#!/bin/bash
  #
  # This script requires that you have run the toplevel run.sh script in TEDLIUM up to stage 7.
  #
  # Results: (Run for x in exp/chain/tdnn/decode*; do [ -d $x ] && grep Sum $x/score_*/*.sys | utils/best_wer.sh; done 2>/dev/null)
  ## Number of parameters: 6172530
  ## %WER 14.1 | 507 17792 | 88.6 7.3 4.1 2.7 14.1 92.9 | 0.075 | exp/chain/tdnn/decode_dev/score_10_0.5/ctm.filt.filt.sys
  ## %WER 13.3 | 507 17792 | 89.7 6.9 3.4 2.9 13.3 92.1 | 0.000 | exp/chain/tdnn/decode_dev_rescore/score_10_0.0/ctm.filt.filt.sys
  ## %WER 13.8 | 1155 27512 | 89.4 7.5 3.1 3.2 13.8 87.9 | 0.101 | exp/chain/tdnn/decode_test/score_10_0.0/ctm.filt.filt.sys
  ## %WER 12.9 | 1155 27512 | 90.1 6.6 3.3 2.9 12.9 86.1 | 0.043 | exp/chain/tdnn/decode_test_rescore/score_10_0.0/ctm.filt.filt.sys
  # The final WER (rescored WER on the test set) is what we are interested in.
  
  # To reproduce the setup used in the paper, set the following variables:
  # affix=_more_ce
  # relu_dim=525
  # xent_regularize=0.2
  #
  # Results: (Run for x in exp/chain/tdnn_more_ce/decode*; do [ -d $x ] && grep Sum $x/score_*/*.sys | utils/best_wer.sh; done 2>/dev/null)
  ## Number of parameters: 8758742
  ## %WER 14.3 | 507 17792 | 89.0 7.8 3.2 3.3 14.3 93.5 | 0.116 | exp/chain/tdnn_more_ce/decode_dev/score_10_0.0/ctm.filt.filt.sys
  ## %WER 13.0 | 507 17792 | 90.0 6.9 3.2 2.9 13.0 91.3 | -0.003 | exp/chain/tdnn_more_ce/decode_devv_rescore/score_10_0.0/ctm.filt.filt.sys
  ## %WER 13.8 | 1155 27512 | 89.1 7.4 3.4 2.9 13.8 87.5 | 0.082 | exp/chain/tdnn_more_ce/decode_test/score_10_0.5/ctm.filt.filt.sys
  ## %WER 12.8 | 1155 27512 | 90.4 6.6 3.1 3.1 12.8 86.7 | 0.014 | exp/chain/tdnn_more_ce/decode_test_rescore/score_10_0.0/ctm.filt.filt.sys
  
  set -uo pipefail
  
  # configs for 'chain'
  affix=
  stage=0 # After running the entire script once, you can set stage=12 to tune the neural net only.
  train_stage=-10
  get_egs_stage=-10
  dir=exp/chain/tdnn
  decode_iter=
  
  # TDNN options
  # this script uses the new tdnn config generator so it needs a final 0 to reflect that the final layer input has no splicing
  self_repair_scale=0.00001
  # training options
  num_epochs=4
  initial_effective_lrate=0.001
  final_effective_lrate=0.0001
  max_param_change=2.0
  final_layer_normalize_target=0.5
  num_jobs_initial=3
  num_jobs_final=8
  minibatch_size=128
  relu_dim=425
  frames_per_eg=150
  remove_egs=false
  xent_regularize=0.1
  
  
  # End configuration section.
  echo "$0 $@"  # Print the command line for logging
  
  . ./cmd.sh
  . ./path.sh
  . ./utils/parse_options.sh
  
  dir=${dir}${affix}
  
  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 9" if you have already
  # run those things.
  
  gmm_dir=exp/tri3
  ali_dir=exp/tri3_ali_sp
  lats_dir=${ali_dir/ali/lats} # note, this is a search-and-replace from 'ali' to 'lats'
  treedir=exp/chain/tri3_tree
  lang=data/lang_chain
  
  mkdir -p $dir
  
  local/nnet3/run_ivector_common.sh --stage $stage \
    --generate-alignments false \
    --speed-perturb true || 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 ${ali_dir}/num_jobs) || exit 1;
    steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/train_sp \
      data/lang $gmm_dir $lats_dir
    rm ${lats_dir}/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" 4000 data/train_sp $lang $ali_dir $treedir
  fi
  
  if [ $stage -le 12 ]; then
    echo "$0: creating neural net configs";
  
    # create the config files for nnet initialization
    repair_opts=${self_repair_scale:+" --self-repair-scale-nonlinearity $self_repair_scale "}
  
    steps/nnet3/tdnn/make_configs.py \
      $repair_opts \
      --feat-dir data/train_sp_hires \
      --ivector-dir exp/nnet3/ivectors_train_sp \
      --tree-dir $treedir \
      --relu-dim $relu_dim \
      --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 $xent_regularize \
      --xent-separate-forward-affine true \
      --include-log-softmax false \
      --final-layer-normalize-target $final_layer_normalize_target \
      $dir/configs || exit 1;
  fi
  
  if [ $stage -le 13 ]; then
    if [[  $(hostname -f) ==  *.clsp.jhu.edu ]]; then
       # spread the egs over various machines.  will help reduce overload of any
       # one machine.
       utils/create_split_dir.pl /export/b{09,10,11,12}/$USER/kaldi-data/egs/tedlium-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
    fi
  
    touch $dir/egs/.nodelete
  
   steps/nnet3/chain/train.py --stage $train_stage \
     --cmd "$decode_cmd" \
     --feat.online-ivector-dir exp/nnet3/ivectors_train_sp \
     --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
     --chain.xent-regularize $xent_regularize \
     --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 $frames_per_eg \
     --trainer.num-chunk-per-minibatch $minibatch_size \
     --trainer.frames-per-iter 1500000 \
     --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 \
     --cleanup.preserve-model-interval 20 \
     --feat-dir data/train_sp_hires \
     --tree-dir $treedir \
     --lat-dir $lats_dir \
     --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_test $dir $dir/graph
  fi
  
  graph_dir=$dir/graph
  if [ $stage -le 15 ]; then
    iter_opts=
    if [ ! -z $decode_iter ]; then
      iter_opts=" --iter $decode_iter "
    fi
  
    for decode_set in dev test; do
      (
      steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
        --nj $(wc -l < data/$decode_set/spk2utt) --cmd "$decode_cmd" $iter_opts \
        --online-ivector-dir exp/nnet3/ivectors_${decode_set} \
        --scoring-opts "--min_lmwt 5 --max_lmwt 15" \
        $graph_dir data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter} || exit 1;
  
      steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
        data/lang_test data/lang_rescore data/${decode_set}_hires \
        $dir/decode_${decode_set}${decode_iter:+_$decode_iter} \
        $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_rescore || exit 1;
      ) &
    done
  fi
  
  wait