chore(format): run black on dev (#23)

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

infer/lib/infer_pack/models.py

@@ -212,7 +212,7 @@ class SynthesizerTrnMsNSFsid(nn.Module):
                 # n_res=return_length2
             )
         del x_mask, z
-        return o # , x_mask, (z, z_p, m_p, logs_p)
+        return o  # , x_mask, (z, z_p, m_p, logs_p)
 
 
 class SynthesizerTrnMs256NSFsid(SynthesizerTrnMsNSFsid):

infer/lib/rtrvc.py

@@ -428,15 +428,19 @@ class RVC:
         # return_length2 = torch.LongTensor([return_length2])
         return_length = torch.LongTensor([return_length])
         with torch.no_grad():
-            infered_audio = self.net_g.infer(
-                feats,
-                p_len,
-                sid,
-                pitch=cache_pitch,
-                pitchf=cache_pitchf,
-                skip_head=skip_head,
-                return_length=return_length,
-            ).squeeze(1).float()
+            infered_audio = (
+                self.net_g.infer(
+                    feats,
+                    p_len,
+                    sid,
+                    pitch=cache_pitch,
+                    pitchf=cache_pitchf,
+                    skip_head=skip_head,
+                    return_length=return_length,
+                )
+                .squeeze(1)
+                .float()
+            )
         upp_res = int(np.floor(factor * self.tgt_sr // 100))
         if upp_res != self.tgt_sr // 100:
             if upp_res not in self.resample_kernel:

infer/modules/vc/pipeline.py

@@ -291,14 +291,19 @@ class Pipeline(object):
         p_len = torch.tensor([p_len], device=self.device).long()
         with torch.no_grad():
             audio1 = (
-                net_g.infer(
-                    feats,
-                    p_len,
-                    sid,
-                    pitch=pitch,
-                    pitchf=pitchf,
-                )[0, 0]
-            ).data.cpu().float().numpy()
+                (
+                    net_g.infer(
+                        feats,
+                        p_len,
+                        sid,
+                        pitch=pitch,
+                        pitchf=pitchf,
+                    )[0, 0]
+                )
+                .data.cpu()
+                .float()
+                .numpy()
+            )
         del feats, p_len, padding_mask
         if torch.cuda.is_available():
             torch.cuda.empty_cache()