # number of decoding jobs.
acwt=0.1 # Just a default value, used for adaptation and beam-pruning..
cmd=run.pl
beam=15.0
gselect=15 # Number of Gaussian-selection indices for SGMMs. [Note:
# the first_pass_gselect variable is used for the 1st pass of
# decoding and can be tighter.
first_pass_gselect=3 # Use a smaller number of Gaussian-selection indices in
# the 1st pass of decoding (lattice generation).
max_active=7000
lat_beam=8.0 # Beam we use in lattice generation.
vecs_beam=4.0 # Beam we use to prune lattices while getting posteriors for
# speaker-vector computation. Can be quite tight (actually we could
# probably just do best-path.
use_fmllr=false
fmllr_iters=10
fmllr_min_count=1000
# 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 [ $# -ne 3 ]; then
echo "Usage: steps/decode_sgmm.sh [options] <graph-dir> <data-dir> <decode-dir>"
echo " e.g.: steps/decode_sgmm.sh --transform-dir exp/tri3b/decode_dev93_tgpr \\"
echo " exp/sgmm3a/graph_tgpr data/test_dev93 exp/sgmm3a/decode_dev93_tgpr"
echo "main options (for others, see top of script file)"
echo " --transform-dir <decoding-dir> # directory of previous decoding"
echo " # where we can find transforms for SAT systems."
echo " --alignment-model <ali-mdl> # Model for the first-pass decoding."
echo " --config <config-file> # config containing options"
echo " --nj <nj> # number of parallel jobs"
echo " --cmd <cmd> # Command to run in parallel with"
echo " --beam <beam> # Decoding beam; default 13.0"
exit 1;
fi
graphdir=$1
data=$2
dir=$3
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
for f in $graphdir/HCLG.fst $data/feats.scp $srcdir/final.mdl; do
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
done
sdata=$data/split$nj;
silphonelist=`cat $graphdir/phones/silence.csl` || exit 1
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.JOB.gz|"
gselect_opt_1stpass="$gselect_opt copy-gselect --n=$first_pass_gselect ark:- ark:- |"
mkdir -p $dir/log
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
echo $nj > $dir/num_jobs
## Set up features.
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
echo "$0: feature type is $feat_type"
case $feat_type in
delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
lda) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
;;
*) echo "$0: invalid feature type $feat_type" && exit 1;
esac
if [ ! -z "$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:$sdata/JOB/utt2spk ark,s,cs:$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
##
## Calculate FMLLR pre-transforms if needed. We are doing this here since this
## step is requried by models both with and without speaker vectors
if $use_fmllr; then
if [ ! -f $srcdir/final.fmllr_mdl ] || [ $srcdir/final.fmllr_mdl -ot $srcdir/final.mdl ]; then
echo "$0: computing pre-transform for fMLLR computation."
sgmm-comp-prexform $srcdir/final.mdl $srcdir/final.occs $srcdir/final.fmllr_mdl || exit 1;
fi
fi
## Save Gaussian-selection info to disk.
# Note: we can use final.mdl regardless of whether there is an alignment model--
# they use the same UBM.
if [ $stage -le 1 ]; then
$cmd JOB=1:$nj $dir/log/gselect.JOB.log \
sgmm-gselect --full-gmm-nbest=$gselect $srcdir/final.mdl \
"$feats" "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
fi
## Work out name of alignment model. ##
if [ -z "$alignment_model" ]; then
if [ -f "$srcdir/final.alimdl" ]; then alignment_model=$srcdir/final.alimdl;
else alignment_model=$srcdir/final.mdl; fi
fi
[ ! -f "$alignment_model" ] && echo "$0: no alignment model $alignment_model " && exit 1;
# Generate state-level lattice which we can rescore. This is done with the
# alignment model and no speaker-vectors.
if [ $stage -le 2 ]; then
$cmd JOB=1:$nj $dir/log/decode_pass1.JOB.log \
sgmm-latgen-faster --max-active=$max_active --beam=$beam --lattice-beam=$lat_beam \
--acoustic-scale=$acwt --determinize-lattice=false --allow-partial=true \
--word-symbol-table=$graphdir/words.txt "$gselect_opt_1stpass" $alignment_model \
$graphdir/HCLG.fst "$feats" "ark:|gzip -c > $dir/pre_lat.JOB.gz" || exit 1;
fi
## Check if the model has speaker vectors
spkdim=`sgmm-info $srcdir/final.mdl | grep 'speaker vector' | awk '{print $NF}'`
if [ $spkdim -gt 0 ]; then ### For models with speaker vectors:
# Estimate speaker vectors (1st pass). Prune before determinizing
# because determinization can take a while on un-pruned lattices.
# Note: the sgmm-post-to-gpost stage is necessary because we have
# a separate alignment-model and final model, otherwise we'd skip it
# and use sgmm-est-spkvecs.
if [ $stage -le 3 ]; then
$cmd JOB=1:$nj $dir/log/vecs_pass1.JOB.log \
gunzip -c $dir/pre_lat.JOB.gz \| \
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
weight-silence-post 0.0 $silphonelist $alignment_model ark:- ark:- \| \
sgmm-post-to-gpost "$gselect_opt" $alignment_model "$feats" ark:- ark:- \| \
sgmm-est-spkvecs-gpost --spk2utt=ark:$sdata/JOB/spk2utt \
$srcdir/final.mdl "$feats" ark,s,cs:- "ark:$dir/pre_vecs.JOB" || exit 1;
fi
# Estimate speaker vectors (2nd pass). Since we already have spk vectors,
# at this point we need to rescore the lattice to get the correct posteriors.
if [ $stage -le 4 ]; then
$cmd JOB=1:$nj $dir/log/vecs_pass2.JOB.log \
gunzip -c $dir/pre_lat.JOB.gz \| \
sgmm-rescore-lattice --spk-vecs=ark:$dir/pre_vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk \
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
sgmm-est-spkvecs --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" --spk-vecs=ark:$dir/pre_vecs.JOB \
$srcdir/final.mdl "$feats" ark,s,cs:- "ark:$dir/vecs.JOB" || exit 1;
fi
rm $dir/pre_vecs.*
if $use_fmllr; then
# Estimate fMLLR transforms (note: these may be on top of any
# fMLLR transforms estimated with the baseline GMM system.
if [ $stage -le 5 ]; then # compute fMLLR transforms.
echo "$0: computing fMLLR transforms."
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
gunzip -c $dir/pre_lat.JOB.gz \| \
sgmm-rescore-lattice --spk-vecs=ark:$dir/vecs.JOB --utt2spk=ark:$sdata/JOB/utt2spk \
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
sgmm-est-fmllr --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" --spk-vecs=ark:$dir/vecs.JOB \
--fmllr-iters=$fmllr_iters --fmllr-min-count=$fmllr_min_count \
$srcdir/final.fmllr_mdl "$feats" ark,s,cs:- "ark:$dir/trans.JOB" || exit 1;
fi
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$dir/trans.JOB ark:- ark:- |"
fi
# Now rescore the state-level lattices with the adapted features and the
# corresponding model. Prune and determinize the lattices to limit
# their size.
if [ $stage -le 6 ]; then
$cmd JOB=1:$nj $dir/log/rescore.JOB.log \
sgmm-rescore-lattice "$gselect_opt" --utt2spk=ark:$sdata/JOB/utt2spk --spk-vecs=ark:$dir/vecs.JOB \
$srcdir/final.mdl "ark:gunzip -c $dir/pre_lat.JOB.gz|" "$feats" ark:- \| \
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$lat_beam ark:- \
"ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
fi
rm $dir/pre_lat.*.gz
else ### For models without speaker vectors:
if $use_fmllr; then
# Estimate fMLLR transforms (note: these may be on top of any
# fMLLR transforms estimated with the baseline GMM system.
if [ $stage -le 5 ]; then # compute fMLLR transforms.
echo "$0: computing fMLLR transforms."
$cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
gunzip -c $dir/pre_lat.JOB.gz \| \
sgmm-rescore-lattice --utt2spk=ark:$sdata/JOB/utt2spk \
"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
sgmm-est-fmllr --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" \
--fmllr-iters=$fmllr_iters --fmllr-min-count=$fmllr_min_count \
$srcdir/final.fmllr_mdl "$feats" ark,s,cs:- "ark:$dir/trans.JOB" || exit 1;
fi
feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$dir/trans.JOB ark:- ark:- |"
fi
# Now rescore the state-level lattices with the adapted features and the
# corresponding model. Prune and determinize the lattices to limit
# their size.
if [ $stage -le 6 ] && $use_fmllr; then
$cmd JOB=1:$nj $dir/log/rescore.JOB.log \
sgmm-rescore-lattice "$gselect_opt" --utt2spk=ark:$sdata/JOB/utt2spk \
$srcdir/final.mdl "ark:gunzip -c $dir/pre_lat.JOB.gz|" "$feats" ark:- \| \
lattice-determinize-pruned --acoustic-scale=$acwt --beam=$lat_beam ark:- \
"ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
rm $dir/pre_lat.*.gz
else # Already done with decoding if no adaptation needed.
for n in `seq 1 $nj`; do
mv $dir/pre_lat.${n}.gz $dir/lat.${n}.gz
done
fi
fi
# The output of this script is the files "lat.*.gz"-- we'll rescore this at
# different acoustic scales to get the final output.
if [ $stage -le 7 ]; then
[ ! -x local/score.sh ] && \
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
echo "score best paths"
local/score.sh --cmd "$cmd" $data $graphdir $dir
echo "score confidence and timing with sclite"
#local/score_sclite_conf.sh --cmd "$cmd" --language turkish $data $graphdir $dir
fi
echo "Decoding done."
exit 0;
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