#!/bin/bash
  # Copyright 2017 University of Chinese Academy of Sciences (UCAS) Gaofeng Cheng
  # Apache 2.0
  
  # The model training procedure is similar to run_blstm_6j.sh under egs/swbd/s5c
  
  # ./local/chain/compare_wer_general.sh blstm_6j_sp
  # System                blstm_6j_sp
  # WER on eval2000(tg)        12.3
  # WER on eval2000(fg)        12.2
  # WER on rt03(tg)        11.7
  # WER on rt03(fg)        11.5
  # Final train prob         -0.061
  # Final valid prob         -0.082
  # Final train prob (xent)        -0.698
  # Final valid prob (xent)       -0.8108
  # num-params=41.3M
  
  # ./steps/info/chain_dir_info.pl exp/chain/blstm_6j_sp
  # exp/chain/blstm_6j_sp: num-iters=2384 nj=3..16 num-params=41.3M dim=40+100->6149 combine=-0.075->-0.074 (over 15) 
  # xent:train/valid[1587,2383,final]=(-0.754,-0.710,-0.698/-0.828,-0.824,-0.811) 
  # logprob:train/valid[1587,2383,final]=(-0.070,-0.063,-0.061/-0.082,-0.084,-0.082)
  
  # ./local/chain/show_chain_wer.sh blstm_6j_sp
  # %WER 16.0 | 2628 21594 | 86.3 8.7 5.0 2.3 16.0 53.8 | exp/chain/blstm_6j_sp/decode_eval2000_fsh_sw1_tg/score_6_0.0/eval2000_hires.ctm.callhm.filt.sys
  # %WER 12.3 | 4459 42989 | 89.3 6.6 4.1 1.6 12.3 49.4 | exp/chain/blstm_6j_sp/decode_eval2000_fsh_sw1_tg/score_8_0.0/eval2000_hires.ctm.filt.sys
  # %WER 8.3 | 1831 21395 | 92.8 4.8 2.4 1.1 8.3 41.8 | exp/chain/blstm_6j_sp/decode_eval2000_fsh_sw1_tg/score_10_1.0/eval2000_hires.ctm.swbd.filt.sys
  # %WER 15.7 | 2628 21594 | 86.5 8.5 5.0 2.3 15.7 53.2 | exp/chain/blstm_6j_sp/decode_eval2000_fsh_sw1_fg/score_6_0.0/eval2000_hires.ctm.callhm.filt.sys
  # %WER 12.2 | 4459 42989 | 89.7 6.9 3.4 2.0 12.2 50.1 | exp/chain/blstm_6j_sp/decode_eval2000_fsh_sw1_fg/score_6_0.0/eval2000_hires.ctm.filt.sys
  # %WER 8.2 | 1831 21395 | 93.0 4.8 2.2 1.2 8.2 41.6 | exp/chain/blstm_6j_sp/decode_eval2000_fsh_sw1_fg/score_10_0.0/eval2000_hires.ctm.swbd.filt.sys
  
  # ./local/chain/show_chain_rt03_wer.sh blstm_6j_sp
  # %WER 9.9 | 3970 36721 | 91.3 5.3 3.4 1.2 9.9 43.6 | exp/chain/blstm_6j_sp/decode_rt03_fsh_sw1_tg/score_7_0.0/rt03_hires.ctm.fsh.filt.sys
  # %WER 11.7 | 8420 76157 | 89.6 6.3 4.1 1.3 11.7 44.7 | exp/chain/blstm_6j_sp/decode_rt03_fsh_sw1_tg/score_8_0.0/rt03_hires.ctm.filt.sys
  # %WER 13.3 | 4450 39436 | 88.2 7.5 4.3 1.5 13.3 45.3 | exp/chain/blstm_6j_sp/decode_rt03_fsh_sw1_tg/score_8_0.0/rt03_hires.ctm.swbd.filt.sys
  # %WER 9.7 | 3970 36721 | 91.4 5.2 3.4 1.1 9.7 43.1 | exp/chain/blstm_6j_sp/decode_rt03_fsh_sw1_fg/score_7_0.0/rt03_hires.ctm.fsh.filt.sys
  # %WER 11.5 | 8420 76157 | 89.8 6.2 4.0 1.3 11.5 44.3 | exp/chain/blstm_6j_sp/decode_rt03_fsh_sw1_fg/score_8_0.0/rt03_hires.ctm.filt.sys
  # %WER 13.2 | 4450 39436 | 88.3 7.3 4.3 1.5 13.2 45.1 | exp/chain/blstm_6j_sp/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
  dir=exp/chain/blstm_6j
  decode_iter=
  decode_dir_affix=
  
  # training options
  # training options
  leftmost_questions_truncate=-1
  chunk_width=150
  chunk_left_context=40
  chunk_right_context=40
  xent_regularize=0.025
  self_repair_scale=0.00001
  label_delay=0
  
  # decode options
  extra_left_context=50
  extra_right_context=50
  frames_per_chunk=
  
  remove_egs=false
  common_egs_dir=
  
  # 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
  
  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 || exit 1;
    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 || exit 1
  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)
  
    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
  
    # check steps/libs/nnet3/xconfig/lstm.py for the other options and defaults
    fast-lstmp-layer name=blstm1-forward input=lda cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3
    fast-lstmp-layer name=blstm1-backward input=lda cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=3
  
    fast-lstmp-layer name=blstm2-forward input=Append(blstm1-forward, blstm1-backward) cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3
    fast-lstmp-layer name=blstm2-backward input=Append(blstm1-forward, blstm1-backward) cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=3
  
    fast-lstmp-layer name=blstm3-forward input=Append(blstm2-forward, blstm2-backward) cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3
    fast-lstmp-layer name=blstm3-backward input=Append(blstm2-forward, blstm2-backward) cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=3
  
    ## adding the layers for chain branch
    output-layer name=output input=Append(blstm3-forward, blstm3-backward) 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=Append(blstm3-forward, blstm3-backward) 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/fisher_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 0.1 \
      --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 \
      --trainer.frames-per-iter 1200000 \
      --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.optimization.momentum 0.0 \
      --trainer.deriv-truncate-margin 8 \
      --egs.stage $get_egs_stage \
      --egs.opts "--frames-overlap-per-eg 0" \
      --egs.chunk-width $chunk_width \
      --egs.chunk-left-context $chunk_left_context \
      --egs.chunk-right-context $chunk_right_context \
      --egs.dir "$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
    iter_opts=
    if [ ! -z $decode_iter ]; then
      iter_opts=" --iter $decode_iter "
    fi
  
    # decoding options
    extra_left_context=$[$chunk_left_context+10]
    extra_right_context=$[$chunk_right_context+10]
  
    for decode_set in eval2000 rt03; do
        (
        num_jobs=`cat data/${decode_set}_hires/utt2spk|cut -d' ' -f2|sort -u|wc -l`
        steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
            --nj $num_jobs --cmd "$decode_cmd" $iter_opts \
            --extra-left-context $extra_left_context \
            --extra-right-context $extra_right_context \
            --frames-per-chunk $chunk_width \
            --online-ivector-dir exp/nnet3/ivectors_${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;
        ) &
    done
  fi
  wait;
  exit 0;