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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)
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