chore(format): run black on dev (#13)

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

infer/lib/infer_pack/models.py

@@ -309,7 +309,7 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
         upsample_kernel_sizes: List[int],
         spk_embed_dim: int,
         gin_channels: int,
-        sr = None,
+        sr=None,
     ):
         super(SynthesizerTrnMs256NSFsid_nono, self).__init__()
         self.spec_channels = spec_channels
@@ -464,7 +464,7 @@ class SynthesizerTrnMs768NSFsid_nono(SynthesizerTrnMs256NSFsid_nono):
         upsample_kernel_sizes: List[int],
         spk_embed_dim: int,
         gin_channels: int,
-        sr = None,
+        sr=None,
     ):
         super(SynthesizerTrnMs768NSFsid_nono, self).__init__(
             spec_channels,

infer/lib/train/utils.py

@@ -256,7 +256,7 @@ def load_filepaths_and_text(filename, split="|"):
     except UnicodeDecodeError:
         with open(filename) as f:
             filepaths_and_text = [line.strip().split(split) for line in f]
-    
+
     return filepaths_and_text
 
 

rvc/attentions.py

@@ -229,6 +229,7 @@ class FFN(nn.Module):
     """
     Feed-Forward Network
     """
+
     def __init__(
         self,
         in_channels: int,

rvc/encoders.py

@@ -161,6 +161,7 @@ class TextEncoder(nn.Module):
         m, logs = torch.split(stats, self.out_channels, dim=1)
         return m, logs, x_mask
 
+
 class PosteriorEncoder(nn.Module):
     def __init__(
         self,

rvc/generators.py

@@ -9,6 +9,7 @@ from torch.nn.utils import remove_weight_norm, weight_norm
 from .residuals import ResBlock1, ResBlock2, LRELU_SLOPE
 from .utils import call_weight_data_normal_if_Conv
 
+
 class Generator(torch.nn.Module):
     def __init__(
         self,
@@ -156,11 +157,15 @@ class SineGenerator(torch.nn.Module):
         self.dim = harmonic_num + 1
         self.sampling_rate = samp_rate
         self.voiced_threshold = voiced_threshold
-    
+
-    def __call__(self, f0: torch.Tensor, upp: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    def __call__(
+        self, f0: torch.Tensor, upp: int
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         return super().__call__(f0, upp)
 
-    def forward(self, f0: torch.Tensor, upp: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    def forward(
+        self, f0: torch.Tensor, upp: int
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """sine_tensor, uv = forward(f0)
         input F0: tensor(batchsize=1, length, dim=1)
                   f0 for unvoiced steps should be 0
@@ -190,7 +195,7 @@ class SineGenerator(torch.nn.Module):
             tmp_over_one *= upp
             tmp_over_one: torch.Tensor = F.interpolate(
                 tmp_over_one.transpose(2, 1),
-                scale_factor = float(upp),
+                scale_factor=float(upp),
                 mode="linear",
                 align_corners=True,
             ).transpose(2, 1)

rvc/nsf.py

@@ -57,7 +57,8 @@ class SourceModuleHnNSF(torch.nn.Module):
         sine_wavs, _, _ = self.l_sin_gen(x, upp)
         sine_wavs = sine_wavs.to(dtype=self.l_linear.weight.dtype)
         sine_merge: torch.Tensor = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge #, None, None  # noise, uv
+        return sine_merge  # , None, None  # noise, uv
+
 
 class NSFGenerator(torch.nn.Module):
     def __init__(