diff --git a/api.py b/api.py
index 4b79d50..164a1f1 100644
--- a/api.py
+++ b/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)
diff --git a/batch_inference.py b/batch_inference.py
new file mode 100644
index 0000000..476d20b
--- /dev/null
+++ b/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)
diff --git a/config.py b/config.py
index 5f90c5c..586963d 100644
--- a/config.py
+++ b/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 = ""
diff --git a/webui.py b/webui.py
index 6434e40..eb60847 100644
--- a/webui.py
+++ b/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.")
- + "
"
- + 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.")+ "
"+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,