#!/bin/bash
  
  
  # run_tdnn_lstm_1a.sh is a TDNN+LSTM system.  Compare with the TDNN
  # system in run_tdnn_1a.sh.  Configuration is similar to
  # the same-named script run_tdnn_lstm_1a.sh in
  # egs/tedlium/s5_r2/local/nnet3/tuning.
  
  # It's a little better than the TDNN-only script on dev93, a little
  # worse on eval92.
  
  # steps/info/nnet3_dir_info.pl exp/nnet3/tdnn_lstm1a_sp
  # exp/nnet3/tdnn_lstm1a_sp: num-iters=102 nj=3..10 num-params=8.8M dim=40+100->3413 combine=-0.55->-0.54 loglike:train/valid[67,101,combined]=(-0.63,-0.55,-0.55/-0.71,-0.63,-0.63) accuracy:train/valid[67,101,combined]=(0.80,0.82,0.82/0.76,0.78,0.78)
  
  
  
  # local/nnet3/compare_wer.sh --looped --online exp/nnet3/tdnn1a_sp exp/nnet3/tdnn_lstm1a_sp 2>/dev/null
  # local/nnet3/compare_wer.sh --looped --online exp/nnet3/tdnn1a_sp exp/nnet3/tdnn_lstm1a_sp
  # System                tdnn1a_sp tdnn_lstm1a_sp
  #WER dev93 (tgpr)                9.18      8.54
  #             [looped:]                    8.54
  #             [online:]                    8.57
  #WER dev93 (tg)                  8.59      8.25
  #             [looped:]                    8.21
  #             [online:]                    8.34
  #WER dev93 (big-dict,tgpr)       6.45      6.24
  #             [looped:]                    6.28
  #             [online:]                    6.40
  #WER dev93 (big-dict,fg)         5.83      5.70
  #             [looped:]                    5.70
  #             [online:]                    5.77
  #WER eval92 (tgpr)               6.15      6.52
  #             [looped:]                    6.45
  #             [online:]                    6.56
  #WER eval92 (tg)                 5.55      6.13
  #             [looped:]                    6.08
  #             [online:]                    6.24
  #WER eval92 (big-dict,tgpr)      3.58      3.88
  #             [looped:]                    3.93
  #             [online:]                    3.88
  #WER eval92 (big-dict,fg)        2.98      3.38
  #             [looped:]                    3.47
  #             [online:]                    3.53
  # Final train prob        -0.7200   -0.5492
  # Final valid prob        -0.8834   -0.6343
  # Final train acc          0.7762    0.8154
  # Final valid acc          0.7301    0.7849
  
  
  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
  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=1a  #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
  common_egs_dir=
  reporting_email=
  
  # LSTM options
  train_stage=-10
  label_delay=5
  
  # training chunk-options
  chunk_width=40,30,20
  chunk_left_context=40
  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.
  
  . ./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 $num_threads_ubm \
    --nnet3-affix "$nnet3_affix"
  
  
  
  gmm_dir=exp/${gmm}
  ali_dir=exp/${gmm}_ali_${train_set}_sp
  lang=data/lang
  dir=exp/nnet3${nnet3_affix}/tdnn_lstm${affix}_sp
  train_data_dir=data/${train_set}_sp_hires
  train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
  
  for f in $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
      $gmm_dir/{graph_tgpr,graph_bd_tgpr}/HCLG.fst \
      $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
    mkdir -p $dir
    echo "$0: creating neural net configs using the xconfig parser";
  
    num_targets=$(tree-info $ali_dir/tree |grep num-pdfs|awk '{print $2}')
  
    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-renorm-layer name=tdnn1 dim=520
    relu-renorm-layer name=tdnn2 dim=520 input=Append(-1,0,1)
    fast-lstmp-layer name=lstm1 cell-dim=520 recurrent-projection-dim=130 non-recurrent-projection-dim=130 decay-time=20 delay=-3
    relu-renorm-layer name=tdnn3 dim=520 input=Append(-3,0,3)
    relu-renorm-layer name=tdnn4 dim=520 input=Append(-3,0,3)
    fast-lstmp-layer name=lstm2 cell-dim=520 recurrent-projection-dim=130 non-recurrent-projection-dim=130 decay-time=20 delay=-3
    relu-renorm-layer name=tdnn5 dim=520 input=Append(-3,0,3)
    relu-renorm-layer name=tdnn6 dim=520 input=Append(-3,0,3)
    fast-lstmp-layer name=lstm3 cell-dim=520 recurrent-projection-dim=130 non-recurrent-projection-dim=130 decay-time=20 delay=-3
  
    output-layer name=output input=lstm3 output-delay=$label_delay dim=$num_targets 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 $dir/egs/storage ]; then
      utils/create_split_dir.pl \
       /export/b0{3,4,5,6}/$USER/kaldi-data/egs/tedlium-$(date +'%m_%d_%H_%M')/s5_r2/$dir/egs/storage $dir/egs/storage
    fi
  
    steps/nnet3/train_rnn.py --stage=$train_stage \
      --cmd="$decode_cmd" \
      --feat.online-ivector-dir=$train_ivector_dir \
      --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
      --trainer.srand=$srand \
      --trainer.max-param-change=2.0 \
      --trainer.num-epochs=6 \
      --trainer.deriv-truncate-margin=10 \
      --trainer.samples-per-iter=20000 \
      --trainer.optimization.num-jobs-initial=3 \
      --trainer.optimization.num-jobs-final=10 \
      --trainer.optimization.initial-effective-lrate=0.0003 \
      --trainer.optimization.final-effective-lrate=0.00003 \
      --trainer.optimization.shrink-value 0.99 \
      --trainer.rnn.num-chunk-per-minibatch=128,64 \
      --trainer.optimization.momentum=0.5 \
      --egs.chunk-width=$chunk_width \
      --egs.chunk-left-context=$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=$remove_egs \
      --use-gpu=true \
      --feat-dir=$train_data_dir \
      --ali-dir=$ali_dir \
      --lang=$lang \
      --reporting.email="$reporting_email" \
      --dir=$dir  || exit 1;
  fi
  
  if [ $stage -le 14 ]; then
    frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
    rm $dir/.error 2>/dev/null || true
  
    for data in $test_sets; do
      (
        frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
        data_affix=$(echo $data | sed s/test_//)
        nj=$(wc -l <data/${data}_hires/spk2utt)
        for lmtype in tgpr bd_tgpr; do
          graph_dir=$gmm_dir/graph_${lmtype}
          steps/nnet3/decode.sh \
            --extra-left-context $chunk_left_context \
            --extra-right-context $chunk_right_context \
            --extra-left-context-initial 0 \
            --extra-right-context-final 0 \
            --frames-per-chunk $frames_per_chunk \
            --nj $nj --cmd "$decode_cmd"  --num-threads 4 \
            --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
            $graph_dir data/${data}_hires ${dir}/decode_${lmtype}_${data_affix} || exit 1
        done
        steps/lmrescore.sh --cmd "$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
  
  if [ $stage -le 15 ]; then
    # 'looped' decoding.
    # note: you should NOT do this decoding step for setups that have bidirectional
    # recurrence, like BLSTMs-- it doesn't make sense and will give bad results.
    # we didn't write a -parallel version of this program yet,
    # so it will take a bit longer as the --num-threads option is not supported.
    # we just hardcode the --frames-per-chunk option as it doesn't have to
    # match any value used in training, and it won't affect the results (unlike
    # regular decoding).
    rm $dir/.error 2>/dev/null || true
  
    for data in $test_sets; do
      (
        data_affix=$(echo $data | sed s/test_//)
        nj=$(wc -l <data/${data}_hires/spk2utt)
        for lmtype in tgpr bd_tgpr; do
          graph_dir=$gmm_dir/graph_${lmtype}
          steps/nnet3/decode_looped.sh \
            --frames-per-chunk 30 \
            --nj $nj --cmd "$decode_cmd" \
            --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
            $graph_dir data/${data}_hires ${dir}/decode_looped_${lmtype}_${data_affix} || exit 1
        done
        steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_{tgpr,tg} \
          data/${data}_hires ${dir}/decode_looped_{tgpr,tg}_${data_affix} || exit 1
        steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
          data/lang_test_bd_{tgpr,fgconst} \
         data/${data}_hires ${dir}/decode_looped_${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
  
  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 \
      $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
  
    rm $dir/.error 2>/dev/null || true
  
    for data in $test_sets; do
      (
        data_affix=$(echo $data | sed s/test_//)
        nj=$(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
          graph_dir=$gmm_dir/graph_${lmtype}
          steps/online/nnet3/decode.sh \
            --nj $nj --cmd "$decode_cmd" \
            $graph_dir data/${data} ${dir}_online/decode_${lmtype}_${data_affix} || exit 1
        done
        steps/lmrescore.sh --cmd "$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;