| import os | |
| import numpy as np | |
| filename = 'E:/uma_voice/output.txt' | |
| split ='|' | |
| with open(filename, encoding='utf-8') as f: | |
| filepaths_and_text = [line.strip().split(split) for line in f] | |
| train_filename = filename.split('.')[0] + '_train' + '.txt' | |
| val_filename = filename.split('.')[0] + '_val' + '.txt' | |
| train_split_ratio = 0.99 | |
| train_f = open(train_filename, 'w', encoding='utf-8') | |
| val_f = open(val_filename, 'w', encoding='utf-8') | |
| for i in range(len(filepaths_and_text)): | |
| if np.random.rand() < train_split_ratio: | |
| train_f.writelines('|'.join(filepaths_and_text[i]) + '\n') | |
| else: | |
| val_f.writelines('|'.join(filepaths_and_text[i]) + '\n') |