#!/bin/bash
  
  # 1c is as 1b but replacing the renorm with batchnorm components
  # (i.e. NormalizeComponent with BatchNormComponent).
  
  # local/chain/compare_wer.sh --online exp/chain/tdnn1b_sp exp/chain/tdnn1c_sp
  # System                tdnn1b_sp tdnn1c_sp
  #WER dev_clean_2 (tgsmall)      14.87     14.69
  #             [online:]         14.99     14.63
  #WER dev_clean_2 (tglarge)      10.86     10.45
  #             [online:]         10.79     10.56
  # Final train prob        -0.0609   -0.0463
  # Final valid prob        -0.0956   -0.0896
  # Final train prob (xent)   -1.3493   -1.0632
  # Final valid prob (xent)   -1.6350   -1.4584
  
  
  # steps/info/chain_dir_info.pl exp/chain/tdnn1{b,c}_sp
  # exp/chain/tdnn1b_sp: num-iters=17 nj=2..5 num-params=7.0M dim=40+100->2353 combine=-0.088->-0.064 xent:train/valid[10,16,final]=(-2.32,-1.61,-1.35/-2.49,-1.86,-1.63) logprob:train/valid[10,16,final]=(-0.164,-0.079,-0.061/-0.196,-0.117,-0.096)
  # exp/chain/tdnn1c_sp: num-iters=17 nj=2..5 num-params=7.0M dim=40+100->2353 combine=-0.061->-0.050 xent:train/valid[10,16,final]=(-1.56,-1.17,-1.06/-1.85,-1.53,-1.46) logprob:train/valid[10,16,final]=(-0.081,-0.053,-0.046/-0.120,-0.096,-0.090)
  
  # Set -e here so that we catch if any executable fails immediately
  set -euo pipefail
  
  # 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=10
  train_set=train_clean_5
  test_sets=dev_clean_2
  gmm=tri3b
  nnet3_affix=
  
  # The rest are configs specific to this script.  Most of the parameters
  # are just hardcoded at this level, in the commands below.
  affix=1c   # affix for the TDNN directory name
  tree_affix=
  train_stage=-10
  get_egs_stage=-10
  decode_iter=
  
  # training options
  # training chunk-options
  chunk_width=140,100,160
  # we don't need extra left/right context for TDNN systems.
  chunk_left_context=0
  chunk_right_context=0
  common_egs_dir=
  xent_regularize=0.1
  
  # training options
  srand=0
  remove_egs=true
  reporting_email=
  
  #decode options
  test_online_decoding=true  # if true, it will run the last decoding stage.
  
  
  # 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;
  
  # Problem: We have removed the "train_" prefix of our training set in
  # the alignment directory names! Bad!
  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${affix}_sp
  train_data_dir=data/${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
  
  if [ $stage -le 10 ]; then
    echo "$0: creating lang directory $lang with chain-type topology"
    # 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.]
    if [ -d $lang ]; then
      if [ $lang/L.fst -nt data/lang/L.fst ]; then
        echo "$0: $lang already exists, not overwriting it; continuing"
      else
        echo "$0: $lang already exists and seems to be older than data/lang..."
        echo " ... not sure what to do.  Exiting."
        exit 1;
      fi
    else
      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
  fi
  
  if [ $stage -le 11 ]; then
    # Get the alignments as lattices (gives the chain training more freedom).
    # use the same num-jobs as the alignments
    steps/align_fmllr_lats.sh --nj 75 --cmd "$train_cmd" ${lores_train_data_dir} \
      data/lang $gmm_dir $lat_dir
    rm $lat_dir/fsts.*.gz # save space
  fi
  
  if [ $stage -le 12 ]; then
    # Build a tree using our new topology.  We know we have alignments for the
    # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
    # those.  The num-leaves is always somewhat less than the num-leaves from
    # the GMM baseline.
     if [ -f $tree_dir/final.mdl ]; then
       echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
       exit 1;
    fi
    steps/nnet3/chain/build_tree.sh \
      --frame-subsampling-factor 3 \
      --context-opts "--context-width=2 --central-position=1" \
      --cmd "$train_cmd" 3500 ${lores_train_data_dir} \
      $lang $ali_dir $tree_dir
  fi
  
  
  if [ $stage -le 13 ]; then
    mkdir -p $dir
    echo "$0: creating neural net configs using the xconfig parser";
  
    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(-2,-1,0,1,2,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=512
    relu-batchnorm-layer name=tdnn2 dim=512 input=Append(-1,0,1)
    relu-batchnorm-layer name=tdnn3 dim=512 input=Append(-1,0,1)
    relu-batchnorm-layer name=tdnn4 dim=512 input=Append(-3,0,3)
    relu-batchnorm-layer name=tdnn5 dim=512 input=Append(-3,0,3)
    relu-batchnorm-layer name=tdnn6 dim=512 input=Append(-6,-3,0)
  
    ## adding the layers for chain branch
    relu-batchnorm-layer name=prefinal-chain dim=512 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=512 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 14 ]; then
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
      utils/create_split_dir.pl \
       /export/b0{3,4,5,6}/$USER/kaldi-data/egs/mini_librispeech-$(date +'%m_%d_%H_%M')/s5/$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 $xent_regularize \
      --chain.leaky-hmm-coefficient=0.1 \
      --chain.l2-regularize=0.00005 \
      --chain.apply-deriv-weights=false \
      --chain.lm-opts="--num-extra-lm-states=2000" \
      --trainer.srand=$srand \
      --trainer.max-param-change=2.0 \
      --trainer.num-epochs=10 \
      --trainer.frames-per-iter=3000000 \
      --trainer.optimization.num-jobs-initial=2 \
      --trainer.optimization.num-jobs-final=5 \
      --trainer.optimization.initial-effective-lrate=0.001 \
      --trainer.optimization.final-effective-lrate=0.0001 \
      --trainer.optimization.shrink-value=1.0 \
      --trainer.optimization.proportional-shrink=150.0 \
      --trainer.num-chunk-per-minibatch=256,128,64 \
      --trainer.optimization.momentum=0.0 \
      --egs.chunk-width=$chunk_width \
      --egs.chunk-left-context=$chunk_left_context \
      --egs.chunk-right-context=$chunk_right_context \
      --egs.chunk-left-context-initial=0 \
      --egs.chunk-right-context-final=0 \
      --egs.dir="$common_egs_dir" \
      --egs.opts="--frames-overlap-per-eg 0" \
      --cleanup.remove-egs=$remove_egs \
      --use-gpu=true \
      --reporting.email="$reporting_email" \
      --feat-dir=$train_data_dir \
      --tree-dir=$tree_dir \
      --lat-dir=$lat_dir \
      --dir=$dir  || exit 1;
  fi
  
  if [ $stage -le 15 ]; then
    # Note: it's not important to give mkgraph.sh the lang directory with the
    # matched topology (since it gets the topology file from the model).
    utils/mkgraph.sh \
      --self-loop-scale 1.0 data/lang_test_tgsmall \
      $tree_dir $tree_dir/graph_tgsmall || exit 1;
  fi
  
  if [ $stage -le 16 ]; then
    frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
    rm $dir/.error 2>/dev/null || true
  
    for data in $test_sets; do
      (
        nspk=$(wc -l <data/${data}_hires/spk2utt)
        steps/nnet3/decode.sh \
            --acwt 1.0 --post-decode-acwt 10.0 \
            --extra-left-context $chunk_left_context \
            --extra-right-context $chunk_right_context \
            --extra-left-context-initial 0 \
            --extra-right-context-final 0 \
            --frames-per-chunk $frames_per_chunk \
            --nj $nspk --cmd "$decode_cmd"  --num-threads 4 \
            --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
            $tree_dir/graph_tgsmall data/${data}_hires ${dir}/decode_tgsmall_${data} || exit 1
        steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
          data/lang_test_{tgsmall,tglarge} \
         data/${data}_hires ${dir}/decode_{tgsmall,tglarge}_${data} || exit 1
      ) || touch $dir/.error &
    done
    wait
    [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
  fi
  
  # Not testing the 'looped' decoding separately, because for
  # TDNN systems it would give exactly the same results as the
  # normal decoding.
  
  if $test_online_decoding && [ $stage -le 17 ]; then
    # note: if the features change (e.g. you add pitch features), you will have to
    # change the options of the following command line.
    steps/online/nnet3/prepare_online_decoding.sh \
      --mfcc-config conf/mfcc_hires.conf \
      $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
  
    rm $dir/.error 2>/dev/null || true
  
    for data in $test_sets; do
      (
        nspk=$(wc -l <data/${data}_hires/spk2utt)
        # note: we just give it "data/${data}" as it only uses the wav.scp, the
        # feature type does not matter.
        steps/online/nnet3/decode.sh \
          --acwt 1.0 --post-decode-acwt 10.0 \
          --nj $nspk --cmd "$decode_cmd" \
          $tree_dir/graph_tgsmall data/${data} ${dir}_online/decode_tgsmall_${data} || exit 1
        steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
          data/lang_test_{tgsmall,tglarge} \
         data/${data}_hires ${dir}_online/decode_{tgsmall,tglarge}_${data} || exit 1
      ) || touch $dir/.error &
    done
    wait
    [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
  fi
  
  
  exit 0;