#!/bin/bash
  
  # this script is a modified version of run_tdnn_5n.sh. It uses
  # a new configs convention for chain model after kaldi 5.2.
  
  
  
  set -e
  
  # configs for 'chain'
  stage=0
  train_stage=-10
  get_egs_stage=-10
  xent_regularize=0.1
  dir=exp/chain/tdnn_5o
  
  # training options
  num_epochs=13
  initial_effective_lrate=0.005
  final_effective_lrate=0.0005
  max_param_change=2.0
  final_layer_normalize_target=0.5
  num_jobs_initial=2
  num_jobs_final=4
  minibatch_size=128
  frames_per_eg=150
  remove_egs=false
  #common_egs_dir=exp/chain/tdnn_5g/egs/
  common_egs_dir=
  # End configuration section.
  echo "$0 $@"  # Print the command line for logging
  
  . ./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
  
  # The iVector-extraction and feature-dumping parts are the same as the standard
  # nnet2 setup, and you can skip them by setting "--stage 4" if you have already
  # run those things.
  
  ali_dir=exp/tri3b_ali
  treedir=exp/chain/tri4_5o_tree
  lang=data/lang_chain_5o
  
  local/online/run_nnet2_common.sh --stage $stage || exit 1;
  
  if [ $stage -le 4 ]; then
    # Get the alignments as lattices (gives the chain training more freedom).
    # use the same num-jobs as the alignments
    nj=$(cat exp/tri3b_ali/num_jobs) || exit 1;
    steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/train \
      data/lang exp/tri3b exp/tri3b_lats
    rm exp/tri3b_lats/fsts.*.gz # save space
  fi
  
  if [ $stage -le 5 ]; then
    # Create a version of the lang/ directory that has one state per phone in the
    # topo file. [note, it really has two states.. the first one is only repeated
    # once, the second one has zero or more repeats.]
    rm -rf $lang
    cp -r data/lang $lang
    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
    # Use our special topology... note that later on may have to tune this
    # topology.
    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
  fi
  
  if [ $stage -le 6 ]; then
    # Build a tree using our new topology.
    steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
      --cmd "$train_cmd" 1200 data/train $lang $ali_dir $treedir
  fi
  
  if [ $stage -le 7 ]; then
    mkdir -p $dir
    echo "$0: creating neural net configs using the xconfig parser";
  
    num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
    learning_rate_factor=$(echo "print(0.5/$xent_regularize)" | python)
    tdnn_opts="l2-regularize=0.01 dropout-proportion=0.0 dropout-per-dim-continuous=true"
    tdnnf_opts="l2-regularize=0.01 dropout-proportion=0.0 bypass-scale=0.66"
    linear_opts="l2-regularize=0.01 orthonormal-constraint=-1.0"
    prefinal_opts="l2-regularize=0.01"
    output_opts="l2-regularize=0.005"
  
    mkdir -p $dir/configs
    cat <<EOF > $dir/configs/network.xconfig
    input dim=50 name=ivector
    input dim=13 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(-1,0,1,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-batchnorm-dropout-layer name=tdnn1 $tdnn_opts dim=768
    tdnnf-layer name=tdnnf2 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=1
    tdnnf-layer name=tdnnf3 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=1
    tdnnf-layer name=tdnnf4 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=1
    tdnnf-layer name=tdnnf5 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=0
    tdnnf-layer name=tdnnf6 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
    tdnnf-layer name=tdnnf7 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
    tdnnf-layer name=tdnnf8 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
    tdnnf-layer name=tdnnf9 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
    tdnnf-layer name=tdnnf10 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
    tdnnf-layer name=tdnnf11 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
    tdnnf-layer name=tdnnf12 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
    tdnnf-layer name=tdnnf13 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
    linear-component name=prefinal-l dim=192 $linear_opts
  
    ## adding the layers for chain branch
    prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts small-dim=192 big-dim=768
    output-layer name=output include-log-softmax=false dim=$num_targets $output_opts
  
    # adding the layers for xent branch
    prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts small-dim=192 big-dim=768
    output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor $output_opts
  EOF
    steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
  fi
  
  if [ $stage -le 8 ]; then
   steps/nnet3/chain/train.py --stage $train_stage \
      --cmd "$decode_cmd" \
      --feat.online-ivector-dir exp/nnet2_online/ivectors \
      --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
      --chain.xent-regularize 0.1 \
      --chain.leaky-hmm-coefficient 0.1 \
      --chain.l2-regularize 0.00005 \
      --chain.apply-deriv-weights false \
      --chain.lm-opts="--num-extra-lm-states=200" \
      --egs.dir "$common_egs_dir" \
      --egs.opts "--frames-overlap-per-eg 0" \
      --egs.chunk-width $frames_per_eg \
      --trainer.num-chunk-per-minibatch $minibatch_size \
      --trainer.frames-per-iter 1000000 \
      --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 \
      --trainer.max-param-change $max_param_change \
      --cleanup.remove-egs $remove_egs \
      --feat-dir data/train_hires \
      --tree-dir $treedir \
      --lat-dir exp/tri3b_lats \
      --dir $dir
  fi
  
  if [ $stage -le 9 ]; then
    steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 4 \
      data/test_hires exp/nnet2_online/extractor exp/nnet2_online/ivectors_test || exit 1;
  fi
  
  if [ $stage -le 10 ]; then
    # Note: it might appear that this $lang directory is mismatched, and it is as
    # far as the 'topo' is concerned, but this script doesn't read the 'topo' from
    # the lang directory.
    utils/mkgraph.sh --self-loop-scale 1.0 data/lang $dir $dir/graph
    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
      --scoring-opts "--min-lmwt 1" \
      --nj 20 --cmd "$decode_cmd" \
      --online-ivector-dir exp/nnet2_online/ivectors_test \
      $dir/graph data/test_hires $dir/decode || exit 1;
  fi
  
  if [ $stage -le 11 ]; then
    utils/mkgraph.sh --self-loop-scale 1.0 data/lang_ug $dir $dir/graph_ug
    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
      --nj 20 --cmd "$decode_cmd" \
      --online-ivector-dir exp/nnet2_online/ivectors_test \
      $dir/graph_ug data/test_hires $dir/decode_ug || exit 1;
  fi
  wait;
  exit 0;