#!/bin/bash
  # Copyright    2017  Hossein Hadian
  
  # This script does end2end chain training (i.e. from scratch)
  # ./local/chain/compare_wer.sh exp/chain/e2e_cnn_1a/
  # System                      e2e_cnn_1a
  # WER                             11.24
  # WER (rescored)                  10.80
  # CER                              5.32
  # CER (rescored)                   5.24
  # Final train prob               0.0568
  # Final valid prob               0.0381
  # Final train prob (xent)
  # Final valid prob (xent)
  # Parameters                      9.13M
  
  # steps/info/chain_dir_info.pl exp/chain/e2e_cnn_1a
  # exp/chain/e2e_cnn_1a: num-iters=42 nj=2..4 num-params=9.1M dim=40->12640 combine=0.049->0.049 (over 1) logprob:train/valid[27,41,final]=(0.035,0.055,0.057/0.016,0.037,0.038)
  
  
  set -e
  
  # configs for 'chain'
  stage=0
  train_stage=-10
  get_egs_stage=-10
  affix=1a
  nj=30
  
  # training options
  tdnn_dim=450
  num_epochs=4
  num_jobs_initial=2
  num_jobs_final=4
  minibatch_size=150=100,64/300=50,32/600=25,16/1200=16,8
  common_egs_dir=
  l2_regularize=0.00005
  frames_per_iter=1000000
  cmvn_opts="--norm-means=true --norm-vars=true"
  train_set=train
  decode_val=true
  lang_decode=data/lang
  lang_rescore=data/lang_rescore_6g
  
  # 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/lang_e2e
  treedir=exp/chain/e2e_bitree  # it's actually just a trivial tree (no tree building)
  dir=exp/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/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 [ $stage -le 1 ]; then
    steps/nnet3/chain/e2e/prepare_e2e.sh --nj 30 --cmd "$cmd" \
                                         --shared-phones true \
                                         --type biphone \
                                         data/$train_set $lang $treedir
    $cmd $treedir/log/make_phone_lm.log \
    cat data/$train_set/text \| \
      steps/nnet3/chain/e2e/text_to_phones.py data/lang \| \
      utils/sym2int.pl -f 2- data/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="$cnn_opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
    common2="$cnn_opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
    common3="$cnn_opts required-time-offsets= height-offsets=-1,0,1 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=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=-1,0,1 $common3
    conv-relu-batchnorm-layer name=cnn7 height-in=10 height-out=10 time-offsets=-1,0,1 $common3
    relu-batchnorm-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$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.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 $num_jobs_final \
      --trainer.optimization.initial-effective-lrate 0.001 \
      --trainer.optimization.final-effective-lrate 0.0001 \
      --trainer.optimization.shrink-value 1.0 \
      --trainer.max-param-change 2.0 \
      --cleanup.remove-egs true \
      --feat-dir data/${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 $lang_decode \
      $dir $dir/graph || exit 1;
  fi
  
  if [ $stage -le 5 ]; then
    for decode_set in test $maybe_val; do
      steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
        --nj $nj --cmd "$cmd" \
        $dir/graph data/$decode_set $dir/decode_$decode_set || exit 1;
  
      steps/lmrescore_const_arpa.sh --cmd "$cmd" $lang_decode $lang_rescore \
                                  data/$decode_set $dir/decode_${decode_set}{,_rescored} || exit 1
    done
  fi
  
  echo "Done. Date: $(date). Results:"
  local/chain/compare_wer.sh $dir