#!/bin/bash
  
  # This is based on tdnn_1d_sp, but adding cnn as the front-end.
  # The cnn-tdnn-f (tdnn_cnn_1a_sp) outperforms the tdnn-f (tdnn_1d_sp).
  
  # bash local/chain/compare_wer.sh exp/chain_cleaned/tdnn_1d_sp exp/chain_cleaned/tdnn_cnn_1a_sp/
  # System                         tdnn_1d_sp  tdnn_cnn_1a_sp
  # WER on dev(fglarge)               3.29          3.34
  # WER on dev(tglarge)               3.44          3.39
  # WER on dev(tgmed)                 4.22          4.29
  # WER on dev(tgsmall)               4.72          4.77
  # WER on dev_other(fglarge)         8.71          8.62
  # WER on dev_other(tglarge)         9.05          9.00
  # WER on dev_other(tgmed)          11.09         10.93
  # WER on dev_other(tgsmall)        12.13         12.02
  # WER on test(fglarge)              3.80          3.69
  # WER on test(tglarge)              3.89          3.80
  # WER on test(tgmed)                4.72          4.64
  # WER on test(tgsmall)              5.19          5.16      
  # WER on test_other(fglarge)        8.76          8.71
  # WER on test_other(tglarge)        9.19          9.11
  # WER on test_other(tgmed)         11.22         11.00
  # WER on test_other(tgsmall)       12.24         12.16
  # Final train prob               -0.0378       -0.0420
  # Final valid prob               -0.0374       -0.0400
  # Final train prob (xent)        -0.6099       -0.6881
  # Final valid prob (xent)        -0.6353       -0.7180
  # Num-parameters                22623456      18100736
  
  
  set -e
  
  # configs for 'chain'
  stage=0
  decode_nj=50
  train_set=train_960_cleaned
  gmm=tri6b_cleaned
  nnet3_affix=_cleaned
  
  # The rest are configs specific to this script.  Most of the parameters
  # are just hardcoded at this level, in the commands below.
  affix=cnn_1a
  tree_affix=
  train_stage=-10
  get_egs_stage=-10
  decode_iter=
  
  # TDNN options
  frames_per_eg=150,110,100
  remove_egs=true
  common_egs_dir=
  xent_regularize=0.1
  dropout_schedule='0,0@0.20,0.5@0.50,0'
  
  test_online_decoding=true  # if true, it will run the last decoding stage.
  
  # 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 11" if you have already
  # run those things.
  
  local/nnet3/run_ivector_common.sh --stage $stage \
                                    --train-set $train_set \
                                    --gmm $gmm \
                                    --num-threads-ubm 6 --num-processes 3 \
                                    --nnet3-affix "$nnet3_affix" || exit 1;
  
  gmm_dir=exp/$gmm
  ali_dir=exp/${gmm}_ali_${train_set}_sp
  tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
  lang=data/lang_chain
  lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
  dir=exp/chain${nnet3_affix}/tdnn${affix:+_$affix}_sp
  train_data_dir=data/${train_set}_sp_hires
  lores_train_data_dir=data/${train_set}_sp
  train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
  
  # if we are using the speed-perturbed data we need to generate
  # alignments for it.
  
  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; do
    [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
  done
  
  # Please take this as a reference on how to specify all the options of
  # local/chain/run_chain_common.sh
  local/chain/run_chain_common.sh --stage $stage \
                                  --gmm-dir $gmm_dir \
                                  --ali-dir $ali_dir \
                                  --lores-train-data-dir ${lores_train_data_dir} \
                                  --lang $lang \
                                  --lat-dir $lat_dir \
                                  --num-leaves 7000 \
                                  --tree-dir $tree_dir || exit 1;
  
  if [ $stage -le 14 ]; 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)
    cnn_opts="l2-regularize=0.01"
    ivector_affine_opts="l2-regularize=0.0"
    affine_opts="l2-regularize=0.008 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
    tdnnf_first_opts="l2-regularize=0.008 dropout-proportion=0.0 bypass-scale=0.0"
    tdnnf_opts="l2-regularize=0.008 dropout-proportion=0.0 bypass-scale=0.75"
    linear_opts="l2-regularize=0.008 orthonormal-constraint=-1.0"
    prefinal_opts="l2-regularize=0.008"
    output_opts="l2-regularize=0.005"
  
    mkdir -p $dir/configs
  
    cat <<EOF > $dir/configs/network.xconfig
    input dim=100 name=ivector
    input dim=40 name=input
  
    # MFCC to filterbank
    idct-layer name=idct input=input dim=40 cepstral-lifter=22 affine-transform-file=$dir/configs/idct.mat
  
    linear-component name=ivector-linear $ivector_affine_opts dim=200 input=ReplaceIndex(ivector, t, 0)
    batchnorm-component name=ivector-batchnorm target-rms=0.025
    batchnorm-component name=idct-batchnorm input=idct
  
    combine-feature-maps-layer name=combine_inputs input=Append(idct-batchnorm, ivector-batchnorm) num-filters1=1 num-filters2=5 height=40
    conv-relu-batchnorm-layer name=cnn1 $cnn_opts height-in=40 height-out=40 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=64
    conv-relu-batchnorm-layer name=cnn2 $cnn_opts height-in=40 height-out=40 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=64
    conv-relu-batchnorm-layer name=cnn3 $cnn_opts height-in=40 height-out=20 height-subsample-out=2 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=128
    conv-relu-batchnorm-layer name=cnn4 $cnn_opts height-in=20 height-out=20 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=128
    conv-relu-batchnorm-layer name=cnn5 $cnn_opts height-in=20 height-out=10 height-subsample-out=2 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=256
    conv-relu-batchnorm-layer name=cnn6 $cnn_opts height-in=10 height-out=10 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=256
  
    # the first TDNN-F layer has no bypass
    tdnnf-layer name=tdnnf7 $tdnnf_first_opts dim=1536 bottleneck-dim=256 time-stride=0
    tdnnf-layer name=tdnnf8 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf9 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf10 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf11 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf12 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf13 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf14 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf15 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf16 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf17 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    tdnnf-layer name=tdnnf18 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
    linear-component name=prefinal-l dim=256 $linear_opts
  
    prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts big-dim=1536 small-dim=256
    output-layer name=output include-log-softmax=false dim=$num_targets $output_opts
  
    prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts big-dim=1536 small-dim=256
    output-layer name=output-xent dim=$num_targets 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 15 ]; then
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
      utils/create_split_dir.pl \
       /export/b{09,10,11,12}/$USER/kaldi-data/egs/swbd-$(date +'%m_%d_%H_%M')/s5c/$dir/egs/storage $dir/egs/storage
    fi
  
    steps/nnet3/chain/train.py --stage $train_stage \
      --use-gpu "wait" \
      --cmd "$decode_cmd" \
      --feat.online-ivector-dir $train_ivector_dir \
      --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
      --chain.xent-regularize $xent_regularize \
      --chain.leaky-hmm-coefficient 0.1 \
      --chain.l2-regularize 0.0 \
      --chain.apply-deriv-weights false \
      --chain.lm-opts="--num-extra-lm-states=2000" \
      --egs.dir "$common_egs_dir" \
      --egs.stage $get_egs_stage \
      --egs.opts "--frames-overlap-per-eg 0 --constrained false" \
      --egs.chunk-width $frames_per_eg \
      --trainer.dropout-schedule $dropout_schedule \
      --trainer.add-option="--optimization.memory-compression-level=2" \
      --trainer.num-chunk-per-minibatch 64 \
      --trainer.frames-per-iter 2500000 \
      --trainer.num-epochs 4 \
      --trainer.optimization.num-jobs-initial 3 \
      --trainer.optimization.num-jobs-final 16 \
      --trainer.optimization.initial-effective-lrate 0.00015 \
      --trainer.optimization.final-effective-lrate 0.000015 \
      --trainer.max-param-change 2.0 \
      --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 16 ]; 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 17 ]; 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" $iter_opts \
            --online-ivector-dir exp/nnet3${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
  
  if $test_online_decoding && [ $stage -le 18 ]; then
    # note: if the features change (e.g. you add pitch features), you will have to
    # change the options of the following command line.
    steps/online/nnet3/prepare_online_decoding.sh \
         --mfcc-config conf/mfcc_hires.conf \
         $lang exp/nnet3${nnet3_affix}/extractor $dir ${dir}_online
  
    rm $dir/.error 2>/dev/null || true
    for data in test_clean test_other dev_clean dev_other; do
      (
        nspk=$(wc -l <data/${data}_hires/spk2utt)
        # note: we just give it "data/${data}" as it only uses the wav.scp, the
        # feature type does not matter.
        steps/online/nnet3/decode.sh \
            --acwt 1.0 --post-decode-acwt 10.0 \
            --nj $nspk --cmd "$decode_cmd" \
            $graph_dir data/${data} ${dir}_online/decode_${data}_tgsmall || 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;