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import numpy as np
import torch
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=None):
if uv is None:
uv = f0 == 0
f0 = np.log2(f0 + uv) # avoid arithmetic error
f0[uv] = -np.inf
return f0
def interp_f0(f0, uv=None):
if uv is None:
uv = f0 == 0
f0 = norm_f0(f0, uv)
if uv.any() and not uv.all():
f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
return denorm_f0(f0, uv=None), uv
def denorm_f0(f0, uv, pitch_padding=None):
f0 = 2 ** f0
if uv is not None:
f0[uv > 0] = 0
if pitch_padding is not None:
f0[pitch_padding] = 0
return f0
def resample_align_curve(points: np.ndarray, original_timestep: float, target_timestep: float, align_length: int):
t_max = (len(points) - 1) * original_timestep
curve_interp = np.interp(
np.arange(0, t_max, target_timestep),
original_timestep * np.arange(len(points)),
points
).astype(points.dtype)
delta_l = align_length - len(curve_interp)
if delta_l < 0:
curve_interp = curve_interp[:align_length]
elif delta_l > 0:
curve_interp = np.concatenate((curve_interp, np.full(delta_l, fill_value=curve_interp[-1])), axis=0)
return curve_interp
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