#!/bin/bash
  
  # example script for online-nnet2 system training and decoding,
  # based on the one for fisher-English.
  
  # note: run_nnet2_ms.sh gives better results, but it's slower to train.
  
  . ./cmd.sh
  
  
  stage=0
  train_stage=-10
  use_gpu=true
  dir=exp/nnet2_online/nnet_a
  
  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
    minibatch_size=512
    # the _a is in case I want to change the parameters.
  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
    minibatch_size=128
    parallel_opts="--num-threads $num_threads"
  fi
  
  local/online/run_nnet2_common.sh --stage $stage
  
  if [ $stage -le 7 ]; then
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
      utils/create_split_dir.pl \
       /export/b0{1,2,3,4}/$USER/kaldi-data/egs/librispeech-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
    fi
  
    # The size of the system is kept rather smaller than the run_7a_960.sh system:
    # this is because we want it to be small enough that we could plausibly run it
    # in real-time.
    steps/nnet2/train_pnorm_simple2.sh --stage $train_stage \
      --num-epochs 8 --num-jobs-nnet 6 \
      --splice-width 7 --feat-type raw \
      --online-ivector-dir exp/nnet2_online/ivectors_train_960_hires \
      --cmvn-opts "--norm-means=false --norm-vars=false" \
      --num-threads "$num_threads" \
      --minibatch-size "$minibatch_size" \
      --parallel-opts "$parallel_opts" \
      --io-opts "--max-jobs-run 12" \
      --initial-learning-rate 0.01 --final-learning-rate 0.001 \
      --cmd "$decode_cmd" \
      --pnorm-input-dim 3500 \
      --pnorm-output-dim 350 \
      --num-hidden-layers 4 \
      --mix-up 12000 \
      data/train_960_hires data/lang exp/tri6b $dir  || exit 1;
  fi
  
  if [ $stage -le 8 ]; then
    # dump iVectors for the testing data.
    for test in dev_clean dev_other; do
      steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 20 \
        data/${test}_hires exp/nnet2_online/extractor exp/nnet2_online/ivectors_$test || exit 1;
    done
  fi
  
  
  if [ $stage -le 9 ]; then
    # this does offline decoding that should give about the same results as the
    # real online decoding (the one with --per-utt true)
    for test in dev_clean dev_other; do
      steps/nnet2/decode.sh --nj 30 --cmd "$decode_cmd" --config conf/decode.config \
        --online-ivector-dir exp/nnet2_online/ivectors_${test} \
        exp/tri6b/graph_tgsmall data/${test}_hires $dir/decode_${test}_tgsmall || exit 1;
      steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_{tgsmall,tgmed} \
        data/${test}_hires $dir/decode_${test}_{tgsmall,tgmed}  || exit 1;
      steps/lmrescore_const_arpa.sh \
        --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
        data/$test $dir/decode_${test}_{tgsmall,tglarge} || exit 1;
    done
  fi
  
  
  if [ $stage -le 10 ]; then
    # If this setup used PLP features, we'd have to give the option --feature-type plp
    # to the script below.
    steps/online/nnet2/prepare_online_decoding.sh --mfcc-config conf/mfcc_hires.conf \
      data/lang exp/nnet2_online/extractor "$dir" ${dir}_online || exit 1;
  fi
  
  if [ $stage -le 11 ]; then
    # do the actual online decoding with iVectors, carrying info forward from
    # previous utterances of the same speaker.
    for test in test_clean test_other dev_clean dev_other; do
      steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 30 \
        exp/tri6b/graph_tgsmall data/$test ${dir}_online/decode_${test}_tgsmall || exit 1;
      steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_{tgsmall,tgmed} \
        data/$test ${dir}_online/decode_${test}_{tgsmall,tgmed}  || exit 1;
      steps/lmrescore_const_arpa.sh \
        --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
        data/$test ${dir}_online/decode_${test}_{tgsmall,tglarge} || exit 1;
      steps/lmrescore_const_arpa.sh \
        --cmd "$decode_cmd" data/lang_test_{tgsmall,fglarge} \
        data/$test ${dir}_online/decode_${test}_{tgsmall,fglarge} || exit 1;
    done
  fi
  
  if [ $stage -le 12 ]; then
    # this version of the decoding treats each utterance separately
    # without carrying forward speaker information.
    for test in dev_clean dev_other; do
      steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 30 \
        --per-utt true exp/tri6b/graph_tgsmall data/$test ${dir}_online/decode_${test}_tgsmall_utt || exit 1;
      steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_{tgsmall,tgmed} \
        data/$test ${dir}_online/decode_${test}_{tgsmall,tgmed}_utt  || exit 1;
      steps/lmrescore_const_arpa.sh \
        --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
        data/$test ${dir}_online/decode_${test}_{tgsmall,tglarge}_utt || exit 1;
    done
  fi
  
  if [ $stage -le 13 ]; then
    # this version of the decoding treats each utterance separately
    # without carrying forward speaker information, but looks to the end
    # of the utterance while computing the iVector (--online false)
    for test in dev_clean dev_other; do
      steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 30 \
        --per-utt true --online false exp/tri6b/graph_tgsmall data/$test \
          ${dir}_online/decode_${test}_tgsmall_utt_offline || exit 1;
      steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_{tgsmall,tgmed} \
        data/$test ${dir}_online/decode_${test}_{tgsmall,tgmed}_utt_offline  || exit 1;
      steps/lmrescore_const_arpa.sh \
        --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
        data/$test ${dir}_online/decode_${test}_{tgsmall,tglarge}_utt_offline || exit 1;
    done
  fi
  
  exit 0;
  ###### Comment out the "exit 0" above to run the multi-threaded decoding. #####
  
  if [ $stage -le 14 ]; then
    # Demonstrate the multi-threaded decoding.
    test=dev_clean
    steps/online/nnet2/decode.sh --threaded true \
      --config conf/decode.config --cmd "$decode_cmd" --nj 30 \
      --per-utt true exp/tri6b/graph_tgsmall data/$test \
      ${dir}_online/decode_${test}_tgsmall_utt_threaded || exit 1;
  fi
  
  if [ $stage -le 15 ]; then
    # Demonstrate the multi-threaded decoding with endpointing.
    test=dev_clean
    steps/online/nnet2/decode.sh --threaded true --do-endpointing true \
      --config conf/decode.config --cmd "$decode_cmd" --nj 30 \
      --per-utt true exp/tri6b/graph_tgsmall data/$test \
      ${dir}_online/decode_${test}_tgsmall_utt_threaded_ep || exit 1;
  fi
  
  exit 0;