#!/bin/bash
  
  # This is modified from run_tdnn_5f.sh, to use the old topology, as a baseline
  # to test the modified transition-model code (by which we hope to be able to
  # create more compact decoding graphs for chain models).
  
  set -e
  
  # configs for 'chain'
  stage=0
  train_stage=-10
  get_egs_stage=-10
  dir=exp/chain/tdnn_5g
  
  # training options
  num_epochs=12
  initial_effective_lrate=0.005
  final_effective_lrate=0.0005
  max_param_change=2.0
  final_layer_normalize_target=0.5
  num_jobs_initial=2
  num_jobs_final=4
  minibatch_size=128
  frames_per_eg=150
  remove_egs=false
  
  # 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
  # nnet2 setup, and you can skip them by setting "--stage 4" if you have already
  # run those things.
  
  ali_dir=exp/tri3b_ali
  treedir=exp/chain/tri4_5g_tree
  lang=data/lang_chain_5g
  
  local/online/run_nnet2_common.sh --stage $stage || exit 1;
  
  if [ $stage -le 4 ]; then
    # Get the alignments as lattices (gives the chain training more freedom).
    # use the same num-jobs as the alignments
    nj=$(cat exp/tri3b_ali/num_jobs) || exit 1;
    steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/train \
      data/lang exp/tri3b exp/tri3b_lats
    rm exp/tri3b_lats/fsts.*.gz # save space
  fi
  
  if [ $stage -le 5 ]; 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_orig.py $nonsilphonelist $silphonelist >$lang/topo
  fi
  
  if [ $stage -le 6 ]; then
    # Build a tree using our new topology.
    steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
      --cmd "$train_cmd" 1200 data/train $lang $ali_dir $treedir
  fi
  
  if [ $stage -le 7 ]; then
    mkdir -p $dir
  
    echo "$0: creating neural net configs";
  
    steps/nnet3/tdnn/make_configs.py \
      --self-repair-scale-nonlinearity 0.00001 \
      --feat-dir data/train \
      --ivector-dir exp/nnet2_online/ivectors \
      --tree-dir $treedir \
      --relu-dim 450 \
      --splice-indexes "-1,0,1 -2,-1,0,1 -3,0,3 -6,-3,0 0" \
      --use-presoftmax-prior-scale false \
      --xent-regularize 0.1 \
      --xent-separate-forward-affine true \
      --include-log-softmax false \
      --final-layer-normalize-target 1.0 \
     $dir/configs || exit 1;
  fi
  
  if [ $stage -le 8 ]; then
   steps/nnet3/chain/train.py --stage $train_stage \
      --cmd "$decode_cmd" \
      --feat.online-ivector-dir exp/nnet2_online/ivectors \
      --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
      --chain.xent-regularize 0.1 \
      --chain.leaky-hmm-coefficient 0.1 \
      --chain.l2-regularize 0.00005 \
      --chain.apply-deriv-weights false \
      --chain.lm-opts="--num-extra-lm-states=200" \
      --egs.dir "$common_egs_dir" \
      --egs.opts "--frames-overlap-per-eg 0" \
      --egs.chunk-width $frames_per_eg \
      --trainer.num-chunk-per-minibatch $minibatch_size \
      --trainer.frames-per-iter 1000000 \
      --trainer.num-epochs $num_epochs \
      --trainer.optimization.num-jobs-initial $num_jobs_initial \
      --trainer.optimization.num-jobs-final $num_jobs_final \
      --trainer.optimization.initial-effective-lrate $initial_effective_lrate \
      --trainer.optimization.final-effective-lrate $final_effective_lrate \
      --trainer.max-param-change $max_param_change \
      --cleanup.remove-egs $remove_egs \
      --feat-dir data/train \
      --tree-dir $treedir \
      --lat-dir exp/tri3b_lats \
      --dir $dir
  fi
  
  if [ $stage -le 9 ]; then
    steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 4 \
      data/test exp/nnet2_online/extractor exp/nnet2_online/ivectors_test || exit 1;
  fi
  
  if [ $stage -le 10 ]; 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 $dir $dir/graph
    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
      --scoring-opts "--min-lmwt 1" \
      --nj 20 --cmd "$decode_cmd" \
      --online-ivector-dir exp/nnet2_online/ivectors_test \
      $dir/graph data/test $dir/decode || exit 1;
  fi
  
  if [ $stage -le 11 ]; then
    utils/mkgraph.sh --self-loop-scale 1.0 data/lang_ug $dir $dir/graph_ug
    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
      --nj 20 --cmd "$decode_cmd" \
      --online-ivector-dir exp/nnet2_online/ivectors_test \
      $dir/graph_ug data/test $dir/decode_ug || exit 1;
  fi
  wait;
  exit 0;