#!/bin/bash
  
  # Copyright 2012   Carnegie Mellon University (Author: Yajie Miao)
  #                  Johns Hopkins University (Author: Daniel Povey)
  
  # Decoding script that does basis fMLLR.  This can be on top of delta+delta-delta,
  # or LDA+MLLT features.
  
  # There are 3 models involved potentially in this script,
  # and for a standard, speaker-independent system they will all be the same.
  # The "alignment model" is for the 1st-pass decoding and to get the 
  # Gaussian-level alignments for the "adaptation model" the first time we
  # do fMLLR.  The "adaptation model" is used to estimate fMLLR transforms
  # and to generate state-level lattices.  The lattices are then rescored
  # with the "final model".
  #
  # The following table explains where we get these 3 models from.
  # Note: $srcdir is one level up from the decoding directory.
  #
  #   Model              Default source:                 
  #
  #  "alignment model"   $srcdir/final.alimdl              --alignment-model <model>
  #                     (or $srcdir/final.mdl if alimdl absent)
  #  "adaptation model"  $srcdir/final.mdl                 --adapt-model <model>
  #  "final model"       $srcdir/final.mdl                 --final-model <model>
  
  
  # Begin configuration section
  first_beam=10.0 # Beam used in initial, speaker-indep. pass
  first_max_active=2000 # max-active used in initial pass.
  alignment_model=
  adapt_model=
  final_model=
  stage=0
  acwt=0.083333 # Acoustic weight used in getting fMLLR transforms, and also in 
                # lattice generation.
  
  # Parameters in alignment of training data
  scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
  align_beam=10
  retry_beam=40
  
  max_active=7000
  beam=13.0
  lattice_beam=6.0
  nj=4
  silence_weight=0.01
  cmd=run.pl
  si_dir=
  # 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 [ $# != 3 ]; then
     echo "Usage: steps/decode_basis_fmllr.sh [options] <graph-dir> <data-dir> <decode-dir>"
     echo " e.g.: steps/decode_basis_fmllr.sh exp/tri2b/graph_tgpr data/train_si84 data/test_dev93 exp/tri2b/decode_dev93_tgpr"
     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 "  --adapt-model <adapt-mdl>                # Model to compute transforms with"
     echo "  --alignment-model <ali-mdl>              # Model to get Gaussian-level alignments for"
     echo "                                           # 1st pass of transform computation."
     echo "  --final-model <finald-mdl>               # Model to finally decode with"
     echo "  --si-dir <speaker-indep-decoding-dir>    # use this to skip 1st pass of decoding"
     echo "                                           # Caution-- must be with same tree"
     echo "  --acwt <acoustic-weight>                 # default 0.08333 ... used to get posteriors"
  
     exit 1;
  fi
  
  
  graphdir=$1
  data=$2
  dir=`echo $3 | sed 's:/$::g'` # remove any trailing slash.
  
  srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
  sdata=$data/split$nj;
  
  mkdir -p $dir/log
  [[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
  echo $nj > $dir/num_jobs
  splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
  
  silphonelist=`cat $graphdir/phones/silence.csl` || exit 1;
  
  # Some checks.  Note: we don't need $srcdir/tree but we expect
  # it should exist, given the current structure of the scripts.
  for f in $graphdir/HCLG.fst $data/feats.scp $srcdir/tree $srcdir/fmllr.basis; do
    [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
  done
  
  ## Work out name of alignment model. ##
  if [ -z "$alignment_model" ]; then
    if [ -f "$srcdir/final.alimdl" ]; then alignment_model=$srcdir/final.alimdl;
    else alignment_model=$srcdir/final.mdl; fi
  fi
  [ ! -f "$alignment_model" ] && echo "$0: no alignment model $alignment_model " && exit 1;
  ##
  
  ## Do the speaker-independent decoding, if --si-dir option not present. ##
  if [ -z "$si_dir" ]; then # we need to do the speaker-independent decoding pass.
    si_dir=${dir}.si # Name it as our decoding dir, but with suffix ".si".
    if [ $stage -le 0 ]; then
      steps/decode.sh --acwt $acwt --nj $nj --cmd "$cmd" --beam $first_beam --model $alignment_model --max-active $first_max_active $graphdir $data $si_dir || exit 1;
    fi
  fi
  ##
  
  ## Some checks, and setting of defaults for variables.
  [ "$nj" -ne "`cat $si_dir/num_jobs`" ] && echo "Mismatch in #jobs with si-dir" && exit 1;
  [ ! -f "$si_dir/lat.1.gz" ] && echo "No such file $si_dir/lat.1.gz" && exit 1;
  [ -z "$adapt_model" ] && adapt_model=$srcdir/final.mdl
  [ -z "$final_model" ] && final_model=$srcdir/final.mdl
  for f in $adapt_model $final_model; do
    [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
  done
  ##
  
  ## Set up the unadapted features "$sifeats" for testing set
  if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
  echo "$0: feature type is $feat_type";
  case $feat_type in
    delta) sifeats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
    lda) sifeats="ark,s,cs:apply-cmvn --norm-vars=false --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 "Invalid feature type $feat_type" && exit 1;
  esac
  ##
  
  ## Now get the first-pass fMLLR transforms.
  ## We give all the default parameters in gmm-est-basis-fmllr
  if [ $stage -le 1 ]; then
    echo "$0: getting first-pass fMLLR transforms."
    $cmd JOB=1:$nj $dir/log/fmllr_pass1.JOB.log \
      gunzip -c $si_dir/lat.JOB.gz \| \
      lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
      weight-silence-post $silence_weight $silphonelist $alignment_model ark:- ark:- \| \
      gmm-post-to-gpost $alignment_model "$sifeats" ark:- ark:- \| \
      gmm-est-basis-fmllr-gpost --spk2utt=ark:$sdata/JOB/spk2utt \
      --fmllr-min-count=200  --num-iters=10 --size-scale=0.2 \
      --step-size-iters=3 --write-weights=ark:$dir/pre_wgt.JOB \
       $adapt_model $srcdir/fmllr.basis "$sifeats" ark,s,cs:- \
      ark:$dir/pre_trans.JOB || exit 1;
  fi
  ##
  
  pass1feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/pre_trans.JOB ark:- ark:- |"
  
  ## Do the main lattice generation pass.  Note: we don't determinize the lattices at
  ## this stage, as we're going to use them in acoustic rescoring with the larger 
  ## model, and it's more correct to store the full state-level lattice for this purpose.
  if [ $stage -le 2 ]; then
    echo "$0: doing main lattice generation phase"
    $cmd JOB=1:$nj $dir/log/decode.JOB.log \
      gmm-latgen-faster --max-active=$max_active --beam=$beam --lattice-beam=$lattice_beam \
      --acoustic-scale=$acwt  \
      --determinize-lattice=false --allow-partial=true --word-symbol-table=$graphdir/words.txt \
      $adapt_model $graphdir/HCLG.fst "$pass1feats" "ark:|gzip -c > $dir/lat.tmp.JOB.gz" \
      || exit 1;
  fi
  ##
  
  ## Do a second pass of estimating the transform-- this time with the lattices
  ## generated from the alignment model.  Compose the transforms to get
  ## $dir/trans.1, etc.
  if [ $stage -le 3 ]; then
    echo "$0: estimating fMLLR transforms a second time."
    $cmd JOB=1:$nj $dir/log/fmllr_pass2.JOB.log \
      lattice-determinize-pruned --acoustic-scale=$acwt --beam=4.0 \
      "ark:gunzip -c $dir/lat.tmp.JOB.gz|" ark:- \| \
      lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
      weight-silence-post $silence_weight $silphonelist $adapt_model ark:- ark:- \| \
      gmm-est-basis-fmllr --fmllr-min-count=200 \
      --spk2utt=ark:$sdata/JOB/spk2utt --write-weights=ark:$dir/trans_tmp_wgt.JOB \
      $adapt_model $srcdir/fmllr.basis "$pass1feats" ark,s,cs:- ark:$dir/trans_tmp.JOB '&&' \
      compose-transforms --b-is-affine=true ark:$dir/trans_tmp.JOB ark:$dir/pre_trans.JOB \
      ark:$dir/trans.JOB  || exit 1;
  fi
  ##
  
  feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans.JOB ark:- ark:- |"
  
  # Rescore the state-level lattices with the final adapted features, and the final model
  # (which by default is $srcdir/final.mdl, but which may be specified on the command line,
  # useful in case of discriminatively trained systems).
  # At this point we prune and determinize the lattices and write them out, ready for 
  # language model rescoring.
  
  if [ $stage -le 4 ]; then
    echo "$0: doing a final pass of acoustic rescoring."
    $cmd JOB=1:$nj $dir/log/acoustic_rescore.JOB.log \
      gmm-rescore-lattice $final_model "ark:gunzip -c $dir/lat.tmp.JOB.gz|" "$feats" ark:- \| \
      lattice-determinize-pruned --acoustic-scale=$acwt --beam=$lattice_beam ark:- \
      "ark:|gzip -c > $dir/lat.JOB.gz" '&&' rm $dir/lat.tmp.JOB.gz || exit 1;
  fi
  
  [ ! -x local/score.sh ] && \
    echo "$0: not scoring because local/score.sh does not exist or not executable." && exit 1;
  local/score.sh --cmd "$cmd" $data $graphdir $dir
  
  rm $dir/{trans_tmp,pre_trans}.*
  
  exit 0;