Merge pull request #743 from IAHispano/formatter/main

chore(format): run black on main

rvc/lib/algorithm/commons.py

@@ -156,6 +156,7 @@ def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
     acts = t_act * s_act
     return acts
 
+
 def convert_pad_shape(pad_shape: List[List[int]]) -> List[int]:
     """
     Convert the pad shape to a list of integers.

rvc/lib/algorithm/modules.py

@@ -1,6 +1,7 @@
 import torch
 from rvc.lib.algorithm.commons import fused_add_tanh_sigmoid_multiply
 
+
 class WaveNet(torch.nn.Module):
     """WaveNet residual blocks as used in WaveGlow
 

rvc/lib/zluda.py

@@ -1,16 +1,18 @@
 import torch
+
 if torch.cuda.is_available() and torch.cuda.get_device_name().endswith("[ZLUDA]"):
     _torch_stft = torch.stft
+
     def z_stft(
         audio: torch.Tensor,
         n_fft: int,
-        hop_length: int = None, 
-        win_length: int = None, 
-        window: torch.Tensor = None, 
-        center: bool = True, 
-        pad_mode: str = "reflect", 
-        normalized: bool = False, 
-        onesided: bool = None, 
+        hop_length: int = None,
+        win_length: int = None,
+        window: torch.Tensor = None,
+        center: bool = True,
+        pad_mode: str = "reflect",
+        normalized: bool = False,
+        onesided: bool = None,
         return_complex: bool = None,
     ):
         sd = audio.device
@@ -26,16 +28,16 @@ def z_stft(
             onesided=onesided,
             return_complex=return_complex,
         ).to(sd)
-        
+
     def z_jit(f, *_, **__):
         f.graph = torch._C.Graph()
         return f
-    
+
     # hijacks
     torch.stft = z_stft
     torch.jit.script = z_jit
     # disabling unsupported cudnn
     torch.backends.cudnn.enabled = False
     torch.backends.cuda.enable_flash_sdp(False)
     torch.backends.cuda.enable_math_sdp(True)
-    torch.backends.cuda.enable_mem_efficient_sdp(False)
+    torch.backends.cuda.enable_mem_efficient_sdp(False)

rvc/train/train.py

@@ -1024,6 +1024,7 @@ def save_to_json(
     with open(file_path, "w") as f:
         json.dump(data, f)
 
+
 if __name__ == "__main__":
     torch.multiprocessing.set_start_method("spawn")
     main()