#!/bin/bash
  
  # Copyright 2012-2013  Johns Hopkins University (Author: Daniel Povey).
  #           2014       Vijayaditya Peddinti
  # 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
  min_active=200
  lattice_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=
  online_ivector_dir=
  minimize=false
  # 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: $0 [options] <graph-dir> <data-dir> <decode-dir>"
    echo " e.g.: $0 --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. '--num-threads 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
  
  
  [ ! -z "$online_ivector_dir" ] && \
    extra_files="$online_ivector_dir/ivector_online.scp $online_ivector_dir/ivector_period"
  
  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}utt;
  cmvn_opts=`cat $srcdir/cmvn_opts 2>/dev/null`
  thread_string=
  [ $num_threads -gt 1 ] && thread_string="-parallel --num-threads=$num_threads"
  
  mkdir -p $dir/log
  split_data.sh --per-utt $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=raw; fi
    echo "$0: feature type is $feat_type"
  fi
  
  splice_opts=`cat $srcdir/splice_opts 2>/dev/null`
  
  case $feat_type in
    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:- |";;
    lda) feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$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"
    [ ! -s $transform_dir/num_jobs ] && \
      echo "$0: expected $transform_dir/num_jobs to contain the number of jobs." && exit 1;
    nj_orig=$(cat $transform_dir/num_jobs)
  
    if [ $feat_type == "raw" ]; then trans=raw_trans;
    else trans=trans; fi
    if [ $feat_type == "lda" ] && \
      ! cmp $transform_dir/../final.mat $srcdir/final.mat && \
      ! cmp $transform_dir/final.mat $srcdir/final.mat; then
      echo "$0: LDA transforms differ between $srcdir and $transform_dir"
      exit 1;
    fi
    if [ ! -f $transform_dir/$trans.1 ]; then
      echo "$0: expected $transform_dir/$trans.1 to exist (--transform-dir option)"
      exit 1;
    fi
    if [ $nj -ne $nj_orig ]; then
      # Copy the transforms into an archive with an index.
      for n in $(seq $nj_orig); do cat $transform_dir/$trans.$n; done | \
         copy-feats ark:- ark,scp:$dir/$trans.ark,$dir/$trans.scp || exit 1;
      feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk scp:$dir/$trans.scp ark:- ark:- |"
    else
      # number of jobs matches with alignment dir.
      feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/$trans.JOB ark:- ark:- |"
    fi
  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 [ ! -z "$online_ivector_dir" ]; then
    ivector_period=$(cat $online_ivector_dir/ivector_period) || exit 1;
    # note: subsample-feats, with negative n, will repeat each feature -n times.
    feats="$feats paste-feats --length-tolerance=$ivector_period ark:- 'ark,s,cs:utils/filter_scp.pl $sdata/JOB/utt2spk $online_ivector_dir/ivector_online.scp | subsample-feats --n=-$ivector_period scp:- ark:- |' ark:- |"
  fi
  
  if [ $stage -le 1 ]; then
    $cmd $parallel_opts JOB=1:$nj $dir/log/decode.JOB.log \
      nnet-latgen-faster$thread_string \
       --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" "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;