#!/bin/bash
  
  set -e
  
  # based on run_tdnn_7b.sh in the swbd recipe
  
  # configs for 'chain'
  affix=
  stage=12
  train_stage=-10
  get_egs_stage=-10
  dir=exp/chain/tdnn_7b
  decode_iter=
  
  # training options
  num_epochs=4
  remove_egs=false
  common_egs_dir=
  
  # 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
  
  dir=${dir}${affix:+_$affix}
  
  train_set=train_nodup_sp
  build_tree_train_set=train_nodup
  build_tree_ali_dir=exp/tri5a_ali
  treedir=exp/chain/tri6_tree
  lang=data/lang_chain
  
  
  # The iVector-extraction and feature-dumping parts are the same as the standard
  # nnet3 setup, and you can skip them by setting "--stage 8" if you have already
  # run those things.
  local/nnet3/run_ivector_common.sh --stage $stage \
    --speed-perturb true \
    --generate-alignments false || exit 1;
  
  if [ $stage -le 9 ]; then
    # Get the alignments as lattices (gives the chain training more freedom).
    # use the same num-jobs as the alignments
    nj=$(cat $build_tree_ali_dir/num_jobs) || exit 1;
    steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/$train_set \
      data/lang exp/tri5a exp/tri5a_lats_nodup_sp || exit 1;
    rm exp/tri5a_lats_nodup_sp/fsts.*.gz # save space
  fi
  
  if [ $stage -le 10 ]; 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.py $nonsilphonelist $silphonelist >$lang/topo
  fi
  
  if [ $stage -le 11 ]; then
    # Build a tree using our new topology.
    steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
        --cmd "$train_cmd" 11000 data/$build_tree_train_set $lang $build_tree_ali_dir $treedir || exit 1;
  fi
  
  if [ $stage -le 12 ]; then
    echo "$0: creating neural net configs";
  
    # create the config files for nnet initialization
    steps/nnet3/tdnn/make_configs.py \
      --self-repair-scale-nonlinearity 0.00001 \
      --feat-dir data/${train_set}_hires \
      --ivector-dir exp/nnet3/ivectors_${train_set} \
      --tree-dir $treedir \
      --relu-dim 725 \
      --splice-indexes "-1,0,1 -1,0,1,2 -3,0,3 -3,0,3 -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 0.5 \
      $dir/configs || exit 1;
  fi
  
  if [ $stage -le 13 ]; then
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
      utils/create_split_dir.pl \
       /export/b0{5,6,7,8}/$USER/kaldi-data/egs/fisher_swbd-$(date +'%m_%d_%H_%M')/s5c/$dir/egs/storage $dir/egs/storage
    fi
  
    touch $dir/egs/.nodelete # keep egs around when that run dies.
  
    steps/nnet3/chain/train.py --stage $train_stage \
      --egs.dir "$common_egs_dir" \
      --cmd "$decode_cmd" \
      --feat.online-ivector-dir exp/nnet3/ivectors_${train_set} \
      --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=2000" \
      --egs.stage $get_egs_stage \
      --egs.opts "--frames-overlap-per-eg 0" \
      --egs.chunk-width 150 \
      --trainer.num-chunk-per-minibatch 128 \
      --trainer.frames-per-iter 1500000 \
      --trainer.num-epochs $num_epochs \
      --trainer.optimization.num-jobs-initial 3 \
      --trainer.optimization.num-jobs-final 16 \
      --trainer.optimization.initial-effective-lrate 0.001 \
      --trainer.optimization.final-effective-lrate 0.0001 \
      --trainer.max-param-change 2.0 \
      --cleanup.remove-egs $remove_egs \
      --feat-dir data/${train_set}_hires \
      --tree-dir $treedir \
      --lat-dir exp/tri5a_lats_nodup_sp \
      --dir $dir  || exit 1;
  fi
  
  if [ $stage -le 14 ]; 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_fsh_sw1_tg $dir $dir/graph_fsh_sw1_tg
  fi
  
  decode_suff=fsh_sw1_tg
  graph_dir=$dir/graph_fsh_sw1_tg
  if [ $stage -le 15 ]; then
    iter_opts=
    if [ ! -z $decode_iter ]; then
      iter_opts=" --iter $decode_iter "
    fi
    for decode_set in eval2000 rt03; do
        (
        num_jobs=`cat data/${decode_set}_hires/utt2spk|cut -d' ' -f2|sort -u|wc -l`
        steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
            --nj $num_jobs --cmd "$decode_cmd" $iter_opts \
            --online-ivector-dir exp/nnet3/ivectors_${decode_set} \
            $graph_dir data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_${decode_suff} || exit 1;
        steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
            data/lang_fsh_sw1_{tg,fg} data/${decode_set}_hires \
            $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_fsh_sw1_{tg,fg} || exit 1;
        ) &
    done
  fi
  wait;
  exit 0;