File size: 1,394 Bytes
7ff2ba3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
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]
|