#!/bin/bash
  
  
  # This script demonstrates discriminative training of p-norm neural nets.  It's on top
  # of run_5d_gpu.sh, which uses adapted 40-dimensional 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/nnet5d exp/nnet5d_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/nnet5d exp/nnet5d_ali
  
  # note, the default options use 16 threads.
  steps/nnet2/train_discriminative.sh --cmd "$decode_cmd" --learning-rate 0.00002 \
      --num-jobs-nnet 4  --transform-dir exp/tri4b_ali_si284 \
      data/train_si284 data/lang \
      exp/nnet5d_ali exp/nnet5d_denlats exp/nnet5d/final.mdl exp/nnet6d_mpe
  
  for epoch in 1 2 3 4; do
    dir=exp/nnet6d_mpe
    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;