#!/bin/bash
  
  #    This is the standard "tdnn" system, built in nnet3 with xconfigs.
  
  
  # local/nnet3/compare_wer.sh exp/nnet3/tdnn1a_sp
  # System                tdnn1a_sp
  #WER dev93 (tgpr)                9.18
  #WER dev93 (tg)                  8.59
  #WER dev93 (big-dict,tgpr)       6.45
  #WER dev93 (big-dict,fg)         5.83
  #WER eval92 (tgpr)               6.15
  #WER eval92 (tg)                 5.55
  #WER eval92 (big-dict,tgpr)      3.58
  #WER eval92 (big-dict,fg)        2.98
  # Final train prob        -0.7200
  # Final valid prob        -0.8834
  # Final train acc          0.7762
  # Final valid acc          0.7301
  
  set -e -o pipefail -u
  
  # 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.
  tdnn_affix=1a  #affix for TDNN directory e.g. "1a" or "1b", in case we change the configuration.
  
  # Options which are not passed through to run_ivector_common.sh
  train_stage=-10
  remove_egs=true
  srand=0
  reporting_email=
  # set common_egs_dir to use previously dumped egs.
  common_egs_dir=
  
  . ./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_${train_set}_sp
  dir=exp/nnet3${nnet3_affix}/tdnn${tdnn_affix}_sp
  train_data_dir=data/${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 $gmm_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=650
    relu-renorm-layer name=tdnn2 dim=650 input=Append(-1,0,1)
    relu-renorm-layer name=tdnn3 dim=650 input=Append(-1,0,1)
    relu-renorm-layer name=tdnn4 dim=650 input=Append(-3,0,3)
    relu-renorm-layer name=tdnn5 dim=650 input=Append(-6,-3,0)
    output-layer name=output 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_dnn.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=3 \
      --trainer.samples-per-iter=400000 \
      --trainer.optimization.num-jobs-initial=2 \
      --trainer.optimization.num-jobs-final=10 \
      --trainer.optimization.initial-effective-lrate=0.0015 \
      --trainer.optimization.final-effective-lrate=0.00015 \
      --trainer.optimization.minibatch-size=256,128 \
      --egs.dir="$common_egs_dir" \
      --cleanup.remove-egs=$remove_egs \
      --use-gpu=true \
      --feat-dir=$train_data_dir \
      --ali-dir=$ali_dir \
      --lang=data/lang \
      --reporting.email="$reporting_email" \
      --dir=$dir  || exit 1;
  fi
  
  if [ $stage -le 14 ]; then
    # note: for TDNNs, looped decoding gives exactly the same results
    # as regular decoding, so there is no point in testing it separately.
    # We use regular decoding because it supports multi-threaded (we just
    # didn't create the binary for that, for looped decoding, so far).
    rm $dir/.error || true 2>/dev/null
    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.sh --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
  
  
  exit 0;