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egs/wsj/s5/local/nnet2/run_5e_gpu.sh
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#!/bin/bash
# This is an ensemble training recipe using pnorm neural nets on top of adapted 40-dimensional features.
ensemble_size=4
initial_beta=0.1
final_beta=5
train_stage=-10
temp_dir= # e.g. --temp-dir /export/m1-02/dpovey/kaldi-dan2/egs/wsj/s5/
parallel_opts="--gpu 1" # This is suitable for the CLSP network, you'll likely have to change it.
dir=exp/nnet5e_gpu
# Note: since we multiplied the num-jobs by 1/4, we halved the
# learning rate, relative to run_5c.sh
. ././cmd.sh
. ./path.sh
! cuda-compiled && 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
. utils/parse_options.sh
(
if [ ! -z "$temp_dir" ] && [ ! -e $dir/egs ]; then
mkdir -p $dir
mkdir -p $temp_dir/$dir/egs
ln -s $temp_dir/$dir/egs $dir/
fi
steps/nnet2/train_pnorm_ensemble.sh --stage $train_stage \
--num-jobs-nnet 4 --num-threads 1 --parallel-opts "$parallel_opts" \
--mix-up 8000 \
--initial-learning-rate 0.02 --final-learning-rate 0.002 \
--num-hidden-layers 4 \
--pnorm-input-dim 2000 --pnorm-output-dim 400 \
--cmd "$decode_cmd" \
--p 2 \
--ensemble-size $ensemble_size --initial-beta $initial_beta --final-beta $final_beta \
data/train_si284 data/lang exp/tri4b_ali_si284 $dir || exit 1
steps/nnet2/decode.sh --cmd "$decode_cmd" --nj 10 \
--transform-dir exp/tri4b/decode_tgpr_dev93 \
exp/tri4b/graph_tgpr data/test_dev93 $dir/decode_tgpr_dev93
steps/nnet2/decode.sh --cmd "$decode_cmd" --nj 8 \
--transform-dir exp/tri4b/decode_tgpr_eval92 \
exp/tri4b/graph_tgpr data/test_eval92 $dir/decode_tgpr_eval92
steps/nnet2/decode.sh --cmd "$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
)
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