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from typing import Optional, Union
import torch
import numpy as np
class F0Predictor(object):
def __init__(
self,
hop_length=512,
f0_min=50,
f0_max=1100,
sampling_rate=44100,
device: Optional[str] = None,
):
self.hop_length = hop_length
self.f0_min = f0_min
self.f0_max = f0_max
self.sampling_rate = sampling_rate
if device is None:
device = "cuda:0" if torch.cuda.is_available() else "cpu"
self.device = device
def compute_f0(
self,
wav: np.ndarray,
p_len: Optional[int] = None,
filter_radius: Optional[Union[int, float]] = None,
): ...
def _interpolate_f0(self, f0: np.ndarray):
"""
对F0进行插值处理
"""
data = np.reshape(f0, (f0.size, 1))
vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
vuv_vector[data > 0.0] = 1.0
vuv_vector[data <= 0.0] = 0.0
ip_data = data
frame_number = data.size
last_value = 0.0
for i in range(frame_number):
if data[i] <= 0.0:
j = i + 1
for j in range(i + 1, frame_number):
if data[j] > 0.0:
break
if j < frame_number - 1:
if last_value > 0.0:
step = (data[j] - data[i - 1]) / float(j - i)
for k in range(i, j):
ip_data[k] = data[i - 1] + step * (k - i + 1)
else:
for k in range(i, j):
ip_data[k] = data[j]
else:
for k in range(i, frame_number):
ip_data[k] = last_value
else:
ip_data[i] = data[i] # 这里可能存在一个没有必要的拷贝
last_value = data[i]
return ip_data[:, 0], vuv_vector[:, 0]
def _resize_f0(self, x: np.ndarray, target_len: int):
source = np.array(x)
source[source < 0.001] = np.nan
target = np.interp(
np.arange(0, len(source) * target_len, len(source)) / target_len,
np.arange(0, len(source)),
source,
)
res = np.nan_to_num(target)
return res
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