#!/bin/bash
  
  
  # This script demonstrates discriminative training of neural nets.  It's on top
  # of run_5c_gpu.sh, which uses adapted 40-dimensional features.  This version of
  # the script uses GPUs.  We distinguish it by putting "_gpu" at the end of the
  # directory name.
  
  
  gpu_opts="--gpu 1"                   # This is suitable for the CLSP network,
                                        # you'll likely have to change it.  we'll
                                        # use it later on, in the training (it's
                                        # not used in denlat creation)
  . ./cmd.sh
  . ./path.sh
  ! cuda-compiled && 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
  
  # The denominator lattice creation currently doesn't use GPUs.
  
  # Note: we specify 1G for --mem, which is per
  # thread... it will likely be less than the default.  Increase the beam relative
  # to the defaults; this is just for this RM setup, where the default beams will
  # likely generate very thin lattices.  Note: the transform-dir is important to
  # specify, since this system is on top of fMLLR features.
  
  set -e # exit on error.
  
  nj=$(cat exp/tri4b_ali_si284/num_jobs)
  
  steps/nnet2/make_denlats.sh --cmd "$decode_cmd --mem 1G" \
        --nj $nj --sub-split 20 --num-threads 6 --parallel-opts "--num-threads 6" \
        --transform-dir exp/tri4b_ali_si284 \
       data/train_si284 data/lang exp/nnet5c_gpu exp/nnet5c_gpu_denlats
  
  steps/nnet2/align.sh  --cmd "$decode_cmd $gpu_opts" \
        --use-gpu yes --transform-dir exp/tri4b_ali_si284 \
        --nj $nj data/train_si284 data/lang exp/nnet5c_gpu exp/nnet5c_gpu_ali
  
  steps/nnet2/train_discriminative.sh --cmd "$decode_cmd" --learning-rate 0.000002 \
      --num-jobs-nnet 4  --transform-dir exp/tri4b_ali_si284 \
      --num-threads 1 --parallel-opts "$gpu_opts" data/train_si284 data/lang \
      exp/nnet5c_gpu_ali exp/nnet5c_gpu_denlats exp/nnet5c_gpu/final.mdl exp/nnet6c_mpe_gpu
  
  for epoch in 1 2 3 4; do
    dir=exp/nnet6c_mpe_gpu
    steps/nnet2/decode.sh --cmd "$decode_cmd" --nj 10 --iter epoch$epoch \
      --transform-dir exp/tri4b/decode_bd_tgpr_dev93 \
       exp/tri4b/graph_bd_tgpr data/test_dev93 $dir/decode_bd_tgpr_dev93_epoch$epoch &
  
    steps/nnet2/decode.sh --cmd "$decode_cmd" --nj 8  --iter epoch$epoch \
      --transform-dir exp/tri4b/decode_bd_tgpr_eval92 \
       exp/tri4b/graph_bd_tgpr data/test_eval92 $dir/decode_bd_tgpr_eval92_epoch$epoch &
  done
  
  
  
  exit 0;