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#!/bin/bash
# Copyright 2012 Brno University of Technology (Author: Karel Vesely)
# Apache 2.0
# Computes training alignments using MLP model
# 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
# Begin configuration.
scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
beam=10
retry_beam=40
use_gpu_id=-1 # disable gpu
# 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 [ $# != 4 ]; then
echo "usage: steps/align_si.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
echo "e.g.: steps/align_si.sh 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 " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
exit 1;
fi
data=$1
lang=$2
srcdir=$3
dir=$4
oov=`cat $lang/oov.int` || exit 1;
mkdir -p $dir/log
echo $nj > $dir/num_jobs
sdata=$data/split$nj
[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
cp $srcdir/{tree,final.mdl} $dir || exit 1;
#Get the files we will need
nnet=$srcdir/final.nnet;
[ -z "$nnet" ] && echo "Error nnet '$nnet' does not exist!" && exit 1;
class_frame_counts=$srcdir/ali_train_pdf.counts
[ -z "$class_frame_counts" ] && echo "Error class_frame_counts '$class_frame_counts' does not exist!" && exit 1;
feature_transform=$srcdir/final.feature_transform
if [ ! -f $feature_transform ]; then
echo "Missing feature_transform '$feature_transform'"
exit 1
fi
model=$dir/final.mdl
[ -z "$model" ] && echo "Error transition model '$model' does not exist!" && exit 1;
###
### Prepare feature pipeline (same as for decoding)
###
# Create the feature stream:
feats="ark,s,cs:copy-feats scp:$sdata/JOB/feats.scp ark:- |"
# Optionally add cmvn
if [ -f $srcdir/norm_vars ]; then
norm_vars=$(cat $srcdir/norm_vars 2>/dev/null)
[ ! -f $sdata/1/cmvn.scp ] && echo "$0: cannot find cmvn stats $sdata/1/cmvn.scp" && exit 1
feats="$feats apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp ark:- ark:- |"
fi
# Optionally add deltas
if [ -f $srcdir/delta_order ]; then
delta_order=$(cat $srcdir/delta_order)
feats="$feats add-deltas --delta-order=$delta_order ark:- ark:- |"
fi
# Finally add feature_transform and the MLP
feats="$feats nnet-forward --feature-transform=$feature_transform --no-softmax=true --class-frame-counts=$class_frame_counts --use-gpu-id=$use_gpu_id $nnet ark:- ark:- |"
###
###
###
echo "$0: aligning data in $data using model from $srcdir, putting alignments in $dir"
tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";
# We could just use gmm-align-mapped 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:- \| \
align-compiled-mapped $scale_opts --beam=$beam --retry-beam=$retry_beam $dir/final.mdl ark:- \
"$feats" "ark,t:|gzip -c >$dir/ali.JOB.gz" || exit 1;
echo "$0: done aligning data."
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