#!/usr/bin/env bash

# NOTE: same as local/chain/tuning/run_cnn_tdnn_1a.sh -- CB

# 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
. ./fb_commons.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

## NOTE: the contents of run_chain_common.sh have been moved in-file here -- CB
## 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 11 ]; 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 $lang ]; then
    if [ $lang/L.fst -nt data/lang/L.fst ]; then
      echo "$0: $lang already exists, not overwriting it; continuing"
    else
      echo "$0: $lang 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 $lang
    silphonelist=$(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
fi

if [ $stage -le 12 ]; then
  # Get the alignments as lattices (gives the chain training more freedom).
  # use the same num-jobs as the alignments
  nj=$(cat ${ali_dir}/num_jobs) || exit 1;
  steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" ${lores_train_data_dir} \
    $lang $gmm_dir $lat_dir
  rm $lat_dir/fsts.*.gz # save space
fi

if [ $stage -le 13 ]; 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: $tree_dir/final.mdl already exists, refusing to overwrite it."
    exit 1;
  fi
  num_leaves=7000  # CB
  steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
      --context-opts "--context-width=2 --central-position=1" \
      --cmd "$train_cmd" $num_leaves $lores_train_data_dir $lang $ali_dir $tree_dir
fi

## end run_chain_common.sh

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
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;
                
