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egs/lre07/v2/local/dnn/run_nnet2_multisplice.sh
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#!/bin/bash
# This script is based on run_nnet2_multisplice.sh in
# egs/fisher_english/s5/local/online. It has been modified
# for language recognition.
. ./cmd.sh
stage=1
train_stage=-10
use_gpu=true
set -e
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
# assume use_gpu=true since it would be way too slow otherwise.
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/nnet2_online/nnet_ms_a
mkdir -p exp/nnet2_online
# Stages 1 through 5 are done in run_nnet2_common.sh,
# so it can be shared with other similar scripts.
local/dnn/run_nnet2_common.sh --stage $stage
if [ $stage -le 6 ]; then
if [[ $(hostname -f) == *.clsp.jhu.edu ]]; then
utils/create_split_dir.pl /export/b0{6,7,8,9}/$(USER)/kaldi-data/egs/lre07/v2/$dir/egs/storage
fi
# Because we have a lot of data here and we don't want the training to take
# too long, we reduce the number of epochs from the defaults (15 + 5) to (3 +
# 1). The option "--io-opts '--max-jobs-run 12'" is to have more than the default number
# (5) of jobs dumping the egs to disk; this is OK since we're splitting our
# data across four filesystems for speed.
lid/nnet2/train_multisplice_accel2.sh --stage $train_stage \
--feat-type raw \
--splice-indexes "layer0/-2:-1:0:1:2 layer1/-1:2 layer3/-3:3 layer4/-7:2" \
--num-epochs 6 \
--num-hidden-layers 6 \
--num-jobs-initial 3 --num-jobs-final 18 \
--num-threads "$num_threads" \
--minibatch-size "$minibatch_size" \
--parallel-opts "$parallel_opts" \
--mix-up 10500 \
--initial-effective-lrate 0.0015 --final-effective-lrate 0.00015 \
--cmd "$decode_cmd" \
--egs-dir "$common_egs_dir" \
--pnorm-input-dim 3500 \
--pnorm-output-dim 350 \
data/train_hires_asr data/lang exp/tri5a $dir || exit 1;
fi
exit 0;
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