chore(format): run black on dev (#3)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

rvc/onnx/f0predictor/dio.py

@@ -9,7 +9,9 @@ class DioF0Predictor(F0Predictor):
     def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
         super().__init__(hop_length, f0_min, f0_max, sampling_rate)
 
-    def compute_f0(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None):
+    def compute_f0(
+        self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
+    ):
         if p_len is None:
             p_len = wav.shape[0] // self.hop_length
         f0, t = pyworld.dio(
@@ -24,7 +26,9 @@ class DioF0Predictor(F0Predictor):
             f0[index] = round(pitch, 1)
         return self.__interpolate_f0(self.__resize_f0(f0, p_len))[0]
 
-    def compute_f0_uv(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None):
+    def compute_f0_uv(
+        self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
+    ):
         if p_len is None:
             p_len = wav.shape[0] // self.hop_length
         f0, t = pyworld.dio(

rvc/onnx/f0predictor/f0.py

@@ -1,6 +1,7 @@
 import numpy as np
 import typing
 
+
 class F0Predictor(object):
     def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
         self.hop_length = hop_length
@@ -8,9 +9,13 @@ class F0Predictor(object):
         self.f0_max = f0_max
         self.sampling_rate = sampling_rate
 
-    def compute_f0(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None): ...
+    def compute_f0(
+        self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
+    ): ...
 
-    def compute_f0_uv(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None): ...
+    def compute_f0_uv(
+        self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
+    ): ...
 
     def __interpolate_f0(self, f0: np.ndarray[typing.Any, np.dtype]):
         """

rvc/onnx/f0predictor/harvest.py

@@ -9,7 +9,9 @@ class HarvestF0Predictor(F0Predictor):
     def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
         super().__init__(hop_length, f0_min, f0_max, sampling_rate)
 
-    def compute_f0(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None):
+    def compute_f0(
+        self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
+    ):
         if p_len is None:
             p_len = wav.shape[0] // self.hop_length
         f0, t = pyworld.harvest(
@@ -22,7 +24,9 @@ class HarvestF0Predictor(F0Predictor):
         f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.fs)
         return self.__interpolate_f0(self.__resize_f0(f0, p_len))[0]
 
-    def compute_f0_uv(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None):
+    def compute_f0_uv(
+        self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
+    ):
         if p_len is None:
             p_len = wav.shape[0] // self.hop_length
         f0, t = pyworld.harvest(

rvc/onnx/f0predictor/pm.py

@@ -9,7 +9,9 @@ class PMF0Predictor(F0Predictor):
     def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
         super().__init__(hop_length, f0_min, f0_max, sampling_rate)
 
-    def compute_f0(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None):
+    def compute_f0(
+        self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
+    ):
         x = wav
         if p_len is None:
             p_len = x.shape[0] // self.hop_length
@@ -33,7 +35,9 @@ class PMF0Predictor(F0Predictor):
         f0, uv = self.__interpolate_f0(f0)
         return f0
 
-    def compute_f0_uv(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None):
+    def compute_f0_uv(
+        self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
+    ):
         x = wav
         if p_len is None:
             p_len = x.shape[0] // self.hop_length

rvc/onnx/infer.py

@@ -4,11 +4,20 @@ import onnxruntime
 import typing
 import os
 
-from onnx.f0predictor import PMF0Predictor, HarvestF0Predictor, DioF0Predictor, F0Predictor
+from onnx.f0predictor import (
+    PMF0Predictor,
+    HarvestF0Predictor,
+    DioF0Predictor,
+    F0Predictor,
+)
 
 
 class Model:
-    def __init__(self, path: str | bytes | os.PathLike, device: typing.Literal["cpu", "cuda", "dml"]="cpu"):
+    def __init__(
+        self,
+        path: str | bytes | os.PathLike,
+        device: typing.Literal["cpu", "cuda", "dml"] = "cpu",
+    ):
         if device == "cpu":
             providers = ["CPUExecutionProvider"]
         elif device == "cuda":
@@ -19,8 +28,13 @@ class Model:
             raise RuntimeError("Unsportted Device")
         self.model = onnxruntime.InferenceSession(path, providers=providers)
 
+
 class ContentVec(Model):
-    def __init__(self, vec_path: str | bytes | os.PathLike, device: typing.Literal["cpu", "cuda", "dml"]="cpu"):
+    def __init__(
+        self,
+        vec_path: str | bytes | os.PathLike,
+        device: typing.Literal["cpu", "cuda", "dml"] = "cpu",
+    ):
         super().__init__(vec_path, device)
 
     def __call__(self, wav: np.ndarray[typing.Any, np.dtype]):
@@ -43,7 +57,9 @@ predictors: typing.Dict[str, F0Predictor] = {
 }
 
 
-def get_f0_predictor(f0_method: str, hop_length: int, sampling_rate: int) -> F0Predictor:
+def get_f0_predictor(
+    f0_method: str, hop_length: int, sampling_rate: int
+) -> F0Predictor:
     return predictors[f0_method](hop_length=hop_length, sampling_rate=sampling_rate)
 
 
@@ -107,7 +123,9 @@ class RVC(Model):
         rnd = np.random.randn(1, 192, hubert_length).astype(np.float32)
         hubert_length = np.array([hubert_length]).astype(np.int64)
 
-        out_wav = self.__forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze()
+        out_wav = self.__forward(
+            hubert, hubert_length, pitch, pitchf, ds, rnd
+        ).squeeze()
         out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant")
         return out_wav[0:org_length]