#!/bin/bash
  # by Johns Hopkins University (Author: Daniel Povey), 2012.  Apache 2.0.
  
  # This script does MMI discriminative training, including
  # feature-space (like fMPE) and model-space components. 
  # If you give the --boost option it does "boosted MMI" (BMMI).
  # On the iterations of training it alternates feature-space
  # and model-space training.  We do 8 iterations in total--
  # 4 of each type ((B)MMI, f(B)MMI)
  
  
  # Begin configuration section.
  cmd=run.pl
  schedule="fmmi mmi fmmi mmi fmmi mmi fmmi mmi"
  boost=0.0
  learning_rate=0.02
  tau=200 # For model.  Note: we're doing smoothing "to the previous iteration",
      # so --smooth-from-model so 200 seems like a more sensible default
      # than 100.  We smooth to the previous iteration because now
      # we are discriminatively training the features (and not using
      # the indirect differential), so it seems like it wouldn't make 
      # sense to use any element of ML.
  cancel=true # if true, cancel num and den counts as described in 
       # the boosted MMI paper. 
  zero_if_disjoint=false # if true, ignore stats from frames where num + den
                         # have no overlap. 
  acwt=0.1
  stage=-1
  ngselect=2; # Just the 2 top Gaussians.  Beyond that, adding more Gaussians
              # wouldn't make much difference since the posteriors would be very small.
  # End configuration section.
  
  echo "$0 $@"  # Print the command line for logging
  
  [ -f ./path.sh ] && . ./path.sh;
  . parse_options.sh || exit 1;
  
  
  if [ $# != 6 ]; then
    echo "Usage: steps/train_mmi_fmmi.sh <data> <lang> <diag-ubm-dir> <ali-dir> <denlat-dir> <exp-dir>"
    echo " e.g.: steps/train_mmi_fmmi.sh data/train_si84 data/lang exp/tri2b_ali_si84 exp/ubm2d exp/tri2b_denlats_si84 exp/tri2b_fmmi"
    echo "Main options (for others, see top of script file)"
    echo "  --boost <boost-weight>                           # (e.g. 0.1) ... boosted MMI."
    echo "  --cancel (true|false)                            # cancel stats (true by default)"
    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 "  --tau                                            # tau for i-smooth to last iter (default 200)"
    echo "  --learning-rate                                  # learning rate for fMMI, default 0.01"
    echo "  --schedule                                       # learning schedule: by default,"
    echo "                                                   # \"fmmi mmi fmmi mmi fmmi mmi fmmi mmi\""
    exit 1;
  fi
  
  
  data=$1
  lang=$2
  alidir=$3
  dubmdir=$4  # where diagonal UBM is.
  denlatdir=$5
  dir=$6
  
  silphonelist=`cat $lang/phones/silence.csl`
  mkdir -p $dir/log
  
  for f in $data/feats.scp $lang/phones.txt $dubmdir/final.dubm $alidir/final.mdl \
    $alidir/ali.1.gz $denlatdir/lat.1.gz; do
    [ ! -f $f ] && echo "Expected file $f to exist" && exit 1;
  done
  cp $alidir/final.mdl $alidir/tree $dir || exit 1;
  nj=`cat $alidir/num_jobs` || exit 1;
  [ "$nj" -ne "`cat $denlatdir/num_jobs`" ] && \
    echo "$alidir and $denlatdir have different num-jobs" && exit 1;
  sdata=$data/split$nj
  splice_opts=`cat $alidir/splice_opts 2>/dev/null` # frame-splicing options.
  mkdir -p $dir/log
  cp $alidir/splice_opts $dir 2>/dev/null # frame-splicing options.
  [[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
  
  
  if [ -f $alidir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
  echo "$0: feature type is $feat_type"
  
  # Note: $feats is the features before fMPE.
  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 $alidir/final.mat ark:- ark:- |"
      cp $alidir/final.mat $dir    
      ;;
    *) echo "Invalid feature type $feat_type" && exit 1;
  esac
  
  [ -f $alidir/trans.1 ] && echo Using transforms from $alidir && \
    feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$alidir/trans.JOB ark:- ark:- |"
  
  lats="ark:gunzip -c $denlatdir/lat.JOB.gz|"
  if [[ "$boost" != "0.0" && "$boost" != 0 ]]; then
    lats="$lats lattice-boost-ali --b=$boost --silence-phones=$silphonelist $alidir/final.mdl ark:- 'ark,s,cs:gunzip -c $alidir/ali.JOB.gz|' ark:- |"
  fi
  
  
  fmpefeats="$feats" # At first, the features "after fMPE" are the same as the 
                     # base features.
  
  
  # Initialize the fMPE object.  Note: we call it .fmpe because
  # that's what it was called in the original paper, but since
  # we're using the MMI objective function, it's really fMMI.
  
  fmpe-init $dubmdir/final.dubm $dir/0.fmpe 2>$dir/log/fmpe_init.log || exit 1;
  
  
  if [ $stage -le -1 ]; then
    # Get the gselect (Gaussian selection) info for fMPE.
    # Note: fMPE object starts with GMM object, so can be read
    # as one.
    $cmd JOB=1:$nj $dir/log/gselect.JOB.log \
      gmm-gselect --n=$ngselect $dir/0.fmpe "$feats" \
      "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
  fi
  
  cp $alidir/final.mdl $dir/0.mdl
  
  x=0
  num_iters=`echo $schedule | wc -w`
  
  while [ $x -lt $num_iters ]; do
    iter_type=`echo $schedule | cut -d ' ' -f $[$x+1]`
    case $iter_type in 
      fmmi) fmmi_iter=true; local_cancel=false;;
      mmi) fmmi_iter=false; local_cancel=$cancel;;
      *) echo "Bad iteration type $iter_type"; exit 1;;
    esac
  
    echo "Getting MMI stats (needed for fMMI and MMI iterations).";
    if [ $stage -le $x ]; then
      $cmd JOB=1:$nj $dir/log/acc.$x.JOB.log \
        gmm-rescore-lattice $dir/$x.mdl "$lats" "$fmpefeats" ark:- \| \
        lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
        sum-post --merge=$local_cancel --scale1=-1 --zero-if-disjoint=$zero_if_disjoint \
        ark:- "ark,s,cs:gunzip -c $alidir/ali.JOB.gz | ali-to-post ark:- ark:- |" ark:- \| \
        gmm-acc-stats2 $dir/$x.mdl "$fmpefeats" ark,s,cs:- \
        $dir/num_acc.$x.JOB.acc $dir/den_acc.$x.JOB.acc || exit 1;
      n=`echo $dir/{num,den}_acc.$x.*.acc | wc -w`;
      [ "$n" -ne $[$nj*2] ] && \
        echo "Wrong number of MMI accumulators $n versus 2*$nj" && exit 1;
      rm $dir/.error 2>/dev/null
      $cmd $dir/log/den_acc_sum.$x.log \
        gmm-sum-accs $dir/den_acc.$x.acc $dir/den_acc.$x.*.acc || touch $dir/.error &
      $cmd $dir/log/num_acc_sum.$x.log \
        gmm-sum-accs $dir/num_acc.$x.acc $dir/num_acc.$x.*.acc || touch $dir/.error &
      wait
      [ -f $dir/.error ] && echo "Error summing accs" && exit 1;
      rm $dir/den_acc.$x.*.acc
      rm $dir/num_acc.$x.*.acc
    fi
  
    if $fmmi_iter; then
      echo "Iteration $x: doing fMMI"
      if [ $stage -le $x ]; then
        # Get model derivative.  Note: the "ml accumulator" is the same as the "numerator"
        # since this is MMI.  We avoided doing the "canceling of stats" on this iteration
        # so that this would be true (this canceling wouldn't affect the derivative anyway,
        # so can have no benefit for fMMI, unlike MMI).
        $cmd $dir/log/get_stats_deriv.$x.log \
          gmm-get-stats-deriv $dir/$x.mdl $dir/num_acc.$x.acc $dir/den_acc.$x.acc \
          $dir/num_acc.$x.acc $dir/model_deriv.$x.gmmacc
        numpost="ark,s,cs:gunzip -c $alidir/ali.JOB.gz| ali-to-post ark:- ark:-|"
          # Note: the command gmm-fmpe-acc-stats below requires the pre-fMPE features.
        $cmd JOB=1:$nj $dir/log/acc_fmmi.$x.JOB.log \
          gmm-rescore-lattice $dir/$x.mdl "$lats" "$fmpefeats" ark:- \| \
          lattice-to-post --acoustic-scale=$acwt ark:- ark:- \| \
          sum-post --zero-if-disjoint=$zero_if_disjoint --merge=false --scale1=-1 \
            ark:- "$numpost" ark:- \| \
          gmm-fmpe-acc-stats --model-derivative=$dir/model_deriv.$x.gmmacc \
            $dir/$x.mdl $dir/$x.fmpe "$feats" \
           "ark,s,cs:gunzip -c $dir/gselect.JOB.gz|" ark,s,cs:-  \
           $dir/$x.JOB.fmpe_acc || exit 1;
        
        ( fmpe-sum-accs $dir/$x.fmpe_acc $dir/$x.*.fmpe_acc && \
          rm $dir/$x.*.fmpe_acc && \
          fmpe-est --learning-rate=$learning_rate $dir/$x.fmpe $dir/$x.fmpe_acc $dir/$[$x+1].fmpe ) \
          2>$dir/log/est_fmpe.$x.log || exit 1;
  
        fmpefeats="$feats fmpe-apply-transform $dir/$[$x+1].fmpe ark:- 'ark,s,cs:gunzip -c $dir/gselect.JOB.gz|' ark:- |" 
        # OK, now we do one iteration of the "rescaling update" where we use the
        # old and new ML accs, and we shift and rescale the model to match the new
        # features.
        $cmd JOB=1:$nj $dir/log/acc_ml.$x.JOB.log \
          gmm-acc-stats-ali $dir/$x.mdl "$fmpefeats" "ark:gunzip -c $alidir/ali.JOB.gz|" \
            $dir/new_ml_acc.$x.JOB.acc || exit 1;
        $cmd $dir/log/new_ml_acc_sum.$x.log \
          gmm-sum-accs $dir/new_ml_acc.$x.acc $dir/new_ml_acc.$x.*.acc || exit 1;
        $cmd $dir/log/update_rescale.$x.log \
          gmm-est-rescale $dir/$x.mdl $dir/num_acc.$x.acc $dir/new_ml_acc.$x.acc \
          $dir/$[$x+1].mdl || exit 1;
      fi
      # We need to set the features to use the correct fMPE object.
      # This is a repeat of a command above-- in case we didn't do this stage.
      fmpefeats="$feats fmpe-apply-transform $dir/$[$x+1].fmpe ark:- 'ark,s,cs:gunzip -c $dir/gselect.JOB.gz|' ark:- |" 
      # Now, diagnostics.
      objf_nf=`grep Overall $dir/log/acc_fmmi.$x.*.log | grep gmm-fmpe-acc-stats | awk '{ p+=$10*$12; nf+=$12; } END{print p/nf, nf;}'`
      objf=`echo $objf_nf | awk '{print $1}'`;
      nf=`echo $objf_nf | awk '{print $2}'`;
      impr=`grep Objf $dir/log/est_fmpe.$x.log | awk '{print $NF}'`
      impr=`perl -e "print ($impr/$nf);"` # normalize by #frames.
      echo On iter $x, objf was $objf, auxf improvement from fMMI was $impr | tee $dir/objf.$x.log
    else # MMI iteration-- on this iteration do model-space update.
      echo "Iteration $x: doing MMI update"
        # note: this tau value is for smoothing to model parameters;
        # you need to use gmm-ismooth-stats to smooth to the ML stats,
        # but anyway this script does canceling of num and den stats on
        # each frame (as suggested in the Boosted MMI paper) which would
        # make smoothing to ML impossible without accumulating extra stats.
      if [ $stage -le $x ]; then
        $cmd $dir/log/update.$x.log \
          gmm-est-gaussians-ebw --tau=$tau $dir/$x.mdl $dir/num_acc.$x.acc $dir/den_acc.$x.acc - \| \
          gmm-est-weights-ebw - $dir/num_acc.$x.acc $dir/den_acc.$x.acc $dir/$[$x+1].mdl || exit 1;
      else 
        echo "not doing this iteration because --stage=$stage"
      fi
      
      # Some diagnostics.. note, this objf is somewhat comparable to the
      # MMI objective function divided by the acoustic weight, and differences in it
      # are comparable to the auxf improvement printed by the update program.
      objf_nf=`grep Overall $dir/log/acc.$x.*.log | grep gmm-acc-stats2 | awk '{ p+=$10*$12; nf+=$12; } END{print p/nf, nf;}'`
      objf=`echo $objf_nf | awk '{print $1}'`;
      nf=`echo $objf_nf | awk '{print $2}'`;
      impr=`grep Overall $dir/log/update.$x.log | head -1 | awk '{print $10*$12;}'`
      impr=`perl -e "print ($impr/$nf);"` # renormalize by "real" #frames, to correct
      # for the canceling of stats.
      echo On iter $x, objf was $objf, auxf improvement was $impr | tee $dir/objf.$x.log
      rm $dir/$[x+1].fmpe 2>/dev/null; ln -s $x.fmpe $dir/$[$x+1].fmpe # link previous fMPE transform
    fi
    x=$[$x+1]
  done
  
  echo "Succeeded with $num_iters iters iterations of MMI+fMMI training (boosting factor = $boost)"
  
  rm $dir/final.mdl 2>/dev/null; ln -s $num_iters.mdl $dir/final.mdl
  rm $dir/final.fmpe 2>/dev/null; ln -s $num_iters.fmpe $dir/final.fmpe 
  
  # Now do some cleanup.
  rm $dir/gselect.*.gz $dir/*.acc $dir/*.fmpe_acc
  exit 0;