chore(format): run black on dev (#55)

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

configs/config.py

@@ -6,6 +6,7 @@ import shutil
 from multiprocessing import cpu_count
 
 import torch
+
 # TODO: move device selection into rvc
 import logging
 

rvc/__init__.py

@@ -1,4 +1,4 @@
 from . import ipex
 import sys
-del sys.modules["rvc.ipex"]
 
+del sys.modules["rvc.ipex"]

rvc/f0/models.py

@@ -1,6 +1,9 @@
 import torch
 
-def get_rmvpe(model_path="assets/rmvpe/rmvpe.pt", device=torch.device("cpu"), is_half=False):
+
+def get_rmvpe(
+    model_path="assets/rmvpe/rmvpe.pt", device=torch.device("cpu"), is_half=False
+):
     from rvc.f0.e2e import E2E
 
     model = E2E(4, 1, (2, 2))

rvc/f0/rmvpe.py

@@ -36,6 +36,7 @@ def rmvpe_jit_export(
     save_pickle(ckpt, save_path)
     return ckpt
 
+
 class RMVPE(F0Predictor):
     def __init__(
         self,
@@ -80,6 +81,7 @@ class RMVPE(F0Predictor):
                 providers=["DmlExecutionProvider"],
             )
         else:
+
             def rmvpe_jit_model():
                 ckpt = get_jit_model(model_path, is_half, self.device, rmvpe_jit_export)
                 model = torch.jit.load(BytesIO(ckpt["model"]), map_location=self.device)

rvc/ipex/__init__.py

@@ -1,7 +1,9 @@
 try:
     import torch
+
     if torch.xpu.is_available():
         from .init import ipex_init
+
         ipex_init()
         from .gradscaler import gradscaler_init
 except Exception:  # pylint: disable=broad-exception-caught

rvc/jit/__init__.py

@@ -1 +1 @@
-from .jit import load_inputs, get_jit_model, export_jit_model, save_pickle
+from .jit import load_inputs, get_jit_model, export_jit_model, save_pickle

rvc/jit/jit.py

@@ -15,6 +15,7 @@ def save_pickle(ckpt: dict, save_path: str):
     with open(save_path, "wb") as f:
         pickle.dump(ckpt, f)
 
+
 def load_inputs(path: torch.serialization.FILE_LIKE, device: str, is_half=False):
     parm = torch.load(path, map_location=torch.device("cpu"))
     for key in parm.keys():
@@ -25,6 +26,7 @@ def load_inputs(path: torch.serialization.FILE_LIKE, device: str, is_half=False)
             parm[key] = parm[key].float()
     return parm
 
+
 def export_jit_model(
     model: torch.nn.Module,
     mode: str = "trace",

rvc/synthesizer.py

@@ -35,6 +35,7 @@ def load_synthesizer(
         device,
     )
 
+
 def synthesizer_jit_export(
     model_path: str,
     mode: str = "script",