Blame view
egs/aspire/s5/local/nnet3/run_autoencoder.sh
2.72 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 89 |
#!/bin/bash
# this is an example to show a "tdnn" system in raw nnet configuration
# i.e. without a transition model
# It uses corrupted (reverberation + noise) speech as input and clean speech
# as output.
. ./cmd.sh
stage=0
affix=
train_stage=-10
common_egs_dir=
egs_opts=
num_data_reps=10
remove_egs=true
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
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
dir=exp/nnet3/tdnn_raw
dir=$dir${affix:+_$affix}
clean_data_dir=data/train
data_dir=data/train_rvb
targets_scp=$dir/targets.scp
mkdir -p $dir
# Create copies of clean feats with prefix "rev$x_" to match utterance names of
# the noisy feats
for x in `seq 1 $num_data_reps`; do
awk -v x=$x '{print "rev"x"_"$0}' $clean_data_dir/feats.scp | sort -k1,1 > $targets_scp
done
if [ $stage -le 9 ]; then
echo "$0: creating neural net configs";
num_targets=`feat-to-dim scp:$targets_scp - 2>/dev/null` || exit 1
feat_dim=`feat-to-dim scp:$data_dir/feats.scp - 2>/dev/null` || exit 1
mkdir -p $dir/configs
cat <<EOF > $dir/configs/network.xconfig
input dim=$feat_dim name=input
relu-renorm-layer name=tdnn1 dim=1024 input=Append(-2,-1,0,1,2)
relu-renorm-layer name=tdnn2 dim=1024 input=Append(-1,2)
relu-renorm-layer name=tdnn3 dim=1024 input=Append(-3,3)
relu-renorm-layer name=tdnn4 dim=1024 input=Append(-7,2)
relu-renorm-layer name=tdnn5 dim=1024
output-layer name=output dim=$num_targets max-change=1.5 objective-type=quadratic include-log-softmax=false
EOF
steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
fi
if [ $stage -le 10 ]; then
if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
utils/create_split_dir.pl \
/export/b0{3,4,5,6}/$USER/kaldi-data/egs/aspire-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
fi
steps/nnet3/train_raw_dnn.py --stage=$train_stage \
--cmd="$decode_cmd" \
--feat.cmvn-opts "--norm-means=false --norm-vars=false" \
--trainer.num-epochs 2 \
--trainer.optimization.num-jobs-initial 3 \
--trainer.optimization.num-jobs-final 16 \
--trainer.optimization.initial-effective-lrate 0.0017 \
--trainer.optimization.final-effective-lrate 0.00017 \
--trainer.optimization.minibatch-size 512 \
--egs.dir "$common_egs_dir" --egs.opts "$egs_opts" \
--cleanup.remove-egs $remove_egs \
--cleanup.preserve-model-interval 50 \
--nj=30 \
--use-dense-targets=true \
--feat-dir=${data_dir} \
--targets-scp=$targets_scp \
--dir=$dir || exit 1;
fi
|