Create vc_webui2.py

GPT_SoVITS/vc_webui2.py

@@ -0,0 +1,1132 @@
+'''
+按中英混合识别
+按日英混合识别
+多语种启动切分识别语种
+全部按中文识别
+全部按英文识别
+全部按日文识别
+'''
+import logging
+import traceback,torchaudio,warnings
+logging.getLogger("markdown_it").setLevel(logging.ERROR)
+logging.getLogger("urllib3").setLevel(logging.ERROR)
+logging.getLogger("httpcore").setLevel(logging.ERROR)
+logging.getLogger("httpx").setLevel(logging.ERROR)
+logging.getLogger("asyncio").setLevel(logging.ERROR)
+logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
+logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
+logging.getLogger("multipart.multipart").setLevel(logging.ERROR)
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+import os, re, sys, json
+import pdb
+import torch
+from text.LangSegmenter import LangSegmenter
+
+try:
+    import gradio.analytics as analytics
+    analytics.version_check = lambda:None
+except:...
+version=model_version="v3"
+pretrained_sovits_name=["GPT_SoVITS/pretrained_models/s2Gv3.pth"]
+pretrained_gpt_name=["GPT_SoVITS/pretrained_models/s1v3.ckpt"]
+
+
+_ =[[],[]]
+for i in range(1):
+    if os.path.exists(pretrained_gpt_name[i]):_[0].append(pretrained_gpt_name[i])
+    if os.path.exists(pretrained_sovits_name[i]):_[-1].append(pretrained_sovits_name[i])
+pretrained_gpt_name,pretrained_sovits_name = _
+
+
+if os.path.exists(f"./weight.json"):
+    pass
+else:
+    with open(f"./weight.json", 'w', encoding="utf-8") as file:json.dump({'GPT':{},'SoVITS':{}},file)
+
+with open(f"./weight.json", 'r', encoding="utf-8") as file:
+    weight_data = file.read()
+    weight_data=json.loads(weight_data)
+    gpt_path = os.environ.get(
+        "gpt_path", weight_data.get('GPT',{}).get(version,pretrained_gpt_name))
+    sovits_path = os.environ.get(
+        "sovits_path", weight_data.get('SoVITS',{}).get(version,pretrained_sovits_name))
+    if isinstance(gpt_path,list):
+        gpt_path = gpt_path[0]
+    if isinstance(sovits_path,list):
+        sovits_path = sovits_path[0]
+
+# gpt_path = os.environ.get(
+#     "gpt_path", pretrained_gpt_name
+# )
+# sovits_path = os.environ.get("sovits_path", pretrained_sovits_name)
+cnhubert_base_path = os.environ.get(
+    "cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base"
+)
+bert_path = os.environ.get(
+    "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
+)
+infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
+infer_ttswebui = int(infer_ttswebui)
+is_share = os.environ.get("is_share", "False")
+is_share = eval(is_share)
+if "_CUDA_VISIBLE_DEVICES" in os.environ:
+    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
+is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
+punctuation = set(['!', '?', '…', ',', '.', '-'," "])
+import gradio as gr
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import numpy as np
+import librosa
+from feature_extractor import cnhubert
+
+cnhubert.cnhubert_base_path = cnhubert_base_path
+
+from GPT_SoVITS.module.models import SynthesizerTrn,SynthesizerTrnV3
+from AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from text import cleaned_text_to_sequence
+from text.cleaner import clean_text
+from time import time as ttime
+from module.mel_processing import spectrogram_torch
+from tools.my_utils import load_audio
+from tools.i18n.i18n import I18nAuto, scan_language_list
+
+language=os.environ.get("language","Auto")
+language=sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
+i18n = I18nAuto(language=language)
+
+# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'  # 确保直接启动推理UI时也能够设置。
+
+if torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
+
+dict_language_v1 = {
+    i18n("中文"): "all_zh",#全部按中文识别
+    i18n("英文"): "en",#全部按英文识别#######不变
+    i18n("日文"): "all_ja",#全部按日文识别
+    i18n("中英混合"): "zh",#按中英混合识别####不变
+    i18n("日英混合"): "ja",#按日英混合识别####不变
+    i18n("多语种混合"): "auto",#多语种启动切分识别语种
+}
+dict_language_v2 = {
+    i18n("中文"): "all_zh",#全部按中文识别
+    i18n("英文"): "en",#全部按英文识别#######不变
+    i18n("日文"): "all_ja",#全部按日文识别
+    i18n("粤语"): "all_yue",#全部按中文识别
+    i18n("韩文"): "all_ko",#全部按韩文识别
+    i18n("中英混合"): "zh",#按中英混合识别####不变
+    i18n("日英混合"): "ja",#按日英混合识别####不变
+    i18n("粤英混合"): "yue",#按粤英混合识别####不变
+    i18n("韩英混合"): "ko",#按韩英混合识别####不变
+    i18n("多语种混合"): "auto",#多语种启动切分识别语种
+    i18n("多语种混合(粤语)"): "auto_yue",#多语种启动切分识别语种
+}
+dict_language = dict_language_v1 if version =='v1' else dict_language_v2
+
+tokenizer = AutoTokenizer.from_pretrained(bert_path)
+bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
+if is_half == True:
+    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)
+        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)
+    return phone_level_feature.T
+
+
+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")
+
+
+ssl_model = cnhubert.get_model()
+if is_half == True:
+    ssl_model = ssl_model.half().to(device)
+else:
+    ssl_model = ssl_model.to(device)
+
+resample_transform_dict={}
+def resample(audio_tensor, sr0):
+    global resample_transform_dict
+    if sr0 not in resample_transform_dict:
+        resample_transform_dict[sr0] = torchaudio.transforms.Resample(
+            sr0, 24000
+        ).to(device)
+    return resample_transform_dict[sr0](audio_tensor)
+
+def change_sovits_weights(sovits_path,prompt_language=None,text_language=None):
+    global vq_model, hps, version, model_version, dict_language
+    '''
+        v1:about 82942KB
+        half thr:82978KB
+        v2:about 83014KB
+        half thr:100MB
+        v1base:103490KB
+        half thr:103520KB
+        v2base:103551KB
+        v3:about 750MB
+        
+        ~82978K~100M~103420~700M
+        v1-v2-v1base-v2base-v3
+        version:
+            symbols version and timebre_embedding version
+        model_version:
+            sovits is v1/2 (VITS) or v3 (shortcut CFM DiT)
+    '''
+    size=os.path.getsize(sovits_path)
+    if size<82978*1024:
+        model_version=version="v1"
+    elif size<100*1024*1024:
+        model_version=version="v2"
+    elif size<103520*1024:
+        model_version=version="v1"
+    elif size<700*1024*1024:
+        model_version = version = "v2"
+    else:
+        version = "v2"
+        model_version="v3"
+
+    dict_language = dict_language_v1 if version =='v1' else dict_language_v2
+    if prompt_language is not None and text_language is not None:
+        if prompt_language in list(dict_language.keys()):
+            prompt_text_update, prompt_language_update = {'__type__':'update'},  {'__type__':'update', 'value':prompt_language}
+        else:
+            prompt_text_update = {'__type__':'update', 'value':''}
+            prompt_language_update = {'__type__':'update', 'value':i18n("中文")}
+        if text_language in list(dict_language.keys()):
+            text_update, text_language_update = {'__type__':'update'}, {'__type__':'update', 'value':text_language}
+        else:
+            text_update = {'__type__':'update', 'value':''}
+            text_language_update = {'__type__':'update', 'value':i18n("中文")}
+        if model_version=="v3":
+            visible_sample_steps=True
+            visible_inp_refs=False
+        else:
+            visible_sample_steps=False
+            visible_inp_refs=True
+        yield  {'__type__':'update', 'choices':list(dict_language.keys())}, {'__type__':'update', 'choices':list(dict_language.keys())}, prompt_text_update, prompt_language_update, text_update, text_language_update,{"__type__": "update", "visible": visible_sample_steps},{"__type__": "update", "visible": visible_inp_refs},{"__type__": "update", "value": False,"interactive":True if model_version!="v3"else False}
+
+    dict_s2 = torch.load(sovits_path, map_location="cpu", weights_only=False)
+    hps = dict_s2["config"]
+    hps = DictToAttrRecursive(hps)
+    hps.model.semantic_frame_rate = "25hz"
+    if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
+        hps.model.version = "v1"
+    else:
+        hps.model.version = "v2"
+    version=hps.model.version
+    # print("sovits版本:",hps.model.version)
+    if model_version!="v3":
+        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
+        )
+        model_version=version
+    else:
+        vq_model = SynthesizerTrnV3(
+            hps.data.filter_length // 2 + 1,
+            hps.train.segment_size // hps.data.hop_length,
+            n_speakers=hps.data.n_speakers,
+            **hps.model
+        )
+    if ("pretrained" not in sovits_path):
+        try:
+            del vq_model.enc_q
+        except:pass
+    if is_half == True:
+        vq_model = vq_model.half().to(device)
+    else:
+        vq_model = vq_model.to(device)
+    vq_model.eval()
+    print("loading sovits_%s"%model_version,vq_model.load_state_dict(dict_s2["weight"], strict=False))
+    with open("./weight.json")as f:
+        data=f.read()
+        data=json.loads(data)
+        data["SoVITS"][version]=sovits_path
+    with open("./weight.json","w")as f:f.write(json.dumps(data))
+
+
+try:next(change_sovits_weights(sovits_path))
+except:pass
+
+def change_gpt_weights(gpt_path):
+    global hz, max_sec, t2s_model, config
+    hz = 50
+    dict_s1 = torch.load(gpt_path, map_location="cpu")
+    config = dict_s1["config"]
+    max_sec = config["data"]["max_sec"]
+    t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
+    t2s_model.load_state_dict(dict_s1["weight"])
+    if is_half == True:
+        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))
+    with open("./weight.json")as f:
+        data=f.read()
+        data=json.loads(data)
+        data["GPT"][version]=gpt_path
+    with open("./weight.json","w")as f:f.write(json.dumps(data))
+
+
+change_gpt_weights(gpt_path)
+os.environ["HF_ENDPOINT"]          = "https://hf-mirror.com"
+import torch,soundfile
+now_dir = os.getcwd()
+import soundfile
+
+def init_bigvgan():
+    global model
+    from BigVGAN import bigvgan
+    model = bigvgan.BigVGAN.from_pretrained("%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,), 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
+    model.remove_weight_norm()
+    model = model.eval()
+    if is_half == True:
+        model = model.half().to(device)
+    else:
+        model = model.to(device)
+
+if model_version!="v3":model=None
+else:init_bigvgan()
+
+
+def get_spepc(hps, filename):
+    audio = load_audio(filename, int(hps.data.sampling_rate))
+    audio = torch.FloatTensor(audio)
+    maxx=audio.abs().max()
+    if(maxx>1):audio/=min(2,maxx)
+    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
+
+def clean_text_inf(text, language, version):
+    phones, word2ph, norm_text = clean_text(text, language, version)
+    phones = cleaned_text_to_sequence(phones, version)
+    return phones, word2ph, norm_text
+
+dtype=torch.float16 if is_half == True else torch.float32
+def get_bert_inf(phones, word2ph, norm_text, language):
+    language=language.replace("all_","")
+    if language == "zh":
+        bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
+    else:
+        bert = torch.zeros(
+            (1024, len(phones)),
+            dtype=torch.float16 if is_half == True else torch.float32,
+        ).to(device)
+
+    return bert
+
+
+splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
+
+
+def get_first(text):
+    pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
+    text = re.split(pattern, text)[0].strip()
+    return text
+
+from text import chinese
+def get_phones_and_bert(text,language,version,final=False):
+    if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
+        language = language.replace("all_","")
+        if language == "en":
+            formattext = text
+        else:
+            # 因无法区别中日韩文汉字,以用户输入为准
+            formattext = text
+        while "  " in formattext:
+            formattext = formattext.replace("  ", " ")
+        if language == "zh":
+            if re.search(r'[A-Za-z]', formattext):
+                formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
+                formattext = chinese.mix_text_normalize(formattext)
+                return get_phones_and_bert(formattext,"zh",version)
+            else:
+                phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
+                bert = get_bert_feature(norm_text, word2ph).to(device)
+        elif language == "yue" and re.search(r'[A-Za-z]', formattext):
+                formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
+                formattext = chinese.mix_text_normalize(formattext)
+                return get_phones_and_bert(formattext,"yue",version)
+        else:
+            phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
+            bert = torch.zeros(
+                (1024, len(phones)),
+                dtype=torch.float16 if is_half == True else torch.float32,
+            ).to(device)
+    elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
+        textlist=[]
+        langlist=[]
+        if language == "auto":
+            for tmp in LangSegmenter.getTexts(text):
+                langlist.append(tmp["lang"])
+                textlist.append(tmp["text"])
+        elif language == "auto_yue":
+            for tmp in LangSegmenter.getTexts(text):
+                if tmp["lang"] == "zh":
+                    tmp["lang"] = "yue"
+                langlist.append(tmp["lang"])
+                textlist.append(tmp["text"])
+        else:
+            for tmp in LangSegmenter.getTexts(text):
+                if tmp["lang"] == "en":
+                    langlist.append(tmp["lang"])
+                else:
+                    # 因无法区别中日韩文汉字,以用户输入为准
+                    langlist.append(language)
+                textlist.append(tmp["text"])
+        print(textlist)
+        print(langlist)
+        phones_list = []
+        bert_list = []
+        norm_text_list = []
+        for i in range(len(textlist)):
+            lang = langlist[i]
+            phones, word2ph, norm_text = clean_text_inf(textlist[i], lang, version)
+            bert = get_bert_inf(phones, word2ph, norm_text, lang)
+            phones_list.append(phones)
+            norm_text_list.append(norm_text)
+            bert_list.append(bert)
+        bert = torch.cat(bert_list, dim=1)
+        phones = sum(phones_list, [])
+        norm_text = ''.join(norm_text_list)
+
+    if not final and len(phones) < 6:
+        return get_phones_and_bert("." + text,language,version,final=True)
+
+    return phones,bert.to(dtype),norm_text
+
+from module.mel_processing import spectrogram_torch,spec_to_mel_torch
+def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
+    spec=spectrogram_torch(y,n_fft,sampling_rate,hop_size,win_size,center)
+    mel=spec_to_mel_torch(spec,n_fft,num_mels,sampling_rate,fmin,fmax)
+    return mel
+mel_fn_args = {
+    "n_fft": 1024,
+    "win_size": 1024,
+    "hop_size": 256,
+    "num_mels": 100,
+    "sampling_rate": 24000,
+    "fmin": 0,
+    "fmax": None,
+    "center": False
+}
+
+spec_min = -12
+spec_max = 2
+def norm_spec(x):
+    return (x - spec_min) / (spec_max - spec_min) * 2 - 1
+def denorm_spec(x):
+    return (x + 1) / 2 * (spec_max - spec_min) + spec_min
+mel_fn=lambda x: mel_spectrogram(x, **mel_fn_args)
+
+
+def merge_short_text_in_array(texts, threshold):
+    if (len(texts)) < 2:
+        return texts
+    result = []
+    text = ""
+    for ele in texts:
+        text += ele
+        if len(text) >= threshold:
+            result.append(text)
+            text = ""
+    if (len(text) > 0):
+        if len(result) == 0:
+            result.append(text)
+        else:
+            result[len(result) - 1] += text
+    return result
+
+##ref_wav_path+prompt_text+prompt_language+text(单个)+text_language+top_k+top_p+temperature
+# cache_tokens={}#暂未实现清理机制
+cache= {}
+def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False,speed=1,if_freeze=False,inp_refs=None,sample_steps=8):
+    global cache
+    if ref_wav_path:pass
+    else:gr.Warning(i18n('请上传参考音频'))
+    if text:pass
+    else:gr.Warning(i18n('请填入推理文本'))
+    t = []
+    if prompt_text is None or len(prompt_text) == 0:
+        ref_free = True
+    if model_version=="v3":ref_free=False#s2v3暂不支持ref_free
+    t0 = ttime()
+    prompt_language = dict_language[prompt_language]
+    text_language = dict_language[text_language]
+
+
+    if not ref_free:
+        prompt_text = prompt_text.strip("\n")
+        if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
+        print(i18n("实际输入的参考文本:"), prompt_text)
+    text = text.strip("\n")
+    # if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
+    
+    print(i18n("实际输入的目标文本:"), text)
+    zero_wav = np.zeros(
+        int(hps.data.sampling_rate * 0.3),
+        dtype=np.float16 if is_half == True else np.float32,
+    )
+    if not ref_free:
+        with torch.no_grad():
+            wav16k, sr = librosa.load(ref_wav_path, sr=16000)
+            if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
+                gr.Warning(i18n("参考音频在3~10秒范围外，请更换！"))
+                raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
+            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]
+            prompt = prompt_semantic.unsqueeze(0).to(device)
+
+    t1 = ttime()
+    t.append(t1-t0)
+
+    if (how_to_cut == i18n("凑四句一切")):
+        text = cut1(text)
+    elif (how_to_cut == i18n("凑50字一切")):
+        text = cut2(text)
+    elif (how_to_cut == i18n("按中文句号。切")):
+        text = cut3(text)
+    elif (how_to_cut == i18n("按英文句号.切")):
+        text = cut4(text)
+    elif (how_to_cut == i18n("按标点符号切")):
+        text = cut5(text)
+    while "\n\n" in text:
+        text = text.replace("\n\n", "\n")
+    print(i18n("实际输入的目标文本(切句后):"), text)
+    texts = text.split("\n")
+    texts = process_text(texts)
+    texts = merge_short_text_in_array(texts, 5)
+    audio_opt = []
+    ###s2v3暂不支持ref_free
+    if not ref_free:
+        phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language, version)
+
+    for i_text,text in enumerate(texts):
+        # 解决输入目标文本的空行导致报错的问题
+        if (len(text.strip()) == 0):
+            continue
+        if (text[-1] not in splits): text += "。" if text_language != "en" else "."
+        print(i18n("实际输入的目标文本(每句):"), text)
+        phones2,bert2,norm_text2=get_phones_and_bert(text, text_language, version)
+        print(i18n("前端处理后的文本(每句):"), norm_text2)
+        if not ref_free:
+            bert = torch.cat([bert1, bert2], 1)
+            all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
+        else:
+            bert = bert2
+            all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
+
+        bert = bert.to(device).unsqueeze(0)
+        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
+
+        t2 = ttime()
+        # cache_key="%s-%s-%s-%s-%s-%s-%s-%s"%(ref_wav_path,prompt_text,prompt_language,text,text_language,top_k,top_p,temperature)
+        # print(cache.keys(),if_freeze)
+        if(i_text in cache and if_freeze==True):pred_semantic=cache[i_text]
+        else:
+            with torch.no_grad():
+                pred_semantic, idx = t2s_model.model.infer_panel(
+                    all_phoneme_ids,
+                    all_phoneme_len,
+                    None if ref_free else prompt,
+                    bert,
+                    # prompt_phone_len=ph_offset,
+                    top_k=top_k,
+                    top_p=top_p,
+                    temperature=temperature,
+                    early_stop_num=hz * max_sec,
+                )
+                pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
+                cache[i_text]=pred_semantic
+        t3 = ttime()
+        ###v3不存在以下逻辑和inp_refs
+        if model_version!="v3":
+            refers=[]
+            if(inp_refs):
+                for path in inp_refs:
+                    try:
+                        refer = get_spepc(hps, path.name).to(dtype).to(device)
+                        refers.append(refer)
+                    except:
+                        traceback.print_exc()
+            if(len(refers)==0):refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
+            audio = (vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers,speed=speed).detach().cpu().numpy()[0, 0])
+        else:
+            refer = get_spepc(hps, ref_wav_path).to(device).to(dtype)#######这里要重采样切到32k,因为src是24k的，没有单独的32k的src，所以不能改成2个路径
+            phoneme_ids0=torch.LongTensor(phones1).to(device).unsqueeze(0)
+            phoneme_ids1=torch.LongTensor(phones2).to(device).unsqueeze(0)
+            fea_ref,ge = vq_model.decode_encp(prompt.unsqueeze(0), phoneme_ids0, refer)
+            ref_audio, sr = torchaudio.load(ref_wav_path)
+            ref_audio=ref_audio.to(device).float()
+            if (ref_audio.shape[0] == 2):
+                ref_audio = ref_audio.mean(0).unsqueeze(0)
+            if sr!=24000:
+                ref_audio=resample(ref_audio,sr)
+            mel2 = mel_fn(ref_audio.to(dtype))
+            mel2 = norm_spec(mel2)
+            T_min = min(mel2.shape[2], fea_ref.shape[2])
+            mel2 = mel2[:, :, :T_min]
+            fea_ref = fea_ref[:, :, :T_min]
+            if (T_min > 468):
+                mel2 = mel2[:, :, -468:]
+                fea_ref = fea_ref[:, :, -468:]
+                T_min = 468
+            chunk_len = 934 - T_min
+            fea_todo, ge = vq_model.decode_encp(pred_semantic, phoneme_ids1, refer, ge)
+            cfm_resss = []
+            idx = 0
+            while (1):
+                fea_todo_chunk = fea_todo[:, :, idx:idx + chunk_len]
+                if (fea_todo_chunk.shape[-1] == 0): break
+                idx += chunk_len
+                fea = torch.cat([fea_ref, fea_todo_chunk], 2).transpose(2, 1)
+                cfm_res = vq_model.cfm.inference(fea, torch.LongTensor([fea.size(1)]).to(fea.device), mel2, sample_steps, inference_cfg_rate=0)
+                cfm_res = cfm_res[:, :, mel2.shape[2]:]
+                mel2 = cfm_res[:, :, -T_min:]
+                fea_ref = fea_todo_chunk[:, :, -T_min:]
+                cfm_resss.append(cfm_res)
+            cmf_res = torch.cat(cfm_resss, 2)
+            cmf_res = denorm_spec(cmf_res)
+            if model==None:init_bigvgan()
+            with torch.inference_mode():
+                wav_gen = model(cmf_res)
+                audio=wav_gen[0][0].cpu().detach().numpy()
+            max_audio=np.abs(audio).max()#简单防止16bit爆音
+            if max_audio>1:audio/=max_audio
+        audio_opt.append(audio)
+        audio_opt.append(zero_wav)
+        t4 = ttime()
+        t.extend([t2 - t1,t3 - t2, t4 - t3])
+        t1 = ttime()
+    print("%.3f\t%.3f\t%.3f\t%.3f" % 
+           (t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3]))
+           )
+    sr=hps.data.sampling_rate if model_version!="v3"else 24000
+    yield sr, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
+
+
+def split(todo_text):
+    todo_text = todo_text.replace("……", "。").replace("——", "，")
+    if todo_text[-1] not in splits:
+        todo_text += "。"
+    i_split_head = i_split_tail = 0
+    len_text = len(todo_text)
+    todo_texts = []
+    while 1:
+        if i_split_head >= len_text:
+            break  # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
+        if todo_text[i_split_head] in splits:
+            i_split_head += 1
+            todo_texts.append(todo_text[i_split_tail:i_split_head])
+            i_split_tail = i_split_head
+        else:
+            i_split_head += 1
+    return todo_texts
+
+
+def cut1(inp):
+    inp = inp.strip("\n")
+    inps = split(inp)
+    split_idx = list(range(0, len(inps), 4))
+    split_idx[-1] = None
+    if len(split_idx) > 1:
+        opts = []
+        for idx in range(len(split_idx) - 1):
+            opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
+    else:
+        opts = [inp]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return "\n".join(opts)
+
+
+def cut2(inp):
+    inp = inp.strip("\n")
+    inps = split(inp)
+    if len(inps) < 2:
+        return inp
+    opts = []
+    summ = 0
+    tmp_str = ""
+    for i in range(len(inps)):
+        summ += len(inps[i])
+        tmp_str += inps[i]
+        if summ > 50:
+            summ = 0
+            opts.append(tmp_str)
+            tmp_str = ""
+    if tmp_str != "":
+        opts.append(tmp_str)
+    # print(opts)
+    if len(opts) > 1 and len(opts[-1]) < 50:  ##如果最后一个太短了，和前一个合一起
+        opts[-2] = opts[-2] + opts[-1]
+        opts = opts[:-1]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return "\n".join(opts)
+
+
+def cut3(inp):
+    inp = inp.strip("\n")
+    opts = ["%s" % item for item in inp.strip("。").split("。")]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return  "\n".join(opts)
+
+def cut4(inp):
+    inp = inp.strip("\n")
+    opts = ["%s" % item for item in inp.strip(".").split(".")]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return "\n".join(opts)
+
+
+# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
+def cut5(inp):
+    inp = inp.strip("\n")
+    punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
+    mergeitems = []
+    items = []
+
+    for i, char in enumerate(inp):
+        if char in punds:
+            if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
+                items.append(char)
+            else:
+                items.append(char)
+                mergeitems.append("".join(items))
+                items = []
+        else:
+            items.append(char)
+
+    if items:
+        mergeitems.append("".join(items))
+
+    opt = [item for item in mergeitems if not set(item).issubset(punds)]
+    return "\n".join(opt)
+
+
+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 process_text(texts):
+    _text=[]
+    if all(text in [None, " ", "\n",""] for text in texts):
+        raise ValueError(i18n("请输入有效文本"))
+    for text in texts:
+        if text in  [None, " ", ""]:
+            pass
+        else:
+            _text.append(text)
+    return _text
+
+
+def change_choices():
+    SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
+    return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
+
+
+SoVITS_weight_root=["SoVITS_weights","SoVITS_weights_v2","SoVITS_weights_v3"]
+GPT_weight_root=["GPT_weights","GPT_weights_v2","GPT_weights_v3"]
+for path in SoVITS_weight_root+GPT_weight_root:
+    os.makedirs(path,exist_ok=True)
+
+
+def get_weights_names(GPT_weight_root, SoVITS_weight_root):
+    SoVITS_names = [i for i in pretrained_sovits_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 = [i for i in pretrained_gpt_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(GPT_weight_root, SoVITS_weight_root)
+
+def html_center(text, label='p'):
+    return f"""<div style="text-align: center; margin: 100; padding: 50;">
+                <{label} style="margin: 0; padding: 0;">{text}</{label}>
+                </div>"""
+
+def html_left(text, label='p'):
+    return f"""<div style="text-align: left; margin: 0; padding: 0;">
+                <{label} style="margin: 0; padding: 0;">{text}</{label}>
+                </div>"""
+
+@torch.no_grad()
+def get_code_from_ssl(ssl):
+    ssl = vq_model.ssl_proj(ssl)
+    quantized, codes, commit_loss, quantized_list = vq_model.quantizer(ssl)
+    # print(codes.shape, codes.dtype)  # [n_q, B, T]
+    return codes.transpose(0, 1)  # [B, n_q, T]
+
+    
+@torch.no_grad()
+def get_code_from_wav(wav_path):
+    wav16k, sr = librosa.load(wav_path, sr=16000)
+    wav16k = torch.from_numpy(wav16k)
+    if is_half == True:
+        wav16k = wav16k.half().to(device)
+    else:
+        wav16k = wav16k.to(device)
+    ssl_content = ssl_model.model(wav16k.unsqueeze(0))[ 
+        "last_hidden_state"
+    ].transpose(
+        1, 2
+    )  # .float()
+    codes = get_code_from_ssl(ssl_content)  # [B, n_q, T]
+
+    prompt_semantic = codes[0, 0] 
+    return prompt_semantic
+
+
+def vc_main(wav_path, text, language, prompt_wav, noise_scale=0.5, top_k=20, top_p=0.6, temperature=0.6, speed=1, sample_steps=8):
+    """
+    Voice Conversion function that supports both v2 and v3 model versions
+    
+    Args:
+        wav_path: Path to source audio for conversion
+        text: Corresponding text for phoneme extraction
+        language: Language of the text
+        prompt_wav: Path to target/reference voice
+        noise_scale: Noise scale for v2 models
+        top_k, top_p, temperature: Parameters for v3 models
+        speed: Speed factor for audio playback
+        sample_steps: Number of sample steps for v3 models
+    
+    Returns:
+        Sampling rate and converted audio
+    """
+    # Get language format
+    language = dict_language[language]
+    
+    # Get phones from text
+    phones, word2ph, norm_text = clean_text_inf(text, language, version)
+    
+    # Get reference audio spectrogram
+    refer = get_spepc(hps, prompt_wav).to(dtype).to(device)
+    
+    # Get codes from source audio
+    source_codes = get_code_from_wav(wav_path)
+    
+    if model_version != "v3":
+        # V1/V2 models voice conversion logic
+        ge = vq_model.ref_enc(refer)  # [B, D, T/1]
+        quantized = vq_model.quantizer.decode(source_codes[None, None])  # [B, D, T]
+        
+        # Interpolate if necessary for 25hz models
+        if hps.model.semantic_frame_rate == "25hz":
+            quantized = F.interpolate(
+                quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
+            )
+        
+        m_p, logs_p, y_mask = vq_model.enc_p(
+            quantized, 
+            torch.LongTensor([quantized.shape[-1]]).to(device),
+            torch.LongTensor(phones).to(device).unsqueeze(0),
+            torch.LongTensor([len(phones)]).to(device),
+            ge
+        )
+        
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        z = vq_model.flow(z_p, y_mask, g=ge, reverse=True)
+        o = vq_model.dec((z * y_mask)[:, :, :], g=ge)  # [B, D=1, T], torch.float32 (-1, 1)
+        audio = o.detach().cpu().numpy()[0, 0]
+        
+    else:
+        # V3 model voice conversion logic
+        if model is None:
+            init_bigvgan()
+        
+        # Handle 1D tensor case (the source_codes from get_code_from_wav is 1D)
+        # The shape of source_codes is [T] - just the sequence length
+        if source_codes.dim() == 1:  # If [T]
+            # For v3 models, we need to reshape to [B, T, D]
+            # We need to determine the feature dimension D
+            # From the error message, we can see the tensor has shape [225]
+            # This is likely just the sequence length, and we need to add feature dimension
+            
+            # First, reshape to [1, T, 1] - adding batch and feature dimensions
+            semantic = source_codes.unsqueeze(0).unsqueeze(-1)
+            
+            # The feature dimension may need to be expanded to match what the model expects
+            # This depends on the model architecture - let's try using the same dimension as SSL features
+            if hasattr(vq_model, 'ssl_dim'):
+                feature_dim = vq_model.ssl_dim
+            else:
+                # If we can't determine it, use a default value that seems reasonable
+                # For v3 models, this is often 768 (BERT/HuBERT hidden size)
+                feature_dim = 768
+                
+            # Expand the feature dimension to match expected size
+            semantic = semantic.expand(-1, -1, feature_dim)
+            
+        elif source_codes.dim() == 2:  # If [T, D]
+            semantic = source_codes.unsqueeze(0)  # Add batch dimension [1, T, D]
+        elif source_codes.dim() == 3:  # If [B, T, D]
+            semantic = source_codes
+        else:
+            # For any other unexpected shape
+            raise ValueError(f"Unexpected source_codes shape: {source_codes.shape}")
+        
+        # Prepare phoneme IDs
+        phoneme_ids = torch.LongTensor(phones).to(device).unsqueeze(0)
+        
+        # Get reference audio features and global embedding
+        fea_ref, ge = vq_model.decode_encp(semantic, phoneme_ids, refer)
+        
+        # Load and process reference audio
+        ref_audio, sr = torchaudio.load(prompt_wav)
+        ref_audio = ref_audio.to(device).float()
+        if ref_audio.shape[0] == 2:  # Convert stereo to mono
+            ref_audio = ref_audio.mean(0).unsqueeze(0)
+        if sr != 24000:
+            ref_audio = resample(ref_audio, sr)
+        
+        # Convert to mel spectrogram and normalize
+        mel2 = mel_fn(ref_audio.to(dtype))
+        mel2 = norm_spec(mel2)
+        
+        # Adjust time dimensions
+        T_min = min(mel2.shape[2], fea_ref.shape[2])
+        mel2 = mel2[:, :, :T_min]
+        fea_ref = fea_ref[:, :, :T_min]
+        
+        if T_min > 468:
+            mel2 = mel2[:, :, -468:]
+            fea_ref = fea_ref[:, :, -468:]
+            T_min = 468
+        
+        # Process source audio features with phoneme conditioning
+        fea_todo, ge = vq_model.decode_encp(semantic, phoneme_ids, refer, ge)
+        
+        # Process audio in chunks
+        chunk_len = 934 - T_min
+        cfm_resss = []
+        idx = 0
+        
+        while True:
+            fea_todo_chunk = fea_todo[:, :, idx:idx + chunk_len]
+            if fea_todo_chunk.shape[-1] == 0:
+                break
+            
+            idx += chunk_len
+            fea = torch.cat([fea_ref, fea_todo_chunk], 2).transpose(2, 1)
+            cfm_res = vq_model.cfm.inference(
+                fea, 
+                torch.LongTensor([fea.size(1)]).to(fea.device), 
+                mel2, 
+                sample_steps, 
+                inference_cfg_rate=0
+            )
+            
+            cfm_res = cfm_res[:, :, mel2.shape[2]:]
+            mel2 = cfm_res[:, :, -T_min:]
+            fea_ref = fea_todo_chunk[:, :, -T_min:]
+            cfm_resss.append(cfm_res)
+        
+        # Concatenate results and convert to audio
+        cmf_res = torch.cat(cfm_resss, 2)
+        cmf_res = denorm_spec(cmf_res)
+        
+        with torch.inference_mode():
+            wav_gen = model(cmf_res)
+            audio = wav_gen[0][0].cpu().detach().numpy()
+    
+    # Normalize audio to prevent clipping
+    max_audio = np.abs(audio).max()
+    if max_audio > 1:
+        audio /= max_audio
+    
+    sr = hps.data.sampling_rate if model_version != "v3" else 24000
+    return sr, (audio * 32768).astype(np.int16)
+
+# Create and launch the standalone Gradio interface for voice conversion
+def launch_vc_ui():
+    with gr.Blocks(title="GPT-SoVITS Voice Conversion") as vc_app:
+        gr.Markdown("# GPT-SoVITS Voice Conversion")
+        gr.Markdown(f"Current Model Version: {model_version}")
+        
+        with gr.Row():
+            with gr.Column():
+                source_audio = gr.Audio(type="filepath", label="Source Audio (to be converted)")
+                text_input = gr.Textbox(label="Text content of the source audio")
+                language_input = gr.Dropdown(
+                    choices=list(dict_language.keys()),
+                    value=i18n("中文"),
+                    label=i18n("语言 / Language")
+                )
+                target_audio = gr.Audio(type="filepath", label="Target Voice (reference)")
+                
+                with gr.Accordion("Advanced Settings", open=False):
+                    with gr.Row():
+                        speed = gr.Slider(
+                            minimum=0.1, maximum=5, value=1, step=0.1, 
+                            label=i18n("语速 / Speed")
+                        )
+                    
+                    if model_version != "v3":
+                        noise_scale = gr.Slider(
+                            minimum=0.1, maximum=1.0, value=0.5, step=0.1, 
+                            label="Noise Scale (V2 models only)"
+                        )
+                    else:
+                        noise_scale = gr.Slider(
+                            minimum=0.1, maximum=1.0, value=0.5, step=0.1, 
+                            label="Noise Scale (ignored for V3)",
+                            visible=False
+                        )
+                    
+                    if model_version == "v3":
+                        sample_steps = gr.Slider(
+                            minimum=1, maximum=30, value=8, step=1, 
+                            label=i18n("采样步数 / Sample Steps")
+                        )
+                        top_k = gr.Slider(
+                            minimum=1, maximum=100, value=20, step=1, 
+                            label=i18n("Top K")
+                        )
+                        top_p = gr.Slider(
+                            minimum=0.1, maximum=1.0, value=0.6, step=0.1, 
+                            label=i18n("Top P")
+                        )
+                        temperature = gr.Slider(
+                            minimum=0.1, maximum=1.0, value=0.6, step=0.1, 
+                            label=i18n("Temperature")
+                        )
+                    else:
+                        sample_steps = gr.Slider(
+                            minimum=1, maximum=30, value=8, step=1, 
+                            label=i18n("采样步数 / Sample Steps"),
+                            visible=False
+                        )
+                        top_k = gr.Slider(
+                            minimum=1, maximum=100, value=20, step=1, 
+                            label=i18n("Top K"),
+                            visible=False
+                        )
+                        top_p = gr.Slider(
+                            minimum=0.1, maximum=1.0, value=0.6, step=0.1, 
+                            label=i18n("Top P"),
+                            visible=False
+                        )
+                        temperature = gr.Slider(
+                            minimum=0.1, maximum=1.0, value=0.6, step=0.1, 
+                            label=i18n("Temperature"),
+                            visible=False
+                        )
+                
+                go_btn = gr.Button(i18n("开始转换 / Start Conversion"), variant="primary")
+            
+            with gr.Column():
+                output_audio = gr.Audio(label=i18n("转换后的声音 / Converted Audio"))
+                status_output = gr.Markdown("Ready")
+        
+        def process_vc(source_path, text, lang, target_path, noise, k, p, temp, spd, steps):
+            try:
+                if not source_path:
+                    return None, "Error: Source audio is required"
+                if not target_path:
+                    return None, "Error: Target audio is required"
+                if not text:
+                    return None, "Error: Text content is required"
+                
+                return vc_main(
+                    source_path, text, lang, target_path, 
+                    noise_scale=noise, 
+                    top_k=k, 
+                    top_p=p, 
+                    temperature=temp, 
+                    speed=spd, 
+                    sample_steps=steps
+                ), "Conversion completed successfully"
+            except Exception as e:
+                import traceback
+                return None, f"Error: {str(e)}\n{traceback.format_exc()}"
+        
+        go_btn.click(
+            fn=process_vc,
+            inputs=[
+                source_audio, text_input, language_input, target_audio, 
+                noise_scale, top_k, top_p, temperature, speed, sample_steps
+            ],
+            outputs=[output_audio, status_output]
+        )
+    
+    # Launch the app with the infer_ttswebui port + 1 to avoid conflicts
+    vc_app.launch(
+        share=True,
+    )
+
+if __name__ == "__main__":
+    print(f"Launching Voice Conversion UI with model version: {model_version}")
+    launch_vc_ui()