#!/bin/bash
  
  # steps/info/chain_dir_info.pl exp/chain/tdnn_lstm1a_sp_bi/
  # exp/chain/tdnn_lstm1a_sp_bi/: num-iters=384 nj=2..12 num-params=9.5M dim=40+100->3557 combine=-0.05->-0.05 xent:train/valid[255,383,final]=(-0.579,-0.518,-0.523/-0.651,-0.616,-0.619) logprob:train/valid[255,383,final]=(-0.046,-0.038,-0.038/-0.063,-0.060,-0.059)
  
  # local/chain/compare_wer_general.sh exp/chain/tdnn_sp_bi/ exp/chain/lstm1e_sp_bi/ exp/chain/tdnn_lstm1a_sp_bi/
  # System               exp/chain/tdnn_sp_bi/exp/chain/lstm1e_sp_bi/exp/chain/tdnn_lstm1a_sp_bi/
  # WER on dev(tg)      10.00      9.39      8.48
  # WER on test(tg)        8.58      7.72      7.20
  # Final train prob        -0.0642   -0.0528   -0.0378
  # Final valid prob        -0.0788   -0.0651   -0.0595
  # Final train prob (xent)       -0.9113   -0.7117   -0.5228
  # Final valid prob (xent)       -0.9525   -0.7607   -0.6185
  
  # run_tdnn_lstm_1a.sh was modified from run_lstm_1e.sh, which is a fairly
  # standard, LSTM, except that some TDNN layers were added in between the
  # LSTM layers.  
  
  ## how you run this (note: this assumes that the run_tdnn_lstm.sh soft link points here;
  ## otherwise call it directly in its location).
  # by default:
  # local/chain/run_tdnn_lstm.sh
  
  # note, that you may want to adjust parallelisation to your setup
  # if you have already run one of the non-chain nnet3 systems
  # (e.g. local/nnet3/run_tdnn.sh), you may want to run with --stage 14.
  
  
  set -e -o pipefail
  
  # First the options that are passed through to run_ivector_common.sh
  # (some of which are also used in this script directly).
  stage=0
  nj=30
  decode_nj=7
  min_seg_len=1.55
  chunk_left_context=40
  chunk_right_context=0
  label_delay=5
  xent_regularize=0.1
  train_set=train
  gmm=tri3b  # the gmm for the target data
  num_threads_ubm=32
  nnet3_affix=  # cleanup affix for nnet3 and chain dirs, e.g. _cleaned
  # decode options
  extra_left_context=50
  extra_right_context=0
  frames_per_chunk=150
  
  # The rest are configs specific to this script.  Most of the parameters
  # are just hardcoded at this level, in the commands below.
  train_stage=-10
  tree_affix=  # affix for tree directory, e.g. "a" or "b", in case we change the configuration.
  tdnn_lstm_affix=1a  #affix for TDNN-LSTM directory, e.g. "a" or "b", in case we change the configuration.
  common_egs_dir=  # you can set this to use previously dumped egs.
  
  # 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
  
  local/nnet3/run_ivector_common.sh --stage $stage \
                                    --nj $nj \
                                    --min-seg-len $min_seg_len \
                                    --train-set $train_set \
                                    --gmm $gmm \
                                    --num-threads-ubm $num_threads_ubm \
                                    --nnet3-affix "$nnet3_affix"
  
  
  gmm_dir=exp/$gmm
  graph_dir=$gmm_dir/graph_tg
  ali_dir=exp/${gmm}_ali_${train_set}_sp_comb
  tree_dir=exp/chain${nnet3_affix}/tree_bi${tree_affix}
  lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_comb_lats
  dir=exp/chain${nnet3_affix}/tdnn_lstm${tdnn_lstm_affix}_sp_bi
  train_data_dir=data/${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
  
  
  for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
      $lores_train_data_dir/feats.scp $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 14 ]; then
    echo "$0: creating lang directory with one state per phone."
    # 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 data/lang_chain ]; then
      if [ data/lang_chain/L.fst -nt data/lang/L.fst ]; then
        echo "$0: data/lang_chain already exists, not overwriting it; continuing"
      else
        echo "$0: data/lang_chain already exists and seems to be older than data/lang..."
        echo " ... not sure what to do.  Exiting."
        exit 1;
      fi
    else
      cp -r data/lang data/lang_chain
      silphonelist=$(cat data/lang_chain/phones/silence.csl) || exit 1;
      nonsilphonelist=$(cat data/lang_chain/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 >data/lang_chain/topo
    fi
  fi
  
  if [ $stage -le 15 ]; then
    # Get the alignments as lattices (gives the chain training more freedom).
    # use the same num-jobs as the alignments
    steps/align_fmllr_lats.sh --nj 100 --cmd "$train_cmd" ${lores_train_data_dir} \
      data/lang $gmm_dir $lat_dir
    rm $lat_dir/fsts.*.gz # save space
  fi
  
  if [ $stage -le 16 ]; 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.
    if [ -f $tree_dir/final.mdl ]; then
      echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
      exit 1;
    fi
    steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
        --context-opts "--context-width=2 --central-position=1" \
        --cmd "$train_cmd" 4000 ${lores_train_data_dir} data/lang_chain $ali_dir $tree_dir
  fi
  
  
  if [ $stage -le 17 ]; 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)
  
    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(-2,-1,0,1,2,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-renorm-layer name=tdnn1 dim=512
    relu-renorm-layer name=tdnn2 dim=512 input=Append(-1,0,1)
    fast-lstmp-layer name=lstm1 cell-dim=512 recurrent-projection-dim=128 non-recurrent-projection-dim=128 delay=-3
    relu-renorm-layer name=tdnn3 dim=512 input=Append(-3,0,3)
    relu-renorm-layer name=tdnn4 dim=512 input=Append(-3,0,3)
    fast-lstmp-layer name=lstm2 cell-dim=512 recurrent-projection-dim=128 non-recurrent-projection-dim=128 delay=-3
    relu-renorm-layer name=tdnn5 dim=512 input=Append(-3,0,3)
    relu-renorm-layer name=tdnn6 dim=512 input=Append(-3,0,3)
    fast-lstmp-layer name=lstm3 cell-dim=512 recurrent-projection-dim=128 non-recurrent-projection-dim=128 delay=-3
  
    ## adding the layers for chain branch
    output-layer name=output input=lstm3 output-delay=$label_delay include-log-softmax=false dim=$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' models... 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.
    output-layer name=output-xent input=lstm3 output-delay=$label_delay dim=$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 18 ]; then
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
      utils/create_split_dir.pl \
       /export/b0{5,6,7,8}/$USER/kaldi-data/egs/ami-$(date +'%m_%d_%H_%M')/s5/$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 $xent_regularize \
      --chain.leaky-hmm-coefficient 0.1 \
      --chain.l2-regularize 0.00005 \
      --chain.apply-deriv-weights false \
      --chain.lm-opts="--num-extra-lm-states=2000" \
      --egs.dir "$common_egs_dir" \
      --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" \
      --trainer.num-chunk-per-minibatch 128 \
      --trainer.frames-per-iter 1500000 \
      --trainer.max-param-change 2.0 \
      --trainer.num-epochs 4 \
      --trainer.deriv-truncate-margin 10 \
      --trainer.optimization.shrink-value 0.99 \
      --trainer.optimization.num-jobs-initial 2 \
      --trainer.optimization.num-jobs-final 12 \
      --trainer.optimization.initial-effective-lrate 0.001 \
      --trainer.optimization.final-effective-lrate 0.0001 \
      --trainer.optimization.momentum 0.0 \
      --cleanup.remove-egs true \
      --feat-dir $train_data_dir \
      --tree-dir $tree_dir \
      --lat-dir $lat_dir \
      --dir $dir
  fi
  
  
  
  if [ $stage -le 19 ]; then
    # Note: it might appear that this data/lang_chain 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 data/lang_test_tg $dir $dir/graph
  fi
  
  if [ $stage -le 20 ]; then
    rm $dir/.error 2>/dev/null || true
    for dset in dev test; do
        (
        steps/nnet3/decode.sh --num-threads 4 --nj $decode_nj --cmd "$decode_cmd" \
            --acwt 1.0 --post-decode-acwt 10.0 \
            --extra-left-context $extra_left_context  \
            --extra-right-context $extra_right_context  \
            --frames-per-chunk "$frames_per_chunk" \
            --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${dset}_hires \
            --scoring-opts "--min-lmwt 5 " \
           $dir/graph data/${dset}_hires $dir/decode_${dset} || exit 1;
      ) || touch $dir/.error &
    done
    wait
    if [ -f $dir/.error ]; then
      echo "$0: something went wrong in decoding"
      exit 1
    fi
  fi
  exit 0