Format code (#1193)

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

infer/lib/audio.py

@@ -3,38 +3,49 @@ import numpy as np
 import av
 from io import BytesIO
 
+
 def wav2(i, o, format):
-    inp = av.open(i, 'rb')
-    if format == "m4a": format = "mp4"
-    out = av.open(o, 'wb', format=format)
-    if format == "ogg": format = "libvorbis"
-    if format == "mp4": format = "aac"
+    inp = av.open(i, "rb")
+    if format == "m4a":
+        format = "mp4"
+    out = av.open(o, "wb", format=format)
+    if format == "ogg":
+        format = "libvorbis"
+    if format == "mp4":
+        format = "aac"
 
     ostream = out.add_stream(format)
 
     for frame in inp.decode(audio=0):
-        for p in ostream.encode(frame): out.mux(p)
+        for p in ostream.encode(frame):
+            out.mux(p)
 
-    for p in ostream.encode(None): out.mux(p)
+    for p in ostream.encode(None):
+        out.mux(p)
 
     out.close()
     inp.close()
 
+
 def audio2(i, o, format, sr):
-    inp = av.open(i, 'rb')
-    out = av.open(o, 'wb', format=format)
-    if format == "ogg": format = "libvorbis"
-    if format == "f32le": format = "pcm_f32le"
+    inp = av.open(i, "rb")
+    out = av.open(o, "wb", format=format)
+    if format == "ogg":
+        format = "libvorbis"
+    if format == "f32le":
+        format = "pcm_f32le"
 
     ostream = out.add_stream(format, channels=1)
     ostream.sample_rate = sr
 
     for frame in inp.decode(audio=0):
-        for p in ostream.encode(frame): out.mux(p)
+        for p in ostream.encode(frame):
+            out.mux(p)
 
     out.close()
     inp.close()
 
+
 def load_audio(file, sr):
     try:
         file = (

infer/lib/infer_pack/models.py

@@ -15,6 +15,7 @@ from infer.lib.infer_pack.commons import get_padding, init_weights
 
 has_xpu = bool(hasattr(torch, "xpu") and torch.xpu.is_available())
 
+
 class TextEncoder256(nn.Module):
     def __init__(
         self,
@@ -1158,7 +1159,9 @@ class DiscriminatorP(torch.nn.Module):
         if t % self.period != 0:  # pad first
             n_pad = self.period - (t % self.period)
             if has_xpu and x.dtype == torch.bfloat16:
-                x = F.pad(x.to(dtype=torch.float16), (0, n_pad), "reflect").to(dtype=torch.bfloat16)
+                x = F.pad(x.to(dtype=torch.float16), (0, n_pad), "reflect").to(
+                    dtype=torch.bfloat16
+                )
             else:
                 x = F.pad(x, (0, n_pad), "reflect")
             t = t + n_pad

infer/lib/rmvpe.py

@@ -2,11 +2,14 @@ import pdb, os
 
 import numpy as np
 import torch
+
 try:
-    #Fix "Torch not compiled with CUDA enabled"
-    import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
+    # Fix "Torch not compiled with CUDA enabled"
+    import intel_extension_for_pytorch as ipex  # pylint: disable=import-error, unused-import
+
     if torch.xpu.is_available():
         from infer.modules.ipex import ipex_init
+
         ipex_init()
 except Exception:
     pass