#!/bin/bash
  
  
  # This is neural net training on top of adapted 40-dimensional features.
  # The same script works for GPUs, and for CPU only (with --use-gpu false).
  
  train_stage=-10
  use_gpu=true
  
  . ./cmd.sh
  . ./path.sh
  . utils/parse_options.sh
  
  
  if $use_gpu; then
    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
    parallel_opts="--gpu 1"
    num_threads=1
    minibatch_size=512
    dir=exp/nnet4c_gpu
  else
    num_threads=16
    minibatch_size=128
    parallel_opts="--num-threads $num_threads"
    dir=exp/nnet4c
  fi
  
  
  
  if [ ! -f $dir/final.mdl ]; then
   steps/nnet2/train_tanh_fast.sh --stage $train_stage \
       --num-jobs-nnet 4 \
       --num-threads "$num_threads" \
       --minibatch-size "$minibatch_size" \
       --parallel-opts "$parallel_opts" \
       --num-epochs 20 \
       --add-layers-period 1 \
       --num-hidden-layers 2 \
       --mix-up 4000 \
       --initial-learning-rate 0.02 --final-learning-rate 0.004 \
       --cmd "$decode_cmd" \
       --hidden-layer-dim 375 \
       data/train data/lang exp/tri3b_ali $dir || exit 1;
  fi
  
  steps/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 \
    --transform-dir exp/tri3b/decode \
    exp/tri3b/graph data/test $dir/decode  &
  
  steps/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 \
    --transform-dir exp/tri3b/decode_ug \
    exp/tri3b/graph_ug data/test $dir/decode_ug
  
  wait