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from typing import Any, Optional, Union
import numpy as np
import torch
import torchcrepe
from .f0 import F0Predictor
class CRePE(F0Predictor):
def __init__(
self,
hop_length=512,
f0_min=50,
f0_max=1100,
sampling_rate=44100,
device="cpu",
):
if "privateuseone" in str(device):
device = "cpu"
super().__init__(
hop_length,
f0_min,
f0_max,
sampling_rate,
device,
)
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
if not torch.is_tensor(wav):
wav = torch.from_numpy(wav)
# Pick a batch size that doesn't cause memory errors on your gpu
batch_size = 512
# Compute pitch using device 'device'
f0, pd = torchcrepe.predict(
wav.float().to(self.device).unsqueeze(dim=0),
self.sampling_rate,
self.hop_length,
self.f0_min,
self.f0_max,
batch_size=batch_size,
device=self.device,
return_periodicity=True,
)
pd = torchcrepe.filter.median(pd, 3)
f0 = torchcrepe.filter.mean(f0, 3)
f0[pd < 0.1] = 0
f0 = f0[0].cpu().numpy()
return self._interpolate_f0(self._resize_f0(f0, p_len))[0]
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