#!/bin/bash
  # this is the tdnn-lstmp based on the run_tdnn_lstm_1a.sh under Librispeech but with larger model size.
  
  # training acoustic model and decoding:
  #     local/chain/tuning/run_tdnn_lstm_1b.sh
  # local/chain/compare_wer.sh exp/chain_cleaned/tdnn_lstm1a_sp exp/chain_cleaned/tdnn_lstm1b_sp
  # System                      tdnn_lstm1a_sp tdnn_lstm1b_sp
  # WER on dev(fglarge)              3.44      3.36
  # WER on dev(tglarge)              3.55      3.48
  # WER on dev(tgmed)                4.41      4.26
  # WER on dev(tgsmall)              4.82      4.71
  # WER on dev_other(fglarge)        8.63      8.43
  # WER on dev_other(tglarge)        9.09      8.94
  # WER on dev_other(tgmed)         10.99     10.65
  # WER on dev_other(tgsmall)       11.95     11.51
  # WER on test(fglarge)             3.78      3.83
  # WER on test(tglarge)             3.94      3.93
  # WER on test(tgmed)               4.68      4.72
  # WER on test(tgsmall)             5.11      5.10
  # WER on test_other(fglarge)       8.83      8.69
  # WER on test_other(tglarge)       9.09      9.10
  # WER on test_other(tgmed)        11.05     10.86
  # WER on test_other(tgsmall)      12.18     11.83
  # Final train prob              -0.0452   -0.0417
  # Final valid prob              -0.0477   -0.0459
  # Final train prob (xent)       -0.7874   -0.7488
  # Final valid prob (xent)       -0.8150   -0.7757
  # Num-parameters               27790288  45245520
  
  # rnn-lm rescoring:
  #     local/rnnlm/tuning/run_tdnn_lstm_1a.sh --ac-model-dir exp/chain_cleaned/tdnn_lstm1b_sp/
  # System                      tdnn_lstm1b_sp
  # WER on dev(fglarge_nbe_rnnlm)      2.73
  # WER on dev(fglarge_lat_rnnlm)        2.83
  # WER on dev(fglarge)              3.36
  # WER on dev(tglarge)              3.48
  # WER on dev_other(fglarge_nbe_rnnlm)      7.20
  # WER on dev_other(fglarge_lat_rnnlm)      7.23
  # WER on dev_other(fglarge)        8.43
  # WER on dev_other(tglarge)        8.94
  # WER on test(fglarge_nbe_rnnlm)      3.10
  # WER on test(fglarge_lat_rnnlm)       3.22
  # WER on test(fglarge)             3.83
  # WER on test(tglarge)             3.93
  # WER on test_other(fglarge_nbe_rnnlm)      7.54
  # WER on test_other(fglarge_lat_rnnlm)      7.65
  # WER on test_other(fglarge)       8.69
  # WER on test_other(tglarge)       9.10
  # Final train prob              -0.0417
  # Final valid prob              -0.0459
  # Final train prob (xent)       -0.7488
  # Final valid prob (xent)       -0.7757
  # Num-parameters               45245520
  
  
  
  set -e
  
  # configs for 'chain'
  stage=12
  train_stage=-10
  get_egs_stage=-10
  speed_perturb=true
  affix=1b
  decode_iter=
  decode_nj=50
  
  # LSTM training options
  frames_per_chunk=140,100,160
  frames_per_chunk_primary=$(echo $frames_per_chunk | cut -d, -f1)
  chunk_left_context=40
  chunk_right_context=0
  xent_regularize=0.025
  self_repair_scale=0.00001
  label_delay=5
  # decode options
  extra_left_context=50
  extra_right_context=0
  dropout_schedule='0,0@0.20,0.3@0.50,0'
  
  remove_egs=false
  common_egs_dir=
  nnet3_affix=_cleaned
  # 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
  # nnet3 setup, and you can skip them by setting "--stage 8" if you have already
  # run those things.
  
  suffix=
  if [ "$speed_perturb" == "true" ]; then
    suffix=_sp
  fi
  
  gmm=tri6b_cleaned
  dir=exp/chain${nnet3_affix}/tdnn_lstm${affix}${suffix}
  train_set=train_960_cleaned
  ali_dir=exp/${gmm}_ali_${train_set}_sp_comb
  tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
  lang=data/lang_chain
  train_data_dir=data/${train_set}_sp_hires_comb
  lores_train_data_dir=data/${train_set}_sp_comb
  train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires_comb
  lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_comb_lats
  
  if [ $stage -le 12 ]; then
    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)
  
    opts="l2-regularize=0.002"
    linear_opts="orthonormal-constraint=1.0"
    lstm_opts="l2-regularize=0.0005 decay-time=40"
    output_opts="l2-regularize=0.0005 output-delay=$label_delay max-change=1.5 dim=$num_targets"
  
  
    mkdir -p $dir/configs
    cat <<EOF > $dir/configs/network.xconfig
    input dim=100 name=ivector
    input dim=40 name=input
  
    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-layer name=tdnn1 $opts dim=1280
    linear-component name=tdnn2l dim=320 $linear_opts input=Append(-1,0)
    relu-batchnorm-layer name=tdnn2 $opts input=Append(0,1) dim=1280
    linear-component name=tdnn3l dim=320 $linear_opts
    relu-batchnorm-layer name=tdnn3 $opts dim=1280
    linear-component name=tdnn4l dim=320 $linear_opts input=Append(-1,0)
    relu-batchnorm-layer name=tdnn4 $opts input=Append(0,1) dim=1280
    linear-component name=tdnn5l dim=320 $linear_opts
    relu-batchnorm-layer name=tdnn5 $opts dim=1280 input=Append(tdnn5l, tdnn3l)
    linear-component name=tdnn6l dim=320 $linear_opts input=Append(-3,0)
    relu-batchnorm-layer name=tdnn6 $opts input=Append(0,3) dim=1280
    linear-component name=lstm1l dim=320 $linear_opts input=Append(-3,0)
    fast-lstmp-layer name=lstm1 cell-dim=1536 recurrent-projection-dim=384 non-recurrent-projection-dim=384 delay=-3 dropout-proportion=0.0 $lstm_opts
    relu-batchnorm-layer name=tdnn7 $opts input=Append(0,3,tdnn6l,tdnn4l,tdnn2l) dim=1280
    linear-component name=tdnn8l dim=320 $linear_opts input=Append(-3,0)
    relu-batchnorm-layer name=tdnn8 $opts input=Append(0,3) dim=1280
    linear-component name=lstm2l dim=320 $linear_opts input=Append(-3,0)
    fast-lstmp-layer name=lstm2 cell-dim=1536 recurrent-projection-dim=384 non-recurrent-projection-dim=384 delay=-3 dropout-proportion=0.0 $lstm_opts
    relu-batchnorm-layer name=tdnn9 $opts input=Append(0,3,tdnn8l,tdnn6l,tdnn4l) dim=1280
    linear-component name=tdnn10l dim=320 $linear_opts input=Append(-3,0)
    relu-batchnorm-layer name=tdnn10 $opts input=Append(0,3) dim=1280
    linear-component name=lstm3l dim=320 $linear_opts input=Append(-3,0)
    fast-lstmp-layer name=lstm3 cell-dim=1536 recurrent-projection-dim=384 non-recurrent-projection-dim=384: delay=-3 dropout-proportion=0.0 $lstm_opts
  
    output-layer name=output input=lstm3  include-log-softmax=false $output_opts
  
    output-layer name=output-xent input=lstm3 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 13 ]; then
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
      utils/create_split_dir.pl \
        /export/c0{1,2,5,7}/$USER/kaldi-data/egs/swbd-$(date +'%m_%d_%H_%M')/s5c/$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.num-chunk-per-minibatch 64,32 \
      --trainer.frames-per-iter 1500000 \
      --trainer.max-param-change 2.0 \
      --trainer.num-epochs 6 \
      --trainer.optimization.num-jobs-initial 3 \
      --trainer.optimization.num-jobs-final 16 \
      --trainer.optimization.initial-effective-lrate 0.001 \
      --trainer.optimization.final-effective-lrate 0.0001 \
      --trainer.optimization.momentum 0.0 \
      --trainer.deriv-truncate-margin 8 \
      --egs.stage $get_egs_stage \
      --egs.opts "--frames-overlap-per-eg 0" \
      --egs.chunk-width $frames_per_chunk \
      --egs.chunk-left-context $chunk_left_context \
      --egs.chunk-right-context $chunk_right_context \
      --egs.chunk-left-context-initial 0 \
      --egs.chunk-right-context-final 0 \
      --egs.dir "$common_egs_dir" \
      --cleanup.remove-egs $remove_egs \
      --feat-dir $train_data_dir \
      --tree-dir $tree_dir \
      --lat-dir $lat_dir \
      --dir $dir  || exit 1;
  fi
  
  
  graph_dir=$dir/graph_tgsmall
  if [ $stage -le 14 ]; 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 --remove-oov data/lang_test_tgsmall $dir $graph_dir
    # remove <UNK> from the graph, and convert back to const-FST.
    fstrmsymbols --apply-to-output=true --remove-arcs=true "echo 3|" $graph_dir/HCLG.fst - | \
      fstconvert --fst_type=const > $graph_dir/temp.fst
    mv $graph_dir/temp.fst $graph_dir/HCLG.fst
  fi
  
  
  iter_opts=
  if [ ! -z $decode_iter ]; then
    iter_opts=" --iter $decode_iter "
  fi
  if [ $stage -le 15 ]; then
    rm $dir/.error 2>/dev/null || true
    for decode_set in test_clean test_other dev_clean dev_other; do
        (
        steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
            --nj $decode_nj --cmd "$decode_cmd" $iter_opts \
  		  --extra-left-context $extra_left_context \
            --extra-right-context $extra_right_context \
            --extra-left-context-initial 0 \
            --extra-right-context-final 0 \
            --frames-per-chunk "$frames_per_chunk_primary" \
            --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${decode_set}_hires \
            $graph_dir data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_tgsmall || exit 1
        steps/lmrescore.sh --cmd "$decode_cmd" --self-loop-scale 1.0 data/lang_test_{tgsmall,tgmed} \
            data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_{tgsmall,tgmed} || exit 1
        steps/lmrescore_const_arpa.sh \
            --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
            data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_{tgsmall,tglarge} || exit 1
        steps/lmrescore_const_arpa.sh \
            --cmd "$decode_cmd" data/lang_test_{tgsmall,fglarge} \
            data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_{tgsmall,fglarge} || exit 1
        ) || touch $dir/.error &
    done
    wait
    if [ -f $dir/.error ]; then
      echo "$0: something went wrong in decoding"
      exit 1
    fi
  fi