#!/bin/bash
  
  # 1c is as 1b but using more 'chain-like' splicing and slightly
  # smaller dim.  Not better; maybe slightly worse.
  
  # note: the num-params is almost the same.
  # steps/info/nnet3_dir_info.pl exp/nnet3_cleaned/tdnn1{b,c}_sp
  # exp/nnet3_cleaned/tdnn1b_sp: num-iters=240 nj=2..12 num-params=10.3M dim=40+100->4187 combine=-0.95->-0.95 loglike:train/valid[159,239,combined]=(-1.01,-0.95,-0.94/-1.18,-1.16,-1.15) accuracy:train/valid[159,239,combined]=(0.71,0.72,0.72/0.67,0.68,0.68)
  # exp/nnet3_cleaned/tdnn1c_sp: num-iters=240 nj=2..12 num-params=10.1M dim=40+100->4187 combine=-1.16->-1.15 loglike:train/valid[159,239,combined]=(-1.22,-1.16,-1.15/-1.41,-1.38,-1.38) accuracy:train/valid[159,239,combined]=(0.66,0.67,0.68/0.62,0.63,0.63)
  
  # local/nnet3/compare_wer.sh exp/nnet3_cleaned/tdnn1{b,c}_sp
  # System                tdnn1b_sp tdnn1c_sp
  # WER on dev(orig)           11.7      11.9
  # WER on dev(rescored)       10.9      11.1
  # WER on test(orig)          11.7      11.8
  # WER on test(rescored)      11.0      11.2
  # Final train prob        -0.9416   -1.1505
  # Final valid prob        -1.1496   -1.3805
  # Final train acc          0.7241    0.6756
  # Final valid acc          0.6788    0.6255
  
  #    This is the standard "tdnn" system, built in nnet3; this script
  # is the version that's meant to run with data-cleanup, that doesn't
  # support parallel alignments.
  
  
  # steps/info/nnet3_dir_info.pl exp/nnet3_cleaned/tdnn1b_sp
  # exp/nnet3_cleaned/tdnn1b_sp: num-iters=240 nj=2..12 num-params=10.3M dim=40+100->4187 combine=-0.95->-0.95 loglike:train/valid[159,239,combined]=(-1.01,-0.95,-0.94/-1.18,-1.16,-1.15) accuracy:train/valid[159,239,combined]=(0.71,0.72,0.72/0.67,0.68,0.68)
  
  # local/nnet3/compare_wer.sh exp/nnet3_cleaned/tdnn1a_sp exp/nnet3_cleaned/tdnn1b_sp
  # System                tdnn1a_sp tdnn1b_sp
  # WER on dev(orig)           11.9      11.7
  # WER on dev(rescored)       11.2      10.9
  # WER on test(orig)          11.6      11.7
  # WER on test(rescored)      11.0      11.0
  # Final train prob        -0.9255   -0.9416
  # Final valid prob        -1.1842   -1.1496
  # Final train acc          0.7245    0.7241
  # Final valid acc          0.6771    0.6788
  
  
  # by default, with cleanup:
  # local/nnet3/run_tdnn.sh
  
  # without cleanup:
  # local/nnet3/run_tdnn.sh  --train-set train --gmm tri3 --nnet3-affix "" &
  
  
  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
  decode_nj=30
  min_seg_len=1.55
  train_set=train_cleaned
  gmm=tri3_cleaned  # this is the source gmm-dir for the data-type of interest; it
                    # should have alignments for the specified training data.
  num_threads_ubm=32
  nnet3_affix=_cleaned  # cleanup affix for exp dirs, e.g. _cleaned
  tdnn_affix=1c  #affix for TDNN directory e.g. "a" or "b", 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=dpovey@gmail.com
  # 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 \
                                    --min-seg-len $min_seg_len \
                                    --train-set $train_set \
                                    --gmm $gmm \
                                    --num-threads-ubm $num_threads_ubm \
                                    --nnet3-affix "$nnet3_affix"
  
  
  
  gmm_dir=exp/${gmm}
  graph_dir=$gmm_dir/graph
  ali_dir=exp/${gmm}_ali_${train_set}_sp_comb
  dir=exp/nnet3${nnet3_affix}/tdnn${tdnn_affix}_sp
  train_data_dir=data/${train_set}_sp_hires_comb
  train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires_comb
  
  
  for f in $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
       $graph_dir/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=750
    relu-renorm-layer name=tdnn2 dim=750 input=Append(-1,0,1)
    relu-renorm-layer name=tdnn3 dim=750 input=Append(-1,0,1)
    relu-renorm-layer name=tdnn4 dim=750 input=Append(-3,0,3)
    relu-renorm-layer name=tdnn5 dim=750 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=12 \
      --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 dset in dev test; do
     (
      steps/nnet3/decode.sh --nj $decode_nj --cmd "$decode_cmd"  --num-threads 4 \
          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${dset}_hires \
        ${graph_dir} data/${dset}_hires ${dir}/decode_${dset} || exit 1
      steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" data/lang data/lang_rescore \
         data/${dset}_hires ${dir}/decode_${dset} ${dir}/decode_${dset}_rescore || exit 1
      ) || touch $dir/.error &
    done
    wait
    [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
  fi
  
  
  exit 0;