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| import librosa
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| import numpy as np
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| import torch
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| gamma = 0
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| mcepInput = 3
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| alpha = 0.45
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| en_floor = 10 ** (-80 / 20)
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| FFT_SIZE = 2048
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| f0_bin = 256
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| f0_max = 1100.0
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| f0_min = 50.0
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| f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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| f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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| def f0_to_coarse(f0):
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| is_torch = isinstance(f0, torch.Tensor)
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| f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
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| f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
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| f0_mel[f0_mel <= 1] = 1
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| f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
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| f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
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| assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
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| return f0_coarse
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| def norm_f0(f0, uv, hparams):
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| is_torch = isinstance(f0, torch.Tensor)
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| if hparams['pitch_norm'] == 'standard':
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| f0 = (f0 - hparams['f0_mean']) / hparams['f0_std']
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| if hparams['pitch_norm'] == 'log':
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| f0 = torch.log2(f0) if is_torch else np.log2(f0)
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| if uv is not None and hparams['use_uv']:
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| f0[uv > 0] = 0
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| return f0
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| def norm_interp_f0(f0, hparams):
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| is_torch = isinstance(f0, torch.Tensor)
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| if is_torch:
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| device = f0.device
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| f0 = f0.data.cpu().numpy()
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| uv = f0 == 0
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| f0 = norm_f0(f0, uv, hparams)
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| if sum(uv) == len(f0):
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| f0[uv] = 0
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| elif sum(uv) > 0:
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| f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
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| uv = torch.FloatTensor(uv)
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| f0 = torch.FloatTensor(f0)
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| if is_torch:
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| f0 = f0.to(device)
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| return f0, uv
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| def denorm_f0(f0, uv, hparams, pitch_padding=None, min=None, max=None):
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| if hparams['pitch_norm'] == 'standard':
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| f0 = f0 * hparams['f0_std'] + hparams['f0_mean']
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| if hparams['pitch_norm'] == 'log':
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| f0 = 2 ** f0
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| if min is not None:
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| f0 = f0.clamp(min=min)
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| if max is not None:
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| f0 = f0.clamp(max=max)
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| if uv is not None and hparams['use_uv']:
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| f0[uv > 0] = 0
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| if pitch_padding is not None:
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| f0[pitch_padding] = 0
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| return f0
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