#! /bin/bash

# This script is an adjusted version of kaldis wsj run_tdnn_1g.sh script.
# We reduced the number tdnn-f layer, batch size and training jobs to
# fit smaller gpus.
# Training and evaluation on wsj_8k leads to the following WER:
#                             this script  tdnn1g_sp
# WER dev93 (tgpr)               7.40        6.68
# WER eval92 (tgpr)              5.59        4.54
# Training and evaluation on sms_wsj with a single speaker leads to the following WER:
# WER cv_dev93 (tgpr)            12.20
# WER test_eval92 (tgpr)         8.93

# Exit on error: https://stackoverflow.com/a/1379904/911441
set -e

dest_dir=
nj=16
dataset=sms
train_set=train_si284
cv_sets=cv_dev93

gmm=tri4b
gmm_data_type=wsj_8k
ali_data_type=sms_early
stage=5

num_threads_ubm=32
nnet3_affix=       # affix for exp dirs, e.g. it was _cleaned in tedlium.

# Options which are not passed through to run_ivector_common.sh
affix=1a   #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
common_egs_dir=
reporting_email=

# LSTM/chain options
train_stage=-10
xent_regularize=0.1
dropout_schedule='0,0@0.20,0.5@0.50,0'

# training chunk-options
chunk_width=140,100,160
# we don't need extra left/right context for TDNN systems.
chunk_left_context=0
chunk_right_context=0

# training options
srand=0
remove_egs=true

. ./cmd.sh
. ./path.sh
. ${KALDI_ROOT}/egs/wsj/s5/utils/parse_options.sh

green='\033[0;32m'
NC='\033[0m' # No Color
trap 'echo -e "${green}$ $BASH_COMMAND ${NC}"' DEBUG

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

gmm_dir=exp/$gmm_data_type/${gmm}
ali_dir=exp/$ali_data_type/${gmm}_ali_${train_set}_sp
lat_dir=exp/$dataset/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
dir=exp/$dataset/chain${nnet3_affix}/tdnn${affix}_sp
train_data_dir=data/$dataset/${train_set}_sp_hires
train_ivector_dir=exp/$dataset/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
lores_train_data_dir=data/$ali_data_type/${train_set}_sp
tree_dir=exp/$dataset/chain${nnet3_affix}/tree_a_sp
# the 'lang' directory is created by this script.
# If you create such a directory with a non-standard topology
# you should probably name it differently.
lang=data/lang_chain
################################################################################
# Define config
#############################################################################
if [ $stage -le 17 ]; then
  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=0.01 dropout-proportion=0.0 dropout-per-dim-continuous=true"
  tdnnf_opts="l2-regularize=0.01 dropout-proportion=0.0 bypass-scale=0.66"
  linear_opts="l2-regularize=0.01 orthonormal-constraint=-1.0"
  prefinal_opts="l2-regularize=0.01"
  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

  # 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-dropout-layer name=tdnn1 $tdnn_opts dim=512
  tdnnf-layer name=tdnnf2 $tdnnf_opts dim=512 bottleneck-dim=128 time-stride=1
  tdnnf-layer name=tdnnf3 $tdnnf_opts dim=512 bottleneck-dim=128 time-stride=1
  tdnnf-layer name=tdnnf4 $tdnnf_opts dim=512 bottleneck-dim=128 time-stride=1
  tdnnf-layer name=tdnnf5 $tdnnf_opts dim=512 bottleneck-dim=128 time-stride=0
  tdnnf-layer name=tdnnf6 $tdnnf_opts dim=512 bottleneck-dim=128 time-stride=3
  tdnnf-layer name=tdnnf7 $tdnnf_opts dim=512 bottleneck-dim=128 time-stride=3
  tdnnf-layer name=tdnnf8 $tdnnf_opts dim=512 bottleneck-dim=128 time-stride=3
  linear-component name=prefinal-l dim=192 $linear_opts


  prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts big-dim=1024 small-dim=192
  output-layer name=output include-log-softmax=false dim=$num_targets $output_opts

  prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts big-dim=1024 small-dim=192
  output-layer name=output-xent dim=$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

################################################################################
# Train network
#############################################################################
if [ $stage -le 18 ]; then

  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.dropout-schedule $dropout_schedule \
    --trainer.add-option="--optimization.memory-compression-level=2" \
    --trainer.srand=$srand \
    --trainer.max-param-change=2.0 \
    --trainer.num-epochs=10 \
    --trainer.frames-per-iter=5000000 \
    --trainer.optimization.num-jobs-initial=1 \
    --trainer.optimization.num-jobs-final=1 \
    --trainer.optimization.initial-effective-lrate=0.0005 \
    --trainer.optimization.final-effective-lrate=0.00005 \
    --trainer.num-chunk-per-minibatch=128,64 \
    --trainer.optimization.momentum=0.0 \
    --egs.chunk-width=$chunk_width \
    --egs.chunk-left-context=0 \
    --egs.chunk-right-context=0 \
    --egs.dir="$common_egs_dir" \
    --egs.opts="--frames-overlap-per-eg 0" \
    --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

################################################################################
# Decode on the dev set with lm rescoring
#############################################################################
if [ $stage -le 19 ]; then
  # The reason we are using data/lang here, instead of $lang, is just to
  # emphasize that it's not actually important to give mkgraph.sh the
  # lang directory with the matched topology (since it gets the
  # topology file from the model).  So you could give it a different
  # lang directory, one that contained a wordlist and LM of your choice,
  # as long as phones.txt was compatible.

  utils/lang/check_phones_compatible.sh \
    data/lang_test_tgpr/phones.txt $lang/phones.txt
  utils/mkgraph.sh \
    --self-loop-scale 1.0 data/lang_test_tgpr \
    $tree_dir $tree_dir/graph_tgpr || exit 1;
fi
if [ $stage -le 20 ]; then
  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
  rm $dir/.error 2>/dev/null || true

  for data in $cv_sets; do
    data_affix=$(echo $data | sed s/test_//)
    nspk=$(wc -l <data/$dataset/${data}_hires/spk2utt)
    lmtype=tgpr
    steps/nnet3/decode.sh \
        --acwt 1.0 --post-decode-acwt 10.0 \
        --extra-left-context 0 --extra-right-context 0 \
        --extra-left-context-initial 0 \
        --extra-right-context-final 0 \
        --frames-per-chunk $frames_per_chunk \
        --nj $nspk --cmd "$decode_cmd"  --num-threads 4 \
        --online-ivector-dir exp/$dataset/nnet3${nnet3_affix}/ivectors_${data}_hires \
        $tree_dir/graph_${lmtype} data/$dataset/${data}_hires ${dir}/decode_${lmtype}_${data_affix} || exit 1
  done
fi