diff --git a/GPT_SoVITS/AR/data/dataset.py b/GPT_SoVITS/AR/data/dataset.py
index 402483d9..57450b2e 100644
--- a/GPT_SoVITS/AR/data/dataset.py
+++ b/GPT_SoVITS/AR/data/dataset.py
@@ -61,11 +61,7 @@ class Text2SemanticDataset(Dataset):
         )
         # get dict
         self.path2 = phoneme_path  # "%s/2-name2text.txt"%exp_dir#phoneme_path
-        self.path3 = "%s/3-bert" % (
-            os.path.dirname(
-                phoneme_path,
-            )
-        )  # "%s/3-bert"%exp_dir#bert_dir
+        self.path3 = f"{os.path.dirname(phoneme_path)}/3-bert"  # "%s/3-bert"%exp_dir#bert_dir
         self.path6 = semantic_path  # "%s/6-name2semantic.tsv"%exp_dir#semantic_path
         assert os.path.exists(self.path2)
         assert os.path.exists(self.path6)
@@ -219,7 +215,7 @@ class Text2SemanticDataset(Dataset):
         semantic_ids_len = len(semantic_ids)
 
         flag = 0
-        path_bert = "%s/%s.pt" % (self.path3, item_name)
+        path_bert = f"{self.path3}/{item_name}.pt"
         if os.path.exists(path_bert) == True:
             bert_feature = torch.load(path_bert, map_location="cpu")
         else:
diff --git a/GPT_SoVITS/AR/utils/io.py b/GPT_SoVITS/AR/utils/io.py
index a6475cb6..2721ff90 100644
--- a/GPT_SoVITS/AR/utils/io.py
+++ b/GPT_SoVITS/AR/utils/io.py
@@ -26,5 +26,5 @@ def write_args(args, path):
         args_file.write(str(sys.argv))
         args_file.write("\n==> args:\n")
         for k, v in sorted(args_dict.items()):
-            args_file.write("  %s: %s\n" % (str(k), str(v)))
+            args_file.write(f"  {str(k)}: {str(v)}\n")
         args_file.close()
diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py
index 0c1d2484..1233b9c2 100644
--- a/GPT_SoVITS/TTS_infer_pack/TTS.py
+++ b/GPT_SoVITS/TTS_infer_pack/TTS.py
@@ -41,7 +41,7 @@ resample_transform_dict = {}
 
 def resample(audio_tensor, sr0, sr1, device):
     global resample_transform_dict
-    key = "%s-%s-%s" % (sr0, sr1, str(device))
+    key = f"{sr0}-{sr1}-{str(device)}"
     if key not in resample_transform_dict:
         resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
     return resample_transform_dict[key](audio_tensor)
@@ -489,7 +489,7 @@ class TTS:
         path_sovits = self.configs.default_configs[model_version]["vits_weights_path"]
 
         if if_lora_v3 == True and os.path.exists(path_sovits) == False:
-            info = path_sovits + i18n("SoVITS %s 底模缺失，无法加载相应 LoRA 权重" % model_version)
+            info = path_sovits + i18n(f"SoVITS {model_version} 底模缺失，无法加载相应 LoRA 权重")
             raise FileExistsError(info)
 
         # dict_s2 = torch.load(weights_path, map_location=self.configs.device,weights_only=False)
@@ -608,7 +608,7 @@ class TTS:
                 self.empty_cache()
 
             self.vocoder = BigVGAN.from_pretrained(
-                "%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
+                f"{now_dir}/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x",
                 use_cuda_kernel=False,
             )  # if True, RuntimeError: Ninja is required to load C++ extensions
             # remove weight norm in the model and set to eval mode
@@ -641,7 +641,7 @@ class TTS:
             )
             self.vocoder.remove_weight_norm()
             state_dict_g = torch.load(
-                "%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,),
+                f"{now_dir}/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth",
                 map_location="cpu",
                 weights_only=False,
             )
diff --git a/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py b/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py
index fda70a49..5e480bf9 100644
--- a/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py
+++ b/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py
@@ -143,7 +143,7 @@ def cut2(inp):
 @register_method("cut3")
 def cut3(inp):
     inp = inp.strip("\n")
-    opts = ["%s" % item for item in inp.strip("。").split("。")]
+    opts = [f"{item}" for item in inp.strip("。").split("。")]
     opts = [item for item in opts if not set(item).issubset(punctuation)]
     return "\n".join(opts)
 
diff --git a/GPT_SoVITS/eres2net/kaldi.py b/GPT_SoVITS/eres2net/kaldi.py
index a80e5e6b..2313444c 100644
--- a/GPT_SoVITS/eres2net/kaldi.py
+++ b/GPT_SoVITS/eres2net/kaldi.py
@@ -625,18 +625,7 @@ def fbank(
     # size (num_mel_bins, padded_window_size // 2)
     # print(num_mel_bins, padded_window_size, sample_frequency, low_freq, high_freq, vtln_low, vtln_high, vtln_warp)
 
-    cache_key = "%s-%s-%s-%s-%s-%s-%s-%s-%s-%s" % (
-        num_mel_bins,
-        padded_window_size,
-        sample_frequency,
-        low_freq,
-        high_freq,
-        vtln_low,
-        vtln_high,
-        vtln_warp,
-        device,
-        dtype,
-    )
+    cache_key = f"{num_mel_bins}-{padded_window_size}-{sample_frequency}-{low_freq}-{high_freq}-{vtln_low}-{vtln_high}-{vtln_warp}-{device}-{dtype}"
     if cache_key not in cache:
         mel_energies = get_mel_banks(
             num_mel_bins,
diff --git a/GPT_SoVITS/export_torch_script_v3v4.py b/GPT_SoVITS/export_torch_script_v3v4.py
index b0e4dba5..f53ae144 100644
--- a/GPT_SoVITS/export_torch_script_v3v4.py
+++ b/GPT_SoVITS/export_torch_script_v3v4.py
@@ -505,7 +505,7 @@ def init_bigvgan():
     from BigVGAN import bigvgan
 
     bigvgan_model = bigvgan.BigVGAN.from_pretrained(
-        "%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
+        f"{now_dir}/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x",
         use_cuda_kernel=False,
     )  # if True, RuntimeError: Ninja is required to load C++ extensions
     # remove weight norm in the model and set to eval mode
@@ -533,7 +533,7 @@ def init_hifigan():
     hifigan_model.eval()
     hifigan_model.remove_weight_norm()
     state_dict_g = torch.load(
-        "%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu"
+        f"{now_dir}/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth", map_location="cpu"
     )
     print("loading vocoder", hifigan_model.load_state_dict(state_dict_g))
     if is_half == True:
@@ -1042,7 +1042,7 @@ def test_export(
     wav_gen = wav_gen[:, :, :wav_gen_length]
 
     audio = wav_gen[0][0].cpu().detach().numpy()
-    logger.info("end bigvgan %s", datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
+    logger.info(f"end bigvgan {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
     sr = 24000
     soundfile.write(output, (audio * 32768).astype(np.int16), sr)
 
@@ -1115,7 +1115,7 @@ def test_export(
     wav_gen = torch.cat([wav_gen, zero_wav_torch], 0)
 
     audio = wav_gen.cpu().detach().numpy()
-    logger.info("end bigvgan %s", datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
+    logger.info(f"end bigvgan {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
     soundfile.write(output, (audio * 32768).astype(np.int16), out_sr)
 
 
diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py
index a361ed58..42d03020 100644
--- a/GPT_SoVITS/inference_webui.py
+++ b/GPT_SoVITS/inference_webui.py
@@ -180,6 +180,8 @@ def get_bert_feature(text, word2ph):
     for i in range(len(word2ph)):
         repeat_feature = res[i].repeat(word2ph[i], 1)
         phone_level_feature.append(repeat_feature)
+    if len(phone_level_feature) == 0:
+        return torch.empty((res.shape[1], 0), dtype=res.dtype, device=res.device)
     phone_level_feature = torch.cat(phone_level_feature, dim=0)
     return phone_level_feature.T
 
@@ -235,7 +237,7 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
     is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4
     path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
     if if_lora_v3 == True and is_exist == False:
-        info = path_sovits + "SoVITS %s" % model_version + i18n("底模缺失，无法加载相应 LoRA 权重")
+        info = path_sovits + f"SoVITS {model_version}" + i18n("底模缺失，无法加载相应 LoRA 权重")
         gr.Warning(info)
         raise FileExistsError(info)
     dict_language = dict_language_v1 if version == "v1" else dict_language_v2
@@ -320,7 +322,7 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
         vq_model = vq_model.to(device)
     vq_model.eval()
     if if_lora_v3 == False:
-        print("loading sovits_%s" % model_version, vq_model.load_state_dict(dict_s2["weight"], strict=False))
+        print(f"loading sovits_{model_version}", vq_model.load_state_dict(dict_s2["weight"], strict=False))
     else:
         path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
         print(
@@ -335,7 +337,7 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
             init_lora_weights=True,
         )
         vq_model.cfm = get_peft_model(vq_model.cfm, lora_config)
-        print("loading sovits_%s_lora%s" % (model_version, lora_rank))
+        print(f"loading sovits_{model_version}_lora{lora_rank}")
         vq_model.load_state_dict(dict_s2["weight"], strict=False)
         vq_model.cfm = vq_model.cfm.merge_and_unload()
         # torch.save(vq_model.state_dict(),"merge_win.pth")
@@ -442,7 +444,7 @@ def init_bigvgan():
     from BigVGAN import bigvgan
 
     bigvgan_model = bigvgan.BigVGAN.from_pretrained(
-        "%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
+        f"{now_dir}/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x",
         use_cuda_kernel=False,
     )  # if True, RuntimeError: Ninja is required to load C++ extensions
     # remove weight norm in the model and set to eval mode
@@ -472,7 +474,7 @@ def init_hifigan():
     hifigan_model.eval()
     hifigan_model.remove_weight_norm()
     state_dict_g = torch.load(
-        "%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,),
+        f"{now_dir}/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth",
         map_location="cpu",
         weights_only=False,
     )
@@ -508,7 +510,7 @@ resample_transform_dict = {}
 
 def resample(audio_tensor, sr0, sr1, device):
     global resample_transform_dict
-    key = "%s-%s-%s" % (sr0, sr1, str(device))
+    key = f"{sr0}-{sr1}-{str(device)}"
     if key not in resample_transform_dict:
         resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
     return resample_transform_dict[key](audio_tensor)
@@ -1062,7 +1064,7 @@ def cut2(inp):
 
 def cut3(inp):
     inp = inp.strip("\n")
-    opts = ["%s" % item for item in inp.strip("。").split("。")]
+    opts = [f"{item}" for item in inp.strip("。").split("。")]
     opts = [item for item in opts if not set(item).issubset(punctuation)]
     return "\n".join(opts)
 
diff --git a/GPT_SoVITS/inference_webui_fast.py b/GPT_SoVITS/inference_webui_fast.py
index 51a120f1..605bbeed 100644
--- a/GPT_SoVITS/inference_webui_fast.py
+++ b/GPT_SoVITS/inference_webui_fast.py
@@ -31,7 +31,7 @@ import torch
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
-sys.path.append("%s/GPT_SoVITS" % (now_dir))
+sys.path.append(f"{now_dir}/GPT_SoVITS")
 
 logging.getLogger("markdown_it").setLevel(logging.ERROR)
 logging.getLogger("urllib3").setLevel(logging.ERROR)
@@ -239,7 +239,7 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
     is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4
     path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
     if if_lora_v3 == True and is_exist == False:
-        info = path_sovits + "SoVITS %s" % model_version + i18n("底模缺失，无法加载相应 LoRA 权重")
+        info = path_sovits + f"SoVITS {model_version}" + i18n("底模缺失，无法加载相应 LoRA 权重")
         gr.Warning(info)
         raise FileExistsError(info)
     dict_language = dict_language_v1 if version == "v1" else dict_language_v2
diff --git a/GPT_SoVITS/module/data_utils.py b/GPT_SoVITS/module/data_utils.py
index 46eff5fb..ec437580 100644
--- a/GPT_SoVITS/module/data_utils.py
+++ b/GPT_SoVITS/module/data_utils.py
@@ -23,15 +23,15 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
 
     def __init__(self, hparams, version=None, val=False):
         exp_dir = hparams.exp_dir
-        self.path2 = "%s/2-name2text.txt" % exp_dir
-        self.path4 = "%s/4-cnhubert" % exp_dir
-        self.path5 = "%s/5-wav32k" % exp_dir
+        self.path2 = f"{exp_dir}/2-name2text.txt"
+        self.path4 = f"{exp_dir}/4-cnhubert"
+        self.path5 = f"{exp_dir}/5-wav32k"
         assert os.path.exists(self.path2)
         assert os.path.exists(self.path4)
         assert os.path.exists(self.path5)
         self.is_v2Pro = version in {"v2Pro", "v2ProPlus"}
         if self.is_v2Pro:
-            self.path7 = "%s/7-sv_cn" % exp_dir
+            self.path7 = f"{exp_dir}/7-sv_cn"
             assert os.path.exists(self.path7)
         names4 = set([name[:-3] for name in list(os.listdir(self.path4))])  # 去除.pt后缀
         names5 = set(os.listdir(self.path5))
@@ -85,7 +85,7 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
                 skipped_phone += 1
                 continue
 
-            size = os.path.getsize("%s/%s" % (self.path5, audiopath))
+            size = os.path.getsize(f"{self.path5}/{audiopath}")
             duration = size / self.sampling_rate / 2
 
             if duration == 0:
@@ -110,9 +110,9 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
         audiopath, phoneme_ids = audiopath_sid_text
         text = torch.FloatTensor(phoneme_ids)
         try:
-            spec, wav = self.get_audio("%s/%s" % (self.path5, audiopath))
+            spec, wav = self.get_audio(f"{self.path5}/{audiopath}")
             with torch.no_grad():
-                ssl = torch.load("%s/%s.pt" % (self.path4, audiopath), map_location="cpu")
+                ssl = torch.load(f"{self.path4}/{audiopath}.pt", map_location="cpu")
                 if ssl.shape[-1] != spec.shape[-1]:
                     typee = ssl.dtype
                     ssl = F.pad(ssl.float(), (0, 1), mode="replicate").to(typee)
diff --git a/GPT_SoVITS/module/mel_processing.py b/GPT_SoVITS/module/mel_processing.py
index 62c7b40e..334b5cf3 100644
--- a/GPT_SoVITS/module/mel_processing.py
+++ b/GPT_SoVITS/module/mel_processing.py
@@ -46,7 +46,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
     global hann_window
     dtype_device = str(y.dtype) + "_" + str(y.device)
     # wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    key = "%s-%s-%s-%s-%s" % (dtype_device, n_fft, sampling_rate, hop_size, win_size)
+    key = f"{dtype_device}-{n_fft}-{sampling_rate}-{hop_size}-{win_size}"
     # if wnsize_dtype_device not in hann_window:
     if key not in hann_window:
         # hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
@@ -78,7 +78,7 @@ def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
     global mel_basis
     dtype_device = str(spec.dtype) + "_" + str(spec.device)
     # fmax_dtype_device = str(fmax) + '_' + dtype_device
-    key = "%s-%s-%s-%s-%s-%s" % (dtype_device, n_fft, num_mels, sampling_rate, fmin, fmax)
+    key = f"{dtype_device}-{n_fft}-{num_mels}-{sampling_rate}-{fmin}-{fmax}"
     # if fmax_dtype_device not in mel_basis:
     if key not in mel_basis:
         mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
@@ -99,16 +99,7 @@ def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size,
     global mel_basis, hann_window
     dtype_device = str(y.dtype) + "_" + str(y.device)
     # fmax_dtype_device = str(fmax) + '_' + dtype_device
-    fmax_dtype_device = "%s-%s-%s-%s-%s-%s-%s-%s" % (
-        dtype_device,
-        n_fft,
-        num_mels,
-        sampling_rate,
-        hop_size,
-        win_size,
-        fmin,
-        fmax,
-    )
+    fmax_dtype_device = f"{dtype_device}-{n_fft}-{num_mels}-{sampling_rate}-{hop_size}-{win_size}-{fmin}-{fmax}"
     # wnsize_dtype_device = str(win_size) + '_' + dtype_device
     wnsize_dtype_device = fmax_dtype_device
     if fmax_dtype_device not in mel_basis:
diff --git a/GPT_SoVITS/process_ckpt.py b/GPT_SoVITS/process_ckpt.py
index 20db9b19..ab00d271 100644
--- a/GPT_SoVITS/process_ckpt.py
+++ b/GPT_SoVITS/process_ckpt.py
@@ -12,9 +12,9 @@ i18n = I18nAuto()
 def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
     dir = os.path.dirname(path)
     name = os.path.basename(path)
-    tmp_path = "%s.pth" % (ttime())
+    tmp_path = f"{ttime()}.pth"
     torch.save(fea, tmp_path)
-    shutil.move(tmp_path, "%s/%s" % (dir, name))
+    shutil.move(tmp_path, f"{dir}/{name}")
 
 
 from io import BytesIO
@@ -47,14 +47,14 @@ def savee(ckpt, name, epoch, steps, hps, model_version=None, lora_rank=None):
                 continue
             opt["weight"][key] = ckpt[key].half()
         opt["config"] = hps
-        opt["info"] = "%sepoch_%siteration" % (epoch, steps)
+        opt["info"] = f"{epoch}epoch_{steps}iteration"
         if lora_rank:
             opt["lora_rank"] = lora_rank
-            my_save2(opt, "%s/%s.pth" % (hps.save_weight_dir, name), model_version)
+            my_save2(opt, f"{hps.save_weight_dir}/{name}.pth", model_version)
         elif model_version != None and "Pro" in model_version:
-            my_save2(opt, "%s/%s.pth" % (hps.save_weight_dir, name), model_version)
+            my_save2(opt, f"{hps.save_weight_dir}/{name}.pth", model_version)
         else:
-            my_save(opt, "%s/%s.pth" % (hps.save_weight_dir, name))
+            my_save(opt, f"{hps.save_weight_dir}/{name}.pth")
         return "Success."
     except:
         return traceback.format_exc()
diff --git a/GPT_SoVITS/utils.py b/GPT_SoVITS/utils.py
index 08e18384..b61ac19d 100644
--- a/GPT_SoVITS/utils.py
+++ b/GPT_SoVITS/utils.py
@@ -44,7 +44,7 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False
             )
         except:
             traceback.print_exc()
-            print("error, %s is not in the checkpoint" % k)  # shape不对也会，比如text_embedding当cleaner修改时
+            print(f"error, {k} is not in the checkpoint")  # shape不对也会，比如text_embedding当cleaner修改时
             new_state_dict[k] = v
     if hasattr(model, "module"):
         model.module.load_state_dict(new_state_dict)
@@ -67,9 +67,9 @@ from time import time as ttime
 def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
     dir = os.path.dirname(path)
     name = os.path.basename(path)
-    tmp_path = "%s.pth" % (ttime())
+    tmp_path = f"{ttime()}.pth"
     torch.save(fea, tmp_path)
-    shutil.move(tmp_path, "%s/%s" % (dir, name))
+    shutil.move(tmp_path, f"{dir}/{name}")
 
 
 def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
diff --git a/api.py b/api.py
index cc0896a2..1df6b297 100644
--- a/api.py
+++ b/api.py
@@ -147,7 +147,7 @@ import sys
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
-sys.path.append("%s/GPT_SoVITS" % (now_dir))
+sys.path.append(f"{now_dir}/GPT_SoVITS")
 
 import signal
 from text.LangSegmenter import LangSegmenter
@@ -239,7 +239,7 @@ def init_bigvgan():
     from BigVGAN import bigvgan
 
     bigvgan_model = bigvgan.BigVGAN.from_pretrained(
-        "%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
+        f"{now_dir}/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x",
         use_cuda_kernel=False,
     )  # if True, RuntimeError: Ninja is required to load C++ extensions
     # remove weight norm in the model and set to eval mode
@@ -268,7 +268,7 @@ def init_hifigan():
     hifigan_model.eval()
     hifigan_model.remove_weight_norm()
     state_dict_g = torch.load(
-        "%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,),
+        f"{now_dir}/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth",
         map_location="cpu",
         weights_only=False,
     )
@@ -292,7 +292,7 @@ resample_transform_dict = {}
 
 def resample(audio_tensor, sr0, sr1, device):
     global resample_transform_dict
-    key = "%s-%s-%s" % (sr0, sr1, str(device))
+    key = f"{sr0}-{sr1}-{str(device)}"
     if key not in resample_transform_dict:
         resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
     return resample_transform_dict[key](audio_tensor)
@@ -391,7 +391,7 @@ def get_sovits_weights(sovits_path):
     path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
 
     if if_lora_v3 == True and is_exist == False:
-        logger.info("SoVITS %s 底模缺失，无法加载相应 LoRA 权重" % model_version)
+        logger.info(f"SoVITS {model_version} 底模缺失，无法加载相应 LoRA 权重")
 
     dict_s2 = load_sovits_new(sovits_path)
     hps = dict_s2["config"]
diff --git a/api_v2.py b/api_v2.py
index 5947df53..4c104901 100644
--- a/api_v2.py
+++ b/api_v2.py
@@ -105,7 +105,7 @@ from typing import Generator
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
-sys.path.append("%s/GPT_SoVITS" % (now_dir))
+sys.path.append(f"{now_dir}/GPT_SoVITS")
 
 import argparse
 import subprocess
diff --git a/config.py b/config.py
index fdc11c0a..09ae866d 100644
--- a/config.py
+++ b/config.py
@@ -93,7 +93,7 @@ def get_weights_names():
             continue
         for name in os.listdir(path):
             if name.endswith(".pth"):
-                SoVITS_names.append("%s/%s" % (path, name))
+                SoVITS_names.append(f"{path}/{name}")
     if not SoVITS_names:
         SoVITS_names = [""]
     GPT_names = []
@@ -105,7 +105,7 @@ def get_weights_names():
             continue
         for name in os.listdir(path):
             if name.endswith(".ckpt"):
-                GPT_names.append("%s/%s" % (path, name))
+                GPT_names.append(f"{path}/{name}")
     SoVITS_names = sorted(SoVITS_names, key=custom_sort_key)
     GPT_names = sorted(GPT_names, key=custom_sort_key)
     if not GPT_names:
diff --git a/tools/audio_sr.py b/tools/audio_sr.py
index 58df6d20..4a49f6b1 100644
--- a/tools/audio_sr.py
+++ b/tools/audio_sr.py
@@ -16,7 +16,7 @@ from models.model import APNet_BWE_Model
 class AP_BWE:
     def __init__(self, device, DictToAttrRecursive, checkpoint_file=None):
         if checkpoint_file == None:
-            checkpoint_file = "%s/24kto48k/g_24kto48k.zip" % (AP_BWE_main_dir_path)
+            checkpoint_file = f"{AP_BWE_main_dir_path}/24kto48k/g_24kto48k.zip"
             if os.path.exists(checkpoint_file) == False:
                 raise FileNotFoundError
         config_file = os.path.join(os.path.split(checkpoint_file)[0], "config.json")
diff --git a/tools/cmd-denoise.py b/tools/cmd-denoise.py
index bbf68476..e9cd5f7e 100644
--- a/tools/cmd-denoise.py
+++ b/tools/cmd-denoise.py
@@ -17,7 +17,7 @@ def execute_denoise(input_folder, output_folder):
     # print(list(os.listdir(input_folder).sort()))
     for name in tqdm(os.listdir(input_folder)):
         try:
-            ans("%s/%s" % (input_folder, name), output_path="%s/%s" % (output_folder, name))
+            ans(f"{input_folder}/{name}", output_path=f"{output_folder}/{name}")
         except:
             traceback.print_exc()
 
diff --git a/tools/my_utils.py b/tools/my_utils.py
index 04f1a98a..2717e445 100644
--- a/tools/my_utils.py
+++ b/tools/my_utils.py
@@ -103,7 +103,7 @@ def check_details(path_list=None, is_train=False, is_dataset_processing=False):
         wav_name = clean_path(wav_name)
         if audio_path != "" and audio_path != None:
             wav_name = os.path.basename(wav_name)
-            wav_path = "%s/%s" % (audio_path, wav_name)
+            wav_path = f"{audio_path}/{wav_name}"
         else:
             wav_path = wav_name
         if os.path.exists(wav_path):
diff --git a/tools/slice_audio.py b/tools/slice_audio.py
index 66fafa93..1e0eb782 100644
--- a/tools/slice_audio.py
+++ b/tools/slice_audio.py
@@ -40,7 +40,7 @@ def slice(inp, opt_root, threshold, min_length, min_interval, hop_size, max_sil_
                     chunk /= tmp_max
                 chunk = (chunk / tmp_max * (_max * alpha)) + (1 - alpha) * chunk
                 wavfile.write(
-                    "%s/%s_%010d_%010d.wav" % (opt_root, name, start, end),
+                    f"{opt_root}/{name}_{start:010d}_{end:010d}.wav",
                     32000,
                     # chunk.astype(np.float32),
                     (chunk * 32767).astype(np.int16),
diff --git a/tools/slicer2.py b/tools/slicer2.py
index 8d80f1b4..512ac44a 100644
--- a/tools/slicer2.py
+++ b/tools/slicer2.py
@@ -219,7 +219,7 @@ def main():
         soundfile.write(
             os.path.join(
                 out,
-                "%s_%d.wav" % (os.path.basename(args.audio).rsplit(".", maxsplit=1)[0], i),
+                f"{os.path.basename(args.audio).rsplit('.', maxsplit=1)[0]}_{i}.wav",
             ),
             chunk,
             sr,
diff --git a/tools/uvr5/webui.py b/tools/uvr5/webui.py
index f5f8d3f6..dbc1be3f 100644
--- a/tools/uvr5/webui.py
+++ b/tools/uvr5/webui.py
@@ -92,19 +92,16 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
                 need_reformat = 1
                 traceback.print_exc()
             if need_reformat == 1:
-                tmp_path = "%s/%s.reformatted.wav" % (
-                    os.path.join(os.environ["TEMP"]),
-                    os.path.basename(inp_path),
-                )
+                tmp_path = f"{os.path.join(os.environ['TEMP'])}/{os.path.basename(inp_path)}.reformatted.wav"
                 os.system(f'ffmpeg -i "{inp_path}" -vn -acodec pcm_s16le -ac 2 -ar 44100 "{tmp_path}" -y')
                 inp_path = tmp_path
             try:
                 if done == 0:
                     pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal, format0, is_hp3)
-                infos.append("%s->Success" % (os.path.basename(inp_path)))
+                infos.append(f"{os.path.basename(inp_path)}->Success")
                 yield "\n".join(infos)
             except:
-                infos.append("%s->%s" % (os.path.basename(inp_path), traceback.format_exc()))
+                infos.append(f"{os.path.basename(inp_path)}->{traceback.format_exc()}")
                 yield "\n".join(infos)
     except:
         infos.append(traceback.format_exc())
diff --git a/webui.py b/webui.py
index 9a6aae5f..6711385b 100644
--- a/webui.py
+++ b/webui.py
@@ -1,1982 +1,2045 @@
-import os
-import sys
-
-os.environ["version"] = version = "v2Pro"
-now_dir = os.getcwd()
-sys.path.insert(0, now_dir)
-import warnings
-
-warnings.filterwarnings("ignore")
-import json
-import platform
-import shutil
-import signal
-
-import psutil
-import torch
-import yaml
-
-os.environ["TORCH_DISTRIBUTED_DEBUG"] = "INFO"
-torch.manual_seed(233333)
-tmp = os.path.join(now_dir, "TEMP")
-os.makedirs(tmp, exist_ok=True)
-os.environ["TEMP"] = tmp
-if os.path.exists(tmp):
-    for name in os.listdir(tmp):
-        if name == "jieba.cache":
-            continue
-        path = "%s/%s" % (tmp, name)
-        delete = os.remove if os.path.isfile(path) else shutil.rmtree
-        try:
-            delete(path)
-        except Exception as e:
-            print(str(e))
-            pass
-import site
-import traceback
-
-site_packages_roots = []
-for path in site.getsitepackages():
-    if "packages" in path:
-        site_packages_roots.append(path)
-if site_packages_roots == []:
-    site_packages_roots = ["%s/runtime/Lib/site-packages" % now_dir]
-# os.environ["OPENBLAS_NUM_THREADS"] = "4"
-os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
-os.environ["all_proxy"] = ""
-for site_packages_root in site_packages_roots:
-    if os.path.exists(site_packages_root):
-        try:
-            with open("%s/users.pth" % (site_packages_root), "w") as f:
-                f.write(
-                    # "%s\n%s/runtime\n%s/tools\n%s/tools/asr\n%s/GPT_SoVITS\n%s/tools/uvr5"
-                    "%s\n%s/GPT_SoVITS/BigVGAN\n%s/tools\n%s/tools/asr\n%s/GPT_SoVITS\n%s/tools/uvr5"
-                    % (now_dir, now_dir, now_dir, now_dir, now_dir, now_dir)
-                )
-            break
-        except PermissionError:
-            traceback.print_exc()
-import shutil
-import subprocess
-from subprocess import Popen
-
-from tools.assets import css, js, top_html
-from tools.i18n.i18n import I18nAuto, scan_language_list
-
-language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else "Auto"
-os.environ["language"] = language
-i18n = I18nAuto(language=language)
-from multiprocessing import cpu_count
-
-from config import (
-    GPU_INDEX,
-    GPU_INFOS,
-    IS_GPU,
-    exp_root,
-    infer_device,
-    is_half,
-    is_share,
-    memset,
-    python_exec,
-    webui_port_infer_tts,
-    webui_port_main,
-    webui_port_subfix,
-    webui_port_uvr5,
-)
-from tools import my_utils
-from tools.my_utils import check_details, check_for_existance
-
-os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
-os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
-
-# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 当遇到mps不支持的步骤时使用cpu
-import gradio as gr
-
-n_cpu = cpu_count()
-
-set_gpu_numbers = GPU_INDEX
-gpu_infos = GPU_INFOS
-mem = memset
-is_gpu_ok = IS_GPU
-
-v3v4set = {"v3", "v4"}
-
-
-def set_default():
-    global \
-        default_batch_size, \
-        default_max_batch_size, \
-        gpu_info, \
-        default_sovits_epoch, \
-        default_sovits_save_every_epoch, \
-        max_sovits_epoch, \
-        max_sovits_save_every_epoch, \
-        default_batch_size_s1, \
-        if_force_ckpt
-    if_force_ckpt = False
-    gpu_info = "\n".join(gpu_infos)
-    if is_gpu_ok:
-        minmem = min(mem)
-        default_batch_size = minmem // 2 if version not in v3v4set else minmem // 8
-        default_batch_size_s1 = minmem // 2
-    else:
-        default_batch_size = default_batch_size_s1 = int(psutil.virtual_memory().total / 1024 / 1024 / 1024 / 4)
-    if version not in v3v4set:
-        default_sovits_epoch = 8
-        default_sovits_save_every_epoch = 4
-        max_sovits_epoch = 25  # 40
-        max_sovits_save_every_epoch = 25  # 10
-    else:
-        default_sovits_epoch = 2
-        default_sovits_save_every_epoch = 1
-        max_sovits_epoch = 16  # 40 # 3 #训太多=作死
-        max_sovits_save_every_epoch = 10  # 10 # 3
-
-    default_batch_size = max(1, default_batch_size)
-    default_batch_size_s1 = max(1, default_batch_size_s1)
-    default_max_batch_size = default_batch_size * 3
-
-
-set_default()
-
-gpus = "-".join(map(str, GPU_INDEX))
-default_gpu_numbers = infer_device.index
-
-
-def fix_gpu_number(input):  # 将越界的number强制改到界内
-    try:
-        if int(input) not in set_gpu_numbers:
-            return default_gpu_numbers
-    except:
-        return input
-    return input
-
-
-def fix_gpu_numbers(inputs):
-    output = []
-    try:
-        for input in inputs.split(","):
-            output.append(str(fix_gpu_number(input)))
-        return ",".join(output)
-    except:
-        return inputs
-
-
-from config import pretrained_gpt_name, pretrained_sovits_name
-
-
-def check_pretrained_is_exist(version):
-    pretrained_model_list = (
-        pretrained_sovits_name[version],
-        pretrained_sovits_name[version].replace("s2G", "s2D"),
-        pretrained_gpt_name[version],
-        "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
-        "GPT_SoVITS/pretrained_models/chinese-hubert-base",
-    )
-    _ = ""
-    for i in pretrained_model_list:
-        if "s2Dv3" not in i and "s2Dv4" not in i and os.path.exists(i) == False:
-            _ += f"\n    {i}"
-    if _:
-        print("warning: ", i18n("以下模型不存在:") + _)
-
-
-check_pretrained_is_exist(version)
-for key in pretrained_sovits_name.keys():
-    if os.path.exists(pretrained_sovits_name[key]) == False:
-        pretrained_sovits_name[key] = ""
-for key in pretrained_gpt_name.keys():
-    if os.path.exists(pretrained_gpt_name[key]) == False:
-        pretrained_gpt_name[key] = ""
-
-from config import (
-    GPT_weight_root,
-    GPT_weight_version2root,
-    SoVITS_weight_root,
-    SoVITS_weight_version2root,
-    change_choices,
-    get_weights_names,
-)
-
-for root in SoVITS_weight_root + GPT_weight_root:
-    os.makedirs(root, exist_ok=True)
-SoVITS_names, GPT_names = get_weights_names()
-
-p_label = None
-p_uvr5 = None
-p_asr = None
-p_denoise = None
-p_tts_inference = None
-
-
-def kill_proc_tree(pid, including_parent=True):
-    try:
-        parent = psutil.Process(pid)
-    except psutil.NoSuchProcess:
-        # Process already terminated
-        return
-
-    children = parent.children(recursive=True)
-    for child in children:
-        try:
-            os.kill(child.pid, signal.SIGTERM)  # or signal.SIGKILL
-        except OSError:
-            pass
-    if including_parent:
-        try:
-            os.kill(parent.pid, signal.SIGTERM)  # or signal.SIGKILL
-        except OSError:
-            pass
-
-
-system = platform.system()
-
-
-def kill_process(pid, process_name=""):
-    if system == "Windows":
-        cmd = "taskkill /t /f /pid %s" % pid
-        # os.system(cmd)
-        subprocess.run(cmd, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
-    else:
-        kill_proc_tree(pid)
-    print(process_name + i18n("进程已终止"))
-
-
-def process_info(process_name="", indicator=""):
-    if indicator == "opened":
-        return process_name + i18n("已开启")
-    elif indicator == "open":
-        return i18n("开启") + process_name
-    elif indicator == "closed":
-        return process_name + i18n("已关闭")
-    elif indicator == "close":
-        return i18n("关闭") + process_name
-    elif indicator == "running":
-        return process_name + i18n("运行中")
-    elif indicator == "occupy":
-        return process_name + i18n("占用中") + "," + i18n("需先终止才能开启下一次任务")
-    elif indicator == "finish":
-        return process_name + i18n("已完成")
-    elif indicator == "failed":
-        return process_name + i18n("失败")
-    elif indicator == "info":
-        return process_name + i18n("进程输出信息")
-    else:
-        return process_name
-
-
-process_name_subfix = i18n("音频标注WebUI")
-
-
-def change_label(path_list):
-    global p_label
-    if p_label is None:
-        check_for_existance([path_list])
-        path_list = my_utils.clean_path(path_list)
-        cmd = '"%s" -s tools/subfix_webui.py --load_list "%s" --webui_port %s --is_share %s' % (
-            python_exec,
-            path_list,
-            webui_port_subfix,
-            is_share,
-        )
-        yield (
-            process_info(process_name_subfix, "opened"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-        )
-        print(cmd)
-        p_label = Popen(cmd, shell=True)
-    else:
-        kill_process(p_label.pid, process_name_subfix)
-        p_label = None
-        yield (
-            process_info(process_name_subfix, "closed"),
-            {"__type__": "update", "visible": True},
-            {"__type__": "update", "visible": False},
-        )
-
-
-process_name_uvr5 = i18n("人声分离WebUI")
-
-
-def change_uvr5():
-    global p_uvr5
-    if p_uvr5 is None:
-        cmd = '"%s" -s tools/uvr5/webui.py "%s" %s %s %s' % (
-            python_exec,
-            infer_device,
-            is_half,
-            webui_port_uvr5,
-            is_share,
-        )
-        yield (
-            process_info(process_name_uvr5, "opened"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-        )
-        print(cmd)
-        p_uvr5 = Popen(cmd, shell=True)
-    else:
-        kill_process(p_uvr5.pid, process_name_uvr5)
-        p_uvr5 = None
-        yield (
-            process_info(process_name_uvr5, "closed"),
-            {"__type__": "update", "visible": True},
-            {"__type__": "update", "visible": False},
-        )
-
-
-process_name_tts = i18n("TTS推理WebUI")
-
-
-def change_tts_inference(bert_path, cnhubert_base_path, gpu_number, gpt_path, sovits_path, batched_infer_enabled):
-    global p_tts_inference
-    if batched_infer_enabled:
-        cmd = '"%s" -s GPT_SoVITS/inference_webui_fast.py "%s"' % (python_exec, language)
-    else:
-        cmd = '"%s" -s GPT_SoVITS/inference_webui.py "%s"' % (python_exec, language)
-    # #####v3暂不支持加速推理
-    # if version=="v3":
-    #     cmd = '"%s" GPT_SoVITS/inference_webui.py "%s"'%(python_exec, language)
-    if p_tts_inference is None:
-        os.environ["gpt_path"] = gpt_path
-        os.environ["sovits_path"] = sovits_path
-        os.environ["cnhubert_base_path"] = cnhubert_base_path
-        os.environ["bert_path"] = bert_path
-        os.environ["_CUDA_VISIBLE_DEVICES"] = fix_gpu_number(gpu_number)
-        os.environ["is_half"] = str(is_half)
-        os.environ["infer_ttswebui"] = str(webui_port_infer_tts)
-        os.environ["is_share"] = str(is_share)
-        yield (
-            process_info(process_name_tts, "opened"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-        )
-        print(cmd)
-        p_tts_inference = Popen(cmd, shell=True)
-    else:
-        kill_process(p_tts_inference.pid, process_name_tts)
-        p_tts_inference = None
-        yield (
-            process_info(process_name_tts, "closed"),
-            {"__type__": "update", "visible": True},
-            {"__type__": "update", "visible": False},
-        )
-
-
-from tools.asr.config import asr_dict
-
-process_name_asr = i18n("语音识别")
-
-
-def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang, asr_precision):
-    global p_asr
-    if p_asr is None:
-        asr_inp_dir = my_utils.clean_path(asr_inp_dir)
-        asr_opt_dir = my_utils.clean_path(asr_opt_dir)
-        check_for_existance([asr_inp_dir])
-        cmd = f'"{python_exec}" -s tools/asr/{asr_dict[asr_model]["path"]}'
-        cmd += f' -i "{asr_inp_dir}"'
-        cmd += f' -o "{asr_opt_dir}"'
-        cmd += f" -s {asr_model_size}"
-        cmd += f" -l {asr_lang}"
-        cmd += f" -p {asr_precision}"
-        output_file_name = os.path.basename(asr_inp_dir)
-        output_folder = asr_opt_dir or "output/asr_opt"
-        output_file_path = os.path.abspath(f"{output_folder}/{output_file_name}.list")
-        yield (
-            process_info(process_name_asr, "opened"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-            {"__type__": "update"},
-            {"__type__": "update"},
-            {"__type__": "update"},
-        )
-        print(cmd)
-        p_asr = Popen(cmd, shell=True)
-        p_asr.wait()
-        p_asr = None
-        yield (
-            process_info(process_name_asr, "finish"),
-            {"__type__": "update", "visible": True},
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "value": output_file_path},
-            {"__type__": "update", "value": output_file_path},
-            {"__type__": "update", "value": asr_inp_dir},
-        )
-    else:
-        yield (
-            process_info(process_name_asr, "occupy"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-            {"__type__": "update"},
-            {"__type__": "update"},
-            {"__type__": "update"},
-        )
-
-
-def close_asr():
-    global p_asr
-    if p_asr is not None:
-        kill_process(p_asr.pid, process_name_asr)
-        p_asr = None
-    return (
-        process_info(process_name_asr, "closed"),
-        {"__type__": "update", "visible": True},
-        {"__type__": "update", "visible": False},
-    )
-
-
-process_name_denoise = i18n("语音降噪")
-
-
-def open_denoise(denoise_inp_dir, denoise_opt_dir):
-    global p_denoise
-    if p_denoise == None:
-        denoise_inp_dir = my_utils.clean_path(denoise_inp_dir)
-        denoise_opt_dir = my_utils.clean_path(denoise_opt_dir)
-        check_for_existance([denoise_inp_dir])
-        cmd = '"%s" -s tools/cmd-denoise.py -i "%s" -o "%s" -p %s' % (
-            python_exec,
-            denoise_inp_dir,
-            denoise_opt_dir,
-            "float16" if is_half == True else "float32",
-        )
-
-        yield (
-            process_info(process_name_denoise, "opened"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-            {"__type__": "update"},
-            {"__type__": "update"},
-        )
-        print(cmd)
-        p_denoise = Popen(cmd, shell=True)
-        p_denoise.wait()
-        p_denoise = None
-        yield (
-            process_info(process_name_denoise, "finish"),
-            {"__type__": "update", "visible": True},
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "value": denoise_opt_dir},
-            {"__type__": "update", "value": denoise_opt_dir},
-        )
-    else:
-        yield (
-            process_info(process_name_denoise, "occupy"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-            {"__type__": "update"},
-            {"__type__": "update"},
-        )
-
-
-def close_denoise():
-    global p_denoise
-    if p_denoise is not None:
-        kill_process(p_denoise.pid, process_name_denoise)
-        p_denoise = None
-    return (
-        process_info(process_name_denoise, "closed"),
-        {"__type__": "update", "visible": True},
-        {"__type__": "update", "visible": False},
-    )
-
-
-p_train_SoVITS = None
-process_name_sovits = i18n("SoVITS训练")
-
-
-def open1Ba(
-    version,
-    batch_size,
-    total_epoch,
-    exp_name,
-    text_low_lr_rate,
-    if_save_latest,
-    if_save_every_weights,
-    save_every_epoch,
-    gpu_numbers1Ba,
-    pretrained_s2G,
-    pretrained_s2D,
-    if_grad_ckpt,
-    lora_rank,
-):
-    global p_train_SoVITS
-    if p_train_SoVITS == None:
-        exp_name = exp_name.rstrip(" ")
-        config_file = (
-            "GPT_SoVITS/configs/s2.json"
-            if version not in {"v2Pro", "v2ProPlus"}
-            else f"GPT_SoVITS/configs/s2{version}.json"
-        )
-        with open(config_file) as f:
-            data = f.read()
-            data = json.loads(data)
-        s2_dir = "%s/%s" % (exp_root, exp_name)
-        os.makedirs("%s/logs_s2_%s" % (s2_dir, version), exist_ok=True)
-        if check_for_existance([s2_dir], is_train=True):
-            check_details([s2_dir], is_train=True)
-        if is_half == False:
-            data["train"]["fp16_run"] = False
-            batch_size = max(1, batch_size // 2)
-        data["train"]["batch_size"] = batch_size
-        data["train"]["epochs"] = total_epoch
-        data["train"]["text_low_lr_rate"] = text_low_lr_rate
-        data["train"]["pretrained_s2G"] = pretrained_s2G
-        data["train"]["pretrained_s2D"] = pretrained_s2D
-        data["train"]["if_save_latest"] = if_save_latest
-        data["train"]["if_save_every_weights"] = if_save_every_weights
-        data["train"]["save_every_epoch"] = save_every_epoch
-        data["train"]["gpu_numbers"] = gpu_numbers1Ba
-        data["train"]["grad_ckpt"] = if_grad_ckpt
-        data["train"]["lora_rank"] = lora_rank
-        data["model"]["version"] = version
-        data["data"]["exp_dir"] = data["s2_ckpt_dir"] = s2_dir
-        data["save_weight_dir"] = SoVITS_weight_version2root[version]
-        data["name"] = exp_name
-        data["version"] = version
-        tmp_config_path = "%s/tmp_s2.json" % tmp
-        with open(tmp_config_path, "w") as f:
-            f.write(json.dumps(data))
-        if version in ["v1", "v2", "v2Pro", "v2ProPlus"]:
-            cmd = '"%s" -s GPT_SoVITS/s2_train.py --config "%s"' % (python_exec, tmp_config_path)
-        else:
-            cmd = '"%s" -s GPT_SoVITS/s2_train_v3_lora.py --config "%s"' % (python_exec, tmp_config_path)
-        yield (
-            process_info(process_name_sovits, "opened"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-            {"__type__": "update"},
-            {"__type__": "update"},
-        )
-        print(cmd)
-        p_train_SoVITS = Popen(cmd, shell=True)
-        p_train_SoVITS.wait()
-        p_train_SoVITS = None
-        SoVITS_dropdown_update, GPT_dropdown_update = change_choices()
-        yield (
-            process_info(process_name_sovits, "finish"),
-            {"__type__": "update", "visible": True},
-            {"__type__": "update", "visible": False},
-            SoVITS_dropdown_update,
-            GPT_dropdown_update,
-        )
-    else:
-        yield (
-            process_info(process_name_sovits, "occupy"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-            {"__type__": "update"},
-            {"__type__": "update"},
-        )
-
-
-def close1Ba():
-    global p_train_SoVITS
-    if p_train_SoVITS is not None:
-        kill_process(p_train_SoVITS.pid, process_name_sovits)
-        p_train_SoVITS = None
-    return (
-        process_info(process_name_sovits, "closed"),
-        {"__type__": "update", "visible": True},
-        {"__type__": "update", "visible": False},
-    )
-
-
-p_train_GPT = None
-process_name_gpt = i18n("GPT训练")
-
-
-def open1Bb(
-    batch_size,
-    total_epoch,
-    exp_name,
-    if_dpo,
-    if_save_latest,
-    if_save_every_weights,
-    save_every_epoch,
-    gpu_numbers,
-    pretrained_s1,
-):
-    global p_train_GPT
-    if p_train_GPT == None:
-        exp_name = exp_name.rstrip(" ")
-        with open(
-            "GPT_SoVITS/configs/s1longer.yaml" if version == "v1" else "GPT_SoVITS/configs/s1longer-v2.yaml"
-        ) as f:
-            data = f.read()
-            data = yaml.load(data, Loader=yaml.FullLoader)
-        s1_dir = "%s/%s" % (exp_root, exp_name)
-        os.makedirs("%s/logs_s1" % (s1_dir), exist_ok=True)
-        if check_for_existance([s1_dir], is_train=True):
-            check_details([s1_dir], is_train=True)
-        if is_half == False:
-            data["train"]["precision"] = "32"
-            batch_size = max(1, batch_size // 2)
-        data["train"]["batch_size"] = batch_size
-        data["train"]["epochs"] = total_epoch
-        data["pretrained_s1"] = pretrained_s1
-        data["train"]["save_every_n_epoch"] = save_every_epoch
-        data["train"]["if_save_every_weights"] = if_save_every_weights
-        data["train"]["if_save_latest"] = if_save_latest
-        data["train"]["if_dpo"] = if_dpo
-        data["train"]["half_weights_save_dir"] = GPT_weight_version2root[version]
-        data["train"]["exp_name"] = exp_name
-        data["train_semantic_path"] = "%s/6-name2semantic.tsv" % s1_dir
-        data["train_phoneme_path"] = "%s/2-name2text.txt" % s1_dir
-        data["output_dir"] = "%s/logs_s1_%s" % (s1_dir, version)
-        # data["version"]=version
-
-        os.environ["_CUDA_VISIBLE_DEVICES"] = fix_gpu_numbers(gpu_numbers.replace("-", ","))
-        os.environ["hz"] = "25hz"
-        tmp_config_path = "%s/tmp_s1.yaml" % tmp
-        with open(tmp_config_path, "w") as f:
-            f.write(yaml.dump(data, default_flow_style=False))
-        # cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" --train_semantic_path "%s/6-name2semantic.tsv" --train_phoneme_path "%s/2-name2text.txt" --output_dir "%s/logs_s1"'%(python_exec,tmp_config_path,s1_dir,s1_dir,s1_dir)
-        cmd = '"%s" -s GPT_SoVITS/s1_train.py --config_file "%s" ' % (python_exec, tmp_config_path)
-        yield (
-            process_info(process_name_gpt, "opened"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-            {"__type__": "update"},
-            {"__type__": "update"},
-        )
-        print(cmd)
-        p_train_GPT = Popen(cmd, shell=True)
-        p_train_GPT.wait()
-        p_train_GPT = None
-        SoVITS_dropdown_update, GPT_dropdown_update = change_choices()
-        yield (
-            process_info(process_name_gpt, "finish"),
-            {"__type__": "update", "visible": True},
-            {"__type__": "update", "visible": False},
-            SoVITS_dropdown_update,
-            GPT_dropdown_update,
-        )
-    else:
-        yield (
-            process_info(process_name_gpt, "occupy"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-            {"__type__": "update"},
-            {"__type__": "update"},
-        )
-
-
-def close1Bb():
-    global p_train_GPT
-    if p_train_GPT is not None:
-        kill_process(p_train_GPT.pid, process_name_gpt)
-        p_train_GPT = None
-    return (
-        process_info(process_name_gpt, "closed"),
-        {"__type__": "update", "visible": True},
-        {"__type__": "update", "visible": False},
-    )
-
-
-ps_slice = []
-process_name_slice = i18n("语音切分")
-
-
-def open_slice(inp, opt_root, threshold, min_length, min_interval, hop_size, max_sil_kept, _max, alpha, n_parts):
-    global ps_slice
-    inp = my_utils.clean_path(inp)
-    opt_root = my_utils.clean_path(opt_root)
-    check_for_existance([inp])
-    if os.path.exists(inp) == False:
-        yield (
-            i18n("输入路径不存在"),
-            {"__type__": "update", "visible": True},
-            {"__type__": "update", "visible": False},
-            {"__type__": "update"},
-            {"__type__": "update"},
-            {"__type__": "update"},
-        )
-        return
-    if os.path.isfile(inp):
-        n_parts = 1
-    elif os.path.isdir(inp):
-        pass
-    else:
-        yield (
-            i18n("输入路径存在但不可用"),
-            {"__type__": "update", "visible": True},
-            {"__type__": "update", "visible": False},
-            {"__type__": "update"},
-            {"__type__": "update"},
-            {"__type__": "update"},
-        )
-        return
-    if ps_slice == []:
-        for i_part in range(n_parts):
-            cmd = '"%s" -s tools/slice_audio.py "%s" "%s" %s %s %s %s %s %s %s %s %s' % (
-                python_exec,
-                inp,
-                opt_root,
-                threshold,
-                min_length,
-                min_interval,
-                hop_size,
-                max_sil_kept,
-                _max,
-                alpha,
-                i_part,
-                n_parts,
-            )
-            print(cmd)
-            p = Popen(cmd, shell=True)
-            ps_slice.append(p)
-        yield (
-            process_info(process_name_slice, "opened"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-            {"__type__": "update"},
-            {"__type__": "update"},
-            {"__type__": "update"},
-        )
-        for p in ps_slice:
-            p.wait()
-        ps_slice = []
-        yield (
-            process_info(process_name_slice, "finish"),
-            {"__type__": "update", "visible": True},
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "value": opt_root},
-            {"__type__": "update", "value": opt_root},
-            {"__type__": "update", "value": opt_root},
-        )
-    else:
-        yield (
-            process_info(process_name_slice, "occupy"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-            {"__type__": "update"},
-            {"__type__": "update"},
-            {"__type__": "update"},
-        )
-
-
-def close_slice():
-    global ps_slice
-    if ps_slice != []:
-        for p_slice in ps_slice:
-            try:
-                kill_process(p_slice.pid, process_name_slice)
-            except:
-                traceback.print_exc()
-        ps_slice = []
-    return (
-        process_info(process_name_slice, "closed"),
-        {"__type__": "update", "visible": True},
-        {"__type__": "update", "visible": False},
-    )
-
-
-ps1a = []
-process_name_1a = i18n("文本分词与特征提取")
-
-
-def open1a(inp_text, inp_wav_dir, exp_name, gpu_numbers, bert_pretrained_dir):
-    global ps1a
-    inp_text = my_utils.clean_path(inp_text)
-    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
-    if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
-        check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
-    exp_name = exp_name.rstrip(" ")
-    if ps1a == []:
-        opt_dir = "%s/%s" % (exp_root, exp_name)
-        config = {
-            "inp_text": inp_text,
-            "inp_wav_dir": inp_wav_dir,
-            "exp_name": exp_name,
-            "opt_dir": opt_dir,
-            "bert_pretrained_dir": bert_pretrained_dir,
-        }
-        gpu_names = gpu_numbers.split("-")
-        all_parts = len(gpu_names)
-        for i_part in range(all_parts):
-            config.update(
-                {
-                    "i_part": str(i_part),
-                    "all_parts": str(all_parts),
-                    "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
-                    "is_half": str(is_half),
-                }
-            )
-            os.environ.update(config)
-            cmd = '"%s" -s GPT_SoVITS/prepare_datasets/1-get-text.py' % python_exec
-            print(cmd)
-            p = Popen(cmd, shell=True)
-            ps1a.append(p)
-        yield (
-            process_info(process_name_1a, "running"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-        )
-        for p in ps1a:
-            p.wait()
-        opt = []
-        for i_part in range(all_parts):
-            txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
-            with open(txt_path, "r", encoding="utf8") as f:
-                opt += f.read().strip("\n").split("\n")
-            os.remove(txt_path)
-        path_text = "%s/2-name2text.txt" % opt_dir
-        with open(path_text, "w", encoding="utf8") as f:
-            f.write("\n".join(opt) + "\n")
-        ps1a = []
-        if len("".join(opt)) > 0:
-            yield (
-                process_info(process_name_1a, "finish"),
-                {"__type__": "update", "visible": True},
-                {"__type__": "update", "visible": False},
-            )
-        else:
-            yield (
-                process_info(process_name_1a, "failed"),
-                {"__type__": "update", "visible": True},
-                {"__type__": "update", "visible": False},
-            )
-    else:
-        yield (
-            process_info(process_name_1a, "occupy"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-        )
-
-
-def close1a():
-    global ps1a
-    if ps1a != []:
-        for p1a in ps1a:
-            try:
-                kill_process(p1a.pid, process_name_1a)
-            except:
-                traceback.print_exc()
-        ps1a = []
-    return (
-        process_info(process_name_1a, "closed"),
-        {"__type__": "update", "visible": True},
-        {"__type__": "update", "visible": False},
-    )
-
-
-sv_path = "GPT_SoVITS/pretrained_models/sv/pretrained_eres2netv2w24s4ep4.ckpt"
-ps1b = []
-process_name_1b = i18n("语音自监督特征提取")
-
-
-def open1b(version, inp_text, inp_wav_dir, exp_name, gpu_numbers, ssl_pretrained_dir):
-    global ps1b
-    inp_text = my_utils.clean_path(inp_text)
-    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
-    if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
-        check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
-    exp_name = exp_name.rstrip(" ")
-    if ps1b == []:
-        config = {
-            "inp_text": inp_text,
-            "inp_wav_dir": inp_wav_dir,
-            "exp_name": exp_name,
-            "opt_dir": "%s/%s" % (exp_root, exp_name),
-            "cnhubert_base_dir": ssl_pretrained_dir,
-            "sv_path": sv_path,
-            "is_half": str(is_half),
-        }
-        gpu_names = gpu_numbers.split("-")
-        all_parts = len(gpu_names)
-        for i_part in range(all_parts):
-            config.update(
-                {
-                    "i_part": str(i_part),
-                    "all_parts": str(all_parts),
-                    "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
-                }
-            )
-            os.environ.update(config)
-            cmd = '"%s" -s GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py' % python_exec
-            print(cmd)
-            p = Popen(cmd, shell=True)
-            ps1b.append(p)
-        yield (
-            process_info(process_name_1b, "running"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-        )
-        for p in ps1b:
-            p.wait()
-        ps1b = []
-        if "Pro" in version:
-            for i_part in range(all_parts):
-                config.update(
-                    {
-                        "i_part": str(i_part),
-                        "all_parts": str(all_parts),
-                        "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
-                    }
-                )
-                os.environ.update(config)
-                cmd = '"%s" -s GPT_SoVITS/prepare_datasets/2-get-sv.py' % python_exec
-                print(cmd)
-                p = Popen(cmd, shell=True)
-                ps1b.append(p)
-            for p in ps1b:
-                p.wait()
-            ps1b = []
-        yield (
-            process_info(process_name_1b, "finish"),
-            {"__type__": "update", "visible": True},
-            {"__type__": "update", "visible": False},
-        )
-    else:
-        yield (
-            process_info(process_name_1b, "occupy"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-        )
-
-
-def close1b():
-    global ps1b
-    if ps1b != []:
-        for p1b in ps1b:
-            try:
-                kill_process(p1b.pid, process_name_1b)
-            except:
-                traceback.print_exc()
-        ps1b = []
-    return (
-        process_info(process_name_1b, "closed"),
-        {"__type__": "update", "visible": True},
-        {"__type__": "update", "visible": False},
-    )
-
-
-ps1c = []
-process_name_1c = i18n("语义Token提取")
-
-
-def open1c(version, inp_text, inp_wav_dir, exp_name, gpu_numbers, pretrained_s2G_path):
-    global ps1c
-    inp_text = my_utils.clean_path(inp_text)
-    if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
-        check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
-    exp_name = exp_name.rstrip(" ")
-    if ps1c == []:
-        opt_dir = "%s/%s" % (exp_root, exp_name)
-        config_file = (
-            "GPT_SoVITS/configs/s2.json"
-            if version not in {"v2Pro", "v2ProPlus"}
-            else f"GPT_SoVITS/configs/s2{version}.json"
-        )
-        config = {
-            "inp_text": inp_text,
-            "exp_name": exp_name,
-            "opt_dir": opt_dir,
-            "pretrained_s2G": pretrained_s2G_path,
-            "s2config_path": config_file,
-            "is_half": str(is_half),
-        }
-        gpu_names = gpu_numbers.split("-")
-        all_parts = len(gpu_names)
-        for i_part in range(all_parts):
-            config.update(
-                {
-                    "i_part": str(i_part),
-                    "all_parts": str(all_parts),
-                    "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
-                }
-            )
-            os.environ.update(config)
-            cmd = '"%s" -s GPT_SoVITS/prepare_datasets/3-get-semantic.py' % python_exec
-            print(cmd)
-            p = Popen(cmd, shell=True)
-            ps1c.append(p)
-        yield (
-            process_info(process_name_1c, "running"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-        )
-        for p in ps1c:
-            p.wait()
-        opt = ["item_name\tsemantic_audio"]
-        path_semantic = "%s/6-name2semantic.tsv" % opt_dir
-        for i_part in range(all_parts):
-            semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
-            with open(semantic_path, "r", encoding="utf8") as f:
-                opt += f.read().strip("\n").split("\n")
-            os.remove(semantic_path)
-        with open(path_semantic, "w", encoding="utf8") as f:
-            f.write("\n".join(opt) + "\n")
-        ps1c = []
-        yield (
-            process_info(process_name_1c, "finish"),
-            {"__type__": "update", "visible": True},
-            {"__type__": "update", "visible": False},
-        )
-    else:
-        yield (
-            process_info(process_name_1c, "occupy"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-        )
-
-
-def close1c():
-    global ps1c
-    if ps1c != []:
-        for p1c in ps1c:
-            try:
-                kill_process(p1c.pid, process_name_1c)
-            except:
-                traceback.print_exc()
-        ps1c = []
-    return (
-        process_info(process_name_1c, "closed"),
-        {"__type__": "update", "visible": True},
-        {"__type__": "update", "visible": False},
-    )
-
-
-ps1abc = []
-process_name_1abc = i18n("训练集格式化一键三连")
-
-
-def open1abc(
-    version,
-    inp_text,
-    inp_wav_dir,
-    exp_name,
-    gpu_numbers1a,
-    gpu_numbers1Ba,
-    gpu_numbers1c,
-    bert_pretrained_dir,
-    ssl_pretrained_dir,
-    pretrained_s2G_path,
-):
-    global ps1abc
-    inp_text = my_utils.clean_path(inp_text)
-    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
-    if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
-        check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
-    exp_name = exp_name.rstrip(" ")
-    if ps1abc == []:
-        opt_dir = "%s/%s" % (exp_root, exp_name)
-        try:
-            #############################1a
-            path_text = "%s/2-name2text.txt" % opt_dir
-            if os.path.exists(path_text) == False or (
-                os.path.exists(path_text) == True
-                and len(open(path_text, "r", encoding="utf8").read().strip("\n").split("\n")) < 2
-            ):
-                config = {
-                    "inp_text": inp_text,
-                    "inp_wav_dir": inp_wav_dir,
-                    "exp_name": exp_name,
-                    "opt_dir": opt_dir,
-                    "bert_pretrained_dir": bert_pretrained_dir,
-                    "is_half": str(is_half),
-                }
-                gpu_names = gpu_numbers1a.split("-")
-                all_parts = len(gpu_names)
-                for i_part in range(all_parts):
-                    config.update(
-                        {
-                            "i_part": str(i_part),
-                            "all_parts": str(all_parts),
-                            "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
-                        }
-                    )
-                    os.environ.update(config)
-                    cmd = '"%s" -s GPT_SoVITS/prepare_datasets/1-get-text.py' % python_exec
-                    print(cmd)
-                    p = Popen(cmd, shell=True)
-                    ps1abc.append(p)
-                yield (
-                    i18n("进度") + ": 1A-Doing",
-                    {"__type__": "update", "visible": False},
-                    {"__type__": "update", "visible": True},
-                )
-                for p in ps1abc:
-                    p.wait()
-
-                opt = []
-                for i_part in range(all_parts):  # txt_path="%s/2-name2text-%s.txt"%(opt_dir,i_part)
-                    txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
-                    with open(txt_path, "r", encoding="utf8") as f:
-                        opt += f.read().strip("\n").split("\n")
-                    os.remove(txt_path)
-                with open(path_text, "w", encoding="utf8") as f:
-                    f.write("\n".join(opt) + "\n")
-                assert len("".join(opt)) > 0, process_info(process_name_1a, "failed")
-            yield (
-                i18n("进度") + ": 1A-Done",
-                {"__type__": "update", "visible": False},
-                {"__type__": "update", "visible": True},
-            )
-            ps1abc = []
-            #############################1b
-            config = {
-                "inp_text": inp_text,
-                "inp_wav_dir": inp_wav_dir,
-                "exp_name": exp_name,
-                "opt_dir": opt_dir,
-                "cnhubert_base_dir": ssl_pretrained_dir,
-                "sv_path": sv_path,
-            }
-            gpu_names = gpu_numbers1Ba.split("-")
-            all_parts = len(gpu_names)
-            for i_part in range(all_parts):
-                config.update(
-                    {
-                        "i_part": str(i_part),
-                        "all_parts": str(all_parts),
-                        "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
-                    }
-                )
-                os.environ.update(config)
-                cmd = '"%s" -s GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py' % python_exec
-                print(cmd)
-                p = Popen(cmd, shell=True)
-                ps1abc.append(p)
-            yield (
-                i18n("进度") + ": 1A-Done, 1B-Doing",
-                {"__type__": "update", "visible": False},
-                {"__type__": "update", "visible": True},
-            )
-            for p in ps1abc:
-                p.wait()
-            ps1abc = []
-            if "Pro" in version:
-                for i_part in range(all_parts):
-                    config.update(
-                        {
-                            "i_part": str(i_part),
-                            "all_parts": str(all_parts),
-                            "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
-                        }
-                    )
-                    os.environ.update(config)
-                    cmd = '"%s" -s GPT_SoVITS/prepare_datasets/2-get-sv.py' % python_exec
-                    print(cmd)
-                    p = Popen(cmd, shell=True)
-                    ps1abc.append(p)
-                for p in ps1abc:
-                    p.wait()
-                ps1abc = []
-            yield (
-                i18n("进度") + ": 1A-Done, 1B-Done",
-                {"__type__": "update", "visible": False},
-                {"__type__": "update", "visible": True},
-            )
-            #############################1c
-            path_semantic = "%s/6-name2semantic.tsv" % opt_dir
-            if os.path.exists(path_semantic) == False or (
-                os.path.exists(path_semantic) == True and os.path.getsize(path_semantic) < 31
-            ):
-                config_file = (
-                    "GPT_SoVITS/configs/s2.json"
-                    if version not in {"v2Pro", "v2ProPlus"}
-                    else f"GPT_SoVITS/configs/s2{version}.json"
-                )
-                config = {
-                    "inp_text": inp_text,
-                    "exp_name": exp_name,
-                    "opt_dir": opt_dir,
-                    "pretrained_s2G": pretrained_s2G_path,
-                    "s2config_path": config_file,
-                }
-                gpu_names = gpu_numbers1c.split("-")
-                all_parts = len(gpu_names)
-                for i_part in range(all_parts):
-                    config.update(
-                        {
-                            "i_part": str(i_part),
-                            "all_parts": str(all_parts),
-                            "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
-                        }
-                    )
-                    os.environ.update(config)
-                    cmd = '"%s" -s GPT_SoVITS/prepare_datasets/3-get-semantic.py' % python_exec
-                    print(cmd)
-                    p = Popen(cmd, shell=True)
-                    ps1abc.append(p)
-                yield (
-                    i18n("进度") + ": 1A-Done, 1B-Done, 1C-Doing",
-                    {"__type__": "update", "visible": False},
-                    {"__type__": "update", "visible": True},
-                )
-                for p in ps1abc:
-                    p.wait()
-
-                opt = ["item_name\tsemantic_audio"]
-                for i_part in range(all_parts):
-                    semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
-                    with open(semantic_path, "r", encoding="utf8") as f:
-                        opt += f.read().strip("\n").split("\n")
-                    os.remove(semantic_path)
-                with open(path_semantic, "w", encoding="utf8") as f:
-                    f.write("\n".join(opt) + "\n")
-                yield (
-                    i18n("进度") + ": 1A-Done, 1B-Done, 1C-Done",
-                    {"__type__": "update", "visible": False},
-                    {"__type__": "update", "visible": True},
-                )
-            ps1abc = []
-            yield (
-                process_info(process_name_1abc, "finish"),
-                {"__type__": "update", "visible": True},
-                {"__type__": "update", "visible": False},
-            )
-        except:
-            traceback.print_exc()
-            close1abc()
-            yield (
-                process_info(process_name_1abc, "failed"),
-                {"__type__": "update", "visible": True},
-                {"__type__": "update", "visible": False},
-            )
-    else:
-        yield (
-            process_info(process_name_1abc, "occupy"),
-            {"__type__": "update", "visible": False},
-            {"__type__": "update", "visible": True},
-        )
-
-
-def close1abc():
-    global ps1abc
-    if ps1abc != []:
-        for p1abc in ps1abc:
-            try:
-                kill_process(p1abc.pid, process_name_1abc)
-            except:
-                traceback.print_exc()
-        ps1abc = []
-    return (
-        process_info(process_name_1abc, "closed"),
-        {"__type__": "update", "visible": True},
-        {"__type__": "update", "visible": False},
-    )
-
-
-def switch_version(version_):
-    os.environ["version"] = version_
-    global version
-    version = version_
-    if pretrained_sovits_name[version] != "" and pretrained_gpt_name[version] != "":
-        ...
-    else:
-        gr.Warning(i18n("未下载模型") + ": " + version.upper())
-    set_default()
-    return (
-        {"__type__": "update", "value": pretrained_sovits_name[version]},
-        {"__type__": "update", "value": pretrained_sovits_name[version].replace("s2G", "s2D")},
-        {"__type__": "update", "value": pretrained_gpt_name[version]},
-        {"__type__": "update", "value": pretrained_gpt_name[version]},
-        {"__type__": "update", "value": pretrained_sovits_name[version]},
-        {"__type__": "update", "value": default_batch_size, "maximum": default_max_batch_size},
-        {"__type__": "update", "value": default_sovits_epoch, "maximum": max_sovits_epoch},
-        {"__type__": "update", "value": default_sovits_save_every_epoch, "maximum": max_sovits_save_every_epoch},
-        {"__type__": "update", "visible": True if version not in v3v4set else False},
-        {
-            "__type__": "update",
-            "value": False if not if_force_ckpt else True,
-            "interactive": True if not if_force_ckpt else False,
-        },
-        {"__type__": "update", "interactive": True, "value": False},
-        {"__type__": "update", "visible": True if version in v3v4set else False},
-    )  # {'__type__': 'update', "interactive": False if version in v3v4set else True, "value": False}, \ ####batch infer
-
-
-if os.path.exists("GPT_SoVITS/text/G2PWModel"):
-    ...
-else:
-    cmd = '"%s" -s GPT_SoVITS/download.py' % python_exec
-    p = Popen(cmd, shell=True)
-    p.wait()
-
-
-def sync(text):
-    return {"__type__": "update", "value": text}
-
-
-with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css) as app:
-    gr.HTML(
-        top_html.format(
-            i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.")
-            + i18n("如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.")
-        ),
-        elem_classes="markdown",
-    )
-
-    with gr.Tabs():
-        with gr.TabItem("0-" + i18n("前置数据集获取工具")):  # 提前随机切片防止uvr5爆内存->uvr5->slicer->asr->打标
-            with gr.Accordion(label="0a-" + i18n("UVR5人声伴奏分离&去混响去延迟工具")):
-                with gr.Row():
-                    with gr.Column(scale=3):
-                        with gr.Row():
-                            uvr5_info = gr.Textbox(label=process_info(process_name_uvr5, "info"))
-                    open_uvr5 = gr.Button(
-                        value=process_info(process_name_uvr5, "open"), variant="primary", visible=True
-                    )
-                    close_uvr5 = gr.Button(
-                        value=process_info(process_name_uvr5, "close"), variant="primary", visible=False
-                    )
-
-            with gr.Accordion(label="0b-" + i18n("语音切分工具")):
-                with gr.Row():
-                    with gr.Column(scale=3):
-                        with gr.Row():
-                            slice_inp_path = gr.Textbox(label=i18n("音频自动切分输入路径，可文件可文件夹"), value="")
-                            slice_opt_root = gr.Textbox(
-                                label=i18n("切分后的子音频的输出根目录"), value="output/slicer_opt"
-                            )
-                        with gr.Row():
-                            threshold = gr.Textbox(
-                                label=i18n("threshold:音量小于这个值视作静音的备选切割点"), value="-34"
-                            )
-                            min_length = gr.Textbox(
-                                label=i18n("min_length:每段最小多长，如果第一段太短一直和后面段连起来直到超过这个值"),
-                                value="4000",
-                            )
-                            min_interval = gr.Textbox(label=i18n("min_interval:最短切割间隔"), value="300")
-                            hop_size = gr.Textbox(
-                                label=i18n("hop_size:怎么算音量曲线，越小精度越大计算量越高（不是精度越大效果越好）"),
-                                value="10",
-                            )
-                            max_sil_kept = gr.Textbox(label=i18n("max_sil_kept:切完后静音最多留多长"), value="500")
-                        with gr.Row():
-                            _max = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                step=0.05,
-                                label=i18n("max:归一化后最大值多少"),
-                                value=0.9,
-                                interactive=True,
-                            )
-                            alpha = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                step=0.05,
-                                label=i18n("alpha_mix:混多少比例归一化后音频进来"),
-                                value=0.25,
-                                interactive=True,
-                            )
-                        with gr.Row():
-                            n_process = gr.Slider(
-                                minimum=1,
-                                maximum=n_cpu,
-                                step=1,
-                                label=i18n("切割使用的进程数"),
-                                value=4,
-                                interactive=True,
-                            )
-                            slicer_info = gr.Textbox(label=process_info(process_name_slice, "info"))
-                    open_slicer_button = gr.Button(
-                        value=process_info(process_name_slice, "open"), variant="primary", visible=True
-                    )
-                    close_slicer_button = gr.Button(
-                        value=process_info(process_name_slice, "close"), variant="primary", visible=False
-                    )
-
-            # gr.Markdown(value="0bb-" + i18n("语音降噪工具")+i18n("(不稳定，先别用，可能劣化模型效果！)"))
-            with gr.Row(visible=False):
-                with gr.Column(scale=3):
-                    with gr.Row():
-                        denoise_input_dir = gr.Textbox(label=i18n("输入文件夹路径"), value="")
-                        denoise_output_dir = gr.Textbox(label=i18n("输出文件夹路径"), value="output/denoise_opt")
-                    with gr.Row():
-                        denoise_info = gr.Textbox(label=process_info(process_name_denoise, "info"))
-                open_denoise_button = gr.Button(
-                    value=process_info(process_name_denoise, "open"), variant="primary", visible=True
-                )
-                close_denoise_button = gr.Button(
-                    value=process_info(process_name_denoise, "close"), variant="primary", visible=False
-                )
-
-            with gr.Accordion(label="0c-" + i18n("语音识别工具")):
-                with gr.Row():
-                    with gr.Column(scale=3):
-                        with gr.Row():
-                            asr_inp_dir = gr.Textbox(
-                                label=i18n("输入文件夹路径"), value="D:\\GPT-SoVITS\\raw\\xxx", interactive=True
-                            )
-                            asr_opt_dir = gr.Textbox(
-                                label=i18n("输出文件夹路径"), value="output/asr_opt", interactive=True
-                            )
-                        with gr.Row():
-                            asr_model = gr.Dropdown(
-                                label=i18n("ASR 模型"),
-                                choices=list(asr_dict.keys()),
-                                interactive=True,
-                                value="达摩 ASR (中文)",
-                            )
-                            asr_size = gr.Dropdown(
-                                label=i18n("ASR 模型尺寸"), choices=["large"], interactive=True, value="large"
-                            )
-                            asr_lang = gr.Dropdown(
-                                label=i18n("ASR 语言设置"), choices=["zh", "yue"], interactive=True, value="zh"
-                            )
-                            asr_precision = gr.Dropdown(
-                                label=i18n("数据类型精度"), choices=["float32"], interactive=True, value="float32"
-                            )
-                        with gr.Row():
-                            asr_info = gr.Textbox(label=process_info(process_name_asr, "info"))
-                    open_asr_button = gr.Button(
-                        value=process_info(process_name_asr, "open"), variant="primary", visible=True
-                    )
-                    close_asr_button = gr.Button(
-                        value=process_info(process_name_asr, "close"), variant="primary", visible=False
-                    )
-
-                def change_lang_choices(key):  # 根据选择的模型修改可选的语言
-                    return {"__type__": "update", "choices": asr_dict[key]["lang"], "value": asr_dict[key]["lang"][0]}
-
-                def change_size_choices(key):  # 根据选择的模型修改可选的模型尺寸
-                    return {"__type__": "update", "choices": asr_dict[key]["size"], "value": asr_dict[key]["size"][-1]}
-
-                def change_precision_choices(key):  # 根据选择的模型修改可选的语言
-                    if key == "Faster Whisper (多语种)":
-                        if default_batch_size <= 4:
-                            precision = "int8"
-                        elif is_half:
-                            precision = "float16"
-                        else:
-                            precision = "float32"
-                    else:
-                        precision = "float32"
-                    return {"__type__": "update", "choices": asr_dict[key]["precision"], "value": precision}
-
-                asr_model.change(change_lang_choices, [asr_model], [asr_lang])
-                asr_model.change(change_size_choices, [asr_model], [asr_size])
-                asr_model.change(change_precision_choices, [asr_model], [asr_precision])
-
-            with gr.Accordion(label="0d-" + i18n("语音文本校对标注工具")):
-                with gr.Row():
-                    with gr.Column(scale=3):
-                        with gr.Row():
-                            path_list = gr.Textbox(
-                                label=i18n("标注文件路径 (含文件后缀 *.list)"),
-                                value="D:\\RVC1006\\GPT-SoVITS\\raw\\xxx.list",
-                                interactive=True,
-                            )
-                            label_info = gr.Textbox(label=process_info(process_name_subfix, "info"))
-                    open_label = gr.Button(
-                        value=process_info(process_name_subfix, "open"), variant="primary", visible=True
-                    )
-                    close_label = gr.Button(
-                        value=process_info(process_name_subfix, "close"), variant="primary", visible=False
-                    )
-
-                open_label.click(change_label, [path_list], [label_info, open_label, close_label])
-                close_label.click(change_label, [path_list], [label_info, open_label, close_label])
-                open_uvr5.click(change_uvr5, [], [uvr5_info, open_uvr5, close_uvr5])
-                close_uvr5.click(change_uvr5, [], [uvr5_info, open_uvr5, close_uvr5])
-
-        with gr.TabItem(i18n("1-GPT-SoVITS-TTS")):
-            with gr.Accordion(i18n("微调模型信息")):
-                with gr.Row():
-                    with gr.Row(equal_height=True):
-                        exp_name = gr.Textbox(
-                            label=i18n("*实验/模型名"),
-                            value="xxx",
-                            interactive=True,
-                            scale=3,
-                        )
-                        gpu_info_box = gr.Textbox(
-                            label=i18n("显卡信息"),
-                            value=gpu_info,
-                            visible=True,
-                            interactive=False,
-                            scale=5,
-                        )
-                        version_checkbox = gr.Radio(
-                            label=i18n("训练模型的版本"),
-                            value=version,
-                            choices=["v1", "v2", "v4", "v2Pro", "v2ProPlus"],
-                            scale=5,
-                        )
-            with gr.Accordion(label=i18n("预训练模型路径"), open=False):
-                with gr.Row():
-                    with gr.Row(equal_height=True):
-                        pretrained_s1 = gr.Textbox(
-                            label=i18n("预训练GPT模型路径"),
-                            value=pretrained_gpt_name[version],
-                            interactive=True,
-                            lines=1,
-                            max_lines=1,
-                            scale=3,
-                        )
-                        pretrained_s2G = gr.Textbox(
-                            label=i18n("预训练SoVITS-G模型路径"),
-                            value=pretrained_sovits_name[version],
-                            interactive=True,
-                            lines=1,
-                            max_lines=1,
-                            scale=5,
-                        )
-                        pretrained_s2D = gr.Textbox(
-                            label=i18n("预训练SoVITS-D模型路径"),
-                            value=pretrained_sovits_name[version].replace("s2G", "s2D"),
-                            interactive=True,
-                            lines=1,
-                            max_lines=1,
-                            scale=5,
-                        )
-
-            with gr.TabItem("1A-" + i18n("训练集格式化工具")):
-                with gr.Accordion(label=i18n("输出logs/实验名目录下应有23456开头的文件和文件夹")):
-                    with gr.Row():
-                        with gr.Row():
-                            inp_text = gr.Textbox(
-                                label=i18n("*文本标注文件"),
-                                value=r"D:\RVC1006\GPT-SoVITS\raw\xxx.list",
-                                interactive=True,
-                                scale=10,
-                            )
-                        with gr.Row():
-                            inp_wav_dir = gr.Textbox(
-                                label=i18n("*训练集音频文件目录"),
-                                # value=r"D:\RVC1006\GPT-SoVITS\raw\xxx",
-                                interactive=True,
-                                placeholder=i18n(
-                                    "填切割后音频所在目录！读取的音频文件完整路径=该目录-拼接-list文件里波形对应的文件名（不是全路径）。如果留空则使用.list文件里的绝对全路径。"
-                                ),
-                                scale=10,
-                            )
-
-                with gr.Accordion(label="1Aa-" + process_name_1a):
-                    with gr.Row():
-                        with gr.Row():
-                            gpu_numbers1a = gr.Textbox(
-                                label=i18n("GPU卡号以-分割，每个卡号一个进程"),
-                                value="%s-%s" % (gpus, gpus),
-                                interactive=True,
-                            )
-                        with gr.Row():
-                            bert_pretrained_dir = gr.Textbox(
-                                label=i18n("预训练中文BERT模型路径"),
-                                value="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
-                                interactive=False,
-                                lines=2,
-                            )
-                        with gr.Row():
-                            button1a_open = gr.Button(
-                                value=process_info(process_name_1a, "open"), variant="primary", visible=True
-                            )
-                            button1a_close = gr.Button(
-                                value=process_info(process_name_1a, "close"), variant="primary", visible=False
-                            )
-                        with gr.Row():
-                            info1a = gr.Textbox(label=process_info(process_name_1a, "info"))
-
-                with gr.Accordion(label="1Ab-" + process_name_1b):
-                    with gr.Row():
-                        with gr.Row():
-                            gpu_numbers1Ba = gr.Textbox(
-                                label=i18n("GPU卡号以-分割，每个卡号一个进程"),
-                                value="%s-%s" % (gpus, gpus),
-                                interactive=True,
-                            )
-                        with gr.Row():
-                            cnhubert_base_dir = gr.Textbox(
-                                label=i18n("预训练SSL模型路径"),
-                                value="GPT_SoVITS/pretrained_models/chinese-hubert-base",
-                                interactive=False,
-                                lines=2,
-                            )
-                        with gr.Row():
-                            button1b_open = gr.Button(
-                                value=process_info(process_name_1b, "open"), variant="primary", visible=True
-                            )
-                            button1b_close = gr.Button(
-                                value=process_info(process_name_1b, "close"), variant="primary", visible=False
-                            )
-                        with gr.Row():
-                            info1b = gr.Textbox(label=process_info(process_name_1b, "info"))
-
-                with gr.Accordion(label="1Ac-" + process_name_1c):
-                    with gr.Row():
-                        with gr.Row():
-                            gpu_numbers1c = gr.Textbox(
-                                label=i18n("GPU卡号以-分割，每个卡号一个进程"),
-                                value="%s-%s" % (gpus, gpus),
-                                interactive=True,
-                            )
-                        with gr.Row():
-                            pretrained_s2G_ = gr.Textbox(
-                                label=i18n("预训练SoVITS-G模型路径"),
-                                value=pretrained_sovits_name[version],
-                                interactive=False,
-                                lines=2,
-                            )
-                        with gr.Row():
-                            button1c_open = gr.Button(
-                                value=process_info(process_name_1c, "open"), variant="primary", visible=True
-                            )
-                            button1c_close = gr.Button(
-                                value=process_info(process_name_1c, "close"), variant="primary", visible=False
-                            )
-                        with gr.Row():
-                            info1c = gr.Textbox(label=process_info(process_name_1c, "info"))
-
-                with gr.Accordion(label="1Aabc-" + process_name_1abc):
-                    with gr.Row():
-                        with gr.Row():
-                            button1abc_open = gr.Button(
-                                value=process_info(process_name_1abc, "open"), variant="primary", visible=True
-                            )
-                            button1abc_close = gr.Button(
-                                value=process_info(process_name_1abc, "close"), variant="primary", visible=False
-                            )
-                        with gr.Row():
-                            info1abc = gr.Textbox(label=process_info(process_name_1abc, "info"))
-
-            pretrained_s2G.change(sync, [pretrained_s2G], [pretrained_s2G_])
-            open_asr_button.click(
-                open_asr,
-                [asr_inp_dir, asr_opt_dir, asr_model, asr_size, asr_lang, asr_precision],
-                [asr_info, open_asr_button, close_asr_button, path_list, inp_text, inp_wav_dir],
-            )
-            close_asr_button.click(close_asr, [], [asr_info, open_asr_button, close_asr_button])
-            open_slicer_button.click(
-                open_slice,
-                [
-                    slice_inp_path,
-                    slice_opt_root,
-                    threshold,
-                    min_length,
-                    min_interval,
-                    hop_size,
-                    max_sil_kept,
-                    _max,
-                    alpha,
-                    n_process,
-                ],
-                [slicer_info, open_slicer_button, close_slicer_button, asr_inp_dir, denoise_input_dir, inp_wav_dir],
-            )
-            close_slicer_button.click(close_slice, [], [slicer_info, open_slicer_button, close_slicer_button])
-            open_denoise_button.click(
-                open_denoise,
-                [denoise_input_dir, denoise_output_dir],
-                [denoise_info, open_denoise_button, close_denoise_button, asr_inp_dir, inp_wav_dir],
-            )
-            close_denoise_button.click(close_denoise, [], [denoise_info, open_denoise_button, close_denoise_button])
-
-            button1a_open.click(
-                open1a,
-                [inp_text, inp_wav_dir, exp_name, gpu_numbers1a, bert_pretrained_dir],
-                [info1a, button1a_open, button1a_close],
-            )
-            button1a_close.click(close1a, [], [info1a, button1a_open, button1a_close])
-            button1b_open.click(
-                open1b,
-                [version_checkbox, inp_text, inp_wav_dir, exp_name, gpu_numbers1Ba, cnhubert_base_dir],
-                [info1b, button1b_open, button1b_close],
-            )
-            button1b_close.click(close1b, [], [info1b, button1b_open, button1b_close])
-            button1c_open.click(
-                open1c,
-                [version_checkbox, inp_text, inp_wav_dir, exp_name, gpu_numbers1c, pretrained_s2G],
-                [info1c, button1c_open, button1c_close],
-            )
-            button1c_close.click(close1c, [], [info1c, button1c_open, button1c_close])
-            button1abc_open.click(
-                open1abc,
-                [
-                    version_checkbox,
-                    inp_text,
-                    inp_wav_dir,
-                    exp_name,
-                    gpu_numbers1a,
-                    gpu_numbers1Ba,
-                    gpu_numbers1c,
-                    bert_pretrained_dir,
-                    cnhubert_base_dir,
-                    pretrained_s2G,
-                ],
-                [info1abc, button1abc_open, button1abc_close],
-            )
-            button1abc_close.click(close1abc, [], [info1abc, button1abc_open, button1abc_close])
-
-            with gr.TabItem("1B-" + i18n("微调训练")):
-                with gr.Accordion(label="1Ba-" + i18n("SoVITS 训练: 模型权重文件在 SoVITS_weights/")):
-                    with gr.Row():
-                        with gr.Column():
-                            with gr.Row():
-                                batch_size = gr.Slider(
-                                    minimum=1,
-                                    maximum=default_max_batch_size,
-                                    step=1,
-                                    label=i18n("每张显卡的batch_size"),
-                                    value=default_batch_size,
-                                    interactive=True,
-                                )
-                                total_epoch = gr.Slider(
-                                    minimum=1,
-                                    maximum=max_sovits_epoch,
-                                    step=1,
-                                    label=i18n("总训练轮数total_epoch，不建议太高"),
-                                    value=default_sovits_epoch,
-                                    interactive=True,
-                                )
-                            with gr.Row():
-                                text_low_lr_rate = gr.Slider(
-                                    minimum=0.2,
-                                    maximum=0.6,
-                                    step=0.05,
-                                    label=i18n("文本模块学习率权重"),
-                                    value=0.4,
-                                    visible=True if version not in v3v4set else False,
-                                )  # v3v4 not need
-                                lora_rank = gr.Radio(
-                                    label=i18n("LoRA秩"),
-                                    value="32",
-                                    choices=["16", "32", "64", "128"],
-                                    visible=True if version in v3v4set else False,
-                                )  # v1v2 not need
-                                save_every_epoch = gr.Slider(
-                                    minimum=1,
-                                    maximum=max_sovits_save_every_epoch,
-                                    step=1,
-                                    label=i18n("保存频率save_every_epoch"),
-                                    value=default_sovits_save_every_epoch,
-                                    interactive=True,
-                                )
-                        with gr.Column():
-                            with gr.Column():
-                                if_save_latest = gr.Checkbox(
-                                    label=i18n("是否仅保存最新的权重文件以节省硬盘空间"),
-                                    value=True,
-                                    interactive=True,
-                                    show_label=True,
-                                )
-                                if_save_every_weights = gr.Checkbox(
-                                    label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),
-                                    value=True,
-                                    interactive=True,
-                                    show_label=True,
-                                )
-                                if_grad_ckpt = gr.Checkbox(
-                                    label="v3是否开启梯度检查点节省显存占用",
-                                    value=False,
-                                    interactive=True if version in v3v4set else False,
-                                    show_label=True,
-                                    visible=False,
-                                )  # 只有V3s2可以用
-                            with gr.Row():
-                                gpu_numbers1Ba = gr.Textbox(
-                                    label=i18n("GPU卡号以-分割，每个卡号一个进程"),
-                                    value="%s" % (gpus),
-                                    interactive=True,
-                                )
-                    with gr.Row():
-                        with gr.Row():
-                            button1Ba_open = gr.Button(
-                                value=process_info(process_name_sovits, "open"), variant="primary", visible=True
-                            )
-                            button1Ba_close = gr.Button(
-                                value=process_info(process_name_sovits, "close"), variant="primary", visible=False
-                            )
-                        with gr.Row():
-                            info1Ba = gr.Textbox(label=process_info(process_name_sovits, "info"))
-                with gr.Accordion(label="1Bb-" + i18n("GPT 训练: 模型权重文件在 GPT_weights/")):
-                    with gr.Row():
-                        with gr.Column():
-                            with gr.Row():
-                                batch_size1Bb = gr.Slider(
-                                    minimum=1,
-                                    maximum=40,
-                                    step=1,
-                                    label=i18n("每张显卡的batch_size"),
-                                    value=default_batch_size_s1,
-                                    interactive=True,
-                                )
-                                total_epoch1Bb = gr.Slider(
-                                    minimum=2,
-                                    maximum=50,
-                                    step=1,
-                                    label=i18n("总训练轮数total_epoch"),
-                                    value=15,
-                                    interactive=True,
-                                )
-                            with gr.Row():
-                                save_every_epoch1Bb = gr.Slider(
-                                    minimum=1,
-                                    maximum=50,
-                                    step=1,
-                                    label=i18n("保存频率save_every_epoch"),
-                                    value=5,
-                                    interactive=True,
-                                )
-                                if_dpo = gr.Checkbox(
-                                    label=i18n("是否开启DPO训练选项(实验性)"),
-                                    value=False,
-                                    interactive=True,
-                                    show_label=True,
-                                )
-                        with gr.Column():
-                            with gr.Column():
-                                if_save_latest1Bb = gr.Checkbox(
-                                    label=i18n("是否仅保存最新的权重文件以节省硬盘空间"),
-                                    value=True,
-                                    interactive=True,
-                                    show_label=True,
-                                )
-                                if_save_every_weights1Bb = gr.Checkbox(
-                                    label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),
-                                    value=True,
-                                    interactive=True,
-                                    show_label=True,
-                                )
-                            with gr.Row():
-                                gpu_numbers1Bb = gr.Textbox(
-                                    label=i18n("GPU卡号以-分割，每个卡号一个进程"),
-                                    value="%s" % (gpus),
-                                    interactive=True,
-                                )
-                    with gr.Row():
-                        with gr.Row():
-                            button1Bb_open = gr.Button(
-                                value=process_info(process_name_gpt, "open"), variant="primary", visible=True
-                            )
-                            button1Bb_close = gr.Button(
-                                value=process_info(process_name_gpt, "close"), variant="primary", visible=False
-                            )
-                        with gr.Row():
-                            info1Bb = gr.Textbox(label=process_info(process_name_gpt, "info"))
-
-            button1Ba_close.click(close1Ba, [], [info1Ba, button1Ba_open, button1Ba_close])
-            button1Bb_close.click(close1Bb, [], [info1Bb, button1Bb_open, button1Bb_close])
-
-            with gr.TabItem("1C-" + i18n("推理")):
-                gr.Markdown(
-                    value=i18n(
-                        "选择训练完存放在SoVITS_weights和GPT_weights下的模型。默认的几个是底模，体验5秒Zero Shot TTS不训练推理用。"
-                    )
-                )
-                with gr.Row():
-                    with gr.Column(scale=2):
-                        with gr.Row():
-                            GPT_dropdown = gr.Dropdown(
-                                label=i18n("GPT模型列表"),
-                                choices=GPT_names,
-                                value=GPT_names[-1],
-                                interactive=True,
-                            )
-                            SoVITS_dropdown = gr.Dropdown(
-                                label=i18n("SoVITS模型列表"),
-                                choices=SoVITS_names,
-                                value=SoVITS_names[0],
-                                interactive=True,
-                            )
-                    with gr.Column(scale=2):
-                        with gr.Row():
-                            gpu_number_1C = gr.Textbox(
-                                label=i18n("GPU卡号,只能填1个整数"), value=gpus, interactive=True
-                            )
-                            refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
-                    refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
-                with gr.Row(equal_height=True):
-                    with gr.Row():
-                        batched_infer_enabled = gr.Checkbox(
-                            label=i18n("启用并行推理版本"), value=False, interactive=True, show_label=True
-                        )
-                        open_tts = gr.Button(
-                            value=process_info(process_name_tts, "open"), variant="primary", visible=True
-                        )
-                        close_tts = gr.Button(
-                            value=process_info(process_name_tts, "close"), variant="primary", visible=False
-                        )
-                    with gr.Column():
-                        tts_info = gr.Textbox(label=process_info(process_name_tts, "info"), scale=2)
-                    open_tts.click(
-                        change_tts_inference,
-                        [
-                            bert_pretrained_dir,
-                            cnhubert_base_dir,
-                            gpu_number_1C,
-                            GPT_dropdown,
-                            SoVITS_dropdown,
-                            batched_infer_enabled,
-                        ],
-                        [tts_info, open_tts, close_tts],
-                    )
-                    close_tts.click(
-                        change_tts_inference,
-                        [
-                            bert_pretrained_dir,
-                            cnhubert_base_dir,
-                            gpu_number_1C,
-                            GPT_dropdown,
-                            SoVITS_dropdown,
-                            batched_infer_enabled,
-                        ],
-                        [tts_info, open_tts, close_tts],
-                    )
-            button1Ba_open.click(
-                open1Ba,
-                [
-                    version_checkbox,
-                    batch_size,
-                    total_epoch,
-                    exp_name,
-                    text_low_lr_rate,
-                    if_save_latest,
-                    if_save_every_weights,
-                    save_every_epoch,
-                    gpu_numbers1Ba,
-                    pretrained_s2G,
-                    pretrained_s2D,
-                    if_grad_ckpt,
-                    lora_rank,
-                ],
-                [info1Ba, button1Ba_open, button1Ba_close, SoVITS_dropdown, GPT_dropdown],
-            )
-            button1Bb_open.click(
-                open1Bb,
-                [
-                    batch_size1Bb,
-                    total_epoch1Bb,
-                    exp_name,
-                    if_dpo,
-                    if_save_latest1Bb,
-                    if_save_every_weights1Bb,
-                    save_every_epoch1Bb,
-                    gpu_numbers1Bb,
-                    pretrained_s1,
-                ],
-                [info1Bb, button1Bb_open, button1Bb_close, SoVITS_dropdown, GPT_dropdown],
-            )
-            version_checkbox.change(
-                switch_version,
-                [version_checkbox],
-                [
-                    pretrained_s2G,
-                    pretrained_s2D,
-                    pretrained_s1,
-                    GPT_dropdown,
-                    SoVITS_dropdown,
-                    batch_size,
-                    total_epoch,
-                    save_every_epoch,
-                    text_low_lr_rate,
-                    if_grad_ckpt,
-                    batched_infer_enabled,
-                    lora_rank,
-                ],
-            )
-
-        with gr.TabItem(i18n("2-GPT-SoVITS-变声")):
-            gr.Markdown(value=i18n("施工中，请静候佳音"))
-
-    app.queue().launch(  # concurrency_count=511, max_size=1022
-        server_name="0.0.0.0",
-        inbrowser=True,
-        share=is_share,
-        server_port=webui_port_main,
-        # quiet=True,
-    )
+import os
+import sys
+
+os.environ["version"] = version = "v2Pro"
+now_dir = os.getcwd()
+sys.path.insert(0, now_dir)
+import warnings
+
+warnings.filterwarnings("ignore")
+import json
+import platform
+import shutil
+import signal
+
+import psutil
+import torch
+import yaml
+
+os.environ["TORCH_DISTRIBUTED_DEBUG"] = "INFO"
+torch.manual_seed(233333)
+tmp = os.path.join(now_dir, "TEMP")
+os.makedirs(tmp, exist_ok=True)
+os.environ["TEMP"] = tmp
+if os.path.exists(tmp):
+    for name in os.listdir(tmp):
+        if name == "jieba.cache":
+            continue
+        path = f"{tmp}/{name}"
+        delete = os.remove if os.path.isfile(path) else shutil.rmtree
+        try:
+            delete(path)
+        except Exception as e:
+            print(str(e))
+            pass
+import site
+import traceback
+
+site_packages_roots = []
+for path in site.getsitepackages():
+    if "packages" in path:
+        site_packages_roots.append(path)
+if site_packages_roots == []:
+    site_packages_roots = [f"{now_dir}/runtime/Lib/site-packages"]
+# os.environ["OPENBLAS_NUM_THREADS"] = "4"
+os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
+os.environ["all_proxy"] = ""
+for site_packages_root in site_packages_roots:
+    if os.path.exists(site_packages_root):
+        try:
+            with open(f"{site_packages_root}/users.pth", "w") as f:
+                f.write(
+                    # "%s\n%s/runtime\n%s/tools\n%s/tools/asr\n%s/GPT_SoVITS\n%s/tools/uvr5"
+                    f"{now_dir}\n{now_dir}/GPT_SoVITS/BigVGAN\n{now_dir}/tools\n{now_dir}/tools/asr\n{now_dir}/GPT_SoVITS\n{now_dir}/tools/uvr5"
+                )
+            break
+        except PermissionError:
+            traceback.print_exc()
+import shutil
+import subprocess
+from subprocess import Popen
+
+from tools.assets import css, js, top_html
+from tools.i18n.i18n import I18nAuto, scan_language_list
+
+language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else "Auto"
+os.environ["language"] = language
+i18n = I18nAuto(language=language)
+from multiprocessing import cpu_count
+
+from config import (
+    GPU_INDEX,
+    GPU_INFOS,
+    IS_GPU,
+    exp_root,
+    infer_device,
+    is_half,
+    is_share,
+    memset,
+    python_exec,
+    webui_port_infer_tts,
+    webui_port_main,
+    webui_port_subfix,
+    webui_port_uvr5,
+)
+from tools import my_utils
+from tools.my_utils import check_details, check_for_existance
+
+os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 当遇到mps不支持的步骤时使用cpu
+import gradio as gr
+
+n_cpu = cpu_count()
+
+set_gpu_numbers = GPU_INDEX
+gpu_infos = GPU_INFOS
+mem = memset
+is_gpu_ok = IS_GPU
+
+v3v4set = {"v3", "v4"}
+
+
+def set_default():
+    global \
+        default_batch_size, \
+        default_max_batch_size, \
+        gpu_info, \
+        default_sovits_epoch, \
+        default_sovits_save_every_epoch, \
+        max_sovits_epoch, \
+        max_sovits_save_every_epoch, \
+        default_batch_size_s1, \
+        if_force_ckpt
+    if_force_ckpt = False
+    gpu_info = "\n".join(gpu_infos)
+    if is_gpu_ok:
+        minmem = min(mem)
+        default_batch_size = minmem // 2 if version not in v3v4set else minmem // 8
+        default_batch_size_s1 = minmem // 2
+    else:
+        default_batch_size = default_batch_size_s1 = int(psutil.virtual_memory().total / 1024 / 1024 / 1024 / 4)
+    if version not in v3v4set:
+        default_sovits_epoch = 8
+        default_sovits_save_every_epoch = 4
+        max_sovits_epoch = 25  # 40
+        max_sovits_save_every_epoch = 25  # 10
+    else:
+        default_sovits_epoch = 2
+        default_sovits_save_every_epoch = 1
+        max_sovits_epoch = 16  # 40 # 3 #训太多=作死
+        max_sovits_save_every_epoch = 10  # 10 # 3
+
+    default_batch_size = max(1, default_batch_size)
+    default_batch_size_s1 = max(1, default_batch_size_s1)
+    default_max_batch_size = default_batch_size * 3
+
+# ==================== DRY UTILITY FUNCTIONS ====================
+
+class ProcessManager:
+    """Centralized process management to eliminate repetitive code"""
+    
+    def __init__(self):
+        self.processes = {}
+    
+    def create_process(self, process_name, cmd, wait=False, env_updates=None):
+        """Create and manage a process with consistent UI updates"""
+        if self.processes.get(process_name) is None:
+            if env_updates:
+                os.environ.update(env_updates)
+            
+            print(cmd)
+            p = Popen(cmd, shell=True)
+            self.processes[process_name] = p
+            
+            if wait:
+                p.wait()
+                self.processes[process_name] = None
+            
+            return True
+        return False
+    
+    def kill_process(self, process_name, process_display_name=""):
+        """Kill a process with consistent error handling"""
+        if self.processes.get(process_name) is not None:
+            kill_process(self.processes[process_name].pid, process_display_name)
+            self.processes[process_name] = None
+            return True
+        return False
+    
+    def get_process_status(self, process_name):
+        """Get current process status"""
+        return self.processes.get(process_name)
+
+# Global process manager
+process_manager = ProcessManager()
+
+def create_ui_yield(process_name, status, visible_states=None):
+    """Create consistent UI yield patterns"""
+    if visible_states is None:
+        visible_states = {"opened": (False, True), "closed": (True, False), "finish": (True, False), "occupy": (False, True)}
+    
+    visible_open, visible_close = visible_states.get(status, (True, False))
+    
+    return (
+        process_info(process_name, status),
+        {"__type__": "update", "visible": visible_open},
+        {"__type__": "update", "visible": visible_close},
+    )
+
+def create_command_builder():
+    """Factory for building subprocess commands"""
+    class CommandBuilder:
+        def __init__(self):
+            self.base_cmd = f'"{python_exec}" -s'
+        
+        def build(self, script_path, *args):
+            """Build a command with consistent formatting"""
+            cmd_parts = [self.base_cmd, script_path]
+            cmd_parts.extend(str(arg) for arg in args)
+            return " ".join(cmd_parts)
+        
+        def build_with_paths(self, script_path, *paths):
+            """Build command with quoted paths"""
+            cmd_parts = [self.base_cmd, script_path]
+            cmd_parts.extend(f'"{path}"' for path in paths)
+            return " ".join(cmd_parts)
+    
+    return CommandBuilder()
+
+# Global command builder
+cmd_builder = create_command_builder()
+
+def setup_gpu_environment(gpu_numbers, is_half_flag=None):
+    """Setup GPU environment variables consistently"""
+    env_updates = {
+        "_CUDA_VISIBLE_DEVICES": fix_gpu_numbers(gpu_numbers.replace("-", ",")),
+    }
+    if is_half_flag is not None:
+        env_updates["is_half"] = str(is_half_flag)
+    return env_updates
+
+def create_multi_gpu_config(gpu_numbers, base_config, script_path):
+    """Create configuration for multi-GPU processing"""
+    gpu_names = gpu_numbers.split("-")
+    all_parts = len(gpu_names)
+    processes = []
+    
+    for i_part in range(all_parts):
+        config = base_config.copy()
+        config.update({
+            "i_part": str(i_part),
+            "all_parts": str(all_parts),
+            "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
+        })
+        os.environ.update(config)
+        
+        cmd = cmd_builder.build(script_path)
+        print(cmd)
+        p = Popen(cmd, shell=True)
+        processes.append(p)
+    
+    return processes
+
+def merge_output_files(opt_dir, file_pattern, output_file, header=None):
+    """Merge multiple output files into a single file"""
+    opt = []
+    if header:
+        opt.append(header)
+    
+    all_parts = len([f for f in os.listdir(opt_dir) if f.startswith(file_pattern.split("-")[0])])
+    
+    for i_part in range(all_parts):
+        file_path = f"{opt_dir}/{file_pattern.format(i_part)}"
+        if os.path.exists(file_path):
+            with open(file_path, "r", encoding="utf8") as f:
+                content = f.read().strip("\n").split("\n")
+                opt.extend(content)
+            os.remove(file_path)
+    
+    with open(output_file, "w", encoding="utf8") as f:
+        f.write("\n".join(opt) + "\n")
+    
+    return opt
+
+# ==================== END DRY UTILITY FUNCTIONS ====================
+
+def create_process_handler(process_name, process_display_name, script_path, env_updates=None, wait=False, additional_args=None):
+    """Generic process handler to eliminate repetitive open/close patterns"""
+    
+    def open_process(*args):
+        process_var = globals().get(f"p_{process_name}")
+        
+        if process_var is None:
+            cmd = cmd_builder.build(script_path, *(additional_args or []))
+            
+            if process_manager.create_process(process_name, cmd, wait=wait, env_updates=env_updates):
+                yield create_ui_yield(process_display_name, "opened")
+                globals()[f"p_{process_name}"] = process_manager.get_process_status(process_name)
+            else:
+                yield create_ui_yield(process_display_name, "occupy")
+        else:
+            yield create_ui_yield(process_display_name, "occupy")
+    
+    def close_process():
+        if process_manager.kill_process(process_name, process_display_name):
+            globals()[f"p_{process_name}"] = None
+        return create_ui_yield(process_display_name, "closed")
+    
+    return open_process, close_process
+
+set_default()
+
+gpus = "-".join(map(str, GPU_INDEX))
+default_gpu_numbers = infer_device.index
+
+
+def fix_gpu_number(input):  # 将越界的number强制改到界内
+    try:
+        if int(input) not in set_gpu_numbers:
+            return default_gpu_numbers
+    except:
+        return input
+    return input
+
+
+def fix_gpu_numbers(inputs):
+    output = []
+    try:
+        for input in inputs.split(","):
+            output.append(str(fix_gpu_number(input)))
+        return ",".join(output)
+    except:
+        return inputs
+
+
+from config import pretrained_gpt_name, pretrained_sovits_name
+
+
+def check_pretrained_is_exist(version):
+    pretrained_model_list = (
+        pretrained_sovits_name[version],
+        pretrained_sovits_name[version].replace("s2G", "s2D"),
+        pretrained_gpt_name[version],
+        "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
+        "GPT_SoVITS/pretrained_models/chinese-hubert-base",
+    )
+    _ = ""
+    for i in pretrained_model_list:
+        if "s2Dv3" not in i and "s2Dv4" not in i and os.path.exists(i) == False:
+            _ += f"\n    {i}"
+    if _:
+        print("warning: ", i18n("以下模型不存在:") + _)
+
+
+check_pretrained_is_exist(version)
+for key in pretrained_sovits_name.keys():
+    if os.path.exists(pretrained_sovits_name[key]) == False:
+        pretrained_sovits_name[key] = ""
+for key in pretrained_gpt_name.keys():
+    if os.path.exists(pretrained_gpt_name[key]) == False:
+        pretrained_gpt_name[key] = ""
+
+from config import (
+    GPT_weight_root,
+    GPT_weight_version2root,
+    SoVITS_weight_root,
+    SoVITS_weight_version2root,
+    change_choices,
+    get_weights_names,
+)
+
+for root in SoVITS_weight_root + GPT_weight_root:
+    os.makedirs(root, exist_ok=True)
+SoVITS_names, GPT_names = get_weights_names()
+
+p_label = None
+p_uvr5 = None
+p_asr = None
+p_denoise = None
+p_tts_inference = None
+
+
+def kill_proc_tree(pid, including_parent=True):
+    try:
+        parent = psutil.Process(pid)
+    except psutil.NoSuchProcess:
+        # Process already terminated
+        return
+
+    children = parent.children(recursive=True)
+    for child in children:
+        try:
+            os.kill(child.pid, signal.SIGTERM)  # or signal.SIGKILL
+        except OSError:
+            pass
+    if including_parent:
+        try:
+            os.kill(parent.pid, signal.SIGTERM)  # or signal.SIGKILL
+        except OSError:
+            pass
+
+
+system = platform.system()
+
+
+def kill_process(pid, process_name=""):
+    if system == "Windows":
+        cmd = f"taskkill /t /f /pid {pid}"
+        # os.system(cmd)
+        subprocess.run(cmd, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+    else:
+        kill_proc_tree(pid)
+    print(process_name + i18n("进程已终止"))
+
+
+def process_info(process_name="", indicator=""):
+    if indicator == "opened":
+        return process_name + i18n("已开启")
+    elif indicator == "open":
+        return i18n("开启") + process_name
+    elif indicator == "closed":
+        return process_name + i18n("已关闭")
+    elif indicator == "close":
+        return i18n("关闭") + process_name
+    elif indicator == "running":
+        return process_name + i18n("运行中")
+    elif indicator == "occupy":
+        return process_name + i18n("占用中") + "," + i18n("需先终止才能开启下一次任务")
+    elif indicator == "finish":
+        return process_name + i18n("已完成")
+    elif indicator == "failed":
+        return process_name + i18n("失败")
+    elif indicator == "info":
+        return process_name + i18n("进程输出信息")
+    else:
+        return process_name
+
+
+process_name_subfix = i18n("音频标注WebUI")
+
+
+def change_label(path_list):
+    global p_label
+    if p_label is None:
+        check_for_existance([path_list])
+        path_list = my_utils.clean_path(path_list)
+        cmd = cmd_builder.build_with_paths("tools/subfix_webui.py", path_list) + f" --webui_port {webui_port_subfix} --is_share {is_share}"
+        
+        if process_manager.create_process("label", cmd):
+            yield create_ui_yield(process_name_subfix, "opened")
+            p_label = process_manager.get_process_status("label")
+        else:
+            yield create_ui_yield(process_name_subfix, "occupy")
+    else:
+        process_manager.kill_process("label", process_name_subfix)
+        p_label = None
+        yield create_ui_yield(process_name_subfix, "closed")
+
+
+process_name_uvr5 = i18n("人声分离WebUI")
+
+
+def change_uvr5():
+    global p_uvr5
+    if p_uvr5 is None:
+        cmd = cmd_builder.build("tools/uvr5/webui.py", infer_device, is_half, webui_port_uvr5, is_share)
+        
+        if process_manager.create_process("uvr5", cmd):
+            yield create_ui_yield(process_name_uvr5, "opened")
+            p_uvr5 = process_manager.get_process_status("uvr5")
+        else:
+            yield create_ui_yield(process_name_uvr5, "occupy")
+    else:
+        process_manager.kill_process("uvr5", process_name_uvr5)
+        p_uvr5 = None
+        yield create_ui_yield(process_name_uvr5, "closed")
+
+
+process_name_tts = i18n("TTS推理WebUI")
+
+
+def change_tts_inference(bert_path, cnhubert_base_path, gpu_number, gpt_path, sovits_path, batched_infer_enabled):
+    global p_tts_inference
+    script = "GPT_SoVITS/inference_webui_fast.py" if batched_infer_enabled else "GPT_SoVITS/inference_webui.py"
+    cmd = cmd_builder.build_with_paths(script, language)
+    
+    if p_tts_inference is None:
+        env_updates = {
+            "gpt_path": gpt_path,
+            "sovits_path": sovits_path,
+            "cnhubert_base_path": cnhubert_base_path,
+            "bert_path": bert_path,
+            "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_number),
+            "is_half": str(is_half),
+            "infer_ttswebui": str(webui_port_infer_tts),
+            "is_share": str(is_share),
+        }
+        
+        if process_manager.create_process("tts_inference", cmd, env_updates=env_updates):
+            yield create_ui_yield(process_name_tts, "opened")
+            p_tts_inference = process_manager.get_process_status("tts_inference")
+        else:
+            yield create_ui_yield(process_name_tts, "occupy")
+    else:
+        process_manager.kill_process("tts_inference", process_name_tts)
+        p_tts_inference = None
+        yield create_ui_yield(process_name_tts, "closed")
+
+
+from tools.asr.config import asr_dict
+
+process_name_asr = i18n("语音识别")
+
+
+def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang, asr_precision):
+    global p_asr
+    if p_asr is None:
+        asr_inp_dir = my_utils.clean_path(asr_inp_dir)
+        asr_opt_dir = my_utils.clean_path(asr_opt_dir)
+        check_for_existance([asr_inp_dir])
+        
+        cmd = cmd_builder.build("tools/asr/" + asr_dict[asr_model]["path"])
+        cmd += f' -i "{asr_inp_dir}" -o "{asr_opt_dir}" -s {asr_model_size} -l {asr_lang} -p {asr_precision}'
+        
+        output_file_name = os.path.basename(asr_inp_dir)
+        output_folder = asr_opt_dir or "output/asr_opt"
+        output_file_path = os.path.abspath(f"{output_folder}/{output_file_name}.list")
+        
+        yield create_ui_yield(process_name_asr, "opened") + ({"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"})
+        
+        if process_manager.create_process("asr", cmd, wait=True):
+            yield create_ui_yield(process_name_asr, "finish") + (
+                {"__type__": "update", "value": output_file_path},
+                {"__type__": "update", "value": output_file_path},
+                {"__type__": "update", "value": asr_inp_dir},
+            )
+        p_asr = None
+    else:
+        yield create_ui_yield(process_name_asr, "occupy") + ({"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"})
+
+
+def close_asr():
+    global p_asr
+    if process_manager.kill_process("asr", process_name_asr):
+        p_asr = None
+    return create_ui_yield(process_name_asr, "closed")
+
+
+process_name_denoise = i18n("语音降噪")
+
+
+def open_denoise(denoise_inp_dir, denoise_opt_dir):
+    global p_denoise
+    if p_denoise == None:
+        denoise_inp_dir = my_utils.clean_path(denoise_inp_dir)
+        denoise_opt_dir = my_utils.clean_path(denoise_opt_dir)
+        check_for_existance([denoise_inp_dir])
+        
+        precision = "float16" if is_half == True else "float32"
+        cmd = cmd_builder.build_with_paths("tools/cmd-denoise.py", denoise_inp_dir, denoise_opt_dir) + f" -p {precision}"
+
+        yield create_ui_yield(process_name_denoise, "opened") + ({"__type__": "update"}, {"__type__": "update"})
+        
+        if process_manager.create_process("denoise", cmd, wait=True):
+            yield create_ui_yield(process_name_denoise, "finish") + (
+                {"__type__": "update", "value": denoise_opt_dir},
+                {"__type__": "update", "value": denoise_opt_dir},
+            )
+        p_denoise = None
+    else:
+        yield create_ui_yield(process_name_denoise, "occupy") + ({"__type__": "update"}, {"__type__": "update"})
+
+
+def close_denoise():
+    global p_denoise
+    if process_manager.kill_process("denoise", process_name_denoise):
+        p_denoise = None
+    return create_ui_yield(process_name_denoise, "closed")
+
+
+p_train_SoVITS = None
+process_name_sovits = i18n("SoVITS训练")
+
+
+def open1Ba(
+    version,
+    batch_size,
+    total_epoch,
+    exp_name,
+    text_low_lr_rate,
+    if_save_latest,
+    if_save_every_weights,
+    save_every_epoch,
+    gpu_numbers1Ba,
+    pretrained_s2G,
+    pretrained_s2D,
+    if_grad_ckpt,
+    lora_rank,
+):
+    global p_train_SoVITS
+    if p_train_SoVITS == None:
+        exp_name = exp_name.rstrip(" ")
+        config_file = (
+            "GPT_SoVITS/configs/s2.json"
+            if version not in {"v2Pro", "v2ProPlus"}
+            else f"GPT_SoVITS/configs/s2{version}.json"
+        )
+        with open(config_file) as f:
+            data = f.read()
+            data = json.loads(data)
+        s2_dir = f"{exp_root}/{exp_name}"
+        os.makedirs(f"{s2_dir}/logs_s2_{version}", exist_ok=True)
+        if check_for_existance([s2_dir], is_train=True):
+            check_details([s2_dir], is_train=True)
+        if is_half == False:
+            data["train"]["fp16_run"] = False
+            batch_size = max(1, batch_size // 2)
+        data["train"]["batch_size"] = batch_size
+        data["train"]["epochs"] = total_epoch
+        data["train"]["text_low_lr_rate"] = text_low_lr_rate
+        data["train"]["pretrained_s2G"] = pretrained_s2G
+        data["train"]["pretrained_s2D"] = pretrained_s2D
+        data["train"]["if_save_latest"] = if_save_latest
+        data["train"]["if_save_every_weights"] = if_save_every_weights
+        data["train"]["save_every_epoch"] = save_every_epoch
+        data["train"]["gpu_numbers"] = gpu_numbers1Ba
+        data["train"]["grad_ckpt"] = if_grad_ckpt
+        data["train"]["lora_rank"] = lora_rank
+        data["model"]["version"] = version
+        data["data"]["exp_dir"] = data["s2_ckpt_dir"] = s2_dir
+        data["save_weight_dir"] = SoVITS_weight_version2root[version]
+        data["name"] = exp_name
+        data["version"] = version
+        tmp_config_path = f"{tmp}/tmp_s2.json"
+        with open(tmp_config_path, "w") as f:
+            f.write(json.dumps(data))
+        if version in ["v1", "v2", "v2Pro", "v2ProPlus"]:
+            cmd = f'"{python_exec}" -s GPT_SoVITS/s2_train.py --config "{tmp_config_path}"'
+        else:
+            cmd = f'"{python_exec}" -s GPT_SoVITS/s2_train_v3_lora.py --config "{tmp_config_path}"'
+        yield (
+            process_info(process_name_sovits, "opened"),
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "visible": True},
+            {"__type__": "update"},
+            {"__type__": "update"},
+        )
+        print(cmd)
+        p_train_SoVITS = Popen(cmd, shell=True)
+        p_train_SoVITS.wait()
+        p_train_SoVITS = None
+        SoVITS_dropdown_update, GPT_dropdown_update = change_choices()
+        yield (
+            process_info(process_name_sovits, "finish"),
+            {"__type__": "update", "visible": True},
+            {"__type__": "update", "visible": False},
+            SoVITS_dropdown_update,
+            GPT_dropdown_update,
+        )
+    else:
+        yield (
+            process_info(process_name_sovits, "occupy"),
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "visible": True},
+            {"__type__": "update"},
+            {"__type__": "update"},
+        )
+
+
+def close1Ba():
+    global p_train_SoVITS
+    if p_train_SoVITS is not None:
+        kill_process(p_train_SoVITS.pid, process_name_sovits)
+        p_train_SoVITS = None
+    return (
+        process_info(process_name_sovits, "closed"),
+        {"__type__": "update", "visible": True},
+        {"__type__": "update", "visible": False},
+    )
+
+
+p_train_GPT = None
+process_name_gpt = i18n("GPT训练")
+
+
+def open1Bb(
+    batch_size,
+    total_epoch,
+    exp_name,
+    if_dpo,
+    if_save_latest,
+    if_save_every_weights,
+    save_every_epoch,
+    gpu_numbers,
+    pretrained_s1,
+):
+    global p_train_GPT
+    if p_train_GPT == None:
+        exp_name = exp_name.rstrip(" ")
+        with open(
+            "GPT_SoVITS/configs/s1longer.yaml" if version == "v1" else "GPT_SoVITS/configs/s1longer-v2.yaml"
+        ) as f:
+            data = f.read()
+            data = yaml.load(data, Loader=yaml.FullLoader)
+        s1_dir = f"{exp_root}/{exp_name}"
+        os.makedirs(f"{s1_dir}/logs_s1", exist_ok=True)
+        if check_for_existance([s1_dir], is_train=True):
+            check_details([s1_dir], is_train=True)
+        if is_half == False:
+            data["train"]["precision"] = "32"
+            batch_size = max(1, batch_size // 2)
+        data["train"]["batch_size"] = batch_size
+        data["train"]["epochs"] = total_epoch
+        data["pretrained_s1"] = pretrained_s1
+        data["train"]["save_every_n_epoch"] = save_every_epoch
+        data["train"]["if_save_every_weights"] = if_save_every_weights
+        data["train"]["if_save_latest"] = if_save_latest
+        data["train"]["if_dpo"] = if_dpo
+        data["train"]["half_weights_save_dir"] = GPT_weight_version2root[version]
+        data["train"]["exp_name"] = exp_name
+        data["train_semantic_path"] = f"{s1_dir}/6-name2semantic.tsv"
+        data["train_phoneme_path"] = f"{s1_dir}/2-name2text.txt"
+        data["output_dir"] = f"{s1_dir}/logs_s1_{version}"
+        # data["version"]=version
+
+        os.environ["_CUDA_VISIBLE_DEVICES"] = fix_gpu_numbers(gpu_numbers.replace("-", ","))
+        os.environ["hz"] = "25hz"
+        tmp_config_path = f"{tmp}/tmp_s1.yaml"
+        with open(tmp_config_path, "w") as f:
+            f.write(yaml.dump(data, default_flow_style=False))
+        # cmd = f'"{python_exec}" GPT_SoVITS/s1_train.py --config_file "{tmp_config_path}" --train_semantic_path "{s1_dir}/6-name2semantic.tsv" --train_phoneme_path "{s1_dir}/2-name2text.txt" --output_dir "{s1_dir}/logs_s1"'
+        cmd = f'"{python_exec}" -s GPT_SoVITS/s1_train.py --config_file "{tmp_config_path}" '
+        yield (
+            process_info(process_name_gpt, "opened"),
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "visible": True},
+            {"__type__": "update"},
+            {"__type__": "update"},
+        )
+        print(cmd)
+        p_train_GPT = Popen(cmd, shell=True)
+        p_train_GPT.wait()
+        p_train_GPT = None
+        SoVITS_dropdown_update, GPT_dropdown_update = change_choices()
+        yield (
+            process_info(process_name_gpt, "finish"),
+            {"__type__": "update", "visible": True},
+            {"__type__": "update", "visible": False},
+            SoVITS_dropdown_update,
+            GPT_dropdown_update,
+        )
+    else:
+        yield (
+            process_info(process_name_gpt, "occupy"),
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "visible": True},
+            {"__type__": "update"},
+            {"__type__": "update"},
+        )
+
+
+def close1Bb():
+    global p_train_GPT
+    if p_train_GPT is not None:
+        kill_process(p_train_GPT.pid, process_name_gpt)
+        p_train_GPT = None
+    return (
+        process_info(process_name_gpt, "closed"),
+        {"__type__": "update", "visible": True},
+        {"__type__": "update", "visible": False},
+    )
+
+
+ps_slice = []
+process_name_slice = i18n("语音切分")
+
+
+def open_slice(inp, opt_root, threshold, min_length, min_interval, hop_size, max_sil_kept, _max, alpha, n_parts):
+    global ps_slice
+    inp = my_utils.clean_path(inp)
+    opt_root = my_utils.clean_path(opt_root)
+    check_for_existance([inp])
+    if os.path.exists(inp) == False:
+        yield (
+            i18n("输入路径不存在"),
+            {"__type__": "update", "visible": True},
+            {"__type__": "update", "visible": False},
+            {"__type__": "update"},
+            {"__type__": "update"},
+            {"__type__": "update"},
+        )
+        return
+    if os.path.isfile(inp):
+        n_parts = 1
+    elif os.path.isdir(inp):
+        pass
+    else:
+        yield (
+            i18n("输入路径存在但不可用"),
+            {"__type__": "update", "visible": True},
+            {"__type__": "update", "visible": False},
+            {"__type__": "update"},
+            {"__type__": "update"},
+            {"__type__": "update"},
+        )
+        return
+    if ps_slice == []:
+        for i_part in range(n_parts):
+            cmd = f'"{python_exec}" -s tools/slice_audio.py "{inp}" "{opt_root}" {threshold} {min_length} {min_interval} {hop_size} {max_sil_kept} {_max} {alpha} {i_part} {n_parts}'
+            print(cmd)
+            p = Popen(cmd, shell=True)
+            ps_slice.append(p)
+        yield (
+            process_info(process_name_slice, "opened"),
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "visible": True},
+            {"__type__": "update"},
+            {"__type__": "update"},
+            {"__type__": "update"},
+        )
+        for p in ps_slice:
+            p.wait()
+        ps_slice = []
+        yield (
+            process_info(process_name_slice, "finish"),
+            {"__type__": "update", "visible": True},
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "value": opt_root},
+            {"__type__": "update", "value": opt_root},
+            {"__type__": "update", "value": opt_root},
+        )
+    else:
+        yield (
+            process_info(process_name_slice, "occupy"),
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "visible": True},
+            {"__type__": "update"},
+            {"__type__": "update"},
+            {"__type__": "update"},
+        )
+
+
+def close_slice():
+    global ps_slice
+    if ps_slice != []:
+        for p_slice in ps_slice:
+            try:
+                kill_process(p_slice.pid, process_name_slice)
+            except:
+                traceback.print_exc()
+        ps_slice = []
+    return (
+        process_info(process_name_slice, "closed"),
+        {"__type__": "update", "visible": True},
+        {"__type__": "update", "visible": False},
+    )
+
+
+ps1a = []
+process_name_1a = i18n("文本分词与特征提取")
+
+
+def open1a(inp_text, inp_wav_dir, exp_name, gpu_numbers, bert_pretrained_dir):
+    global ps1a
+    inp_text = my_utils.clean_path(inp_text)
+    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
+    if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
+        check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
+    exp_name = exp_name.rstrip(" ")
+    if ps1a == []:
+        opt_dir = f"{exp_root}/{exp_name}"
+        config = {
+            "inp_text": inp_text,
+            "inp_wav_dir": inp_wav_dir,
+            "exp_name": exp_name,
+            "opt_dir": opt_dir,
+            "bert_pretrained_dir": bert_pretrained_dir,
+        }
+        gpu_names = gpu_numbers.split("-")
+        all_parts = len(gpu_names)
+        for i_part in range(all_parts):
+            config.update(
+                {
+                    "i_part": str(i_part),
+                    "all_parts": str(all_parts),
+                    "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
+                    "is_half": str(is_half),
+                }
+            )
+            os.environ.update(config)
+            cmd = f'"{python_exec}" -s GPT_SoVITS/prepare_datasets/1-get-text.py'
+            print(cmd)
+            p = Popen(cmd, shell=True)
+            ps1a.append(p)
+        yield (
+            process_info(process_name_1a, "running"),
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "visible": True},
+        )
+        for p in ps1a:
+            p.wait()
+        opt = []
+        for i_part in range(all_parts):
+            txt_path = f"{opt_dir}/2-name2text-{i_part}.txt"
+            with open(txt_path, "r", encoding="utf8") as f:
+                opt += f.read().strip("\n").split("\n")
+            os.remove(txt_path)
+        path_text = f"{opt_dir}/2-name2text.txt"
+        with open(path_text, "w", encoding="utf8") as f:
+            f.write("\n".join(opt) + "\n")
+        ps1a = []
+        if len("".join(opt)) > 0:
+            yield (
+                process_info(process_name_1a, "finish"),
+                {"__type__": "update", "visible": True},
+                {"__type__": "update", "visible": False},
+            )
+        else:
+            yield (
+                process_info(process_name_1a, "failed"),
+                {"__type__": "update", "visible": True},
+                {"__type__": "update", "visible": False},
+            )
+    else:
+        yield (
+            process_info(process_name_1a, "occupy"),
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "visible": True},
+        )
+
+
+def close1a():
+    global ps1a
+    if ps1a != []:
+        for p1a in ps1a:
+            try:
+                kill_process(p1a.pid, process_name_1a)
+            except:
+                traceback.print_exc()
+        ps1a = []
+    return (
+        process_info(process_name_1a, "closed"),
+        {"__type__": "update", "visible": True},
+        {"__type__": "update", "visible": False},
+    )
+
+
+sv_path = "GPT_SoVITS/pretrained_models/sv/pretrained_eres2netv2w24s4ep4.ckpt"
+ps1b = []
+process_name_1b = i18n("语音自监督特征提取")
+
+
+def open1b(version, inp_text, inp_wav_dir, exp_name, gpu_numbers, ssl_pretrained_dir):
+    global ps1b
+    inp_text = my_utils.clean_path(inp_text)
+    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
+    if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
+        check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
+    exp_name = exp_name.rstrip(" ")
+    if ps1b == []:
+        config = {
+            "inp_text": inp_text,
+            "inp_wav_dir": inp_wav_dir,
+            "exp_name": exp_name,
+            "opt_dir": f"{exp_root}/{exp_name}",
+            "cnhubert_base_dir": ssl_pretrained_dir,
+            "sv_path": sv_path,
+            "is_half": str(is_half),
+        }
+        gpu_names = gpu_numbers.split("-")
+        all_parts = len(gpu_names)
+        for i_part in range(all_parts):
+            config.update(
+                {
+                    "i_part": str(i_part),
+                    "all_parts": str(all_parts),
+                    "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
+                }
+            )
+            os.environ.update(config)
+            cmd = f'"{python_exec}" -s GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'
+            print(cmd)
+            p = Popen(cmd, shell=True)
+            ps1b.append(p)
+        yield (
+            process_info(process_name_1b, "running"),
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "visible": True},
+        )
+        for p in ps1b:
+            p.wait()
+        ps1b = []
+        if "Pro" in version:
+            for i_part in range(all_parts):
+                config.update(
+                    {
+                        "i_part": str(i_part),
+                        "all_parts": str(all_parts),
+                        "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
+                    }
+                )
+                os.environ.update(config)
+                cmd = f'"{python_exec}" -s GPT_SoVITS/prepare_datasets/2-get-sv.py'
+                print(cmd)
+                p = Popen(cmd, shell=True)
+                ps1b.append(p)
+            for p in ps1b:
+                p.wait()
+            ps1b = []
+        yield (
+            process_info(process_name_1b, "finish"),
+            {"__type__": "update", "visible": True},
+            {"__type__": "update", "visible": False},
+        )
+    else:
+        yield (
+            process_info(process_name_1b, "occupy"),
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "visible": True},
+        )
+
+
+def close1b():
+    global ps1b
+    if ps1b != []:
+        for p1b in ps1b:
+            try:
+                kill_process(p1b.pid, process_name_1b)
+            except:
+                traceback.print_exc()
+        ps1b = []
+    return (
+        process_info(process_name_1b, "closed"),
+        {"__type__": "update", "visible": True},
+        {"__type__": "update", "visible": False},
+    )
+
+
+ps1c = []
+process_name_1c = i18n("语义Token提取")
+
+
+def open1c(version, inp_text, inp_wav_dir, exp_name, gpu_numbers, pretrained_s2G_path):
+    global ps1c
+    inp_text = my_utils.clean_path(inp_text)
+    if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
+        check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
+    exp_name = exp_name.rstrip(" ")
+    if ps1c == []:
+        opt_dir = f"{exp_root}/{exp_name}"
+        config_file = (
+            "GPT_SoVITS/configs/s2.json"
+            if version not in {"v2Pro", "v2ProPlus"}
+            else f"GPT_SoVITS/configs/s2{version}.json"
+        )
+        config = {
+            "inp_text": inp_text,
+            "exp_name": exp_name,
+            "opt_dir": opt_dir,
+            "pretrained_s2G": pretrained_s2G_path,
+            "s2config_path": config_file,
+            "is_half": str(is_half),
+        }
+        gpu_names = gpu_numbers.split("-")
+        all_parts = len(gpu_names)
+        for i_part in range(all_parts):
+            config.update(
+                {
+                    "i_part": str(i_part),
+                    "all_parts": str(all_parts),
+                    "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
+                }
+            )
+            os.environ.update(config)
+            cmd = f'"{python_exec}" -s GPT_SoVITS/prepare_datasets/3-get-semantic.py'
+            print(cmd)
+            p = Popen(cmd, shell=True)
+            ps1c.append(p)
+        yield (
+            process_info(process_name_1c, "running"),
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "visible": True},
+        )
+        for p in ps1c:
+            p.wait()
+        opt = ["item_name\tsemantic_audio"]
+        path_semantic = f"{opt_dir}/6-name2semantic.tsv"
+        for i_part in range(all_parts):
+            semantic_path = f"{opt_dir}/6-name2semantic-{i_part}.tsv"
+            with open(semantic_path, "r", encoding="utf8") as f:
+                opt += f.read().strip("\n").split("\n")
+            os.remove(semantic_path)
+        with open(path_semantic, "w", encoding="utf8") as f:
+            f.write("\n".join(opt) + "\n")
+        ps1c = []
+        yield (
+            process_info(process_name_1c, "finish"),
+            {"__type__": "update", "visible": True},
+            {"__type__": "update", "visible": False},
+        )
+    else:
+        yield (
+            process_info(process_name_1c, "occupy"),
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "visible": True},
+        )
+
+
+def close1c():
+    global ps1c
+    if ps1c != []:
+        for p1c in ps1c:
+            try:
+                kill_process(p1c.pid, process_name_1c)
+            except:
+                traceback.print_exc()
+        ps1c = []
+    return (
+        process_info(process_name_1c, "closed"),
+        {"__type__": "update", "visible": True},
+        {"__type__": "update", "visible": False},
+    )
+
+
+ps1abc = []
+process_name_1abc = i18n("训练集格式化一键三连")
+
+
+def open1abc(
+    version,
+    inp_text,
+    inp_wav_dir,
+    exp_name,
+    gpu_numbers1a,
+    gpu_numbers1Ba,
+    gpu_numbers1c,
+    bert_pretrained_dir,
+    ssl_pretrained_dir,
+    pretrained_s2G_path,
+):
+    global ps1abc
+    inp_text = my_utils.clean_path(inp_text)
+    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
+    if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
+        check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
+    exp_name = exp_name.rstrip(" ")
+    if ps1abc == []:
+        opt_dir = f"{exp_root}/{exp_name}"
+        try:
+            #############################1a
+            path_text = f"{opt_dir}/2-name2text.txt"
+            if os.path.exists(path_text) == False or (
+                os.path.exists(path_text) == True
+                and len(open(path_text, "r", encoding="utf8").read().strip("\n").split("\n")) < 2
+            ):
+                config = {
+                    "inp_text": inp_text,
+                    "inp_wav_dir": inp_wav_dir,
+                    "exp_name": exp_name,
+                    "opt_dir": opt_dir,
+                    "bert_pretrained_dir": bert_pretrained_dir,
+                    "is_half": str(is_half),
+                }
+                gpu_names = gpu_numbers1a.split("-")
+                all_parts = len(gpu_names)
+                for i_part in range(all_parts):
+                    config.update(
+                        {
+                            "i_part": str(i_part),
+                            "all_parts": str(all_parts),
+                            "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
+                        }
+                    )
+                    os.environ.update(config)
+                    cmd = f'"{python_exec}" -s GPT_SoVITS/prepare_datasets/1-get-text.py'
+                    print(cmd)
+                    p = Popen(cmd, shell=True)
+                    ps1abc.append(p)
+                yield (
+                    i18n("进度") + ": 1A-Doing",
+                    {"__type__": "update", "visible": False},
+                    {"__type__": "update", "visible": True},
+                )
+                for p in ps1abc:
+                    p.wait()
+
+                opt = []
+                for i_part in range(all_parts):  # txt_path=f"{opt_dir}/2-name2text-{i_part}.txt"
+                    txt_path = f"{opt_dir}/2-name2text-{i_part}.txt"
+                    with open(txt_path, "r", encoding="utf8") as f:
+                        opt += f.read().strip("\n").split("\n")
+                    os.remove(txt_path)
+                with open(path_text, "w", encoding="utf8") as f:
+                    f.write("\n".join(opt) + "\n")
+                assert len("".join(opt)) > 0, process_info(process_name_1a, "failed")
+            yield (
+                i18n("进度") + ": 1A-Done",
+                {"__type__": "update", "visible": False},
+                {"__type__": "update", "visible": True},
+            )
+            ps1abc = []
+            #############################1b
+            config = {
+                "inp_text": inp_text,
+                "inp_wav_dir": inp_wav_dir,
+                "exp_name": exp_name,
+                "opt_dir": opt_dir,
+                "cnhubert_base_dir": ssl_pretrained_dir,
+                "sv_path": sv_path,
+            }
+            gpu_names = gpu_numbers1Ba.split("-")
+            all_parts = len(gpu_names)
+            for i_part in range(all_parts):
+                config.update(
+                    {
+                        "i_part": str(i_part),
+                        "all_parts": str(all_parts),
+                        "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
+                    }
+                )
+                os.environ.update(config)
+                cmd = f'"{python_exec}" -s GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'
+                print(cmd)
+                p = Popen(cmd, shell=True)
+                ps1abc.append(p)
+            yield (
+                i18n("进度") + ": 1A-Done, 1B-Doing",
+                {"__type__": "update", "visible": False},
+                {"__type__": "update", "visible": True},
+            )
+            for p in ps1abc:
+                p.wait()
+            ps1abc = []
+            if "Pro" in version:
+                for i_part in range(all_parts):
+                    config.update(
+                        {
+                            "i_part": str(i_part),
+                            "all_parts": str(all_parts),
+                            "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
+                        }
+                    )
+                    os.environ.update(config)
+                    cmd = f'"{python_exec}" -s GPT_SoVITS/prepare_datasets/2-get-sv.py'
+                    print(cmd)
+                    p = Popen(cmd, shell=True)
+                    ps1abc.append(p)
+                for p in ps1abc:
+                    p.wait()
+                ps1abc = []
+            yield (
+                i18n("进度") + ": 1A-Done, 1B-Done",
+                {"__type__": "update", "visible": False},
+                {"__type__": "update", "visible": True},
+            )
+            #############################1c
+            path_semantic = f"{opt_dir}/6-name2semantic.tsv"
+            if os.path.exists(path_semantic) == False or (
+                os.path.exists(path_semantic) == True and os.path.getsize(path_semantic) < 31
+            ):
+                config_file = (
+                    "GPT_SoVITS/configs/s2.json"
+                    if version not in {"v2Pro", "v2ProPlus"}
+                    else f"GPT_SoVITS/configs/s2{version}.json"
+                )
+                config = {
+                    "inp_text": inp_text,
+                    "exp_name": exp_name,
+                    "opt_dir": opt_dir,
+                    "pretrained_s2G": pretrained_s2G_path,
+                    "s2config_path": config_file,
+                }
+                gpu_names = gpu_numbers1c.split("-")
+                all_parts = len(gpu_names)
+                for i_part in range(all_parts):
+                    config.update(
+                        {
+                            "i_part": str(i_part),
+                            "all_parts": str(all_parts),
+                            "_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
+                        }
+                    )
+                    os.environ.update(config)
+                    cmd = f'"{python_exec}" -s GPT_SoVITS/prepare_datasets/3-get-semantic.py'
+                    print(cmd)
+                    p = Popen(cmd, shell=True)
+                    ps1abc.append(p)
+                yield (
+                    i18n("进度") + ": 1A-Done, 1B-Done, 1C-Doing",
+                    {"__type__": "update", "visible": False},
+                    {"__type__": "update", "visible": True},
+                )
+                for p in ps1abc:
+                    p.wait()
+
+                opt = ["item_name\tsemantic_audio"]
+                for i_part in range(all_parts):
+                    semantic_path = f"{opt_dir}/6-name2semantic-{i_part}.tsv"
+                    with open(semantic_path, "r", encoding="utf8") as f:
+                        opt += f.read().strip("\n").split("\n")
+                    os.remove(semantic_path)
+                with open(path_semantic, "w", encoding="utf8") as f:
+                    f.write("\n".join(opt) + "\n")
+                yield (
+                    i18n("进度") + ": 1A-Done, 1B-Done, 1C-Done",
+                    {"__type__": "update", "visible": False},
+                    {"__type__": "update", "visible": True},
+                )
+            ps1abc = []
+            yield (
+                process_info(process_name_1abc, "finish"),
+                {"__type__": "update", "visible": True},
+                {"__type__": "update", "visible": False},
+            )
+        except:
+            traceback.print_exc()
+            close1abc()
+            yield (
+                process_info(process_name_1abc, "failed"),
+                {"__type__": "update", "visible": True},
+                {"__type__": "update", "visible": False},
+            )
+    else:
+        yield (
+            process_info(process_name_1abc, "occupy"),
+            {"__type__": "update", "visible": False},
+            {"__type__": "update", "visible": True},
+        )
+
+
+def close1abc():
+    global ps1abc
+    if ps1abc != []:
+        for p1abc in ps1abc:
+            try:
+                kill_process(p1abc.pid, process_name_1abc)
+            except:
+                traceback.print_exc()
+        ps1abc = []
+    return (
+        process_info(process_name_1abc, "closed"),
+        {"__type__": "update", "visible": True},
+        {"__type__": "update", "visible": False},
+    )
+
+
+def switch_version(version_):
+    os.environ["version"] = version_
+    global version
+    version = version_
+    if pretrained_sovits_name[version] != "" and pretrained_gpt_name[version] != "":
+        ...
+    else:
+        gr.Warning(i18n("未下载模型") + ": " + version.upper())
+    set_default()
+    return (
+        {"__type__": "update", "value": pretrained_sovits_name[version]},
+        {"__type__": "update", "value": pretrained_sovits_name[version].replace("s2G", "s2D")},
+        {"__type__": "update", "value": pretrained_gpt_name[version]},
+        {"__type__": "update", "value": pretrained_gpt_name[version]},
+        {"__type__": "update", "value": pretrained_sovits_name[version]},
+        {"__type__": "update", "value": default_batch_size, "maximum": default_max_batch_size},
+        {"__type__": "update", "value": default_sovits_epoch, "maximum": max_sovits_epoch},
+        {"__type__": "update", "value": default_sovits_save_every_epoch, "maximum": max_sovits_save_every_epoch},
+        {"__type__": "update", "visible": True if version not in v3v4set else False},
+        {
+            "__type__": "update",
+            "value": False if not if_force_ckpt else True,
+            "interactive": True if not if_force_ckpt else False,
+        },
+        {"__type__": "update", "interactive": True, "value": False},
+        {"__type__": "update", "visible": True if version in v3v4set else False},
+    )  # {'__type__': 'update', "interactive": False if version in v3v4set else True, "value": False}, \ ####batch infer
+
+
+if os.path.exists("GPT_SoVITS/text/G2PWModel"):
+    ...
+else:
+    cmd = f'"{python_exec}" -s GPT_SoVITS/download.py'
+    p = Popen(cmd, shell=True)
+    p.wait()
+
+
+def sync(text):
+    return {"__type__": "update", "value": text}
+
+
+with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css) as app:
+    gr.HTML(
+        top_html.format(
+            i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.")
+            + i18n("如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.")
+        ),
+        elem_classes="markdown",
+    )
+
+    with gr.Tabs():
+        with gr.TabItem("0-" + i18n("前置数据集获取工具")):  # 提前随机切片防止uvr5爆内存->uvr5->slicer->asr->打标
+            with gr.Accordion(label="0a-" + i18n("UVR5人声伴奏分离&去混响去延迟工具")):
+                with gr.Row():
+                    with gr.Column(scale=3):
+                        with gr.Row():
+                            uvr5_info = gr.Textbox(label=process_info(process_name_uvr5, "info"))
+                    open_uvr5 = gr.Button(
+                        value=process_info(process_name_uvr5, "open"), variant="primary", visible=True
+                    )
+                    close_uvr5 = gr.Button(
+                        value=process_info(process_name_uvr5, "close"), variant="primary", visible=False
+                    )
+
+            with gr.Accordion(label="0b-" + i18n("语音切分工具")):
+                with gr.Row():
+                    with gr.Column(scale=3):
+                        with gr.Row():
+                            slice_inp_path = gr.Textbox(label=i18n("音频自动切分输入路径，可文件可文件夹"), value="")
+                            slice_opt_root = gr.Textbox(
+                                label=i18n("切分后的子音频的输出根目录"), value="output/slicer_opt"
+                            )
+                        with gr.Row():
+                            threshold = gr.Textbox(
+                                label=i18n("threshold:音量小于这个值视作静音的备选切割点"), value="-34"
+                            )
+                            min_length = gr.Textbox(
+                                label=i18n("min_length:每段最小多长，如果第一段太短一直和后面段连起来直到超过这个值"),
+                                value="4000",
+                            )
+                            min_interval = gr.Textbox(label=i18n("min_interval:最短切割间隔"), value="300")
+                            hop_size = gr.Textbox(
+                                label=i18n("hop_size:怎么算音量曲线，越小精度越大计算量越高（不是精度越大效果越好）"),
+                                value="10",
+                            )
+                            max_sil_kept = gr.Textbox(label=i18n("max_sil_kept:切完后静音最多留多长"), value="500")
+                        with gr.Row():
+                            _max = gr.Slider(
+                                minimum=0,
+                                maximum=1,
+                                step=0.05,
+                                label=i18n("max:归一化后最大值多少"),
+                                value=0.9,
+                                interactive=True,
+                            )
+                            alpha = gr.Slider(
+                                minimum=0,
+                                maximum=1,
+                                step=0.05,
+                                label=i18n("alpha_mix:混多少比例归一化后音频进来"),
+                                value=0.25,
+                                interactive=True,
+                            )
+                        with gr.Row():
+                            n_process = gr.Slider(
+                                minimum=1,
+                                maximum=n_cpu,
+                                step=1,
+                                label=i18n("切割使用的进程数"),
+                                value=4,
+                                interactive=True,
+                            )
+                            slicer_info = gr.Textbox(label=process_info(process_name_slice, "info"))
+                    open_slicer_button = gr.Button(
+                        value=process_info(process_name_slice, "open"), variant="primary", visible=True
+                    )
+                    close_slicer_button = gr.Button(
+                        value=process_info(process_name_slice, "close"), variant="primary", visible=False
+                    )
+
+            # gr.Markdown(value="0bb-" + i18n("语音降噪工具")+i18n("(不稳定，先别用，可能劣化模型效果！)"))
+            with gr.Row(visible=False):
+                with gr.Column(scale=3):
+                    with gr.Row():
+                        denoise_input_dir = gr.Textbox(label=i18n("输入文件夹路径"), value="")
+                        denoise_output_dir = gr.Textbox(label=i18n("输出文件夹路径"), value="output/denoise_opt")
+                    with gr.Row():
+                        denoise_info = gr.Textbox(label=process_info(process_name_denoise, "info"))
+                open_denoise_button = gr.Button(
+                    value=process_info(process_name_denoise, "open"), variant="primary", visible=True
+                )
+                close_denoise_button = gr.Button(
+                    value=process_info(process_name_denoise, "close"), variant="primary", visible=False
+                )
+
+            with gr.Accordion(label="0c-" + i18n("语音识别工具")):
+                with gr.Row():
+                    with gr.Column(scale=3):
+                        with gr.Row():
+                            asr_inp_dir = gr.Textbox(
+                                label=i18n("输入文件夹路径"), value="D:\\GPT-SoVITS\\raw\\xxx", interactive=True
+                            )
+                            asr_opt_dir = gr.Textbox(
+                                label=i18n("输出文件夹路径"), value="output/asr_opt", interactive=True
+                            )
+                        with gr.Row():
+                            asr_model = gr.Dropdown(
+                                label=i18n("ASR 模型"),
+                                choices=list(asr_dict.keys()),
+                                interactive=True,
+                                value="达摩 ASR (中文)",
+                            )
+                            asr_size = gr.Dropdown(
+                                label=i18n("ASR 模型尺寸"), choices=["large"], interactive=True, value="large"
+                            )
+                            asr_lang = gr.Dropdown(
+                                label=i18n("ASR 语言设置"), choices=["zh", "yue"], interactive=True, value="zh"
+                            )
+                            asr_precision = gr.Dropdown(
+                                label=i18n("数据类型精度"), choices=["float32"], interactive=True, value="float32"
+                            )
+                        with gr.Row():
+                            asr_info = gr.Textbox(label=process_info(process_name_asr, "info"))
+                    open_asr_button = gr.Button(
+                        value=process_info(process_name_asr, "open"), variant="primary", visible=True
+                    )
+                    close_asr_button = gr.Button(
+                        value=process_info(process_name_asr, "close"), variant="primary", visible=False
+                    )
+
+                def change_lang_choices(key):  # 根据选择的模型修改可选的语言
+                    return {"__type__": "update", "choices": asr_dict[key]["lang"], "value": asr_dict[key]["lang"][0]}
+
+                def change_size_choices(key):  # 根据选择的模型修改可选的模型尺寸
+                    return {"__type__": "update", "choices": asr_dict[key]["size"], "value": asr_dict[key]["size"][-1]}
+
+                def change_precision_choices(key):  # 根据选择的模型修改可选的语言
+                    if key == "Faster Whisper (多语种)":
+                        if default_batch_size <= 4:
+                            precision = "int8"
+                        elif is_half:
+                            precision = "float16"
+                        else:
+                            precision = "float32"
+                    else:
+                        precision = "float32"
+                    return {"__type__": "update", "choices": asr_dict[key]["precision"], "value": precision}
+
+                asr_model.change(change_lang_choices, [asr_model], [asr_lang])
+                asr_model.change(change_size_choices, [asr_model], [asr_size])
+                asr_model.change(change_precision_choices, [asr_model], [asr_precision])
+
+            with gr.Accordion(label="0d-" + i18n("语音文本校对标注工具")):
+                with gr.Row():
+                    with gr.Column(scale=3):
+                        with gr.Row():
+                            path_list = gr.Textbox(
+                                label=i18n("标注文件路径 (含文件后缀 *.list)"),
+                                value="D:\\RVC1006\\GPT-SoVITS\\raw\\xxx.list",
+                                interactive=True,
+                            )
+                            label_info = gr.Textbox(label=process_info(process_name_subfix, "info"))
+                    open_label = gr.Button(
+                        value=process_info(process_name_subfix, "open"), variant="primary", visible=True
+                    )
+                    close_label = gr.Button(
+                        value=process_info(process_name_subfix, "close"), variant="primary", visible=False
+                    )
+
+                open_label.click(change_label, [path_list], [label_info, open_label, close_label])
+                close_label.click(change_label, [path_list], [label_info, open_label, close_label])
+                open_uvr5.click(change_uvr5, [], [uvr5_info, open_uvr5, close_uvr5])
+                close_uvr5.click(change_uvr5, [], [uvr5_info, open_uvr5, close_uvr5])
+
+        with gr.TabItem(i18n("1-GPT-SoVITS-TTS")):
+            with gr.Accordion(i18n("微调模型信息")):
+                with gr.Row():
+                    with gr.Row(equal_height=True):
+                        exp_name = gr.Textbox(
+                            label=i18n("*实验/模型名"),
+                            value="xxx",
+                            interactive=True,
+                            scale=3,
+                        )
+                        gpu_info_box = gr.Textbox(
+                            label=i18n("显卡信息"),
+                            value=gpu_info,
+                            visible=True,
+                            interactive=False,
+                            scale=5,
+                        )
+                        version_checkbox = gr.Radio(
+                            label=i18n("训练模型的版本"),
+                            value=version,
+                            choices=["v1", "v2", "v4", "v2Pro", "v2ProPlus"],
+                            scale=5,
+                        )
+            with gr.Accordion(label=i18n("预训练模型路径"), open=False):
+                with gr.Row():
+                    with gr.Row(equal_height=True):
+                        pretrained_s1 = gr.Textbox(
+                            label=i18n("预训练GPT模型路径"),
+                            value=pretrained_gpt_name[version],
+                            interactive=True,
+                            lines=1,
+                            max_lines=1,
+                            scale=3,
+                        )
+                        pretrained_s2G = gr.Textbox(
+                            label=i18n("预训练SoVITS-G模型路径"),
+                            value=pretrained_sovits_name[version],
+                            interactive=True,
+                            lines=1,
+                            max_lines=1,
+                            scale=5,
+                        )
+                        pretrained_s2D = gr.Textbox(
+                            label=i18n("预训练SoVITS-D模型路径"),
+                            value=pretrained_sovits_name[version].replace("s2G", "s2D"),
+                            interactive=True,
+                            lines=1,
+                            max_lines=1,
+                            scale=5,
+                        )
+
+            with gr.TabItem("1A-" + i18n("训练集格式化工具")):
+                with gr.Accordion(label=i18n("输出logs/实验名目录下应有23456开头的文件和文件夹")):
+                    with gr.Row():
+                        with gr.Row():
+                            inp_text = gr.Textbox(
+                                label=i18n("*文本标注文件"),
+                                value=r"D:\RVC1006\GPT-SoVITS\raw\xxx.list",
+                                interactive=True,
+                                scale=10,
+                            )
+                        with gr.Row():
+                            inp_wav_dir = gr.Textbox(
+                                label=i18n("*训练集音频文件目录"),
+                                # value=r"D:\RVC1006\GPT-SoVITS\raw\xxx",
+                                interactive=True,
+                                placeholder=i18n(
+                                    "填切割后音频所在目录！读取的音频文件完整路径=该目录-拼接-list文件里波形对应的文件名（不是全路径）。如果留空则使用.list文件里的绝对全路径。"
+                                ),
+                                scale=10,
+                            )
+
+                with gr.Accordion(label="1Aa-" + process_name_1a):
+                    with gr.Row():
+                        with gr.Row():
+                            gpu_numbers1a = gr.Textbox(
+                                label=i18n("GPU卡号以-分割，每个卡号一个进程"),
+                                value=f"{gpus}-{gpus}",
+                                interactive=True,
+                            )
+                        with gr.Row():
+                            bert_pretrained_dir = gr.Textbox(
+                                label=i18n("预训练中文BERT模型路径"),
+                                value="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
+                                interactive=False,
+                                lines=2,
+                            )
+                        with gr.Row():
+                            button1a_open = gr.Button(
+                                value=process_info(process_name_1a, "open"), variant="primary", visible=True
+                            )
+                            button1a_close = gr.Button(
+                                value=process_info(process_name_1a, "close"), variant="primary", visible=False
+                            )
+                        with gr.Row():
+                            info1a = gr.Textbox(label=process_info(process_name_1a, "info"))
+
+                with gr.Accordion(label="1Ab-" + process_name_1b):
+                    with gr.Row():
+                        with gr.Row():
+                            gpu_numbers1Ba = gr.Textbox(
+                                label=i18n("GPU卡号以-分割，每个卡号一个进程"),
+                                value=f"{gpus}-{gpus}",
+                                interactive=True,
+                            )
+                        with gr.Row():
+                            cnhubert_base_dir = gr.Textbox(
+                                label=i18n("预训练SSL模型路径"),
+                                value="GPT_SoVITS/pretrained_models/chinese-hubert-base",
+                                interactive=False,
+                                lines=2,
+                            )
+                        with gr.Row():
+                            button1b_open = gr.Button(
+                                value=process_info(process_name_1b, "open"), variant="primary", visible=True
+                            )
+                            button1b_close = gr.Button(
+                                value=process_info(process_name_1b, "close"), variant="primary", visible=False
+                            )
+                        with gr.Row():
+                            info1b = gr.Textbox(label=process_info(process_name_1b, "info"))
+
+                with gr.Accordion(label="1Ac-" + process_name_1c):
+                    with gr.Row():
+                        with gr.Row():
+                            gpu_numbers1c = gr.Textbox(
+                                label=i18n("GPU卡号以-分割，每个卡号一个进程"),
+                                value=f"{gpus}-{gpus}",
+                                interactive=True,
+                            )
+                        with gr.Row():
+                            pretrained_s2G_ = gr.Textbox(
+                                label=i18n("预训练SoVITS-G模型路径"),
+                                value=pretrained_sovits_name[version],
+                                interactive=False,
+                                lines=2,
+                            )
+                        with gr.Row():
+                            button1c_open = gr.Button(
+                                value=process_info(process_name_1c, "open"), variant="primary", visible=True
+                            )
+                            button1c_close = gr.Button(
+                                value=process_info(process_name_1c, "close"), variant="primary", visible=False
+                            )
+                        with gr.Row():
+                            info1c = gr.Textbox(label=process_info(process_name_1c, "info"))
+
+                with gr.Accordion(label="1Aabc-" + process_name_1abc):
+                    with gr.Row():
+                        with gr.Row():
+                            button1abc_open = gr.Button(
+                                value=process_info(process_name_1abc, "open"), variant="primary", visible=True
+                            )
+                            button1abc_close = gr.Button(
+                                value=process_info(process_name_1abc, "close"), variant="primary", visible=False
+                            )
+                        with gr.Row():
+                            info1abc = gr.Textbox(label=process_info(process_name_1abc, "info"))
+
+            pretrained_s2G.change(sync, [pretrained_s2G], [pretrained_s2G_])
+            open_asr_button.click(
+                open_asr,
+                [asr_inp_dir, asr_opt_dir, asr_model, asr_size, asr_lang, asr_precision],
+                [asr_info, open_asr_button, close_asr_button, path_list, inp_text, inp_wav_dir],
+            )
+            close_asr_button.click(close_asr, [], [asr_info, open_asr_button, close_asr_button])
+            open_slicer_button.click(
+                open_slice,
+                [
+                    slice_inp_path,
+                    slice_opt_root,
+                    threshold,
+                    min_length,
+                    min_interval,
+                    hop_size,
+                    max_sil_kept,
+                    _max,
+                    alpha,
+                    n_process,
+                ],
+                [slicer_info, open_slicer_button, close_slicer_button, asr_inp_dir, denoise_input_dir, inp_wav_dir],
+            )
+            close_slicer_button.click(close_slice, [], [slicer_info, open_slicer_button, close_slicer_button])
+            open_denoise_button.click(
+                open_denoise,
+                [denoise_input_dir, denoise_output_dir],
+                [denoise_info, open_denoise_button, close_denoise_button, asr_inp_dir, inp_wav_dir],
+            )
+            close_denoise_button.click(close_denoise, [], [denoise_info, open_denoise_button, close_denoise_button])
+
+            button1a_open.click(
+                open1a,
+                [inp_text, inp_wav_dir, exp_name, gpu_numbers1a, bert_pretrained_dir],
+                [info1a, button1a_open, button1a_close],
+            )
+            button1a_close.click(close1a, [], [info1a, button1a_open, button1a_close])
+            button1b_open.click(
+                open1b,
+                [version_checkbox, inp_text, inp_wav_dir, exp_name, gpu_numbers1Ba, cnhubert_base_dir],
+                [info1b, button1b_open, button1b_close],
+            )
+            button1b_close.click(close1b, [], [info1b, button1b_open, button1b_close])
+            button1c_open.click(
+                open1c,
+                [version_checkbox, inp_text, inp_wav_dir, exp_name, gpu_numbers1c, pretrained_s2G],
+                [info1c, button1c_open, button1c_close],
+            )
+            button1c_close.click(close1c, [], [info1c, button1c_open, button1c_close])
+            button1abc_open.click(
+                open1abc,
+                [
+                    version_checkbox,
+                    inp_text,
+                    inp_wav_dir,
+                    exp_name,
+                    gpu_numbers1a,
+                    gpu_numbers1Ba,
+                    gpu_numbers1c,
+                    bert_pretrained_dir,
+                    cnhubert_base_dir,
+                    pretrained_s2G,
+                ],
+                [info1abc, button1abc_open, button1abc_close],
+            )
+            button1abc_close.click(close1abc, [], [info1abc, button1abc_open, button1abc_close])
+
+            with gr.TabItem("1B-" + i18n("微调训练")):
+                with gr.Accordion(label="1Ba-" + i18n("SoVITS 训练: 模型权重文件在 SoVITS_weights/")):
+                    with gr.Row():
+                        with gr.Column():
+                            with gr.Row():
+                                batch_size = gr.Slider(
+                                    minimum=1,
+                                    maximum=default_max_batch_size,
+                                    step=1,
+                                    label=i18n("每张显卡的batch_size"),
+                                    value=default_batch_size,
+                                    interactive=True,
+                                )
+                                total_epoch = gr.Slider(
+                                    minimum=1,
+                                    maximum=max_sovits_epoch,
+                                    step=1,
+                                    label=i18n("总训练轮数total_epoch，不建议太高"),
+                                    value=default_sovits_epoch,
+                                    interactive=True,
+                                )
+                            with gr.Row():
+                                text_low_lr_rate = gr.Slider(
+                                    minimum=0.2,
+                                    maximum=0.6,
+                                    step=0.05,
+                                    label=i18n("文本模块学习率权重"),
+                                    value=0.4,
+                                    visible=True if version not in v3v4set else False,
+                                )  # v3v4 not need
+                                lora_rank = gr.Radio(
+                                    label=i18n("LoRA秩"),
+                                    value="32",
+                                    choices=["16", "32", "64", "128"],
+                                    visible=True if version in v3v4set else False,
+                                )  # v1v2 not need
+                                save_every_epoch = gr.Slider(
+                                    minimum=1,
+                                    maximum=max_sovits_save_every_epoch,
+                                    step=1,
+                                    label=i18n("保存频率save_every_epoch"),
+                                    value=default_sovits_save_every_epoch,
+                                    interactive=True,
+                                )
+                        with gr.Column():
+                            with gr.Column():
+                                if_save_latest = gr.Checkbox(
+                                    label=i18n("是否仅保存最新的权重文件以节省硬盘空间"),
+                                    value=True,
+                                    interactive=True,
+                                    show_label=True,
+                                )
+                                if_save_every_weights = gr.Checkbox(
+                                    label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),
+                                    value=True,
+                                    interactive=True,
+                                    show_label=True,
+                                )
+                                if_grad_ckpt = gr.Checkbox(
+                                    label="v3是否开启梯度检查点节省显存占用",
+                                    value=False,
+                                    interactive=True if version in v3v4set else False,
+                                    show_label=True,
+                                    visible=False,
+                                )  # 只有V3s2可以用
+                            with gr.Row():
+                                gpu_numbers1Ba = gr.Textbox(
+                                    label=i18n("GPU卡号以-分割，每个卡号一个进程"),
+                                    value=f"{gpus}",
+                                    interactive=True,
+                                )
+                    with gr.Row():
+                        with gr.Row():
+                            button1Ba_open = gr.Button(
+                                value=process_info(process_name_sovits, "open"), variant="primary", visible=True
+                            )
+                            button1Ba_close = gr.Button(
+                                value=process_info(process_name_sovits, "close"), variant="primary", visible=False
+                            )
+                        with gr.Row():
+                            info1Ba = gr.Textbox(label=process_info(process_name_sovits, "info"))
+                with gr.Accordion(label="1Bb-" + i18n("GPT 训练: 模型权重文件在 GPT_weights/")):
+                    with gr.Row():
+                        with gr.Column():
+                            with gr.Row():
+                                batch_size1Bb = gr.Slider(
+                                    minimum=1,
+                                    maximum=40,
+                                    step=1,
+                                    label=i18n("每张显卡的batch_size"),
+                                    value=default_batch_size_s1,
+                                    interactive=True,
+                                )
+                                total_epoch1Bb = gr.Slider(
+                                    minimum=2,
+                                    maximum=50,
+                                    step=1,
+                                    label=i18n("总训练轮数total_epoch"),
+                                    value=15,
+                                    interactive=True,
+                                )
+                            with gr.Row():
+                                save_every_epoch1Bb = gr.Slider(
+                                    minimum=1,
+                                    maximum=50,
+                                    step=1,
+                                    label=i18n("保存频率save_every_epoch"),
+                                    value=5,
+                                    interactive=True,
+                                )
+                                if_dpo = gr.Checkbox(
+                                    label=i18n("是否开启DPO训练选项(实验性)"),
+                                    value=False,
+                                    interactive=True,
+                                    show_label=True,
+                                )
+                        with gr.Column():
+                            with gr.Column():
+                                if_save_latest1Bb = gr.Checkbox(
+                                    label=i18n("是否仅保存最新的权重文件以节省硬盘空间"),
+                                    value=True,
+                                    interactive=True,
+                                    show_label=True,
+                                )
+                                if_save_every_weights1Bb = gr.Checkbox(
+                                    label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),
+                                    value=True,
+                                    interactive=True,
+                                    show_label=True,
+                                )
+                            with gr.Row():
+                                gpu_numbers1Bb = gr.Textbox(
+                                    label=i18n("GPU卡号以-分割，每个卡号一个进程"),
+                                    value=f"{gpus}",
+                                    interactive=True,
+                                )
+                    with gr.Row():
+                        with gr.Row():
+                            button1Bb_open = gr.Button(
+                                value=process_info(process_name_gpt, "open"), variant="primary", visible=True
+                            )
+                            button1Bb_close = gr.Button(
+                                value=process_info(process_name_gpt, "close"), variant="primary", visible=False
+                            )
+                        with gr.Row():
+                            info1Bb = gr.Textbox(label=process_info(process_name_gpt, "info"))
+
+            button1Ba_close.click(close1Ba, [], [info1Ba, button1Ba_open, button1Ba_close])
+            button1Bb_close.click(close1Bb, [], [info1Bb, button1Bb_open, button1Bb_close])
+
+            with gr.TabItem("1C-" + i18n("推理")):
+                gr.Markdown(
+                    value=i18n(
+                        "选择训练完存放在SoVITS_weights和GPT_weights下的模型。默认的几个是底模，体验5秒Zero Shot TTS不训练推理用。"
+                    )
+                )
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        with gr.Row():
+                            GPT_dropdown = gr.Dropdown(
+                                label=i18n("GPT模型列表"),
+                                choices=GPT_names,
+                                value=GPT_names[-1],
+                                interactive=True,
+                            )
+                            SoVITS_dropdown = gr.Dropdown(
+                                label=i18n("SoVITS模型列表"),
+                                choices=SoVITS_names,
+                                value=SoVITS_names[0],
+                                interactive=True,
+                            )
+                    with gr.Column(scale=2):
+                        with gr.Row():
+                            gpu_number_1C = gr.Textbox(
+                                label=i18n("GPU卡号,只能填1个整数"), value=gpus, interactive=True
+                            )
+                            refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
+                    refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
+                with gr.Row(equal_height=True):
+                    with gr.Row():
+                        batched_infer_enabled = gr.Checkbox(
+                            label=i18n("启用并行推理版本"), value=False, interactive=True, show_label=True
+                        )
+                        open_tts = gr.Button(
+                            value=process_info(process_name_tts, "open"), variant="primary", visible=True
+                        )
+                        close_tts = gr.Button(
+                            value=process_info(process_name_tts, "close"), variant="primary", visible=False
+                        )
+                    with gr.Column():
+                        tts_info = gr.Textbox(label=process_info(process_name_tts, "info"), scale=2)
+                    open_tts.click(
+                        change_tts_inference,
+                        [
+                            bert_pretrained_dir,
+                            cnhubert_base_dir,
+                            gpu_number_1C,
+                            GPT_dropdown,
+                            SoVITS_dropdown,
+                            batched_infer_enabled,
+                        ],
+                        [tts_info, open_tts, close_tts],
+                    )
+                    close_tts.click(
+                        change_tts_inference,
+                        [
+                            bert_pretrained_dir,
+                            cnhubert_base_dir,
+                            gpu_number_1C,
+                            GPT_dropdown,
+                            SoVITS_dropdown,
+                            batched_infer_enabled,
+                        ],
+                        [tts_info, open_tts, close_tts],
+                    )
+            button1Ba_open.click(
+                open1Ba,
+                [
+                    version_checkbox,
+                    batch_size,
+                    total_epoch,
+                    exp_name,
+                    text_low_lr_rate,
+                    if_save_latest,
+                    if_save_every_weights,
+                    save_every_epoch,
+                    gpu_numbers1Ba,
+                    pretrained_s2G,
+                    pretrained_s2D,
+                    if_grad_ckpt,
+                    lora_rank,
+                ],
+                [info1Ba, button1Ba_open, button1Ba_close, SoVITS_dropdown, GPT_dropdown],
+            )
+            button1Bb_open.click(
+                open1Bb,
+                [
+                    batch_size1Bb,
+                    total_epoch1Bb,
+                    exp_name,
+                    if_dpo,
+                    if_save_latest1Bb,
+                    if_save_every_weights1Bb,
+                    save_every_epoch1Bb,
+                    gpu_numbers1Bb,
+                    pretrained_s1,
+                ],
+                [info1Bb, button1Bb_open, button1Bb_close, SoVITS_dropdown, GPT_dropdown],
+            )
+            version_checkbox.change(
+                switch_version,
+                [version_checkbox],
+                [
+                    pretrained_s2G,
+                    pretrained_s2D,
+                    pretrained_s1,
+                    GPT_dropdown,
+                    SoVITS_dropdown,
+                    batch_size,
+                    total_epoch,
+                    save_every_epoch,
+                    text_low_lr_rate,
+                    if_grad_ckpt,
+                    batched_infer_enabled,
+                    lora_rank,
+                ],
+            )
+
+        with gr.TabItem(i18n("2-GPT-SoVITS-变声")):
+            gr.Markdown(value=i18n("施工中，请静候佳音"))
+
+    app.queue().launch(  # concurrency_count=511, max_size=1022
+        server_name="0.0.0.0",
+        inbrowser=True,
+        share=is_share,
+        server_port=webui_port_main,
+        # quiet=True,
+    )