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""" |
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I didn't extract features from the test set of LibriSpeech, the features extracted |
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from train-100 was split into train and test set into two separate folders. |
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This was again done to read them easily using torch vision's Dataset Folder |
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""" |
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import os |
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import shutil |
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from pathlib import Path |
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import numpy as np |
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def assert_out_dir_exists(root, index): |
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dir_ = root + '/' + str(index) |
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if not os.path.exists(dir_): |
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os.makedirs(dir_) |
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print('crated dir {}'.format(dir_)) |
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else: |
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print('dir {} already exists'.format(dir_)) |
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return dir_ |
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def train_test_split(root, test_size=0.05): |
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train_dir = root + '_train' |
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test_dir = root + '_test' |
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os.makedirs(train_dir) |
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os.makedirs(test_dir) |
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for label in os.listdir(root): |
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files_iter = Path(root + '/' + label).glob('**/*.npy') |
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files_ = [str(f) for f in files_iter] |
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files_ = np.array(files_) |
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assert_out_dir_exists(train_dir, label) |
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assert_out_dir_exists(test_dir, label) |
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choices = np.random.choice([0, 1], size=files_.shape[0], p=(1 - test_size, test_size)) |
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train_files = files_[choices == 0] |
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test_files = files_[choices == 1] |
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for train_sample in train_files: |
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src = train_sample |
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dest = train_dir + '/' + label + '/' + train_sample.split('/')[-1] |
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print('copying file {} to {}'.format(src, dest)) |
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shutil.copyfile(train_sample, train_dir + '/' + label + '/' + train_sample.split('/')[-1]) |
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for test_sample in test_files: |
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src = test_sample |
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dest = test_dir + '/' + label + '/' + test_sample.split('/')[-1] |
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print('copying file {} to {}'.format(src, dest)) |
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shutil.copyfile(test_sample, test_dir + '/' + label + '/' + test_sample.split('/')[-1]) |
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print('done for label: {}'.format(label)) |
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print('All done') |
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if __name__ == '__main__': |
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train_test_split('fbanks') |
