#!/bin/bash
  
  # local/chain/compare_wer.sh exp/chain/tdnn1a_sp
  # ./local/chain/compare_wer.sh exp/chain/tdnn1a_sp
  # System                  tdnn1a_sp
  # %WER        devtest       13.10
  # %WER           test       15.53
  # %WER         native       10.14
  # %WER      nonnative       19.78
  # Final train prob          -0.0233
  # Final valid prob          -0.0720
  # Final train prob (xent)   -0.8107
  # Final valid prob (xent)   -0.9898
  # Num-params                 6559440
  
  # steps/info/chain_dir_info.pl  exp/chain/tdnn1a_sp/
  # exp/chain/tdnn1a_sp: num-iters=105 nj=1..1 num-params=6.6M dim=40+100->1384 combine=-0.032->-0.026 (over 7) xent:train/valid[69,104,final]=(-1.14,-0.892,-0.811/-1.19,-1.07,-0.990) logprob:train/valid[69,104,final]=(-0.045,-0.029,-0.023/-0.083,-0.080,-0.072)
  
  # 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
  test_sets="native nonnative devtest test"
  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=1a   # affix for the TDNN directory name
  tree_affix=
  train_stage=-10
  get_egs_stage=-10
  decode_iter=
  
  num_leaves=3500
  
  # 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=false  # 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: $tree_dir/final.mdl already exists, refusing to overwrite it."
       exit 1;
    fi
    steps/nnet3/chain/build_tree.sh \
      --cmd "$train_cmd" \
      --frame-subsampling-factor 3 \
      --context-opts "--context-width=2 --central-position=1" \
      $num_leaves \
      ${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)
    opts="l2-regularize=0.01"
    output_opts="l2-regularize=0.0025"
  
    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 $opts dim=512
    relu-batchnorm-layer name=tdnn2 $opts dim=512 input=Append(-1,0,1)
    relu-batchnorm-layer name=tdnn3 $opts dim=512
    relu-batchnorm-layer name=tdnn4 $opts dim=512 input=Append(-1,0,1)
    relu-batchnorm-layer name=tdnn5 $opts dim=512
    relu-batchnorm-layer name=tdnn6 $opts dim=512 input=Append(-3,0,3)
    relu-batchnorm-layer name=tdnn7 $opts dim=512 input=Append(-3,0,3)
    relu-batchnorm-layer name=tdnn8 $opts dim=512 input=Append(-6,-3,0)
  
    # adding the layers for chain branch
    relu-batchnorm-layer name=prefinal-chain $opts dim=512 target-rms=0.5
    output-layer name=output $output_opts include-log-softmax=false dim=$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 $opts input=tdnn8 dim=512 target-rms=0.5
    output-layer name=output-xent $output_opts dim=$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 [ $stage -le 14 ]; then
    steps/nnet3/chain/train.py \
      --stage=$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=7 \
      --trainer.frames-per-iter=3000000 \
      --trainer.optimization.num-jobs-initial=1 \
      --trainer.optimization.num-jobs-final=1 \
      --trainer.optimization.initial-effective-lrate=0.001 \
      --trainer.optimization.final-effective-lrate=0.0001 \
      --trainer.optimization.shrink-value=1.0 \
      --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 \
      $tree_dir \
      $tree_dir/graph || 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 \
        $tree_dir/graph \
        data/${data}_hires \
        ${dir}/decode_${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" \
        $tree_dir/graph data/${data} ${dir}_online/decode_${data} || exit 1
      ) || touch $dir/.error &
    done
    wait
    [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
  fi
  
  exit 0;
  
  # Local Variables:
  # tab-width: 2
  # indent-tabs-mode: nil
  # End: