#!/bin/bash
  
  # this is the standard "tdnn" system, built in nnet3; it's what we use to
  # call multi-splice.
  
  # Results (2 epochs):
  # Number of parameters: 6056880
  # %WER 15.3 | 507 17792 | 87.4 9.0 3.6 2.7 15.3 90.1 | -0.081 | exp/nnet3/tdnn_sp/decode_dev/score_10_0.5/ctm.filt.filt.sys
  # %WER 13.9 | 507 17792 | 88.4 8.0 3.6 2.3 13.9 85.8 | -0.164 | exp/nnet3/tdnn_sp/decode_dev_rescore/score_10_0.5/ctm.filt.filt.sys
  # %WER 13.8 | 1155 27512 | 88.5 8.7 2.7 2.3 13.8 84.2 | -0.076 | exp/nnet3/tdnn_sp/decode_test/score_10_0.0/ctm.filt.filt.sys
  # %WER 12.5 | 1155 27512 | 89.6 7.7 2.6 2.1 12.5 81.5 | -0.133 | exp/nnet3/tdnn_sp/decode_test_rescore/score_10_0.0/ctm.filt.filt.sys
  
  # 4 epochs
  # %WER 14.6 | 507 17792 | 87.9 8.7 3.4 2.5 14.6 88.6 | -0.111 | exp/nnet3/tdnn/decode_dev/score_10_0.5/ctm.filt.filt.sys
  # %WER 13.2 | 507 17792 | 89.4 7.7 2.9 2.6 13.2 85.0 | -0.170 | exp/nnet3/tdnn/decode_dev_rescore/score_10_0.0/ctm.filt.filt.sys
  # %WER 13.5 | 1155 27512 | 88.7 8.5 2.7 2.3 13.5 83.6 | -0.110 | exp/nnet3/tdnn/decode_test/score_10_0.0/ctm.filt.filt.sys
  # %WER 12.1 | 1155 27512 | 89.9 7.5 2.6 2.1 12.1 80.3 | -0.178 | exp/nnet3/tdnn/decode_test_rescore/score_10_0.0/ctm.filt.filt.sys
  
  # At this script level we don't support not running on GPU, as it would be painfully slow.
  # If you want to run without GPU you'd have to call train_tdnn.sh with --gpu false,
  # --num-threads 16 and --minibatch-size 128.
  
  stage=1
  affix=
  train_stage=-10
  common_egs_dir=
  reporting_email=
  remove_egs=true
  decode_iter=
  
  . ./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=exp/nnet3/tdnn
  dir=$dir${affix:+_$affix}
  train_set=train_sp #_sp stands for speed-perturbed. This is hard-coded to speed 
                     # pertub data.
  ali_dir=exp/tri3_ali_sp
  
  local/nnet3/run_ivector_common.sh --stage $stage --generate-alignments true || exit 1;
  
  if [ $stage -le 9 ]; then
    echo "$0: creating neural net configs";
  
    # create the config files for nnet initialization
    python steps/nnet3/tdnn/make_configs.py  \
      --feat-dir data/${train_set}_hires \
      --ivector-dir exp/nnet3/ivectors_${train_set} \
      --ali-dir $ali_dir \
      --relu-dim 500 \
      --splice-indexes "-1,0,1 -1,0,1,2 -3,0,3 -3,0,3 -3,0,3 -6,-3,0" \
      --use-presoftmax-prior-scale true \
     $dir/configs || exit 1;
  fi
  
  if [ $stage -le 10 ]; then
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
      utils/create_split_dir.pl \
       /export/b{09,10,11,12}/$USER/kaldi-data/egs/tedlium-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
    fi
  
    steps/nnet3/train_dnn.py --stage=$train_stage \
      --cmd="$decode_cmd" \
      --feat.online-ivector-dir exp/nnet3/ivectors_${train_set} \
      --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
      --trainer.num-epochs 2 \
      --trainer.optimization.num-jobs-initial 3 \
      --trainer.optimization.num-jobs-final 8 \
      --trainer.optimization.initial-effective-lrate 0.0015 \
      --trainer.optimization.final-effective-lrate 0.00015 \
      --egs.dir "$common_egs_dir" \
      --cleanup.remove-egs $remove_egs \
      --cleanup.preserve-model-interval 20 \
      --feat-dir=data/${train_set}_hires \
      --ali-dir $ali_dir \
      --lang data/lang \
      --reporting.email="$reporting_email" \
      --dir=$dir  || exit 1;
  
  fi
  
  graph_dir=exp/tri3/graph
  if [ $stage -le 11 ]; 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 \
        --nj $(wc -l < data/$decode_set/spk2utt) --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} || 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;