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import librosa |
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import numpy as np |
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import torch |
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def f0_to_coarse(f0,hparams): |
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f0_bin = hparams['f0_bin'] |
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f0_max = hparams['f0_max'] |
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f0_min = hparams['f0_min'] |
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is_torch = isinstance(f0, torch.Tensor) |
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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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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(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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