#!/bin/bash
  
  # ./local/chain/compare_wer.sh exp/chain/tdnn_1a_sp
  # System                      tdnn_1a_sp
  # WER                             16.47
  # CER                              6.68
  # Final train prob              -0.0652
  # Final valid prob              -0.0831
  # Final train prob (xent)       -0.8965
  # Final valid prob (xent)       -0.9964
  
  # steps/info/chain_dir_info.pl exp/chain/tdnn_1a_sp/
  # exp/chain/tdnn_1a_sp/: num-iters=441 nj=3..16 num-params=18.6M dim=40+100->5816 combine=-0.063->-0.062 (over 6) xent:train/valid[293,440,final]=(-1.22,-0.912,-0.896/-1.29,-1.01,-0.996) logprob:train/valid[293,440,final]=(-0.097,-0.066,-0.065/-0.108,-0.084,-0.083)
  
  
  set -e -o pipefail
  stage=0
  nj=30
  train_set=train
  test_set=test
  gmm=tri3b        # this is the source gmm-dir that we'll use for alignments; it
                   # should have alignments for the specified training data.
  num_threads_ubm=32
  nnet3_affix=       # affix for exp dirs, e.g. it was _cleaned in tedlium.
  
  # Options which are not passed through to run_ivector_common.sh
  affix=_1a   #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
  common_egs_dir=
  reporting_email=
  
  # LSTM/chain options
  train_stage=-10
  xent_regularize=0.1
  dropout_schedule='0,0@0.20,0.5@0.50,0'
  
  # training chunk-options
  chunk_width=150,110,100
  get_egs_stage=-10
  
  # training options
  srand=0
  remove_egs=true
  run_ivector_common=true
  run_chain_common=true
  # 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
  
  if $run_ivector_common; then
    local/nnet3/run_ivector_common.sh \
      --stage $stage --nj $nj \
      --train-set $train_set --gmm $gmm \
      --num-threads-ubm $num_threads_ubm \
      --nnet3-affix "$nnet3_affix"
  fi
  
  gmm_dir=exp/${gmm}
  ali_dir=exp/${gmm}_ali_${train_set}_sp
  lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
  dir=exp/chain${nnet3_affix}/tdnn${affix}_sp
  train_data_dir=data/${train_set}_sp_hires
  train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
  lores_train_data_dir=data/${train_set}_sp
  
  # note: you don't necessarily have to change the treedir name
  # each time you do a new experiment-- only if you change the
  # configuration in a way that affects the tree.
  tree_dir=exp/chain${nnet3_affix}/tree_a_sp
  # 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_ivector_dir/ivector_online.scp \
      $lores_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
  
  # Please take this as a reference on how to specify all the options of
  # local/chain/run_chain_common.sh
  if $run_chain_common; then
    local/chain/run_chain_common.sh --stage $stage \
                                    --gmm-dir $gmm_dir \
                                    --ali-dir $ali_dir \
                                    --lores-train-data-dir ${lores_train_data_dir} \
                                    --lang $lang \
                                    --lat-dir $lat_dir \
                                    --num-leaves 7000 \
                                    --tree-dir $tree_dir || exit 1;
  fi
  
  if [ $stage -le 15 ]; 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)
    affine_opts="l2-regularize=0.01 dropout-proportion=0.0 dropout-per-dim=true 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=0.002"
  
    mkdir -p $dir/configs
  
    cat <<EOF > $dir/configs/network.xconfig
    input dim=100 name=ivector
    input dim=40 name=input
    # please note that it is important to have input layer with the name=input
    # as the layer immediately preceding the fixed-affine-layer to enable
    # the use of short notation for the descriptor
    fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
    # the first splicing is moved before the lda layer, so no splicing here
    relu-batchnorm-dropout-layer name=tdnn1 $affine_opts dim=1536
    tdnnf-layer name=tdnnf2 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=1
    tdnnf-layer name=tdnnf3 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=1
    tdnnf-layer name=tdnnf4 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=1
    tdnnf-layer name=tdnnf5 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=0
    tdnnf-layer name=tdnnf6 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf7 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf8 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf9 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf10 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf11 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf12 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf13 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf14 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf15 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    linear-component name=prefinal-l dim=256 $linear_opts
    prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts big-dim=1536 small-dim=256
    output-layer name=output include-log-softmax=false dim=$num_targets $output_opts
    prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts big-dim=1536 small-dim=256
    output-layer name=output-xent dim=$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 16 ]; 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/wsj-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
    fi
  
    steps/nnet3/chain/train.py --stage $train_stage \
      --cmd "$decode_cmd" \
      --feat.online-ivector-dir $train_ivector_dir \
      --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
      --chain.xent-regularize $xent_regularize \
      --chain.leaky-hmm-coefficient 0.1 \
      --chain.l2-regularize 0.0 \
      --chain.apply-deriv-weights false \
      --chain.lm-opts="--num-extra-lm-states=2000" \
      --trainer.dropout-schedule $dropout_schedule \
      --trainer.srand=$srand \
      --trainer.max-param-change=2.0 \
      --trainer.num-epochs 6 \
      --trainer.frames-per-iter 1500000 \
      --trainer.optimization.num-jobs-initial 3 \
      --trainer.optimization.num-jobs-final 16 \
      --trainer.optimization.initial-effective-lrate 0.00025 \
      --trainer.optimization.final-effective-lrate 0.000025 \
      --trainer.num-chunk-per-minibatch=64,32 \
      --trainer.add-option="--optimization.memory-compression-level=2" \
      --egs.chunk-width=$chunk_width \
      --egs.dir="$common_egs_dir" \
      --egs.opts "--frames-overlap-per-eg 0 --constrained false" \
      --egs.stage $get_egs_stage \
      --reporting.email="$reporting_email" \
      --cleanup.remove-egs=$remove_egs \
      --feat-dir=$train_data_dir \
      --tree-dir $tree_dir \
      --lat-dir=$lat_dir \
      --dir $dir  || exit 1;
  
  fi
  
  if [ $stage -le 17 ]; 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/lang/check_phones_compatible.sh \
      data/lang_test/phones.txt $lang/phones.txt
    utils/mkgraph.sh \
      --self-loop-scale 1.0 data/lang_test \
      $tree_dir $tree_dir/graph || exit 1;
  fi
  
  if [ $stage -le 18 ]; then
    frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
    rm $dir/.error 2>/dev/null || true
  
      steps/nnet3/decode.sh \
        --acwt 1.0 --post-decode-acwt 10.0 \
        --extra-left-context 0 --extra-right-context 0 \
        --extra-left-context-initial 0 \
        --extra-right-context-final 0 \
        --frames-per-chunk $frames_per_chunk \
        --nj $nj --cmd "$decode_cmd"  --num-threads 4 \
        --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${test_set}_hires \
        $tree_dir/graph data/${test_set}_hires ${dir}/decode_${test_set} || exit 1
  fi