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:

infer/modules/train/extract/extract_f0_print.py

@@ -1,10 +1,16 @@
-import os, traceback, sys, parselmouth
+import os
+import sys
+import traceback
+
+import parselmouth
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
-from lib.audio import load_audio
+import logging
+
+import numpy as np
 import pyworld
-import numpy as np, logging
+from lib.audio import load_audio
 
 logging.getLogger("numba").setLevel(logging.WARNING)
 from multiprocessing import Process

infer/modules/train/extract/extract_f0_rmvpe.py

@@ -1,10 +1,16 @@
-import os, traceback, sys, parselmouth
+import os
+import sys
+import traceback
+
+import parselmouth
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
-from lib.audio import load_audio
+import logging
+
+import numpy as np
 import pyworld
-import numpy as np, logging
+from lib.audio import load_audio
 
 logging.getLogger("numba").setLevel(logging.WARNING)
 

infer/modules/train/extract/extract_f0_rmvpe_dml.py

@@ -1,10 +1,16 @@
-import os, traceback, sys, parselmouth
+import os
+import sys
+import traceback
+
+import parselmouth
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
-from lib.audio import load_audio
+import logging
+
+import numpy as np
 import pyworld
-import numpy as np, logging
+from lib.audio import load_audio
 
 logging.getLogger("numba").setLevel(logging.WARNING)
 

infer/modules/train/extract_feature_print.py

@@ -1,4 +1,6 @@
-import os, sys, traceback
+import os
+import sys
+import traceback
 
 os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
 os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"
@@ -14,11 +16,11 @@ else:
     exp_dir = sys.argv[5]
     os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
     version = sys.argv[6]
+import fairseq
+import numpy as np
+import soundfile as sf
 import torch
 import torch.nn.functional as F
-import soundfile as sf
-import numpy as np
-import fairseq
 
 if "privateuseone" not in device:
     device = "cpu"

infer/modules/train/preprocess.py

@@ -1,4 +1,7 @@
-import sys, os, multiprocessing
+import multiprocessing
+import os
+import sys
+
 from scipy import signal
 
 now_dir = os.getcwd()
@@ -9,12 +12,15 @@ sr = int(sys.argv[2])
 n_p = int(sys.argv[3])
 exp_dir = sys.argv[4]
 noparallel = sys.argv[5] == "True"
-import numpy as np, os, traceback
-from lib.slicer2 import Slicer
-import librosa, traceback
-from scipy.io import wavfile
 import multiprocessing
+import os
+import traceback
+
+import librosa
+import numpy as np
 from lib.audio import load_audio
+from lib.slicer2 import Slicer
+from scipy.io import wavfile
 
 mutex = multiprocessing.Lock()
 f = open("%s/preprocess.log" % exp_dir, "a+")

infer/modules/train/train.py

@@ -1,43 +1,47 @@
-import os, sys
+import os
+import sys
 
 now_dir = os.getcwd()
 sys.path.append(os.path.join(now_dir))
 
-from infer.lib.train import utils
 import datetime
 
+from infer.lib.train import utils
+
 hps = utils.get_hparams()
 os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
 n_gpus = len(hps.gpus.split("-"))
-from random import shuffle, randint
+from random import randint, shuffle
 
 import torch
 
 torch.backends.cudnn.deterministic = False
 torch.backends.cudnn.benchmark = False
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-from infer.lib.infer_pack import commons
 from time import sleep
 from time import time as ttime
+
+import torch.distributed as dist
+import torch.multiprocessing as mp
+from torch.cuda.amp import GradScaler, autocast
+from torch.nn import functional as F
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+
+from infer.lib.infer_pack import commons
 from infer.lib.train.data_utils import (
-    TextAudioLoaderMultiNSFsid,
-    TextAudioLoader,
-    TextAudioCollateMultiNSFsid,
-    TextAudioCollate,
     DistributedBucketSampler,
+    TextAudioCollate,
+    TextAudioCollateMultiNSFsid,
+    TextAudioLoader,
+    TextAudioLoaderMultiNSFsid,
 )
 
 if hps.version == "v1":
+    from infer.lib.infer_pack.models import MultiPeriodDiscriminator
+    from infer.lib.infer_pack.models import SynthesizerTrnMs256NSFsid as RVC_Model_f0
     from infer.lib.infer_pack.models import (
-        SynthesizerTrnMs256NSFsid as RVC_Model_f0,
         SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
-        MultiPeriodDiscriminator,
     )
 else:
     from infer.lib.infer_pack.models import (
@@ -45,10 +49,11 @@ else:
         SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
         MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
     )
+
 from infer.lib.train.losses import (
-    generator_loss,
     discriminator_loss,
     feature_loss,
+    generator_loss,
     kl_loss,
 )
 from infer.lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch

infer/modules/uvr5/mdxnet.py

@@ -1,12 +1,12 @@
 import os
 import warnings
 
-import soundfile as sf
 import librosa
 import numpy as np
 import onnxruntime as ort
-from tqdm import tqdm
+import soundfile as sf
 import torch
+from tqdm import tqdm
 
 cpu = torch.device("cpu")
 

infer/modules/uvr5/modules.py

@@ -1,12 +1,12 @@
 import os
 import traceback
 
-import torch
 import ffmpeg
+import torch
 
 from configs.config import Config
-from infer.modules.uvr5.preprocess import AudioPre, AudioPreDeEcho
 from infer.modules.uvr5.mdxnet import MDXNetDereverb
+from infer.modules.uvr5.preprocess import AudioPre, AudioPreDeEcho
 
 config = Config()
 

infer/modules/uvr5/preprocess.py

@@ -1,16 +1,15 @@
 import os
-import torch
 
 import librosa
 import numpy as np
 import soundfile as sf
+import torch
 
-from infer.lib.uvr5_pack.lib_v5 import spec_utils
-from infer.lib.uvr5_pack.utils import inference
-from infer.lib.uvr5_pack.lib_v5.model_param_init import ModelParameters
-
-from infer.lib.uvr5_pack.lib_v5.nets_new import CascadedNet
 from infer.lib.uvr5_pack.lib_v5 import nets_61968KB as Nets
+from infer.lib.uvr5_pack.lib_v5 import spec_utils
+from infer.lib.uvr5_pack.lib_v5.model_param_init import ModelParameters
+from infer.lib.uvr5_pack.lib_v5.nets_new import CascadedNet
+from infer.lib.uvr5_pack.utils import inference
 
 
 class AudioPre:

infer/modules/vc/modules.py

@@ -1,9 +1,10 @@
 import traceback
 
 import numpy as np
-import torch
 import soundfile as sf
+import torch
 
+from infer.lib.audio import load_audio
 from infer.lib.infer_pack.models import (
     SynthesizerTrnMs256NSFsid,
     SynthesizerTrnMs256NSFsid_nono,
@@ -12,7 +13,6 @@ from infer.lib.infer_pack.models import (
 )
 from infer.modules.vc.pipeline import Pipeline
 from infer.modules.vc.utils import *
-from infer.lib.audio import load_audio
 
 
 class VC:

infer/modules/vc/pipeline.py

@@ -1,13 +1,18 @@
+import os
 import sys
+import traceback
+from functools import lru_cache
 from time import time as ttime
 
+import faiss
+import librosa
 import numpy as np
 import parselmouth
+import pyworld
 import torch
 import torch.nn.functional as F
-import pyworld, os, traceback, faiss, librosa, torchcrepe
+import torchcrepe
 from scipy import signal
-from functools import lru_cache
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)