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run_aug.sh
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#!/bin/bash
# Copyright 2020 Zhao Yi
#需要建立路径，存放下载的数据
. ./cmd.sh
. ./path.sh
set -e
cmd=run.pl
#input data
data=data
NOISE_DIR=../kaldi-master/egs/sre16/v2/noise
audio_dir=../speech/aishell/train/
out_data=data/wav/train_aug
logdir=log
nj=10
stage=-1
#prepare data
if [ $stage -le 0 ]; then
echo "$0: preparing data"
find $audio_dir -iname "*.wav" > $tmp_dir/wav.list
sed -e 's/\.wav//' $tmp_dir/wav.list | awk -F '/' '{print $NF}' > $tmp_dir/utt.list
sed -e 's/\.wav//' $tmp_dir/wav.list | awk -F '/' '{i=NF-1;printf("%s %s\n",$NF,$i)}' > $tmp_dir/utt2spk
paste -d' ' $tmp_dir/utt.list $tmp_dir/wav.list > $tmp_dir/wav.scp
utils/utt2spk_to_spk2utt.pl $data/train/utt2spk > $data/train/spk2utt
fi
#speed-perturbation
if [ $stage -le 1 ]; then
echo "$0: preparing directory for speed-perturbed data"
utils/data/perturb_data_dir_speed_zy.sh $data/train $data/train_speed $out_data/train_speed
fi
# do volume-perturbation
if [ $stage -le 2 ]; then
echo "$0: preparing directory for volume-perturbed data"
utils/data/perturb_data_dir_volume_zy.sh $data/train $data/train_volume $out_data/train_volume
awk '{printf("%s_vol %s\n",$1,$NF)}' $data/train/utt2spk > $data/train_volume/utt2spk
awk '{printf("%s_vol %s\n",$1,$NF)}' $data/train/utt2dur > $data/train_volume/utt2dur
cat $data/train/utt2spk | awk -v p=_vol '{printf("%s %s%s\n", $1, $1, p);}' > $data/train_volume/utt_map
utils/apply_map.pl -f 1 $data/train_volume/utt_map <$data/train_volume/wav.scp > $data/train_volume/wav.scp_new
rm $data/train_volume/wav.scp
mv $data/train_volume/wav.scp_new $data/train_volume/wav.scp
utils/apply_map.pl -f 1 $data/train_volume/utt_map <$data/train/text > $data/train_volume/text
utils/utt2spk_to_spk2utt.pl $data/train_volume/utt2spk > $data/train_volume/spk2utt
fi
#RIRS
if [ $stage -le 3 ]; then
echo "$0: preparing directory for RIRS-perturbed data"
#frame_shift=0.01
#awk -v frame_shift=$frame_shift '{print $1, $2*frame_shift;}' $aug_data/utt2num_frames > $aug_data/reco2dur
#if [ ! -d "RIRS_NOISES" ]; then
# Download the package that includes the real RIRs, simulated RIRs, isotropic noises and point-source noises
#wget --no-check-certificate http://www.openslr.org/resources/28/rirs_noises.zip
#unzip rirs_noises.zip
#fi
# Make a version with reverberated speech
rvb_opts=()
rvb_opts+=(--rir-set-parameters "0.5, RIRS_NOISES/simulated_rirs/smallroom/rir_list")
rvb_opts+=(--rir-set-parameters "0.5, RIRS_NOISES/simulated_rirs/mediumroom/rir_list")
# Make a reverberated version of the SWBD+SRE list. Note that we don't add any
# additive noise here.
python steps/data/reverberate_data_dir_zy.py \
"${rvb_opts[@]}" \
--speech-rvb-probability 1 \
--pointsource-noise-addition-probability 0 \
--isotropic-noise-addition-probability 0 \
--num-replications 1 \
--source-sampling-rate 16000 \
--out_dir $out_data/train_reverb \
$data/train $data/train_reverb
#cp $aug_data/vad.scp $data/train_reverb/
utils/copy_data_dir.sh --utt-suffix "-reverb" $data/train_reverb $data/train_reverb.new
rm -rf $data/train_reverb
mv $data/train_reverb.new $data/train_reverb
fi
#MUSAN
if [ $stage -le 4 ]; then
echo "$0: preparing directory for MUSAN-perturbed data"
# Prepare the MUSAN corpus, which consists of music, speech, and noise
# suitable for augmentation.
local/make_musan.sh $NOISE_DIR/musan $data
# Get the duration of the MUSAN recordings. This will be used by the
# script augment_data_dir.py.
for name in speech noise music; do
utils/data/get_utt2dur.sh $data/musan_${name}
mv $data/musan_${name}/utt2dur $data/musan_${name}/reco2dur
#augment data
python steps/data/augment.py --utt-suffix "noise" --fg-interval 1 --fg-snrs "15:10:5:0" --fg-noise-dir "$data/musan_noise" --out_dir "$out_data/train_noise" $data/train $data/train_noise
# Augment with musan_music
python steps/data/augment.py --utt-suffix "music" --bg-snrs "15:10:8:5" --num-bg-noises "1" --bg-noise-dir "$data/musan_music" --out_dir "$out_data/train_music" $data/train $data/train_music
# Augment with musan_speech
python steps/data/augment.py --utt-suffix "babble" --bg-snrs "20:17:15:13" --num-bg-noises "3:4:5:6:7" --bg-noise-dir "$data/musan_speech" --out_dir "$out_data/train_babble" $data/train $data/train_babble
fi
#combine data
if [ $stage -le 5 ]; then
echo "$0: combine augment data and random select a subset about twice the origin data"
# Combine reverb, noise, music, and babble into one directory.
utils/combine_data.sh $data/train_aug $data/train_reverb $data/train_noise $data/train_music $data/train_babble $data/train_speed $data/train_volume
utils/subset_data_dir.sh $data/train_aug 240000 $data/train_aug_24k
utils/fix_data_dir.sh $data/train_aug_24k
fi
if [ $stage -le 6 ]; then
echo "$0: convert the subset to audio file and save the audio and text to dest dir"
cat $data/train_aug_24k/wav.scp | awk '{printf("%s \n", $1);}' > $data/train_aug_24k/utt_map
utils/apply_map.pl -f 1 $data/train_aug_24k/utt_map <$data/train_aug_24k/wav.scp > $data/train_aug_24k/wav.scp_new
cp $data/train_aug_24k/text $data/wav/train_aug/text
split_command=""
for n in $(seq $nj); do
split_command="$split_command $logdir/command.$n.sh"
done
utils/split_scp.pl $data/train_aug_24k/wav.scp_new $split_command || exit 1;
$cmd JOB=1:$nj $logdir/run_aug.JOB.log \
bash $logdir/command.JOB.sh || exit 1;
echo "Sucessed generate the wave files."
fi