#!/bin/bash
  
  # Copyright 2012  Johns Hopkins University (Author: Daniel Povey)
  #                 Korbinian Riedhammer
  # Apache 2.0.
  
  # Begin configuration.
  cmd=run.pl
  config=
  stage=-5
  scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
  realign_iters="10 20 30";
  mllt_iters="2 4 6 12";
  num_iters=35    # Number of iterations of training
  max_iter_inc=25  # Last iter to increase #Gauss on.
  beam=10
  retry_beam=40
  boost_silence=1.0 # Factor by which to boost silence likelihoods in alignment
  power=0.2 # Exponent for number of gaussians according to occurrence counts
  randprune=4.0 # This is approximately the ratio by which we will speed up the
                # LDA and MLLT calculations via randomized pruning.
  splice_opts=
  cluster_thresh=-1  # for build-tree control final bottom-up clustering of leaves
  
  dim1=30  # dimension first stream (spectral features)
  dim2=40  # dimension second stream (pasted features, usually bn/posteriors)
  
  # apply CMVN to the second feature stream
  normft2=true
  
  # do an extra LDA after pasting the features?
  extra_lda=false
  
  # End configuration.
  
  echo "$0 $@"  # Print the command line for logging
  
  [ -f path.sh ] && . ./path.sh
  . parse_options.sh || exit 1;
  
  if [ $# != 7 ]; then
    echo "Usage: steps/tandem/train_lda_mllt.sh [options] <#leaves> <#gauss> <data1> <data2> <lang> <alignments> <dir>"
    echo " e.g.: steps/tandem/train_lda_mllt.sh 2500 15000 {mfcc,bottleneck}/data/train_si84 data/lang exp/tri1_ali_si84 exp/tri2b"
    echo "Main options (for others, see top of script file)"
    echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
    echo "  --config <config-file>                           # config containing options"
    echo "  --stage <stage>                                  # stage to do partial re-run from."
    echo "  --normft2 (true|false)                           # apply CMVN to second data set (true)"
    echo "  --extra-lda (true|false)                         # apply extra LDA after feature paste (false)"
    echo "  --dim1 <n>                                       # dimension of the first feature stream by HLDA"
    echo "  --dim2 <m>                                       # dimension of of the pasted features after 2nd HLDA"
    exit 1;
  fi
  
  numleaves=$1
  totgauss=$2
  data1=$3
  data2=$4
  lang=$5
  alidir=$6
  dir=$7
  
  for f in $alidir/final.mdl $alidir/ali.1.gz $data1/feats.scp $data2/feats.scp $lang/phones.txt; do
    [ ! -f $f ] && echo "train_tandem_lda_mllt.sh: no such file $f" && exit 1;
  done
  
  numgauss=$numleaves
  incgauss=$[($totgauss-$numgauss)/$max_iter_inc] # per-iter #gauss increment
  oov=`cat $lang/oov.int` || exit 1;
  nj=`cat $alidir/num_jobs` || exit 1;
  silphonelist=`cat $lang/phones/silence.csl` || exit 1;
  ciphonelist=`cat $lang/phones/context_indep.csl` || exit 1;
  
  mkdir -p $dir/log
  echo $nj >$dir/num_jobs
  
  
  # Set up features.
  
  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 feature stream 1;  here we assume spectral features which we will 
  # splice instead of deltas
  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:- | splice-feats $splice_opts ark:- ark:- |"
  
  # Now estimate LDA, which will only be applied to the spectral features
  # (assuming that the tandem features were already discriminatively trained).
  # This is instead of the deltas.
  if [ $stage -le -5 ]; then
    echo "Accumulating LDA statistics (this only applies to the base feature part)."
    $cmd JOB=1:$nj $dir/log/lda_acc.JOB.log \
      ali-to-post "ark:gunzip -c $alidir/ali.JOB.gz|" ark:- \| \
        weight-silence-post 0.0 $silphonelist $alidir/final.mdl ark:- ark:- \| \
        acc-lda --rand-prune=$randprune $alidir/final.mdl "$feats1" ark,s,cs:- \
         $dir/lda.JOB.acc || exit 1;
    est-lda --write-full-matrix=$dir/full.mat --dim=$dim1 $dir/lda.mat $dir/lda.*.acc \
        2>$dir/log/lda_est.log || exit 1;
    rm $dir/lda.*.acc
  fi
  
  # add transform to the features
  feats1="$feats1 transform-feats $dir/lda.mat ark:- ark:- |"
  
  # 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 [ "$normft2" == "true" ]; then
    feats2="ark,s,cs:apply-cmvn --norm-vars=false --utt2spk=ark:$sdata2/JOB/utt2spk scp:$sdata2/JOB/cmvn.scp $feats2 ark:- |"
  fi
  
  # assemble tandem features;  note: $feats gets overwritten later in the script
  # once we have MLLT matrices
  tandemfeats="ark,s,cs:paste-feats '$feats1' '$feats2' ark:- |"
  feats="$tandemfeats"
  
  # keep track of splicing/normalization options
  echo $splice_opts > $dir/splice_opts
  echo $normft2 > $dir/normft2
  
  
  # Begin training;  initially, we have no MLLT matrix
  cur_mllt_iter=0
  
  if [ $stage -le -4 -a $extra_lda == true ]; then
    echo "Accumulating LDA statistics (for tandem features this time)."
    $cmd JOB=1:$nj $dir/log/lda_acc.JOB.log \
      ali-to-post "ark:gunzip -c $alidir/ali.JOB.gz|" ark:- \| \
      weight-silence-post 0.0 $silphonelist $alidir/final.mdl ark:- ark:- \| \
      acc-lda --rand-prune=$randprune $alidir/final.mdl "$tandemfeats" ark,s,cs:- \
      $dir/lda.JOB.acc || exit 1;
    est-lda --write-full-matrix=$dir/full.mat --dim=$dim2 $dir/0.mat $dir/lda.*.acc \
      2>$dir/log/lda_est.log || exit 1;
    rm $dir/lda.*.acc
    
    feats="$tandemfeats transform-feats $dir/0.mat ark:- ark:- |"
  fi
  
  # keep track of the features
  echo $feats > $dir/tandem
  
  if [ $stage -le -3 ]; then
    echo "Accumulating tree stats"
    $cmd JOB=1:$nj $dir/log/acc_tree.JOB.log \
     acc-tree-stats  --ci-phones=$ciphonelist $alidir/final.mdl "$feats" \
       "ark:gunzip -c $alidir/ali.JOB.gz|" $dir/JOB.treeacc || exit 1;
    [ `ls $dir/*.treeacc | wc -w` -ne "$nj" ] && echo "Wrong #tree-accs" && exit 1;
    $cmd $dir/log/sum_tree_acc.log \
      sum-tree-stats $dir/treeacc $dir/*.treeacc || exit 1;
    rm $dir/*.treeacc
  fi
  
  
  if [ $stage -le -2 ]; then
    echo "Getting questions for tree clustering."
    # preparing questions, roots file...
    cluster-phones $dir/treeacc $lang/phones/sets.int $dir/questions.int 2> $dir/log/questions.log || exit 1;
    cat $lang/phones/extra_questions.int >> $dir/questions.int
    compile-questions $lang/topo $dir/questions.int $dir/questions.qst 2>$dir/log/compile_questions.log || exit 1;
  
    echo "Building the tree"
    $cmd $dir/log/build_tree.log \
      build-tree --verbose=1 --max-leaves=$numleaves \
      --cluster-thresh=$cluster_thresh $dir/treeacc $lang/phones/roots.int \
      $dir/questions.qst $lang/topo $dir/tree || exit 1;
  
    gmm-init-model  --write-occs=$dir/1.occs  \
      $dir/tree $dir/treeacc $lang/topo $dir/1.mdl 2> $dir/log/init_model.log || exit 1;
    grep 'no stats' $dir/log/init_model.log && echo "This is a bad warning.";
  
    # could mix up if we wanted:
    # gmm-mixup --mix-up=$numgauss $dir/1.mdl $dir/1.occs $dir/1.mdl 2>$dir/log/mixup.log || exit 1;
    rm $dir/treeacc
  fi
  
  
  if [ $stage -le -1 ]; then
    # Convert the alignments.
    echo "Converting alignments from $alidir to use current tree"
    $cmd JOB=1:$nj $dir/log/convert.JOB.log \
      convert-ali $alidir/final.mdl $dir/1.mdl $dir/tree \
       "ark:gunzip -c $alidir/ali.JOB.gz|" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
  fi
  
  if [ $stage -le 0 ]; then
    echo "Compiling graphs of transcripts"
    $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \
      compile-train-graphs $dir/tree $dir/1.mdl  $lang/L.fst  \
       "ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt < $data1/split$nj/JOB/text |" \
        "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
  fi
  
  
  x=1
  while [ $x -lt $num_iters ]; do
    echo Training pass $x
    if echo $realign_iters | grep -w $x >/dev/null && [ $stage -le $x ]; then
      echo Aligning data
      mdl="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $dir/$x.mdl - |"
      $cmd JOB=1:$nj $dir/log/align.$x.JOB.log \
        gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$mdl" \
        "ark:gunzip -c $dir/fsts.JOB.gz|" "$feats" \
        "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
    fi
    if echo $mllt_iters | grep -w $x >/dev/null; then
      if [ $stage -le $x ]; then
        echo "Estimating MLLT"
        $cmd JOB=1:$nj $dir/log/macc.$x.JOB.log \
          ali-to-post "ark:gunzip -c $dir/ali.JOB.gz|" ark:- \| \
          weight-silence-post 0.0 $silphonelist $dir/$x.mdl ark:- ark:- \| \
          gmm-acc-mllt --rand-prune=$randprune  $dir/$x.mdl "$feats" ark:- $dir/$x.JOB.macc \
          || exit 1;
        est-mllt $dir/$x.mat.new $dir/$x.*.macc 2> $dir/log/mupdate.$x.log || exit 1;
        gmm-transform-means  $dir/$x.mat.new $dir/$x.mdl $dir/$x.mdl \
          2> $dir/log/transform_means.$x.log || exit 1;
        
        # see if this is the first MLLT iteration and there is no lda;  otherwise compose transforms
        if [ $cur_mllt_iter == 0 -a $extra_lda == false ]; then
          mv $dir/$x.mat.new $dir/$x.mat || exit 1;
        else
          compose-transforms --print-args=false $dir/$x.mat.new $dir/$cur_mllt_iter.mat $dir/$x.mat || exit 1;
        fi
  
        rm $dir/$x.*.macc
      fi
  
      # update features
      feats="$tandemfeats transform-feats $dir/$x.mat ark:- ark:- |"
      cur_mllt_iter=$x
    fi
  
    if [ $stage -le $x ]; then
      $cmd JOB=1:$nj $dir/log/acc.$x.JOB.log \
        gmm-acc-stats-ali  $dir/$x.mdl "$feats" \
        "ark,s,cs:gunzip -c $dir/ali.JOB.gz|" $dir/$x.JOB.acc || exit 1;
      $cmd $dir/log/update.$x.log \
        gmm-est --write-occs=$dir/$[$x+1].occs --mix-up=$numgauss --power=$power \
          $dir/$x.mdl "gmm-sum-accs - $dir/$x.*.acc |" $dir/$[$x+1].mdl || exit 1;
      rm $dir/$x.mdl $dir/$x.*.acc $dir/$x.occs 
    fi
    [ $x -le $max_iter_inc ] && numgauss=$[$numgauss+$incgauss];
    x=$[$x+1];
  done
  
  rm $dir/final.{mdl,mat,occs} 2>/dev/null
  ln -s $x.mdl $dir/final.mdl
  ln -s $x.occs $dir/final.occs
  ln -s $cur_mllt_iter.mat $dir/final.mat
  
  # Summarize warning messages...
  
  utils/summarize_warnings.pl $dir/log
  
  echo Done training system with LDA+MLLT tandem features in $dir