chore: make Ruff happy

.ruff.toml

@@ -1,4 +1,4 @@
 select = ["E", "F", "I"]
 
 # Never enforce `E501` (line length violations).
-ignore = ["E501"]
+ignore = ["E501", "E741"]

modules/DSConv.py

@@ -1,6 +1,6 @@
-import torch
 import torch.nn as nn
-from torch.nn.utils import weight_norm, remove_weight_norm
+from torch.nn.utils import remove_weight_norm, weight_norm
+
 
 class Depthwise_Separable_Conv1D(nn.Module):
     def __init__(

modules/mel_processing.py

@@ -53,7 +53,9 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
     y = y.squeeze(1)
 
     y_dtype = y.dtype
-    if y.dtype == torch.bfloat16: y = y.to(torch.float32)
+    if y.dtype == torch.bfloat16:
+        y = y.to(torch.float32)
+
     spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
                       center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
     spec = torch.view_as_real(spec).to(y_dtype)

modules/modules.py

@@ -1,12 +1,14 @@
 import torch
 from torch import nn
-from torch.nn import Conv1d
 from torch.nn import functional as F
 
-from modules.DSConv import weight_norm_modules, remove_weight_norm_modules, Depthwise_Separable_Conv1D
-
 import modules.commons as commons
-from modules.commons import init_weights, get_padding
+from modules.commons import get_padding, init_weights
+from modules.DSConv import (
+    Depthwise_Separable_Conv1D,
+    remove_weight_norm_modules,
+    weight_norm_modules,
+)
 
 LRELU_SLOPE = 0.1
 

webUI.py

@@ -6,6 +6,7 @@
 import time
 import traceback
 from itertools import chain
+from pathlib import Path
 
 # os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
 import gradio as gr
@@ -15,7 +16,6 @@
 import soundfile
 import torch
 from scipy.io import wavfile
-from pathlib import Path
 
 from compress_model import removeOptimizer
 from inference.infer_tool import Svc
@@ -172,14 +172,18 @@ def vc_fn(sid, input_audio, output_format, vc_transform, auto_f0,cluster_ratio,
         model.clear_empty()
         #os.remove(temp_path)
         #构建保存文件的路径，并保存到results文件夹内
-        timestamp = str(int(time.time()))
+        str(int(time.time()))
         if not os.path.exists("results"):
             os.makedirs("results")
         key = "auto" if auto_f0 else f"{int(vc_transform)}key"
         cluster = "_" if cluster_ratio == 0 else f"_{cluster_ratio}_"
         isdiffusion = "sovits"
-        if model.shallow_diffusion : isdiffusion = "sovdiff"
-        if model.only_diffusion : isdiffusion = "diff"
+        if model.shallow_diffusion:
+            isdiffusion = "sovdiff"
+
+        if model.only_diffusion:
+            isdiffusion = "diff"
+
         output_file_name = 'result_'+truncated_basename+f'_{sid}_{key}{cluster}{isdiffusion}.{output_format}'
         output_file = os.path.join("results", output_file_name)
         soundfile.write(output_file, _audio, model.target_sample, format=output_format)