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| ''' | |
| Author: wuxulong19950206 [email protected] | |
| Date: 2024-03-12 20:23:34 | |
| LastEditors: wuxulong19950206 [email protected] | |
| LastEditTime: 2024-03-12 21:44:46 | |
| FilePath: \mandarin-tts-mtts\wav2mel.py | |
| Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE | |
| ''' | |
| import os | |
| import glob | |
| import tqdm | |
| import torch | |
| import argparse | |
| import numpy as np | |
| from mtts.utils.stft import TacotronSTFT | |
| from scipy.io.wavfile import read | |
| from mtts.utils.logging import get_logger | |
| import librosa | |
| import yaml | |
| logger = get_logger(__file__) | |
| def read_wav_np(path): | |
| sr, wav = read(path) | |
| if len(wav.shape) == 2: | |
| wav = wav[:, 0] | |
| if wav.dtype == np.int16: | |
| wav = wav / 32768.0 | |
| elif wav.dtype == np.int32: | |
| wav = wav / 2147483648.0 | |
| elif wav.dtype == np.uint8: | |
| wav = (wav - 128) / 128.0 | |
| wav = wav.astype(np.float32) | |
| return sr, wav | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('-c', '--config', type=str, required=True, | |
| help="yaml file for config.") | |
| parser.add_argument('-w', '--wav_path', type=str, required=True, | |
| help="root directory of wav files") | |
| parser.add_argument('-m', '--mel_path', type=str, required=True, | |
| help="root directory of mel files") | |
| parser.add_argument('-d', '--device', type=str, required=False, | |
| help="device, cpu or cuda:0, cuda:1,...") | |
| parser.add_argument('-r', '--resample_mode', type=str, required=False,default='kaiser_fast', | |
| help="use kaiser_best for high-quality audio") | |
| args = parser.parse_args() | |
| logger.info(f'using resample mode {args.resample_mode}') | |
| with open(args.config) as f: | |
| config = yaml.safe_load(f) | |
| logger.info('loading TacotronSTFT') | |
| stft = TacotronSTFT(filter_length=config['fbank']['n_fft'], | |
| hop_length=config['fbank']['hop_length'], | |
| win_length=config['fbank']['win_length'], | |
| n_mel_channels=config['fbank']['n_mels'], | |
| sampling_rate=config['fbank']['sample_rate'], | |
| mel_fmin=config['fbank']['fmin'], | |
| mel_fmax=config['fbank']['fmax'], | |
| device=args.device) | |
| logger.info('done') | |
| wav_files = glob.glob(os.path.join(args.wav_path, '*.wav'),recursive=False) | |
| logger.info(f'{len(wav_files)} found in {args.wav_path}') | |
| mel_path = args.mel_path | |
| logger.info(f'mel files will be saved to {mel_path}') | |
| # Create all folders | |
| os.makedirs(mel_path, exist_ok=True) | |
| for wavpath in tqdm.tqdm(wav_files, desc='preprocess wav to mel'): | |
| wav,r = librosa.load(wavpath,sr=config['fbank']['sample_rate'],res_type=args.resample_mode) | |
| wav = torch.from_numpy(wav).unsqueeze(0) | |
| mel = stft.mel_spectrogram(wav) # mel [1, num_mel, T] | |
| mel = mel.squeeze(0) # [num_mel, T] | |
| id = os.path.basename(wavpath).split(".")[0] | |
| np.save('{}/{}.npy'.format(mel_path, id), mel.numpy(), allow_pickle=False) | |