#!/bin/bash
  # Copyright 2017 University of Chinese Academy of Sciences (UCAS) Gaofeng Cheng
  # Apache 2.0
  
  # Similar to swbd\s5c\local\chain\tuning\run_tdnn_lstm_1e.sh
  # Difference between tdnn_lstm_1a and tdnn_lstm_1b:
  # chunk width        150  140,100,160
  # xent_regularize    0.025  0.01
  # minibatch          64  64,32
  # frames-per-iter    1200000  1500000
  # batchnorm in TDNN  No  Yes
  # Dropout in LSTM    No  Yes
  
  # ./local/chain/compare_wer_general.sh --looped tdnn_lstm_1a_sp tdnn_lstm_1b_sp
  # System                tdnn_lstm_1a_sp tdnn_lstm_1b_sp
  # num-params                 39.7M     39.7M
  # WER on eval2000(tg)        12.3      12.3
  #           [looped:]        12.2      12.3
  # WER on eval2000(fg)        12.1      12.0
  #           [looped:]        12.1      12.2
  # WER on rt03(tg)            11.6      11.4
  #           [looped:]        11.6      11.6
  # WER on rt03(fg)            11.3      11.1
  #           [looped:]        11.3      11.3
  # Final train prob         -0.074    -0.087
  # Final valid prob         -0.084    -0.088
  # Final train prob (xent)        -0.882    -1.015
  # Final valid prob (xent)       -0.9393   -0.9837
  
  #./steps/info/chain_dir_info.pl exp/chain/tdnn_lstm_1b_sp
  #exp/chain/tdnn_lstm_1b_sp: num-iters=1909 nj=3..16 num-params=39.7M dim=40+100->6149 combine=-0.087->-0.086 (over 5) 
  #xent:train/valid[1270,1908,final]=(-1.37,-1.02,-1.01/-1.31,-1.00,-0.984) 
  #logprob:train/valid[1270,1908,final]=(-0.108,-0.088,-0.087/-0.103,-0.091,-0.088)
  
  
  # online results
  # Eval2000
  #%WER 15.9 | 2628 21594 | 86.0 8.6 5.4 1.9 15.9 53.5 | exp/chain/tdnn_lstm_1b_online/decode_eval2000_fsh_sw1_tg/score_7_0.0/eval2000_hires.ctm.callhm.filt.sys
  #%WER 12.3 | 4459 42989 | 89.1 6.8 4.1 1.5 12.3 49.2 | exp/chain/tdnn_lstm_1b_online/decode_eval2000_fsh_sw1_tg/score_8_0.0/eval2000_hires.ctm.filt.sys
  #%WER 8.6 | 1831 21395 | 92.5 5.2 2.3 1.1 8.6 42.6 | exp/chain/tdnn_lstm_1b_online/decode_eval2000_fsh_sw1_tg/score_8_1.0/eval2000_hires.ctm.swbd.filt.sys
  #%WER 15.7 | 2628 21594 | 86.2 8.5 5.3 1.9 15.7 53.0 | exp/chain/tdnn_lstm_1b_online/decode_eval2000_fsh_sw1_fg/score_7_0.0/eval2000_hires.ctm.callhm.filt.sys
  #%WER 12.1 | 4459 42989 | 89.3 6.6 4.0 1.5 12.1 48.4 | exp/chain/tdnn_lstm_1b_online/decode_eval2000_fsh_sw1_fg/score_8_0.0/eval2000_hires.ctm.filt.sys
  #%WER 8.5 | 1831 21395 | 92.5 4.9 2.5 1.0 8.5 41.1 | exp/chain/tdnn_lstm_1b_online/decode_eval2000_fsh_sw1_fg/score_10_0.0/eval2000_hires.ctm.swbd.filt.sys
  
  # online results
  # RT03
  #%WER 9.4 | 3970 36721 | 91.4 5.0 3.5 0.9 9.4 39.5 | exp/chain/tdnn_lstm_1b_online/decode_rt03_fsh_sw1_tg/score_8_0.0/rt03_hires.ctm.fsh.filt.sys
  #%WER 11.6 | 8420 76157 | 89.5 6.4 4.1 1.1 11.6 42.0 | exp/chain/tdnn_lstm_1b_online/decode_rt03_fsh_sw1_tg/score_8_0.0/rt03_hires.ctm.filt.sys
  #%WER 13.5 | 4450 39436 | 87.6 7.3 5.0 1.1 13.5 44.5 | exp/chain/tdnn_lstm_1b_online/decode_rt03_fsh_sw1_tg/score_9_0.0/rt03_hires.ctm.swbd.filt.sys
  #%WER 9.2 | 3970 36721 | 91.6 4.9 3.5 0.9 9.2 39.3 | exp/chain/tdnn_lstm_1b_online/decode_rt03_fsh_sw1_fg/score_8_0.0/rt03_hires.ctm.fsh.filt.sys
  #%WER 11.3 | 8420 76157 | 89.8 6.2 4.0 1.1 11.3 41.6 | exp/chain/tdnn_lstm_1b_online/decode_rt03_fsh_sw1_fg/score_8_0.0/rt03_hires.ctm.filt.sys
  #%WER 13.2 | 4450 39436 | 88.0 7.4 4.6 1.2 13.2 43.6 | exp/chain/tdnn_lstm_1b_online/decode_rt03_fsh_sw1_fg/score_8_0.0/rt03_hires.ctm.swbd.filt.sys
  
  set -e
  
  # configs for 'chain'
  stage=12
  train_stage=-10
  get_egs_stage=-10
  speed_perturb=true
  dir=exp/chain/tdnn_lstm_1b # Note: _sp will get added to this if $speed_perturb == true.
  decode_iter=
  decode_dir_affix=
  
  # training options
  leftmost_questions_truncate=-1
  frames_per_chunk=140,100,160
  chunk_left_context=40
  chunk_right_context=0
  xent_regularize=0.01
  self_repair_scale=0.00001
  label_delay=5
  dropout_schedule='0,0@0.20,0.2@0.50,0'
  # decode options
  extra_left_context=50
  extra_right_context=0
  frames_per_chunk_primary=$(echo $frames_per_chunk | cut -d, -f1)
  
  remove_egs=false
  common_egs_dir=
  
  affix=
  # 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 8" if you have already
  # run those things.
  
  suffix=
  if [ "$speed_perturb" == "true" ]; then
    suffix=_sp
  fi
  
  dir=${dir}$suffix
  build_tree_train_set=train_nodup
  train_set=train_nodup_sp
  build_tree_ali_dir=exp/tri5a_ali
  treedir=exp/chain/tri6_tree
  lang=data/lang_chain
  
  # if we are using the speed-perturbed data we need to generate
  # alignments for it.
  local/nnet3/run_ivector_common.sh --stage $stage \
    --speed-perturb $speed_perturb \
    --generate-alignments $speed_perturb || exit 1;
  
  if [ $stage -le 9 ]; then
    # Get the alignments as lattices (gives the CTC training more freedom).
    # use the same num-jobs as the alignments
    nj=$(cat $build_tree_ali_dir/num_jobs) || exit 1;
    steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/$train_set \
      data/lang exp/tri5a exp/tri5a_lats_nodup$suffix
    rm exp/tri5a_lats_nodup$suffix/fsts.*.gz # save space
  fi
  
  if [ $stage -le 10 ]; then
    # 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.]
    rm -rf $lang
    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
  
  if [ $stage -le 11 ]; then
    # Build a tree using our new topology.
    steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
        --leftmost-questions-truncate $leftmost_questions_truncate \
        --context-opts "--context-width=2 --central-position=1" \
        --cmd "$train_cmd" 11000 data/$build_tree_train_set $lang $build_tree_ali_dir $treedir
  fi
  
  if [ $stage -le 12 ]; then
    echo "$0: creating neural net configs using the xconfig parser";
  
    num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
    learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
    lstm_opts="decay-time=20 dropout-proportion=0.0"
  
    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=1024
    relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1) dim=1024
    relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=1024
  
    # check steps/libs/nnet3/xconfig/lstm.py for the other options and defaults
    fast-lstmp-layer name=fastlstm1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $lstm_opts
    relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=1024
    relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=1024
    fast-lstmp-layer name=fastlstm2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $lstm_opts
    relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=1024
    relu-batchnorm-layer name=tdnn7 input=Append(-3,0,3) dim=1024
    fast-lstmp-layer name=fastlstm3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $lstm_opts
  
    ## adding the layers for chain branch
    output-layer name=output input=fastlstm3 output-delay=$label_delay include-log-softmax=false dim=$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.
    output-layer name=output-xent input=fastlstm3 output-delay=$label_delay dim=$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 13 ]; then
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
      utils/create_split_dir.pl \
       /export/b0{5,6,7,8}/$USER/kaldi-data/egs/swbd-$(date +'%m_%d_%H_%M')/s5c/$dir/egs/storage $dir/egs/storage
    fi
  
    steps/nnet3/chain/train.py --stage $train_stage \
      --cmd "$decode_cmd" \
      --feat.online-ivector-dir exp/nnet3/ivectors_${train_set} \
      --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.num-chunk-per-minibatch 64,32 \
      --trainer.frames-per-iter 1500000 \
      --trainer.max-param-change 2.0 \
      --trainer.num-epochs 4 \
      --trainer.optimization.shrink-value 0.99 \
      --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.dropout-schedule $dropout_schedule \
      --trainer.optimization.momentum 0.0 \
      --trainer.deriv-truncate-margin 8 \
      --egs.stage $get_egs_stage \
      --egs.opts "--frames-overlap-per-eg 0" \
      --egs.chunk-width $frames_per_chunk \
      --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" \
      --cleanup.remove-egs $remove_egs \
      --feat-dir data/${train_set}_hires \
      --tree-dir $treedir \
      --lat-dir exp/tri5a_lats_nodup$suffix \
      --dir $dir  || exit 1;
  fi
  
  if [ $stage -le 14 ]; 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 data/lang_fsh_sw1_tg $dir $dir/graph_fsh_sw1_tg
  fi
  
  decode_suff=fsh_sw1_tg
  graph_dir=$dir/graph_fsh_sw1_tg
  if [ $stage -le 15 ]; then
    rm $dir/.error 2>/dev/null || true
    [ -z $extra_left_context ] && extra_left_context=$chunk_left_context;
    [ -z $extra_right_context ] && extra_right_context=$chunk_right_context;
    if [ ! -z $decode_iter ]; then
      iter_opts=" --iter $decode_iter "
    fi
    for decode_set in rt03 eval2000; do
        (
        steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
            --nj 50 --cmd "$decode_cmd" $iter_opts \
            --extra-left-context $extra_left_context  \
            --extra-right-context $extra_right_context  \
            --extra-left-context-initial 0 \
            --extra-right-context-final 0 \
            --frames-per-chunk  "$frames_per_chunk_primary" \
            --online-ivector-dir exp/nnet3/ivectors_${decode_set} \
           $graph_dir data/${decode_set}_hires \
           $dir/decode_${decode_set}${decode_dir_affix:+_$decode_dir_affix}_${decode_suff} || exit 1;
        steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
              data/lang_fsh_sw1_{tg,fg} data/${decode_set}_hires \
              $dir/decode_${decode_set}${decode_dir_affix:+_$decode_dir_affix}_fsh_sw1_{tg,fg} || exit 1;
        ) || touch $dir/.error &
    done
    wait
    if [ -f $dir/.error ]; then
      echo "$0: something went wrong in decoding"
      exit 1
    fi
  fi
  
  test_online_decoding=false
  lang=data/lang_fsh_sw1_tg
  if $test_online_decoding && [ $stage -le 16 ]; 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/extractor $dir ${dir}_online
  
    rm $dir/.error 2>/dev/null || true
    for decode_set in rt03 eval2000; do
      (
        # note: we just give it "$decode_set" as it only uses the wav.scp, the
        # feature type does not matter.
  
        steps/online/nnet3/decode.sh --nj 50 --cmd "$decode_cmd" $iter_opts \
            --acwt 1.0 --post-decode-acwt 10.0 \
           $graph_dir data/${decode_set}_hires \
           ${dir}_online/decode_${decode_set}${decode_iter:+_$decode_iter}_${decode_suff} || exit 1;
  	    steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
  		      data/lang_fsh_sw1_{tg,fg} data/${decode_set}_hires \
  		      ${dir}_online/decode_${decode_set}${decode_dir_affix:+_$decode_dir_affix}_fsh_sw1_{tg,fg} || exit 1;
      ) || touch $dir/.error &
    done
    wait
    if [ -f $dir/.error ]; then
      echo "$0: something went wrong in online decoding"
      exit 1
    fi
  fi
  
  exit 0;