#!/bin/bash
  
  set -e
  
  # based on run_tdnn_7b.sh in the swbd recipe
  
  # System                      exp/chain/tdnn_lstm_1a
  # WER on dev_aspire (fg)                    22.9
  # Final train prob                        -0.118
  # Final valid prob                        -0.123
  # Final train prob (xent)                 -1.243
  # Final valid prob (xent)                -1.2350
  # Num-parameters                        49945168
  
  # configs for 'chain'
  stage=0
  train_stage=-10
  get_egs_stage=-10
  test_stage=1
  nj=70
  
  tdnn_affix=_1a
  
  hidden_dim=1024
  cell_dim=1024
  projection_dim=256
  
  # training options
  minibatch_size=64,32
  chunk_left_context=40
  chunk_right_context=0
  dropout_schedule='0,0@0.20,0.3@0.50,0'
  xent_regularize=0.025
  label_delay=5
  
  # decode options
  extra_left_context=50
  extra_right_context=0
  
  # training options
  num_epochs=4
  remove_egs=false
  common_egs_dir=
  
  num_data_reps=3
  # 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
  
  train_set=train_rvb
  
  gmm_dir=exp/tri5a   # used to get training lattices (for chain supervision)
  treedir=exp/chain/tree_bi_a
  lat_dir=exp/chain/tri5a_${train_set}_lats  # training lattices directory
  dir=exp/chain/tdnn_lstm${tdnn_affix}
  train_data_dir=data/${train_set}_hires
  train_ivector_dir=exp/nnet3/ivectors_${train_set}
  lang=data/lang_chain
  
  
  # 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.
  local/nnet3/run_ivector_common.sh --stage $stage --num-data-reps 3 || exit 1
  
  mkdir -p $dir
  
  norvb_lat_dir=exp/chain/tri5a_train_lats
  
  if [ $stage -le 7 ]; then
    # Get the alignments as lattices (gives the chain training more freedom).
    # use the same num-jobs as the alignments
    steps/align_fmllr_lats.sh --nj 30 --cmd "$train_cmd" \
      --generate-ali-from-lats true data/train \
      data/lang $gmm_dir $norvb_lat_dir || exit 1;
    rm $norvb_lat_dir/fsts.*.gz # save space
  fi
  
  if [ $stage -le 8 ]; then
    mkdir -p $lat_dir
  
    utils/split_data.sh data/${train_set} $nj
  
    for n in `seq $nj`; do
      awk '{print $1}' data/${train_set}/split$nj/$n/utt2spk | \
        perl -ane 's/rev[1-3]-//g' > $lat_dir/uttlist.$n.$nj
    done
  
    rm -f $lat_dir/lat_tmp.*.{ark,scp} 2>/dev/null
  
    norvb_nj=$(cat $norvb_lat_dir/num_jobs)
    $train_cmd JOB=1:$norvb_nj $lat_dir/log/copy_lattices.JOB.log \
      lattice-copy "ark:gunzip -c $norvb_lat_dir/lat.JOB.gz |" \
      ark,scp:$lat_dir/lat_tmp.JOB.ark,$lat_dir/lat_tmp.JOB.scp || exit 1
  
    for n in `seq 3`; do
      cat $lat_dir/lat_tmp.*.scp | awk -v n=$n '{print "rev"n"-"$1" "$2}'
    done > $lat_dir/lat_rvb.scp
  
    $train_cmd JOB=1:$nj $lat_dir/log/copy_rvb_lattices.JOB.log \
      lattice-copy \
      "scp:utils/filter_scp.pl data/${train_set}/split$nj/JOB/utt2spk $lat_dir/lat_rvb.scp |" \
      "ark:| gzip -c > $lat_dir/lat.JOB.gz" || exit 1
  
    rm $lat_dir/lat_tmp.* $lat_dir/lat_rvb.scp
  
    echo $nj > $lat_dir/num_jobs
  
    for f in cmvn_opts final.mdl splice_opts tree; do
      cp $norvb_lat_dir/$f $lat_dir/$f
    done
  fi
  
  if [ $stage -le 10 ]; 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 11 ]; then
    # Build a tree using our new topology.
    # we build the tree using clean features (data/train) rather than
    # the augmented features (data/train_rvb) to get better alignments
  
    steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
        --leftmost-questions-truncate -1 \
        --context-opts "--context-width=2 --central-position=1" \
        --cmd "$train_cmd" 7000 data/train $lang exp/tri5a $treedir || exit 1
  fi
  
  if [ $stage -le 12 ]; then
    echo "$0: creating neural net configs using the xconfig parser";
  
    num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
    learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
  
    lstm_opts="decay-time=40"
  
    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-batchnorm-layer name=tdnn1 dim=$hidden_dim
    relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1) dim=$hidden_dim
    relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=$hidden_dim
  
    fast-lstmp-layer name=lstm1 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
    relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=$hidden_dim
    relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=$hidden_dim
    fast-lstmp-layer name=lstm2 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
    relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=$hidden_dim
    relu-batchnorm-layer name=tdnn7 input=Append(-3,0,3) dim=$hidden_dim
    fast-lstmp-layer name=lstm3 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
    relu-batchnorm-layer name=tdnn8 input=Append(-3,0,3) dim=$hidden_dim
    relu-batchnorm-layer name=tdnn9 input=Append(-3,0,3) dim=$hidden_dim
    fast-lstmp-layer name=lstm4 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
  
    ## adding the layers for chain branch
    output-layer name=output input=lstm4 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=lstm4 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 13 ]; 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/aspire-$(date +'%m_%d_%H_%M')/s5c/$dir/egs/storage $dir/egs/storage
    fi
  
    mkdir -p $dir/egs
    touch $dir/egs/.nodelete # keep egs around when that run dies.
  
    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" \
      --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 $num_epochs \
      --trainer.optimization.shrink-value 0.99 \
      --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 --generate-egs-scp true" \
      --egs.chunk-width 160,140,110,80 \
      --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 $treedir \
      --lat-dir $lat_dir \
      --dir $dir  || exit 1;
  fi
  
  graph_dir=$dir/graph_pp
  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 data/lang_pp_test $dir $graph_dir
  fi
  
  if [ $stage -le 15 ]; then
    rm $dir/.error 2>/dev/null || true
  
    for d in dev_rvb test_rvb; do
      (
        if [ ! -f exp/nnet3/ivectors_${d}/ivector_online.scp ]; then
          steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 30 \
            data/${d}_hires exp/nnet3/extractor \
            exp/nnet3/ivectors_${d} || { echo "Failed i-vector extraction for data/${d}_hires"; touch $dir/.error; }
        fi
  
        decode_dir=$dir/decode_${d}_pp
        steps/nnet3/decode.sh --nj 30 --cmd "$decode_cmd" --config conf/decode.config \
          --acwt 1.0 --post-decode-acwt 10.0 \
          --extra-left-context $extra_left_context \
          --extra-right-context $extra_right_context \
          --extra-left-context-initial 0 --extra-right-context-final 0 \
          --frames-per-chunk 160 \
          --online-ivector-dir exp/nnet3/ivectors_${d} \
          $graph_dir data/${d}_hires $decode_dir || { echo "Failed decoding in $decode_dir"; touch $dir/.error; }
      ) &
    done
    wait
  
    if [ -f $dir/.error ]; then
      echo "Failed decoding."
      exit 1
    fi
  fi
  
  if [ $stage -le 16 ]; then
    # %WER 22.9 | 2083 25834 | 81.6 12.0 6.4 4.5 22.9 70.7 | -0.546 | exp/chain/tdnn_lstm_1a/decode_dev_aspire_uniformsegmented_v9_pp_fg/score_8/penalty_0.0/ctm.filt.filt.sys
    local/nnet3/decode.sh --stage $test_stage --decode-num-jobs 30 --affix "v9" \
     --acwt 1.0 --post-decode-acwt 10.0 \
     --window 10 --overlap 5 --frames-per-chunk 160 \
     --extra-left-context $extra_left_context \
     --extra-right-context $extra_right_context \
     --extra-left-context-initial 0 --extra-right-context-final 0 \
     --sub-speaker-frames 6000 --max-count 75 --ivector-scale 0.75 \
     --pass2-decode-opts "--min-active 1000" \
     dev_aspire data/lang $dir/graph_pp $dir
  fi
  
  if [ $stage -le 17 ]; then
    # %WER 24.0 | 2083 25820 | 79.9 12.0 8.1 4.0 24.0 71.8 | -0.444 | exp/chain/tdnn_lstm_1a_online/decode_dev_aspire_uniformsegmented_v9_pp_fg/score_10/penalty_0.0/ctm.filt.filt.sys
    local/nnet3/decode_online.sh --stage $test_stage --decode-num-jobs 30 --affix "v9" \
     --acwt 1.0 --post-decode-acwt 10.0 \
     --window 10 --overlap 5 --frames-per-chunk 160 \
     --extra-left-context $extra_left_context \
     --extra-right-context $extra_right_context \
     --extra-left-context-initial 0 \
     --max-count 75 \
     --pass2-decode-opts "--min-active 1000" \
     dev_aspire data/lang $dir/graph_pp $dir
  fi
  
  exit 0;