| import os | |
| import argparse | |
| import numpy as np | |
| import torch | |
| from scipy.io.wavfile import read | |
| from omegaconf import OmegaConf | |
| MATPLOTLIB_FLAG = False | |
| def load_wav_to_torch(full_path): | |
| sampling_rate, data = read(full_path) | |
| return torch.FloatTensor(data.astype(np.float32)), sampling_rate | |
| f0_bin = 256 | |
| f0_max = 1100.0 | |
| f0_min = 50.0 | |
| f0_mel_min = 1127 * np.log(1 + f0_min / 700) | |
| f0_mel_max = 1127 * np.log(1 + f0_max / 700) | |
| def f0_to_coarse(f0): | |
| is_torch = isinstance(f0, torch.Tensor) | |
| f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * \ | |
| np.log(1 + f0 / 700) | |
| f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * \ | |
| (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1 | |
| f0_mel[f0_mel <= 1] = 1 | |
| f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1 | |
| f0_coarse = ( | |
| f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int) | |
| assert f0_coarse.max() <= 255 and f0_coarse.min( | |
| ) >= 1, (f0_coarse.max(), f0_coarse.min()) | |
| return f0_coarse | |
| def get_hparams(init=True): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('-c', '--config', type=str, default="./configs/base.yaml", | |
| help='YAML file for configuration') | |
| args = parser.parse_args() | |
| hparams = OmegaConf.load(args.config) | |
| model_dir = os.path.join("./logs", hparams.train.model) | |
| if not os.path.exists(model_dir): | |
| os.makedirs(model_dir) | |
| config_save_path = os.path.join(model_dir, "config.json") | |
| os.system(f"cp {args.config} {config_save_path}") | |
| hparams.model_dir = model_dir | |
| return hparams | |