#!/bin/bash
  
  # this is the standard "tdnn" system, built in nnet3; it's what we use to
  # call multi-splice.
  
  # At this script level we don't support not running on GPU, as it would be painfully slow.
  # If you want to run without GPU you'd have to call train_tdnn.sh with --gpu false,
  # --num-threads 16 and --minibatch-size 128.
  
  stage=0
  train_stage=-10
  dir=exp/nnet3/nnet_tdnn_a
  . ./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 || exit 1;
  
  if [ $stage -le 8 ]; 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/eca-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
    fi
  
    # Note that the alignments used come from the pnorm-ensemble model
    # If you choose to skip ensemble training (which is slow), use the best
    # fmllr alignments available (tri4a)
    steps/nnet3/train_tdnn.sh --stage $train_stage \
      --num-epochs 8 --num-jobs-initial 2 --num-jobs-final 14 \
      --splice-indexes "-4,-3,-2,-1,0,1,2,3,4  0  -2,2  0  -4,4 0" \
      --feat-type raw \
      --online-ivector-dir exp/nnet3/ivectors_train \
      --cmvn-opts "--norm-means=false --norm-vars=false" \
      --initial-effective-lrate 0.005 --final-effective-lrate 0.0005 \
      --cmd "$decode_cmd" \
      --pnorm-input-dim 2000 \
      --pnorm-output-dim 250 \
      data/train_hires data/lang exp/tri5a_ali $dir  || exit 1;
  fi
  
  
  if [ $stage -le 9 ]; then
    # this does offline decoding that should give the same results as the real
    # online decoding.
    graph_dir=exp/tri5a/graph
    # use already-built graphs.
    for data in dev test sup h5; do
      steps/nnet3/decode.sh --nj 8 --cmd "$decode_cmd" \
          --online-ivector-dir exp/nnet3/ivectors_${data} \
         $graph_dir data/${data}_hires $dir/decode_${data} || exit 1;
    done
  fi