set -e
  # The run_tdnn_1a.sh is a first attempt at an TDNN system, based on configs.
  # It created befroe we introduced xconfigs.
  # See run_tdnn_1b.sh for comparative results.
  
  
  # chain_cleaned/tdnn_1a_sp: num_params=16.8M (12.7M after excluding the xent branch), average training time=71.8s per job(on Tesla K80), real-time factor=0.558894
  
  
  # for x in exp/chain_cleaned/tdnn_1a_sp/decode_*; do grep WER $x/wer_* | utils/best_wer.sh ; done
  #System                      tdnn_1a_sp
  #WER on dev(fglarge)           3.87
  #WER on dev(tglarge)           3.97
  #WER on dev(tgmed)             4.95
  #WER on dev(tgsmall)           5.57
  #WER on dev_other(fglarge)    10.22
  #WER on dev_other(tglarge)    10.79
  #WER on dev_other(tgmed)      13.01
  #WER on dev_other(tgsmall)    14.36
  #WER on test(fglarge)          4.17
  #WER on test(tglarge)          4.36
  #WER on test(tgmed)            5.33
  #WER on test(tgsmall)          5.93
  #WER on test_other(fglarge)   10.62
  #WER on test_other(tglarge)   10.96
  #WER on test_other(tgmed)     13.24
  #WER on test_other(tgsmall)   14.53
  
  ## how you run this (note: this assumes that the run_tdnn.sh soft link points here;
  ## otherwise call it directly in its location).
  # by default, with cleanup:
  # local/chain/run_tdnn.sh
  
  # without cleanup:
  # local/chain/run_tdnn_1a.sh  --train-set train_960 --gmm tri6b --nnet3-affix "" &
  
  # configs for 'chain'
  # this script is adapted from swbd's 7b script, but the relu-dim is larger.
  
  # First the options that are passed through to run_ivector_common.sh
  # (some of which are also used in this script directly).
  stage=0
  decode_nj=50
  train_set=train_960_cleaned
  gmm=tri6b_cleaned # the gmm for the target data
  nnet3_affix=_cleaned  # cleanup affix for nnet3 and chain dirs, e.g. _cleaned
  
  # The rest are configs specific to this script.  Most of the parameters
  # are just hardcoded at this level, in the commands below.
  affix=1a
  tree_affix=
  train_stage=-10
  get_egs_stage=-10
  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
  # training options
  frames_per_eg=150
  relu_dim=725
  remove_egs=false
  common_egs_dir=
  xent_regularize=0.1
  self_repair_scale=0.00001
  
  
  # 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
  # nnet3 setup, and you can skip them by setting "--stage 11" if you have already
  # run those things.
  local/nnet3/run_ivector_common.sh --stage $stage \
                                    --train-set $train_set \
                                    --gmm $gmm \
                                    --nnet3-affix "$nnet3_affix" || exit 1;
  
  gmm_dir=exp/$gmm
  ali_dir=exp/${gmm}_ali_${train_set}_sp
  tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
  lang=data/lang_chain
  lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
  dir=exp/chain${nnet3_affix}/tdnn${affix:+_$affix}_sp
  train_data_dir=data/${train_set}_sp_hires
  lores_train_data_dir=data/${train_set}_sp
  train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
  
  for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
      $lores_train_data_dir/feats.scp $ali_dir/ali.1.gz; do
    [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
  done
  
  # Please take this as a reference on how to specify all the options of
  # local/chain/run_chain_common.sh
  local/chain/run_chain_common.sh --stage $stage \
                                  --gmm-dir $gmm_dir \
                                  --ali-dir $ali_dir \
                                  --lores-train-data-dir ${lores_train_data_dir} \
                                  --lang $lang \
                                  --lat-dir $lat_dir \
                                  --tree-dir $tree_dir || exit 1;
  
  
  if [ $stage -le 14 ]; then
    mkdir -p $dir
  
    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 $train_data_dir \
      --ivector-dir $train_ivector_dir \
      --tree-dir $tree_dir \
      --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 0.5 \
      $dir/configs || exit 1;
  fi
  
  
  
  if [ $stage -le 15 ]; 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/librispeech-$(date +'%m_%d_%H_%M')/s5c/$dir/egs/storage $dir/egs/storage
    fi
  
    touch $dir/egs/.nodelete # keep egs around when that run dies.
  
    steps/nnet3/chain/train.py --stage $train_stage \
      --cmd "$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 0.00005 \
      --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 $frames_per_eg \
      --egs.dir "$common_egs_dir" \
      --trainer.num-chunk-per-minibatch 128 \
      --trainer.frames-per-iter 1500000 \
      --trainer.num-epochs 4 \
      --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 \
      --cleanup.remove-egs $remove_egs \
      --feat-dir $train_data_dir \
      --tree-dir $tree_dir \
      --lat-dir $lat_dir \
      --dir $dir  || exit 1;
  fi
  
  
  graph_dir=$dir/graph_tgsmall
  if [ $stage -le 16 ]; 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 --remove-oov data/lang_test_tgsmall $dir $graph_dir
    # romove <UNK> from the graph
    fstrmsymbols --apply-to-output=true --remove-arcs=true "echo 3|" $graph_dir/HCLG.fst $graph_dir/HCLG.fst
  fi
  
  
  if [ $stage -le 17 ]; then
    iter_opts=
    if [ ! -z $decode_iter ]; then
      iter_opts=" --iter $decode_iter "
    fi
    rm $dir/.error 2>/dev/null || true
    for decode_set in test_clean test_other dev_clean dev_other; do
        (
        steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
            --nj $decode_nj --cmd "$decode_cmd" $iter_opts \
            --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${decode_set}_hires \
            $graph_dir data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_tgsmall || exit 1
        steps/lmrescore.sh --cmd "$decode_cmd" --self-loop-scale 1.0 data/lang_test_{tgsmall,tgmed} \
            data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_{tgsmall,tgmed} || exit 1
        steps/lmrescore_const_arpa.sh \
            --cmd "$decode_cmd" data/lang_test_{tgsmall,tglarge} \
            data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_{tgsmall,tglarge} || exit 1
        steps/lmrescore_const_arpa.sh \
            --cmd "$decode_cmd" data/lang_test_{tgsmall,fglarge} \
            data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_{tgsmall,fglarge} || exit 1
        ) || touch $dir/.error &
    done
    wait
    [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
  fi
  
  exit 0;