#!/bin/bash
  
  # This script is based on run_tdnn_1a.sh, but with pitch features applied
  
  # this is the standard "tdnn" system, built in nnet3; it's what we use to
  # call multi-splice.
  
  # 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.
  
  # results
  # local/nnet3/compare_wer.sh exp/nnet3/tdnn_sp/
  # Model                  tdnn_sp
  # WER(%)                    11.02
  # Final train prob        -1.1265
  # Final valid prob        -1.2600
  
  set -e
  
  stage=0
  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
  nj=30
  remove_egs=true
  
  # feature options
  use_ivectors=false
  
  # 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
  
  # we use 43-dim high-resolution mfcc features (w pitch and w/o ivector) for nn training
  # no utt- and spk- level cmvn
  
  dir=exp/nnet3/tdnn_sp${affix:+_$affix}
  gmm_dir=exp/tri3
  test_sets="dev test"
  train_set=train
  ali_dir=${gmm_dir}_ali
  graph_dir=${gmm_dir}/graph
  
  if [ $stage -le 6 ]; then
    mfccdir=mfcc_hires
    for datadir in ${train_set} ${test_sets}; do
      utils/copy_data_dir.sh data/${datadir} data/${datadir}_hires
      utils/data/perturb_data_dir_volume.sh data/${datadir}_hires || exit 1;
      steps/make_mfcc_pitch.sh --mfcc-config conf/mfcc_hires.conf --pitch-config conf/pitch.conf \
        --nj $nj data/${datadir}_hires exp/make_mfcc/ ${mfccdir}
    done
  fi
  
  if [ $stage -le 7 ]; then
    echo "$0: creating neural net configs";
  
    num_targets=$(tree-info $ali_dir/tree |grep num-pdfs|awk '{print $2}')
    input_dim=$(feat-to-dim scp:data/${train_set}_hires/feats.scp -)  
  
    mkdir -p $dir/configs
    cat <<EOF > $dir/configs/network.xconfig
    input dim=$input_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) affine-transform-file=$dir/configs/lda.mat
  
    # the first splicing is moved before the lda layer, so no splicing here
    relu-batchnorm-layer name=tdnn1 dim=850
    relu-batchnorm-layer name=tdnn2 dim=850 input=Append(-1,0,2)
    relu-batchnorm-layer name=tdnn3 dim=850 input=Append(-3,0,3)
    relu-batchnorm-layer name=tdnn4 dim=850 input=Append(-7,0,2)
    relu-batchnorm-layer name=tdnn5 dim=850 input=Append(-3,0,3)
    relu-batchnorm-layer name=tdnn6 dim=850
    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 8 ]; 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/aishell-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
    #fi
  
    steps/nnet3/train_dnn.py --stage=$train_stage \
      --cmd="$decode_cmd" \
      --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
      --trainer.num-epochs $num_epochs \
      --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 9 ]; then
    for decode_set in $test_sets; do
      # this version of the decoding treats each utterance separately
      # without carrying forward speaker information.
      num_jobs=`cat data/${decode_set}_hires/utt2spk|cut -d' ' -f2|sort -u|wc -l`
      decode_dir=${dir}/decode_$decode_set
      steps/nnet3/decode.sh --nj $num_jobs --cmd "$decode_cmd" \
         $graph_dir data/${decode_set}_hires $decode_dir || exit 1;
    done
  fi
  
  wait;
  echo "local/nnet3/run_tdnn.sh succeeded"
  exit 0;