Format code (#526)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

extract_feature_print.py

@@ -64,8 +64,11 @@ def readwave(wav_path, normalize=False):
 # HuBERT model
 printt("load model(s) from {}".format(model_path))
 # if hubert model is exist
-if (os.access(model_path, os.F_OK) == False):
+if os.access(model_path, os.F_OK) == False:
-    printt("Error: Extracting is shut down because %s does not exist, you may download it from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main" % model_path)
+    printt(
+        "Error: Extracting is shut down because %s does not exist, you may download it from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main"
+        % model_path
+    )
     exit(0)
 models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
     [model_path],

gui.py

@@ -469,21 +469,21 @@ class GUI:
                 self.flag_vc = False
 
     def set_values(self, values):
-        if(len(values["pth_path"].strip()) == 0):
+        if len(values["pth_path"].strip()) == 0:
-            sg.popup(i18n('请选择pth文件'))  
+            sg.popup(i18n("请选择pth文件"))
             return False
-        if(len(values["index_path"].strip()) == 0):
+        if len(values["index_path"].strip()) == 0:
-            sg.popup(i18n('请选择index文件'))  
+            sg.popup(i18n("请选择index文件"))
             return False
         pattern = re.compile("[^\x00-\x7F]+")
-        if(pattern.findall(values["hubert_path"])):
+        if pattern.findall(values["hubert_path"]):
-            sg.popup(i18n('hubert模型路径不可包含中文'))  
+            sg.popup(i18n("hubert模型路径不可包含中文"))
             return False
-        if(pattern.findall(values["pth_path"])):
+        if pattern.findall(values["pth_path"]):
-            sg.popup(i18n('pth文件路径不可包含中文'))  
+            sg.popup(i18n("pth文件路径不可包含中文"))
             return False
-        if(pattern.findall(values["index_path"])):
+        if pattern.findall(values["index_path"]):
-            sg.popup(i18n('index文件路径不可包含中文'))  
+            sg.popup(i18n("index文件路径不可包含中文"))
             return False
         self.set_devices(values["sg_input_device"], values["sg_output_device"])
         self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")

infer-web.py

@@ -1,6 +1,7 @@
 import os
 import shutil
 import sys
+
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 import traceback, pdb
@@ -434,11 +435,23 @@ def get_vc(sid,to_return_protect0,to_return_protect1):
     tgt_sr = cpt["config"][-1]
     cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
     if_f0 = cpt.get("f0", 1)
-    if(if_f0==0):
+    if if_f0 == 0:
-        to_return_protect0=to_return_protect1={"visible": False, "value": 0.5, "__type__": "update"}
+        to_return_protect0 = to_return_protect1 = {
+            "visible": False,
+            "value": 0.5,
+            "__type__": "update",
+        }
     else:
-        to_return_protect0 ={"visible": True, "value": to_return_protect0, "__type__": "update"}
+        to_return_protect0 = {
-        to_return_protect1 ={"visible": True, "value": to_return_protect1, "__type__": "update"}
+            "visible": True,
+            "value": to_return_protect0,
+            "__type__": "update",
+        }
+        to_return_protect1 = {
+            "visible": True,
+            "value": to_return_protect1,
+            "__type__": "update",
+        }
     version = cpt.get("version", "v1")
     if version == "v1":
         if if_f0 == 1:
@@ -459,7 +472,11 @@ def get_vc(sid,to_return_protect0,to_return_protect1):
         net_g = net_g.float()
     vc = VC(tgt_sr, config)
     n_spk = cpt["config"][-3]
-    return {"visible": True, "maximum": n_spk, "__type__": "update"},to_return_protect0,to_return_protect1
+    return (
+        {"visible": True, "maximum": n_spk, "__type__": "update"},
+        to_return_protect0,
+        to_return_protect1,
+    )
 
 
 def change_choices():
@@ -665,8 +682,13 @@ def change_sr2(sr2, if_f0_3, version19):
 
 def change_version19(sr2, if_f0_3, version19):
     path_str = "" if version19 == "v1" else "_v2"
-    if(sr2=="32k"and version19=="v1"):sr2="40k"
+    if sr2 == "32k" and version19 == "v1":
-    to_return_sr2= {"choices": ["40k","48k"], "__type__": "update"} if version19=="v1"else {"choices": ["32k","40k","48k"], "__type__": "update"}
+        sr2 = "40k"
+    to_return_sr2 = (
+        {"choices": ["40k", "48k"], "__type__": "update"}
+        if version19 == "v1"
+        else {"choices": ["32k", "40k", "48k"], "__type__": "update"}
+    )
     f0_str = "f0" if if_f0_3 else ""
     if_pretrained_generator_exist = os.access(
         "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
@@ -691,7 +713,7 @@ def change_version19(sr2, if_f0_3, version19):
         "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
         if if_pretrained_discriminator_exist
         else "",
-        to_return_sr2
+        to_return_sr2,
     )
 
 
@@ -893,12 +915,22 @@ def train_index(exp_dir1, version19):
     big_npy_idx = np.arange(big_npy.shape[0])
     np.random.shuffle(big_npy_idx)
     big_npy = big_npy[big_npy_idx]
-    if(big_npy.shape[0]>2e5):
+    if big_npy.shape[0] > 2e5:
         # if(1):
         infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0])
         yield "\n".join(infos)
         try:
-            big_npy = MiniBatchKMeans(n_clusters=10000, verbose=True, batch_size=256 * config.n_cpu, compute_labels=False, init="random").fit(big_npy).cluster_centers_
+            big_npy = (
+                MiniBatchKMeans(
+                    n_clusters=10000,
+                    verbose=True,
+                    batch_size=256 * config.n_cpu,
+                    compute_labels=False,
+                    init="random",
+                )
+                .fit(big_npy)
+                .cluster_centers_
+            )
         except:
             info = traceback.format_exc()
             print(info)
@@ -1147,13 +1179,23 @@ def train1key(
     np.random.shuffle(big_npy_idx)
     big_npy = big_npy[big_npy_idx]
 
-    if(big_npy.shape[0]>2e5):
+    if big_npy.shape[0] > 2e5:
         # if(1):
         info = "Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]
         print(info)
         yield get_info_str(info)
         try:
-            big_npy = MiniBatchKMeans(n_clusters=10000, verbose=True, batch_size=256 * config.n_cpu, compute_labels=False, init="random").fit(big_npy).cluster_centers_
+            big_npy = (
+                MiniBatchKMeans(
+                    n_clusters=10000,
+                    verbose=True,
+                    batch_size=256 * config.n_cpu,
+                    compute_labels=False,
+                    init="random",
+                )
+                .fit(big_npy)
+                .cluster_centers_
+            )
         except:
             info = traceback.format_exc()
             print(info)
@@ -1207,7 +1249,6 @@ def change_info_(ckpt_path):
         return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
 
 
-
 def export_onnx(ModelPath, ExportedPath):
     cpt = torch.load(ModelPath, map_location="cpu")
     cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]

infer_pack/onnx_inference.py

@@ -3,6 +3,7 @@ import librosa
 import numpy as np
 import soundfile
 
+
 class ContentVec:
     def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
         print("load model(s) from {}".format(vec_path))

train_nsf_sim_cache_sid_load_pretrain.py

@@ -192,7 +192,6 @@ def run(rank, n_gpus, hps):
         epoch_str = 1
         global_step = 0
         if hps.pretrainG != "":
-
             if rank == 0:
                 logger.info("loaded pretrained %s" % (hps.pretrainG))
             print(
@@ -201,7 +200,6 @@ def run(rank, n_gpus, hps):
                 )
             )  ##测试不加载优化器
         if hps.pretrainD != "":
-
             if rank == 0:
                 logger.info("loaded pretrained %s" % (hps.pretrainD))
             print(

trainset_preprocess_pipeline_print.py

@@ -54,7 +54,7 @@ class PreProcess:
 
     def norm_write(self, tmp_audio, idx0, idx1):
         tmp_max = np.abs(tmp_audio).max()
-        if(tmp_max>2.5):
+        if tmp_max > 2.5:
             print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max))
             return
         tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + (