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manipulating_data.py
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manipulating_data.py
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import numpy as np
import os
import pickle
from utils import read_feature
from DB_reader import read_DB_structure
import config as c
def padding(DB, feature_directory, label_directory, padding_time):
num_of_DB = len(DB)
for i in range(num_of_DB):
feat_path = DB['filename'][i]
output_file = feat_path.split('/')[-1]
output_file_path = os.path.join(feature_directory, output_file)
label_path = DB['label_path'][i]
output_label = label_path.split('/')[-1]
output_label = output_label.split('.')[0] + '.pkl'
output_label_path = os.path.join(label_directory, output_label)
feature, label = read_feature(feat_path, label_path)
start_seg_feat, end_seg_feat = feature[:100], feature[-100:]
start_seg_label, end_seg_label = label[:100], label[-100:]
# Data have already been padded with 1 seconds silence at at both ends of speech.
if padding_time == 0.0:
final_feature = feature[100:-100]
final_label = label[100:-100]
elif padding_time == 2.0:
final_feature = np.concatenate((start_seg_feat, feature, end_seg_feat))
final_label = np.concatenate((start_seg_label, label, end_seg_label))
elif padding_time == 3.0:
final_feature = np.concatenate((start_seg_feat, start_seg_feat, feature, end_seg_feat, end_seg_feat))
final_label = np.concatenate((start_seg_label, start_seg_label, label, end_seg_label, end_seg_label))
else:
raise ValueError
if os.path.isfile(output_file_path):
print('\'' + output_file + '\'' + 'feature already extracted!')
else:
with open(output_file_path, 'wb') as fp:
pickle.dump(final_feature, fp)
print('[Padding] Feature : %s is done!' % output_file)
if os.path.isfile(output_label_path):
print('\'' + output_label + '\'' + 'label already extracted!')
else:
with open(output_label_path, 'wb') as fp:
pickle.dump(final_label, fp)
print('[Padding] Label : %s is done!' % output_label)
# Trimming the data based on extracted label
def Aurora_EPD(DB, feature_directory, label_directory):
num_of_DB = len(DB)
for i in range(num_of_DB):
feat_path = DB['filename'][i]
output_file = feat_path.split('/')[-1]
output_file_path = os.path.join(feature_directory, output_file)
label_path = DB['label_path'][i]
output_label = label_path.split('/')[-1]
output_label = output_label.split('.')[0] + '.pkl'
output_label_path = os.path.join(label_directory, output_label)
feature, label = read_feature(feat_path, label_path)
# Getting start and end points in utterance
start_point = np.where(label == 1)[0][0]
end_point = np.where(label == 1)[0][-1]
epd_feature = feature[start_point:end_point]
epd_label = label[start_point:end_point]
if os.path.isfile(output_file_path):
print('\'' + output_file + '\'' + 'feature already extracted!')
else:
with open(output_file_path, 'wb') as fp:
pickle.dump(epd_feature, fp)
print('[EPD] Feature : %s is done!' % output_file)
if os.path.isfile(output_label_path):
print('\'' + output_label + '\'' + 'label already extracted!')
else:
with open(output_label_path, 'wb') as fp:
pickle.dump(epd_label, fp)
print('[EPD] Label : %s is done!' % output_label)
def main():
padding_time = 0.0
train_feat_dir = os.path.join(c.MFB_DIR + '_' + str(1.0), 'train_folder')
output_feat_dir = os.path.join(c.MFB_DIR + '_' + str(padding_time), 'train_folder')
output_label_dir = os.path.join(c.LABEL_DIR + '_' + str(padding_time), 'train_folder')
if not os.path.exists(output_feat_dir):
os.makedirs(output_feat_dir)
if not os.path.exists(output_label_dir):
os.makedirs(output_label_dir)
train_DB = read_DB_structure(train_feat_dir, 'train')
# -1.0 means EPD
if padding_time == '-1.0':
Aurora_EPD(train_DB, output_feat_dir, output_label_dir)
else:
padding(train_DB, output_feat_dir, output_label_dir, padding_time)
if __name__ == '__main__':
main()