#!/bin/bash
  # Copyright    2017  Hossein Hadian
  
  # This script does end2end chain training (i.e. from scratch)
  
  # local/chain/compare_wer.sh exp_yomdle_farsi/chain/e2e_cnn_1a exp_yomdle_farsi/chain/cnn_e2eali_1b
  # System                      e2e_cnn_1a cnn_e2eali_1b
  # WER                             19.55     18.45
  # CER                              5.64      4.94
  # Final train prob              -0.0065   -0.0633
  # Final valid prob               0.0015   -0.0619
  # Final train prob (xent)                 -0.2636
  # Final valid prob (xent)                 -0.2511
  
  set -e
  
  data_dir=data
  exp_dir=exp
  
  # configs for 'chain'
  stage=0
  nj=30
  train_stage=-10
  get_egs_stage=-10
  affix=1a
  
  # training options
  tdnn_dim=450
  num_epochs=4
  num_jobs_initial=4
  num_jobs_final=8
  minibatch_size=150=64,32/300=32,16/600=16,8/1200=8,4
  common_egs_dir=
  l2_regularize=0.00005
  frames_per_iter=1000000
  cmvn_opts="--norm-means=false --norm-vars=false"
  train_set=train
  lang_test=lang_test
  
  # 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
  
  lang=$data_dir/lang_e2e
  treedir=$exp_dir/chain/e2e_monotree  # it's actually just a trivial tree (no tree building)
  dir=$exp_dir/chain/e2e_cnn_${affix}
  
  if [ $stage -le 0 ]; 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_dir/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 1 ]; then
    steps/nnet3/chain/e2e/prepare_e2e.sh --nj $nj --cmd "$cmd" \
                                         --shared-phones true \
                                         --type mono \
                                         $data_dir/$train_set $lang $treedir
    $cmd $treedir/log/make_phone_lm.log \
    cat $data_dir/$train_set/text \| \
      steps/nnet3/chain/e2e/text_to_phones.py $data_dir/lang \| \
      utils/sym2int.pl -f 2- $data_dir/lang/phones.txt \| \
      chain-est-phone-lm --num-extra-lm-states=500 \
                         ark:- $treedir/phone_lm.fst
  fi
  
  if [ $stage -le 2 ]; then
    echo "$0: creating neural net configs using the xconfig parser";
    num_targets=$(tree-info $treedir/tree | grep num-pdfs | awk '{print $2}')
    
    cnn_opts="l2-regularize=0.075"
    tdnn_opts="l2-regularize=0.075"
    output_opts="l2-regularize=0.1"
  
    common1="$cnn_opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=72"
    common2="$cnn_opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=144"
    common3="$cnn_opts required-time-offsets= height-offsets=-1,0,1 num-filters-out=144"
    mkdir -p $dir/configs
    cat <<EOF > $dir/configs/network.xconfig
    input dim=120 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=20 time-offsets=-4,-2,0,2,4 $common2
    conv-relu-batchnorm-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
    conv-relu-batchnorm-layer name=cnn6 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
    conv-relu-batchnorm-layer name=cnn7 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
    relu-batchnorm-layer name=tdnn1 input=Append(-8,-4,0,4,8) dim=$tdnn_dim $tdnn_opts
    relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim $tdnn_opts
    relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim $tdnn_opts
    ## adding the layers for chain branch
    relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5 $output_opts
    output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5 $output_opts
  EOF
  
    steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs
  fi
  
  if [ $stage -le 3 ]; then
    # no need to store the egs in a shared storage because we always
    # remove them. Anyway, it takes only 5 minutes to generate them.
  
    steps/nnet3/chain/e2e/train_e2e.py --stage $train_stage \
      --cmd "$cmd" \
      --feat.cmvn-opts "$cmvn_opts" \
      --chain.leaky-hmm-coefficient 0.1 \
      --chain.l2-regularize $l2_regularize \
      --chain.apply-deriv-weights false \
      --egs.dir "$common_egs_dir" \
      --egs.stage $get_egs_stage \
      --egs.opts "--num_egs_diagnostic 100 --num_utts_subset 400" \
      --chain.frame-subsampling-factor 4 \
      --chain.alignment-subsampling-factor 4 \
      --trainer.add-option="--optimization.memory-compression-level=2" \
      --trainer.num-chunk-per-minibatch $minibatch_size \
      --trainer.frames-per-iter $frames_per_iter \
      --trainer.num-epochs $num_epochs \
      --trainer.optimization.momentum 0 \
      --trainer.optimization.num-jobs-initial $num_jobs_initial \
      --trainer.optimization.num-jobs-final $num_jobs_final \
      --trainer.optimization.initial-effective-lrate 0.001 \
      --trainer.optimization.final-effective-lrate 0.0001 \
      --trainer.optimization.shrink-value 1.0 \
      --trainer.max-param-change 2.0 \
      --cleanup.remove-egs true \
      --feat-dir $data_dir/${train_set} \
      --tree-dir $treedir \
      --dir $dir  || exit 1;
  fi
  
  if [ $stage -le 4 ]; 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 $data_dir/$lang_test \
      $dir $dir/graph || exit 1;
  fi
  
  if [ $stage -le 5 ]; then
    frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
      --nj $nj --cmd "$cmd" \
      $dir/graph $data_dir/test $dir/decode_test || exit 1;
  fi
  
  echo "Done. Date: $(date). Results:"
  local/chain/compare_wer.sh $dir