#!/bin/bash
  # Copyright 2012  Johns Hopkins University (Author: Daniel Povey)
  # Apache 2.0
  
  # Computes training alignments and (if needed) speaker-vectors, given an 
  # SGMM system.  If the system is built on top of SAT, you should supply
  # transforms with the --transform-dir option.
  
  # If you supply the --use-graphs option, it will use the training
  # graphs from the source directory.
  
  # Begin configuration section.  
  stage=0
  nj=4
  cmd=run.pl
  use_graphs=false # use graphs from srcdir
  use_gselect=false # use gselect info from srcdir [regardless, we use
     # Gaussian-selection info, we might have to compute it though.]
  gselect=15  # Number of Gaussian-selection indices for SGMMs.
  # Begin configuration.
  scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
  beam=10
  retry_beam=40
  transform_dir=  # directory to find fMLLR transforms in.
  # 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: steps/align_sgmm.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
     echo "e.g.:  steps/align_sgmm.sh --transform-dir exp/tri3b data/train data/lang \\"
     echo "           exp/sgmm4a exp/sgmm5a_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 "  --use-graphs true                                # use graphs in src-dir"
     echo "  --transform-dir <transform-dir>                  # directory to find fMLLR transforms"
     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;
  silphonelist=`cat $lang/phones/silence.csl` || exit 1;
  splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
  sdata=$data/split$nj
  
  mkdir -p $dir/log
  cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
  echo $nj > $dir/num_jobs
  [[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
  
  cp $srcdir/{tree,final.mdl} $dir || exit 1;
  [ -f $srcdir/final.alimdl ] && cp $srcdir/final.alimdl $dir
  cp $srcdir/final.occs $dir;
  
  ## Set up features.
  if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
  echo "$0: feature type is $feat_type"
  
  case $feat_type in
    delta) feats="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
    lda) feats="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:- |"
      cp $srcdir/final.mat $dir    
     ;;
    *) echo "Invalid feature type $feat_type" && exit 1;
  esac
  if [ ! -z "$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:$sdata/JOB/utt2spk ark,s,cs:$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 during alignment."
  fi
  ##
  
  ## Set up model and alignment model.
  mdl=$srcdir/final.mdl
  if [ -f $srcdir/final.alimdl ]; then
    alimdl=$srcdir/final.alimdl
  else
    alimdl=$srcdir/final.mdl
  fi
  [ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
  
  ## Work out where we're getting the graphs from.
  if $use_graphs; then
    [ "$nj" != "`cat $srcdir/num_jobs`" ] && \
      echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1;
    [ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1;
    graphdir=$srcdir
    ln.pl $srcdir/fsts.*.gz $dir
  else
    graphdir=$dir
    if [ $stage -le 0 ]; then
      echo "$0: compiling training graphs"
      tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";   
      $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log  \
        compile-train-graphs $dir/tree $dir/final.mdl  $lang/L.fst "$tra" \
          "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
    fi
  fi
  
  ## Work out where we're getting the Gaussian-selection info from
  if $use_gselect; then
    [ "$nj" != "`cat $srcdir/num_jobs`" ] && \
      echo "$0: you specified --use-gselect true, but #jobs mismatch." && exit 1;
    [ ! -f $srcdir/gselect.1.gz ] && echo "No gselect info in $srcdir" && exit 1;
    graphdir=$srcdir
    gselect_opt="--gselect=ark,s,cs:gunzip -c $srcdir/gselect.JOB.gz|"
    ln.pl $srcdir/gselect.*.gz $dir
  else
    graphdir=$dir
    if [ $stage -le 1 ]; then
      echo "$0: computing Gaussian-selection info"
      # Note: doesn't matter whether we use $alimdl or $mdl, they will
      # have the same gselect info.
      $cmd JOB=1:$nj $dir/log/gselect.JOB.log \
        sgmm-gselect --full-gmm-nbest=$gselect $alimdl \
        "$feats" "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
    fi
    gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.JOB.gz|"
  fi
  
  
  if [ $alimdl == $mdl ]; then 
    # Speaker-independent decoding-- just one pass.  Not normal.
    T=`sgmm-info $mdl | grep 'speaker vector space' | awk '{print $NF}'` || exit 1;
    [ "$T" -ne 0 ] && echo "No alignment model, yet speaker vector space nonempty" && exit 1;
  
    if [ $stage -le 2 ]; then
      echo "$0: aligning data in $data using model $mdl (no speaker-vectors)"
      $cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
        sgmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam $alimdl \
        "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
    fi
    echo "$0: done aligning data."
    exit 0;
  fi
  
  # Continue with system with speaker vectors.
  if [ $stage -le 2 ]; then
    echo "$0: aligning data in $data using model $alimdl"
    $cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
      sgmm-align-compiled $scale_opts "$gselect_opt" --beam=$beam --retry-beam=$retry_beam $alimdl \
      "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
  fi
  
  if [ $stage -le 3 ]; then
    echo "$0: computing speaker vectors (1st pass)"
    $cmd JOB=1:$nj $dir/log/spk_vecs1.JOB.log \
      ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
      weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
      sgmm-post-to-gpost "$gselect_opt" $alimdl "$feats" ark:- ark:- \| \
      sgmm-est-spkvecs-gpost --spk2utt=ark:$sdata/JOB/spk2utt \
       $mdl "$feats" ark,s,cs:- ark:$dir/pre_vecs.JOB || exit 1;
  fi
  
  if [ $stage -le 4 ]; then
    echo "$0: computing speaker vectors (2nd pass)"
    $cmd JOB=1:$nj $dir/log/spk_vecs2.JOB.log \
      ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
      weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
      sgmm-est-spkvecs --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" \
       --spk-vecs=ark:$dir/pre_vecs.JOB $mdl "$feats" ark,s,cs:- ark:$dir/vecs.JOB || exit 1;
    rm $dir/pre_vecs.*
  fi
  
  if [ $stage -le 5 ]; then
    echo "$0: doing final alignment."
    $cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \
      sgmm-align-compiled $scale_opts "$gselect_opt" --beam=$beam --retry-beam=$retry_beam \
       --utt2spk=ark:$sdata/JOB/utt2spk --spk-vecs=ark:$dir/vecs.JOB \
       $mdl "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
  fi
  
  rm $dir/pre_ali.*.gz
  
  echo "$0: done aligning data."
  
  utils/summarize_warnings.pl $dir/log
  
  exit 0;