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Configuration error
Configuration error
| ######### | |
| # world | |
| ########## | |
| import librosa | |
| import numpy as np | |
| import torch | |
| # gamma = 0 | |
| # mcepInput = 3 # 0 for dB, 3 for magnitude | |
| # alpha = 0.45 | |
| # en_floor = 10 ** (-80 / 20) | |
| # FFT_SIZE = 2048 | |
| def f0_to_coarse(f0,hparams): | |
| f0_bin = hparams['f0_bin'] | |
| f0_max = hparams['f0_max'] | |
| f0_min = hparams['f0_min'] | |
| is_torch = isinstance(f0, torch.Tensor) | |
| f0_mel_min = 1127 * np.log(1 + f0_min / 700) | |
| f0_mel_max = 1127 * np.log(1 + f0_max / 700) | |
| 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()) | |
| return f0_coarse | |
| def norm_f0(f0, uv, hparams): | |
| is_torch = isinstance(f0, torch.Tensor) | |
| if hparams['pitch_norm'] == 'standard': | |
| f0 = (f0 - hparams['f0_mean']) / hparams['f0_std'] | |
| if hparams['pitch_norm'] == 'log': | |
| f0 = torch.log2(f0) if is_torch else np.log2(f0) | |
| if uv is not None and hparams['use_uv']: | |
| f0[uv > 0] = 0 | |
| return f0 | |
| def norm_interp_f0(f0, hparams): | |
| 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, hparams) | |
| 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]) | |
| uv = torch.FloatTensor(uv) | |
| f0 = torch.FloatTensor(f0) | |
| if is_torch: | |
| f0 = f0.to(device) | |
| return f0, uv | |
| def denorm_f0(f0, uv, hparams, pitch_padding=None, min=None, max=None): | |
| if hparams['pitch_norm'] == 'standard': | |
| f0 = f0 * hparams['f0_std'] + hparams['f0_mean'] | |
| if hparams['pitch_norm'] == 'log': | |
| f0 = 2 ** f0 | |
| if min is not None: | |
| f0 = f0.clamp(min=min) | |
| if max is not None: | |
| f0 = f0.clamp(max=max) | |
| if uv is not None and hparams['use_uv']: | |
| f0[uv > 0] = 0 | |
| if pitch_padding is not None: | |
| f0[pitch_padding] = 0 | |
| return f0 | |