#!/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 -l mem_free=1G,ram_free=1G" \
      --nj $nj --sub-split 20 --num-threads 6 --parallel-opts "-pe smp 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;