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| # Copyright (c) 2022 NVIDIA CORPORATION. | |
| # Licensed under the MIT license. | |
| import os, glob | |
| def get_wav_and_text_filelist(data_root, data_type, subsample=1): | |
| wav_list = sorted([path.replace(data_root, "")[1:] for path in glob.glob(os.path.join(data_root, data_type, "**/**/*.wav"))]) | |
| wav_list = wav_list[::subsample] | |
| txt_filelist = [path.replace('.wav', '.normalized.txt') for path in wav_list] | |
| txt_list = [] | |
| for txt_file in txt_filelist: | |
| with open(os.path.join(data_root, txt_file), 'r') as f_txt: | |
| text = f_txt.readline().strip('\n') | |
| txt_list.append(text) | |
| wav_list = [path.replace('.wav', '') for path in wav_list] | |
| return wav_list, txt_list | |
| def write_filelist(output_path, wav_list, txt_list): | |
| with open(output_path, 'w') as f: | |
| for i in range(len(wav_list)): | |
| filename = wav_list[i] + '|' + txt_list[i] | |
| f.write(filename + '\n') | |
| if __name__ == "__main__": | |
| data_root = "LibriTTS" | |
| # dev and test sets. subsample each sets to get ~100 utterances | |
| data_type_list = ["dev-clean", "dev-other", "test-clean", "test-other"] | |
| subsample_list = [50, 50, 50, 50] | |
| for (data_type, subsample) in zip(data_type_list, subsample_list): | |
| print("processing {}".format(data_type)) | |
| data_path = os.path.join(data_root, data_type) | |
| assert os.path.exists(data_path),\ | |
| "path {} not found. make sure the path is accessible by creating the symbolic link using the following command: "\ | |
| "ln -s /path/to/your/{} {}".format(data_path, data_path, data_path) | |
| wav_list, txt_list = get_wav_and_text_filelist(data_root, data_type, subsample) | |
| write_filelist(os.path.join(data_root, data_type+".txt"), wav_list, txt_list) | |
| # training and seen speaker validation datasets (libritts-full): train-clean-100 + train-clean-360 + train-other-500 | |
| wav_list_train, txt_list_train = [], [] | |
| for data_type in ["train-clean-100", "train-clean-360", "train-other-500"]: | |
| print("processing {}".format(data_type)) | |
| data_path = os.path.join(data_root, data_type) | |
| assert os.path.exists(data_path),\ | |
| "path {} not found. make sure the path is accessible by creating the symbolic link using the following command: "\ | |
| "ln -s /path/to/your/{} {}".format(data_path, data_path, data_path) | |
| wav_list, txt_list = get_wav_and_text_filelist(data_root, data_type) | |
| wav_list_train.extend(wav_list) | |
| txt_list_train.extend(txt_list) | |
| # split the training set so that the seen speaker validation set contains ~100 utterances | |
| subsample_val = 3000 | |
| wav_list_val, txt_list_val = wav_list_train[::subsample_val], txt_list_train[::subsample_val] | |
| del wav_list_train[::subsample_val] | |
| del txt_list_train[::subsample_val] | |
| write_filelist(os.path.join(data_root, "train-full.txt"), wav_list_train, txt_list_train) | |
| write_filelist(os.path.join(data_root, "val-full.txt"), wav_list_val, txt_list_val) | |
| print("done") |