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egs/rm/s5/local/nnet2/run_4d.sh
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#!/bin/bash
# local/nnet2/run_4d.sh is the new, faster version of the p-norm training script.
# 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/nnet4d_gpu
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"
dir=exp/nnet4d
fi
if [ ! -f $dir/final.mdl ]; then
steps/nnet2/train_pnorm_fast.sh --stage $train_stage \
--num-threads "$num_threads" \
--minibatch-size "$minibatch_size" \
--parallel-opts "$parallel_opts" \
--num-jobs-nnet 4 \
--num-epochs 8 --num-epochs-extra 5 --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 $dir || exit 1;
fi
steps/nnet2/decode.sh --config conf/decode.config --cmd "$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
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