format

configs/config.py

@@ -1,9 +1,10 @@
-import os
 import argparse
+import os
 import sys
-import torch
 from multiprocessing import cpu_count
 
+import torch
+
 
 def use_fp32_config():
     for config_file in [
@@ -198,6 +199,3 @@ class Config:
                 except:
                     pass
         return x_pad, x_query, x_center, x_max
-
-
-defaultconfig = Config()

gui_v1.py

@@ -1,4 +1,6 @@
-import os, sys, pdb
+import os
+import pdb
+import sys
 
 os.environ["OMP_NUM_THREADS"] = "2"
 if sys.platform == "darwin":
@@ -16,7 +18,8 @@ class Harvest(multiprocessing.Process):
         self.opt_q = opt_q
 
     def run(self):
-        import numpy as np, pyworld
+        import numpy as np
+        import pyworld
 
         while 1:
             idx, x, res_f0, n_cpu, ts = self.inp_q.get()
@@ -33,21 +36,26 @@ class Harvest(multiprocessing.Process):
 
 
 if __name__ == "__main__":
-    from multiprocessing import Queue
-    from queue import Empty
-    import numpy as np
-    import multiprocessing
-    import traceback, re
     import json
-    import PySimpleGUI as sg
-    import sounddevice as sd
+    import multiprocessing
+    import re
+    import threading
+    import time
+    import traceback
+    from multiprocessing import Queue, cpu_count
+    from queue import Empty
+
+    import librosa
     import noisereduce as nr
-    from multiprocessing import cpu_count
-    import librosa, torch, time, threading
+    import numpy as np
+    import PySimpleGUI as sg
+    import rvc_for_realtime
+    import sounddevice as sd
+    import torch
     import torch.nn.functional as F
     import torchaudio.transforms as tat
+
     from i18n import I18nAuto
-    import rvc_for_realtime
 
     i18n = I18nAuto()
     device = rvc_for_realtime.config.device

i18n/i18n.py

@@ -1,5 +1,5 @@
-import locale
 import json
+import locale
 import os
 
 

i18n/locale/scan_i18n.py

@@ -1,7 +1,6 @@
 import ast
 import glob
 import json
-
 from collections import OrderedDict
 
 

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)

tools/calc_rvc_model_similarity.py

@@ -1,6 +1,8 @@
 # This code references https://huggingface.co/JosephusCheung/ASimilarityCalculatior/blob/main/qwerty.py
 # Fill in the path of the model to be queried and the root directory of the reference models, and this script will return the similarity between the model to be queried and all reference models.
-import sys, os
+import os
+import sys
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

tools/export_onnx.py

@@ -1,5 +1,5 @@
-from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
 import torch
+from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
 
 if __name__ == "__main__":
     MoeVS = True  # 模型是否为MoeVoiceStudio（原MoeSS）使用

tools/infer/infer-pm-index256.py

@@ -2,34 +2,36 @@
 
 对源特征进行检索
 """
-import torch, pdb, os, parselmouth
+import os
+import pdb
+
+import parselmouth
+import torch
 
 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+# import torchcrepe
+from time import time as ttime
+
+# import pyworld
+import librosa
 import numpy as np
+import scipy.signal as signal
 import soundfile as sf
+import torch.nn.functional as F
+from fairseq import checkpoint_utils
 
 # from models import SynthesizerTrn256#hifigan_nonsf
 # from lib.infer_pack.models import SynthesizerTrn256NSF as SynthesizerTrn256#hifigan_nsf
 from lib.infer_pack.models import (
     SynthesizerTrnMs256NSFsid as SynthesizerTrn256,
 )  # hifigan_nsf
+from scipy.io import wavfile
 
 # from lib.infer_pack.models import SynthesizerTrnMs256NSFsid_sim as SynthesizerTrn256#hifigan_nsf
 # from models import SynthesizerTrn256NSFsim as SynthesizerTrn256#hifigan_nsf
 # from models import SynthesizerTrn256NSFsimFlow as SynthesizerTrn256#hifigan_nsf
 
 
-from scipy.io import wavfile
-from fairseq import checkpoint_utils
-
-# import pyworld
-import librosa
-import torch.nn.functional as F
-import scipy.signal as signal
-
-# import torchcrepe
-from time import time as ttime
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model_path = r"E:\codes\py39\vits_vc_gpu_train\hubert_base.pt"  #
 print("load model(s) from {}".format(model_path))

tools/infer/train-index-v2.py

@@ -1,11 +1,14 @@
 """
 格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个
 """
-import faiss, numpy as np, os
-from sklearn.cluster import MiniBatchKMeans
+import os
 import traceback
 from multiprocessing import cpu_count
 
+import faiss
+import numpy as np
+from sklearn.cluster import MiniBatchKMeans
+
 # ###########如果是原始特征要先写save
 n_cpu = 0
 if n_cpu == 0:

tools/infer/train-index.py

@@ -1,7 +1,10 @@
 """
 格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个
 """
-import faiss, numpy as np, os
+import os
+
+import faiss
+import numpy as np
 
 # ###########如果是原始特征要先写save
 inp_root = r"E:\codes\py39\dataset\mi\2-co256"

tools/infer/trans_weights.py

@@ -1,4 +1,6 @@
-import torch, pdb
+import pdb
+
+import torch
 
 # a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-suc\G_1000.pth")["model"]#sim_nsf#
 # a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-freeze-vocoder-flow-enc_q\G_1000.pth")["model"]#sim_nsf#

tools/onnx_inference_demo.py

@@ -1,4 +1,5 @@
 import soundfile
+
 from ..lib.infer_pack.onnx_inference import OnnxRVC
 
 hop_size = 512