support sovits v2Pro v2ProPlus

support sovits v2Pro v2ProPlus

api.py

@@ -374,7 +374,7 @@ hz = 50
 
 
 def get_gpt_weights(gpt_path):
-    dict_s1 = torch.load(gpt_path, map_location="cpu")
+    dict_s1 = torch.load(gpt_path, map_location="cpu", weights_only=False)
     config = dict_s1["config"]
     max_sec = config["data"]["max_sec"]
     t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)

batch_inference.py

@@ -0,0 +1,442 @@
+import argparse
+import os
+import pdb
+import signal
+import sys
+from time import time as ttime
+import torch
+import librosa
+import soundfile as sf
+from fastapi import FastAPI, Request, HTTPException
+from fastapi.responses import StreamingResponse
+import uvicorn
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import numpy as np
+from feature_extractor import cnhubert
+from io import BytesIO
+from module.models import SynthesizerTrn
+from AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from text import cleaned_text_to_sequence
+from text.cleaner import clean_text
+from module.mel_processing import spectrogram_torch
+from my_utils import load_audio
+import config as global_config
+
+g_config = global_config.Config()
+
+# AVAILABLE_COMPUTE = "cuda" if torch.cuda.is_available() else "cpu"
+
+parser = argparse.ArgumentParser(description="GPT-SoVITS api")
+
+parser.add_argument("-s", "--sovits_path", type=str, default=g_config.sovits_path, help="SoVITS模型路径")
+parser.add_argument("-g", "--gpt_path", type=str, default=g_config.gpt_path, help="GPT模型路径")
+
+parser.add_argument("-dr", "--default_refer_path", type=str, default="",
+                    help="默认参考音频路径, 请求缺少参考音频时调用")
+parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
+parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
+
+parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu")
+parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
+parser.add_argument("-a", "--bind_addr", type=str, default="127.0.0.1", help="default: 127.0.0.1")
+parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
+parser.add_argument("-hp", "--half_precision", action="store_true", default=False, help="覆盖config.is_half为True, 使用半精度")
+# bool值的用法为 `python ./api.py -fp ...`
+# 此时 full_precision==True, half_precision==False
+
+parser.add_argument("-hb", "--hubert_path", type=str, default=g_config.cnhubert_path, help="覆盖config.cnhubert_path")
+parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, help="覆盖config.bert_path")
+
+args = parser.parse_args()
+
+sovits_path = args.sovits_path
+gpt_path = args.gpt_path
+
+default_refer_path = args.default_refer_path
+default_refer_text = args.default_refer_text
+default_refer_language = args.default_refer_language
+has_preset = False
+
+device = args.device
+port = args.port
+host = args.bind_addr
+
+if sovits_path == "":
+    sovits_path = g_config.pretrained_sovits_path
+    print(f"[WARN] 未指定SoVITS模型路径, fallback后当前值: {sovits_path}")
+if gpt_path == "":
+    gpt_path = g_config.pretrained_gpt_path
+    print(f"[WARN] 未指定GPT模型路径, fallback后当前值: {gpt_path}")
+
+# 指定默认参考音频, 调用方 未提供/未给全 参考音频参数时使用
+if default_refer_path == "" or default_refer_text == "" or default_refer_language == "":
+    default_refer_path, default_refer_text, default_refer_language = "", "", ""
+    print("[INFO] 未指定默认参考音频")
+    has_preset = False
+else:
+    print(f"[INFO] 默认参考音频路径: {default_refer_path}")
+    print(f"[INFO] 默认参考音频文本: {default_refer_text}")
+    print(f"[INFO] 默认参考音频语种: {default_refer_language}")
+    has_preset = True
+
+is_half = g_config.is_half
+if args.full_precision:
+    is_half = False
+if args.half_precision:
+    is_half = True
+if args.full_precision and args.half_precision:
+    is_half = g_config.is_half  # 炒饭fallback
+
+print(f"[INFO] 半精: {is_half}")
+
+cnhubert_base_path = args.hubert_path
+bert_path = args.bert_path
+
+cnhubert.cnhubert_base_path = cnhubert_base_path
+tokenizer = AutoTokenizer.from_pretrained(bert_path)
+bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
+if is_half:
+    bert_model = bert_model.half().to(device)
+else:
+    bert_model = bert_model.to(device)
+
+
+def get_bert_feature(text, word2ph):
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)  #####输入是long不用管精度问题，精度随bert_model
+        res = bert_model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
+    assert len(word2ph) == len(text)
+    phone_level_feature = []
+    for i in range(len(word2ph)):
+        repeat_feature = res[i].repeat(word2ph[i], 1)
+        phone_level_feature.append(repeat_feature)
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+    # if(is_half==True):phone_level_feature=phone_level_feature.half()
+    return phone_level_feature.T
+
+
+n_semantic = 1024
+dict_s2 = torch.load(sovits_path, map_location="cpu", weights_only=False)
+hps = dict_s2["config"]
+print(hps)
+
+class DictToAttrRecursive(dict):
+    def __init__(self, input_dict):
+        super().__init__(input_dict)
+        for key, value in input_dict.items():
+            if isinstance(value, dict):
+                value = DictToAttrRecursive(value)
+            self[key] = value
+            setattr(self, key, value)
+
+    def __getattr__(self, item):
+        try:
+            return self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+    def __setattr__(self, key, value):
+        if isinstance(value, dict):
+            value = DictToAttrRecursive(value)
+        super(DictToAttrRecursive, self).__setitem__(key, value)
+        super().__setattr__(key, value)
+
+    def __delattr__(self, item):
+        try:
+            del self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+
+hps = DictToAttrRecursive(hps)
+hps.model.semantic_frame_rate = "25hz"
+dict_s1 = torch.load(gpt_path, map_location="cpu", weights_only=False)
+config = dict_s1["config"]
+ssl_model = cnhubert.get_model()
+if is_half:
+    ssl_model = ssl_model.half().to(device)
+else:
+    ssl_model = ssl_model.to(device)
+
+vq_model = SynthesizerTrn(
+    hps.data.filter_length // 2 + 1,
+    hps.train.segment_size // hps.data.hop_length,
+    n_speakers=hps.data.n_speakers,
+    **hps.model)
+if is_half:
+    vq_model = vq_model.half().to(device)
+else:
+    vq_model = vq_model.to(device)
+vq_model.eval()
+print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
+hz = 50
+max_sec = config['data']['max_sec']
+t2s_model = Text2SemanticLightningModule(config, "ojbk", is_train=False)
+t2s_model.load_state_dict(dict_s1["weight"])
+if is_half:
+    t2s_model = t2s_model.half()
+t2s_model = t2s_model.to(device)
+t2s_model.eval()
+total = sum([param.nelement() for param in t2s_model.parameters()])
+print("Number of parameter: %.2fM" % (total / 1e6))
+
+
+def get_spepc(hps, filename):
+    audio = load_audio(filename, int(hps.data.sampling_rate))
+    audio = torch.FloatTensor(audio)
+    audio_norm = audio
+    audio_norm = audio_norm.unsqueeze(0)
+    spec = spectrogram_torch(audio_norm, hps.data.filter_length, hps.data.sampling_rate, hps.data.hop_length,
+                             hps.data.win_length, center=False)
+    return spec
+
+
+dict_language = {
+    "中文": "zh",
+    "英文": "en",
+    "日文": "ja",
+    "ZH": "zh",
+    "EN": "en",
+    "JA": "ja",
+    "zh": "zh",
+    "en": "en",
+    "ja": "ja"
+}
+
+
+def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
+    t0 = ttime()
+    prompt_text = prompt_text.strip("\n")
+    prompt_language, text = prompt_language, text.strip("\n")
+    zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
+    with torch.no_grad():
+        wav16k, sr = librosa.load(ref_wav_path, sr=16000)
+        wav16k = torch.from_numpy(wav16k)
+        zero_wav_torch = torch.from_numpy(zero_wav)
+        if (is_half == True):
+            wav16k = wav16k.half().to(device)
+            zero_wav_torch = zero_wav_torch.half().to(device)
+        else:
+            wav16k = wav16k.to(device)
+            zero_wav_torch = zero_wav_torch.to(device)
+        wav16k=torch.cat([wav16k,zero_wav_torch])
+        ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2)  # .float()
+        codes = vq_model.extract_latent(ssl_content)
+        prompt_semantic = codes[0, 0]
+    t1 = ttime()
+    prompt_language = dict_language[prompt_language]
+    text_language = dict_language[text_language]
+    phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
+    phones1 = cleaned_text_to_sequence(phones1)
+    texts = text.split("\n")
+    audio_opt = []
+
+    for text in texts:
+        phones2, word2ph2, norm_text2 = clean_text(text, text_language)
+        phones2 = cleaned_text_to_sequence(phones2)
+        if (prompt_language == "zh"):
+            bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
+        else:
+            bert1 = torch.zeros((1024, len(phones1)), dtype=torch.float16 if is_half == True else torch.float32).to(
+                device)
+        if (text_language == "zh"):
+            bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
+        else:
+            bert2 = torch.zeros((1024, len(phones2))).to(bert1)
+        bert = torch.cat([bert1, bert2], 1)
+
+        all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
+        bert = bert.to(device).unsqueeze(0)
+        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
+        prompt = prompt_semantic.unsqueeze(0).to(device)
+        t2 = ttime()
+        with torch.no_grad():
+            # pred_semantic = t2s_model.model.infer(
+            pred_semantic, idx = t2s_model.model.infer_panel(
+                all_phoneme_ids,
+                all_phoneme_len,
+                prompt,
+                bert,
+                # prompt_phone_len=ph_offset,
+                top_k=config['inference']['top_k'],
+                early_stop_num=hz * max_sec)
+        t3 = ttime()
+        # print(pred_semantic.shape,idx)
+        pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)  # .unsqueeze(0)#mq要多unsqueeze一次
+        refer = get_spepc(hps, ref_wav_path)  # .to(device)
+        if (is_half == True):
+            refer = refer.half().to(device)
+        else:
+            refer = refer.to(device)
+        # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
+        audio = \
+            vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
+                            refer).detach().cpu().numpy()[
+                0, 0]  ###试试重建不带上prompt部分
+        audio_opt.append(audio)
+        audio_opt.append(zero_wav)
+        t4 = ttime()
+    print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
+    # yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
+    return hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
+def get_tts_wavs(ref_wav_path, prompt_text, prompt_language, textss, text_language):
+    t0 = ttime()
+    prompt_text = prompt_text.strip("\n")
+    zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
+    with torch.no_grad():
+        wav16k, sr = librosa.load(ref_wav_path, sr=16000)
+        wav16k = torch.from_numpy(wav16k)
+        zero_wav_torch = torch.from_numpy(zero_wav)
+        if (is_half == True):
+            wav16k = wav16k.half().to(device)
+            zero_wav_torch = zero_wav_torch.half().to(device)
+        else:
+            wav16k = wav16k.to(device)
+            zero_wav_torch = zero_wav_torch.to(device)
+        wav16k=torch.cat([wav16k,zero_wav_torch])
+        ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2)  # .float()
+        codes = vq_model.extract_latent(ssl_content)
+        prompt_semantic = codes[0, 0]
+    t1 = ttime()
+    prompt_language = dict_language[prompt_language]
+    text_language = dict_language[text_language]
+    phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
+    phones1 = cleaned_text_to_sequence(phones1)
+    audios_opt=[]
+    for text0 in textss:
+        texts = text0.strip("\n").split("\n")
+        audio_opt = []
+        for text in texts:
+            text=text.strip("。")+"。"
+            phones2, word2ph2, norm_text2 = clean_text(text, text_language)
+            phones2 = cleaned_text_to_sequence(phones2)
+            if (prompt_language == "zh"):
+                bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
+            else:
+                bert1 = torch.zeros((1024, len(phones1)), dtype=torch.float16 if is_half == True else torch.float32).to(
+                    device)
+            if (text_language == "zh"):
+                bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
+            else:
+                bert2 = torch.zeros((1024, len(phones2))).to(bert1)
+            bert = torch.cat([bert1, bert2], 1)
+
+            all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
+            bert = bert.to(device).unsqueeze(0)
+            all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
+            prompt = prompt_semantic.unsqueeze(0).to(device)
+            t2 = ttime()
+            with torch.no_grad():
+                # pred_semantic = t2s_model.model.infer(
+                pred_semantic, idx = t2s_model.model.infer_panel(
+                    all_phoneme_ids,
+                    all_phoneme_len,
+                    prompt,
+                    bert,
+                    # prompt_phone_len=ph_offset,
+                    top_k=config['inference']['top_k'],
+                    early_stop_num=hz * max_sec)
+            t3 = ttime()
+            # print(pred_semantic.shape,idx)
+            pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)  # .unsqueeze(0)#mq要多unsqueeze一次
+            refer = get_spepc(hps, ref_wav_path)  # .to(device)
+            if (is_half == True):
+                refer = refer.half().to(device)
+            else:
+                refer = refer.to(device)
+            # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
+            audio = \
+                vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
+                                refer).detach().cpu().numpy()[
+                    0, 0]  ###试试重建不带上prompt部分
+            audio_opt.append(audio)
+            audio_opt.append(zero_wav)
+            t4 = ttime()
+        print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
+        audios_opt.append([text0,(np.concatenate(audio_opt, 0) * 32768).astype(np.int16)])
+    return audios_opt
+
+
+# get_tts_wav(r"D:\BaiduNetdiskDownload\gsv\speech\萧逸声音-你得先从滑雪的基本技巧学起.wav", "你得先从滑雪的基本技巧学起。", "中文", "我觉得还是该给喜欢的女孩子一场认真的告白。", "中文")
+# with open(r"D:\BaiduNetdiskDownload\gsv\烟嗓-todo1.txt","r",encoding="utf8")as f:
+# with open(r"D:\BaiduNetdiskDownload\gsv\年下-todo1.txt","r",encoding="utf8")as f:
+# with open(r"D:\BaiduNetdiskDownload\gsv\萧逸3b.txt","r",encoding="utf8")as f:
+with open(r"D:\BaiduNetdiskDownload\gsv\萧逸4.txt","r",encoding="utf8")as f:
+    textss=f.read().split("\n")
+for idx,(text,audio)in enumerate(get_tts_wavs(r"D:\BaiduNetdiskDownload\gsv\speech\萧逸声音-你得先从滑雪的基本技巧学起.wav", "你得先从滑雪的基本技巧学起。", "中文", textss, "中文")):
+
+# for idx,(text,audio)in enumerate(get_tts_wavs(r"D:\BaiduNetdiskDownload\gsv\足够的能力，去制定好自己的生活规划。低沉烟嗓.MP3_1940480_2095360.wav", "足够的能力，去制定好自己的生活规划。", "中文", textss, "中文")):
+# for idx,(text,audio)in enumerate(get_tts_wavs(r"D:\BaiduNetdiskDownload\gsv\不会呀！你前几天才吃过你还说好吃来着。年下少年音.MP3_537600_711040.wav", "不会呀！你前几天才吃过你还说好吃来着。", "中文", textss, "中文")):
+    print(idx,text)
+    # sf.write(r"D:\BaiduNetdiskDownload\gsv\output\烟嗓第一批\%04d-%s.wav"%(idx,text),audio,32000)
+    # sf.write(r"D:\BaiduNetdiskDownload\gsv\output\年下\%04d-%s.wav"%(idx,text),audio,32000)
+    sf.write(r"D:\BaiduNetdiskDownload\gsv\output\萧逸第4批\%04d-%s.wav"%(idx,text),audio,32000)
+
+
+# def handle(command, refer_wav_path, prompt_text, prompt_language, text, text_language):
+#     if command == "/restart":
+#         os.execl(g_config.python_exec, g_config.python_exec, *sys.argv)
+#     elif command == "/exit":
+#         os.kill(os.getpid(), signal.SIGTERM)
+#         exit(0)
+#
+#     if (
+#             refer_wav_path == "" or refer_wav_path is None
+#             or prompt_text == "" or prompt_text is None
+#             or prompt_language == "" or prompt_language is None
+#     ):
+#         refer_wav_path, prompt_text, prompt_language = (
+#             default_refer_path,
+#             default_refer_text,
+#             default_refer_language,
+#         )
+#         if not has_preset:
+#             raise HTTPException(status_code=400, detail="未指定参考音频且接口无预设")
+#
+#     with torch.no_grad():
+#         gen = get_tts_wav(
+#             refer_wav_path, prompt_text, prompt_language, text, text_language
+#         )
+#         sampling_rate, audio_data = next(gen)
+#
+#     wav = BytesIO()
+#     sf.write(wav, audio_data, sampling_rate, format="wav")
+#     wav.seek(0)
+#
+#     torch.cuda.empty_cache()
+#     return StreamingResponse(wav, media_type="audio/wav")
+
+
+# app = FastAPI()
+#
+#
+# @app.post("/")
+# async def tts_endpoint(request: Request):
+#     json_post_raw = await request.json()
+#     return handle(
+#         json_post_raw.get("command"),
+#         json_post_raw.get("refer_wav_path"),
+#         json_post_raw.get("prompt_text"),
+#         json_post_raw.get("prompt_language"),
+#         json_post_raw.get("text"),
+#         json_post_raw.get("text_language"),
+#     )
+#
+#
+# @app.get("/")
+# async def tts_endpoint(
+#         command: str = None,
+#         refer_wav_path: str = None,
+#         prompt_text: str = None,
+#         prompt_language: str = None,
+#         text: str = None,
+#         text_language: str = None,
+# ):
+#     return handle(command, refer_wav_path, prompt_text, prompt_language, text, text_language)
+#
+#
+# if __name__ == "__main__":
+#     uvicorn.run(app, host=host, port=port, workers=1)

config.py

@@ -1,7 +1,93 @@
 import sys
 import os
 
-import torch
+import torch,re
+
+from tools.i18n.i18n import I18nAuto, scan_language_list
+i18n = I18nAuto(language=os.environ["language"])
+
+
+pretrained_sovits_name = {
+    "v1":"GPT_SoVITS/pretrained_models/s2G488k.pth",
+    "v2":"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
+    "v3":"GPT_SoVITS/pretrained_models/s2Gv3.pth",###v3v4还要检查vocoder，算了。。。
+    "v4":"GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
+    "v2Pro":"GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro_pre1.pth",
+    "v2ProPlus":"GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus_pre1.pth",
+}
+
+pretrained_gpt_name = {
+    "v1":"GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
+    "v2":"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
+    "v3":"GPT_SoVITS/pretrained_models/s1v3.ckpt",
+    "v4":"GPT_SoVITS/pretrained_models/s1v3.ckpt",
+    "v2Pro":"GPT_SoVITS/pretrained_models/s1v3.ckpt",
+    "v2ProPlus":"GPT_SoVITS/pretrained_models/s1v3.ckpt",
+}
+name2sovits_path={
+    # i18n("不训练直接推v1底模！"): "GPT_SoVITS/pretrained_models/s2G488k.pth",
+    i18n("不训练直接推v2底模！"): "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
+    # i18n("不训练直接推v3底模！"): "GPT_SoVITS/pretrained_models/s2Gv3.pth",
+    # i18n("不训练直接推v4底模！"): "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
+    i18n("不训练直接推v2Pro底模！"): "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro_pre1.pth",
+    i18n("不训练直接推v2ProPlus底模！"): "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus_pre1.pth",
+}
+name2gpt_path={
+    # i18n("不训练直接推v1底模！"):"GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
+    i18n("不训练直接推v2底模！"):"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
+    i18n("不训练直接推v3底模！"):"GPT_SoVITS/pretrained_models/s1v3.ckpt",
+}
+SoVITS_weight_root = ["SoVITS_weights", "SoVITS_weights_v2", "SoVITS_weights_v3", "SoVITS_weights_v4", "SoVITS_weights_v2Pro", "SoVITS_weights_v2ProPlus"]
+GPT_weight_root = ["GPT_weights", "GPT_weights_v2", "GPT_weights_v3", "GPT_weights_v4", "GPT_weights_v2Pro", "GPT_weights_v2ProPlus"]
+SoVITS_weight_version2root={
+    "v1":"SoVITS_weights",
+    "v2":"SoVITS_weights_v2",
+    "v3":"SoVITS_weights_v3",
+    "v4":"SoVITS_weights_v4",
+    "v2Pro":"SoVITS_weights_v2Pro",
+    "v2ProPlus":"SoVITS_weights_v2ProPlus",
+}
+GPT_weight_version2root={
+    "v1":"GPT_weights",
+    "v2":"GPT_weights_v2",
+    "v3":"GPT_weights_v3",
+    "v4":"GPT_weights_v4",
+    "v2Pro":"GPT_weights_v2Pro",
+    "v2ProPlus":"GPT_weights_v2ProPlus",
+}
+def custom_sort_key(s):
+    # 使用正则表达式提取字符串中的数字部分和非数字部分
+    parts = re.split("(\d+)", s)
+    # 将数字部分转换为整数，非数字部分保持不变
+    parts = [int(part) if part.isdigit() else part for part in parts]
+    return parts
+
+def get_weights_names():
+    SoVITS_names = []
+    for key in name2sovits_path:
+        if os.path.exists(name2sovits_path[key]):SoVITS_names.append(key)
+    for path in SoVITS_weight_root:
+        for name in os.listdir(path):
+            if name.endswith(".pth"):
+                SoVITS_names.append("%s/%s" % (path, name))
+    GPT_names = []
+    for key in name2gpt_path:
+        if os.path.exists(name2gpt_path[key]):GPT_names.append(key)
+    for path in GPT_weight_root:
+        for name in os.listdir(path):
+            if name.endswith(".ckpt"):
+                GPT_names.append("%s/%s" % (path, name))
+    SoVITS_names=sorted(SoVITS_names, key=custom_sort_key)
+    GPT_names=sorted(GPT_names, key=custom_sort_key)
+    return SoVITS_names, GPT_names
+
+def change_choices():
+    SoVITS_names, GPT_names = get_weights_names()
+    return {"choices": SoVITS_names, "__type__": "update"}, {
+        "choices": GPT_names,
+        "__type__": "update",
+    }
+
 
 # 推理用的指定模型
 sovits_path = ""

webui.py

@@ -1,10 +1,6 @@
 import os
 import sys
-
-if len(sys.argv) == 1:
-    sys.argv.append("v2")
-version = "v1" if sys.argv[1] == "v1" else "v2"
-os.environ["version"] = version
+os.environ["version"] = version="v2Pro"
 now_dir = os.getcwd()
 sys.path.insert(0, now_dir)
 import warnings
@@ -63,7 +59,11 @@ for site_packages_root in site_packages_roots:
 import shutil
 import subprocess
 from subprocess import Popen
+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 config import (
     exp_root,
     infer_device,
@@ -76,11 +76,6 @@ from config import (
     webui_port_uvr5,
 )
 from tools import my_utils
-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 tools.my_utils import check_details, check_for_existance
@@ -232,86 +227,32 @@ def fix_gpu_numbers(inputs):
         return inputs
 
 
-pretrained_sovits_name = [
-    "GPT_SoVITS/pretrained_models/s2G488k.pth",
-    "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
-    "GPT_SoVITS/pretrained_models/s2Gv3.pth",
-    "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
-]
-pretrained_gpt_name = [
-    "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
-    "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
-    "GPT_SoVITS/pretrained_models/s1v3.ckpt",
-    "GPT_SoVITS/pretrained_models/s1v3.ckpt",
-]
+from config import pretrained_sovits_name,pretrained_gpt_name
 
-pretrained_model_list = (
-    pretrained_sovits_name[int(version[-1]) - 1],
-    pretrained_sovits_name[int(version[-1]) - 1].replace("s2G", "s2D"),
-    pretrained_gpt_name[int(version[-1]) - 1],
-    "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
-    "GPT_SoVITS/pretrained_models/chinese-hubert-base",
-)
+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]=""
 
-_ = ""
-for i in pretrained_model_list:
-    if "s2Dv3" not in i and os.path.exists(i) == False:
-        _ += f"\n    {i}"
-if _:
-    print("warning: ", i18n("以下模型不存在:") + _)
-
-_ = [[], []]
-for i in range(4):
-    if os.path.exists(pretrained_gpt_name[i]):
-        _[0].append(pretrained_gpt_name[i])
-    else:
-        _[0].append("")  ##没有下pretrained模型的，说不定他们是想自己从零训底模呢
-    if os.path.exists(pretrained_sovits_name[i]):
-        _[-1].append(pretrained_sovits_name[i])
-    else:
-        _[-1].append("")
-pretrained_gpt_name, pretrained_sovits_name = _
-
-SoVITS_weight_root = ["SoVITS_weights", "SoVITS_weights_v2", "SoVITS_weights_v3", "SoVITS_weights_v4"]
-GPT_weight_root = ["GPT_weights", "GPT_weights_v2", "GPT_weights_v3", "GPT_weights_v4"]
+from config import SoVITS_weight_root,GPT_weight_root,get_weights_names,change_choices,SoVITS_weight_version2root,GPT_weight_version2root
 for root in SoVITS_weight_root + GPT_weight_root:
     os.makedirs(root, exist_ok=True)
-
-
-def get_weights_names():
-    SoVITS_names = [name for name in pretrained_sovits_name if name != ""]
-    for path in SoVITS_weight_root:
-        for name in os.listdir(path):
-            if name.endswith(".pth"):
-                SoVITS_names.append("%s/%s" % (path, name))
-    GPT_names = [name for name in pretrained_gpt_name if name != ""]
-    for path in GPT_weight_root:
-        for name in os.listdir(path):
-            if name.endswith(".ckpt"):
-                GPT_names.append("%s/%s" % (path, name))
-    return SoVITS_names, GPT_names
-
-
 SoVITS_names, GPT_names = get_weights_names()
-for path in SoVITS_weight_root + GPT_weight_root:
-    os.makedirs(path, exist_ok=True)
-
-
-def custom_sort_key(s):
-    # 使用正则表达式提取字符串中的数字部分和非数字部分
-    parts = re.split("(\d+)", s)
-    # 将数字部分转换为整数，非数字部分保持不变
-    parts = [int(part) if part.isdigit() else part for part in parts]
-    return parts
-
-
-def change_choices():
-    SoVITS_names, GPT_names = get_weights_names()
-    return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {
-        "choices": sorted(GPT_names, key=custom_sort_key),
-        "__type__": "update",
-    }
-
 
 p_label = None
 p_uvr5 = None
@@ -450,8 +391,8 @@ def change_tts_inference(bert_path, cnhubert_base_path, gpu_number, gpt_path, so
     # 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 if "/" in gpt_path else "%s/%s" % (GPT_weight_root, gpt_path)
-        os.environ["sovits_path"] = sovits_path if "/" in sovits_path else "%s/%s" % (SoVITS_weight_root, sovits_path)
+        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)
@@ -599,6 +540,7 @@ process_name_sovits = i18n("SoVITS训练")
 
 
 def open1Ba(
+    version,
     batch_size,
     total_epoch,
     exp_name,
@@ -614,7 +556,8 @@ def open1Ba(
 ):
     global p_train_SoVITS
     if p_train_SoVITS == None:
-        with open("GPT_SoVITS/configs/s2.json") as f:
+        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)
@@ -637,13 +580,13 @@ def open1Ba(
         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_root[int(version[-1]) - 1]
+        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"]:
+        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)
@@ -724,7 +667,7 @@ def open1Bb(
         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_root[int(version[-1]) - 1]
+        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
@@ -964,12 +907,10 @@ def close1a():
         {"__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(inp_text, inp_wav_dir, exp_name, gpu_numbers, ssl_pretrained_dir):
+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)
@@ -982,6 +923,7 @@ def open1b(inp_text, inp_wav_dir, exp_name, gpu_numbers, ssl_pretrained_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("-")
@@ -1007,6 +949,23 @@ def open1b(inp_text, inp_wav_dir, exp_name, gpu_numbers, ssl_pretrained_dir):
         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},
@@ -1040,19 +999,20 @@ ps1c = []
 process_name_1c = i18n("语义Token提取")
 
 
-def open1c(inp_text, exp_name, gpu_numbers, pretrained_s2G_path):
+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, ""], is_dataset_processing=True):
-        check_details([inp_text, ""], is_dataset_processing=True)
+    if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
+        check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
     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": "GPT_SoVITS/configs/s2.json",
+            "s2config_path": config_file,
             "is_half": str(is_half),
         }
         gpu_names = gpu_numbers.split("-")
@@ -1121,6 +1081,7 @@ process_name_1abc = i18n("训练集格式化一键三连")
 
 
 def open1abc(
+    version,
     inp_text,
     inp_wav_dir,
     exp_name,
@@ -1198,6 +1159,7 @@ def open1abc(
                 "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)
@@ -1221,23 +1183,41 @@ def open1abc(
             )
             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},
             )
-            ps1abc = []
             #############################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": "GPT_SoVITS/configs/s2.json",
+                    "s2config_path": config_file,
                 }
                 gpu_names = gpu_numbers1c.split("-")
                 all_parts = len(gpu_names)
@@ -1317,17 +1297,17 @@ def switch_version(version_):
     os.environ["version"] = version_
     global version
     version = version_
-    if pretrained_sovits_name[int(version[-1]) - 1] != "" and pretrained_gpt_name[int(version[-1]) - 1] != "":
+    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[int(version[-1]) - 1]},
-        {"__type__": "update", "value": pretrained_sovits_name[int(version[-1]) - 1].replace("s2G", "s2D")},
-        {"__type__": "update", "value": pretrained_gpt_name[int(version[-1]) - 1]},
-        {"__type__": "update", "value": pretrained_gpt_name[int(version[-1]) - 1]},
-        {"__type__": "update", "value": pretrained_sovits_name[int(version[-1]) - 1]},
+        {"__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},
@@ -1357,10 +1337,7 @@ def sync(text):
 with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
     gr.Markdown(
         value=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.")
-        + "<br>"
-        + 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.")
-    )
-    gr.Markdown(value=i18n("中文教程文档") + ": " + "https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e")
+        + 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.")+ "<br>"+i18n("中文教程文档") + ": " + "https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e")
 
     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
@@ -1419,8 +1396,8 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
                     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():
+            # 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="")
@@ -1512,33 +1489,33 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
         with gr.TabItem(i18n("1-GPT-SoVITS-TTS")):
             with gr.Row():
                 with gr.Row():
-                    exp_name = gr.Textbox(label=i18n("*实验/模型名"), value="xxx", interactive=True)
-                    gpu_info = gr.Textbox(label=i18n("显卡信息"), value=gpu_info, visible=True, interactive=False)
-                    version_checkbox = gr.Radio(label=i18n("版本"), value=version, choices=["v1", "v2", "v4"])  # , "v3"
-                with gr.Row():
+                    exp_name = gr.Textbox(label=i18n("*实验/模型名"), value="xxx", interactive=True,scale=3,)
+                    gpu_info = 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,)
+                    # version_checkbox = gr.Radio(label=i18n("训练模型的版本"), value=version, choices=["v2", "v4", "v2Pro", "v2ProPlus", "v2ProMax"],scale=5,)
                     pretrained_s2G = gr.Textbox(
                         label=i18n("预训练SoVITS-G模型路径"),
-                        value=pretrained_sovits_name[int(version[-1]) - 1],
+                        value=pretrained_sovits_name[version],
                         interactive=True,
                         lines=2,
                         max_lines=3,
-                        scale=9,
+                        scale=5,
                     )
                     pretrained_s2D = gr.Textbox(
                         label=i18n("预训练SoVITS-D模型路径"),
-                        value=pretrained_sovits_name[int(version[-1]) - 1].replace("s2G", "s2D"),
+                        value=pretrained_sovits_name[version].replace("s2G", "s2D"),
                         interactive=True,
                         lines=2,
                         max_lines=3,
-                        scale=9,
+                        scale=5,
                     )
                     pretrained_s1 = gr.Textbox(
                         label=i18n("预训练GPT模型路径"),
-                        value=pretrained_gpt_name[int(version[-1]) - 1],
+                        value=pretrained_gpt_name[version],
                         interactive=True,
                         lines=2,
                         max_lines=3,
-                        scale=10,
+                        scale=5,
                     )
 
             with gr.TabItem("1A-" + i18n("训练集格式化工具")):
@@ -1623,7 +1600,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
                     with gr.Row():
                         pretrained_s2G_ = gr.Textbox(
                             label=i18n("预训练SoVITS-G模型路径"),
-                            value=pretrained_sovits_name[int(version[-1]) - 1],
+                            value=pretrained_sovits_name[version],
                             interactive=False,
                             lines=2,
                         )
@@ -1688,17 +1665,18 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
             button1a_close.click(close1a, [], [info1a, button1a_open, button1a_close])
             button1b_open.click(
                 open1b,
-                [inp_text, inp_wav_dir, exp_name, gpu_numbers1Ba, cnhubert_base_dir],
+                [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, [inp_text, exp_name, gpu_numbers1c, pretrained_s2G], [info1c, button1c_open, button1c_close]
+                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,
@@ -1862,21 +1840,21 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
             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。默认的一个是底模，体验5秒Zero Shot TTS用。"
+                        "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.Row():
                         GPT_dropdown = gr.Dropdown(
                             label=i18n("GPT模型列表"),
-                            choices=sorted(GPT_names, key=custom_sort_key),
-                            value=pretrained_gpt_name[0],
+                            choices=GPT_names,
+                            value=GPT_names[-1],
                             interactive=True,
                         )
                         SoVITS_dropdown = gr.Dropdown(
                             label=i18n("SoVITS模型列表"),
-                            choices=sorted(SoVITS_names, key=custom_sort_key),
-                            value=pretrained_sovits_name[0],
+                            choices=SoVITS_names,
+                            value=SoVITS_names[0],
                             interactive=True,
                         )
                     with gr.Row():
@@ -1924,6 +1902,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
             button1Ba_open.click(
                 open1Ba,
                 [
+                    version_checkbox,
                     batch_size,
                     total_epoch,
                     exp_name,