chore: code cleanup by ruff suggested

diffusion/unit2mel.py

@@ -24,9 +24,11 @@ def load_model_vocoder(
         device='cpu',
         config_path = None
         ):
-    if config_path is None: config_file = os.path.join(os.path.split(model_path)[0], 'config.yaml')
-    else: config_file = config_path
-    
+    if config_path is None:
+        config_file = os.path.join(os.path.split(model_path)[0], 'config.yaml')
+    else:
+        config_file = config_path
+
     with open(config_file, "r") as config:
         args = yaml.safe_load(config)
     args = DotDict(args)

inference/infer_tool.py

@@ -179,7 +179,8 @@ class Svc(object):
         else:
             self.feature_retrieval=False
 
-        if self.shallow_diffusion : self.nsf_hifigan_enhance = False
+        if self.shallow_diffusion :
+            self.nsf_hifigan_enhance = False
         if self.nsf_hifigan_enhance:
             from modules.enhancer import Enhancer
             self.enhancer = Enhancer('nsf-hifigan', 'pretrain/nsf_hifigan/model',device=self.dev)
@@ -442,7 +443,8 @@ class Svc(object):
                 datas = [data]
             for k,dat in enumerate(datas):
                 per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) if clip_seconds!=0 else length
-                if clip_seconds!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
+                if clip_seconds!=0: 
+                    print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
                 # padd
                 pad_len = int(audio_sr * pad_seconds)
                 dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])

inference_main.py

@@ -132,8 +132,10 @@ def main():
             key = "auto" if auto_predict_f0 else f"{tran}key"
             cluster_name = "" if cluster_infer_ratio == 0 else f"_{cluster_infer_ratio}"
             isdiffusion = "sovits"
-            if shallow_diffusion : isdiffusion = "sovdiff"
-            if only_diffusion : isdiffusion = "diff"
+            if shallow_diffusion :
+                isdiffusion = "sovdiff"
+            if only_diffusion :
+                isdiffusion = "diff"
             if use_spk_mix:
                 spk = "spk_mix"
             res_path = f'results/{clean_name}_{key}_{spk}{cluster_name}_{isdiffusion}_{f0p}.{wav_format}'

modules/commons.py

@@ -134,12 +134,6 @@ def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
   return acts
 
 
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
 def shift_1d(x):
   x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
   return x

train.py

@@ -1,11 +1,7 @@
 import logging
 import multiprocessing
-import time
-
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-logging.getLogger('numba').setLevel(logging.WARNING)
-
 import os
+import time
 
 import torch
 import torch.distributed as dist
@@ -26,6 +22,9 @@ from models import (
 from modules.losses import discriminator_loss, feature_loss, generator_loss, kl_loss
 from modules.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
 
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+logging.getLogger('numba').setLevel(logging.WARNING)
+
 torch.backends.cudnn.benchmark = True
 global_step = 0
 start_time = time.time()

webUI.py

@@ -45,7 +45,8 @@ def upload_mix_append_file(files,sfiles):
         p = {file:100 for file in file_paths}
         return file_paths,mix_model_output1.update(value=json.dumps(p,indent=2))
     except Exception as e:
-        if debug: traceback.print_exc()
+        if debug:
+            traceback.print_exc()
         raise gr.Error(e)
 
 def mix_submit_click(js,mode):
@@ -59,16 +60,19 @@ def mix_submit_click(js,mode):
         path = mix_model(model_path,mix_rate,mode)
         return f"成功，文件被保存在了{path}"
     except Exception as e:
-        if debug: traceback.print_exc()
+        if debug:
+            traceback.print_exc()
         raise gr.Error(e)
 
 def updata_mix_info(files):
     try:
-        if files is None : return mix_model_output1.update(value="")
+        if files is None :
+            return mix_model_output1.update(value="")
         p = {file.name:100 for file in files}
         return mix_model_output1.update(value=json.dumps(p,indent=2))
     except Exception as e:
-        if debug: traceback.print_exc()
+        if debug:
+            traceback.print_exc()
         raise gr.Error(e)
 
 def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance,diff_model_path,diff_config_path,only_diffusion,use_spk_mix):
@@ -108,7 +112,8 @@ def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance,diff_
             msg += i + " "
         return sid.update(choices = spks,value=spks[0]), msg
     except Exception as e:
-        if debug: traceback.print_exc()
+        if debug:
+            traceback.print_exc()
         raise gr.Error(e)
 
     
@@ -168,7 +173,8 @@ def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise
         soundfile.write(output_file, _audio, model.target_sample, format="wav")
         return "Success", output_file
     except Exception as e:
-        if debug: traceback.print_exc()
+        if debug:
+            traceback.print_exc()
         raise gr.Error(e)
 
 def tts_func(_text,_rate,_voice):
@@ -176,7 +182,8 @@ def tts_func(_text,_rate,_voice):
     # voice = "zh-CN-XiaoyiNeural"#女性，较高音
     # voice = "zh-CN-YunxiNeural"#男性
     voice = "zh-CN-YunxiNeural"#男性
-    if ( _voice == "女" ) : voice = "zh-CN-XiaoyiNeural"
+    if ( _voice == "女" ) :
+        voice = "zh-CN-XiaoyiNeural"
     output_file = _text[0:10]+".wav"
     # communicate = edge_tts.Communicate(_text, voice)
     # await communicate.save(output_file)