#!/bin/bash
  
  # Copyright 2017-2018  Johns Hopkins University (author: Daniel Povey)
  #           2017-2018  Yiming Wang
  
  # 1b is trying a more complicated architecture with factored parameter matrices with dropout.
  
  # cat exp/chain/tdnn_1b/decode_dev/scoring_kaldi/best_wer
  # %WER 17.73 [ 1951 / 11006, 247 ins, 364 del, 1340 sub ] exp/chain/tdnn_1b/decode_dev/wer_10_0.0
  # cat exp/chain/tdnn_1b/decode_dev.rescored/scoring_kaldi/best_wer
  # %WER 16.14 [ 1776 / 11006, 210 ins, 377 del, 1189 sub ] exp/chain/tdnn_1b/decode_dev.rescored/wer_10_0.5
  
  # steps/info/chain_dir_info.pl exp/chain/tdnn_1b
  # exp/chain/tdnn_1b: num-iters=38 nj=2..5 num-params=12.0M dim=40+50->1592 combine=-0.062->-0.061 (over 2) xent:train/valid[24,37,final]=(-1.28,-1.03,-0.988/-1.61,-1.43,-1.36) logprob:train/valid[24,37,final]=(-0.069,-0.053,-0.049/-0.128,-0.124,-0.120)
  
  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
  test_sets="dev"
  gmm=tri3b
  
  # Options which are not passed through to run_ivector_common.sh
  affix=1b   #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
  get_egs_stage=-10
  xent_regularize=0.1
  
  # 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
  dropout_schedule='0,0@0.20,0.3@0.50,0'
  num_epochs=15
  
  # training options
  srand=0
  remove_egs=true
  
  
  # 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 \
                                    --train-set $train_set \
                                    --gmm $gmm || exit 1;
  
  
  gmm_dir=exp/$gmm
  ali_dir=exp/${gmm}_ali_${train_set}_sp
  tree_dir=exp/chain/tree_sp
  lang=data/lang_chain
  lat_dir=exp/chain/${gmm}_${train_set}_sp_lats
  dir=exp/chain/tdnn_${affix}
  train_data_dir=data/${train_set}_sp_hires
  train_ivector_dir=exp/nnet3/ivectors_${train_set}_sp_hires
  lores_train_data_dir=data/${train_set}_sp
  
  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 9 ]; 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_test/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_test ..."
        echo " ... not sure what to do.  Exiting."
        exit 1;
      fi
    else
      cp -r data/lang_test $lang
      silphonelist=$(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 [ $stage -le 10 ]; then
    # Get the alignments as lattices (gives the chain training more freedom).
    # use the same num-jobs as the alignments
    steps/align_fmllr_lats.sh --nj 50 --cmd "$train_cmd" ${lores_train_data_dir} \
      data/lang $gmm_dir $lat_dir
    rm $lat_dir/fsts.*.gz # save space
  fi
  
  if [ $stage -le 11 ]; 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 12 ]; 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=0.08 dropout-per-dim=true dropout-per-dim-continuous=true"
    linear_opts="orthonormal-constraint=-1.0"
    output_opts="l2-regularize=0.04"
  
    mkdir -p $dir/configs
    cat <<EOF > $dir/configs/network.xconfig
    input dim=50 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 $opts dim=768
    linear-component name=tdnn2l dim=256 $linear_opts input=Append(-1,0)
    relu-batchnorm-dropout-layer name=tdnn2 $opts input=Append(0,1) dim=768
    linear-component name=tdnn3l dim=256 $linear_opts
    relu-batchnorm-dropout-layer name=tdnn3 $opts dim=768
    linear-component name=tdnn4l dim=256 $linear_opts input=Append(-1,0)
    relu-batchnorm-dropout-layer name=tdnn4 $opts input=Append(0,1) dim=768
    linear-component name=tdnn5l dim=256 $linear_opts
    relu-batchnorm-dropout-layer name=tdnn5 $opts dim=768 input=Append(0, tdnn3l)
    linear-component name=tdnn6l dim=256 $linear_opts input=Append(-3,0)
    relu-batchnorm-dropout-layer name=tdnn6 $opts input=Append(0,3) dim=1024
    linear-component name=tdnn7l dim=256 $linear_opts input=Append(-3,0)
    relu-batchnorm-dropout-layer name=tdnn7 $opts input=Append(0,3,tdnn6l,tdnn4l,tdnn2l) dim=768
    linear-component name=tdnn8l dim=256 $linear_opts input=Append(-3,0)
    relu-batchnorm-dropout-layer name=tdnn8 $opts input=Append(0,3) dim=1024
    linear-component name=tdnn9l dim=256 $linear_opts input=Append(-3,0)
    relu-batchnorm-dropout-layer name=tdnn9 $opts input=Append(0,3,tdnn8l,tdnn6l,tdnn5l) dim=768
    linear-component name=tdnn10l dim=256 $linear_opts input=Append(-3,0)
    relu-batchnorm-dropout-layer name=tdnn10 $opts input=Append(0,3) dim=1024
    linear-component name=tdnn11l dim=256 $linear_opts input=Append(-3,0)
    relu-batchnorm-dropout-layer name=tdnn11 $opts input=Append(0,3,tdnn10l,tdnn9l,tdnn7l) dim=768
    linear-component name=prefinal-l dim=256 $linear_opts
  
    relu-batchnorm-layer name=prefinal-chain input=prefinal-l $opts dim=1024
    output-layer name=output include-log-softmax=false dim=$num_targets bottleneck-dim=256 max-change=1.5 $output_opts
  
    # 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=prefinal-l $opts dim=1024
    output-layer name=output-xent $output_opts dim=$num_targets learning-rate-factor=$learning_rate_factor bottleneck-dim=256 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 13 ]; 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.dropout-schedule $dropout_schedule \
      --trainer.add-option="--optimization.memory-compression-level=2" \
      --trainer.srand=$srand \
      --trainer.max-param-change=2.0 \
      --trainer.num-epochs=$num_epochs \
      --trainer.frames-per-iter=3000000 \
      --trainer.optimization.num-jobs-initial=2 \
      --trainer.optimization.num-jobs-final=5 \
      --trainer.optimization.initial-effective-lrate=0.001 \
      --trainer.optimization.final-effective-lrate=0.0001 \
      --trainer.num-chunk-per-minibatch=256,128,64 \
      --trainer.optimization.momentum=0.0 \
      --egs.chunk-width=$chunk_width \
      --egs.chunk-left-context=0 \
      --egs.chunk-right-context=0 \
      --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 14 ]; 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 [ $stage -le 15 ]; then
    frames_per_chunk=$(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 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/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
  
  if [ $stage -le 16 ]; then
    for data in $test_sets; do
      (
        steps/lmrescore_const_arpa.sh  --cmd "$decode_cmd" \
          data/lang_test/ data/lang_big/ data/${data} \
          ${dir}/decode_${data} ${dir}/decode_${data}.rescored
      )
    done
    wait
  fi
  
  exit 0;