From 6556af6feee5daf034f224ee6a82f7839c2e7dcc Mon Sep 17 00:00:00 2001
From: DD-MASTERT <15737567532@163.com>
Date: Sun, 21 Jul 2024 01:09:56 +0800
Subject: [PATCH 1/5] Add files via upload

---
 inference_webui.py | 858 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 858 insertions(+)
 create mode 100644 inference_webui.py

diff --git a/inference_webui.py b/inference_webui.py
new file mode 100644
index 00000000..39e1c9c3
--- /dev/null
+++ b/inference_webui.py
@@ -0,0 +1,858 @@
+'''
+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
+import torch
+import shutil
+from scipy.io import wavfile
+
+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")) and torch.cuda.is_available()
+punctuation = set(['!', '?', '…', ',', '.', '-'," "])
+import gradio as gr
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import numpy as np
+import librosa
+from feature_extractor import cnhubert
+
+cnhubert.cnhubert_base_path = cnhubert_base_path
+
+from 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 tools.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"
+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 clean_text_inf(text, language):
+    phones, word2ph, norm_text = clean_text(text, 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
+
+
+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_phones_and_bert(text,language):
+    if language in {"en","all_zh","all_ja"}:
+        language = language.replace("all_","")
+        if language == "en":
+            LangSegment.setfilters(["en"])
+            formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
+        else:
+            # 因无法区别中日文汉字,以用户输入为准
+            formattext = text
+        while "  " in formattext:
+            formattext = formattext.replace("  ", " ")
+        phones, word2ph, norm_text = clean_text_inf(formattext, language)
+        if language == "zh":
+            bert = get_bert_feature(norm_text, word2ph).to(device)
+        else:
+            bert = torch.zeros(
+                (1024, len(phones)),
+                dtype=torch.float16 if is_half == True else torch.float32,
+            ).to(device)
+    elif language in {"zh", "ja","auto"}:
+        textlist=[]
+        langlist=[]
+        LangSegment.setfilters(["zh","ja","en","ko"])
+        if language == "auto":
+            for tmp in LangSegment.getTexts(text):
+                if tmp["lang"] == "ko":
+                    langlist.append("zh")
+                    textlist.append(tmp["text"])
+                else:
+                    langlist.append(tmp["lang"])
+                    textlist.append(tmp["text"])
+        else:
+            for tmp in LangSegment.getTexts(text):
+                if tmp["lang"] == "en":
+                    langlist.append(tmp["lang"])
+                else:
+                    # 因无法区别中日文汉字,以用户输入为准
+                    langlist.append(language)
+                textlist.append(tmp["text"])
+        print(textlist)
+        print(langlist)
+        phones_list = []
+        bert_list = []
+        norm_text_list = []
+        for i in range(len(textlist)):
+            lang = langlist[i]
+            phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
+            bert = get_bert_inf(phones, word2ph, norm_text, lang)
+            phones_list.append(phones)
+            norm_text_list.append(norm_text)
+            bert_list.append(bert)
+        bert = torch.cat(bert_list, dim=1)
+        phones = sum(phones_list, [])
+        norm_text = ''.join(norm_text_list)
+
+    return phones,bert.to(dtype),norm_text
+
+
+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, interval=0.3, 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")
+    text = replace_consecutive_punctuation(text)
+    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 * interval),
+        dtype=np.float16 if is_half == True else np.float32,
+    )
+    if not ref_free:
+        with torch.no_grad():
+            wav16k, sr = librosa.load(ref_wav_path, sr=16000)
+            if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
+                raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
+            wav16k = torch.from_numpy(wav16k)
+            zero_wav_torch = torch.from_numpy(zero_wav)
+            if is_half == True:
+                wav16k = wav16k.half().to(device)
+                zero_wav_torch = zero_wav_torch.half().to(device)
+            else:
+                wav16k = wav16k.to(device)
+                zero_wav_torch = zero_wav_torch.to(device)
+            wav16k = torch.cat([wav16k, zero_wav_torch])
+            ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
+                "last_hidden_state"
+            ].transpose(
+                1, 2
+            )  # .float()
+            codes = vq_model.extract_latent(ssl_content)
+            prompt_semantic = codes[0, 0]
+            prompt = prompt_semantic.unsqueeze(0).to(device)
+
+    t1 = ttime()
+
+    if (how_to_cut == i18n("凑四句一切")):
+        text = cut1(text)
+    elif (how_to_cut == i18n("凑50字一切")):
+        text = cut2(text)
+    elif (how_to_cut == i18n("按中文句号。切")):
+        text = cut3(text)
+    elif (how_to_cut == i18n("按英文句号.切")):
+        text = cut4(text)
+    elif (how_to_cut == i18n("按标点符号切")):
+        text = cut5(text)
+    while "\n\n" in text:
+        text = text.replace("\n\n", "\n")
+    print(i18n("实际输入的目标文本(切句后):"), text)
+    texts = text.split("\n")
+    texts = process_text(texts)
+    texts = merge_short_text_in_array(texts, 5)
+    audio_opt = []
+    if not ref_free:
+        phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
+
+    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,bert2,norm_text2=get_phones_and_bert(text, text_language)
+        print(i18n("前端处理后的文本(每句):"), norm_text2)
+        if not ref_free:
+            bert = torch.cat([bert1, bert2], 1)
+            all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
+        else:
+            bert = bert2
+            all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
+
+        bert = bert.to(device).unsqueeze(0)
+        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
+
+        t2 = ttime()
+        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
+    )
+    # 指定保存音频的文件路径
+    file_path = 'moys/temp/audio.wav'
+
+    # 调用保存音频的函数
+    save_audio(hps.data.sampling_rate, np.concatenate(audio_opt, 0), file_path)
+
+# 保存音频数据到文件
+def save_audio(sampling_rate, audio_data, file_path):
+    # 确保音频数据是16位PCM格式
+    audio_data = np.int16(audio_data / np.max(np.abs(audio_data)) * 32767)
+    wavfile.write(file_path, sampling_rate, audio_data)
+
+def split(todo_text):
+    todo_text = todo_text.replace("……", "。").replace("——", "，")
+    if todo_text[-1] not in splits:
+        todo_text += "。"
+    i_split_head = i_split_tail = 0
+    len_text = len(todo_text)
+    todo_texts = []
+    while 1:
+        if i_split_head >= len_text:
+            break  # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
+        if todo_text[i_split_head] in splits:
+            i_split_head += 1
+            todo_texts.append(todo_text[i_split_tail:i_split_head])
+            i_split_tail = i_split_head
+        else:
+            i_split_head += 1
+    return todo_texts
+
+
+def cut1(inp):
+    inp = inp.strip("\n")
+    inps = split(inp)
+    split_idx = list(range(0, len(inps), 4))
+    split_idx[-1] = None
+    if len(split_idx) > 1:
+        opts = []
+        for idx in range(len(split_idx) - 1):
+            opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
+    else:
+        opts = [inp]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return "\n".join(opts)
+
+
+def cut2(inp):
+    inp = inp.strip("\n")
+    inps = split(inp)
+    if len(inps) < 2:
+        return inp
+    opts = []
+    summ = 0
+    tmp_str = ""
+    for i in range(len(inps)):
+        summ += len(inps[i])
+        tmp_str += inps[i]
+        if summ > 50:
+            summ = 0
+            opts.append(tmp_str)
+            tmp_str = ""
+    if tmp_str != "":
+        opts.append(tmp_str)
+    # print(opts)
+    if len(opts) > 1 and len(opts[-1]) < 50:  ##如果最后一个太短了，和前一个合一起
+        opts[-2] = opts[-2] + opts[-1]
+        opts = opts[:-1]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return "\n".join(opts)
+
+
+def cut3(inp):
+    inp = inp.strip("\n")
+    opts = ["%s" % item for item in inp.strip("。").split("。")]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return  "\n".join(opts)
+
+def cut4(inp):
+    inp = inp.strip("\n")
+    opts = ["%s" % item for item in inp.strip(".").split(".")]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return "\n".join(opts)
+
+
+# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
+def cut5(inp):
+    inp = inp.strip("\n")
+    punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
+    mergeitems = []
+    items = []
+
+    for i, char in enumerate(inp):
+        if char in punds:
+            if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
+                items.append(char)
+            else:
+                items.append(char)
+                mergeitems.append("".join(items))
+                items = []
+        else:
+            items.append(char)
+
+    if items:
+        mergeitems.append("".join(items))
+
+    opt = [item for item in mergeitems if not set(item).issubset(punds)]
+    return "\n".join(opt)
+
+
+def custom_sort_key(s):
+    # 使用正则表达式提取字符串中的数字部分和非数字部分
+    parts = re.split('(\d+)', s)
+    # 将数字部分转换为整数，非数字部分保持不变
+    parts = [int(part) if part.isdigit() else part for part in parts]
+    return parts
+
+def process_text(texts):
+    _text=[]
+    if all(text in [None, " ", "\n",""] for text in texts):
+        raise ValueError(i18n("请输入有效文本"))
+    for text in texts:
+        if text in  [None, " ", ""]:
+            pass
+        else:
+            _text.append(text)
+    return _text
+
+
+def replace_consecutive_punctuation(text):
+    punctuations = ''.join(re.escape(p) for p in punctuation)
+    pattern = f'([{punctuations}])([{punctuations}])+'
+    result = re.sub(pattern, r'\1', text)
+    return result
+
+
+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
+
+
+def save_model_config(GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language):
+    config_dir = "moys"
+    config_dir1 = r"moys\audio"
+    if not os.path.exists(config_dir):
+        os.makedirs(config_dir)
+    
+    # 复制参考音频文件到配置目录
+    copy_ref_audio_path = os.path.join(config_dir1, os.path.basename(inp_ref))
+    shutil.copy(inp_ref, copy_ref_audio_path)
+    
+    gpt_model_name = os.path.basename(GPT_dropdown).split('-')[0]
+    config_file_path = os.path.join(config_dir, f"{gpt_model_name}.txt")
+    
+    with open(config_file_path, 'w', encoding='utf-8') as f:
+        f.write(f"GPT_model_path={GPT_dropdown}\n")
+        f.write(f"SoVITS_model_path={SoVITS_dropdown}\n")
+        f.write(f"ref_audio_path={copy_ref_audio_path}\n")  # 修改写入的路径为复制文件的路径
+        f.write(f"ref_text={prompt_text}\n")
+        f.write(f"ref_audio_language={prompt_language}\n")
+    
+    return f"Configuration saved to {config_file_path}"
+
+def load_model_config(config_file_name):
+    config_dir = "moys"
+    # 因为 config_file_name 现在是字符串，我们直接使用它来构造文件路径
+    config_file_path = os.path.join(config_dir, config_file_name)
+    
+    with open(config_file_path, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+    
+    config = {}
+    for line in lines:
+        key, value = line.strip().split('=')
+        config[key] = value
+    
+    # 返回一个包含所有组件期望值的字典
+    return (
+        config["GPT_model_path"],
+        config["SoVITS_model_path"],
+        config["ref_audio_path"],
+        config["ref_text"],
+        config["ref_audio_language"]
+    )
+def refresh_config_files():
+    # 获取最新的配置文件列表
+    config_files = get_config_files()
+    # 创建一个新的文件名列表，只包含文件名
+    config_file_names = [os.path.basename(path) for path in config_files]
+    
+    # 返回一个更新的配置，告诉 Gradio 更新下拉菜单的选项
+    return {"choices": config_file_names, "__type__": "update"}
+
+
+
+    # 辅助函数，用于处理 load_model_config 函数的输出
+def handle_load_model_config(config_file_name, GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language):
+    # 调用原始函数获取配置
+    config = load_model_config(config_file_name)
+    
+    # 更新组件的值
+    GPT_dropdown.update(value=config.get("GPT_model_path"))
+    SoVITS_dropdown.update(value=config.get("SoVITS_model_path"))
+    inp_ref.update(value=config.get("ref_audio_path"))
+    prompt_text.update(value=config.get("ref_text"))
+    prompt_language.update(value=config.get("ref_audio_language"))
+
+def get_config_files():
+    config_dir = "moys"
+    if not os.path.exists(config_dir):
+        return []
+    
+    return [os.path.join(config_dir, f) for f in os.listdir(config_dir) if f.endswith('.txt')]
+
+def echo(input_text):
+    # 直接返回输入的文本
+    return input_text
+
+
+def find_latest_wav(source_dir, dest_dir):
+    # 确保目标文件夹存在
+    if not os.path.exists(dest_dir):
+        os.makedirs(dest_dir)
+
+    # 初始化找到的wav文件路径
+    wav_file_path = None
+
+    # 遍历源文件夹
+    for root, dirs, files in os.walk(source_dir):
+        for file in files:
+            if file.lower().endswith('.wav'):
+                wav_file_path = os.path.join(root, file)
+                # 找到第一个wav文件就退出循环
+                break
+        if wav_file_path:
+            break  # 确保找到文件后不再继续遍历
+
+    # 如果找到了wav文件，复制到目标文件夹
+    if wav_file_path:
+        base_name = os.path.basename(wav_file_path)
+        file_name, file_ext = os.path.splitext(base_name)
+        dest_file_path = os.path.join(dest_dir, base_name)
+        
+       # 检查目标文件夹中是否存在同名文件，并添加后缀以避免覆盖
+        counter = 1
+        while os.path.exists(dest_file_path):
+            new_name = f"{file_name}({counter}){file_ext}"
+            dest_file_path = os.path.join(dest_dir, new_name)
+            counter += 1
+        # 复制文件
+        shutil.copy2(wav_file_path, dest_file_path)
+        print(f"Copied WAV file to {dest_file_path}")
+        return dest_file_path  # 返回复制的文件路径
+    else:
+        print("No WAV files found.")
+        return None  # 没有找到 WAV 文件时返回 None
+
+
+
+
+def on_download_click(textq_value):
+    source_directory = r'moys/temp'  # 源文件夹路径
+    destination_directory = textq_value
+   # outputs.update_value(f"开始查找最新的WAV文件...")
+
+    result = find_latest_wav(source_directory, destination_directory)  # 调用函数
+  #  outputs.update_value(f"已保存到: {destination_directory}")
+    return f"{result}已保存到: {destination_directory}"
+
+
+SoVITS_names, GPT_names = get_weights_names()
+
+with gr.Blocks(title="GPT-SoVITS WebUI") as app:
+    gr.Markdown(
+        value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
+    )
+    with gr.Group():
+        gr.Markdown(value=i18n("模型切换"))
+        with gr.Row():
+            GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True)
+            SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
+            refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
+            refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
+            SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
+            GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
+        gr.Markdown(value=i18n("*请上传并填写参考信息"))
+        with gr.Row():
+            inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
+            with gr.Column():
+                ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True)
+                gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。"))
+                prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
+            prompt_language = gr.Dropdown(
+                label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
+            )
+        gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
+        with gr.Row():
+            text = gr.Textbox(label=i18n("需要合成的文本"), value="")
+            text_language = gr.Dropdown(
+                label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
+            )
+            how_to_cut = gr.Radio(
+                label=i18n("怎么切"),
+                choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
+                value=i18n("凑四句一切"),
+                interactive=True,
+            )
+            with gr.Row():
+                gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)："))
+                top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
+                top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
+                temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
+                interval = gr.Slider(minimum=0,maximum=5,step=0.02,label=i18n("interval"),value=0.3,interactive=True)                
+            inference_button = gr.Button(i18n("合成语音"), variant="primary")
+            output = gr.Audio(label=i18n("输出的语音"))
+
+            with gr.Row():
+                # 创建文本框和下载按钮
+                download_button = gr.Button("下载语音", variant="primary")
+                textq = gr.Textbox(label="保存的语音路径", value="")
+                outputs0 = gr.Textbox(label=i18n("保存状态"), value="", interactive=False)
+                # 将事件处理函数绑定到按钮的点击事件
+                download_button.click(
+                    on_download_click, 
+                    inputs=[textq],  # 这里确保 textq 是正确的组件引用
+                    outputs=[outputs0]  # 这里确保 outputs 是正确的组件引用
+                )
+
+        inference_button.click(
+            get_tts_wav,
+            [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, interval, ref_text_free],
+            [output],
+        )
+        # Add new UI elements for saving and loading configurations
+        with gr.Row():
+
+
+            # 初始加载配置文件列表
+            config_files = get_config_files()
+            # 创建一个新的列表，只包含文件名
+            config_file_names = [os.path.basename(path) for path in config_files]
+
+            # 使用文件名列表作为 Dropdown 组件的选项
+            config_dropdown = gr.Dropdown(
+                label=i18n("加载模型配置"),
+                choices=config_file_names,
+                value=config_file_names[0] if config_file_names else None
+            )
+
+            # Output textbox for displaying save confirmation
+            save_output = gr.Textbox(label=i18n("保存配置状态"), value="", interactive=False)
+
+            # 绑定刷新按钮的点击事件
+            refresh_button = gr.Button(i18n("刷新配置文件列表"), variant="primary")
+            refresh_button.click(
+                fn=refresh_config_files,  # 使用新创建的 refresh_config_files 函数
+                inputs=[],  # 刷新按钮不需要输入
+                outputs=[config_dropdown]  # 指定输出为 config_dropdown 组件，以更新其选项
+            )
+
+            # 绑定保存按钮的点击事件
+            save_button = gr.Button(i18n("保存模型配置"), variant="primary")
+            save_button.click(
+                fn=save_model_config,
+                inputs=[GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language],
+                outputs=[save_output]
+            )
+
+            # 绑定加载按钮的点击事件
+            load_button = gr.Button(i18n("加载模型配置"), variant="primary")
+            load_button.click(
+                fn=load_model_config,  # 直接使用 load_model_config 函数
+                inputs=[config_dropdown],  # config_dropdown 组件本身作为输入
+                outputs=[GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language]  # 期望更新的组件列表
+            )
+        gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
+        with gr.Row():
+            text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
+            button1 = gr.Button(i18n("凑四句一切"), variant="primary")
+            button2 = gr.Button(i18n("凑50字一切"), variant="primary")
+            button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
+            button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
+            button5 = gr.Button(i18n("按标点符号切"), variant="primary")
+            button6 = gr.Button(i18n("推送"), variant="primary")            
+            text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
+            button1.click(cut1, [text_inp], [text_opt])
+            button2.click(cut2, [text_inp], [text_opt])
+            button3.click(cut3, [text_inp], [text_opt])
+            button4.click(cut4, [text_inp], [text_opt])
+            button5.click(cut5, [text_inp], [text_opt])
+            button6.click(echo, [text_opt], [text])             
+        gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。"))
+
+if __name__ == '__main__':
+    app.queue(concurrency_count=511, max_size=1022).launch(
+        server_name="0.0.0.0",
+        inbrowser=True,
+        share=is_share,
+        server_port=infer_ttswebui,
+        quiet=True,
+    )

From ca323cec56ed1a9824b07fe58e037b6eed482dd4 Mon Sep 17 00:00:00 2001
From: DD-MASTERT <15737567532@163.com>
Date: Sun, 21 Jul 2024 01:15:46 +0800
Subject: [PATCH 2/5] Update inference_webui.py

---
 inference_webui.py | 857 ---------------------------------------------
 1 file changed, 857 deletions(-)

diff --git a/inference_webui.py b/inference_webui.py
index 39e1c9c3..d3f5a12f 100644
--- a/inference_webui.py
+++ b/inference_webui.py
@@ -1,858 +1 @@
-'''
-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
-import torch
-import shutil
-from scipy.io import wavfile
 
-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")) and torch.cuda.is_available()
-punctuation = set(['!', '?', '…', ',', '.', '-'," "])
-import gradio as gr
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-import numpy as np
-import librosa
-from feature_extractor import cnhubert
-
-cnhubert.cnhubert_base_path = cnhubert_base_path
-
-from 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 tools.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"
-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 clean_text_inf(text, language):
-    phones, word2ph, norm_text = clean_text(text, 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
-
-
-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_phones_and_bert(text,language):
-    if language in {"en","all_zh","all_ja"}:
-        language = language.replace("all_","")
-        if language == "en":
-            LangSegment.setfilters(["en"])
-            formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
-        else:
-            # 因无法区别中日文汉字,以用户输入为准
-            formattext = text
-        while "  " in formattext:
-            formattext = formattext.replace("  ", " ")
-        phones, word2ph, norm_text = clean_text_inf(formattext, language)
-        if language == "zh":
-            bert = get_bert_feature(norm_text, word2ph).to(device)
-        else:
-            bert = torch.zeros(
-                (1024, len(phones)),
-                dtype=torch.float16 if is_half == True else torch.float32,
-            ).to(device)
-    elif language in {"zh", "ja","auto"}:
-        textlist=[]
-        langlist=[]
-        LangSegment.setfilters(["zh","ja","en","ko"])
-        if language == "auto":
-            for tmp in LangSegment.getTexts(text):
-                if tmp["lang"] == "ko":
-                    langlist.append("zh")
-                    textlist.append(tmp["text"])
-                else:
-                    langlist.append(tmp["lang"])
-                    textlist.append(tmp["text"])
-        else:
-            for tmp in LangSegment.getTexts(text):
-                if tmp["lang"] == "en":
-                    langlist.append(tmp["lang"])
-                else:
-                    # 因无法区别中日文汉字,以用户输入为准
-                    langlist.append(language)
-                textlist.append(tmp["text"])
-        print(textlist)
-        print(langlist)
-        phones_list = []
-        bert_list = []
-        norm_text_list = []
-        for i in range(len(textlist)):
-            lang = langlist[i]
-            phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
-            bert = get_bert_inf(phones, word2ph, norm_text, lang)
-            phones_list.append(phones)
-            norm_text_list.append(norm_text)
-            bert_list.append(bert)
-        bert = torch.cat(bert_list, dim=1)
-        phones = sum(phones_list, [])
-        norm_text = ''.join(norm_text_list)
-
-    return phones,bert.to(dtype),norm_text
-
-
-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, interval=0.3, 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")
-    text = replace_consecutive_punctuation(text)
-    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 * interval),
-        dtype=np.float16 if is_half == True else np.float32,
-    )
-    if not ref_free:
-        with torch.no_grad():
-            wav16k, sr = librosa.load(ref_wav_path, sr=16000)
-            if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
-                raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
-            wav16k = torch.from_numpy(wav16k)
-            zero_wav_torch = torch.from_numpy(zero_wav)
-            if is_half == True:
-                wav16k = wav16k.half().to(device)
-                zero_wav_torch = zero_wav_torch.half().to(device)
-            else:
-                wav16k = wav16k.to(device)
-                zero_wav_torch = zero_wav_torch.to(device)
-            wav16k = torch.cat([wav16k, zero_wav_torch])
-            ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
-                "last_hidden_state"
-            ].transpose(
-                1, 2
-            )  # .float()
-            codes = vq_model.extract_latent(ssl_content)
-            prompt_semantic = codes[0, 0]
-            prompt = prompt_semantic.unsqueeze(0).to(device)
-
-    t1 = ttime()
-
-    if (how_to_cut == i18n("凑四句一切")):
-        text = cut1(text)
-    elif (how_to_cut == i18n("凑50字一切")):
-        text = cut2(text)
-    elif (how_to_cut == i18n("按中文句号。切")):
-        text = cut3(text)
-    elif (how_to_cut == i18n("按英文句号.切")):
-        text = cut4(text)
-    elif (how_to_cut == i18n("按标点符号切")):
-        text = cut5(text)
-    while "\n\n" in text:
-        text = text.replace("\n\n", "\n")
-    print(i18n("实际输入的目标文本(切句后):"), text)
-    texts = text.split("\n")
-    texts = process_text(texts)
-    texts = merge_short_text_in_array(texts, 5)
-    audio_opt = []
-    if not ref_free:
-        phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
-
-    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,bert2,norm_text2=get_phones_and_bert(text, text_language)
-        print(i18n("前端处理后的文本(每句):"), norm_text2)
-        if not ref_free:
-            bert = torch.cat([bert1, bert2], 1)
-            all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
-        else:
-            bert = bert2
-            all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
-
-        bert = bert.to(device).unsqueeze(0)
-        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
-
-        t2 = ttime()
-        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
-    )
-    # 指定保存音频的文件路径
-    file_path = 'moys/temp/audio.wav'
-
-    # 调用保存音频的函数
-    save_audio(hps.data.sampling_rate, np.concatenate(audio_opt, 0), file_path)
-
-# 保存音频数据到文件
-def save_audio(sampling_rate, audio_data, file_path):
-    # 确保音频数据是16位PCM格式
-    audio_data = np.int16(audio_data / np.max(np.abs(audio_data)) * 32767)
-    wavfile.write(file_path, sampling_rate, audio_data)
-
-def split(todo_text):
-    todo_text = todo_text.replace("……", "。").replace("——", "，")
-    if todo_text[-1] not in splits:
-        todo_text += "。"
-    i_split_head = i_split_tail = 0
-    len_text = len(todo_text)
-    todo_texts = []
-    while 1:
-        if i_split_head >= len_text:
-            break  # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
-        if todo_text[i_split_head] in splits:
-            i_split_head += 1
-            todo_texts.append(todo_text[i_split_tail:i_split_head])
-            i_split_tail = i_split_head
-        else:
-            i_split_head += 1
-    return todo_texts
-
-
-def cut1(inp):
-    inp = inp.strip("\n")
-    inps = split(inp)
-    split_idx = list(range(0, len(inps), 4))
-    split_idx[-1] = None
-    if len(split_idx) > 1:
-        opts = []
-        for idx in range(len(split_idx) - 1):
-            opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
-    else:
-        opts = [inp]
-    opts = [item for item in opts if not set(item).issubset(punctuation)]
-    return "\n".join(opts)
-
-
-def cut2(inp):
-    inp = inp.strip("\n")
-    inps = split(inp)
-    if len(inps) < 2:
-        return inp
-    opts = []
-    summ = 0
-    tmp_str = ""
-    for i in range(len(inps)):
-        summ += len(inps[i])
-        tmp_str += inps[i]
-        if summ > 50:
-            summ = 0
-            opts.append(tmp_str)
-            tmp_str = ""
-    if tmp_str != "":
-        opts.append(tmp_str)
-    # print(opts)
-    if len(opts) > 1 and len(opts[-1]) < 50:  ##如果最后一个太短了，和前一个合一起
-        opts[-2] = opts[-2] + opts[-1]
-        opts = opts[:-1]
-    opts = [item for item in opts if not set(item).issubset(punctuation)]
-    return "\n".join(opts)
-
-
-def cut3(inp):
-    inp = inp.strip("\n")
-    opts = ["%s" % item for item in inp.strip("。").split("。")]
-    opts = [item for item in opts if not set(item).issubset(punctuation)]
-    return  "\n".join(opts)
-
-def cut4(inp):
-    inp = inp.strip("\n")
-    opts = ["%s" % item for item in inp.strip(".").split(".")]
-    opts = [item for item in opts if not set(item).issubset(punctuation)]
-    return "\n".join(opts)
-
-
-# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
-def cut5(inp):
-    inp = inp.strip("\n")
-    punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
-    mergeitems = []
-    items = []
-
-    for i, char in enumerate(inp):
-        if char in punds:
-            if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
-                items.append(char)
-            else:
-                items.append(char)
-                mergeitems.append("".join(items))
-                items = []
-        else:
-            items.append(char)
-
-    if items:
-        mergeitems.append("".join(items))
-
-    opt = [item for item in mergeitems if not set(item).issubset(punds)]
-    return "\n".join(opt)
-
-
-def custom_sort_key(s):
-    # 使用正则表达式提取字符串中的数字部分和非数字部分
-    parts = re.split('(\d+)', s)
-    # 将数字部分转换为整数，非数字部分保持不变
-    parts = [int(part) if part.isdigit() else part for part in parts]
-    return parts
-
-def process_text(texts):
-    _text=[]
-    if all(text in [None, " ", "\n",""] for text in texts):
-        raise ValueError(i18n("请输入有效文本"))
-    for text in texts:
-        if text in  [None, " ", ""]:
-            pass
-        else:
-            _text.append(text)
-    return _text
-
-
-def replace_consecutive_punctuation(text):
-    punctuations = ''.join(re.escape(p) for p in punctuation)
-    pattern = f'([{punctuations}])([{punctuations}])+'
-    result = re.sub(pattern, r'\1', text)
-    return result
-
-
-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
-
-
-def save_model_config(GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language):
-    config_dir = "moys"
-    config_dir1 = r"moys\audio"
-    if not os.path.exists(config_dir):
-        os.makedirs(config_dir)
-    
-    # 复制参考音频文件到配置目录
-    copy_ref_audio_path = os.path.join(config_dir1, os.path.basename(inp_ref))
-    shutil.copy(inp_ref, copy_ref_audio_path)
-    
-    gpt_model_name = os.path.basename(GPT_dropdown).split('-')[0]
-    config_file_path = os.path.join(config_dir, f"{gpt_model_name}.txt")
-    
-    with open(config_file_path, 'w', encoding='utf-8') as f:
-        f.write(f"GPT_model_path={GPT_dropdown}\n")
-        f.write(f"SoVITS_model_path={SoVITS_dropdown}\n")
-        f.write(f"ref_audio_path={copy_ref_audio_path}\n")  # 修改写入的路径为复制文件的路径
-        f.write(f"ref_text={prompt_text}\n")
-        f.write(f"ref_audio_language={prompt_language}\n")
-    
-    return f"Configuration saved to {config_file_path}"
-
-def load_model_config(config_file_name):
-    config_dir = "moys"
-    # 因为 config_file_name 现在是字符串，我们直接使用它来构造文件路径
-    config_file_path = os.path.join(config_dir, config_file_name)
-    
-    with open(config_file_path, 'r', encoding='utf-8') as f:
-        lines = f.readlines()
-    
-    config = {}
-    for line in lines:
-        key, value = line.strip().split('=')
-        config[key] = value
-    
-    # 返回一个包含所有组件期望值的字典
-    return (
-        config["GPT_model_path"],
-        config["SoVITS_model_path"],
-        config["ref_audio_path"],
-        config["ref_text"],
-        config["ref_audio_language"]
-    )
-def refresh_config_files():
-    # 获取最新的配置文件列表
-    config_files = get_config_files()
-    # 创建一个新的文件名列表，只包含文件名
-    config_file_names = [os.path.basename(path) for path in config_files]
-    
-    # 返回一个更新的配置，告诉 Gradio 更新下拉菜单的选项
-    return {"choices": config_file_names, "__type__": "update"}
-
-
-
-    # 辅助函数，用于处理 load_model_config 函数的输出
-def handle_load_model_config(config_file_name, GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language):
-    # 调用原始函数获取配置
-    config = load_model_config(config_file_name)
-    
-    # 更新组件的值
-    GPT_dropdown.update(value=config.get("GPT_model_path"))
-    SoVITS_dropdown.update(value=config.get("SoVITS_model_path"))
-    inp_ref.update(value=config.get("ref_audio_path"))
-    prompt_text.update(value=config.get("ref_text"))
-    prompt_language.update(value=config.get("ref_audio_language"))
-
-def get_config_files():
-    config_dir = "moys"
-    if not os.path.exists(config_dir):
-        return []
-    
-    return [os.path.join(config_dir, f) for f in os.listdir(config_dir) if f.endswith('.txt')]
-
-def echo(input_text):
-    # 直接返回输入的文本
-    return input_text
-
-
-def find_latest_wav(source_dir, dest_dir):
-    # 确保目标文件夹存在
-    if not os.path.exists(dest_dir):
-        os.makedirs(dest_dir)
-
-    # 初始化找到的wav文件路径
-    wav_file_path = None
-
-    # 遍历源文件夹
-    for root, dirs, files in os.walk(source_dir):
-        for file in files:
-            if file.lower().endswith('.wav'):
-                wav_file_path = os.path.join(root, file)
-                # 找到第一个wav文件就退出循环
-                break
-        if wav_file_path:
-            break  # 确保找到文件后不再继续遍历
-
-    # 如果找到了wav文件，复制到目标文件夹
-    if wav_file_path:
-        base_name = os.path.basename(wav_file_path)
-        file_name, file_ext = os.path.splitext(base_name)
-        dest_file_path = os.path.join(dest_dir, base_name)
-        
-       # 检查目标文件夹中是否存在同名文件，并添加后缀以避免覆盖
-        counter = 1
-        while os.path.exists(dest_file_path):
-            new_name = f"{file_name}({counter}){file_ext}"
-            dest_file_path = os.path.join(dest_dir, new_name)
-            counter += 1
-        # 复制文件
-        shutil.copy2(wav_file_path, dest_file_path)
-        print(f"Copied WAV file to {dest_file_path}")
-        return dest_file_path  # 返回复制的文件路径
-    else:
-        print("No WAV files found.")
-        return None  # 没有找到 WAV 文件时返回 None
-
-
-
-
-def on_download_click(textq_value):
-    source_directory = r'moys/temp'  # 源文件夹路径
-    destination_directory = textq_value
-   # outputs.update_value(f"开始查找最新的WAV文件...")
-
-    result = find_latest_wav(source_directory, destination_directory)  # 调用函数
-  #  outputs.update_value(f"已保存到: {destination_directory}")
-    return f"{result}已保存到: {destination_directory}"
-
-
-SoVITS_names, GPT_names = get_weights_names()
-
-with gr.Blocks(title="GPT-SoVITS WebUI") as app:
-    gr.Markdown(
-        value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
-    )
-    with gr.Group():
-        gr.Markdown(value=i18n("模型切换"))
-        with gr.Row():
-            GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True)
-            SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
-            refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
-            refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
-            SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
-            GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
-        gr.Markdown(value=i18n("*请上传并填写参考信息"))
-        with gr.Row():
-            inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
-            with gr.Column():
-                ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True)
-                gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。"))
-                prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
-            prompt_language = gr.Dropdown(
-                label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
-            )
-        gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
-        with gr.Row():
-            text = gr.Textbox(label=i18n("需要合成的文本"), value="")
-            text_language = gr.Dropdown(
-                label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
-            )
-            how_to_cut = gr.Radio(
-                label=i18n("怎么切"),
-                choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
-                value=i18n("凑四句一切"),
-                interactive=True,
-            )
-            with gr.Row():
-                gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)："))
-                top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
-                top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
-                temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
-                interval = gr.Slider(minimum=0,maximum=5,step=0.02,label=i18n("interval"),value=0.3,interactive=True)                
-            inference_button = gr.Button(i18n("合成语音"), variant="primary")
-            output = gr.Audio(label=i18n("输出的语音"))
-
-            with gr.Row():
-                # 创建文本框和下载按钮
-                download_button = gr.Button("下载语音", variant="primary")
-                textq = gr.Textbox(label="保存的语音路径", value="")
-                outputs0 = gr.Textbox(label=i18n("保存状态"), value="", interactive=False)
-                # 将事件处理函数绑定到按钮的点击事件
-                download_button.click(
-                    on_download_click, 
-                    inputs=[textq],  # 这里确保 textq 是正确的组件引用
-                    outputs=[outputs0]  # 这里确保 outputs 是正确的组件引用
-                )
-
-        inference_button.click(
-            get_tts_wav,
-            [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, interval, ref_text_free],
-            [output],
-        )
-        # Add new UI elements for saving and loading configurations
-        with gr.Row():
-
-
-            # 初始加载配置文件列表
-            config_files = get_config_files()
-            # 创建一个新的列表，只包含文件名
-            config_file_names = [os.path.basename(path) for path in config_files]
-
-            # 使用文件名列表作为 Dropdown 组件的选项
-            config_dropdown = gr.Dropdown(
-                label=i18n("加载模型配置"),
-                choices=config_file_names,
-                value=config_file_names[0] if config_file_names else None
-            )
-
-            # Output textbox for displaying save confirmation
-            save_output = gr.Textbox(label=i18n("保存配置状态"), value="", interactive=False)
-
-            # 绑定刷新按钮的点击事件
-            refresh_button = gr.Button(i18n("刷新配置文件列表"), variant="primary")
-            refresh_button.click(
-                fn=refresh_config_files,  # 使用新创建的 refresh_config_files 函数
-                inputs=[],  # 刷新按钮不需要输入
-                outputs=[config_dropdown]  # 指定输出为 config_dropdown 组件，以更新其选项
-            )
-
-            # 绑定保存按钮的点击事件
-            save_button = gr.Button(i18n("保存模型配置"), variant="primary")
-            save_button.click(
-                fn=save_model_config,
-                inputs=[GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language],
-                outputs=[save_output]
-            )
-
-            # 绑定加载按钮的点击事件
-            load_button = gr.Button(i18n("加载模型配置"), variant="primary")
-            load_button.click(
-                fn=load_model_config,  # 直接使用 load_model_config 函数
-                inputs=[config_dropdown],  # config_dropdown 组件本身作为输入
-                outputs=[GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language]  # 期望更新的组件列表
-            )
-        gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
-        with gr.Row():
-            text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
-            button1 = gr.Button(i18n("凑四句一切"), variant="primary")
-            button2 = gr.Button(i18n("凑50字一切"), variant="primary")
-            button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
-            button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
-            button5 = gr.Button(i18n("按标点符号切"), variant="primary")
-            button6 = gr.Button(i18n("推送"), variant="primary")            
-            text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
-            button1.click(cut1, [text_inp], [text_opt])
-            button2.click(cut2, [text_inp], [text_opt])
-            button3.click(cut3, [text_inp], [text_opt])
-            button4.click(cut4, [text_inp], [text_opt])
-            button5.click(cut5, [text_inp], [text_opt])
-            button6.click(echo, [text_opt], [text])             
-        gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。"))
-
-if __name__ == '__main__':
-    app.queue(concurrency_count=511, max_size=1022).launch(
-        server_name="0.0.0.0",
-        inbrowser=True,
-        share=is_share,
-        server_port=infer_ttswebui,
-        quiet=True,
-    )

From f40e9893f50dc3062abdd95c42d1f5e5b8487052 Mon Sep 17 00:00:00 2001
From: DD-MASTERT <15737567532@163.com>
Date: Sun, 21 Jul 2024 01:16:41 +0800
Subject: [PATCH 3/5] Delete inference_webui.py

---
 inference_webui.py | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 inference_webui.py

diff --git a/inference_webui.py b/inference_webui.py
deleted file mode 100644
index d3f5a12f..00000000
--- a/inference_webui.py
+++ /dev/null
@@ -1 +0,0 @@
-

From 9150e2ca32ab7029366fba746c4e793fbf4cceb2 Mon Sep 17 00:00:00 2001
From: DD-MASTERT <15737567532@163.com>
Date: Sun, 21 Jul 2024 01:17:19 +0800
Subject: [PATCH 4/5] Add files via upload

---
 moys/audio/audio.wav | Bin 0 -> 384044 bytes
 moys/temp/audio.wav  | Bin 0 -> 384044 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 moys/audio/audio.wav
 create mode 100644 moys/temp/audio.wav

diff --git a/moys/audio/audio.wav b/moys/audio/audio.wav
new file mode 100644
index 0000000000000000000000000000000000000000..0e0215dbdcad5deeb572fce443117ddef6376ce5
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literal 0
HcmV?d00001


From 7cdaca185a93615bd3fd51c86e53a207b41f1d65 Mon Sep 17 00:00:00 2001
From: DD-MASTERT <15737567532@163.com>
Date: Sun, 21 Jul 2024 01:17:48 +0800
Subject: [PATCH 5/5] Add files via upload

---
 GPT_SoVITS/inference_webui.py | 1519 +++++++++++++++++++--------------
 1 file changed, 858 insertions(+), 661 deletions(-)

diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py
index cc79817f..39e1c9c3 100644
--- a/GPT_SoVITS/inference_webui.py
+++ b/GPT_SoVITS/inference_webui.py
@@ -1,661 +1,858 @@
-'''
-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
-import torch
-
-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")) and torch.cuda.is_available()
-punctuation = set(['!', '?', '…', ',', '.', '-'," "])
-import gradio as gr
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-import numpy as np
-import librosa
-from feature_extractor import cnhubert
-
-cnhubert.cnhubert_base_path = cnhubert_base_path
-
-from 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 tools.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"
-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 clean_text_inf(text, language):
-    phones, word2ph, norm_text = clean_text(text, 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
-
-
-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_phones_and_bert(text,language):
-    if language in {"en","all_zh","all_ja"}:
-        language = language.replace("all_","")
-        if language == "en":
-            LangSegment.setfilters(["en"])
-            formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
-        else:
-            # 因无法区别中日文汉字,以用户输入为准
-            formattext = text
-        while "  " in formattext:
-            formattext = formattext.replace("  ", " ")
-        phones, word2ph, norm_text = clean_text_inf(formattext, language)
-        if language == "zh":
-            bert = get_bert_feature(norm_text, word2ph).to(device)
-        else:
-            bert = torch.zeros(
-                (1024, len(phones)),
-                dtype=torch.float16 if is_half == True else torch.float32,
-            ).to(device)
-    elif language in {"zh", "ja","auto"}:
-        textlist=[]
-        langlist=[]
-        LangSegment.setfilters(["zh","ja","en","ko"])
-        if language == "auto":
-            for tmp in LangSegment.getTexts(text):
-                if tmp["lang"] == "ko":
-                    langlist.append("zh")
-                    textlist.append(tmp["text"])
-                else:
-                    langlist.append(tmp["lang"])
-                    textlist.append(tmp["text"])
-        else:
-            for tmp in LangSegment.getTexts(text):
-                if tmp["lang"] == "en":
-                    langlist.append(tmp["lang"])
-                else:
-                    # 因无法区别中日文汉字,以用户输入为准
-                    langlist.append(language)
-                textlist.append(tmp["text"])
-        print(textlist)
-        print(langlist)
-        phones_list = []
-        bert_list = []
-        norm_text_list = []
-        for i in range(len(textlist)):
-            lang = langlist[i]
-            phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
-            bert = get_bert_inf(phones, word2ph, norm_text, lang)
-            phones_list.append(phones)
-            norm_text_list.append(norm_text)
-            bert_list.append(bert)
-        bert = torch.cat(bert_list, dim=1)
-        phones = sum(phones_list, [])
-        norm_text = ''.join(norm_text_list)
-
-    return phones,bert.to(dtype),norm_text
-
-
-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")
-    text = replace_consecutive_punctuation(text)
-    if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
-    
-    print(i18n("实际输入的目标文本:"), text)
-    zero_wav = np.zeros(
-        int(hps.data.sampling_rate * 0.3),
-        dtype=np.float16 if is_half == True else np.float32,
-    )
-    if not ref_free:
-        with torch.no_grad():
-            wav16k, sr = librosa.load(ref_wav_path, sr=16000)
-            if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
-                raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
-            wav16k = torch.from_numpy(wav16k)
-            zero_wav_torch = torch.from_numpy(zero_wav)
-            if is_half == True:
-                wav16k = wav16k.half().to(device)
-                zero_wav_torch = zero_wav_torch.half().to(device)
-            else:
-                wav16k = wav16k.to(device)
-                zero_wav_torch = zero_wav_torch.to(device)
-            wav16k = torch.cat([wav16k, zero_wav_torch])
-            ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
-                "last_hidden_state"
-            ].transpose(
-                1, 2
-            )  # .float()
-            codes = vq_model.extract_latent(ssl_content)
-            prompt_semantic = codes[0, 0]
-            prompt = prompt_semantic.unsqueeze(0).to(device)
-
-    t1 = ttime()
-
-    if (how_to_cut == i18n("凑四句一切")):
-        text = cut1(text)
-    elif (how_to_cut == i18n("凑50字一切")):
-        text = cut2(text)
-    elif (how_to_cut == i18n("按中文句号。切")):
-        text = cut3(text)
-    elif (how_to_cut == i18n("按英文句号.切")):
-        text = cut4(text)
-    elif (how_to_cut == i18n("按标点符号切")):
-        text = cut5(text)
-    while "\n\n" in text:
-        text = text.replace("\n\n", "\n")
-    print(i18n("实际输入的目标文本(切句后):"), text)
-    texts = text.split("\n")
-    texts = process_text(texts)
-    texts = merge_short_text_in_array(texts, 5)
-    audio_opt = []
-    if not ref_free:
-        phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
-
-    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,bert2,norm_text2=get_phones_and_bert(text, text_language)
-        print(i18n("前端处理后的文本(每句):"), norm_text2)
-        if not ref_free:
-            bert = torch.cat([bert1, bert2], 1)
-            all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
-        else:
-            bert = bert2
-            all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
-
-        bert = bert.to(device).unsqueeze(0)
-        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
-
-        t2 = ttime()
-        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]
-    opts = [item for item in opts if not set(item).issubset(punctuation)]
-    return "\n".join(opts)
-
-
-def cut2(inp):
-    inp = inp.strip("\n")
-    inps = split(inp)
-    if len(inps) < 2:
-        return inp
-    opts = []
-    summ = 0
-    tmp_str = ""
-    for i in range(len(inps)):
-        summ += len(inps[i])
-        tmp_str += inps[i]
-        if summ > 50:
-            summ = 0
-            opts.append(tmp_str)
-            tmp_str = ""
-    if tmp_str != "":
-        opts.append(tmp_str)
-    # print(opts)
-    if len(opts) > 1 and len(opts[-1]) < 50:  ##如果最后一个太短了，和前一个合一起
-        opts[-2] = opts[-2] + opts[-1]
-        opts = opts[:-1]
-    opts = [item for item in opts if not set(item).issubset(punctuation)]
-    return "\n".join(opts)
-
-
-def cut3(inp):
-    inp = inp.strip("\n")
-    opts = ["%s" % item for item in inp.strip("。").split("。")]
-    opts = [item for item in opts if not set(item).issubset(punctuation)]
-    return  "\n".join(opts)
-
-def cut4(inp):
-    inp = inp.strip("\n")
-    opts = ["%s" % item for item in inp.strip(".").split(".")]
-    opts = [item for item in opts if not set(item).issubset(punctuation)]
-    return "\n".join(opts)
-
-
-# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
-def cut5(inp):
-    inp = inp.strip("\n")
-    punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
-    mergeitems = []
-    items = []
-
-    for i, char in enumerate(inp):
-        if char in punds:
-            if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
-                items.append(char)
-            else:
-                items.append(char)
-                mergeitems.append("".join(items))
-                items = []
-        else:
-            items.append(char)
-
-    if items:
-        mergeitems.append("".join(items))
-
-    opt = [item for item in mergeitems if not set(item).issubset(punds)]
-    return "\n".join(opt)
-
-
-def custom_sort_key(s):
-    # 使用正则表达式提取字符串中的数字部分和非数字部分
-    parts = re.split('(\d+)', s)
-    # 将数字部分转换为整数，非数字部分保持不变
-    parts = [int(part) if part.isdigit() else part for part in parts]
-    return parts
-
-def process_text(texts):
-    _text=[]
-    if all(text in [None, " ", "\n",""] for text in texts):
-        raise ValueError(i18n("请输入有效文本"))
-    for text in texts:
-        if text in  [None, " ", ""]:
-            pass
-        else:
-            _text.append(text)
-    return _text
-
-
-def replace_consecutive_punctuation(text):
-    punctuations = ''.join(re.escape(p) for p in punctuation)
-    pattern = f'([{punctuations}])([{punctuations}])+'
-    result = re.sub(pattern, r'\1', text)
-    return result
-
-
-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()
-
-with gr.Blocks(title="GPT-SoVITS WebUI") as app:
-    gr.Markdown(
-        value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
-    )
-    with gr.Group():
-        gr.Markdown(value=i18n("模型切换"))
-        with gr.Row():
-            GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True)
-            SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
-            refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
-            refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
-            SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
-            GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
-        gr.Markdown(value=i18n("*请上传并填写参考信息"))
-        with gr.Row():
-            inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
-            with gr.Column():
-                ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True)
-                gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。"))
-                prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
-            prompt_language = gr.Dropdown(
-                label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
-            )
-        gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
-        with gr.Row():
-            text = gr.Textbox(label=i18n("需要合成的文本"), value="")
-            text_language = gr.Dropdown(
-                label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
-            )
-            how_to_cut = gr.Radio(
-                label=i18n("怎么切"),
-                choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
-                value=i18n("凑四句一切"),
-                interactive=True,
-            )
-            with gr.Row():
-                gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)："))
-                top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
-                top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
-                temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
-            inference_button = gr.Button(i18n("合成语音"), variant="primary")
-            output = gr.Audio(label=i18n("输出的语音"))
-
-        inference_button.click(
-            get_tts_wav,
-            [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free],
-            [output],
-        )
-
-        gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
-        with gr.Row():
-            text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
-            button1 = gr.Button(i18n("凑四句一切"), variant="primary")
-            button2 = gr.Button(i18n("凑50字一切"), variant="primary")
-            button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
-            button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
-            button5 = gr.Button(i18n("按标点符号切"), variant="primary")
-            text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
-            button1.click(cut1, [text_inp], [text_opt])
-            button2.click(cut2, [text_inp], [text_opt])
-            button3.click(cut3, [text_inp], [text_opt])
-            button4.click(cut4, [text_inp], [text_opt])
-            button5.click(cut5, [text_inp], [text_opt])
-        gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。"))
-
-if __name__ == '__main__':
-    app.queue(concurrency_count=511, max_size=1022).launch(
-        server_name="0.0.0.0",
-        inbrowser=True,
-        share=is_share,
-        server_port=infer_ttswebui,
-        quiet=True,
-    )
+'''
+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
+import torch
+import shutil
+from scipy.io import wavfile
+
+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")) and torch.cuda.is_available()
+punctuation = set(['!', '?', '…', ',', '.', '-'," "])
+import gradio as gr
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import numpy as np
+import librosa
+from feature_extractor import cnhubert
+
+cnhubert.cnhubert_base_path = cnhubert_base_path
+
+from 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 tools.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"
+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 clean_text_inf(text, language):
+    phones, word2ph, norm_text = clean_text(text, 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
+
+
+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_phones_and_bert(text,language):
+    if language in {"en","all_zh","all_ja"}:
+        language = language.replace("all_","")
+        if language == "en":
+            LangSegment.setfilters(["en"])
+            formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
+        else:
+            # 因无法区别中日文汉字,以用户输入为准
+            formattext = text
+        while "  " in formattext:
+            formattext = formattext.replace("  ", " ")
+        phones, word2ph, norm_text = clean_text_inf(formattext, language)
+        if language == "zh":
+            bert = get_bert_feature(norm_text, word2ph).to(device)
+        else:
+            bert = torch.zeros(
+                (1024, len(phones)),
+                dtype=torch.float16 if is_half == True else torch.float32,
+            ).to(device)
+    elif language in {"zh", "ja","auto"}:
+        textlist=[]
+        langlist=[]
+        LangSegment.setfilters(["zh","ja","en","ko"])
+        if language == "auto":
+            for tmp in LangSegment.getTexts(text):
+                if tmp["lang"] == "ko":
+                    langlist.append("zh")
+                    textlist.append(tmp["text"])
+                else:
+                    langlist.append(tmp["lang"])
+                    textlist.append(tmp["text"])
+        else:
+            for tmp in LangSegment.getTexts(text):
+                if tmp["lang"] == "en":
+                    langlist.append(tmp["lang"])
+                else:
+                    # 因无法区别中日文汉字,以用户输入为准
+                    langlist.append(language)
+                textlist.append(tmp["text"])
+        print(textlist)
+        print(langlist)
+        phones_list = []
+        bert_list = []
+        norm_text_list = []
+        for i in range(len(textlist)):
+            lang = langlist[i]
+            phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
+            bert = get_bert_inf(phones, word2ph, norm_text, lang)
+            phones_list.append(phones)
+            norm_text_list.append(norm_text)
+            bert_list.append(bert)
+        bert = torch.cat(bert_list, dim=1)
+        phones = sum(phones_list, [])
+        norm_text = ''.join(norm_text_list)
+
+    return phones,bert.to(dtype),norm_text
+
+
+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, interval=0.3, 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")
+    text = replace_consecutive_punctuation(text)
+    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 * interval),
+        dtype=np.float16 if is_half == True else np.float32,
+    )
+    if not ref_free:
+        with torch.no_grad():
+            wav16k, sr = librosa.load(ref_wav_path, sr=16000)
+            if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
+                raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
+            wav16k = torch.from_numpy(wav16k)
+            zero_wav_torch = torch.from_numpy(zero_wav)
+            if is_half == True:
+                wav16k = wav16k.half().to(device)
+                zero_wav_torch = zero_wav_torch.half().to(device)
+            else:
+                wav16k = wav16k.to(device)
+                zero_wav_torch = zero_wav_torch.to(device)
+            wav16k = torch.cat([wav16k, zero_wav_torch])
+            ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
+                "last_hidden_state"
+            ].transpose(
+                1, 2
+            )  # .float()
+            codes = vq_model.extract_latent(ssl_content)
+            prompt_semantic = codes[0, 0]
+            prompt = prompt_semantic.unsqueeze(0).to(device)
+
+    t1 = ttime()
+
+    if (how_to_cut == i18n("凑四句一切")):
+        text = cut1(text)
+    elif (how_to_cut == i18n("凑50字一切")):
+        text = cut2(text)
+    elif (how_to_cut == i18n("按中文句号。切")):
+        text = cut3(text)
+    elif (how_to_cut == i18n("按英文句号.切")):
+        text = cut4(text)
+    elif (how_to_cut == i18n("按标点符号切")):
+        text = cut5(text)
+    while "\n\n" in text:
+        text = text.replace("\n\n", "\n")
+    print(i18n("实际输入的目标文本(切句后):"), text)
+    texts = text.split("\n")
+    texts = process_text(texts)
+    texts = merge_short_text_in_array(texts, 5)
+    audio_opt = []
+    if not ref_free:
+        phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
+
+    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,bert2,norm_text2=get_phones_and_bert(text, text_language)
+        print(i18n("前端处理后的文本(每句):"), norm_text2)
+        if not ref_free:
+            bert = torch.cat([bert1, bert2], 1)
+            all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
+        else:
+            bert = bert2
+            all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
+
+        bert = bert.to(device).unsqueeze(0)
+        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
+
+        t2 = ttime()
+        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
+    )
+    # 指定保存音频的文件路径
+    file_path = 'moys/temp/audio.wav'
+
+    # 调用保存音频的函数
+    save_audio(hps.data.sampling_rate, np.concatenate(audio_opt, 0), file_path)
+
+# 保存音频数据到文件
+def save_audio(sampling_rate, audio_data, file_path):
+    # 确保音频数据是16位PCM格式
+    audio_data = np.int16(audio_data / np.max(np.abs(audio_data)) * 32767)
+    wavfile.write(file_path, sampling_rate, audio_data)
+
+def split(todo_text):
+    todo_text = todo_text.replace("……", "。").replace("——", "，")
+    if todo_text[-1] not in splits:
+        todo_text += "。"
+    i_split_head = i_split_tail = 0
+    len_text = len(todo_text)
+    todo_texts = []
+    while 1:
+        if i_split_head >= len_text:
+            break  # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
+        if todo_text[i_split_head] in splits:
+            i_split_head += 1
+            todo_texts.append(todo_text[i_split_tail:i_split_head])
+            i_split_tail = i_split_head
+        else:
+            i_split_head += 1
+    return todo_texts
+
+
+def cut1(inp):
+    inp = inp.strip("\n")
+    inps = split(inp)
+    split_idx = list(range(0, len(inps), 4))
+    split_idx[-1] = None
+    if len(split_idx) > 1:
+        opts = []
+        for idx in range(len(split_idx) - 1):
+            opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
+    else:
+        opts = [inp]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return "\n".join(opts)
+
+
+def cut2(inp):
+    inp = inp.strip("\n")
+    inps = split(inp)
+    if len(inps) < 2:
+        return inp
+    opts = []
+    summ = 0
+    tmp_str = ""
+    for i in range(len(inps)):
+        summ += len(inps[i])
+        tmp_str += inps[i]
+        if summ > 50:
+            summ = 0
+            opts.append(tmp_str)
+            tmp_str = ""
+    if tmp_str != "":
+        opts.append(tmp_str)
+    # print(opts)
+    if len(opts) > 1 and len(opts[-1]) < 50:  ##如果最后一个太短了，和前一个合一起
+        opts[-2] = opts[-2] + opts[-1]
+        opts = opts[:-1]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return "\n".join(opts)
+
+
+def cut3(inp):
+    inp = inp.strip("\n")
+    opts = ["%s" % item for item in inp.strip("。").split("。")]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return  "\n".join(opts)
+
+def cut4(inp):
+    inp = inp.strip("\n")
+    opts = ["%s" % item for item in inp.strip(".").split(".")]
+    opts = [item for item in opts if not set(item).issubset(punctuation)]
+    return "\n".join(opts)
+
+
+# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
+def cut5(inp):
+    inp = inp.strip("\n")
+    punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
+    mergeitems = []
+    items = []
+
+    for i, char in enumerate(inp):
+        if char in punds:
+            if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
+                items.append(char)
+            else:
+                items.append(char)
+                mergeitems.append("".join(items))
+                items = []
+        else:
+            items.append(char)
+
+    if items:
+        mergeitems.append("".join(items))
+
+    opt = [item for item in mergeitems if not set(item).issubset(punds)]
+    return "\n".join(opt)
+
+
+def custom_sort_key(s):
+    # 使用正则表达式提取字符串中的数字部分和非数字部分
+    parts = re.split('(\d+)', s)
+    # 将数字部分转换为整数，非数字部分保持不变
+    parts = [int(part) if part.isdigit() else part for part in parts]
+    return parts
+
+def process_text(texts):
+    _text=[]
+    if all(text in [None, " ", "\n",""] for text in texts):
+        raise ValueError(i18n("请输入有效文本"))
+    for text in texts:
+        if text in  [None, " ", ""]:
+            pass
+        else:
+            _text.append(text)
+    return _text
+
+
+def replace_consecutive_punctuation(text):
+    punctuations = ''.join(re.escape(p) for p in punctuation)
+    pattern = f'([{punctuations}])([{punctuations}])+'
+    result = re.sub(pattern, r'\1', text)
+    return result
+
+
+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
+
+
+def save_model_config(GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language):
+    config_dir = "moys"
+    config_dir1 = r"moys\audio"
+    if not os.path.exists(config_dir):
+        os.makedirs(config_dir)
+    
+    # 复制参考音频文件到配置目录
+    copy_ref_audio_path = os.path.join(config_dir1, os.path.basename(inp_ref))
+    shutil.copy(inp_ref, copy_ref_audio_path)
+    
+    gpt_model_name = os.path.basename(GPT_dropdown).split('-')[0]
+    config_file_path = os.path.join(config_dir, f"{gpt_model_name}.txt")
+    
+    with open(config_file_path, 'w', encoding='utf-8') as f:
+        f.write(f"GPT_model_path={GPT_dropdown}\n")
+        f.write(f"SoVITS_model_path={SoVITS_dropdown}\n")
+        f.write(f"ref_audio_path={copy_ref_audio_path}\n")  # 修改写入的路径为复制文件的路径
+        f.write(f"ref_text={prompt_text}\n")
+        f.write(f"ref_audio_language={prompt_language}\n")
+    
+    return f"Configuration saved to {config_file_path}"
+
+def load_model_config(config_file_name):
+    config_dir = "moys"
+    # 因为 config_file_name 现在是字符串，我们直接使用它来构造文件路径
+    config_file_path = os.path.join(config_dir, config_file_name)
+    
+    with open(config_file_path, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+    
+    config = {}
+    for line in lines:
+        key, value = line.strip().split('=')
+        config[key] = value
+    
+    # 返回一个包含所有组件期望值的字典
+    return (
+        config["GPT_model_path"],
+        config["SoVITS_model_path"],
+        config["ref_audio_path"],
+        config["ref_text"],
+        config["ref_audio_language"]
+    )
+def refresh_config_files():
+    # 获取最新的配置文件列表
+    config_files = get_config_files()
+    # 创建一个新的文件名列表，只包含文件名
+    config_file_names = [os.path.basename(path) for path in config_files]
+    
+    # 返回一个更新的配置，告诉 Gradio 更新下拉菜单的选项
+    return {"choices": config_file_names, "__type__": "update"}
+
+
+
+    # 辅助函数，用于处理 load_model_config 函数的输出
+def handle_load_model_config(config_file_name, GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language):
+    # 调用原始函数获取配置
+    config = load_model_config(config_file_name)
+    
+    # 更新组件的值
+    GPT_dropdown.update(value=config.get("GPT_model_path"))
+    SoVITS_dropdown.update(value=config.get("SoVITS_model_path"))
+    inp_ref.update(value=config.get("ref_audio_path"))
+    prompt_text.update(value=config.get("ref_text"))
+    prompt_language.update(value=config.get("ref_audio_language"))
+
+def get_config_files():
+    config_dir = "moys"
+    if not os.path.exists(config_dir):
+        return []
+    
+    return [os.path.join(config_dir, f) for f in os.listdir(config_dir) if f.endswith('.txt')]
+
+def echo(input_text):
+    # 直接返回输入的文本
+    return input_text
+
+
+def find_latest_wav(source_dir, dest_dir):
+    # 确保目标文件夹存在
+    if not os.path.exists(dest_dir):
+        os.makedirs(dest_dir)
+
+    # 初始化找到的wav文件路径
+    wav_file_path = None
+
+    # 遍历源文件夹
+    for root, dirs, files in os.walk(source_dir):
+        for file in files:
+            if file.lower().endswith('.wav'):
+                wav_file_path = os.path.join(root, file)
+                # 找到第一个wav文件就退出循环
+                break
+        if wav_file_path:
+            break  # 确保找到文件后不再继续遍历
+
+    # 如果找到了wav文件，复制到目标文件夹
+    if wav_file_path:
+        base_name = os.path.basename(wav_file_path)
+        file_name, file_ext = os.path.splitext(base_name)
+        dest_file_path = os.path.join(dest_dir, base_name)
+        
+       # 检查目标文件夹中是否存在同名文件，并添加后缀以避免覆盖
+        counter = 1
+        while os.path.exists(dest_file_path):
+            new_name = f"{file_name}({counter}){file_ext}"
+            dest_file_path = os.path.join(dest_dir, new_name)
+            counter += 1
+        # 复制文件
+        shutil.copy2(wav_file_path, dest_file_path)
+        print(f"Copied WAV file to {dest_file_path}")
+        return dest_file_path  # 返回复制的文件路径
+    else:
+        print("No WAV files found.")
+        return None  # 没有找到 WAV 文件时返回 None
+
+
+
+
+def on_download_click(textq_value):
+    source_directory = r'moys/temp'  # 源文件夹路径
+    destination_directory = textq_value
+   # outputs.update_value(f"开始查找最新的WAV文件...")
+
+    result = find_latest_wav(source_directory, destination_directory)  # 调用函数
+  #  outputs.update_value(f"已保存到: {destination_directory}")
+    return f"{result}已保存到: {destination_directory}"
+
+
+SoVITS_names, GPT_names = get_weights_names()
+
+with gr.Blocks(title="GPT-SoVITS WebUI") as app:
+    gr.Markdown(
+        value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
+    )
+    with gr.Group():
+        gr.Markdown(value=i18n("模型切换"))
+        with gr.Row():
+            GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True)
+            SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
+            refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
+            refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
+            SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
+            GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
+        gr.Markdown(value=i18n("*请上传并填写参考信息"))
+        with gr.Row():
+            inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
+            with gr.Column():
+                ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True)
+                gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。"))
+                prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
+            prompt_language = gr.Dropdown(
+                label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
+            )
+        gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
+        with gr.Row():
+            text = gr.Textbox(label=i18n("需要合成的文本"), value="")
+            text_language = gr.Dropdown(
+                label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
+            )
+            how_to_cut = gr.Radio(
+                label=i18n("怎么切"),
+                choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
+                value=i18n("凑四句一切"),
+                interactive=True,
+            )
+            with gr.Row():
+                gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)："))
+                top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
+                top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
+                temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
+                interval = gr.Slider(minimum=0,maximum=5,step=0.02,label=i18n("interval"),value=0.3,interactive=True)                
+            inference_button = gr.Button(i18n("合成语音"), variant="primary")
+            output = gr.Audio(label=i18n("输出的语音"))
+
+            with gr.Row():
+                # 创建文本框和下载按钮
+                download_button = gr.Button("下载语音", variant="primary")
+                textq = gr.Textbox(label="保存的语音路径", value="")
+                outputs0 = gr.Textbox(label=i18n("保存状态"), value="", interactive=False)
+                # 将事件处理函数绑定到按钮的点击事件
+                download_button.click(
+                    on_download_click, 
+                    inputs=[textq],  # 这里确保 textq 是正确的组件引用
+                    outputs=[outputs0]  # 这里确保 outputs 是正确的组件引用
+                )
+
+        inference_button.click(
+            get_tts_wav,
+            [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, interval, ref_text_free],
+            [output],
+        )
+        # Add new UI elements for saving and loading configurations
+        with gr.Row():
+
+
+            # 初始加载配置文件列表
+            config_files = get_config_files()
+            # 创建一个新的列表，只包含文件名
+            config_file_names = [os.path.basename(path) for path in config_files]
+
+            # 使用文件名列表作为 Dropdown 组件的选项
+            config_dropdown = gr.Dropdown(
+                label=i18n("加载模型配置"),
+                choices=config_file_names,
+                value=config_file_names[0] if config_file_names else None
+            )
+
+            # Output textbox for displaying save confirmation
+            save_output = gr.Textbox(label=i18n("保存配置状态"), value="", interactive=False)
+
+            # 绑定刷新按钮的点击事件
+            refresh_button = gr.Button(i18n("刷新配置文件列表"), variant="primary")
+            refresh_button.click(
+                fn=refresh_config_files,  # 使用新创建的 refresh_config_files 函数
+                inputs=[],  # 刷新按钮不需要输入
+                outputs=[config_dropdown]  # 指定输出为 config_dropdown 组件，以更新其选项
+            )
+
+            # 绑定保存按钮的点击事件
+            save_button = gr.Button(i18n("保存模型配置"), variant="primary")
+            save_button.click(
+                fn=save_model_config,
+                inputs=[GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language],
+                outputs=[save_output]
+            )
+
+            # 绑定加载按钮的点击事件
+            load_button = gr.Button(i18n("加载模型配置"), variant="primary")
+            load_button.click(
+                fn=load_model_config,  # 直接使用 load_model_config 函数
+                inputs=[config_dropdown],  # config_dropdown 组件本身作为输入
+                outputs=[GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language]  # 期望更新的组件列表
+            )
+        gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
+        with gr.Row():
+            text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
+            button1 = gr.Button(i18n("凑四句一切"), variant="primary")
+            button2 = gr.Button(i18n("凑50字一切"), variant="primary")
+            button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
+            button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
+            button5 = gr.Button(i18n("按标点符号切"), variant="primary")
+            button6 = gr.Button(i18n("推送"), variant="primary")            
+            text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
+            button1.click(cut1, [text_inp], [text_opt])
+            button2.click(cut2, [text_inp], [text_opt])
+            button3.click(cut3, [text_inp], [text_opt])
+            button4.click(cut4, [text_inp], [text_opt])
+            button5.click(cut5, [text_inp], [text_opt])
+            button6.click(echo, [text_opt], [text])             
+        gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。"))
+
+if __name__ == '__main__':
+    app.queue(concurrency_count=511, max_size=1022).launch(
+        server_name="0.0.0.0",
+        inbrowser=True,
+        share=is_share,
+        server_port=infer_ttswebui,
+        quiet=True,
+    )