#!/bin/bash
  
  # Copyright    2017 Hossein Hadian
  #              2017 Chun Chieh Chang
  #              2017 Ashish Arora
  
  # local/chain/compare_wer.sh exp/chain/cnn_1a/
  # System                         cnn_1a(dict_50k)      cnn_1a(dict_50k + unk model)
  # WER                              16.88                    15.18
  # CER                               8.52                    7.58
  # WER val                          16.17                    13.53
  # CER val                           7.15                    5.89
  # Final train prob               -0.0299
  # Final valid prob               -0.0574
  # Final train prob (xent)        -0.3912
  # Final valid prob (xent)        -0.6439
  # Parameters                       4.36M
  
  # steps/info/chain_dir_info.pl exp/chain/cnn_1a/
  # exp/chain/cnn_1a/: num-iters=42 nj=2..4 num-params=4.4M dim=40->368 combine=-0.029->-0.029 (over 2) xent:train/valid[27,41,final]=(-0.522,-0.394,-0.391/-0.695,-0.644,-0.644) logprob:train/valid[27,41,final]=(-0.035,-0.030,-0.030/-0.056,-0.057,-0.057)
  
  set -e -o pipefail
  stage=0
  nj=30
  train_set=train
  gmm=tri3        # this is the source gmm-dir that we'll use for alignments; it
                  # should have alignments for the specified training data.
  nnet3_affix=    # affix for exp dirs, e.g. it was _cleaned in tedlium.
  affix=_1a  #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
  ali=tri3_ali
  common_egs_dir=
  reporting_email=
  
  # chain options
  train_stage=-10
  xent_regularize=0.1
  # training chunk-options
  chunk_width=340,300,200,100
  num_leaves=500
  tdnn_dim=450
  # training options
  lang_decode=lang_unk
  decode_val=true
  if $decode_val; then maybe_val=val; else maybe_val= ; fi
  # 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
  
  gmm_dir=exp/${gmm}
  ali_dir=exp/${ali}
  lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_lats
  dir=exp/chain${nnet3_affix}/cnn${affix}
  train_data_dir=data/${train_set}
  tree_dir=exp/chain${nnet3_affix}/tree
  
  # the 'lang' directory is created by this script.
  # If you create such a directory with a non-standard topology
  # you should probably name it differently.
  lang=data/lang_chain
  
  for f in $train_data_dir/feats.scp \
      $train_data_dir/feats.scp $gmm_dir/final.mdl \
      $ali_dir/ali.1.gz $gmm_dir/final.mdl; do
    [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
  done
  
  
  if [ $stage -le 1 ]; then
    echo "$0: creating lang directory $lang with chain-type topology"
    # 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.]
    if [ -d $lang ]; then
      if [ $lang/L.fst -nt data/lang/L.fst ]; then
        echo "$0: $lang already exists, not overwriting it; continuing"
      else
        echo "$0: $lang already exists and seems to be older than data/lang..."
        echo " ... not sure what to do.  Exiting."
        exit 1;
      fi
    else
      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
  fi
  
  if [ $stage -le 2 ]; then
    # Get the alignments as lattices (gives the chain training more freedom).
    # use the same num-jobs as the alignments
    steps/align_fmllr_lats.sh --nj $nj --cmd "$cmd" $train_data_dir \
      data/lang $gmm_dir $lat_dir
    rm $lat_dir/fsts.*.gz # save space
  fi
  
  if [ $stage -le 3 ]; then
    # Build a tree using our new topology.  We know we have alignments for the
    # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
    # those.  The num-leaves is always somewhat less than the num-leaves from
    # the GMM baseline.
     if [ -f $tree_dir/final.mdl ]; then
       echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
       exit 1;
    fi
    steps/nnet3/chain/build_tree.sh \
      --frame-subsampling-factor 4 \
      --context-opts "--context-width=2 --central-position=1" \
      --cmd "$cmd" $num_leaves $train_data_dir \
      $lang $ali_dir $tree_dir
  fi
  
  
  if [ $stage -le 4 ]; then
    mkdir -p $dir
    echo "$0: creating neural net configs using the xconfig parser";
    num_targets=$(tree-info $tree_dir/tree | grep num-pdfs | awk '{print $2}')
    learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
    common1="height-offsets=-2,-1,0,1,2 num-filters-out=36"
    common2="height-offsets=-2,-1,0,1,2 num-filters-out=70"
    mkdir -p $dir/configs
    cat <<EOF > $dir/configs/network.xconfig
    input dim=40 name=input
  
    conv-relu-batchnorm-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1
    conv-relu-batchnorm-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2
    conv-relu-batchnorm-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
    conv-relu-batchnorm-layer name=cnn4 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
    relu-batchnorm-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim
    relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim
    relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim
    relu-batchnorm-layer name=tdnn4 input=Append(-4,0,4) dim=$tdnn_dim
  
    ## adding the layers for chain branch
    relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5
    output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
    # adding the layers for xent branch
    # This block prints the configs for a separate output that will be
    # trained with a cross-entropy objective in the 'chain' mod?els... this
    # has the effect of regularizing the hidden parts of the model.  we use
    # 0.5 / args.xent_regularize as the learning rate factor- the factor of
    # 0.5 / args.xent_regularize is suitable as it means the xent
    # final-layer learns at a rate independent of the regularization
    # constant; and the 0.5 was tuned so as to make the relative progress
    # similar in the xent and regular final layers.
    relu-batchnorm-layer name=prefinal-xent input=tdnn4 dim=$tdnn_dim target-rms=0.5
    output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor 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 5 ]; then
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
      utils/create_split_dir.pl \
       /export/b0{3,4,5,6}/$USER/kaldi-data/egs/iam-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
    fi
  
    steps/nnet3/chain/train.py --stage=$train_stage \
      --cmd="$cmd" \
      --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
      --chain.xent-regularize $xent_regularize \
      --chain.leaky-hmm-coefficient=0.1 \
      --chain.l2-regularize=0.00005 \
      --chain.apply-deriv-weights=false \
      --chain.lm-opts="--num-extra-lm-states=500" \
      --chain.frame-subsampling-factor=4 \
      --chain.alignment-subsampling-factor=4 \
      --trainer.srand=0 \
      --trainer.max-param-change=2.0 \
      --trainer.num-epochs=4 \
      --trainer.frames-per-iter=1000000 \
      --trainer.optimization.num-jobs-initial=2 \
      --trainer.optimization.num-jobs-final=4 \
      --trainer.optimization.initial-effective-lrate=0.001 \
      --trainer.optimization.final-effective-lrate=0.0001 \
      --trainer.optimization.shrink-value=1.0 \
      --trainer.num-chunk-per-minibatch=64,32 \
      --egs.chunk-width=$chunk_width \
      --egs.dir="$common_egs_dir" \
      --egs.opts="--frames-overlap-per-eg 0" \
      --cleanup.remove-egs=false \
      --use-gpu=true \
      --reporting.email="$reporting_email" \
      --feat-dir=$train_data_dir \
      --tree-dir=$tree_dir \
      --lat-dir=$lat_dir \
      --dir=$dir  || exit 1;
  fi
  
  if [ $stage -le 6 ]; then
    # The reason we are using data/lang here, instead of $lang, is just to
    # emphasize that it's not actually important to give mkgraph.sh the
    # lang directory with the matched topology (since it gets the
    # topology file from the model).  So you could give it a different
    # lang directory, one that contained a wordlist and LM of your choice,
    # as long as phones.txt was compatible.
    utils/mkgraph.sh \
      --self-loop-scale 1.0 data/$lang_decode \
      $dir $dir/graph || exit 1;
  fi
  
  if [ $stage -le 7 ]; then
    frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
    for decode_set in test $maybe_val; do
      steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
        --frames-per-chunk $frames_per_chunk \
        --nj $nj --cmd "$cmd" \
        $dir/graph data/$decode_set $dir/decode_$decode_set || exit 1;
    done
  fi
  
  echo "$0 Done. Date: $(date). Results:"
  local/chain/compare_wer.sh $dir