run_5c.sh
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#!/bin/bash
# This is neural net training on top of adapted 40-dimensional features.
#
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/nnet5c_gpu
else
num_threads=16
parallel_opts="--num-threads $num_threads"
dir=exp/nnet5c
minibatch_size=128
fi
if [ ! -f $dir/final.mdl ]; then
if [ "$USER" == dpovey ]; then
# spread the egs over various machines. will help reduce overload of any
# one machine.
utils/create_split_dir.pl /export/b0{1,2,3,4}/dpovey/kaldi-pure/egs/wsj/s5/$dir/egs $dir/egs/storage
fi
steps/nnet2/train_tanh_fast.sh --stage $train_stage \
--num-threads "$num_threads" \
--parallel-opts "$parallel_opts" \
--minibatch-size "$minibatch_size" \
--num-jobs-nnet 8 \
--samples-per-iter 400000 \
--mix-up 8000 \
--initial-learning-rate 0.01 --final-learning-rate 0.001 \
--num-hidden-layers 4 --hidden-layer-dim 1024 \
--cmd "$decode_cmd" \
data/train_si284 data/lang exp/tri4b_ali_si284 $dir || exit 1
fi
steps/nnet2/decode.sh --cmd "$decode_cmd" --nj 10 \
--transform-dir exp/tri4b/decode_bd_tgpr_dev93 \
exp/tri4b/graph_bd_tgpr data/test_dev93 $dir/decode_bd_tgpr_dev93 &
steps/nnet2/decode.sh --cmd "$decode_cmd" --nj 8 \
--transform-dir exp/tri4b/decode_bd_tgpr_eval92 \
exp/tri4b/graph_bd_tgpr data/test_eval92 $dir/decode_bd_tgpr_eval92
wait