| from typing import Optional, Union | |
| import numpy as np | |
| import torch | |
| from .f0 import F0Predictor | |
| class FCPE(F0Predictor): | |
| def __init__( | |
| self, | |
| hop_length=512, | |
| f0_min=50, | |
| f0_max=1100, | |
| sampling_rate=44100, | |
| device="cpu", | |
| ): | |
| super().__init__( | |
| hop_length, | |
| f0_min, | |
| f0_max, | |
| sampling_rate, | |
| device, | |
| ) | |
| from torchfcpe import ( | |
| spawn_bundled_infer_model, | |
| ) # must be imported at here, or it will cause fairseq crash on training | |
| self.model = spawn_bundled_infer_model(self.device) | |
| def compute_f0( | |
| self, | |
| wav: np.ndarray, | |
| p_len: Optional[int] = None, | |
| filter_radius: Optional[Union[int, float]] = 0.006, | |
| ): | |
| if p_len is None: | |
| p_len = wav.shape[0] // self.hop_length | |
| if not torch.is_tensor(wav): | |
| wav = torch.from_numpy(wav) | |
| f0 = ( | |
| self.model.infer( | |
| wav.float().to(self.device).unsqueeze(0), | |
| sr=self.sampling_rate, | |
| decoder_mode="local_argmax", | |
| threshold=filter_radius, | |
| ) | |
| .squeeze() | |
| .cpu() | |
| .numpy() | |
| ) | |
| return self._interpolate_f0(self._resize_f0(f0, p_len))[0] | |