#!/bin/bash
  
  set -e
  
  # based on run_tdnn_7b.sh in the swbd recipe
  
  # configs for 'chain'
  stage=7 # assuming you already ran the xent systems
  train_stage=-10
  get_egs_stage=-10
  dir=exp/chain/tdnn_7b
  decode_iter=
  
  # training options
  num_epochs=4
  remove_egs=false
  common_egs_dir=
  num_data_reps=3
  
  
  min_seg_len=
  xent_regularize=0.1
  frames_per_eg=150
  # 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
  
  ali_dir=exp/tri5a_rvb_ali
  treedir=exp/chain/tri6_tree_11000
  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;
  
  if [ $stage -le 7 ]; 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 8 ]; 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 \
        --cmd "$train_cmd" 11000 data/train $lang exp/tri5a $treedir
  fi
  
  if [ -z $min_seg_len ]; then
    min_seg_len=$(python -c "print ($frames_per_eg+5)/100.0")
  fi
  
  if [ $stage -le 9 ]; then
    rm -rf data/train_rvb_min${min_seg_len}_hires
    utils/data/combine_short_segments.sh \
        data/train_rvb_hires $min_seg_len data/train_rvb_min${min_seg_len}_hires
    steps/compute_cmvn_stats.sh data/train_rvb_min${min_seg_len}_hires exp/make_reverb_hires/train_rvb_min${min_seg_len} mfcc_reverb || exit 1;
  
    #extract ivectors for the new data
    steps/online/nnet2/copy_data_dir.sh --utts-per-spk-max 2 \
      data/train_rvb_min${min_seg_len}_hires data/train_rvb_min${min_seg_len}_hires_max2
    ivectordir=exp/nnet3/ivectors_train_min${min_seg_len}
    if [[ $(hostname -f) == *.clsp.jhu.edu ]]; then # this shows how you can split across multiple file-systems.
      utils/create_split_dir.pl /export/b0{1,2,3,4}/$USER/kaldi-data/egs/aspire/s5/$ivectordir/storage $ivectordir/storage
    fi
  
    steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 200 \
      data/train_rvb_min${min_seg_len}_hires_max2 \
      exp/nnet3/extractor $ivectordir || exit 1;
  
   # combine the non-hires features for alignments/lattices
   rm -rf data/${latgen_train_set}_min${min_seg_len}
    utt_prefix="THISISUNIQUESTRING_"
    spk_prefix="THISISUNIQUESTRING_"
    utils/copy_data_dir.sh --spk-prefix "$spk_prefix" --utt-prefix "$utt_prefix" \
      data/train data/train_temp_for_lats
    utils/data/combine_short_segments.sh \
        data/train_temp_for_lats $min_seg_len data/train_min${min_seg_len}
    steps/compute_cmvn_stats.sh data/train_min${min_seg_len} || exit 1;
  fi
  
  if [ $stage -le 10 ]; then
    # Get the alignments as lattices (gives the chain training more freedom).
    # use the same num-jobs as the alignments
    nj=200
    lat_dir=exp/tri5a_min${min_seg_len}_lats
    steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/train_min${min_seg_len} \
      data/lang exp/tri5a $lat_dir
    rm -f $lat_dir/fsts.*.gz # save space
  
    rvb_lat_dir=exp/tri5a_rvb_min${min_seg_len}_lats
    mkdir -p $rvb_lat_dir/temp/
    lattice-copy "ark:gunzip -c $lat_dir/lat.*.gz |" ark,scp:$rvb_lat_dir/temp/lats.ark,$rvb_lat_dir/temp/lats.scp
  
    # copy the lattices for the reverberated data
    rm -f $rvb_lat_dir/temp/combined_lats.scp
    touch $rvb_lat_dir/temp/combined_lats.scp
    for i in `seq 1 $num_data_reps`; do
      cat $rvb_lat_dir/temp/lats.scp | sed -e "s/THISISUNIQUESTRING/rev${i}/g" >> $rvb_lat_dir/temp/combined_lats.scp
    done
    sort -u $rvb_lat_dir/temp/combined_lats.scp > $rvb_lat_dir/temp/combined_lats_sorted.scp
  
    lattice-copy scp:$rvb_lat_dir/temp/combined_lats_sorted.scp "ark:|gzip -c >$rvb_lat_dir/lat.1.gz" || exit 1;
    echo "1" > $rvb_lat_dir/num_jobs
  
    # copy other files from original lattice dir
    for f in cmvn_opts final.mdl splice_opts tree; do
      cp $lat_dir/$f $rvb_lat_dir/$f
    done
  
  fi
  
  if [ $stage -le 11 ]; 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)
  
    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(-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 dim=1024
    relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1,2) dim=1024
    relu-batchnorm-layer name=tdnn3 input=Append(-3,0,3) dim=1024
    relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=1024
    relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=1024
    relu-batchnorm-layer name=tdnn6 input=Append(-6,-3,0) dim=1024
  
    ## adding the layers for chain branch
    relu-batchnorm-layer name=prefinal-chain input=tdnn6 dim=1024 target-rms=0.5
    output-layer name=output 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.
    relu-batchnorm-layer name=prefinal-xent input=tdnn6 dim=1024 target-rms=0.5
    output-layer name=output-xent 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 12 ]; 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 $train_stage \
      --egs.dir "$common_egs_dir" \
      --cmd "$decode_cmd" \
      --feat.online-ivector-dir exp/nnet3/ivectors_train_min${min_seg_len} \
      --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.stage $get_egs_stage \
      --egs.opts "--frames-overlap-per-eg 0" \
      --egs.chunk-width 150 \
      --trainer.num-chunk-per-minibatch 128 \
      --trainer.frames-per-iter 1500000 \
      --trainer.num-epochs $num_epochs \
      --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.max-param-change 2.0 \
      --cleanup.remove-egs $remove_egs \
      --feat-dir data/train_rvb_min${min_seg_len}_hires \
      --tree-dir $treedir \
      --lat-dir exp/tri5a_rvb_min${min_seg_len}_lats \
      --dir $dir  || exit 1;
  fi
  
  if [ $stage -le 13 ]; 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 $dir/graph_pp
  fi
  
  if [ $stage -le 14 ]; then
  #%WER 27.8 | 2120 27217 | 78.2 13.6 8.2 6.0 27.8 75.9 | -0.613 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iterfinal_pp_fg/score_9/penalty_0.0/ctm.filt.filt.sys
    local/nnet3/decode.sh --stage 1 --decode-num-jobs 30 --affix "v7" \
     --acwt 1.0 --post-decode-acwt 10.0 \
     --window 10 --overlap 5 \
     --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 15 ]; then
  #  #Online decoding example
  # %WER 31.5 | 2120 27224 | 74.0 13.0 13.0 5.5 31.5 77.1 | -0.558 | exp/chain/tdnn_7b_online/decode_dev_aspire_whole_uniformsegmented_win10_over5_v9_online_iterfinal_pp_fg/score_10/penalty_0.0/ctm.filt.filt.sys
  
  #  local/nnet3/decode_online.sh --stage 2 --decode-num-jobs 30 --affix "v7" \
  #   --acwt 1.0 --post-decode-acwt 10.0 \
  #   --window 10 --overlap 5 \
  #   --max-count 75 \
  #   --pass2-decode-opts "--min-active 1000" \
  #   dev_aspire data/lang $dir/graph_pp exp/chain/tdnn_7b
  #fi
  
  
  
  
  exit 0;
  
  # %WER 32.7 | 2120 27222 | 73.6 15.3 11.2 6.3 32.7 78.5 | -0.530 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter100_pp_fg/score_9/penalty_0.0/ctm.filt.filt.sys
  # %WER 30.4 | 2120 27211 | 74.8 12.7 12.5 5.1 30.4 77.0 | -0.458 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter200_pp_fg/score_10/penalty_0.0/ctm.filt.filt.sys
  # %WER 29.1 | 2120 27216 | 76.6 13.8 9.6 5.7 29.1 76.8 | -0.527 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter300_pp_fg/score_9/penalty_0.0/ctm.filt.filt.sys
  # %WER 28.8 | 2120 27211 | 77.0 13.8 9.2 5.8 28.8 76.3 | -0.587 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter400_pp_fg/score_9/penalty_0.0/ctm.filt.filt.sys
  # %WER 28.7 | 2120 27218 | 77.1 13.8 9.1 5.8 28.7 77.0 | -0.566 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter500_pp_fg/score_9/penalty_0.0/ctm.filt.filt.sys
  # %WER 28.5 | 2120 27210 | 77.5 13.9 8.7 6.0 28.5 76.1 | -0.596 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter600_pp_fg/score_9/penalty_0.0/ctm.filt.filt.sys
  # %WER 28.2 | 2120 27217 | 77.0 12.4 10.6 5.2 28.2 75.8 | -0.540 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter700_pp_fg/score_10/penalty_0.0/ctm.filt.filt.sys
  # %WER 28.4 | 2120 27218 | 77.6 13.6 8.8 6.0 28.4 76.3 | -0.607 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter800_pp_fg/score_9/penalty_0.0/ctm.filt.filt.sys
  # %WER 28.2 | 2120 27208 | 77.4 12.6 10.0 5.6 28.2 76.6 | -0.555 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter900_pp_fg/score_10/penalty_0.0/ctm.filt.filt.sys
  # %WER 27.8 | 2120 27214 | 78.0 13.5 8.5 5.9 27.8 75.9 | -0.631 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter1000_pp_fg/score_9/penalty_0.0/ctm.filt.filt.sys
  # %WER 27.9 | 2120 27216 | 77.6 13.0 9.4 5.5 27.9 76.1 | -0.544 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter1200_pp_fg/score_10/penalty_0.0/ctm.filt.filt.sys
  # %WER 27.8 | 2120 27216 | 77.4 13.1 9.5 5.3 27.8 75.7 | -0.615 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter1300_pp_fg/score_9/penalty_0.25/ctm.filt.filt.sys
  # %WER 27.7 | 2120 27220 | 78.1 13.6 8.3 5.8 27.7 75.1 | -0.569 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter1400_pp_fg/score_9/penalty_0.0/ctm.filt.filt.sys
  # %WER 27.7 | 2120 27217 | 78.1 13.6 8.3 5.9 27.7 75.1 | -0.605 | exp/chain/tdnn_7b/decode_dev_aspire_whole_uniformsegmented_win10_over5_v6_200jobs_iter1500_pp_fg/score_9/penalty_0.0/ctm.filt.filt.sys