#!/bin/bash
  
  
  # This script does discriminative training on top of the online,
  # system trained in run_nnet2.sh.
  # 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.
  #
  # Note: rather than using any features we have dumped on disk, this script
  # regenerates them from the wav data three times-- when we do lattice
  # generation, numerator alignment and discriminative training.  This made the
  # script easier to write and more generic, because we don't have to know where
  # the features and the iVectors are, but of course it's a little inefficient.
  # The time taken is dominated by the lattice generation anyway, so this isn't
  # a huge deal.
  
  . ./cmd.sh
  
  
  stage=0
  train_stage=-10
  use_gpu=true
  srcdir=exp/nnet2_online/nnet_a
  criterion=smbr
  drop_frames=false  # only relevant for MMI actually.
  learning_rate=0.0001
  train_stage=-10 # can be used to start training in the middle.
  decode_start_epoch=0 # can be used to avoid decoding all epochs, e.g. if we decided to run more.
  num_epochs=4
  cleanup=false  # run with --cleanup true --stage 6 to clean up (remove large things like denlats,
                 # alignments and degs).
  
  set -e
  . ./cmd.sh
  . ./path.sh
  . ./utils/parse_options.sh
  
  
  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
    parallel_opts="--gpu 1"
    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
    parallel_opts="--num-threads $num_threads"
  fi
  
  if [ ! -f ${srcdir}_online/final.mdl ]; then
    echo "$0: expected ${srcdir}_online/final.mdl to exist; first run run_nnet2_ms.sh."
    exit 1;
  fi
  
  
  if [ $stage -le 1 ]; then
    nj=50  # this doesn't really affect anything strongly, except the num-jobs for one of
           # the phases of get_egs_discriminative2.sh below.
    num_threads_denlats=6
    subsplit=40 # number of jobs that run per job (but 2 run at a time, so total jobs is 80, giving
                # total slots = 80 * 6 = 480.
    steps/nnet2/make_denlats.sh --cmd "$decode_cmd --mem 1G --num-threads $num_threads_denlats" \
        --online-ivector-dir exp/nnet2_online/ivectors_train_960_hires \
        --nj $nj --sub-split $subsplit --num-threads "$num_threads_denlats" --config conf/decode.config \
       data/train_960_hires data/lang $srcdir ${srcdir}_denlats || exit 1;
  
    # the command below is a more generic, but slower, way to do it.
    #steps/online/nnet2/make_denlats.sh --cmd "$decode_cmd --mem 1G --num-threads $num_threads_denlats" \
    #    --nj $nj --sub-split $subsplit --num-threads "$num_threads_denlats" --config conf/decode.config \
    #   data/train_960 data/lang ${srcdir}_online ${srcdir}_denlats || exit 1;
  
  fi
  
  if [ $stage -le 2 ]; then
    # hardcode no-GPU for alignment, although you could use GPU [you wouldn't
    # get excellent GPU utilization though.]
    nj=350 # have a high number of jobs because this could take a while, and we might
           # have some stragglers.
    use_gpu=no
    gpu_opts=
  
    steps/nnet2/align.sh  --cmd "$decode_cmd $gpu_opts" --use-gpu "$use_gpu" \
       --online-ivector-dir exp/nnet2_online/ivectors_train_960_hires \
       --nj $nj data/train_960_hires data/lang $srcdir ${srcdir}_ali || exit 1;
  
    # the command below is a more generic, but slower, way to do it.
    # steps/online/nnet2/align.sh --cmd "$decode_cmd $gpu_opts" --use-gpu "$use_gpu" \
    #    --nj $nj data/train_960 data/lang ${srcdir}_online ${srcdir}_ali || exit 1;
  fi
  
  
  if [ $stage -le 3 ]; then
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d ${srcdir}_degs/storage ]; then
      utils/create_split_dir.pl \
       /export/b0{1,2,5,6}/$USER/kaldi-data/egs/librispeech-$(date +'%m_%d_%H_%M')/s5/${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/nnet2/get_egs_discriminative2.sh \
      --cmd "$decode_cmd --max-jobs-run $max_jobs" \
      --online-ivector-dir exp/nnet2_online/ivectors_train_960_hires \
      --criterion $criterion --drop-frames $drop_frames \
       data/train_960_hires data/lang ${srcdir}{_ali,_denlats,/final.mdl,_degs} || exit 1;
  
    # the command below is a more generic, but slower, way to do it.
    #steps/online/nnet2/get_egs_discriminative2.sh \
    #  --cmd "$decode_cmd --max-jobs-run $max_jobs" \
    #  --criterion $criterion --drop-frames $drop_frames \
    #   data/train_960 data/lang ${srcdir}{_ali,_denlats,_online,_degs} || exit 1;
  fi
  
  if [ $stage -le 4 ]; then
    steps/nnet2/train_discriminative2.sh --cmd "$decode_cmd $parallel_opts" \
      --stage $train_stage \
      --learning-rate $learning_rate \
      --criterion $criterion --drop-frames $drop_frames \
      --num-epochs $num_epochs \
      --num-jobs-nnet 2 --num-threads $num_threads \
        ${srcdir}_degs ${srcdir}_${criterion}_${learning_rate} || exit 1;
  fi
  
  if [ $stage -le 5 ]; then
    dir=${srcdir}_${criterion}_${learning_rate}
    ln -sf $(utils/make_absolute.sh ${srcdir}_online/conf) $dir/conf # so it acts like an online-decoding directory
  
    for epoch in $(seq $decode_start_epoch $num_epochs); do
      for test in test_clean test_other dev_clean dev_other; do
        (
          steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 50 \
            --iter epoch$epoch exp/tri6b/graph_tgsmall data/${test} $dir/decode_epoch${epoch}_${test}_tgsmall || exit 1
          steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_{tgsmall,tgmed} \
            data/${test} $dir/decode_epoch${epoch}_${test}_{tgsmall,tgmed}  || exit 1;
          steps/lmrescore_const_arpa.sh \
            --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
            data/$test $dir/decode_epoch${epoch}_${test}_{tgsmall,tglarge} || exit 1;
          steps/lmrescore_const_arpa.sh \
            --cmd "$decode_cmd" data/lang_test_{tgsmall,fglarge} \
            data/$test $dir/decode_epoch${epoch}_${test}_{tgsmall,fglarge} || exit 1;
        ) &
      done
    done
    wait
    for dir in $dir/decode*; do grep WER $dir/wer_* | utils/best_wer.sh; done
  fi
  
  if [ $stage -le 6 ] && $cleanup; then
    # if you run with "--cleanup true --stage 6" you can clean up.
    rm ${srcdir}_denlats/lat.*.gz || true
    rm ${srcdir}_ali/ali.*.gz || true
    steps/nnet2/remove_egs.sh ${srcdir}_degs || true
  fi
  
  
  exit 0;