| import numpy as np | |
| import torch | |
| def to_lf0(f0): | |
| f0[f0 < 1.0e-5] = 1.0e-6 | |
| lf0 = f0.log() if isinstance(f0, torch.Tensor) else np.log(f0) | |
| lf0[f0 < 1.0e-5] = - 1.0E+10 | |
| return lf0 | |
| def to_f0(lf0): | |
| f0 = np.where(lf0 <= 0, 0.0, np.exp(lf0)) | |
| return f0.flatten() | |
| def f0_to_coarse(f0, f0_bin=256, f0_max=900.0, f0_min=50.0): | |
| f0_mel_min = 1127 * np.log(1 + f0_min / 700) | |
| f0_mel_max = 1127 * np.log(1 + f0_max / 700) | |
| 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(int) | |
| assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min(), f0.min(), f0.max()) | |
| return f0_coarse | |
| def coarse_to_f0(f0_coarse, f0_bin=256, f0_max=900.0, f0_min=50.0): | |
| f0_mel_min = 1127 * np.log(1 + f0_min / 700) | |
| f0_mel_max = 1127 * np.log(1 + f0_max / 700) | |
| uv = f0_coarse == 1 | |
| f0 = f0_mel_min + (f0_coarse - 1) * (f0_mel_max - f0_mel_min) / (f0_bin - 2) | |
| f0 = ((f0 / 1127).exp() - 1) * 700 | |
| f0[uv] = 0 | |
| return f0 | |
| def norm_f0(f0, uv, pitch_norm='log', f0_mean=400, f0_std=100): | |
| is_torch = isinstance(f0, torch.Tensor) | |
| if pitch_norm == 'standard': | |
| f0 = (f0 - f0_mean) / f0_std | |
| if pitch_norm == 'log': | |
| f0 = torch.log2(f0 + 1e-8) if is_torch else np.log2(f0 + 1e-8) | |
| if uv is not None: | |
| f0[uv > 0] = 0 | |
| return f0 | |
| def norm_interp_f0(f0, pitch_norm='log', f0_mean=None, f0_std=None): | |
| is_torch = isinstance(f0, torch.Tensor) | |
| if is_torch: | |
| device = f0.device | |
| f0 = f0.data.cpu().numpy() | |
| uv = f0 == 0 | |
| f0 = norm_f0(f0, uv, pitch_norm, f0_mean, f0_std) | |
| if sum(uv) == len(f0): | |
| f0[uv] = 0 | |
| elif sum(uv) > 0: | |
| f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv]) | |
| if is_torch: | |
| uv = torch.FloatTensor(uv) | |
| f0 = torch.FloatTensor(f0) | |
| f0 = f0.to(device) | |
| uv = uv.to(device) | |
| return f0, uv | |
| def denorm_f0(f0, uv, pitch_norm='log', f0_mean=400, f0_std=100, pitch_padding=None, min=50, max=900): | |
| is_torch = isinstance(f0, torch.Tensor) | |
| if pitch_norm == 'standard': | |
| f0 = f0 * f0_std + f0_mean | |
| if pitch_norm == 'log': | |
| f0 = 2 ** f0 | |
| f0 = f0.clamp(min=min, max=max) if is_torch else np.clip(f0, a_min=min, a_max=max) | |
| if uv is not None: | |
| f0[uv > 0] = 0 | |
| if pitch_padding is not None: | |
| f0[pitch_padding] = 0 | |
| return f0 | |