decode.sh
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#!/bin/bash
# Copyright 2012-2015 Johns Hopkins University (Author: Daniel Povey).
# Apache 2.0.
# This script does decoding with a neural-net.
# Begin configuration section.
stage=1
nj=4 # number of decoding jobs.
acwt=0.1 # Just a default value, used for adaptation and beam-pruning..
post_decode_acwt=1.0 # can be used in 'chain' systems to scale acoustics by 10 so the
# regular scoring script works.
cmd=run.pl
beam=15.0
frames_per_chunk=50
max_active=7000
min_active=200
ivector_scale=1.0
lattice_beam=8.0 # Beam we use in lattice generation.
iter=final
num_threads=1 # if >1, will use gmm-latgen-faster-parallel
use_gpu=false # If true, will use a GPU, with nnet3-latgen-faster-batch.
# In that case it is recommended to set num-threads to a large
# number, e.g. 20 if you have that many free CPU slots on a GPU
# node, and to use a small number of jobs.
scoring_opts=
skip_diagnostics=false
skip_scoring=false
extra_left_context=0
extra_right_context=0
extra_left_context_initial=-1
extra_right_context_final=-1
online_ivector_dir=
minimize=false
# End configuration section.
echo "$0 $@" # Print the command line for logging
[ -f ./path.sh ] && . ./path.sh; # source the path.
. utils/parse_options.sh || exit 1;
if [ $# -ne 3 ]; then
echo "Usage: $0 [options] <graph-dir> <data-dir> <decode-dir>"
echo "e.g.: steps/nnet3/decode.sh --nj 8 \\"
echo "--online-ivector-dir exp/nnet2_online/ivectors_test_eval92 \\"
echo " exp/tri4b/graph_bg data/test_eval92_hires $dir/decode_bg_eval92"
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 " --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 " --use-gpu <true|false> # default: false. If true, we recommend"
echo " # to use large --num-threads as the graph"
echo " # search becomes the limiting factor."
exit 1;
fi
graphdir=$1
data=$2
dir=$3
srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
model=$srcdir/$iter.mdl
extra_files=
if [ ! -z "$online_ivector_dir" ]; then
steps/nnet2/check_ivectors_compatible.sh $srcdir $online_ivector_dir || exit 1
extra_files="$online_ivector_dir/ivector_online.scp $online_ivector_dir/ivector_period"
fi
utils/lang/check_phones_compatible.sh {$srcdir,$graphdir}/phones.txt || exit 1
for f in $graphdir/HCLG.fst $data/feats.scp $model $extra_files; do
[ ! -f $f ] && echo "$0: no such file $f" && exit 1;
done
sdata=$data/split$nj;
cmvn_opts=`cat $srcdir/cmvn_opts` || exit 1;
thread_string=
if $use_gpu; then
if [ $num_threads -eq 1 ]; then
echo "$0: **Warning: we recommend to use --num-threads > 1 for GPU-based decoding."
fi
thread_string="-batch --num-threads=$num_threads"
queue_opt="--num-threads $num_threads --gpu 1"
elif [ $num_threads -gt 1 ]; then
thread_string="-parallel --num-threads=$num_threads"
queue_opt="--num-threads $num_threads"
fi
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.
echo "$0: feature type is raw"
feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- |"
if [ ! -z "$online_ivector_dir" ]; then
ivector_period=$(cat $online_ivector_dir/ivector_period) || exit 1;
ivector_opts="--online-ivectors=scp:$online_ivector_dir/ivector_online.scp --online-ivector-period=$ivector_period"
fi
if [ "$post_decode_acwt" == 1.0 ]; then
lat_wspecifier="ark:|gzip -c >$dir/lat.JOB.gz"
else
lat_wspecifier="ark:|lattice-scale --acoustic-scale=$post_decode_acwt ark:- ark:- | gzip -c >$dir/lat.JOB.gz"
fi
frame_subsampling_opt=
if [ -f $srcdir/frame_subsampling_factor ]; then
# e.g. for 'chain' systems
frame_subsampling_opt="--frame-subsampling-factor=$(cat $srcdir/frame_subsampling_factor)"
fi
if [ $stage -le 1 ]; then
$cmd $queue_opt JOB=1:$nj $dir/log/decode.JOB.log \
nnet3-latgen-faster$thread_string $ivector_opts $frame_subsampling_opt \
--frames-per-chunk=$frames_per_chunk \
--extra-left-context=$extra_left_context \
--extra-right-context=$extra_right_context \
--extra-left-context-initial=$extra_left_context_initial \
--extra-right-context-final=$extra_right_context_final \
--minimize=$minimize --max-active=$max_active --min-active=$min_active --beam=$beam \
--lattice-beam=$lattice_beam --acoustic-scale=$acwt --allow-partial=true \
--word-symbol-table=$graphdir/words.txt "$model" \
$graphdir/HCLG.fst "$feats" "$lat_wspecifier" || exit 1;
fi
if [ $stage -le 2 ]; then
if ! $skip_diagnostics ; then
[ ! -z $iter ] && iter_opt="--iter $iter"
steps/diagnostic/analyze_lats.sh --cmd "$cmd" $iter_opt $graphdir $dir
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 3 ]; then
if ! $skip_scoring ; then
[ ! -x local/score.sh ] && \
echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
echo "score best paths"
[ "$iter" != "final" ] && iter_opt="--iter $iter"
local/score.sh $scoring_opts --cmd "$cmd" $data $graphdir $dir
echo "score confidence and timing with sclite"
fi
fi
echo "Decoding done."
exit 0;