| from typing import Any, Optional, Union | |
| import numpy as np | |
| import pyworld | |
| from scipy import signal | |
| from .f0 import F0Predictor | |
| class Harvest(F0Predictor): | |
| def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100): | |
| super().__init__(hop_length, f0_min, f0_max, sampling_rate) | |
| def compute_f0( | |
| self, | |
| wav: np.ndarray, | |
| p_len: Optional[int] = None, | |
| filter_radius: Optional[Union[int, float]] = None, | |
| ): | |
| if p_len is None: | |
| p_len = wav.shape[0] // self.hop_length | |
| f0, t = pyworld.harvest( | |
| wav.astype(np.double), | |
| fs=self.sampling_rate, | |
| f0_ceil=self.f0_max, | |
| f0_floor=self.f0_min, | |
| frame_period=1000 * self.hop_length / self.sampling_rate, | |
| ) | |
| f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate) | |
| if filter_radius is not None and filter_radius > 2: | |
| f0 = signal.medfilt(f0, filter_radius) | |
| return self._interpolate_f0(self._resize_f0(f0, p_len))[0] | |