#!/bin/bash
  # Copyright 2013  Brno University of Technology (Author: Karel Vesely)  
  # Apache 2.0.
  
  # Sequence-discriminative MPE/sMBR training of DNN.
  # 4 iterations (by default) of Stochastic Gradient Descent with per-utterance updates.
  # We select between MPE/sMBR optimization by '--do-smbr <bool>' option.
  
  # For the numerator we have a fixed alignment rather than a lattice--
  # this actually follows from the way lattices are defined in Kaldi, which
  # is to have a single path for each word (output-symbol) sequence.
  
  
  # Begin configuration section.
  cmd=run.pl
  num_iters=4
  acwt=0.1
  lmwt=1.0
  learn_rate=0.00001
  halving_factor=1.0 #ie. disable halving
  do_smbr=true
  use_silphones=false #setting this to something will enable giving siphones to nnet-mpe
  verbose=1
  use_gpu_id=
  
  seed=777    # seed value used for training data shuffling
  # 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 6 ]; then
    echo "Usage: steps/$0 <data> <lang> <srcdir> <ali> <denlats> <exp>"
    echo " e.g.: steps/$0 data/train_all data/lang exp/tri3b_dnn exp/tri3b_dnn_ali exp/tri3b_dnn_denlats exp/tri3b_dnn_smbr"
    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 "  --num-iters <N>                                  # number of iterations to run"
    echo "  --acwt <float>                                   # acoustic score scaling"
    echo "  --lmwt <float>                                   # linguistic score scaling"
    echo "  --learn-rate <float>                             # learning rate for NN training"
    echo "  --do-smbr <bool>                                 # do sMBR training, otherwise MPE"
    
    exit 1;
  fi
  
  data=$1
  lang=$2
  srcdir=$3
  alidir=$4
  denlatdir=$5
  dir=$6
  mkdir -p $dir/log
  
  for f in $data/feats.scp $alidir/{tree,final.mdl,ali.1.gz} $denlatdir/lat.scp $srcdir/{final.nnet,final.feature_transform}; do
    [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
  done
  
  mkdir -p $dir/log
  
  cp $alidir/{final.mdl,tree} $dir
  
  silphonelist=`cat $lang/phones/silence.csl` || exit 1;
  
  
  
  #Get the files we will need
  nnet=$srcdir/$(readlink $srcdir/final.nnet || echo final.nnet);
  [ -z "$nnet" ] && echo "Error nnet '$nnet' does not exist!" && exit 1;
  cp $nnet $dir/0.nnet; nnet=$dir/0.nnet
  
  class_frame_counts=$srcdir/ali_train_pdf.counts
  [ -z "$class_frame_counts" ] && echo "Error class_frame_counts '$class_frame_counts' does not exist!" && exit 1;
  cp $srcdir/ali_train_pdf.counts $dir
  
  feature_transform=$srcdir/final.feature_transform
  if [ ! -f $feature_transform ]; then
    echo "Missing feature_transform '$feature_transform'"
    exit 1
  fi
  cp $feature_transform $dir/final.feature_transform
  
  model=$dir/final.mdl
  [ -z "$model" ] && echo "Error transition model '$model' does not exist!" && exit 1;
  
  #enable/disable silphones from MPE training
  mpe_silphones_arg= #empty
  [ "$use_silphones" == "true" ] && mpe_silphones_arg="--silence-phones=$silphonelist"
  
  
  # Shuffle the feature list to make the GD stochastic!
  # By shuffling features, we have to use lattices with random access (indexed by .scp file).
  cat $data/feats.scp | utils/shuffle_list.pl --srand $seed > $dir/train.scp
  
  
  ###
  ### Prepare feature pipeline
  ###
  # Create the feature stream:
  feats="ark,s,cs:copy-feats scp:$dir/train.scp ark:- |"
  # Optionally add cmvn
  if [ -f $srcdir/norm_vars ]; then
    norm_vars=$(cat $srcdir/norm_vars 2>/dev/null)
    [ ! -f $data/cmvn.scp ] && echo "$0: cannot find cmvn stats $data/cmvn.scp" && exit 1
    feats="$feats apply-cmvn --norm-vars=$norm_vars --utt2spk=ark:$data/utt2spk scp:$data/cmvn.scp ark:- ark:- |"
    cp $srcdir/norm_vars $dir
  fi
  # Optionally add deltas
  if [ -f $srcdir/delta_order ]; then
    delta_order=$(cat $srcdir/delta_order)
    feats="$feats add-deltas --delta-order=$delta_order ark:- ark:- |"
    cp $srcdir/delta_order $dir
  fi
  ###
  ###
  ###
  
  
  ###
  ### Prepare the alignments
  ### 
  # Assuming all alignments will fit into memory
  ali="ark:gunzip -c $alidir/ali.*.gz |"
  
  
  ###
  ### Prepare the lattices
  ###
  # The lattices are indexed by SCP (they are not gziped because of the random access in SGD)
  lats="scp:$denlatdir/lat.scp"
  
  
  # Run several iterations of the MPE/sMBR training
  cur_mdl=$nnet
  x=1
  while [ $x -le $num_iters ]; do
    echo "Pass $x (learnrate $learn_rate)"
    if [ -f $dir/$x.nnet ]; then
      echo "Skipped, file $dir/$x.nnet exists"
    else
      #train
      $cmd $dir/log/mpe.$x.log \
       nnet-train-mpe-sequential \
         --feature-transform=$feature_transform \
         --class-frame-counts=$class_frame_counts \
         --acoustic-scale=$acwt \
         --lm-scale=$lmwt \
         --learn-rate=$learn_rate \
         --do-smbr=$do_smbr \
         --verbose=$verbose \
         $mpe_silphones_arg \
         ${use_gpu_id:+ --use-gpu-id=$use_gpu_id} \
         $cur_mdl $alidir/final.mdl "$feats" "$lats" "$ali" $dir/$x.nnet || exit 1
    fi
    cur_mdl=$dir/$x.nnet
  
    #report the progress
    grep -B 2 "Overall average frame-accuracy" $dir/log/mpe.$x.log | sed -e 's|.*)||'
  
    x=$((x+1))
    learn_rate=$(awk "BEGIN{print($learn_rate*$halving_factor)}")
    
  done
  
  (cd $dir; [ -e final.nnet ] && unlink final.nnet; ln -s $((x-1)).nnet final.nnet)
  
  echo "MPE/sMBR training finished"
  
  
  
  exit 0