split train && interence ui

GPT_SoVITS/inference_webui.py

@@ -1,606 +1,13 @@
-'''
-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 os, re, logging
-import LangSegment
-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)
-import pdb
-
-if os.path.exists("./gweight.txt"):
-    with open("./gweight.txt", 'r', encoding="utf-8") as file:
-        gweight_data = file.read()
-        gpt_path = os.environ.get(
-            "gpt_path", gweight_data)
-else:
-    gpt_path = os.environ.get(
-        "gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
-
-if os.path.exists("./sweight.txt"):
-    with open("./sweight.txt", 'r', encoding="utf-8") as file:
-        sweight_data = file.read()
-        sovits_path = os.environ.get("sovits_path", sweight_data)
-else:
-    sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth")
-# gpt_path = os.environ.get(
-#     "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
-# )
-# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth")
-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"))
-import gradio as gr
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-import numpy as np
-import librosa, torch
-from feature_extractor import cnhubert
-
-cnhubert.cnhubert_base_path = cnhubert_base_path
-
-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 time import time as ttime
-from module.mel_processing import spectrogram_torch
-from my_utils import load_audio
+import os
 from tools.i18n.i18n import I18nAuto
+from interence_base import sovits_path, gpt_path, change_choices, GPT_names, custom_sort_key, SoVITS_names, change_sovits_weights, change_gpt_weights, get_tts_wav, cut1, cut2, cut3, cut4, cut5
+import gradio as gr
 
 i18n = I18nAuto()
-
-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"
-elif torch.backends.mps.is_available():
-    device = "mps"
-else:
-    device = "cpu"
-
-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)
-
-
-def change_sovits_weights(sovits_path):
-    global vq_model, hps
-    dict_s2 = torch.load(sovits_path, map_location="cpu")
-    hps = dict_s2["config"]
-    hps = DictToAttrRecursive(hps)
-    hps.model.semantic_frame_rate = "25hz"
-    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 ("pretrained" not in sovits_path):
-        del vq_model.enc_q
-    if is_half == True:
-        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))
-    with open("./sweight.txt", "w", encoding="utf-8") as f:
-        f.write(sovits_path)
-
-
-change_sovits_weights(sovits_path)
-
-
-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("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
-
-
-change_gpt_weights(gpt_path)
-
-
-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 = {
-    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
-}
-
-
-def splite_en_inf(sentence, language):
-    pattern = re.compile(r'[a-zA-Z ]+')
-    textlist = []
-    langlist = []
-    pos = 0
-    for match in pattern.finditer(sentence):
-        start, end = match.span()
-        if start > pos:
-            textlist.append(sentence[pos:start])
-            langlist.append(language)
-        textlist.append(sentence[start:end])
-        langlist.append("en")
-        pos = end
-    if pos < len(sentence):
-        textlist.append(sentence[pos:])
-        langlist.append(language)
-    # Merge punctuation into previous word
-    for i in range(len(textlist)-1, 0, -1):
-        if re.match(r'^[\W_]+$', textlist[i]):
-            textlist[i-1] += textlist[i]
-            del textlist[i]
-            del langlist[i]
-    # Merge consecutive words with the same language tag
-    i = 0
-    while i < len(langlist) - 1:
-        if langlist[i] == langlist[i+1]:
-            textlist[i] += textlist[i+1]
-            del textlist[i+1]
-            del langlist[i+1]
-        else:
-            i += 1
-
-    return textlist, langlist
-
-
-def clean_text_inf(text, language):
-    formattext = ""
-    language = language.replace("all_","")
-    for tmp in LangSegment.getTexts(text):
-        if language == "ja":
-            if tmp["lang"] == language or tmp["lang"] == "zh":
-                formattext += tmp["text"] + " "
-            continue
-        if tmp["lang"] == language:
-            formattext += tmp["text"] + " "
-    while "  " in formattext:
-        formattext = formattext.replace("  ", " ")
-    phones, word2ph, norm_text = clean_text(formattext, language)
-    phones = cleaned_text_to_sequence(phones)
-    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
-
-
-def nonen_clean_text_inf(text, language):
-    if(language!="auto"):
-        textlist, langlist = splite_en_inf(text, language)
-    else:
-        textlist=[]
-        langlist=[]
-        for tmp in LangSegment.getTexts(text):
-            langlist.append(tmp["lang"])
-            textlist.append(tmp["text"])
-    phones_list = []
-    word2ph_list = []
-    norm_text_list = []
-    for i in range(len(textlist)):
-        lang = langlist[i]
-        phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
-        phones_list.append(phones)
-        if lang == "zh":
-            word2ph_list.append(word2ph)
-        norm_text_list.append(norm_text)
-    print(word2ph_list)
-    phones = sum(phones_list, [])
-    word2ph = sum(word2ph_list, [])
-    norm_text = ' '.join(norm_text_list)
-
-    return phones, word2ph, norm_text
-
-
-def nonen_get_bert_inf(text, language):
-    if(language!="auto"):
-        textlist, langlist = splite_en_inf(text, language)
-    else:
-        textlist=[]
-        langlist=[]
-        for tmp in LangSegment.getTexts(text):
-            langlist.append(tmp["lang"])
-            textlist.append(tmp["text"])
-    print(textlist)
-    print(langlist)
-    bert_list = []
-    for i in range(len(textlist)):
-        lang = langlist[i]
-        phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
-        bert = get_bert_inf(phones, word2ph, norm_text, lang)
-        bert_list.append(bert)
-    bert = torch.cat(bert_list, dim=1)
-
-    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
-
-
-def get_cleaned_text_final(text,language):
-    if language in {"en","all_zh","all_ja"}:
-        phones, word2ph, norm_text = clean_text_inf(text, language)
-    elif language in {"zh", "ja","auto"}:
-        phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
-    return phones, word2ph, norm_text
-
-def get_bert_final(phones, word2ph, text,language,device):
-    if language == "en":
-        bert = get_bert_inf(phones, word2ph, text, language)
-    elif language in {"zh", "ja","auto"}:
-        bert = nonen_get_bert_inf(text, language)
-    elif language == "all_zh":
-        bert = get_bert_feature(text, word2ph).to(device)
-    else:
-        bert = torch.zeros((1024, len(phones))).to(device)
-    return bert
-
-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
-
-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):
-    if prompt_text is None or len(prompt_text) == 0:
-        ref_free = True
-    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,
-    )
-    with torch.no_grad():
-        wav16k, sr = librosa.load(ref_wav_path, sr=16000)
-        if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
-            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]
-    t1 = ttime()
-
-    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 = merge_short_text_in_array(texts, 5)
-    audio_opt = []
-    if not ref_free:
-        phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
-        bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
-
-    for text in 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, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
-        bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
-        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)
-        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,
-                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,
-            )
-        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
-        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()
-    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
-    )
-
-
-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]
-    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]
-    return "\n".join(opts)
-
-
-def cut3(inp):
-    inp = inp.strip("\n")
-    return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
-
-
-def cut4(inp):
-    inp = inp.strip("\n")
-    return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
-
-
-# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
-def cut5(inp):
-    # if not re.search(r'[^\w\s]', inp[-1]):
-    # inp += '。'
-    inp = inp.strip("\n")
-    punds = r'[,.;?!、，。？！;：]'
-    items = re.split(f'({punds})', inp)
-    items = ["".join(group) for group in zip(items[::2], items[1::2])]
-    opt = "\n".join(items)
-    return 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 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"}
-
-
-pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
-pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
-SoVITS_weight_root = "SoVITS_weights"
-GPT_weight_root = "GPT_weights"
-os.makedirs(SoVITS_weight_root, exist_ok=True)
-os.makedirs(GPT_weight_root, exist_ok=True)
-
-
-def get_weights_names():
-    SoVITS_names = [pretrained_sovits_name]
-    for name in os.listdir(SoVITS_weight_root):
-        if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name))
-    GPT_names = [pretrained_gpt_name]
-    for name in os.listdir(GPT_weight_root):
-        if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name))
-    return SoVITS_names, GPT_names
-
-
-SoVITS_names, GPT_names = get_weights_names()
+is_share = os.environ.get("is_share", "False")
+is_share = eval(is_share)
+infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
+infer_ttswebui = int(infer_ttswebui)
 
 with gr.Blocks(title="GPT-SoVITS WebUI") as app:
     gr.Markdown(

GPT_SoVITS/interence_base.py

@@ -0,0 +1,602 @@
+'''
+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 os, re, logging
+import LangSegment
+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)
+import pdb
+
+if os.path.exists("./gweight.txt"):
+    with open("./gweight.txt", 'r', encoding="utf-8") as file:
+        gweight_data = file.read()
+        gpt_path = os.environ.get(
+            "gpt_path", gweight_data)
+else:
+    gpt_path = os.environ.get(
+        "gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
+
+if os.path.exists("./sweight.txt"):
+    with open("./sweight.txt", 'r', encoding="utf-8") as file:
+        sweight_data = file.read()
+        sovits_path = os.environ.get("sovits_path", sweight_data)
+else:
+    sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth")
+# gpt_path = os.environ.get(
+#     "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
+# )
+# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth")
+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"))
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import numpy as np
+import librosa, torch
+from feature_extractor import cnhubert
+
+cnhubert.cnhubert_base_path = cnhubert_base_path
+
+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 time import time as ttime
+from module.mel_processing import spectrogram_torch
+from my_utils import load_audio
+from tools.i18n.i18n import I18nAuto
+
+i18n = I18nAuto()
+
+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"
+elif torch.backends.mps.is_available():
+    device = "mps"
+else:
+    device = "cpu"
+
+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)
+
+
+def change_sovits_weights(sovits_path):
+    global vq_model, hps
+    dict_s2 = torch.load(sovits_path, map_location="cpu")
+    hps = dict_s2["config"]
+    hps = DictToAttrRecursive(hps)
+    hps.model.semantic_frame_rate = "25hz"
+    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 ("pretrained" not in sovits_path):
+        del vq_model.enc_q
+    if is_half == True:
+        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))
+    with open("./sweight.txt", "w", encoding="utf-8") as f:
+        f.write(sovits_path)
+
+
+change_sovits_weights(sovits_path)
+
+
+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("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
+
+
+change_gpt_weights(gpt_path)
+
+
+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 = {
+    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
+}
+
+
+def splite_en_inf(sentence, language):
+    pattern = re.compile(r'[a-zA-Z ]+')
+    textlist = []
+    langlist = []
+    pos = 0
+    for match in pattern.finditer(sentence):
+        start, end = match.span()
+        if start > pos:
+            textlist.append(sentence[pos:start])
+            langlist.append(language)
+        textlist.append(sentence[start:end])
+        langlist.append("en")
+        pos = end
+    if pos < len(sentence):
+        textlist.append(sentence[pos:])
+        langlist.append(language)
+    # Merge punctuation into previous word
+    for i in range(len(textlist)-1, 0, -1):
+        if re.match(r'^[\W_]+$', textlist[i]):
+            textlist[i-1] += textlist[i]
+            del textlist[i]
+            del langlist[i]
+    # Merge consecutive words with the same language tag
+    i = 0
+    while i < len(langlist) - 1:
+        if langlist[i] == langlist[i+1]:
+            textlist[i] += textlist[i+1]
+            del textlist[i+1]
+            del langlist[i+1]
+        else:
+            i += 1
+
+    return textlist, langlist
+
+
+def clean_text_inf(text, language):
+    formattext = ""
+    language = language.replace("all_","")
+    for tmp in LangSegment.getTexts(text):
+        if language == "ja":
+            if tmp["lang"] == language or tmp["lang"] == "zh":
+                formattext += tmp["text"] + " "
+            continue
+        if tmp["lang"] == language:
+            formattext += tmp["text"] + " "
+    while "  " in formattext:
+        formattext = formattext.replace("  ", " ")
+    phones, word2ph, norm_text = clean_text(formattext, language)
+    phones = cleaned_text_to_sequence(phones)
+    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
+
+
+def nonen_clean_text_inf(text, language):
+    if(language!="auto"):
+        textlist, langlist = splite_en_inf(text, language)
+    else:
+        textlist=[]
+        langlist=[]
+        for tmp in LangSegment.getTexts(text):
+            langlist.append(tmp["lang"])
+            textlist.append(tmp["text"])
+    phones_list = []
+    word2ph_list = []
+    norm_text_list = []
+    for i in range(len(textlist)):
+        lang = langlist[i]
+        phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
+        phones_list.append(phones)
+        if lang == "zh":
+            word2ph_list.append(word2ph)
+        norm_text_list.append(norm_text)
+    print(word2ph_list)
+    phones = sum(phones_list, [])
+    word2ph = sum(word2ph_list, [])
+    norm_text = ' '.join(norm_text_list)
+
+    return phones, word2ph, norm_text
+
+
+def nonen_get_bert_inf(text, language):
+    if(language!="auto"):
+        textlist, langlist = splite_en_inf(text, language)
+    else:
+        textlist=[]
+        langlist=[]
+        for tmp in LangSegment.getTexts(text):
+            langlist.append(tmp["lang"])
+            textlist.append(tmp["text"])
+    print(textlist)
+    print(langlist)
+    bert_list = []
+    for i in range(len(textlist)):
+        lang = langlist[i]
+        phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
+        bert = get_bert_inf(phones, word2ph, norm_text, lang)
+        bert_list.append(bert)
+    bert = torch.cat(bert_list, dim=1)
+
+    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
+
+
+def get_cleaned_text_final(text,language):
+    if language in {"en","all_zh","all_ja"}:
+        phones, word2ph, norm_text = clean_text_inf(text, language)
+    elif language in {"zh", "ja","auto"}:
+        phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
+    return phones, word2ph, norm_text
+
+def get_bert_final(phones, word2ph, text,language,device):
+    if language == "en":
+        bert = get_bert_inf(phones, word2ph, text, language)
+    elif language in {"zh", "ja","auto"}:
+        bert = nonen_get_bert_inf(text, language)
+    elif language == "all_zh":
+        bert = get_bert_feature(text, word2ph).to(device)
+    else:
+        bert = torch.zeros((1024, len(phones))).to(device)
+    return bert
+
+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
+
+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):
+    if prompt_text is None or len(prompt_text) == 0:
+        ref_free = True
+    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,
+    )
+    with torch.no_grad():
+        wav16k, sr = librosa.load(ref_wav_path, sr=16000)
+        if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
+            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]
+    t1 = ttime()
+
+    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 = merge_short_text_in_array(texts, 5)
+    audio_opt = []
+    if not ref_free:
+        phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
+        bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
+
+    for text in 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, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
+        bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
+        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)
+        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,
+                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,
+            )
+        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
+        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()
+    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
+    )
+
+
+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]
+    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]
+    return "\n".join(opts)
+
+
+def cut3(inp):
+    inp = inp.strip("\n")
+    return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
+
+
+def cut4(inp):
+    inp = inp.strip("\n")
+    return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
+
+
+# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
+def cut5(inp):
+    # if not re.search(r'[^\w\s]', inp[-1]):
+    # inp += '。'
+    inp = inp.strip("\n")
+    punds = r'[,.;?!、，。？！;：]'
+    items = re.split(f'({punds})', inp)
+    items = ["".join(group) for group in zip(items[::2], items[1::2])]
+    opt = "\n".join(items)
+    return 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 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"}
+
+
+pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
+pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
+SoVITS_weight_root = "SoVITS_weights"
+GPT_weight_root = "GPT_weights"
+os.makedirs(SoVITS_weight_root, exist_ok=True)
+os.makedirs(GPT_weight_root, exist_ok=True)
+
+
+def get_weights_names():
+    SoVITS_names = [pretrained_sovits_name]
+    for name in os.listdir(SoVITS_weight_root):
+        if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name))
+    GPT_names = [pretrained_gpt_name]
+    for name in os.listdir(GPT_weight_root):
+        if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name))
+    return SoVITS_names, GPT_names
+
+
+SoVITS_names, GPT_names = get_weights_names()

train_base.py

@@ -0,0 +1,626 @@
+import os,shutil,sys,pdb,re
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+import json,yaml,warnings,torch
+import platform
+import psutil
+import signal
+
+warnings.filterwarnings("ignore")
+torch.manual_seed(233333)
+tmp = os.path.join(now_dir, "TEMP")
+os.makedirs(tmp, exist_ok=True)
+os.environ["TEMP"] = tmp
+if(os.path.exists(tmp)):
+    for name in os.listdir(tmp):
+        if(name=="jieba.cache"):continue
+        path="%s/%s"%(tmp,name)
+        delete=os.remove if os.path.isfile(path) else shutil.rmtree
+        try:
+            delete(path)
+        except Exception as e:
+            print(str(e))
+            pass
+import site
+site_packages_roots = []
+for path in site.getsitepackages():
+    if "packages" in path:
+        site_packages_roots.append(path)
+if(site_packages_roots==[]):site_packages_roots=["%s/runtime/Lib/site-packages" % now_dir]
+#os.environ["OPENBLAS_NUM_THREADS"] = "4"
+os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
+os.environ["all_proxy"] = ""
+for site_packages_root in site_packages_roots:
+    if os.path.exists(site_packages_root):
+        try:
+            with open("%s/users.pth" % (site_packages_root), "w") as f:
+                f.write(
+                    "%s\n%s/tools\n%s/tools/damo_asr\n%s/GPT_SoVITS\n%s/tools/uvr5"
+                    % (now_dir, now_dir, now_dir, now_dir, now_dir)
+                )
+            break
+        except PermissionError:
+            pass
+from tools import my_utils
+import traceback
+import shutil
+import pdb
+from subprocess import Popen
+import signal
+from config import python_exec,infer_device,is_half,exp_root,webui_port_main,webui_port_infer_tts,webui_port_uvr5,webui_port_subfix,is_share
+from tools.i18n.i18n import I18nAuto
+i18n = I18nAuto()
+from scipy.io import wavfile
+from tools.my_utils import load_audio
+from multiprocessing import cpu_count
+
+os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 当遇到mps不支持的步骤时使用cpu
+
+n_cpu=cpu_count()
+           
+ngpu = torch.cuda.device_count()
+gpu_infos = []
+mem = []
+if_gpu_ok = False
+
+# 判断是否有能用来训练和加速推理的N卡
+if torch.cuda.is_available() or ngpu != 0:
+    for i in range(ngpu):
+        gpu_name = torch.cuda.get_device_name(i)
+        if any(value in gpu_name.upper()for value in ["10","16","20","30","40","A2","A3","A4","P4","A50","500","A60","70","80","90","M4","T4","TITAN","L4","4060"]):
+            # A10#A100#V100#A40#P40#M40#K80#A4500
+            if_gpu_ok = True  # 至少有一张能用的N卡
+            gpu_infos.append("%s\t%s" % (i, gpu_name))
+            mem.append(int(torch.cuda.get_device_properties(i).total_memory/ 1024/ 1024/ 1024+ 0.4))
+# 判断是否支持mps加速
+if torch.backends.mps.is_available():
+    if_gpu_ok = True
+    gpu_infos.append("%s\t%s" % ("0", "Apple GPU"))
+    mem.append(psutil.virtual_memory().total/ 1024 / 1024 / 1024) # 实测使用系统内存作为显存不会爆显存
+
+if if_gpu_ok and len(gpu_infos) > 0:
+    gpu_info = "\n".join(gpu_infos)
+    default_batch_size = min(mem) // 2
+else:
+    gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
+    default_batch_size = 1
+gpus = "-".join([i[0] for i in gpu_infos])
+
+pretrained_sovits_name="GPT_SoVITS/pretrained_models/s2G488k.pth"
+pretrained_gpt_name="GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
+def get_weights_names():
+    SoVITS_names = [pretrained_sovits_name]
+    for name in os.listdir(SoVITS_weight_root):
+        if name.endswith(".pth"):SoVITS_names.append(name)
+    GPT_names = [pretrained_gpt_name]
+    for name in os.listdir(GPT_weight_root):
+        if name.endswith(".ckpt"): GPT_names.append(name)
+    return SoVITS_names,GPT_names
+SoVITS_weight_root="SoVITS_weights"
+GPT_weight_root="GPT_weights"
+os.makedirs(SoVITS_weight_root,exist_ok=True)
+os.makedirs(GPT_weight_root,exist_ok=True)
+SoVITS_names,GPT_names = get_weights_names()
+
+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
+p_asr=None
+p_tts_inference=None
+
+def kill_proc_tree(pid, including_parent=True):  
+    try:
+        parent = psutil.Process(pid)
+    except psutil.NoSuchProcess:
+        # Process already terminated
+        return
+
+    children = parent.children(recursive=True)
+    for child in children:
+        try:
+            os.kill(child.pid, signal.SIGTERM)  # or signal.SIGKILL
+        except OSError:
+            pass
+    if including_parent:
+        try:
+            os.kill(parent.pid, signal.SIGTERM)  # or signal.SIGKILL
+        except OSError:
+            pass
+
+system=platform.system()
+def kill_process(pid):
+    if(system=="Windows"):
+        cmd = "taskkill /t /f /pid %s" % pid
+        os.system(cmd)
+    else:
+        kill_proc_tree(pid)
+    
+
+def change_label(if_label,path_list):
+    global p_label
+    if(if_label==True and p_label==None):
+        path_list=my_utils.clean_path(path_list)
+        cmd = '"%s" tools/subfix_webui.py --load_list "%s" --webui_port %s --is_share %s'%(python_exec,path_list,webui_port_subfix,is_share)
+        yield i18n("打标工具WebUI已开启")
+        print(cmd)
+        p_label = Popen(cmd, shell=True)
+    elif(if_label==False and p_label!=None):
+        kill_process(p_label.pid)
+        p_label=None
+        yield i18n("打标工具WebUI已关闭")
+
+def change_uvr5(if_uvr5):
+    global p_uvr5
+    if(if_uvr5==True and p_uvr5==None):
+        cmd = '"%s" tools/uvr5/webui.py "%s" %s %s %s'%(python_exec,infer_device,is_half,webui_port_uvr5,is_share)
+        yield i18n("UVR5已开启")
+        print(cmd)
+        p_uvr5 = Popen(cmd, shell=True)
+    elif(if_uvr5==False and p_uvr5!=None):
+        kill_process(p_uvr5.pid)
+        p_uvr5=None
+        yield i18n("UVR5已关闭")
+
+
+from tools.asr.config import asr_dict
+def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang):
+    global p_asr
+    if(p_asr==None):
+        asr_inp_dir=my_utils.clean_path(asr_inp_dir)
+        cmd = f'"{python_exec}" tools/asr/{asr_dict[asr_model]["path"]}'
+        cmd += f' -i "{asr_inp_dir}"'
+        cmd += f' -o "{asr_opt_dir}"'
+        cmd += f' -s {asr_model_size}'
+        cmd += f' -l {asr_lang}'
+        cmd += " -p %s"%("float16"if is_half==True else "float32")
+
+        yield "ASR任务开启：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        print(cmd)
+        p_asr = Popen(cmd, shell=True)
+        p_asr.wait()
+        p_asr=None
+        yield f"ASR任务完成, 查看终端进行下一步",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的ASR任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        # return None
+
+def close_asr():
+    global p_asr
+    if(p_asr!=None):
+        kill_process(p_asr.pid)
+        p_asr=None
+    return "已终止ASR进程",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+
+p_train_SoVITS=None
+def open1Ba(batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers1Ba,pretrained_s2G,pretrained_s2D):
+    global p_train_SoVITS
+    if(p_train_SoVITS==None):
+        with open("GPT_SoVITS/configs/s2.json")as f:
+            data=f.read()
+            data=json.loads(data)
+        s2_dir="%s/%s"%(exp_root,exp_name)
+        os.makedirs("%s/logs_s2"%(s2_dir),exist_ok=True)
+        if(is_half==False):
+            data["train"]["fp16_run"]=False
+            batch_size=max(1,batch_size//2)
+        data["train"]["batch_size"]=batch_size
+        data["train"]["epochs"]=total_epoch
+        data["train"]["text_low_lr_rate"]=text_low_lr_rate
+        data["train"]["pretrained_s2G"]=pretrained_s2G
+        data["train"]["pretrained_s2D"]=pretrained_s2D
+        data["train"]["if_save_latest"]=if_save_latest
+        data["train"]["if_save_every_weights"]=if_save_every_weights
+        data["train"]["save_every_epoch"]=save_every_epoch
+        data["train"]["gpu_numbers"]=gpu_numbers1Ba
+        data["data"]["exp_dir"]=data["s2_ckpt_dir"]=s2_dir
+        data["save_weight_dir"]=SoVITS_weight_root
+        data["name"]=exp_name
+        tmp_config_path="%s/tmp_s2.json"%tmp
+        with open(tmp_config_path,"w")as f:f.write(json.dumps(data))
+
+        cmd = '"%s" GPT_SoVITS/s2_train.py --config "%s"'%(python_exec,tmp_config_path)
+        yield "SoVITS训练开始：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        print(cmd)
+        p_train_SoVITS = Popen(cmd, shell=True)
+        p_train_SoVITS.wait()
+        p_train_SoVITS=None
+        yield "SoVITS训练完成",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的SoVITS训练任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+
+def close1Ba():
+    global p_train_SoVITS
+    if(p_train_SoVITS!=None):
+        kill_process(p_train_SoVITS.pid)
+        p_train_SoVITS=None
+    return "已终止SoVITS训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+
+p_train_GPT=None
+def open1Bb(batch_size,total_epoch,exp_name,if_dpo,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers,pretrained_s1):
+    global p_train_GPT
+    if(p_train_GPT==None):
+        with open("GPT_SoVITS/configs/s1longer.yaml")as f:
+            data=f.read()
+            data=yaml.load(data, Loader=yaml.FullLoader)
+        s1_dir="%s/%s"%(exp_root,exp_name)
+        os.makedirs("%s/logs_s1"%(s1_dir),exist_ok=True)
+        if(is_half==False):
+            data["train"]["precision"]="32"
+            batch_size = max(1, batch_size // 2)
+        data["train"]["batch_size"]=batch_size
+        data["train"]["epochs"]=total_epoch
+        data["pretrained_s1"]=pretrained_s1
+        data["train"]["save_every_n_epoch"]=save_every_epoch
+        data["train"]["if_save_every_weights"]=if_save_every_weights
+        data["train"]["if_save_latest"]=if_save_latest
+        data["train"]["if_dpo"]=if_dpo
+        data["train"]["half_weights_save_dir"]=GPT_weight_root
+        data["train"]["exp_name"]=exp_name
+        data["train_semantic_path"]="%s/6-name2semantic.tsv"%s1_dir
+        data["train_phoneme_path"]="%s/2-name2text.txt"%s1_dir
+        data["output_dir"]="%s/logs_s1"%s1_dir
+
+        os.environ["_CUDA_VISIBLE_DEVICES"]=gpu_numbers.replace("-",",")
+        os.environ["hz"]="25hz"
+        tmp_config_path="%s/tmp_s1.yaml"%tmp
+        with open(tmp_config_path, "w") as f:f.write(yaml.dump(data, default_flow_style=False))
+        # cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" --train_semantic_path "%s/6-name2semantic.tsv" --train_phoneme_path "%s/2-name2text.txt" --output_dir "%s/logs_s1"'%(python_exec,tmp_config_path,s1_dir,s1_dir,s1_dir)
+        cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" '%(python_exec,tmp_config_path)
+        yield "GPT训练开始：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        print(cmd)
+        p_train_GPT = Popen(cmd, shell=True)
+        p_train_GPT.wait()
+        p_train_GPT=None
+        yield "GPT训练完成",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的GPT训练任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+
+def close1Bb():
+    global p_train_GPT
+    if(p_train_GPT!=None):
+        kill_process(p_train_GPT.pid)
+        p_train_GPT=None
+    return "已终止GPT训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+
+ps_slice=[]
+def open_slice(inp,opt_root,threshold,min_length,min_interval,hop_size,max_sil_kept,_max,alpha,n_parts):
+    global ps_slice
+    inp = my_utils.clean_path(inp)
+    opt_root = my_utils.clean_path(opt_root)
+    if(os.path.exists(inp)==False):
+        yield "输入路径不存在",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+        return
+    if os.path.isfile(inp):n_parts=1
+    elif os.path.isdir(inp):pass
+    else:
+        yield "输入路径存在但既不是文件也不是文件夹",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+        return
+    if (ps_slice == []):
+        for i_part in range(n_parts):
+            cmd = '"%s" tools/slice_audio.py "%s" "%s" %s %s %s %s %s %s %s %s %s''' % (python_exec,inp, opt_root, threshold, min_length, min_interval, hop_size, max_sil_kept, _max, alpha, i_part, n_parts)
+            print(cmd)
+            p = Popen(cmd, shell=True)
+            ps_slice.append(p)
+        yield "切割执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+        for p in ps_slice:
+            p.wait()
+        ps_slice=[]
+        yield "切割结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的切割任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+
+def close_slice():
+    global ps_slice
+    if (ps_slice != []):
+        for p_slice in ps_slice:
+            try:
+                kill_process(p_slice.pid)
+            except:
+                traceback.print_exc()
+        ps_slice=[]
+    return "已终止所有切割进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
+
+ps1a=[]
+def open1a(inp_text,inp_wav_dir,exp_name,gpu_numbers,bert_pretrained_dir):
+    global ps1a
+    inp_text = my_utils.clean_path(inp_text)
+    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
+    if (ps1a == []):
+        opt_dir="%s/%s"%(exp_root,exp_name)
+        config={
+            "inp_text":inp_text,
+            "inp_wav_dir":inp_wav_dir,
+            "exp_name":exp_name,
+            "opt_dir":opt_dir,
+            "bert_pretrained_dir":bert_pretrained_dir,
+        }
+        gpu_names=gpu_numbers.split("-")
+        all_parts=len(gpu_names)
+        for i_part in range(all_parts):
+            config.update(
+                {
+                    "i_part": str(i_part),
+                    "all_parts": str(all_parts),
+                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
+                    "is_half": str(is_half)
+                }
+            )
+            os.environ.update(config)
+            cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py'%python_exec
+            print(cmd)
+            p = Popen(cmd, shell=True)
+            ps1a.append(p)
+        yield "文本进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+        for p in ps1a:
+            p.wait()
+        opt = []
+        for i_part in range(all_parts):
+            txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
+            with open(txt_path, "r", encoding="utf8") as f:
+                opt += f.read().strip("\n").split("\n")
+            os.remove(txt_path)
+        path_text = "%s/2-name2text.txt" % opt_dir
+        with open(path_text, "w", encoding="utf8") as f:
+            f.write("\n".join(opt) + "\n")
+        ps1a=[]
+        yield "文本进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的文本任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+
+def close1a():
+    global ps1a
+    if (ps1a != []):
+        for p1a in ps1a:
+            try:
+                kill_process(p1a.pid)
+            except:
+                traceback.print_exc()
+        ps1a=[]
+    return "已终止所有1a进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
+
+ps1b=[]
+def open1b(inp_text,inp_wav_dir,exp_name,gpu_numbers,ssl_pretrained_dir):
+    global ps1b
+    inp_text = my_utils.clean_path(inp_text)
+    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
+    if (ps1b == []):
+        config={
+            "inp_text":inp_text,
+            "inp_wav_dir":inp_wav_dir,
+            "exp_name":exp_name,
+            "opt_dir":"%s/%s"%(exp_root,exp_name),
+            "cnhubert_base_dir":ssl_pretrained_dir,
+            "is_half": str(is_half)
+        }
+        gpu_names=gpu_numbers.split("-")
+        all_parts=len(gpu_names)
+        for i_part in range(all_parts):
+            config.update(
+                {
+                    "i_part": str(i_part),
+                    "all_parts": str(all_parts),
+                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
+                }
+            )
+            os.environ.update(config)
+            cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'%python_exec
+            print(cmd)
+            p = Popen(cmd, shell=True)
+            ps1b.append(p)
+        yield "SSL提取进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+        for p in ps1b:
+            p.wait()
+        ps1b=[]
+        yield "SSL提取进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的SSL提取任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+
+def close1b():
+    global ps1b
+    if (ps1b != []):
+        for p1b in ps1b:
+            try:
+                kill_process(p1b.pid)
+            except:
+                traceback.print_exc()
+        ps1b=[]
+    return "已终止所有1b进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
+
+ps1c=[]
+def open1c(inp_text,exp_name,gpu_numbers,pretrained_s2G_path):
+    global ps1c
+    inp_text = my_utils.clean_path(inp_text)
+    if (ps1c == []):
+        opt_dir="%s/%s"%(exp_root,exp_name)
+        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",
+            "is_half": str(is_half)
+        }
+        gpu_names=gpu_numbers.split("-")
+        all_parts=len(gpu_names)
+        for i_part in range(all_parts):
+            config.update(
+                {
+                    "i_part": str(i_part),
+                    "all_parts": str(all_parts),
+                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
+                }
+            )
+            os.environ.update(config)
+            cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py'%python_exec
+            print(cmd)
+            p = Popen(cmd, shell=True)
+            ps1c.append(p)
+        yield "语义token提取进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+        for p in ps1c:
+            p.wait()
+        opt = ["item_name\tsemantic_audio"]
+        path_semantic = "%s/6-name2semantic.tsv" % opt_dir
+        for i_part in range(all_parts):
+            semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
+            with open(semantic_path, "r", encoding="utf8") as f:
+                opt += f.read().strip("\n").split("\n")
+            os.remove(semantic_path)
+        with open(path_semantic, "w", encoding="utf8") as f:
+            f.write("\n".join(opt) + "\n")
+        ps1c=[]
+        yield "语义token提取进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的语义token提取任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+
+def close1c():
+    global ps1c
+    if (ps1c != []):
+        for p1c in ps1c:
+            try:
+                kill_process(p1c.pid)
+            except:
+                traceback.print_exc()
+        ps1c=[]
+    return "已终止所有语义token进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
+#####inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numbers1c,bert_pretrained_dir,cnhubert_base_dir,pretrained_s2G
+ps1abc=[]
+def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numbers1c,bert_pretrained_dir,ssl_pretrained_dir,pretrained_s2G_path):
+    global ps1abc
+    inp_text = my_utils.clean_path(inp_text)
+    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
+    if (ps1abc == []):
+        opt_dir="%s/%s"%(exp_root,exp_name)
+        try:
+            #############################1a
+            path_text="%s/2-name2text.txt" % opt_dir
+            if(os.path.exists(path_text)==False or (os.path.exists(path_text)==True and len(open(path_text,"r",encoding="utf8").read().strip("\n").split("\n"))<2)):
+                config={
+                    "inp_text":inp_text,
+                    "inp_wav_dir":inp_wav_dir,
+                    "exp_name":exp_name,
+                    "opt_dir":opt_dir,
+                    "bert_pretrained_dir":bert_pretrained_dir,
+                    "is_half": str(is_half)
+                }
+                gpu_names=gpu_numbers1a.split("-")
+                all_parts=len(gpu_names)
+                for i_part in range(all_parts):
+                    config.update(
+                        {
+                            "i_part": str(i_part),
+                            "all_parts": str(all_parts),
+                            "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
+                        }
+                    )
+                    os.environ.update(config)
+                    cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py'%python_exec
+                    print(cmd)
+                    p = Popen(cmd, shell=True)
+                    ps1abc.append(p)
+                yield "进度：1a-ing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+                for p in ps1abc:p.wait()
+
+                opt = []
+                for i_part in range(all_parts):#txt_path="%s/2-name2text-%s.txt"%(opt_dir,i_part)
+                    txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
+                    with open(txt_path, "r",encoding="utf8") as f:
+                        opt += f.read().strip("\n").split("\n")
+                    os.remove(txt_path)
+                with open(path_text, "w",encoding="utf8") as f:
+                    f.write("\n".join(opt) + "\n")
+
+            yield "进度：1a-done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+            ps1abc=[]
+            #############################1b
+            config={
+                "inp_text":inp_text,
+                "inp_wav_dir":inp_wav_dir,
+                "exp_name":exp_name,
+                "opt_dir":opt_dir,
+                "cnhubert_base_dir":ssl_pretrained_dir,
+            }
+            gpu_names=gpu_numbers1Ba.split("-")
+            all_parts=len(gpu_names)
+            for i_part in range(all_parts):
+                config.update(
+                    {
+                        "i_part": str(i_part),
+                        "all_parts": str(all_parts),
+                        "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
+                    }
+                )
+                os.environ.update(config)
+                cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'%python_exec
+                print(cmd)
+                p = Popen(cmd, shell=True)
+                ps1abc.append(p)
+            yield "进度：1a-done, 1b-ing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+            for p in ps1abc:p.wait()
+            yield "进度：1a1b-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={
+                    "inp_text":inp_text,
+                    "exp_name":exp_name,
+                    "opt_dir":opt_dir,
+                    "pretrained_s2G":pretrained_s2G_path,
+                    "s2config_path":"GPT_SoVITS/configs/s2.json",
+                }
+                gpu_names=gpu_numbers1c.split("-")
+                all_parts=len(gpu_names)
+                for i_part in range(all_parts):
+                    config.update(
+                        {
+                            "i_part": str(i_part),
+                            "all_parts": str(all_parts),
+                            "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
+                        }
+                    )
+                    os.environ.update(config)
+                    cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py'%python_exec
+                    print(cmd)
+                    p = Popen(cmd, shell=True)
+                    ps1abc.append(p)
+                yield "进度：1a1b-done, 1cing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+                for p in ps1abc:p.wait()
+
+                opt = ["item_name\tsemantic_audio"]
+                for i_part in range(all_parts):
+                    semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
+                    with open(semantic_path, "r",encoding="utf8") as f:
+                        opt += f.read().strip("\n").split("\n")
+                    os.remove(semantic_path)
+                with open(path_semantic, "w",encoding="utf8") as f:
+                    f.write("\n".join(opt) + "\n")
+                yield "进度：all-done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+            ps1abc = []
+            yield "一键三连进程结束", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
+        except:
+            traceback.print_exc()
+            close1abc()
+            yield "一键三连中途报错", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
+    else:
+        yield "已有正在进行的一键三连任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+
+def close1abc():
+    global ps1abc
+    if (ps1abc != []):
+        for p1abc in ps1abc:
+            try:
+                kill_process(p1abc.pid)
+            except:
+                traceback.print_exc()
+        ps1abc=[]
+    return "已终止所有一键三连进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}

webui.py

@@ -1,176 +1,12 @@
-import os,shutil,sys,pdb,re
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import json,yaml,warnings,torch
-import platform
-import psutil
-import signal
 
-warnings.filterwarnings("ignore")
-torch.manual_seed(233333)
-tmp = os.path.join(now_dir, "TEMP")
-os.makedirs(tmp, exist_ok=True)
-os.environ["TEMP"] = tmp
-if(os.path.exists(tmp)):
-    for name in os.listdir(tmp):
-        if(name=="jieba.cache"):continue
-        path="%s/%s"%(tmp,name)
-        delete=os.remove if os.path.isfile(path) else shutil.rmtree
-        try:
-            delete(path)
-        except Exception as e:
-            print(str(e))
-            pass
-import site
-site_packages_roots = []
-for path in site.getsitepackages():
-    if "packages" in path:
-        site_packages_roots.append(path)
-if(site_packages_roots==[]):site_packages_roots=["%s/runtime/Lib/site-packages" % now_dir]
-#os.environ["OPENBLAS_NUM_THREADS"] = "4"
-os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
-os.environ["all_proxy"] = ""
-for site_packages_root in site_packages_roots:
-    if os.path.exists(site_packages_root):
-        try:
-            with open("%s/users.pth" % (site_packages_root), "w") as f:
-                f.write(
-                    "%s\n%s/tools\n%s/tools/damo_asr\n%s/GPT_SoVITS\n%s/tools/uvr5"
-                    % (now_dir, now_dir, now_dir, now_dir, now_dir)
-                )
-            break
-        except PermissionError:
-            pass
-from tools import my_utils
-import traceback
-import shutil
-import pdb
+import os
 import gradio as gr
-from subprocess import Popen
-import signal
-from config import python_exec,infer_device,is_half,exp_root,webui_port_main,webui_port_infer_tts,webui_port_uvr5,webui_port_subfix,is_share
 from tools.i18n.i18n import I18nAuto
 i18n = I18nAuto()
-from scipy.io import wavfile
-from tools.my_utils import load_audio
-from multiprocessing import cpu_count
-
-os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 当遇到mps不支持的步骤时使用cpu
-
-n_cpu=cpu_count()
-           
-ngpu = torch.cuda.device_count()
-gpu_infos = []
-mem = []
-if_gpu_ok = False
-
-# 判断是否有能用来训练和加速推理的N卡
-if torch.cuda.is_available() or ngpu != 0:
-    for i in range(ngpu):
-        gpu_name = torch.cuda.get_device_name(i)
-        if any(value in gpu_name.upper()for value in ["10","16","20","30","40","A2","A3","A4","P4","A50","500","A60","70","80","90","M4","T4","TITAN","L4","4060"]):
-            # A10#A100#V100#A40#P40#M40#K80#A4500
-            if_gpu_ok = True  # 至少有一张能用的N卡
-            gpu_infos.append("%s\t%s" % (i, gpu_name))
-            mem.append(int(torch.cuda.get_device_properties(i).total_memory/ 1024/ 1024/ 1024+ 0.4))
-# 判断是否支持mps加速
-if torch.backends.mps.is_available():
-    if_gpu_ok = True
-    gpu_infos.append("%s\t%s" % ("0", "Apple GPU"))
-    mem.append(psutil.virtual_memory().total/ 1024 / 1024 / 1024) # 实测使用系统内存作为显存不会爆显存
-
-if if_gpu_ok and len(gpu_infos) > 0:
-    gpu_info = "\n".join(gpu_infos)
-    default_batch_size = min(mem) // 2
-else:
-    gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
-    default_batch_size = 1
-gpus = "-".join([i[0] for i in gpu_infos])
-
-pretrained_sovits_name="GPT_SoVITS/pretrained_models/s2G488k.pth"
-pretrained_gpt_name="GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
-def get_weights_names():
-    SoVITS_names = [pretrained_sovits_name]
-    for name in os.listdir(SoVITS_weight_root):
-        if name.endswith(".pth"):SoVITS_names.append(name)
-    GPT_names = [pretrained_gpt_name]
-    for name in os.listdir(GPT_weight_root):
-        if name.endswith(".ckpt"): GPT_names.append(name)
-    return SoVITS_names,GPT_names
-SoVITS_weight_root="SoVITS_weights"
-GPT_weight_root="GPT_weights"
-os.makedirs(SoVITS_weight_root,exist_ok=True)
-os.makedirs(GPT_weight_root,exist_ok=True)
-SoVITS_names,GPT_names = get_weights_names()
-
-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
-p_asr=None
-p_tts_inference=None
-
-def kill_proc_tree(pid, including_parent=True):  
-    try:
-        parent = psutil.Process(pid)
-    except psutil.NoSuchProcess:
-        # Process already terminated
-        return
-
-    children = parent.children(recursive=True)
-    for child in children:
-        try:
-            os.kill(child.pid, signal.SIGTERM)  # or signal.SIGKILL
-        except OSError:
-            pass
-    if including_parent:
-        try:
-            os.kill(parent.pid, signal.SIGTERM)  # or signal.SIGKILL
-        except OSError:
-            pass
-
-system=platform.system()
-def kill_process(pid):
-    if(system=="Windows"):
-        cmd = "taskkill /t /f /pid %s" % pid
-        os.system(cmd)
-    else:
-        kill_proc_tree(pid)
-    
-
-def change_label(if_label,path_list):
-    global p_label
-    if(if_label==True and p_label==None):
-        path_list=my_utils.clean_path(path_list)
-        cmd = '"%s" tools/subfix_webui.py --load_list "%s" --webui_port %s --is_share %s'%(python_exec,path_list,webui_port_subfix,is_share)
-        yield i18n("打标工具WebUI已开启")
-        print(cmd)
-        p_label = Popen(cmd, shell=True)
-    elif(if_label==False and p_label!=None):
-        kill_process(p_label.pid)
-        p_label=None
-        yield i18n("打标工具WebUI已关闭")
-
-def change_uvr5(if_uvr5):
-    global p_uvr5
-    if(if_uvr5==True and p_uvr5==None):
-        cmd = '"%s" tools/uvr5/webui.py "%s" %s %s %s'%(python_exec,infer_device,is_half,webui_port_uvr5,is_share)
-        yield i18n("UVR5已开启")
-        print(cmd)
-        p_uvr5 = Popen(cmd, shell=True)
-    elif(if_uvr5==False and p_uvr5!=None):
-        kill_process(p_uvr5.pid)
-        p_uvr5=None
-        yield i18n("UVR5已关闭")
+from train_base import gpu_info, n_cpu, SoVITS_names, pretrained_sovits_name, pretrained_gpt_name, custom_sort_key, GPT_names, default_batch_size, kill_process, SoVITS_weight_root, GPT_weight_root, change_choices, change_label, change_uvr5, open_asr, change_tts_inference, open1Ba, open1Bb, close1Bb, open_slice, close_asr, open1a, close1a, open1b, close1Ba, close_slice, close1b, open1c, close1c, open1abc, close1abc, gpus
+from tools.asr.config import asr_dict
+from config import python_exec,infer_device,is_half,exp_root,webui_port_main,webui_port_infer_tts,webui_port_uvr5,webui_port_subfix,is_share
+from subprocess import Popen
 
 def change_tts_inference(if_tts,bert_path,cnhubert_base_path,gpu_number,gpt_path,sovits_path):
     global p_tts_inference
@@ -192,459 +28,6 @@ def change_tts_inference(if_tts,bert_path,cnhubert_base_path,gpu_number,gpt_path
         p_tts_inference=None
         yield i18n("TTS推理进程已关闭")
 
-from tools.asr.config import asr_dict
-def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang):
-    global p_asr
-    if(p_asr==None):
-        asr_inp_dir=my_utils.clean_path(asr_inp_dir)
-        cmd = f'"{python_exec}" tools/asr/{asr_dict[asr_model]["path"]}'
-        cmd += f' -i "{asr_inp_dir}"'
-        cmd += f' -o "{asr_opt_dir}"'
-        cmd += f' -s {asr_model_size}'
-        cmd += f' -l {asr_lang}'
-        cmd += " -p %s"%("float16"if is_half==True else "float32")
-
-        yield "ASR任务开启：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
-        print(cmd)
-        p_asr = Popen(cmd, shell=True)
-        p_asr.wait()
-        p_asr=None
-        yield f"ASR任务完成, 查看终端进行下一步",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
-    else:
-        yield "已有正在进行的ASR任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
-        # return None
-
-def close_asr():
-    global p_asr
-    if(p_asr!=None):
-        kill_process(p_asr.pid)
-        p_asr=None
-    return "已终止ASR进程",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
-
-p_train_SoVITS=None
-def open1Ba(batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers1Ba,pretrained_s2G,pretrained_s2D):
-    global p_train_SoVITS
-    if(p_train_SoVITS==None):
-        with open("GPT_SoVITS/configs/s2.json")as f:
-            data=f.read()
-            data=json.loads(data)
-        s2_dir="%s/%s"%(exp_root,exp_name)
-        os.makedirs("%s/logs_s2"%(s2_dir),exist_ok=True)
-        if(is_half==False):
-            data["train"]["fp16_run"]=False
-            batch_size=max(1,batch_size//2)
-        data["train"]["batch_size"]=batch_size
-        data["train"]["epochs"]=total_epoch
-        data["train"]["text_low_lr_rate"]=text_low_lr_rate
-        data["train"]["pretrained_s2G"]=pretrained_s2G
-        data["train"]["pretrained_s2D"]=pretrained_s2D
-        data["train"]["if_save_latest"]=if_save_latest
-        data["train"]["if_save_every_weights"]=if_save_every_weights
-        data["train"]["save_every_epoch"]=save_every_epoch
-        data["train"]["gpu_numbers"]=gpu_numbers1Ba
-        data["data"]["exp_dir"]=data["s2_ckpt_dir"]=s2_dir
-        data["save_weight_dir"]=SoVITS_weight_root
-        data["name"]=exp_name
-        tmp_config_path="%s/tmp_s2.json"%tmp
-        with open(tmp_config_path,"w")as f:f.write(json.dumps(data))
-
-        cmd = '"%s" GPT_SoVITS/s2_train.py --config "%s"'%(python_exec,tmp_config_path)
-        yield "SoVITS训练开始：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
-        print(cmd)
-        p_train_SoVITS = Popen(cmd, shell=True)
-        p_train_SoVITS.wait()
-        p_train_SoVITS=None
-        yield "SoVITS训练完成",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
-    else:
-        yield "已有正在进行的SoVITS训练任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
-
-def close1Ba():
-    global p_train_SoVITS
-    if(p_train_SoVITS!=None):
-        kill_process(p_train_SoVITS.pid)
-        p_train_SoVITS=None
-    return "已终止SoVITS训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
-
-p_train_GPT=None
-def open1Bb(batch_size,total_epoch,exp_name,if_dpo,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers,pretrained_s1):
-    global p_train_GPT
-    if(p_train_GPT==None):
-        with open("GPT_SoVITS/configs/s1longer.yaml")as f:
-            data=f.read()
-            data=yaml.load(data, Loader=yaml.FullLoader)
-        s1_dir="%s/%s"%(exp_root,exp_name)
-        os.makedirs("%s/logs_s1"%(s1_dir),exist_ok=True)
-        if(is_half==False):
-            data["train"]["precision"]="32"
-            batch_size = max(1, batch_size // 2)
-        data["train"]["batch_size"]=batch_size
-        data["train"]["epochs"]=total_epoch
-        data["pretrained_s1"]=pretrained_s1
-        data["train"]["save_every_n_epoch"]=save_every_epoch
-        data["train"]["if_save_every_weights"]=if_save_every_weights
-        data["train"]["if_save_latest"]=if_save_latest
-        data["train"]["if_dpo"]=if_dpo
-        data["train"]["half_weights_save_dir"]=GPT_weight_root
-        data["train"]["exp_name"]=exp_name
-        data["train_semantic_path"]="%s/6-name2semantic.tsv"%s1_dir
-        data["train_phoneme_path"]="%s/2-name2text.txt"%s1_dir
-        data["output_dir"]="%s/logs_s1"%s1_dir
-
-        os.environ["_CUDA_VISIBLE_DEVICES"]=gpu_numbers.replace("-",",")
-        os.environ["hz"]="25hz"
-        tmp_config_path="%s/tmp_s1.yaml"%tmp
-        with open(tmp_config_path, "w") as f:f.write(yaml.dump(data, default_flow_style=False))
-        # cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" --train_semantic_path "%s/6-name2semantic.tsv" --train_phoneme_path "%s/2-name2text.txt" --output_dir "%s/logs_s1"'%(python_exec,tmp_config_path,s1_dir,s1_dir,s1_dir)
-        cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" '%(python_exec,tmp_config_path)
-        yield "GPT训练开始：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
-        print(cmd)
-        p_train_GPT = Popen(cmd, shell=True)
-        p_train_GPT.wait()
-        p_train_GPT=None
-        yield "GPT训练完成",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
-    else:
-        yield "已有正在进行的GPT训练任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
-
-def close1Bb():
-    global p_train_GPT
-    if(p_train_GPT!=None):
-        kill_process(p_train_GPT.pid)
-        p_train_GPT=None
-    return "已终止GPT训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
-
-ps_slice=[]
-def open_slice(inp,opt_root,threshold,min_length,min_interval,hop_size,max_sil_kept,_max,alpha,n_parts):
-    global ps_slice
-    inp = my_utils.clean_path(inp)
-    opt_root = my_utils.clean_path(opt_root)
-    if(os.path.exists(inp)==False):
-        yield "输入路径不存在",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
-        return
-    if os.path.isfile(inp):n_parts=1
-    elif os.path.isdir(inp):pass
-    else:
-        yield "输入路径存在但既不是文件也不是文件夹",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
-        return
-    if (ps_slice == []):
-        for i_part in range(n_parts):
-            cmd = '"%s" tools/slice_audio.py "%s" "%s" %s %s %s %s %s %s %s %s %s''' % (python_exec,inp, opt_root, threshold, min_length, min_interval, hop_size, max_sil_kept, _max, alpha, i_part, n_parts)
-            print(cmd)
-            p = Popen(cmd, shell=True)
-            ps_slice.append(p)
-        yield "切割执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-        for p in ps_slice:
-            p.wait()
-        ps_slice=[]
-        yield "切割结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
-    else:
-        yield "已有正在进行的切割任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-
-def close_slice():
-    global ps_slice
-    if (ps_slice != []):
-        for p_slice in ps_slice:
-            try:
-                kill_process(p_slice.pid)
-            except:
-                traceback.print_exc()
-        ps_slice=[]
-    return "已终止所有切割进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
-
-ps1a=[]
-def open1a(inp_text,inp_wav_dir,exp_name,gpu_numbers,bert_pretrained_dir):
-    global ps1a
-    inp_text = my_utils.clean_path(inp_text)
-    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
-    if (ps1a == []):
-        opt_dir="%s/%s"%(exp_root,exp_name)
-        config={
-            "inp_text":inp_text,
-            "inp_wav_dir":inp_wav_dir,
-            "exp_name":exp_name,
-            "opt_dir":opt_dir,
-            "bert_pretrained_dir":bert_pretrained_dir,
-        }
-        gpu_names=gpu_numbers.split("-")
-        all_parts=len(gpu_names)
-        for i_part in range(all_parts):
-            config.update(
-                {
-                    "i_part": str(i_part),
-                    "all_parts": str(all_parts),
-                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
-                    "is_half": str(is_half)
-                }
-            )
-            os.environ.update(config)
-            cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py'%python_exec
-            print(cmd)
-            p = Popen(cmd, shell=True)
-            ps1a.append(p)
-        yield "文本进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-        for p in ps1a:
-            p.wait()
-        opt = []
-        for i_part in range(all_parts):
-            txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
-            with open(txt_path, "r", encoding="utf8") as f:
-                opt += f.read().strip("\n").split("\n")
-            os.remove(txt_path)
-        path_text = "%s/2-name2text.txt" % opt_dir
-        with open(path_text, "w", encoding="utf8") as f:
-            f.write("\n".join(opt) + "\n")
-        ps1a=[]
-        yield "文本进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
-    else:
-        yield "已有正在进行的文本任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-
-def close1a():
-    global ps1a
-    if (ps1a != []):
-        for p1a in ps1a:
-            try:
-                kill_process(p1a.pid)
-            except:
-                traceback.print_exc()
-        ps1a=[]
-    return "已终止所有1a进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
-
-ps1b=[]
-def open1b(inp_text,inp_wav_dir,exp_name,gpu_numbers,ssl_pretrained_dir):
-    global ps1b
-    inp_text = my_utils.clean_path(inp_text)
-    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
-    if (ps1b == []):
-        config={
-            "inp_text":inp_text,
-            "inp_wav_dir":inp_wav_dir,
-            "exp_name":exp_name,
-            "opt_dir":"%s/%s"%(exp_root,exp_name),
-            "cnhubert_base_dir":ssl_pretrained_dir,
-            "is_half": str(is_half)
-        }
-        gpu_names=gpu_numbers.split("-")
-        all_parts=len(gpu_names)
-        for i_part in range(all_parts):
-            config.update(
-                {
-                    "i_part": str(i_part),
-                    "all_parts": str(all_parts),
-                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
-                }
-            )
-            os.environ.update(config)
-            cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'%python_exec
-            print(cmd)
-            p = Popen(cmd, shell=True)
-            ps1b.append(p)
-        yield "SSL提取进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-        for p in ps1b:
-            p.wait()
-        ps1b=[]
-        yield "SSL提取进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
-    else:
-        yield "已有正在进行的SSL提取任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-
-def close1b():
-    global ps1b
-    if (ps1b != []):
-        for p1b in ps1b:
-            try:
-                kill_process(p1b.pid)
-            except:
-                traceback.print_exc()
-        ps1b=[]
-    return "已终止所有1b进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
-
-ps1c=[]
-def open1c(inp_text,exp_name,gpu_numbers,pretrained_s2G_path):
-    global ps1c
-    inp_text = my_utils.clean_path(inp_text)
-    if (ps1c == []):
-        opt_dir="%s/%s"%(exp_root,exp_name)
-        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",
-            "is_half": str(is_half)
-        }
-        gpu_names=gpu_numbers.split("-")
-        all_parts=len(gpu_names)
-        for i_part in range(all_parts):
-            config.update(
-                {
-                    "i_part": str(i_part),
-                    "all_parts": str(all_parts),
-                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
-                }
-            )
-            os.environ.update(config)
-            cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py'%python_exec
-            print(cmd)
-            p = Popen(cmd, shell=True)
-            ps1c.append(p)
-        yield "语义token提取进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-        for p in ps1c:
-            p.wait()
-        opt = ["item_name\tsemantic_audio"]
-        path_semantic = "%s/6-name2semantic.tsv" % opt_dir
-        for i_part in range(all_parts):
-            semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
-            with open(semantic_path, "r", encoding="utf8") as f:
-                opt += f.read().strip("\n").split("\n")
-            os.remove(semantic_path)
-        with open(path_semantic, "w", encoding="utf8") as f:
-            f.write("\n".join(opt) + "\n")
-        ps1c=[]
-        yield "语义token提取进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
-    else:
-        yield "已有正在进行的语义token提取任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-
-def close1c():
-    global ps1c
-    if (ps1c != []):
-        for p1c in ps1c:
-            try:
-                kill_process(p1c.pid)
-            except:
-                traceback.print_exc()
-        ps1c=[]
-    return "已终止所有语义token进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
-#####inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numbers1c,bert_pretrained_dir,cnhubert_base_dir,pretrained_s2G
-ps1abc=[]
-def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numbers1c,bert_pretrained_dir,ssl_pretrained_dir,pretrained_s2G_path):
-    global ps1abc
-    inp_text = my_utils.clean_path(inp_text)
-    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
-    if (ps1abc == []):
-        opt_dir="%s/%s"%(exp_root,exp_name)
-        try:
-            #############################1a
-            path_text="%s/2-name2text.txt" % opt_dir
-            if(os.path.exists(path_text)==False or (os.path.exists(path_text)==True and len(open(path_text,"r",encoding="utf8").read().strip("\n").split("\n"))<2)):
-                config={
-                    "inp_text":inp_text,
-                    "inp_wav_dir":inp_wav_dir,
-                    "exp_name":exp_name,
-                    "opt_dir":opt_dir,
-                    "bert_pretrained_dir":bert_pretrained_dir,
-                    "is_half": str(is_half)
-                }
-                gpu_names=gpu_numbers1a.split("-")
-                all_parts=len(gpu_names)
-                for i_part in range(all_parts):
-                    config.update(
-                        {
-                            "i_part": str(i_part),
-                            "all_parts": str(all_parts),
-                            "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
-                        }
-                    )
-                    os.environ.update(config)
-                    cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py'%python_exec
-                    print(cmd)
-                    p = Popen(cmd, shell=True)
-                    ps1abc.append(p)
-                yield "进度：1a-ing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-                for p in ps1abc:p.wait()
-
-                opt = []
-                for i_part in range(all_parts):#txt_path="%s/2-name2text-%s.txt"%(opt_dir,i_part)
-                    txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
-                    with open(txt_path, "r",encoding="utf8") as f:
-                        opt += f.read().strip("\n").split("\n")
-                    os.remove(txt_path)
-                with open(path_text, "w",encoding="utf8") as f:
-                    f.write("\n".join(opt) + "\n")
-
-            yield "进度：1a-done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-            ps1abc=[]
-            #############################1b
-            config={
-                "inp_text":inp_text,
-                "inp_wav_dir":inp_wav_dir,
-                "exp_name":exp_name,
-                "opt_dir":opt_dir,
-                "cnhubert_base_dir":ssl_pretrained_dir,
-            }
-            gpu_names=gpu_numbers1Ba.split("-")
-            all_parts=len(gpu_names)
-            for i_part in range(all_parts):
-                config.update(
-                    {
-                        "i_part": str(i_part),
-                        "all_parts": str(all_parts),
-                        "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
-                    }
-                )
-                os.environ.update(config)
-                cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'%python_exec
-                print(cmd)
-                p = Popen(cmd, shell=True)
-                ps1abc.append(p)
-            yield "进度：1a-done, 1b-ing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-            for p in ps1abc:p.wait()
-            yield "进度：1a1b-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={
-                    "inp_text":inp_text,
-                    "exp_name":exp_name,
-                    "opt_dir":opt_dir,
-                    "pretrained_s2G":pretrained_s2G_path,
-                    "s2config_path":"GPT_SoVITS/configs/s2.json",
-                }
-                gpu_names=gpu_numbers1c.split("-")
-                all_parts=len(gpu_names)
-                for i_part in range(all_parts):
-                    config.update(
-                        {
-                            "i_part": str(i_part),
-                            "all_parts": str(all_parts),
-                            "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
-                        }
-                    )
-                    os.environ.update(config)
-                    cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py'%python_exec
-                    print(cmd)
-                    p = Popen(cmd, shell=True)
-                    ps1abc.append(p)
-                yield "进度：1a1b-done, 1cing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-                for p in ps1abc:p.wait()
-
-                opt = ["item_name\tsemantic_audio"]
-                for i_part in range(all_parts):
-                    semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
-                    with open(semantic_path, "r",encoding="utf8") as f:
-                        opt += f.read().strip("\n").split("\n")
-                    os.remove(semantic_path)
-                with open(path_semantic, "w",encoding="utf8") as f:
-                    f.write("\n".join(opt) + "\n")
-                yield "进度：all-done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-            ps1abc = []
-            yield "一键三连进程结束", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
-        except:
-            traceback.print_exc()
-            close1abc()
-            yield "一键三连中途报错", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
-    else:
-        yield "已有正在进行的一键三连任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
-
-def close1abc():
-    global ps1abc
-    if (ps1abc != []):
-        for p1abc in ps1abc:
-            try:
-                kill_process(p1abc.pid)
-            except:
-                traceback.print_exc()
-        ps1abc=[]
-    return "已终止所有一键三连进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
-
 with gr.Blocks(title="GPT-SoVITS WebUI") as app:
     gr.Markdown(
         value=