format

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)