#!/bin/bash
  
  set -o pipefail
  set -e
  # this is run_discriminative.sh
  
  # This script does discriminative training on top of CE nnet3 system.
  # note: this relies on having a cluster that has plenty of CPUs as well as GPUs,
  # since the lattice generation runs in about real-time, so takes of the order of
  # 1000 hours of CPU time.
  #
  . ./cmd.sh
  
  
  stage=0
  train_stage=-10 # can be used to start training in the middle.
  get_egs_stage=-10
  use_gpu=true  # for training
  cleanup=false  # run with --cleanup true --stage 6 to clean up (remove large things like denlats,
                 # alignments and degs).
  
  . ./cmd.sh
  . ./path.sh
  . ./utils/parse_options.sh
  
  srcdir=exp/nnet3/nnet_ms_a
  train_data_dir=data/train_si284_hires
  online_ivector_dir=exp/nnet3/ivectors_train_si284
  degs_dir=                     # If provided, will skip the degs directory creation
  lats_dir=                     # If provided, will skip denlats creation
  
  ## Objective options
  criterion=smbr
  one_silence_class=true
  
  dir=${srcdir}_${criterion}
  
  ## Egs options
  frames_per_eg=150
  frames_overlap_per_eg=30
  
  ## Nnet training options
  effective_learning_rate=0.0000125
  max_param_change=1
  num_jobs_nnet=4
  num_epochs=4
  regularization_opts=          # Applicable for providing --xent-regularize and --l2-regularize options
  minibatch_size=64
  
  ## Decode options
  decode_start_epoch=1 # can be used to avoid decoding all epochs, e.g. if we decided to run more.
  
  if $use_gpu; then
    if ! cuda-compiled; then
      cat <<EOF && exit 1
  This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
  If you want to use GPUs (and have them), go to src/, and configure and make on a machine
  where "nvcc" is installed.  Otherwise, call this script with --use-gpu false
  EOF
    fi
    num_threads=1
  else
    # Use 4 nnet jobs just like run_4d_gpu.sh so the results should be
    # almost the same, but this may be a little bit slow.
    num_threads=16
  fi
  
  if [ ! -f ${srcdir}/final.mdl ]; then
    echo "$0: expected ${srcdir}/final.mdl to exist; first run run_tdnn.sh or run_lstm.sh"
    exit 1;
  fi
  
  if [ $stage -le 1 ]; then
    # hardcode no-GPU for alignment, although you could use GPU [you wouldn't
    # get excellent GPU utilization though.]
    nj=100 # have a high number of jobs because this could take a while, and we might
           # have some stragglers.
    steps/nnet3/align.sh  --cmd "$decode_cmd" --use-gpu false \
      --online-ivector-dir $online_ivector_dir \
       --nj $nj $train_data_dir data/lang $srcdir ${srcdir}_ali ;
  
  fi
  
  if [ -z "$lats_dir" ]; then
    lats_dir=${srcdir}_denlats
    if [ $stage -le 2 ]; then
      nj=50
      # this doesn't really affect anything strongly, except the num-jobs for one of
      # the phases of get_egs_discriminative.sh below.
      num_threads_denlats=6
      subsplit=40 # number of jobs that run per job (but 2 run at a time, so total jobs is 80, giving
      # total slots = 80 * 6 = 480.
      steps/nnet3/make_denlats.sh --cmd "$decode_cmd" --determinize true \
        --online-ivector-dir $online_ivector_dir \
        --nj $nj --sub-split $subsplit --num-threads "$num_threads_denlats" --config conf/decode_dnn.config \
        $train_data_dir data/lang $srcdir ${lats_dir} ;
    fi
  fi
  
  model_left_context=`nnet3-am-info $srcdir/final.mdl | grep "left-context:" | awk '{print $2}'`
  model_right_context=`nnet3-am-info $srcdir/final.mdl | grep "right-context:" | awk '{print $2}'`
  
  left_context=$[model_left_context + extra_left_context]
  right_context=$[model_right_context + extra_right_context]
  
  frame_subsampling_opt=
  if [ -f $srcdir/frame_subsampling_factor ]; then
    frame_subsampling_opt="--frame-subsampling-factor $(cat $srcdir/frame_subsampling_factor)"
  fi
  
  cmvn_opts=`cat $srcdir/cmvn_opts`
  
  if [ -z "$degs_dir" ]; then
    degs_dir=${srcdir}_degs
  
    if [ $stage -le 3 ]; then
      if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d ${srcdir}_degs/storage ]; then
        utils/create_split_dir.pl \
          /export/b0{1,2,12,13}/$USER/kaldi-data/egs/swbd-$(date +'%m_%d_%H_%M')/s5/${srcdir}_degs/storage ${srcdir}_degs/storage
      fi
      # have a higher maximum num-jobs if
      if [ -d ${srcdir}_degs/storage ]; then max_jobs=10; else max_jobs=5; fi
  
      steps/nnet3/get_egs_discriminative.sh \
        --cmd "$decode_cmd --max-jobs-run $max_jobs --mem 20G" --stage $get_egs_stage --cmvn-opts "$cmvn_opts" \
        --online-ivector-dir $online_ivector_dir \
        --left-context $left_context --right-context $right_context \
        $frame_subsampling_opt \
        --frames-per-eg $frames_per_eg --frames-overlap-per-eg $frames_overlap_per_eg \
        $train_data_dir data/lang ${srcdir}_ali $lats_dir $srcdir/final.mdl $degs_dir ;
    fi
  fi
  
  if [ $stage -le 4 ]; then
    steps/nnet3/train_discriminative.sh --cmd "$decode_cmd" \
      --stage $train_stage \
      --effective-lrate $effective_learning_rate --max-param-change $max_param_change \
      --criterion $criterion --drop-frames true \
      --num-epochs $num_epochs --one-silence-class $one_silence_class --minibatch-size $minibatch_size \
      --num-jobs-nnet $num_jobs_nnet --num-threads $num_threads \
      --regularization-opts "$regularization_opts" \
      ${degs_dir} $dir
  fi
  
  if [ $stage -le 5 ]; then
    for x in `seq $decode_start_epoch $num_epochs`; do
      iter=epoch${x}_adj
      for lm_suffix in tgpr bd_tgpr; do
        graph_dir=exp/tri4b/graph_${lm_suffix}
        # use already-built graphs.
        for year in eval92 dev93; do
          (
            steps/nnet3/decode.sh --nj 8 --cmd "$decode_cmd" --iter $iter \
              --online-ivector-dir exp/nnet3/ivectors_test_$year \
              $graph_dir data/test_${year}_hires $dir/decode_${lm_suffix}_${year}_$iter ;
          ) &
        done
      done
    done
  fi
  wait;
  
  if [ $stage -le 6 ] && $cleanup; then
    # if you run with "--cleanup true --stage 6" you can clean up.
    rm ${lats_dir}/lat.*.gz || true
    rm ${srcdir}_ali/ali.*.gz || true
    steps/nnet2/remove_egs.sh ${srcdir}_degs || true
  fi
  
  
  exit 0;