mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-08 12:00:49 +08:00
chore(format): run black on dev (#3)
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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c94f3f6748
@@ -9,7 +9,9 @@ class DioF0Predictor(F0Predictor):
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def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
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super().__init__(hop_length, f0_min, f0_max, sampling_rate)
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def compute_f0(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None):
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def compute_f0(
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self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
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):
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if p_len is None:
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p_len = wav.shape[0] // self.hop_length
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f0, t = pyworld.dio(
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@@ -24,7 +26,9 @@ class DioF0Predictor(F0Predictor):
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f0[index] = round(pitch, 1)
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return self.__interpolate_f0(self.__resize_f0(f0, p_len))[0]
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def compute_f0_uv(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None):
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def compute_f0_uv(
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self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
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):
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if p_len is None:
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p_len = wav.shape[0] // self.hop_length
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f0, t = pyworld.dio(
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@@ -1,6 +1,7 @@
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import numpy as np
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import typing
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class F0Predictor(object):
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def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
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self.hop_length = hop_length
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@@ -8,9 +9,13 @@ class F0Predictor(object):
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self.f0_max = f0_max
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self.sampling_rate = sampling_rate
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def compute_f0(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None): ...
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def compute_f0(
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self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
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): ...
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def compute_f0_uv(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None): ...
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def compute_f0_uv(
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self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
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): ...
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def __interpolate_f0(self, f0: np.ndarray[typing.Any, np.dtype]):
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"""
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@@ -49,7 +54,7 @@ class F0Predictor(object):
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last_value = data[i]
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return ip_data[:, 0], vuv_vector[:, 0]
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def __resize_f0(self, x: np.ndarray[typing.Any, np.dtype], target_len: int):
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source = np.array(x)
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source[source < 0.001] = np.nan
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@@ -9,7 +9,9 @@ class HarvestF0Predictor(F0Predictor):
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def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
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super().__init__(hop_length, f0_min, f0_max, sampling_rate)
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def compute_f0(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None):
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def compute_f0(
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self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
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):
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if p_len is None:
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p_len = wav.shape[0] // self.hop_length
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f0, t = pyworld.harvest(
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@@ -22,7 +24,9 @@ class HarvestF0Predictor(F0Predictor):
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f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.fs)
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return self.__interpolate_f0(self.__resize_f0(f0, p_len))[0]
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def compute_f0_uv(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None):
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def compute_f0_uv(
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self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
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):
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if p_len is None:
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p_len = wav.shape[0] // self.hop_length
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f0, t = pyworld.harvest(
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@@ -9,7 +9,9 @@ class PMF0Predictor(F0Predictor):
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def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
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super().__init__(hop_length, f0_min, f0_max, sampling_rate)
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def compute_f0(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None):
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def compute_f0(
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self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
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):
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x = wav
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if p_len is None:
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p_len = x.shape[0] // self.hop_length
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@@ -33,7 +35,9 @@ class PMF0Predictor(F0Predictor):
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f0, uv = self.__interpolate_f0(f0)
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return f0
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def compute_f0_uv(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None):
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def compute_f0_uv(
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self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
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):
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x = wav
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if p_len is None:
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p_len = x.shape[0] // self.hop_length
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