Blame view
egs/rm/s5/local/online/run_nnet2_baseline.sh
2.49 KB
8dcb6dfcb
first commit
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
#!/bin/bash
# this is a baseline for ./run_nnet2.sh, without
# the iVectors, to see whether they make a difference.
. ./cmd.sh
stage=1
train_stage=-10
use_gpu=true
dir=exp/nnet2_online/nnet_a_baseline
. ./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
else
# Use 4 nnet jobs just like run_4d_gpu.sh so the results should be
# almost the same, but this may be a little bit slow.
num_threads=16
minibatch_size=128
parallel_opts="--num-threads $num_threads"
fi
if [ $stage -le 1 ]; then
steps/nnet2/train_pnorm_simple.sh --stage $train_stage \
--splice-width 7 \
--feat-type raw \
--cmvn-opts "--norm-means=false --norm-vars=false" \
--num-threads "$num_threads" \
--minibatch-size "$minibatch_size" \
--parallel-opts "$parallel_opts" \
--num-jobs-nnet 4 \
--num-epochs 25 \
--add-layers-period 1 \
--num-hidden-layers 2 \
--mix-up 4000 \
--initial-learning-rate 0.02 --final-learning-rate 0.004 \
--cmd "$decode_cmd" \
--pnorm-input-dim 1000 \
--pnorm-output-dim 200 \
data/train data/lang exp/tri3b_ali $dir || exit 1;
fi
if [ $stage -le 2 ]; then
steps/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 \
exp/tri3b/graph data/test $dir/decode &
steps/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 \
exp/tri3b/graph_ug data/test $dir/decode_ug || exit 1;
wait
fi
if [ $stage -le 3 ]; then
# If this setup used PLP features, we'd have to give the option --feature-type plp
# to the script below.
steps/online/nnet2/prepare_online_decoding.sh data/lang "$dir" ${dir}_online || exit 1;
fi
if [ $stage -le 4 ]; then
# Doing the real online decoding. The --per-utt true option actually
# makes no difference to the output as there is no adaptation at all.
steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 \
--per-utt true exp/tri3b/graph data/test ${dir}_online/decode &
steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 \
--per-utt true exp/tri3b/graph_ug data/test ${dir}_online/decode_ug || exit 1;
wait
fi
# for results, see the end of ./run_nnet2.sh
|