#!/bin/bash
  # Copyright 2012-2015 Brno University of Technology (author: Karel Vesely)
  # Apache 2.0
  
  # Aligns 'data' to sequences of transition-ids using Neural Network based acoustic model.
  # Optionally produces alignment in lattice format, this is handy to get word alignment.
  
  # Begin configuration section.
  nj=4
  cmd=run.pl
  stage=0
  # Begin configuration.
  scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
  beam=10
  retry_beam=40
  nnet_forward_opts="--no-softmax=true --prior-scale=1.0"
  ivector=            # rx-specifier with i-vectors (ark-with-vectors),
  text= # (optional) transcipts we align to,
  
  align_to_lats=false # optionally produce alignment in lattice format
   lats_decode_opts="--acoustic-scale=0.1 --beam=20 --lattice_beam=10"
   lats_graph_scales="--transition-scale=1.0 --self-loop-scale=0.1"
  
  use_gpu="no" # yes|no|optionaly
  # End configuration options.
  
  [ $# -gt 0 ] && echo "$0 $@"  # Print the command line for logging
  
  [ -f path.sh ] && . ./path.sh # source the path.
  . parse_options.sh || exit 1;
  
  set -euo pipefail
  
  if [ $# != 4 ]; then
     echo "usage: $0 <data-dir> <lang-dir> <src-dir> <align-dir>"
     echo "e.g.:  $0 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
  
  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;
  
  utils/lang/check_phones_compatible.sh $lang/phones.txt $srcdir/phones.txt
  cp $lang/phones.txt $dir
  
  cp $srcdir/{tree,final.mdl} $dir || exit 1;
  
  # Select default locations to model files
  nnet=$srcdir/final.nnet;
  class_frame_counts=$srcdir/ali_train_pdf.counts
  feature_transform=$srcdir/final.feature_transform
  model=$dir/final.mdl
  
  # Check that files exist
  for f in $sdata/1/feats.scp $lang/L.fst $nnet $model $feature_transform $class_frame_counts; do
    [ ! -f $f ] && echo "$0: missing file $f" && exit 1;
  done
  [ -z "$text" -a ! -f $sdata/1/text ] && echo "$0: missing file $f" && exit 1
  
  
  # PREPARE FEATURE EXTRACTION PIPELINE
  # import config,
  online_cmvn_opts=
  cmvn_opts=
  delta_opts=
  D=$srcdir
  [ -e $D/online_cmvn_opts ] && online_cmvn_opts=$(cat $D/online_cmvn_opts)
  [ -e $D/cmvn_opts ] && cmvn_opts=$(cat $D/cmvn_opts)
  [ -e $D/delta_opts ] && delta_opts=$(cat $D/delta_opts)
  #
  # Create the feature stream,
  feats="ark,s,cs:copy-feats scp:$sdata/JOB/feats.scp ark:- |"
  # apply-cmvn-online (optional),
  [ -n "$online_cmvn_opts" -a ! -f $nndir/global_cmvn_stats.mat ] && echo "$0: Missing $nndir/global_cmvn_stats.mat" && exit 1
  [ -n "$online_cmvn_opts" ] && feats="$feats apply-cmvn-online $online_cmvn_opts --spk2utt=ark:$srcdata/spk2utt $nndir/global_cmvn_stats.mat ark:- ark:- |"
  # apply-cmvn (optional),
  [ -n "$cmvn_opts" -a ! -f $sdata/1/cmvn.scp ] && echo "$0: Missing $sdata/1/cmvn.scp" && exit 1
  [ -n "$cmvn_opts" ] && feats="$feats apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp ark:- ark:- |"
  # add-deltas (optional),
  [ -n "$delta_opts" ] && feats="$feats add-deltas $delta_opts ark:- ark:- |"
  
  # add-ivector (optional),
  if [ -e $D/ivector_dim ]; then
    [ -z $ivector ] && echo "Missing --ivector, they were used in training!" && exit 1
    # Get the tool,
    ivector_append_tool=append-vector-to-feats # default,
    [ -e $D/ivector_append_tool ] && ivector_append_tool=$(cat $D/ivector_append_tool)
    # Check dims,
    feats_job_1=$(sed 's:JOB:1:g' <(echo $feats))
    dim_raw=$(feat-to-dim "$feats_job_1" -)
    dim_raw_and_ivec=$(feat-to-dim "$feats_job_1 $ivector_append_tool ark:- '$ivector' ark:- |" -)
    dim_ivec=$((dim_raw_and_ivec - dim_raw))
    [ $dim_ivec != "$(cat $D/ivector_dim)" ] && \
      echo "Error, i-vector dim. mismatch (expected $(cat $D/ivector_dim), got $dim_ivec in '$ivector')" && \
      exit 1
    # Append to feats,
    feats="$feats $ivector_append_tool ark:- '$ivector' ark:- |"
  fi
  
  # nnet-forward,
  feats="$feats nnet-forward $nnet_forward_opts --feature-transform=$feature_transform --class-frame-counts=$class_frame_counts --use-gpu=$use_gpu $nnet ark:- ark:- |"
  #
  
  echo "$0: aligning data '$data' using nnet/model '$srcdir', putting alignments in '$dir'"
  
  # Map oovs in reference transcription,
  oov=`cat $lang/oov.int` || exit 1;
  [ -z "$text" ] && text=$sdata/JOB/text
  tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $text |";
  # We could just use align-mapped in the next line, but it's less efficient as it compiles the
  # training graphs one by one.
  if [ $stage -le 0 ]; then
    $cmd JOB=1:$nj $dir/log/align.JOB.log \
      compile-train-graphs --read-disambig-syms=$lang/phones/disambig.int $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;
  fi
  
  # Optionally align to lattice format (handy to get word alignment)
  if [ "$align_to_lats" == "true" ]; then
    echo "$0: aligning also to lattices '$dir/lat.*.gz'"
    $cmd JOB=1:$nj $dir/log/align_lat.JOB.log \
      compile-train-graphs --read-disambig-syms=$lang/phones/disambig.int $lats_graph_scales $dir/tree $dir/final.mdl  $lang/L.fst "$tra" ark:- \| \
      latgen-faster-mapped $lats_decode_opts --word-symbol-table=$lang/words.txt $dir/final.mdl ark:- \
        "$feats" "ark:|gzip -c >$dir/lat.JOB.gz" || exit 1;
  fi
  
  echo "$0: done aligning data."