ruff fix

modules/F0Predictor/fcpe/__init__.py

@@ -1,3 +1,3 @@
-from .pcmer import PCmer
-from .nvSTFT import STFT
-from .model import FCPEInfer
+from .model import FCPEInfer  # noqa: F401
+from .nvSTFT import STFT  # noqa: F401
+from .pcmer import PCmer  # noqa: F401

modules/F0Predictor/fcpe/model.py

@@ -1,14 +1,15 @@
+import os
+
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from torch.nn.utils import weight_norm
-import os
 import yaml
+from torch.nn.utils import weight_norm
 from torchaudio.transforms import Resample
-import numpy as np
 
-from .pcmer import PCmer
 from .nvSTFT import STFT
+from .pcmer import PCmer
 
 
 def l2_regularization(model, l2_alpha):

modules/F0Predictor/fcpe/nvSTFT.py

@@ -1,16 +1,14 @@
-import math
 import os
-os.environ["LRU_CACHE_CAPACITY"] = "3"
-import random
-import torch
-import torch.utils.data
-import numpy as np
+
 import librosa
-from librosa.util import normalize
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
+import numpy as np
 import soundfile as sf
+import torch
 import torch.nn.functional as F
+import torch.utils.data
+from librosa.filters import mel as librosa_mel_fn
+
+os.environ["LRU_CACHE_CAPACITY"] = "3"
 
 def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
     sampling_rate = None