#!/bin/bash
  
  # Copyright 2012  Johns Hopkins University (Author: Daniel Povey).  Apache 2.0.
  #                 Korbinian Riedhammer
  
  # This script does decoding with an SGMM system, with speaker vectors. 
  # If the SGMM system was
  # built on top of fMLLR transforms from a conventional system, you should
  # provide the --transform-dir option.
  
  # Begin configuration section.
  stage=1
  alignment_model=
  transform_dir=    # dir to find fMLLR transforms.
  nj=4 # number of decoding jobs.
  acwt=0.1  # Just a default value, used for adaptation and beam-pruning..
  cmd=run.pl
  beam=15.0
  gselect=15  # Number of Gaussian-selection indices for SGMMs.  [Note:
              # the first_pass_gselect variable is used for the 1st pass of
              # decoding and can be tighter.
  first_pass_gselect=3 # Use a smaller number of Gaussian-selection indices in 
              # the 1st pass of decoding (lattice generation).
  max_active=7000
  
  #WARNING: This option is renamed lat_beam (it was renamed to follow the naming 
  #         in the other scripts
  lattice_beam=8.0 # Beam we use in lattice generation.
  vecs_beam=4.0 # Beam we use to prune lattices while getting posteriors for 
      # speaker-vector computation.  Can be quite tight (actually we could
      # probably just do best-path.
  use_fmllr=false
  fmllr_iters=10
  fmllr_min_count=1000
  
  # 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 4 ]; then
    echo "Usage: steps/tandem/decode_sgmm.sh [options] <graph-dir> <data1-dir> <data2-dir> <decode-dir>"
    echo " e.g.: steps/tandem/decode_sgmm.sh --transform-dir exp/tri3b/decode_dev93_tgpr \\"
    echo "      exp/sgmm3a/graph_tgpr {mfcc,bottleneck}/data/test_dev93 exp/sgmm3a/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 "  --alignment-model <ali-mdl>              # Model for the first-pass decoding."
    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 13.0"
    exit 1;
  fi
  
  graphdir=$1
  data1=$2
  data2=$3
  dir=$4
  srcdir=`dirname $dir`; # Assume model directory one level up from decoding directory.
  
  for f in $graphdir/HCLG.fst $data1/feats.scp $data2/feats.scp $srcdir/final.mdl; do
    [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
  done
  
  silphonelist=`cat $graphdir/phones/silence.csl` || exit 1
  gselect_opt="--gselect=ark:gunzip -c $dir/gselect.JOB.gz|"
  gselect_opt_1stpass="$gselect_opt copy-gselect --n=$first_pass_gselect ark:- ark:- |"
  
  mkdir -p $dir/log
  echo $nj > $dir/num_jobs
  
  sdata1=$data1/split$nj;
  sdata2=$data2/split$nj;
  [[ -d $sdata1 && $data1/feats.scp -ot $sdata1 ]] || split_data.sh $data1 $nj || exit 1;
  [[ -d $sdata2 && $data2/feats.scp -ot $sdata2 ]] || split_data.sh $data2 $nj || exit 1;
  
  
  ## Set up features.
  
  splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
  normft2=`cat $srcdir/normft2 2>/dev/null`
  
  if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
  
  case $feat_type in
    delta) 
    	echo "$0: feature type is $feat_type"
    	;;
    lda) 
    	echo "$0: feature type is $feat_type"
      cp $srcdir/{lda,final}.mat $dir/   
      ;;
    *) echo "$0: invalid feature type $feat_type" && exit 1;
  esac
  
  # set up feature stream 1;  this are usually spectral features, so we will add
  # deltas or splice them
  feats1="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata1/JOB/utt2spk scp:$sdata1/JOB/cmvn.scp scp:$sdata1/JOB/feats.scp ark:- |"
  
  if [ "$feat_type" == "delta" ]; then
    feats1="$feats1 add-deltas ark:- ark:- |"
  elif [ "$feat_type" == "lda" ]; then
    feats1="$feats1 splice-feats $splice_opts ark:- ark:- | transform-feats $dir/lda.mat ark:- ark:- |"
  fi
  
  # set up feature stream 2;  this are usually bottleneck or posterior features, 
  # which may be normalized if desired
  feats2="scp:$sdata2/JOB/feats.scp"
  
  if [ "$normft2" == "true" ]; then
    echo "Using cmvn for feats2"
    feats2="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata2/JOB/utt2spk scp:$sdata2/JOB/cmvn.scp $feats2 ark:- |"
  fi
  
  # assemble tandem features
  feats="ark,s,cs:paste-feats '$feats1' '$feats2' ark:- |"
  
  # add transformation, if applicable
  if [ "$feat_type" == "lda" ]; then
    feats="$feats transform-feats $dir/final.mat ark:- ark:- |"
  fi
  
  # splicing/normalization options
  cp $srcdir/{splice_opts,normft2,tandem} $dir 2>/dev/null
  
  if [ ! -z "$transform_dir" ]; then
    echo "$0: using transforms from $transform_dir"
    [ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" && exit 1;
    [ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \
      && echo "$0: #jobs mismatch with transform-dir." && exit 1;
    feats="$feats transform-feats --utt2spk=ark:$sdata1/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |"
  elif grep 'transform-feats --utt2spk' $srcdir/log/acc.0.1.log 2>/dev/null; then
    echo "$0: **WARNING**: you seem to be using an SGMM system trained with transforms,"
    echo "  but you are not providing the --transform-dir option in test time."
  fi
  ##
  
  
  ## Calculate FMLLR pre-transforms if needed. We are doing this here since this
  ## step is requried by models both with and without speaker vectors
  if $use_fmllr; then
    if [ ! -f $srcdir/final.fmllr_mdl ] || [ $srcdir/final.fmllr_mdl -ot $srcdir/final.mdl ]; then
      echo "$0: computing pre-transform for fMLLR computation."
      sgmm-comp-prexform $srcdir/final.mdl $srcdir/final.occs $srcdir/final.fmllr_mdl || exit 1;
    fi
  fi
  
  ## Save Gaussian-selection info to disk.
  # Note: we can use final.mdl regardless of whether there is an alignment model--
  # they use the same UBM.
  if [ $stage -le 1 ]; then
    $cmd JOB=1:$nj $dir/log/gselect.JOB.log \
      sgmm-gselect --full-gmm-nbest=$gselect $srcdir/final.mdl \
      "$feats" "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
  fi
  
  ## 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;
  
  # Generate state-level lattice which we can rescore.  This is done with the 
  # alignment model and no speaker-vectors.
  if [ $stage -le 2 ]; then
    $cmd JOB=1:$nj $dir/log/decode_pass1.JOB.log \
      sgmm-latgen-faster --max-active=$max_active --beam=$beam --lattice-beam=$lattice_beam \
      --acoustic-scale=$acwt --determinize-lattice=false --allow-partial=true \
      --word-symbol-table=$graphdir/words.txt "$gselect_opt_1stpass" $alignment_model \
      $graphdir/HCLG.fst "$feats" "ark:|gzip -c > $dir/pre_lat.JOB.gz" || exit 1;
  fi
  
  ## Check if the model has speaker vectors
  spkdim=`sgmm-info $srcdir/final.mdl | grep 'speaker vector' | awk '{print $NF}'`
  
  if [ $spkdim -gt 0 ]; then  ### For models with speaker vectors:
  
  # Estimate speaker vectors (1st pass).  Prune before determinizing
  # because determinization can take a while on un-pruned lattices.
  # Note: the sgmm-post-to-gpost stage is necessary because we have
  # a separate alignment-model and final model, otherwise we'd skip it 
  # and use sgmm-est-spkvecs.
    if [ $stage -le 3 ]; then
      $cmd JOB=1:$nj $dir/log/vecs_pass1.JOB.log \
        gunzip -c $dir/pre_lat.JOB.gz \| \
        lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
        lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
        lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
        weight-silence-post 0.0 $silphonelist $alignment_model ark:- ark:- \| \
        sgmm-post-to-gpost "$gselect_opt" $alignment_model "$feats" ark:- ark:- \| \
        sgmm-est-spkvecs-gpost --spk2utt=ark:$sdata1/JOB/spk2utt \
        $srcdir/final.mdl "$feats" ark,s,cs:- "ark:$dir/pre_vecs.JOB" || exit 1;
    fi
  
  # Estimate speaker vectors (2nd pass).  Since we already have spk vectors,
  # at this point we need to rescore the lattice to get the correct posteriors.
    if [ $stage -le 4 ]; then
      $cmd JOB=1:$nj $dir/log/vecs_pass2.JOB.log \
        gunzip -c $dir/pre_lat.JOB.gz \| \
        sgmm-rescore-lattice --spk-vecs=ark:$dir/pre_vecs.JOB --utt2spk=ark:$sdata1/JOB/utt2spk \
        "$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
        lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
        lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
        lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
        weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
        sgmm-est-spkvecs --spk2utt=ark:$sdata1/JOB/spk2utt "$gselect_opt" --spk-vecs=ark:$dir/pre_vecs.JOB \
        $srcdir/final.mdl "$feats" ark,s,cs:- "ark:$dir/vecs.JOB" || exit 1;
    fi
    rm $dir/pre_vecs.*
  
    if $use_fmllr; then
    # Estimate fMLLR transforms (note: these may be on top of any
    # fMLLR transforms estimated with the baseline GMM system.
      if [ $stage -le 5 ]; then # compute fMLLR transforms.
        echo "$0: computing fMLLR transforms."
        $cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
  	gunzip -c $dir/pre_lat.JOB.gz \| \
  	sgmm-rescore-lattice --spk-vecs=ark:$dir/vecs.JOB --utt2spk=ark:$sdata1/JOB/utt2spk \
  	"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
  	lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
  	lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
  	lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
  	weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
  	sgmm-est-fmllr --spk2utt=ark:$sdata1/JOB/spk2utt "$gselect_opt" --spk-vecs=ark:$dir/vecs.JOB \
  	--fmllr-iters=$fmllr_iters --fmllr-min-count=$fmllr_min_count \
  	$srcdir/final.fmllr_mdl "$feats" ark,s,cs:- "ark:$dir/trans.JOB" || exit 1;
      fi
      feats="$feats transform-feats --utt2spk=ark:$sdata1/JOB/utt2spk ark,s,cs:$dir/trans.JOB ark:- ark:- |"  
    fi
  
  # Now rescore the state-level lattices with the adapted features and the
  # corresponding model.  Prune and determinize the lattices to limit
  # their size.
    if [ $stage -le 6 ]; then
      $cmd JOB=1:$nj $dir/log/rescore.JOB.log \
        sgmm-rescore-lattice "$gselect_opt" --utt2spk=ark:$sdata1/JOB/utt2spk --spk-vecs=ark:$dir/vecs.JOB \
        $srcdir/final.mdl "ark:gunzip -c $dir/pre_lat.JOB.gz|" "$feats" ark:- \| \
        lattice-determinize-pruned --acoustic-scale=$acwt --beam=$lattice_beam ark:- \
        "ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
    fi
    rm $dir/pre_lat.*.gz
  
  else  ### For models without speaker vectors:
  
    if $use_fmllr; then
    # Estimate fMLLR transforms (note: these may be on top of any
    # fMLLR transforms estimated with the baseline GMM system.
      if [ $stage -le 5 ]; then # compute fMLLR transforms.
        echo "$0: computing fMLLR transforms."
        $cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
  	gunzip -c $dir/pre_lat.JOB.gz \| \
  	sgmm-rescore-lattice --utt2spk=ark:$sdata1/JOB/utt2spk \
  	"$gselect_opt" $srcdir/final.mdl ark:- "$feats" ark:- \| \
  	lattice-prune --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
  	lattice-determinize-pruned --acoustic-scale=$acwt --beam=$vecs_beam ark:- ark:- \| \
  	lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
  	weight-silence-post 0.0 $silphonelist $srcdir/final.mdl ark:- ark:- \| \
  	sgmm-est-fmllr --spk2utt=ark:$sdata1/JOB/spk2utt "$gselect_opt" \
  	--fmllr-iters=$fmllr_iters --fmllr-min-count=$fmllr_min_count \
  	$srcdir/final.fmllr_mdl "$feats" ark,s,cs:- "ark:$dir/trans.JOB" || exit 1;
      fi
      feats="$feats transform-feats --utt2spk=ark:$sdata1/JOB/utt2spk ark,s,cs:$dir/trans.JOB ark:- ark:- |"  
    fi
  
  # Now rescore the state-level lattices with the adapted features and the
  # corresponding model.  Prune and determinize the lattices to limit
  # their size.
    if [ $stage -le 6 ] && $use_fmllr; then
      $cmd JOB=1:$nj $dir/log/rescore.JOB.log \
        sgmm-rescore-lattice "$gselect_opt" --utt2spk=ark:$sdata1/JOB/utt2spk \
        $srcdir/final.mdl "ark:gunzip -c $dir/pre_lat.JOB.gz|" "$feats" ark:- \| \
        lattice-determinize-pruned --acoustic-scale=$acwt --beam=$lattice_beam ark:- \
        "ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
      rm $dir/pre_lat.*.gz
    else  # Already done with decoding if no adaptation needed.
      for n in `seq 1 $nj`; do
        mv $dir/pre_lat.${n}.gz $dir/lat.${n}.gz
      done
    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 [ $stage -le 7 ]; then
    [ ! -x local/score.sh ] && \
      echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
    echo "score best paths"
    local/score.sh --cmd "$cmd" $data $graphdir $dir
    echo "score confidence and timing with sclite"
    #local/score_sclite_conf.sh --cmd "$cmd" --language turkish $data $graphdir $dir
  fi
  echo "Decoding done."
  exit 0;