#!/bin/bash
  
  # This script is based on run_tdnn_7h.sh in swbd chain recipe.
  # exp 2a: change the step of making configs, using xconfig with
  #         minor changes on training configs, referencing wsj
  
  # Results:
  # local/nnet3/compare_wer_general.sh --online exp/nnet3/tdnn_sp_pr43_2a
  # Model                tdnn_sp_pr43_2a
  # WER(%)                    32.86
  # WER(%)[online]            33.08
  # WER(%)[per-utt]           34.51
  # Final train prob        -1.2331
  # Final valid prob        -1.6510
  
  # At this script level we don't support not running on GPU, as it would be painfully slow.
  # If you want to run without GPU you'd have to call train_tdnn.sh with --gpu false,
  # --num-threads 16 and --minibatch-size 128.
  set -euxo pipefail
  
  stage=0
  nj=10
  train_stage=-10
  affix=
  common_egs_dir=
  
  # training options
  initial_effective_lrate=0.0015
  final_effective_lrate=0.00015
  num_epochs=4
  num_jobs_initial=2
  num_jobs_final=12
  remove_egs=true
  
  # feature options
  use_ivectors=true
  
  # End configuration section.
  
  . ./cmd.sh
  . ./path.sh
  . ./utils/parse_options.sh
  
  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.
  EOF
  fi
  
  dir=exp/nnet3/tdnn_sp${affix:+_$affix}
  gmm_dir=exp/tri5a
  train_set=train_sp
  ali_dir=${gmm_dir}_sp_ali
  graph_dir=$gmm_dir/graph
  
  if [ $stage -le 0 ]; then
    local/nnet3/run_ivector_common.sh --stage $stage \
      --ivector-extractor exp/nnet3/extractor || exit 1;
  fi
  
  if [ $stage -le 8 ]; then
    echo "$0: creating neural net configs";
  
    ivector_dim=$(feat-to-dim scp:exp/nnet3/ivectors_${train_set}/ivector_online.scp - || exit 1;)
    feat_dim=$(feat-to-dim scp:data/${train_set}_hires/feats.scp - || exit 1;)
    num_targets=$(tree-info $ali_dir/tree | grep num-pdfs | awk '{print $2}')
  
    mkdir -p $dir/configs
    cat <<EOF > $dir/configs/network.xconfig
    input dim=$ivector_dim name=ivector
    input dim=$feat_dim name=input
  
    # please note that it is important to have input layer with the name=input
    # as the layer immediately preceding the fixed-affine-layer to enable
    # the use of short notation for the descriptor
    fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
  
    # the first splicing is moved before the lda layer, so no splicing here
    relu-renorm-layer name=tdnn1 dim=1024
    relu-renorm-layer name=tdnn2 input=Append(-1,2) dim=1024
    relu-renorm-layer name=tdnn3 input=Append(-3,3) dim=1024
    relu-renorm-layer name=tdnn4 input=Append(-7,2) dim=1024
    relu-renorm-layer name=tdnn5 input=Append(-3,3) dim=1024
    relu-renorm-layer name=tdnn6 dim=1024
  
    output-layer name=output input=tdnn6 dim=$num_targets max-change=1.5
  EOF
  
    steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
  fi
  
  if [ $stage -le 9 ]; then
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
      utils/create_split_dir.pl \
       /export/b0{5,6,7,8}/$USER/kaldi-data/egs/hkust-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
    fi
  
    steps/nnet3/train_dnn.py --stage=$train_stage \
      --cmd="$decode_cmd" \
      --feat.online-ivector-dir exp/nnet3/ivectors_${train_set} \
      --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
      --trainer.num-epochs $num_epochs \
      --trainer.samples-per-iter=400000 \
      --trainer.optimization.num-jobs-initial $num_jobs_initial \
      --trainer.optimization.num-jobs-final $num_jobs_final \
      --trainer.optimization.initial-effective-lrate $initial_effective_lrate \
      --trainer.optimization.final-effective-lrate $final_effective_lrate \
      --egs.dir "$common_egs_dir" \
      --cleanup.remove-egs $remove_egs \
      --cleanup.preserve-model-interval 500 \
      --use-gpu true \
      --feat-dir=data/${train_set}_hires \
      --ali-dir $ali_dir \
      --lang data/lang \
      --reporting.email="$reporting_email" \
      --dir=$dir  || exit 1;
  fi
  
  if [ $stage -le 10 ]; then
    # this version of the decoding treats each utterance separately
    # without carrying forward speaker information.
    for decode_set in dev; do
        (
        num_jobs=`cat data/${decode_set}_hires/utt2spk|cut -d' ' -f2|sort -u|wc -l`
        decode_dir=${dir}/decode
        ivector_opts=" --online-ivector-dir exp/nnet3/ivectors_${decode_set} "
  
        steps/nnet3/decode.sh --nj $num_jobs --cmd "$decode_cmd" $ivector_opts \
           $graph_dir data/${decode_set}_hires $decode_dir || exit 1;
        ) &
    done
  fi
  
  if [ $stage -le 11 ]; then
    steps/online/nnet3/prepare_online_decoding.sh --mfcc-config conf/mfcc_hires.conf \
      --add-pitch true \
      data/lang exp/nnet3/extractor "$dir" ${dir}_online || exit 1;
  fi
  
  if [ $stage -le 12 ]; then
    # do the actual online decoding with iVectors, carrying info forward from
    # previous utterances of the same speaker.
    graph_dir=exp/tri5a/graph
    steps/online/nnet3/decode.sh --config conf/decode.config \
      --cmd "$decode_cmd" --nj $nj \
      "$graph_dir" data/dev_hires \
      ${dir}_online/decode || exit 1;
  fi
  
  if [ $stage -le 13 ]; then
    # this version of the decoding treats each utterance separately
    # without carrying forward speaker information.
    graph_dir=exp/tri5a/graph
    steps/online/nnet3/decode.sh --config conf/decode.config \
      --cmd "$decode_cmd" --nj $nj --per-utt true \
      "$graph_dir" data/dev_hires \
      ${dir}_online/decode_per_utt || exit 1;
  fi
  
  wait;
  exit 0;