#!/usr/bin/env bash
#
# edited on apr 2020 by cassio batista

# 1k is like 1j, while it introduces 'apply-cmvn-online' that does
# cmn normalization both for i-extractor and TDNN input.

# local/chain/compare_wer.sh --online exp/chain/tdnn1j_sp exp/chain_online_cmn/tdnn1k_sp
# System                tdnn1j_sp tdnn1k_sp
#WER dev_clean_2 (tgsmall)      10.97     10.64
#             [online:]         10.97     10.62
#WER dev_clean_2 (tglarge)       7.57      7.17
#             [online:]          7.65      7.16
# Final train prob        -0.0623   -0.0618
# Final valid prob        -0.0793   -0.0793
# Final train prob (xent)   -1.4448   -1.4376
# Final valid prob (xent)   -1.5605   -1.5461
# Num-params                 5210944   5210944

# steps/info/chain_dir_info.pl exp/chain/tdnn1j_sp
# exp/chain/tdnn1j_sp: num-iters=34 nj=2..5 num-params=5.2M dim=40+100->2336 combine=-0.068->-0.064 (over 4) xent:train/valid[21,33,final]=(-1.65,-1.48,-1.44/-1.77,-1.58,-1.56) logprob:train/valid[21,33,final]=(-0.076,-0.068,-0.062/-0.091,-0.084,-0.079)

# steps/info/chain_dir_info.pl exp/chain_online_cmn/tdnn1k_sp
# exp/chain_online_cmn/tdnn1k_sp: num-iters=34 nj=2..5 num-params=5.2M dim=40+100->2336 combine=-0.067->-0.062 (over 5) xent:train/valid[21,33,final]=(-1.63,-1.47,-1.44/-1.73,-1.57,-1.55) logprob:train/valid[21,33,final]=(-0.074,-0.067,-0.062/-0.093,-0.085,-0.079)


# Set -e here so that we catch if any executable fails immediately
set -euo pipefail

# First the options that are passed through to run_ivector_common.sh
# (some of which are also used in this script directly).
nj=12
stage=0
train_set=train_all  # CB: changed
test_sets="test_coddef test_cetuc test_constituicao test_coraa test_cv test_lapsbm test_lapsstory test_mls test_mtedx test_spoltech test_vf test_westpoint"
gmm=tri3b
nnet3_affix=

# The rest are configs specific to this script.  Most of the parameters
# are just hardcoded at this level, in the commands below.
affix=1j   # affix for the TDNN directory name
tree_affix=
train_stage=-10
get_egs_stage=-10
decode_iter=

# training options
# training chunk-options
chunk_width=140,100,160
common_egs_dir=
xent_regularize=0.1

# training options
srand=0
remove_egs=true
reporting_email=

# End configuration section.
echo "$0 $@"  # Print the command line for logging

. ./cmd.sh
. ./path.sh
. ./commons.sh
. ./utils/parse_options.sh

# 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.
prf bash run_ivector_common.sh --nj $nj --stage $stage \
                               --train-set "$train_set" --test-sets "$test_sets" \
                               --gmm $gmm --nnet3-affix "$nnet3_affix" || exit 1;

# Problem: We have removed the "train_" prefix of our training set in
# the alignment directory names! Bad!
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${affix}_sp
train_data_dir=data/${train_set}_sp_hires
lores_train_data_dir=data/${train_set}_sp
train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires

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

if [ $stage -le 10 ]; then
  msg "$0: creating lang directory $lang with chain-type topology"
  # 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 11 ]; then
  # Get the alignments as lattices (gives the chain training more freedom).
  # use the same num-jobs as the alignments
  msg "$0: align lattices with fmllr"
  prf steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" ${lores_train_data_dir} \
      data/lang $gmm_dir $lat_dir
  rm $lat_dir/fsts.*.gz # save space
fi

if [ $stage -le 12 ]; 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.  The num-leaves is always somewhat less than the num-leaves from
  # the GMM baseline.
  msg "$0: building tree"
  if [ -f $tree_dir/final.mdl ]; then
     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
     exit 1;
  fi
  prf steps/nnet3/chain/build_tree.sh \
      --frame-subsampling-factor 3 \
      --context-opts "--context-width=2 --central-position=1" \
      --cmd "$train_cmd" 3500 ${lores_train_data_dir} \
      $lang $ali_dir $tree_dir
fi

if [ $stage -le 13 ]; then
  msg "$0: xconfigs to configs"
  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)

  tdnn_opts="l2-regularize=0.03"
  tdnnf_opts="l2-regularize=0.03 bypass-scale=0.66"
  linear_opts="l2-regularize=0.03 orthonormal-constraint=-1.0"
  prefinal_opts="l2-regularize=0.03"
  output_opts="l2-regularize=0.015"

  mkdir -p $dir/configs
  cat <<EOF > $dir/configs/network.xconfig
  input dim=100 name=ivector
  input dim=40 name=input

  # this takes the MFCCs and generates filterbank coefficients.  The MFCCs
  # are more compressible so we prefer to dump the MFCCs to disk rather
  # than filterbanks.
  idct-layer name=idct input=input dim=40 cepstral-lifter=22 affine-transform-file=$dir/configs/idct.mat
  batchnorm-component name=batchnorm0 input=idct
  spec-augment-layer name=spec-augment freq-max-proportion=0.5 time-zeroed-proportion=0.2 time-mask-max-frames=20

  delta-layer name=delta input=spec-augment
  no-op-component name=input2 input=Append(delta, Scale(0.4, ReplaceIndex(ivector, t, 0)))

  # the first splicing is moved before the lda layer, so no splicing here
  relu-batchnorm-layer name=tdnn1 $tdnn_opts dim=768 input=input2
  tdnnf-layer name=tdnnf2 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=1
  tdnnf-layer name=tdnnf3 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=1
  tdnnf-layer name=tdnnf4 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=1
  tdnnf-layer name=tdnnf5 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=0
  tdnnf-layer name=tdnnf6 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf7 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
  tdnnf-layer name=tdnnf8 $tdnnf_opts dim=768 bottleneck-dim=96 time-stride=3
  linear-component name=prefinal-l dim=192 $linear_opts

  ## adding the layers for chain branch
  prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts small-dim=192 big-dim=768
  output-layer name=output include-log-softmax=false dim=$num_targets $output_opts

  # adding the layers for xent branch
  prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts small-dim=192 big-dim=768
  output-layer name=output-xent dim=$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 14 ]; then
  msg "$0: training DNN"
  prf steps/nnet3/chain/train.py --stage=$train_stage \
      --cmd="$decode_cmd" \
      --feat.online-ivector-dir=$train_ivector_dir \
      --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
      --chain.xent-regularize $xent_regularize \
      --chain.leaky-hmm-coefficient=0.1 \
      --chain.l2-regularize=0.0 \
      --chain.apply-deriv-weights=false \
      --chain.lm-opts="--num-extra-lm-states=2000" \
      --trainer.add-option="--optimization.memory-compression-level=2" \
      --trainer.srand=$srand \
      --trainer.max-param-change=2.0 \
      --trainer.num-epochs=4 \
      --trainer.frames-per-iter=3000000 \
      --trainer.optimization.num-jobs-initial=1 \
      --trainer.optimization.num-jobs-final=1 \
      --trainer.optimization.initial-effective-lrate=0.002 \
      --trainer.optimization.final-effective-lrate=0.0002 \
      --trainer.num-chunk-per-minibatch=128,64 \
      --egs.chunk-width=$chunk_width \
      --egs.dir="$common_egs_dir" \
      --egs.opts="--frames-overlap-per-eg 0 --max-jobs-run $nj --max-shuffle-jobs-run $nj" \
      --cleanup.remove-egs=$remove_egs \
      --use-gpu=true \
      --reporting.email="$reporting_email" \
      --feat-dir=$train_data_dir \
      --tree-dir=$tree_dir \
      --lat-dir=$lat_dir \
      --dir=$dir  || exit 1;
fi

if [ $stage -le 15 ]; then
  # Note: it's not important to give mkgraph.sh the lang directory with the
  # matched topology (since it gets the topology file from the model).
  msg "$0: generating dnn graph"
  prf utils/mkgraph.sh --self-loop-scale 1.0 \
      data/lang_test_small $tree_dir $tree_dir/graph_small || exit 1;
fi

# https://github.com/kaldi-asr/kaldi/blob/master/egs/librispeech/s5/local/lookahead/run_lookahead.sh
if [ $stage -le 16 ]; then
  msg "$0: generating dnn graph (lookahead)"
  export LD_LIBRARY_PATH=$KALDI_ROOT/tools/openfst/lib/fst
  prf utils/mkgraph_lookahead.sh --self-loop-scale 1.0 \
      data/lang_test_small $tree_dir $tree_dir/graph_small_lookahead || exit 1;
fi

model_dir=model-$(date +'%Y%m%d_%H%M')
local/vosk/create_model_dir.sh $model_dir || exit 1
msg "$0: success! check your model at '$model_dir'"
