decode_nnet_cpu.sh.svn-base
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#!/bin/bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey).
# Apache 2.0.
# This script does decoding with a neural-net. If the neural net was built on
# top of fMLLR transforms from a conventional system, you should provide the
# --transform-dir option.
# Begin configuration section.
stage=1
transform_dir= # dir to find fMLLR transforms.
nj=4 # number of decoding jobs. If --transform-dir set, must match that number!
acwt=0.1 # Just a default value, used for adaptation and beam-pruning..
cmd=run.pl
beam=15.0
max_active=7000
lat_beam=8.0 # Beam we use in lattice generation.
iter=final
num_threads=1 # if >1, will use gmm-latgen-faster-parallel
parallel_opts= # If you supply num-threads, you should supply this too.
scoring_opts=
skip_scoring=false
feat_type=
# 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_nnet_cpu.sh [options] <graph-dir> <data-dir> <decode-dir>"
echo " e.g.: steps/decode_nnet_cpu.sh --transform-dir exp/tri3b/decode_dev93_tgpr \\"
echo " exp/tri3b/graph_tgpr data/test_dev93 exp/tri4a_nnet/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 " --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 15.0"
echo " --iter <iter> # Iteration of model to decode; default is final."
echo " --scoring-opts <string> # options to local/score.sh"
echo " --num-threads <n> # number of threads to use, default 1."
echo " --parallel-opts <opts> # e.g. '-pe smp 4' if you supply --num-threads 4"
exit 1;
fi
graphdir=$1
data=$2
dir=$3
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
model=$srcdir/$iter.mdl
for f in $graphdir/HCLG.fst $data/feats.scp $model; do
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
done
sdata=$data/split$nj;
splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
thread_string=
[ $num_threads -gt 1 ] && thread_string="-parallel --num-threads=$num_threads"
mkdir -p $dir/log
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
echo $nj > $dir/num_jobs
## Set up features.
if [ -z "$feat_type" ]; then
if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
echo "$0: feature type is $feat_type"
fi
case $feat_type in
raw) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- |";;
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/train.1.log >&/dev/null; then
echo "$0: **WARNING**: you seem to be using a neural net system trained with transforms,"
echo " but you are not providing the --transform-dir option in test time."
fi
##
if [ $stage -le 1 ]; then
$cmd $parallel_opts JOB=1:$nj $dir/log/decode.JOB.log \
nnet-latgen-faster$thread_string --max-active=$max_active --beam=$beam --lattice-beam=$lat_beam \
--acoustic-scale=$acwt --allow-partial=true --word-symbol-table=$graphdir/words.txt "$model" \
$graphdir/HCLG.fst "$feats" "ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
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 2 ]; then
if ! $skip_scoring ; 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 $scoring_opts --cmd "$cmd" $data $graphdir $dir
echo "score confidence and timing with sclite"
fi
fi
echo "Decoding done."
exit 0;