#!/bin/bash
  set -e
  
  # run_tdnn_1b.sh's topo is similiar with run_tdnn_1a.sh but we used the xconfigs. Otherwise "frames_per_eg=150,140,100".
  
  #exp/chain_cleaned/tdnn_1b_sp: num-iters=871 nj=3..16 num-params=17.1M dim=40+100->5151 combine=-0.074->-0.074 xent:train/valid[579,870,final]=(-1.02,-0.986,-0.990/-0.985,-0.953,-0.957) logprob:train/valid[579,870,final]=(-0.066,-0.062,-0.063/-0.070,-0.069,-0.069)
  
  # by default, with cleanup:
  # local/chain/run_tdnn.sh
  
  # local/chain/compare_wer.sh exp/chain_cleaned/tdnn_1b_sp
  # System                      tdnn_1b_sp
  # WER on dev(fglarge)              3.87
  # WER on dev(tglarge)              3.99
  # WER on dev(tgmed)                4.96
  # WER on dev(tgsmall)              5.42
  # WER on dev_other(fglarge)       10.15
  # WER on dev_other(tglarge)       10.77
  # WER on dev_other(tgmed)         12.94
  # WER on dev_other(tgsmall)       14.39
  # WER on test(fglarge)             4.14
  # WER on test(tglarge)             4.32
  # WER on test(tgmed)               5.28
  # WER on test(tgsmall)             5.88
  # WER on test_other(fglarge)      10.80
  # WER on test_other(tglarge)      11.13
  # WER on test_other(tgmed)        13.37
  # WER on test_other(tgsmall)      14.92
  # Final train prob              -0.0626
  # Final valid prob              -0.0687
  # Final train prob (xent)       -0.9905
  # Final valid prob (xent)       -0.9566
  
  ## how you run this (note: this assumes that the run_tdnn.sh soft link points here;
  ## otherwise call it directly in its location).
  # without cleanup:
  # local/chain/run_tdnn.sh  --train-set train_960 --gmm tri6b --nnet3-affix "" &
  
  # configs for 'chain'
  # this script is adapted from librispeech's 1c script.
  
  # 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=1b
  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=150,140,100
  relu_dim=725
  remove_egs=true
  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
  
    num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
    learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
  
    mkdir -p $dir/configs
    cat <<EOF > $dir/configs/network.xconfig
    input dim=100 name=ivector
    input dim=40 name=input
  
    # please note that it is important to have input layer with the name=input
    # as the layer immediately preceding the fixed-affine-layer to enable
    # the use of short notation for the descriptor
    fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
  
    # the first splicing is moved before the lda layer, so no splicing here
    relu-batchnorm-layer name=tdnn1 dim=$relu_dim
    relu-batchnorm-layer name=tdnn2 dim=$relu_dim input=Append(-1,0,1,2)
    relu-batchnorm-layer name=tdnn3 dim=$relu_dim input=Append(-3,0,3)
    relu-batchnorm-layer name=tdnn4 dim=$relu_dim input=Append(-3,0,3)
    relu-batchnorm-layer name=tdnn5 dim=$relu_dim input=Append(-3,0,3)
    relu-batchnorm-layer name=tdnn6 dim=$relu_dim input=Append(-6,-3,0)
  
    ## adding the layers for chain branch
    relu-batchnorm-layer name=prefinal-chain dim=$relu_dim target-rms=0.5
    output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
  
    # adding the layers for xent branch
    # This block prints the configs for a separate output that will be
    # trained with a cross-entropy objective in the 'chain' models... this
    # has the effect of regularizing the hidden parts of the model.  we use
    # 0.5 / args.xent_regularize as the learning rate factor- the factor of
    # 0.5 / args.xent_regularize is suitable as it means the xent
    # final-layer learns at a rate independent of the regularization
    # constant; and the 0.5 was tuned so as to make the relative progress
    # similar in the xent and regular final layers.
    relu-batchnorm-layer name=prefinal-xent input=tdnn6 dim=$relu_dim target-rms=0.5
    output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
  EOF
    steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
  
  
  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
  
    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
    # remove <UNK> from the graph, and convert back to const-FST.
    fstrmsymbols --apply-to-output=true --remove-arcs=true "echo 3|" $graph_dir/HCLG.fst - | \
      fstconvert --fst_type=const > $graph_dir/temp.fst
    mv $graph_dir/temp.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;