#!/bin/bash
  # Copyright 2013-2015  Brno University of Technology (author: Karel Vesely)
  # Apache 2.0.
  
  # Sequence-discriminative MMI/BMMI training of DNN.
  # 4 iterations (by default) of Stochastic Gradient Descent with per-utterance updates.
  # Boosting of paths with more errors (BMMI) gets activated by '--boost <float>' 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
  boost=0.0 #ie. disable boosting
  acwt=0.1
  lmwt=1.0
  learn_rate=0.00001
  halving_factor=1.0 #ie. disable halving
  drop_frames=true
  verbose=0 # 0 No GPU time-stats, 1 with GPU time-stats (slower),
  ivector=
  
  seed=777    # seed value used for training data shuffling
  skip_cuda_check=false
  # End configuration section
  
  echo "$0 $@"  # Print the command line for logging
  
  [ -f ./path.sh ] && . ./path.sh; # source the path.
  . parse_options.sh || exit 1;
  
  set -euo pipefail
  
  if [ $# -ne 6 ]; then
    echo "Usage: $0 <data> <lang> <srcdir> <ali> <denlats> <exp>"
    echo " e.g.: $0 data/train_all data/lang exp/tri3b_dnn exp/tri3b_dnn_ali exp/tri3b_dnn_denlats exp/tri3b_dnn_mmi"
    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 "  --drop-frames <bool>                             # drop frames num/den completely disagree"
    echo "  --boost <boost-weight>                           # (e.g. 0.1), for boosted MMI.  (default 0)"
  
    exit 1;
  fi
  
  data=$1
  lang=$2
  srcdir=$3
  alidir=$4
  denlatdir=$5
  dir=$6
  
  for f in $data/feats.scp $denlatdir/lat.scp \
           $alidir/{tree,final.mdl,ali.1.gz} \
           $srcdir/{final.nnet,final.feature_transform}; do
    [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
  done
  
  # check if CUDA compiled in,
  if ! $skip_cuda_check; then cuda-compiled || { echo "Error, CUDA not compiled-in!"; exit 1; } fi
  
  mkdir -p $dir/log
  
  utils/lang/check_phones_compatible.sh $lang/phones.txt $srcdir/phones.txt
  utils/lang/check_phones_compatible.sh $lang/phones.txt $alidir/phones.txt
  cp $lang/phones.txt $dir
  
  cp $alidir/{final.mdl,tree} $dir
  
  silphonelist=`cat $lang/phones/silence.csl`
  
  
  #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;
  
  
  
  # 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 EXTRACTION PIPELINE
  ###
  # import config,
  cmvn_opts=
  delta_opts=
  D=$srcdir
  [ -e $D/norm_vars ] && cmvn_opts="--norm-means=true --norm-vars=$(cat $D/norm_vars)" # Bwd-compatibility,
  [ -e $D/cmvn_opts ] && cmvn_opts=$(cat $D/cmvn_opts)
  [ -e $D/delta_order ] && delta_opts="--delta-order=$(cat $D/delta_order)" # Bwd-compatibility,
  [ -e $D/delta_opts ] && delta_opts=$(cat $D/delta_opts)
  #
  # Create the feature stream,
  feats="ark,o:copy-feats scp:$dir/train.scp ark:- |"
  # apply-cmvn (optional),
  [ ! -z "$cmvn_opts" -a ! -f $data/cmvn.scp ] && echo "$0: Missing $data/cmvn.scp" && exit 1
  [ ! -z "$cmvn_opts" ] && feats="$feats apply-cmvn $cmvn_opts --utt2spk=ark:$data/utt2spk scp:$data/cmvn.scp ark:- ark:- |"
  # add-deltas (optional),
  [ ! -z "$delta_opts" ] && feats="$feats add-deltas $delta_opts ark:- ark:- |"
  # add-pytel transform (optional),
  [ -e $D/pytel_transform.py ] && feats="$feats /bin/env python $D/pytel_transform.py |"
  
  # add-ivector (optional),
  if [ -e $D/ivector_dim ]; then
    [ -z $ivector ] && echo "Missing --ivector, they were used in training!" && exit 1
    # Get the tool,
    ivector_append_tool=append-vector-to-feats # default,
    [ -e $D/ivector_append_tool ] && ivector_append_tool=$(cat $D/ivector_append_tool)
    # Check dims,
    dim_raw=$(feat-to-dim "$feats" -)
    dim_raw_and_ivec=$(feat-to-dim "$feats $ivector_append_tool ark:- '$ivector' ark:- |" -)
    dim_ivec=$((dim_raw_and_ivec - dim_raw))
    [ $dim_ivec != "$(cat $D/ivector_dim)" ] && \
      echo "Error, i-vector dim. mismatch (expected $(cat $D/ivector_dim), got $dim_ivec in '$ivector')" && \
      exit 1
    # Append to feats,
    feats="$feats $ivector_append_tool ark:- '$ivector' ark:- |"
  fi
  
  ### Record the setup,
  [ ! -z "$cmvn_opts" ] && echo $cmvn_opts >$dir/cmvn_opts
  [ ! -z "$delta_opts" ] && echo $delta_opts >$dir/delta_opts
  [ -e $D/pytel_transform.py ] && cp $D/pytel_transform.py $dir/pytel_transform.py
  [ -e $D/ivector_dim ] && cp $D/ivector_dim $dir/ivector_dim
  [ -e $D/ivector_append_tool ] && cp $D/ivector_append_tool $dir/ivector_append_tool
  ###
  
  ###
  ### 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"
  
  # Optionally apply boosting
  if [[ "$boost" != "0.0" && "$boost" != 0 ]]; then
    # make lattice scp with same order as the shuffled feature scp,
    awk '{ if(r==0) { utt_id=$1; latH[$1]=$0; } # lat.scp
           if(r==1) { if(latH[$1] != "") { print latH[$1]; } } # train.scp
    }' r=0 $denlatdir/lat.scp r=1 $dir/train.scp > $dir/lat.scp
    # get the list of alignments,
    ali-to-phones $alidir/final.mdl "$ali" ark,t:- | awk '{print $1;}' > $dir/ali.lst
    # remove from features sentences which have no lattice or no alignment,
    # (so that the mmi training tool does not blow-up due to lattice caching),
    mv $dir/train.scp $dir/train.scp_unfilt
    awk '{ if(r==0) { latH[$1]="1"; } # lat.scp
           if(r==1) { aliH[$1]="1"; } # ali.lst
           if(r==2) { if((latH[$1] != "") && (aliH[$1] != "")) { print $0; } } # train.scp_
    }' r=0 $dir/lat.scp r=1 $dir/ali.lst r=2 $dir/train.scp_unfilt > $dir/train.scp
    # create the lat pipeline,
    lats="ark,o:lattice-boost-ali --b=$boost --silence-phones=$silphonelist $alidir/final.mdl scp:$dir/lat.scp '$ali' ark:- |"
  fi
  ###
  ###
  ###
  
  # Run several iterations of the MMI/BMMI 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
      $cmd $dir/log/mmi.$x.log \
       nnet-train-mmi-sequential \
         --feature-transform=$feature_transform \
         --class-frame-counts=$class_frame_counts \
         --acoustic-scale=$acwt \
         --lm-scale=$lmwt \
         --learn-rate=$learn_rate \
         --drop-frames=$drop_frames \
         --verbose=$verbose \
         $cur_mdl $alidir/final.mdl "$feats" "$lats" "$ali" $dir/$x.nnet
    fi
    cur_mdl=$dir/$x.nnet
  
    #report the progress
    grep -B 2 MMI-objective $dir/log/mmi.$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 "MMI/BMMI training finished"
  
  if [ -e $dir/prior_counts ]; then
    echo "Priors are already re-estimated, skipping... ($dir/prior_counts)"
  else
    echo "Re-estimating priors by forwarding 10k utterances from training set."
    . ./cmd.sh
    nj=$(cat $alidir/num_jobs)
    steps/nnet/make_priors.sh --cmd "$train_cmd" --nj $nj \
      ${ivector:+ --ivector "$ivector"} $data $dir
  fi
  
  echo "$0: Done. '$dir'"
  exit 0