chore(format): run black on dev (#19)

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

infer/modules/train/train.py

@@ -304,7 +304,17 @@ def run(rank, n_gpus, hps, logger: logging.Logger):
 
 
 def train_and_evaluate(
-    rank, epoch, hps, nets: Tuple[RVC_Model_f0, MultiPeriodDiscriminator], optims, schedulers, scaler, loaders, logger, writers, cache
+    rank,
+    epoch,
+    hps,
+    nets: Tuple[RVC_Model_f0, MultiPeriodDiscriminator],
+    optims,
+    schedulers,
+    scaler,
+    loaders,
+    logger,
+    writers,
+    cache,
 ):
     net_g, net_d = nets
     optim_g, optim_d = optims

rvc/discriminators.py

@@ -14,21 +14,41 @@ class MultiPeriodDiscriminator(torch.nn.Module):
     """
     version: 'v1' or 'v2'
     """
-    def __init__(self, version: str, use_spectral_norm: bool = False, has_xpu: bool = False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = (2, 3, 5, 7, 11, 17) if version == "v1" else (2, 3, 5, 7, 11, 17, 23, 37)
 
-        self.discriminators = nn.ModuleList([
+    def __init__(
+        self, version: str, use_spectral_norm: bool = False, has_xpu: bool = False
+    ):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = (
+            (2, 3, 5, 7, 11, 17) if version == "v1" else (2, 3, 5, 7, 11, 17, 23, 37)
+        )
+
+        self.discriminators = nn.ModuleList(
+            [
                 DiscriminatorS(use_spectral_norm=use_spectral_norm),
                 *(
-                DiscriminatorP(i, use_spectral_norm=use_spectral_norm, has_xpu=has_xpu) for i in periods
+                    DiscriminatorP(
+                        i, use_spectral_norm=use_spectral_norm, has_xpu=has_xpu
+                    )
+                    for i in periods
+                ),
+            ]
         )
-        ])
 
-    def __call__(self, y: torch.Tensor, y_hat: torch.Tensor) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[List[torch.Tensor]], List[List[torch.Tensor]]]:
+    def __call__(self, y: torch.Tensor, y_hat: torch.Tensor) -> Tuple[
+        List[torch.Tensor],
+        List[torch.Tensor],
+        List[List[torch.Tensor]],
+        List[List[torch.Tensor]],
+    ]:
         return super().__call__(y, y_hat)
 
-    def forward(self, y: torch.Tensor, y_hat: torch.Tensor) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[List[torch.Tensor]], List[List[torch.Tensor]]]:
+    def forward(self, y: torch.Tensor, y_hat: torch.Tensor) -> Tuple[
+        List[torch.Tensor],
+        List[torch.Tensor],
+        List[List[torch.Tensor]],
+        List[List[torch.Tensor]],
+    ]:
         y_d_rs = []
         y_d_gs = []
         fmap_rs = []
@@ -98,7 +118,8 @@ class DiscriminatorP(torch.nn.Module):
 
         self.convs = nn.ModuleList()
         for i in range(len(sequence) - 1):
-            self.convs.append(norm_f(
+            self.convs.append(
+                norm_f(
                     Conv2d(
                         sequence[i],
                         sequence[i + 1],
@@ -106,8 +127,10 @@ class DiscriminatorP(torch.nn.Module):
                         (stride, 1),
                         padding=convs_padding,
                     )
-            ))
+                )
-        self.convs.append(norm_f(
+            )
+        self.convs.append(
+            norm_f(
                 Conv2d(
                     1024,
                     1024,
@@ -115,7 +138,8 @@ class DiscriminatorP(torch.nn.Module):
                     1,
                     padding=convs_padding,
                 )
-        ))
+            )
+        )
         self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
 
     def __call__(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]: