format

rvc/cli/cli.py

@@ -6,11 +6,11 @@ import click
 from rvc.cli.handler.infer import infer
 from rvc.cli.handler.train import train
 from rvc.cli.handler.uvr5 import uvr
-
 from rvc.cli.utils.dlmodel import dlmodel
 from rvc.cli.utils.env import env
 from rvc.cli.utils.initialize import init
 
+
 @click.group(
     context_settings={"help_option_names": ["-h", "--help"]},
     help="rvc cli feature list",
@@ -18,6 +18,7 @@ from rvc.cli.utils.initialize import init
 def cli():
     pass
 
+
 def main():
     cli.add_command(infer)
     cli.add_command(train)

rvc/cli/handler/infer.py

@@ -7,7 +7,6 @@ from scipy.io import wavfile
 
 from rvc.modules.vc.modules import VC
 
-
 logging.getLogger("numba").setLevel(logging.WARNING)
 
 

rvc/cli/utils/dlmodel.py

@@ -1,4 +1,5 @@
 import urllib
+
 import click
 
 

rvc/cli/utils/initialize.py

@@ -6,9 +6,6 @@ download model and setup environmmnt file
 import click
 
 
-import click
-
-
 @click.command()
 def init():
     pass

rvc/lib/rmvpe.py

@@ -18,15 +18,13 @@ try:
 except Exception:  # pylint: disable=broad-exception-caught
     pass
 import logging
+from time import time as ttime
 
 import torch.nn as nn
 import torch.nn.functional as F
+from librosa.filters import mel
 from librosa.util import normalize, pad_center, tiny
 from scipy.signal import get_window
-from librosa.filters import mel
-
-from time import time as ttime
-
 
 logger = logging.getLogger(__name__)
 
@@ -161,7 +159,6 @@ class STFT(torch.nn.Module):
         return reconstruction
 
 
-
 class BiGRU(nn.Module):
     def __init__(self, input_features, hidden_features, num_layers):
         super(BiGRU, self).__init__()
@@ -415,8 +412,6 @@ class E2E(nn.Module):
         return x
 
 
-
-
 class MelSpectrogram(torch.nn.Module):
     def __init__(
         self,

rvc/lib/slicer2.py

@@ -64,11 +64,11 @@ class Slicer:
     def _apply_slice(self, waveform, begin, end):
         if len(waveform.shape) > 1:
             return waveform[
-                :, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)
+                :, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size)
             ]
         else:
             return waveform[
-                begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)
+                begin * self.hop_size : min(waveform.shape[0], end * self.hop_size)
             ]
 
     # @timeit
@@ -106,7 +106,7 @@ class Slicer:
                 continue
             # Need slicing. Record the range of silent frames to be removed.
             if i - silence_start <= self.max_sil_kept:
-                pos = rms_list[silence_start: i + 1].argmin() + silence_start
+                pos = rms_list[silence_start : i + 1].argmin() + silence_start
                 if silence_start == 0:
                     sil_tags.append((0, pos))
                 else:
@@ -114,17 +114,17 @@ class Slicer:
                 clip_start = pos
             elif i - silence_start <= self.max_sil_kept * 2:
                 pos = rms_list[
-                    i - self.max_sil_kept: silence_start + self.max_sil_kept + 1
+                    i - self.max_sil_kept : silence_start + self.max_sil_kept + 1
                 ].argmin()
                 pos += i - self.max_sil_kept
                 pos_l = (
                     rms_list[
-                        silence_start: silence_start + self.max_sil_kept + 1
+                        silence_start : silence_start + self.max_sil_kept + 1
                     ].argmin()
                     + silence_start
                 )
                 pos_r = (
-                    rms_list[i - self.max_sil_kept: i + 1].argmin()
+                    rms_list[i - self.max_sil_kept : i + 1].argmin()
                     + i
                     - self.max_sil_kept
                 )
@@ -137,12 +137,12 @@ class Slicer:
             else:
                 pos_l = (
                     rms_list[
-                        silence_start: silence_start + self.max_sil_kept + 1
+                        silence_start : silence_start + self.max_sil_kept + 1
                     ].argmin()
                     + silence_start
                 )
                 pos_r = (
-                    rms_list[i - self.max_sil_kept: i + 1].argmin()
+                    rms_list[i - self.max_sil_kept : i + 1].argmin()
                     + i
                     - self.max_sil_kept
                 )
@@ -159,7 +159,7 @@ class Slicer:
             and total_frames - silence_start >= self.min_interval
         ):
             silence_end = min(total_frames, silence_start + self.max_sil_kept)
-            pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start
+            pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start
             sil_tags.append((pos, total_frames + 1))
         # Apply and return slices.
         if len(sil_tags) == 0:

rvc/modules/onnx/export.py

@@ -9,7 +9,9 @@ def export_onnx(ModelPath, ExportedPath):
     vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768
 
     test_phone = torch.rand(1, 200, vec_channels)  # hidden unit
-    test_phone_lengths = torch.tensor([200]).long()  # hidden unit length (doesn't seem to help)）
+    test_phone_lengths = torch.tensor(
+        [200]
+    ).long()  # hidden unit length (doesn't seem to help)）
     test_pitch = torch.randint(size=(1, 200), low=5, high=255)  # Base frequency (in Hz)
     test_pitchf = torch.rand(1, 200)  # nsf base frequency
     test_ds = torch.LongTensor([0])  # Speaker ID