#!/bin/bash
  set -e
  
  ## Adapted from swbd for librispeech by David van Leeuwen
  
  # 7n is a kind of factorized TDNN, with skip connections
  
  # steps/info/chain_dir_info.pl exp/chain_cleaned/tdnn_1c_sp
  # exp/chain_cleaned/tdnn_1c_sp: num-iters=1307 nj=3..16 num-params=20.1M dim=40+100->6024 combine=-0.051->-0.050 (over 23) xent:train/valid[869,1306,final]=(-0.808,-0.767,-0.771/-0.828,-0.780,-0.787) logprob:train/valid[869,1306,final]=(-0.051,-0.049,-0.047/-0.059,-0.056,-0.056)
  
  # local/chain/compare_wer.sh exp/chain_cleaned/tdnn_1b_sp exp/chain_cleaned/tdnn_1c_sp
  # System                      tdnn_1b_sp tdnn_1c_sp
  # WER on dev(fglarge)              3.77      3.35
  # WER on dev(tglarge)              3.90      3.49
  # WER on dev(tgmed)                4.89      4.30
  # WER on dev(tgsmall)              5.47      4.78
  # WER on dev_other(fglarge)       10.05      8.76
  # WER on dev_other(tglarge)       10.80      9.26
  # WER on dev_other(tgmed)         13.07     11.21
  # WER on dev_other(tgsmall)       14.46     12.47
  # WER on test(fglarge)             4.20      3.87
  # WER on test(tglarge)             4.28      4.08
  # WER on test(tgmed)               5.31      4.80
  # WER on test(tgsmall)             5.97      5.25
  # WER on test_other(fglarge)      10.44      8.95
  # WER on test_other(tglarge)      11.05      9.41
  # WER on test_other(tgmed)        13.36     11.52
  # WER on test_other(tgsmall)      14.90     12.66
  # Final train prob              -0.0670   -0.0475
  # Final valid prob              -0.0704   -0.0555
  # Final train prob (xent)       -1.0502   -0.7708
  # Final valid prob (xent)       -1.0441   -0.7874
  
  # configs for 'chain'
  stage=0
  decode_nj=50
  train_set=train_960_cleaned
  gmm=tri6b_cleaned
  nnet3_affix=_cleaned
  
  # The rest are configs specific to this script.  Most of the parameters
  # are just hardcoded at this level, in the commands below.
  affix=1c
  tree_affix=
  train_stage=-10
  get_egs_stage=-10
  decode_iter=
  
  # TDNN options
  frames_per_eg=150,110,100
  remove_egs=true
  common_egs_dir=
  xent_regularize=0.1
  
  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 $gmm \
                                    --num-threads-ubm 6 --num-processes 3 \
                                    --nnet3-affix "$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
  
  # if we are using the speed-perturbed data we need to generate
  # alignments for it.
  
  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 \
  				--num-leaves 7000 \
                                  --tree-dir $tree_dir || exit 1;
  
  if [ $stage -le 14 ]; then
    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=0.002"
    linear_opts="orthonormal-constraint=1.0"
    output_opts="l2-regularize=0.0005 bottleneck-dim=256"
  
    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 $opts dim=1280
    linear-component name=tdnn2l dim=256 $linear_opts input=Append(-1,0)
    relu-batchnorm-layer name=tdnn2 $opts input=Append(0,1) dim=1280
    linear-component name=tdnn3l dim=256 $linear_opts
    relu-batchnorm-layer name=tdnn3 $opts dim=1280
    linear-component name=tdnn4l dim=256 $linear_opts input=Append(-1,0)
    relu-batchnorm-layer name=tdnn4 $opts input=Append(0,1) dim=1280
    linear-component name=tdnn5l dim=256 $linear_opts
    relu-batchnorm-layer name=tdnn5 $opts dim=1280 input=Append(tdnn5l, tdnn3l)
    linear-component name=tdnn6l dim=256 $linear_opts input=Append(-3,0)
    relu-batchnorm-layer name=tdnn6 $opts input=Append(0,3) dim=1280
    linear-component name=tdnn7l dim=256 $linear_opts input=Append(-3,0)
    relu-batchnorm-layer name=tdnn7 $opts input=Append(0,3,tdnn6l,tdnn4l,tdnn2l) dim=1280
    linear-component name=tdnn8l dim=256 $linear_opts input=Append(-3,0)
    relu-batchnorm-layer name=tdnn8 $opts input=Append(0,3) dim=1280
    linear-component name=tdnn9l dim=256 $linear_opts input=Append(-3,0)
    relu-batchnorm-layer name=tdnn9 $opts input=Append(0,3,tdnn8l,tdnn6l,tdnn4l) dim=1280
    linear-component name=tdnn10l dim=256 $linear_opts input=Append(-3,0)
    relu-batchnorm-layer name=tdnn10 $opts input=Append(0,3) dim=1280
    linear-component name=tdnn11l dim=256 $linear_opts input=Append(-3,0)
    relu-batchnorm-layer name=tdnn11 $opts input=Append(0,3,tdnn10l,tdnn8l,tdnn6l) dim=1280
    linear-component name=prefinal-l dim=256 $linear_opts
  
    relu-batchnorm-layer name=prefinal-chain input=prefinal-l $opts dim=1280
    output-layer name=output include-log-softmax=false dim=$num_targets $output_opts
  
    relu-batchnorm-layer name=prefinal-xent input=prefinal-l $opts dim=1280
    output-layer name=output-xent dim=$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 15 ]; 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 $xent_regularize \
      --chain.leaky-hmm-coefficient 0.1 \
      --chain.l2-regularize 0.0 \
      --chain.apply-deriv-weights false \
      --chain.lm-opts="--num-extra-lm-states=2000" \
      --egs.dir "$common_egs_dir" \
      --egs.stage $get_egs_stage \
      --egs.opts "--frames-overlap-per-eg 0" \
      --egs.chunk-width $frames_per_eg \
      --trainer.num-chunk-per-minibatch 128 \
      --trainer.frames-per-iter 1500000 \
      --trainer.num-epochs 6 \
      --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 2.0 \
      --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
  
  iter_opts=
  if [ ! -z $decode_iter ]; then
    iter_opts=" --iter $decode_iter "
  fi
  if [ $stage -le 17 ]; then
    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
    if [ -f $dir/.error ]; then
      echo "$0: something went wrong in decoding"
      exit 1
    fi
  fi
  
  if $test_online_decoding && [ $stage -le 18 ]; 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 $dir/.error 2>/dev/null || true
    for data in test_clean test_other dev_clean dev_other; do
      (
        nspk=$(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" \
            $graph_dir data/${data} ${dir}_online/decode_${data}_tgsmall || exit 1
  
      ) || touch $dir/.error &
    done
    wait
    if [ -f $dir/.error ]; then
      echo "$0: something went wrong in decoding"
      exit 1
    fi
  fi
  
  
  exit 0;