| |
| |
| |
| import librosa |
| import numpy as np |
| import torch |
|
|
| gamma = 0 |
| mcepInput = 3 |
| 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 |
|
|
