#!/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."