#!/bin/bash
  
  # Copyright 2012-2015  Johns Hopkins University (Author: Daniel Povey).
  # Apache 2.0.
  
  
  # This is like decode.sh except it uses "looped" decoding.  This is an nnet3
  # mechanism for reusing previously computed activations when we evaluate the
  # neural net for successive chunks of data.  It is applicable to TDNNs and LSTMs
  # and similar forward-recurrent topologies, but not to backward-recurrent
  # topologies like BLSTMs.  Be careful because the script itself does not have a
  # way to figure out what kind of topology you are using.
  #
  # Also be aware that this decoding mechanism means that you have effectively
  # unlimited context within the utterance.  Unless your models were trained (at
  # least partly) on quite large chunk-sizes, e.g. 100 or more (although the
  # longer the BLSTM recurrence the larger chunk-size you'd need in training),
  # there is a possibility that this effectively infinite left-context will cause
  # a mismatch with the training condition.  Also, for recurrent topologies, you may want to make sure
  # that the --extra-left-context-initial matches the --egs.chunk-left-context-initial
  # that you trained with, .  [note: if not specified during training, it defaults to
  # the same as the regular --extra-left-context
  
  # 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
  scoring_opts=
  skip_diagnostics=false
  skip_scoring=false
  extra_left_context_initial=0
  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.:   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"
    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;
  cmvn_opts=`cat $srcdir/cmvn_opts` || exit 1;
  
  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"
  
  splice_opts=`cat $srcdir/splice_opts 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:- |"
  
  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
    $cmd JOB=1:$nj $dir/log/decode.JOB.log \
      nnet3-latgen-faster-looped $ivector_opts $frame_subsampling_opt \
       --frames-per-chunk=$frames_per_chunk \
       --extra-left-context-initial=$extra_left_context_initial \
       --minimize=$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" ]
      local/score.sh $scoring_opts --cmd "$cmd" $data $graphdir $dir
      echo "score confidence and timing with sclite"
    fi
  fi
  echo "Decoding done."
  exit 0;