#!/bin/bash
  
  # 1g is like 1f but upgrading to a "resnet-style TDNN-F model", i.e.
  #   with bypass resnet connections, and re-tuned.
  
  # local/chain/compare_wer.sh exp/chain/tdnn1f_sp exp/chain/tdnn1g_sp
  # System                tdnn1f_sp tdnn1g_sp
  #WER dev93 (tgpr)                7.03      6.68
  #WER dev93 (tg)                  6.83      6.57
  #WER dev93 (big-dict,tgpr)       4.99      4.60
  #WER dev93 (big-dict,fg)         4.52      4.26
  #WER eval92 (tgpr)               5.19      4.54
  #WER eval92 (tg)                 4.73      4.32
  #WER eval92 (big-dict,tgpr)      2.94      2.62
  #WER eval92 (big-dict,fg)        2.68      2.32
  # Final train prob        -0.0461   -0.0417
  # Final valid prob        -0.0588   -0.0487
  # Final train prob (xent)   -0.9042   -0.6461
  # Final valid prob (xent)   -0.9447   -0.6882
  # Num-params                 6071244   8354636
  
  # steps/info/chain_dir_info.pl exp/chain/tdnn1g_sp
  # exp/chain/tdnn1g_sp: num-iters=108 nj=2..8 num-params=8.4M dim=40+100->2854 combine=-0.042->-0.042 (over 2) xent:train/valid[71,107,final]=(-0.975,-0.640,-0.646/-0.980,-0.678,-0.688) logprob:train/valid[71,107,final]=(-0.067,-0.043,-0.042/-0.069,-0.050,-0.049)
  
  set -e -o 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
  nj=30
  train_set=train_si284
  test_sets="test_dev93 test_eval92"
  gmm=tri4b        # this is the source gmm-dir that we'll use for alignments; it
                   # should have alignments for the specified training data.
  
  num_threads_ubm=32
  
  nj_extractor=10
  # It runs a JOB with '-pe smp N', where N=$[threads*processes]
  num_threads_extractor=4
  num_processes_extractor=4
  
  nnet3_affix=       # affix for exp dirs, e.g. it was _cleaned in tedlium.
  
  # Options which are not passed through to run_ivector_common.sh
  affix=1g   #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
  common_egs_dir=
  reporting_email=
  
  # LSTM/chain options
  train_stage=-10
  xent_regularize=0.1
  dropout_schedule='0,0@0.20,0.5@0.50,0'
  
  # 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
  
  # training options
  srand=0
  remove_egs=true
  
  #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
  
  local/nnet3/run_ivector_common.sh \
    --stage $stage --nj $nj \
    --train-set $train_set --gmm $gmm \
    --num-threads-ubm $num_threads_ubm \
    --nj-extractor $nj_extractor \
    --num-processes-extractor $num_processes_extractor \
    --num-threads-extractor $num_threads_extractor \
    --nnet3-affix "$nnet3_affix"
  
  
  
  gmm_dir=exp/${gmm}
  ali_dir=exp/${gmm}_ali_${train_set}_sp
  lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
  dir=exp/chain${nnet3_affix}/tdnn${affix}_sp
  train_data_dir=data/${train_set}_sp_hires
  train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
  lores_train_data_dir=data/${train_set}_sp
  
  # note: you don't necessarily have to change the treedir name
  # each time you do a new experiment-- only if you change the
  # configuration in a way that affects the tree.
  tree_dir=exp/chain${nnet3_affix}/tree_a_sp
  # the 'lang' directory is created by this script.
  # If you create such a directory with a non-standard topology
  # you should probably name it differently.
  lang=data/lang_chain
  
  for f in $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
      $lores_train_data_dir/feats.scp $gmm_dir/final.mdl \
      $ali_dir/ali.1.gz $gmm_dir/final.mdl; do
    [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
  done
  
  
  if [ $stage -le 12 ]; 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 [ $stage -le 13 ]; then
    # Get the alignments as lattices (gives the chain training more freedom).
    # use the same num-jobs as the alignments
    steps/align_fmllr_lats.sh --nj 100 --cmd "$train_cmd" ${lores_train_data_dir} \
      data/lang $gmm_dir $lat_dir
    rm $lat_dir/fsts.*.gz # save space
  fi
  
  if [ $stage -le 14 ]; 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 15 ]; 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)
    tdnn_opts="l2-regularize=0.01 dropout-proportion=0.0 dropout-per-dim-continuous=true"
    tdnnf_opts="l2-regularize=0.01 dropout-proportion=0.0 bypass-scale=0.66"
    linear_opts="l2-regularize=0.01 orthonormal-constraint=-1.0"
    prefinal_opts="l2-regularize=0.01"
    output_opts="l2-regularize=0.005"
  
    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-dropout-layer name=tdnn1 $tdnn_opts dim=1024
    tdnnf-layer name=tdnnf2 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=1
    tdnnf-layer name=tdnnf3 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=1
    tdnnf-layer name=tdnnf4 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=1
    tdnnf-layer name=tdnnf5 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=0
    tdnnf-layer name=tdnnf6 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf7 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf8 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf9 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf10 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf11 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf12 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf13 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    linear-component name=prefinal-l dim=192 $linear_opts
  
  
    prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts big-dim=1024 small-dim=192
    output-layer name=output include-log-softmax=false dim=$num_targets $output_opts
  
    prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts big-dim=1024 small-dim=192
    output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor $output_opts
  EOF
    steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
  fi
  
  
  if [ $stage -le 16 ]; 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/wsj-$(date +'%m_%d_%H_%M')/s5/$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=0.0 \
      --chain.apply-deriv-weights=false \
      --chain.lm-opts="--num-extra-lm-states=2000" \
      --trainer.dropout-schedule $dropout_schedule \
      --trainer.add-option="--optimization.memory-compression-level=2" \
      --trainer.srand=$srand \
      --trainer.max-param-change=2.0 \
      --trainer.num-epochs=10 \
      --trainer.frames-per-iter=5000000 \
      --trainer.optimization.num-jobs-initial=2 \
      --trainer.optimization.num-jobs-final=8 \
      --trainer.optimization.initial-effective-lrate=0.0005 \
      --trainer.optimization.final-effective-lrate=0.00005 \
      --trainer.num-chunk-per-minibatch=128,64 \
      --trainer.optimization.momentum=0.0 \
      --egs.chunk-width=$chunk_width \
      --egs.chunk-left-context=0 \
      --egs.chunk-right-context=0 \
      --egs.dir="$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 17 ]; then
    # The reason we are using data/lang here, instead of $lang, is just to
    # emphasize that it's not actually important to give mkgraph.sh the
    # lang directory with the matched topology (since it gets the
    # topology file from the model).  So you could give it a different
    # lang directory, one that contained a wordlist and LM of your choice,
    # as long as phones.txt was compatible.
  
    utils/lang/check_phones_compatible.sh \
      data/lang_test_tgpr/phones.txt $lang/phones.txt
    utils/mkgraph.sh \
      --self-loop-scale 1.0 data/lang_test_tgpr \
      $tree_dir $tree_dir/graph_tgpr || exit 1;
  
    utils/lang/check_phones_compatible.sh \
      data/lang_test_bd_tgpr/phones.txt $lang/phones.txt
    utils/mkgraph.sh \
      --self-loop-scale 1.0 data/lang_test_bd_tgpr \
      $tree_dir $tree_dir/graph_bd_tgpr || exit 1;
  fi
  
  if [ $stage -le 18 ]; then
    frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
    rm $dir/.error 2>/dev/null || true
  
    for data in $test_sets; do
      (
        data_affix=$(echo $data | sed s/test_//)
        nspk=$(wc -l <data/${data}_hires/spk2utt)
        for lmtype in tgpr bd_tgpr; do
          steps/nnet3/decode.sh \
            --acwt 1.0 --post-decode-acwt 10.0 \
            --extra-left-context 0 --extra-right-context 0 \
            --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 \
            $tree_dir/graph_${lmtype} data/${data}_hires ${dir}/decode_${lmtype}_${data_affix} || exit 1
        done
        steps/lmrescore.sh \
          --self-loop-scale 1.0 \
          --cmd "$decode_cmd" data/lang_test_{tgpr,tg} \
          data/${data}_hires ${dir}/decode_{tgpr,tg}_${data_affix} || exit 1
        steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
          data/lang_test_bd_{tgpr,fgconst} \
         data/${data}_hires ${dir}/decode_${lmtype}_${data_affix}{,_fg} || 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 19 ]; 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 $test_sets; do
      (
        data_affix=$(echo $data | sed s/test_//)
        nspk=$(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.
        for lmtype in tgpr bd_tgpr; do
          steps/online/nnet3/decode.sh \
            --acwt 1.0 --post-decode-acwt 10.0 \
            --nj $nspk --cmd "$decode_cmd" \
            $tree_dir/graph_${lmtype} data/${data} ${dir}_online/decode_${lmtype}_${data_affix} || exit 1
        done
        steps/lmrescore.sh \
          --self-loop-scale 1.0 \
          --cmd "$decode_cmd" data/lang_test_{tgpr,tg} \
          data/${data}_hires ${dir}_online/decode_{tgpr,tg}_${data_affix} || exit 1
        steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
          data/lang_test_bd_{tgpr,fgconst} \
         data/${data}_hires ${dir}_online/decode_${lmtype}_${data_affix}{,_fg} || exit 1
      ) || touch $dir/.error &
    done
    wait
    [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
  fi
  
  
  exit 0;