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

@@ -1,9 +1,10 @@
 import os
 import shutil
 import sys
+
 now_dir = os.getcwd()
 sys.path.append(now_dir)
-import traceback,pdb
+import traceback, pdb
 import warnings
 
 import numpy as np
@@ -396,7 +397,7 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
 
 
 # 一个选项卡全局只能有一个音色
-def get_vc(sid,to_return_protect0,to_return_protect1):
+def get_vc(sid, to_return_protect0, to_return_protect1):
     global n_spk, tgt_sr, net_g, vc, cpt, version
     if sid == "" or sid == []:
         global hubert_model
@@ -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,
     )
 
 
@@ -893,14 +915,24 @@ 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):
+        # if(1):
-        infos.append("Trying doing kmeans %s shape to 10k centers."%big_npy.shape[0])
+        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()
+            info = traceback.format_exc()
             print(info)
             infos.append(info)
             yield "\n".join(infos)
@@ -1147,15 +1179,25 @@ 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):
+        # if(1):
-        info="Trying doing kmeans %s shape to 10k centers."%big_npy.shape[0]
+        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()
+            info = traceback.format_exc()
             print(info)
             yield get_info_str(info)
 
@@ -1207,11 +1249,10 @@ 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]
-    vec_channels = 256 if cpt.get("version","v1")=="v1"else 768
+    vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768
 
     test_phone = torch.rand(1, 200, vec_channels)  # hidden unit
     test_phone_lengths = torch.tensor([200]).long()  # hidden unit 长度（貌似没啥用）
@@ -1223,7 +1264,7 @@ def export_onnx(ModelPath, ExportedPath):
     device = "cpu"  # 导出时设备（不影响使用模型）
 
     net_g = SynthesizerTrnMsNSFsidM(
-        *cpt["config"], is_half=False,version=cpt.get("version","v1")
+        *cpt["config"], is_half=False, version=cpt.get("version", "v1")
     )  # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
     net_g.load_state_dict(cpt["weight"], strict=False)
     input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
@@ -1504,8 +1545,8 @@ with gr.Blocks() as app:
                     )
             sid0.change(
                 fn=get_vc,
-                inputs=[sid0,protect0,protect1],
+                inputs=[sid0, protect0, protect1],
-                outputs=[spk_item,protect0,protect1],
+                outputs=[spk_item, protect0, protect1],
             )
         with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
             with gr.Group():
@@ -1604,7 +1645,7 @@ with gr.Blocks() as app:
                     maximum=config.n_cpu,
                     step=1,
                     label=i18n("提取音高和处理数据使用的CPU进程数"),
-                    value=int(np.ceil(config.n_cpu/1.5)),
+                    value=int(np.ceil(config.n_cpu / 1.5)),
                     interactive=True,
                 )
             with gr.Group():  # 暂时单人的, 后面支持最多4人的#数据处理
@@ -1722,7 +1763,7 @@ with gr.Blocks() as app:
                     version19.change(
                         change_version19,
                         [sr2, if_f0_3, version19],
-                        [pretrained_G14, pretrained_D15,sr2],
+                        [pretrained_G14, pretrained_D15, sr2],
                     )
                     if_f0_3.change(
                         change_f0,
@@ -1915,7 +1956,7 @@ with gr.Blocks() as app:
                     [ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
                     info7,
                 )
-                
+
         with gr.TabItem(i18n("Onnx导出")):
             with gr.Row():
                 ckpt_dir = gr.Textbox(label=i18n("RVC模型路径"), value="", interactive=True)

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

@@ -53,9 +53,9 @@ class PreProcess:
         os.makedirs(self.wavs16k_dir, exist_ok=True)
 
     def norm_write(self, tmp_audio, idx0, idx1):
-        tmp_max=np.abs(tmp_audio).max()
+        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))
+            print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max))
             return
         tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + (
             1 - self.alpha

vc_infer_pipeline.py

@@ -184,7 +184,7 @@ class VC(object):
         with torch.no_grad():
             logits = model.extract_features(**inputs)
             feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
-        if protect < 0.5 and pitch!=None and pitchf!=None:
+        if protect < 0.5 and pitch != None and pitchf != None:
             feats0 = feats.clone()
         if (
             isinstance(index, type(None)) == False
@@ -211,7 +211,7 @@ class VC(object):
             )
 
         feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        if protect < 0.5 and pitch!=None and pitchf!=None:
+        if protect < 0.5 and pitch != None and pitchf != None:
             feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
                 0, 2, 1
             )
@@ -223,7 +223,7 @@ class VC(object):
                 pitch = pitch[:, :p_len]
                 pitchf = pitchf[:, :p_len]
 
-        if protect < 0.5 and pitch!=None and pitchf!=None:
+        if protect < 0.5 and pitch != None and pitchf != None:
             pitchff = pitchf.clone()
             pitchff[pitchf > 0] = 1
             pitchff[pitchf < 1] = protect