######### # 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 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 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