format

infer/lib/audio.py

@@ -1,5 +1,5 @@
-import librosa
 import ffmpeg
+import librosa
 import numpy as np
 
 

infer/lib/infer_pack/attentions.py

@@ -1,12 +1,12 @@
 import copy
 import math
+
 import numpy as np
 import torch
 from torch import nn
 from torch.nn import functional as F
 
-from infer.lib.infer_pack import commons
-from infer.lib.infer_pack import modules
+from infer.lib.infer_pack import commons, modules
 from infer.lib.infer_pack.modules import LayerNorm
 
 

infer/lib/infer_pack/commons.py

@@ -1,4 +1,5 @@
 import math
+
 import numpy as np
 import torch
 from torch import nn

infer/lib/infer_pack/models.py

@@ -1,17 +1,17 @@
-import math, pdb, os
+import math
+import os
+import pdb
 from time import time as ttime
+
+import numpy as np
 import torch
 from torch import nn
+from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
 from torch.nn import functional as F
-from infer.lib.infer_pack import modules
-from infer.lib.infer_pack import attentions
-from infer.lib.infer_pack import commons
-from infer.lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer.lib.infer_pack.commons import init_weights
-import numpy as np
-from infer.lib.infer_pack import commons
+from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
+
+from infer.lib.infer_pack import attentions, commons, modules
+from infer.lib.infer_pack.commons import get_padding, init_weights
 
 
 class TextEncoder256(nn.Module):

infer/lib/infer_pack/models_onnx.py

@@ -1,17 +1,17 @@
-import math, pdb, os
+import math
+import os
+import pdb
 from time import time as ttime
+
+import numpy as np
 import torch
 from torch import nn
+from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
 from torch.nn import functional as F
-from infer.lib.infer_pack import modules
-from infer.lib.infer_pack import attentions
-from infer.lib.infer_pack import commons
-from infer.lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer.lib.infer_pack.commons import init_weights
-import numpy as np
-from infer.lib.infer_pack import commons
+from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
+
+from infer.lib.infer_pack import attentions, commons, modules
+from infer.lib.infer_pack.commons import get_padding, init_weights
 
 
 class TextEncoder256(nn.Module):

infer/lib/infer_pack/modules.py

@@ -1,19 +1,18 @@
 import copy
 import math
+
 import numpy as np
 import scipy
 import torch
 from torch import nn
+from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
 from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
+from torch.nn.utils import remove_weight_norm, weight_norm
 
 from infer.lib.infer_pack import commons
-from infer.lib.infer_pack.commons import init_weights, get_padding
+from infer.lib.infer_pack.commons import get_padding, init_weights
 from infer.lib.infer_pack.transforms import piecewise_rational_quadratic_transform
 
-
 LRELU_SLOPE = 0.1
 
 

infer/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py

@@ -1,6 +1,7 @@
-from infer.lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
 import numpy as np
+import pyworld
+
+from infer.lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
 
 
 class DioF0Predictor(F0Predictor):

infer/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py

@@ -1,6 +1,7 @@
-from infer.lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
 import numpy as np
+import pyworld
+
+from infer.lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
 
 
 class HarvestF0Predictor(F0Predictor):

infer/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py

@@ -1,6 +1,7 @@
-from infer.lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import parselmouth
 import numpy as np
+import parselmouth
+
+from infer.lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
 
 
 class PMF0Predictor(F0Predictor):

infer/lib/infer_pack/onnx_inference.py

@@ -1,6 +1,6 @@
-import onnxruntime
 import librosa
 import numpy as np
+import onnxruntime
 import soundfile
 
 

infer/lib/infer_pack/transforms.py

@@ -1,9 +1,7 @@
+import numpy as np
 import torch
 from torch.nn import functional as F
 
-import numpy as np
-
-
 DEFAULT_MIN_BIN_WIDTH = 1e-3
 DEFAULT_MIN_BIN_HEIGHT = 1e-3
 DEFAULT_MIN_DERIVATIVE = 1e-3

infer/lib/rmvpe.py

@@ -1,11 +1,11 @@
-import torch, numpy as np, pdb
+import pdb
+
+import numpy as np
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import torch, pdb
-import numpy as np
-import torch.nn.functional as F
+from librosa.util import normalize, pad_center, tiny
 from scipy.signal import get_window
-from librosa.util import pad_center, tiny, normalize
 
 
 ###stft codes from https://github.com/pseeth/torch-stft/blob/master/torch_stft/util.py
@@ -670,7 +670,8 @@ class RMVPE:
 
 
 if __name__ == "__main__":
-    import soundfile as sf, librosa
+    import librosa
+    import soundfile as sf
 
     audio, sampling_rate = sf.read(r"C:\Users\liujing04\Desktop\Z\冬之花clip1.wav")
     if len(audio.shape) > 1:

infer/lib/train/data_utils.py

@@ -1,10 +1,12 @@
-import os, traceback
+import os
+import traceback
+
 import numpy as np
 import torch
 import torch.utils.data
 
 from infer.lib.train.mel_processing import spectrogram_torch
-from infer.lib.train.utils import load_wav_to_torch, load_filepaths_and_text
+from infer.lib.train.utils import load_filepaths_and_text, load_wav_to_torch
 
 
 class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):

infer/lib/train/mel_processing.py

@@ -2,7 +2,6 @@ import torch
 import torch.utils.data
 from librosa.filters import mel as librosa_mel_fn
 
-
 MAX_WAV_VALUE = 32768.0
 
 

infer/lib/train/process_ckpt.py

@@ -1,7 +1,10 @@
-import torch, traceback, os, sys
-
-
+import os
+import sys
+import traceback
 from collections import OrderedDict
+
+import torch
+
 from i18n.i18n import I18nAuto
 
 i18n = I18nAuto()

infer/lib/train/utils.py

@@ -1,13 +1,15 @@
-import os, traceback
-import glob
-import sys
 import argparse
-import logging
+import glob
 import json
+import logging
+import os
 import subprocess
+import sys
+import traceback
+
 import numpy as np
-from scipy.io.wavfile import read
 import torch
+from scipy.io.wavfile import read
 
 MATPLOTLIB_FLAG = False
 

infer/lib/uvr5_pack/lib_v5/layers.py

@@ -1,6 +1,6 @@
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from . import spec_utils
 

infer/lib/uvr5_pack/lib_v5/layers_123812KB .py

@@ -1,6 +1,6 @@
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from . import spec_utils
 

infer/lib/uvr5_pack/lib_v5/layers_123821KB.py

@@ -1,6 +1,6 @@
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from . import spec_utils
 

infer/lib/uvr5_pack/lib_v5/layers_33966KB.py

@@ -1,6 +1,6 @@
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from . import spec_utils
 

infer/lib/uvr5_pack/lib_v5/layers_537227KB.py

@@ -1,6 +1,6 @@
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from . import spec_utils
 

infer/lib/uvr5_pack/lib_v5/layers_537238KB.py

@@ -1,6 +1,6 @@
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from . import spec_utils
 

infer/lib/uvr5_pack/lib_v5/layers_new.py

@@ -1,6 +1,6 @@
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from . import spec_utils
 

infer/lib/uvr5_pack/lib_v5/nets.py

@@ -1,8 +1,8 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
 import layers
+import torch
+import torch.nn.functional as F
+from torch import nn
+
 from . import spec_utils
 
 

infer/lib/uvr5_pack/lib_v5/nets_123812KB.py

@@ -1,6 +1,6 @@
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from . import layers_123821KB as layers
 

infer/lib/uvr5_pack/lib_v5/nets_123821KB.py

@@ -1,6 +1,6 @@
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from . import layers_123821KB as layers
 

infer/lib/uvr5_pack/lib_v5/nets_33966KB.py

@@ -1,6 +1,6 @@
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from . import layers_33966KB as layers
 

infer/lib/uvr5_pack/lib_v5/nets_537227KB.py

@@ -1,7 +1,7 @@
-import torch
 import numpy as np
-from torch import nn
+import torch
 import torch.nn.functional as F
+from torch import nn
 
 from . import layers_537238KB as layers
 

infer/lib/uvr5_pack/lib_v5/nets_537238KB.py

@@ -1,7 +1,7 @@
-import torch
 import numpy as np
-from torch import nn
+import torch
 import torch.nn.functional as F
+from torch import nn
 
 from . import layers_537238KB as layers
 

infer/lib/uvr5_pack/lib_v5/nets_61968KB.py

@@ -1,6 +1,6 @@
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from . import layers_123821KB as layers
 

infer/lib/uvr5_pack/lib_v5/nets_new.py

@@ -1,6 +1,7 @@
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
+
 from . import layers_new
 
 

infer/lib/uvr5_pack/lib_v5/spec_utils.py

@@ -1,8 +1,12 @@
-import os, librosa
+import hashlib
+import json
+import math
+import os
+
+import librosa
 import numpy as np
 import soundfile as sf
 from tqdm import tqdm
-import json, math, hashlib
 
 
 def crop_center(h1, h2):
@@ -519,10 +523,11 @@ def istft(spec, hl):
 
 
 if __name__ == "__main__":
-    import cv2
+    import argparse
     import sys
     import time
-    import argparse
+
+    import cv2
     from model_param_init import ModelParameters
 
     p = argparse.ArgumentParser()

infer/lib/uvr5_pack/utils.py

@@ -1,8 +1,9 @@
-import torch
-import numpy as np
-from tqdm import tqdm
 import json
 
+import numpy as np
+import torch
+from tqdm import tqdm
+
 
 def load_data(file_name: str = "./infer/lib/uvr5_pack/name_params.json") -> dict:
     with open(file_name, "r") as f: