diff --git a/webui.py b/webui.py
deleted file mode 100644
index 1f31e94..0000000
--- a/webui.py
+++ /dev/null
@@ -1,559 +0,0 @@
-"""
-Original `webui.py` for Bert-VITS2, not working with Style-Bert-VITS2 yet.
-"""
-
-# flake8: noqa: E402
-import os
-import logging
-import re_matching
-from tools.sentence import split_by_language
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-logging.basicConfig(
-    level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
-)
-
-logger = logging.getLogger(__name__)
-
-import gradio as gr
-import librosa
-import numpy as np
-import torch
-import webbrowser
-
-import utils
-from config import config
-from style_bert_vits2.models.infer import infer, latest_version, get_net_g, infer_multilang
-from tools.translate import translate
-
-net_g = None
-
-device = config.webui_config.device
-if device == "mps":
-    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-
-
-def generate_audio(
-    slices,
-    sdp_ratio,
-    noise_scale,
-    noise_scale_w,
-    length_scale,
-    speaker,
-    language,
-    reference_audio,
-    emotion,
-    style_text,
-    style_weight,
-    skip_start=False,
-    skip_end=False,
-):
-    audio_list = []
-    # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
-    with torch.no_grad():
-        for idx, piece in enumerate(slices):
-            skip_start = idx != 0
-            skip_end = idx != len(slices) - 1
-            audio = infer(
-                piece,
-                reference_audio=reference_audio,
-                emotion=emotion,
-                sdp_ratio=sdp_ratio,
-                noise_scale=noise_scale,
-                noise_scale_w=noise_scale_w,
-                length_scale=length_scale,
-                sid=speaker,
-                language=language,
-                hps=hps,
-                net_g=net_g,
-                device=device,
-                skip_start=skip_start,
-                skip_end=skip_end,
-                assist_text=style_text,
-                assist_text_weight=style_weight,
-            )
-            audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
-            audio_list.append(audio16bit)
-    return audio_list
-
-
-def generate_audio_multilang(
-    slices,
-    sdp_ratio,
-    noise_scale,
-    noise_scale_w,
-    length_scale,
-    speaker,
-    language,
-    reference_audio,
-    emotion,
-    skip_start=False,
-    skip_end=False,
-):
-    audio_list = []
-    # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
-    with torch.no_grad():
-        for idx, piece in enumerate(slices):
-            skip_start = idx != 0
-            skip_end = idx != len(slices) - 1
-            audio = infer_multilang(
-                piece,
-                reference_audio=reference_audio,
-                emotion=emotion,
-                sdp_ratio=sdp_ratio,
-                noise_scale=noise_scale,
-                noise_scale_w=noise_scale_w,
-                length_scale=length_scale,
-                sid=speaker,
-                language=language[idx],
-                hps=hps,
-                net_g=net_g,
-                device=device,
-                skip_start=skip_start,
-                skip_end=skip_end,
-            )
-            audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
-            audio_list.append(audio16bit)
-    return audio_list
-
-
-def tts_split(
-    text: str,
-    speaker,
-    sdp_ratio,
-    noise_scale,
-    noise_scale_w,
-    length_scale,
-    language,
-    cut_by_sent,
-    interval_between_para,
-    interval_between_sent,
-    reference_audio,
-    emotion,
-    style_text,
-    style_weight,
-):
-    while text.find("\n\n") != -1:
-        text = text.replace("\n\n", "\n")
-    text = text.replace("|", "")
-    para_list = re_matching.cut_para(text)
-    para_list = [p for p in para_list if p != ""]
-    audio_list = []
-    for p in para_list:
-        if not cut_by_sent:
-            audio_list += process_text(
-                p,
-                speaker,
-                sdp_ratio,
-                noise_scale,
-                noise_scale_w,
-                length_scale,
-                language,
-                reference_audio,
-                emotion,
-                style_text,
-                style_weight,
-            )
-            silence = np.zeros((int)(44100 * interval_between_para), dtype=np.int16)
-            audio_list.append(silence)
-        else:
-            audio_list_sent = []
-            sent_list = re_matching.cut_sent(p)
-            sent_list = [s for s in sent_list if s != ""]
-            for s in sent_list:
-                audio_list_sent += process_text(
-                    s,
-                    speaker,
-                    sdp_ratio,
-                    noise_scale,
-                    noise_scale_w,
-                    length_scale,
-                    language,
-                    reference_audio,
-                    emotion,
-                    style_text,
-                    style_weight,
-                )
-                silence = np.zeros((int)(44100 * interval_between_sent))
-                audio_list_sent.append(silence)
-            if (interval_between_para - interval_between_sent) > 0:
-                silence = np.zeros(
-                    (int)(44100 * (interval_between_para - interval_between_sent))
-                )
-                audio_list_sent.append(silence)
-            audio16bit = gr.processing_utils.convert_to_16_bit_wav(
-                np.concatenate(audio_list_sent)
-            )  # 对完整句子做音量归一
-            audio_list.append(audio16bit)
-    audio_concat = np.concatenate(audio_list)
-    return ("Success", (hps.data.sampling_rate, audio_concat))
-
-
-def process_mix(slice):
-    _speaker = slice.pop()
-    _text, _lang = [], []
-    for lang, content in slice:
-        content = content.split("|")
-        content = [part for part in content if part != ""]
-        if len(content) == 0:
-            continue
-        if len(_text) == 0:
-            _text = [[part] for part in content]
-            _lang = [[lang] for part in content]
-        else:
-            _text[-1].append(content[0])
-            _lang[-1].append(lang)
-            if len(content) > 1:
-                _text += [[part] for part in content[1:]]
-                _lang += [[lang] for part in content[1:]]
-    return _text, _lang, _speaker
-
-
-def process_auto(text):
-    _text, _lang = [], []
-    for slice in text.split("|"):
-        if slice == "":
-            continue
-        temp_text, temp_lang = [], []
-        sentences_list = split_by_language(slice, target_languages=["zh", "ja", "en"])
-        for sentence, lang in sentences_list:
-            if sentence == "":
-                continue
-            temp_text.append(sentence)
-            if lang == "ja":
-                lang = "jp"
-            temp_lang.append(lang.upper())
-        _text.append(temp_text)
-        _lang.append(temp_lang)
-    return _text, _lang
-
-
-def process_text(
-    text: str,
-    speaker,
-    sdp_ratio,
-    noise_scale,
-    noise_scale_w,
-    length_scale,
-    language,
-    reference_audio,
-    emotion,
-    style_text=None,
-    style_weight=0,
-):
-    audio_list = []
-    if language == "mix":
-        bool_valid, str_valid = re_matching.validate_text(text)
-        if not bool_valid:
-            return str_valid, (
-                hps.data.sampling_rate,
-                np.concatenate([np.zeros(hps.data.sampling_rate // 2)]),
-            )
-        for slice in re_matching.text_matching(text):
-            _text, _lang, _speaker = process_mix(slice)
-            if _speaker is None:
-                continue
-            print(f"Text: {_text}\nLang: {_lang}")
-            audio_list.extend(
-                generate_audio_multilang(
-                    _text,
-                    sdp_ratio,
-                    noise_scale,
-                    noise_scale_w,
-                    length_scale,
-                    _speaker,
-                    _lang,
-                    reference_audio,
-                    emotion,
-                )
-            )
-    elif language.lower() == "auto":
-        _text, _lang = process_auto(text)
-        print(f"Text: {_text}\nLang: {_lang}")
-        audio_list.extend(
-            generate_audio_multilang(
-                _text,
-                sdp_ratio,
-                noise_scale,
-                noise_scale_w,
-                length_scale,
-                speaker,
-                _lang,
-                reference_audio,
-                emotion,
-            )
-        )
-    else:
-        audio_list.extend(
-            generate_audio(
-                text.split("|"),
-                sdp_ratio,
-                noise_scale,
-                noise_scale_w,
-                length_scale,
-                speaker,
-                language,
-                reference_audio,
-                emotion,
-                style_text,
-                style_weight,
-            )
-        )
-    return audio_list
-
-
-def tts_fn(
-    text: str,
-    speaker,
-    sdp_ratio,
-    noise_scale,
-    noise_scale_w,
-    length_scale,
-    language,
-    reference_audio,
-    emotion,
-    prompt_mode,
-    style_text=None,
-    style_weight=0,
-):
-    if style_text == "":
-        style_text = None
-    if prompt_mode == "Audio prompt":
-        if reference_audio == None:
-            return ("Invalid audio prompt", None)
-        else:
-            reference_audio = load_audio(reference_audio)[1]
-    else:
-        reference_audio = None
-
-    audio_list = process_text(
-        text,
-        speaker,
-        sdp_ratio,
-        noise_scale,
-        noise_scale_w,
-        length_scale,
-        language,
-        reference_audio,
-        emotion,
-        style_text,
-        style_weight,
-    )
-
-    audio_concat = np.concatenate(audio_list)
-    return "Success", (hps.data.sampling_rate, audio_concat)
-
-
-def format_utils(text, speaker):
-    _text, _lang = process_auto(text)
-    res = f"[{speaker}]"
-    for lang_s, content_s in zip(_lang, _text):
-        for lang, content in zip(lang_s, content_s):
-            res += f"<{lang.lower()}>{content}"
-        res += "|"
-    return "mix", res[:-1]
-
-
-def load_audio(path):
-    audio, sr = librosa.load(path, 48000)
-    # audio = librosa.resample(audio, 44100, 48000)
-    return sr, audio
-
-
-def gr_util(item):
-    if item == "Text prompt":
-        return {"visible": True, "__type__": "update"}, {
-            "visible": False,
-            "__type__": "update",
-        }
-    else:
-        return {"visible": False, "__type__": "update"}, {
-            "visible": True,
-            "__type__": "update",
-        }
-
-
-if __name__ == "__main__":
-    if config.webui_config.debug:
-        logger.info("Enable DEBUG-LEVEL log")
-        logging.basicConfig(level=logging.DEBUG)
-    hps = utils.get_hparams_from_file(config.webui_config.config_path)
-    # 若config.json中未指定版本则默认为最新版本
-    version = hps.version if hasattr(hps, "version") else latest_version
-    net_g = get_net_g(
-        model_path=config.webui_config.model, version=version, device=device, hps=hps
-    )
-    speaker_ids = hps.data.spk2id
-    speakers = list(speaker_ids.keys())
-    languages = ["ZH", "JP", "EN", "mix", "auto"]
-    with gr.Blocks() as app:
-        with gr.Row():
-            with gr.Column():
-                text = gr.TextArea(
-                    label="输入文本内容",
-                    placeholder="""
-                    如果你选择语言为\'mix\'，必须按照格式输入，否则报错:
-                        格式举例(zh是中文，jp是日语，不区分大小写；说话人举例:gongzi):
-                         [说话人1]<zh>你好，こんにちは！ <jp>こんにちは，世界。
-                         [说话人2]<zh>你好吗？<jp>元気ですか？
-                         [说话人3]<zh>谢谢。<jp>どういたしまして。
-                         ...
-                    另外，所有的语言选项都可以用'|'分割长段实现分句生成。
-                    """,
-                )
-                trans = gr.Button("中翻日", variant="primary")
-                slicer = gr.Button("快速切分", variant="primary")
-                formatter = gr.Button("检测语言，并整理为 MIX 格式", variant="primary")
-                speaker = gr.Dropdown(
-                    choices=speakers, value=speakers[0], label="Speaker"
-                )
-                _ = gr.Markdown(
-                    value="提示模式（Prompt mode）：可选文字提示或音频提示，用于生成文字或音频指定风格的声音。\n",
-                    visible=False,
-                )
-                prompt_mode = gr.Radio(
-                    ["Text prompt", "Audio prompt"],
-                    label="Prompt Mode",
-                    value="Text prompt",
-                    visible=False,
-                )
-                text_prompt = gr.Textbox(
-                    label="Text prompt",
-                    placeholder="用文字描述生成风格。如：Happy",
-                    value="Happy",
-                    visible=False,
-                )
-                audio_prompt = gr.Audio(
-                    label="Audio prompt", type="filepath", visible=False
-                )
-                sdp_ratio = gr.Slider(
-                    minimum=0, maximum=1, value=0.5, step=0.1, label="SDP Ratio"
-                )
-                noise_scale = gr.Slider(
-                    minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise"
-                )
-                noise_scale_w = gr.Slider(
-                    minimum=0.1, maximum=2, value=0.9, step=0.1, label="Noise_W"
-                )
-                length_scale = gr.Slider(
-                    minimum=0.1, maximum=2, value=1.0, step=0.1, label="Length"
-                )
-                language = gr.Dropdown(
-                    choices=languages, value=languages[0], label="Language"
-                )
-                btn = gr.Button("生成音频！", variant="primary")
-            with gr.Column():
-                with gr.Accordion("融合文本语义", open=False):
-                    gr.Markdown(
-                        value="使用辅助文本的语意来辅助生成对话（语言保持与主文本相同）\n\n"
-                        "**注意**：不要使用**指令式文本**（如：开心），要使用**带有强烈情感的文本**（如：我好快乐！！！）\n\n"
-                        "效果较不明确，留空即为不使用该功能"
-                    )
-                    style_text = gr.Textbox(label="辅助文本")
-                    style_weight = gr.Slider(
-                        minimum=0,
-                        maximum=1,
-                        value=0.7,
-                        step=0.1,
-                        label="Weight",
-                        info="主文本和辅助文本的bert混合比率，0表示仅主文本，1表示仅辅助文本",
-                    )
-                with gr.Row():
-                    with gr.Column():
-                        interval_between_sent = gr.Slider(
-                            minimum=0,
-                            maximum=5,
-                            value=0.2,
-                            step=0.1,
-                            label="句间停顿(秒)，勾选按句切分才生效",
-                        )
-                        interval_between_para = gr.Slider(
-                            minimum=0,
-                            maximum=10,
-                            value=1,
-                            step=0.1,
-                            label="段间停顿(秒)，需要大于句间停顿才有效",
-                        )
-                        opt_cut_by_sent = gr.Checkbox(
-                            label="按句切分    在按段落切分的基础上再按句子切分文本"
-                        )
-                        slicer = gr.Button("切分生成", variant="primary")
-                text_output = gr.Textbox(label="状态信息")
-                audio_output = gr.Audio(label="输出音频")
-                # explain_image = gr.Image(
-                #     label="参数解释信息",
-                #     show_label=True,
-                #     show_share_button=False,
-                #     show_download_button=False,
-                #     value=os.path.abspath("./img/参数说明.png"),
-                # )
-        btn.click(
-            tts_fn,
-            inputs=[
-                text,
-                speaker,
-                sdp_ratio,
-                noise_scale,
-                noise_scale_w,
-                length_scale,
-                language,
-                audio_prompt,
-                text_prompt,
-                prompt_mode,
-                style_text,
-                style_weight,
-            ],
-            outputs=[text_output, audio_output],
-        )
-
-        trans.click(
-            translate,
-            inputs=[text],
-            outputs=[text],
-        )
-        slicer.click(
-            tts_split,
-            inputs=[
-                text,
-                speaker,
-                sdp_ratio,
-                noise_scale,
-                noise_scale_w,
-                length_scale,
-                language,
-                opt_cut_by_sent,
-                interval_between_para,
-                interval_between_sent,
-                audio_prompt,
-                text_prompt,
-                style_text,
-                style_weight,
-            ],
-            outputs=[text_output, audio_output],
-        )
-
-        prompt_mode.change(
-            lambda x: gr_util(x),
-            inputs=[prompt_mode],
-            outputs=[text_prompt, audio_prompt],
-        )
-
-        audio_prompt.upload(
-            lambda x: load_audio(x),
-            inputs=[audio_prompt],
-            outputs=[audio_prompt],
-        )
-
-        formatter.click(
-            format_utils,
-            inputs=[text, speaker],
-            outputs=[language, text],
-        )
-
-    print("推理页面已开启!")
-    webbrowser.open(f"http://127.0.0.1:{config.webui_config.port}")
-    app.launch(share=config.webui_config.share, server_port=config.webui_config.port)