#!/bin/bash
  # Copyright 2012  Brno University of Technology (Author: Karel Vesely)
  #           2013  Johns Hopkins University (Author: Daniel Povey)
  #           2015  Vijayaditya Peddinti
  #           2016  Vimal Manohar
  #           2017  Pegah Ghahremani
  # Apache 2.0
  
  # Computes training alignments using nnet3 DNN, with output to lattices.
  
  # Begin configuration section.
  nj=4
  cmd=run.pl
  stage=-1
  # Begin configuration.
  scale_opts="--transition-scale=1.0 --self-loop-scale=0.1"
  acoustic_scale=0.1
  beam=20
  iter=final
  frames_per_chunk=50
  extra_left_context=0
  extra_right_context=0
  extra_left_context_initial=-1
  extra_right_context_final=-1
  online_ivector_dir=
  graphs_scp=
  generate_ali_from_lats=false # If true, alingments generated from lattices.
  # 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: $0 <data-dir> <lang-dir> <src-dir> <align-dir>"
     echo "e.g.: $0 data/train data/lang exp/nnet4 exp/nnet4_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;
  
  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
  
  for f in $srcdir/tree $srcdir/${iter}.mdl $data/feats.scp $lang/L.fst $extra_files; do
    [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
  done
  
  cp $srcdir/{tree,${iter}.mdl} $dir || exit 1;
  
  utils/lang/check_phones_compatible.sh $lang/phones.txt $srcdir/phones.txt || exit 1;
  cp $lang/phones.txt $dir || exit 1;
  ## Set up features.  Note: these are different from the normal features
  ## because we have one rspecifier that has the features for the entire
  ## training set, not separate ones for each batch.
  echo "$0: feature type is raw"
  
  cmvn_opts=`cat $srcdir/cmvn_opts 2>/dev/null`
  cp $srcdir/cmvn_opts $dir 2>/dev/null
  
  feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- |"
  
  ivector_opts=
  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
  
  echo "$0: aligning data in $data using model from $srcdir, putting alignments in $dir"
  
  frame_subsampling_opt=
  if [ -f $srcdir/frame_subsampling_factor ]; then
    # e.g. for 'chain' systems
    frame_subsampling_factor=$(cat $srcdir/frame_subsampling_factor)
    frame_subsampling_opt="--frame-subsampling-factor=$frame_subsampling_factor"
    cp $srcdir/frame_subsampling_factor $dir
    if [[ $frame_subsampling_factor -gt 1 ]]; then
      # Assume a chain system, check agrument sanity.
      if [[ ! ($scale_opts == *--self-loop-scale=1.0* &&
               $scale_opts == *--transition-scale=1.0* &&
               $acoustic_scale = '1.0') ]]; then
        echo "$0: ERROR: frame-subsampling-factor is not 1, assuming a chain system."
        echo "... You should pass the following options to this script:"
        echo "  --scale-opts '--transition-scale=1.0 --self-loop-scale=1.0'" \
             "--acoustic_scale 1.0"
      fi
    fi
  fi
  
  if [ ! -z "$graphs_scp" ]; then
    if [ ! -f $graphs_scp ]; then
      echo "Could not find graphs $graphs_scp" && exit 1
    fi
    tra="scp:utils/filter_scp.pl $sdata/JOB/utt2spk $graphs_scp |"
    prog=compile-train-graphs-fsts
  else
    tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";
    prog=compile-train-graphs
  fi
  
  if [ $stage -le 0 ]; then
    ## because nnet3-latgen-faster doesn't support adding the transition-probs to the
    ## graph itself, we need to bake them into the compiled graphs.  This means we can't reuse previously compiled graphs,
    ## because the other scripts write them without transition probs.
    $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \
      $prog --read-disambig-syms=$lang/phones/disambig.int \
      $scale_opts \
      $dir/tree $srcdir/${iter}.mdl  $lang/L.fst "$tra" \
      "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1
  fi
  
  if [ $stage -le 1 ]; then
    # Warning: nnet3-latgen-faster doesn't support a retry-beam so you may get more
    # alignment errors (however, it does have a default min-active=200 so this
    # will tend to reduce alignment errors).
    # --allow_partial=false makes sure we reach the end of the decoding graph.
    # --word-determinize=false makes sure we retain the alternative pronunciations of
    #   words (including alternatives regarding optional silences).
    #  --lattice-beam=$beam keeps all the alternatives that were within the beam,
    #    it means we do no pruning of the lattice (lattices from a training transcription
    #    will be small anyway).
    $cmd JOB=1:$nj $dir/log/generate_lattices.JOB.log \
      nnet3-latgen-faster --acoustic-scale=$acoustic_scale $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 \
      --beam=$beam --lattice-beam=$beam \
      --allow-partial=false --word-determinize=false \
      $srcdir/${iter}.mdl "ark:gunzip -c $dir/fsts.JOB.gz |" \
      "$feats" "ark:|gzip -c >$dir/lat.JOB.gz" || exit 1;
  fi
  
  if [ $stage -le 2 ] && $generate_ali_from_lats; then
    # If generate_alignments is true, ali.*.gz is generated in lats dir
    $cmd JOB=1:$nj $dir/log/generate_alignments.JOB.log \
      lattice-best-path --acoustic-scale=$acoustic_scale "ark:gunzip -c $dir/lat.JOB.gz |" \
      ark:/dev/null "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
  fi
  echo "$0: done generating lattices from training transcripts."