chore(format): run black on dev (#11)

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

infer/lib/infer_pack/models.py

@@ -355,9 +355,7 @@ class GeneratorNSF(torch.nn.Module):
         self.num_upsamples = len(upsample_rates)
 
         self.f0_upsamp = torch.nn.Upsample(scale_factor=math.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0
-        )
+        self.m_source = SourceModuleHnNSF(sampling_rate=sr, harmonic_num=0)
         self.noise_convs = nn.ModuleList()
         self.conv_pre = Conv1d(
             initial_channel, upsample_initial_channel, 7, 1, padding=3

rvc/norms.py

@@ -6,6 +6,7 @@ from torch.nn import functional as F
 
 from .utils import activate_add_tanh_sigmoid_multiply
 
+
 class LayerNorm(nn.Module):
     def __init__(self, channels: int, eps: float = 1e-5):
         super(LayerNorm, self).__init__()
@@ -20,6 +21,7 @@ class LayerNorm(nn.Module):
         x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
         return x.transpose(1, -1)
 
+
 class WN(torch.nn.Module):
     def __init__(
         self,

rvc/onnx/f0predictors/dio.py

@@ -10,9 +10,7 @@ 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[Any, np.dtype], p_len: Optional[int] = None
-    ):
+    def compute_f0(self, wav: np.ndarray[Any, np.dtype], p_len: Optional[int] = None):
         if p_len is None:
             p_len = wav.shape[0] // self.hop_length
         f0, t = pyworld.dio(

rvc/onnx/f0predictors/harvest.py

@@ -10,9 +10,7 @@ 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[Any, np.dtype], p_len: Optional[int] = None
-    ):
+    def compute_f0(self, wav: np.ndarray[Any, np.dtype], p_len: Optional[int] = None):
         if p_len is None:
             p_len = wav.shape[0] // self.hop_length
         f0, t = pyworld.harvest(

rvc/onnx/f0predictors/pm.py

@@ -10,9 +10,7 @@ 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[Any, np.dtype], p_len: Optional[int] = None
-    ):
+    def compute_f0(self, wav: np.ndarray[Any, np.dtype], p_len: Optional[int] = None):
         x = wav
         if p_len is None:
             p_len = x.shape[0] // self.hop_length

rvc/residuals.py

@@ -14,6 +14,7 @@ from .utils import (
 
 LRELU_SLOPE = 0.1
 
+
 class ResBlock1(torch.nn.Module):
     def __init__(
         self,
@@ -111,6 +112,7 @@ class ResBlock2(torch.nn.Module):
     Actually this module is not used currently
     because all configs specified "resblock": "1"
     """
+
     def __init__(
         self,
         channels: int,
@@ -255,6 +257,7 @@ class ResidualCouplingLayer(nn.Module):
                 torch.nn.utils.remove_weight_norm(self.enc)
         return self
 
+
 class ResidualCouplingBlock(nn.Module):
     class Flip(nn.Module):
         """
@@ -262,6 +265,7 @@ class ResidualCouplingBlock(nn.Module):
         can't take variable number of arguments or
         use keyword-only arguments with defaults
         """
+
         def forward(
             self,
             x: torch.Tensor,