diff --git a/tools/my_utils.py b/tools/my_utils.py
new file mode 100644
index 0000000..776939d
--- /dev/null
+++ b/tools/my_utils.py
@@ -0,0 +1,21 @@
+import ffmpeg
+import numpy as np
+
+
+def load_audio(file, sr):
+    try:
+        # https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
+        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+        file = (
+            file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        )  # 防止小白拷路径头尾带了空格和"和回车
+        out, _ = (
+            ffmpeg.input(file, threads=0)
+            .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
+            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
+        )
+    except Exception as e:
+        raise RuntimeError(f"Failed to load audio: {e}")
+
+    return np.frombuffer(out, np.float32).flatten()
diff --git a/tools/slice_audio.py b/tools/slice_audio.py
new file mode 100644
index 0000000..79592de
--- /dev/null
+++ b/tools/slice_audio.py
@@ -0,0 +1,48 @@
+import os,sys,numpy as np
+import traceback
+from scipy.io import wavfile
+# parent_directory = os.path.dirname(os.path.abspath(__file__))
+# sys.path.append(parent_directory)
+from my_utils import load_audio
+from slicer2 import Slicer
+
+def slice(inp,opt_root,threshold,min_length,min_interval,hop_size,max_sil_kept,_max,alpha,i_part,all_part):
+    os.makedirs(opt_root,exist_ok=True)
+    if os.path.isfile(inp):
+        input=[inp]
+    elif os.path.isdir(inp):
+        input=["%s/%s"%(inp,name)for name in sorted(list(os.listdir(inp)))]
+    else:
+        return "输入路径存在但既不是文件也不是文件夹"
+    slicer = Slicer(
+        sr=32000,  # 长音频采样率
+        threshold=      int(threshold),  # 音量小于这个值视作静音的备选切割点
+        min_length=     int(min_length),  # 每段最小多长，如果第一段太短一直和后面段连起来直到超过这个值
+        min_interval=   int(min_interval),  # 最短切割间隔
+        hop_size=       int(hop_size),  # 怎么算音量曲线，越小精度越大计算量越高（不是精度越大效果越好）
+        max_sil_kept=   int(max_sil_kept),  # 切完后静音最多留多长
+    )
+    _max=float(_max)
+    alpha=float(alpha)
+    for inp_path in input[int(i_part)::int(all_part)]:
+        # print(inp_path)
+        try:
+            name = os.path.basename(inp_path)
+            audio = load_audio(inp_path, 32000)
+            # print(audio.shape)
+            for chunk, start, end in slicer.slice(audio):  # start和end是帧数
+                tmp_max = np.abs(chunk).max()
+                if(tmp_max>1):chunk/=tmp_max
+                chunk = (chunk / tmp_max * (_max * alpha)) + (1 - alpha) * chunk
+                wavfile.write(
+                    "%s/%s_%s_%s.wav" % (opt_root, name, start, end),
+                    32000,
+                    # chunk.astype(np.float32),
+                    (chunk * 32767).astype(np.int16),
+                )
+        except:
+            print(inp_path,"->fail->",traceback.format_exc())
+    return "执行完毕，请检查输出文件"
+
+print(slice(*sys.argv[1:]))
+
diff --git a/tools/slicer2.py b/tools/slicer2.py
new file mode 100644
index 0000000..ba6794b
--- /dev/null
+++ b/tools/slicer2.py
@@ -0,0 +1,261 @@
+import numpy as np
+
+
+# This function is obtained from librosa.
+def get_rms(
+    y,
+    frame_length=2048,
+    hop_length=512,
+    pad_mode="constant",
+):
+    padding = (int(frame_length // 2), int(frame_length // 2))
+    y = np.pad(y, padding, mode=pad_mode)
+
+    axis = -1
+    # put our new within-frame axis at the end for now
+    out_strides = y.strides + tuple([y.strides[axis]])
+    # Reduce the shape on the framing axis
+    x_shape_trimmed = list(y.shape)
+    x_shape_trimmed[axis] -= frame_length - 1
+    out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
+    xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
+    if axis < 0:
+        target_axis = axis - 1
+    else:
+        target_axis = axis + 1
+    xw = np.moveaxis(xw, -1, target_axis)
+    # Downsample along the target axis
+    slices = [slice(None)] * xw.ndim
+    slices[axis] = slice(0, None, hop_length)
+    x = xw[tuple(slices)]
+
+    # Calculate power
+    power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
+
+    return np.sqrt(power)
+
+
+class Slicer:
+    def __init__(
+        self,
+        sr: int,
+        threshold: float = -40.0,
+        min_length: int = 5000,
+        min_interval: int = 300,
+        hop_size: int = 20,
+        max_sil_kept: int = 5000,
+    ):
+        if not min_length >= min_interval >= hop_size:
+            raise ValueError(
+                "The following condition must be satisfied: min_length >= min_interval >= hop_size"
+            )
+        if not max_sil_kept >= hop_size:
+            raise ValueError(
+                "The following condition must be satisfied: max_sil_kept >= hop_size"
+            )
+        min_interval = sr * min_interval / 1000
+        self.threshold = 10 ** (threshold / 20.0)
+        self.hop_size = round(sr * hop_size / 1000)
+        self.win_size = min(round(min_interval), 4 * self.hop_size)
+        self.min_length = round(sr * min_length / 1000 / self.hop_size)
+        self.min_interval = round(min_interval / self.hop_size)
+        self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
+
+    def _apply_slice(self, waveform, begin, end):
+        if len(waveform.shape) > 1:
+            return waveform[
+                :, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size)
+            ]
+        else:
+            return waveform[
+                begin * self.hop_size : min(waveform.shape[0], end * self.hop_size)
+            ]
+
+    # @timeit
+    def slice(self, waveform):
+        if len(waveform.shape) > 1:
+            samples = waveform.mean(axis=0)
+        else:
+            samples = waveform
+        if samples.shape[0] <= self.min_length:
+            return [waveform]
+        rms_list = get_rms(
+            y=samples, frame_length=self.win_size, hop_length=self.hop_size
+        ).squeeze(0)
+        sil_tags = []
+        silence_start = None
+        clip_start = 0
+        for i, rms in enumerate(rms_list):
+            # Keep looping while frame is silent.
+            if rms < self.threshold:
+                # Record start of silent frames.
+                if silence_start is None:
+                    silence_start = i
+                continue
+            # Keep looping while frame is not silent and silence start has not been recorded.
+            if silence_start is None:
+                continue
+            # Clear recorded silence start if interval is not enough or clip is too short
+            is_leading_silence = silence_start == 0 and i > self.max_sil_kept
+            need_slice_middle = (
+                i - silence_start >= self.min_interval
+                and i - clip_start >= self.min_length
+            )
+            if not is_leading_silence and not need_slice_middle:
+                silence_start = None
+                continue
+            # Need slicing. Record the range of silent frames to be removed.
+            if i - silence_start <= self.max_sil_kept:
+                pos = rms_list[silence_start : i + 1].argmin() + silence_start
+                if silence_start == 0:
+                    sil_tags.append((0, pos))
+                else:
+                    sil_tags.append((pos, pos))
+                clip_start = pos
+            elif i - silence_start <= self.max_sil_kept * 2:
+                pos = rms_list[
+                    i - self.max_sil_kept : silence_start + self.max_sil_kept + 1
+                ].argmin()
+                pos += i - self.max_sil_kept
+                pos_l = (
+                    rms_list[
+                        silence_start : silence_start + self.max_sil_kept + 1
+                    ].argmin()
+                    + silence_start
+                )
+                pos_r = (
+                    rms_list[i - self.max_sil_kept : i + 1].argmin()
+                    + i
+                    - self.max_sil_kept
+                )
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                    clip_start = pos_r
+                else:
+                    sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
+                    clip_start = max(pos_r, pos)
+            else:
+                pos_l = (
+                    rms_list[
+                        silence_start : silence_start + self.max_sil_kept + 1
+                    ].argmin()
+                    + silence_start
+                )
+                pos_r = (
+                    rms_list[i - self.max_sil_kept : i + 1].argmin()
+                    + i
+                    - self.max_sil_kept
+                )
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                else:
+                    sil_tags.append((pos_l, pos_r))
+                clip_start = pos_r
+            silence_start = None
+        # Deal with trailing silence.
+        total_frames = rms_list.shape[0]
+        if (
+            silence_start is not None
+            and total_frames - silence_start >= self.min_interval
+        ):
+            silence_end = min(total_frames, silence_start + self.max_sil_kept)
+            pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start
+            sil_tags.append((pos, total_frames + 1))
+        # Apply and return slices.
+        ####音频+起始时间+终止时间
+        if len(sil_tags) == 0:
+            return [[waveform,0,int(total_frames*self.hop_size)]]
+        else:
+            chunks = []
+            if sil_tags[0][0] > 0:
+                chunks.append([self._apply_slice(waveform, 0, sil_tags[0][0]),0,int(sil_tags[0][0]*self.hop_size)])
+            for i in range(len(sil_tags) - 1):
+                chunks.append(
+                    [self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]),int(sil_tags[i][1]*self.hop_size),int(sil_tags[i + 1][0]*self.hop_size)]
+                )
+            if sil_tags[-1][1] < total_frames:
+                chunks.append(
+                    [self._apply_slice(waveform, sil_tags[-1][1], total_frames),int(sil_tags[-1][1]*self.hop_size),int(total_frames*self.hop_size)]
+                )
+            return chunks
+
+
+def main():
+    import os.path
+    from argparse import ArgumentParser
+
+    import librosa
+    import soundfile
+
+    parser = ArgumentParser()
+    parser.add_argument("audio", type=str, help="The audio to be sliced")
+    parser.add_argument(
+        "--out", type=str, help="Output directory of the sliced audio clips"
+    )
+    parser.add_argument(
+        "--db_thresh",
+        type=float,
+        required=False,
+        default=-40,
+        help="The dB threshold for silence detection",
+    )
+    parser.add_argument(
+        "--min_length",
+        type=int,
+        required=False,
+        default=5000,
+        help="The minimum milliseconds required for each sliced audio clip",
+    )
+    parser.add_argument(
+        "--min_interval",
+        type=int,
+        required=False,
+        default=300,
+        help="The minimum milliseconds for a silence part to be sliced",
+    )
+    parser.add_argument(
+        "--hop_size",
+        type=int,
+        required=False,
+        default=10,
+        help="Frame length in milliseconds",
+    )
+    parser.add_argument(
+        "--max_sil_kept",
+        type=int,
+        required=False,
+        default=500,
+        help="The maximum silence length kept around the sliced clip, presented in milliseconds",
+    )
+    args = parser.parse_args()
+    out = args.out
+    if out is None:
+        out = os.path.dirname(os.path.abspath(args.audio))
+    audio, sr = librosa.load(args.audio, sr=None, mono=False)
+    slicer = Slicer(
+        sr=sr,
+        threshold=args.db_thresh,
+        min_length=args.min_length,
+        min_interval=args.min_interval,
+        hop_size=args.hop_size,
+        max_sil_kept=args.max_sil_kept,
+    )
+    chunks = slicer.slice(audio)
+    if not os.path.exists(out):
+        os.makedirs(out)
+    for i, chunk in enumerate(chunks):
+        if len(chunk.shape) > 1:
+            chunk = chunk.T
+        soundfile.write(
+            os.path.join(
+                out,
+                f"%s_%d.wav"
+                % (os.path.basename(args.audio).rsplit(".", maxsplit=1)[0], i),
+            ),
+            chunk,
+            sr,
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/subfix_webui.py b/tools/subfix_webui.py
new file mode 100644
index 0000000..0e6585e
--- /dev/null
+++ b/tools/subfix_webui.py
@@ -0,0 +1,492 @@
+import argparse,os
+import copy
+import json
+import os
+import uuid
+
+import librosa
+import gradio as gr
+import numpy as np
+import soundfile
+
+g_json_key_text = ""
+g_json_key_path = ""
+g_load_file = ""
+g_load_format = ""
+
+g_max_json_index = 0
+g_index = 0
+g_batch = 10
+g_text_list = []
+g_audio_list = []
+g_checkbox_list = []
+g_data_json = []
+
+
+def reload_data(index, batch):
+    global g_index
+    g_index = index
+    global g_batch
+    g_batch = batch
+    datas = g_data_json[index:index+batch]
+    output = []
+    for d in datas:
+        output.append(
+            {
+                g_json_key_text: d[g_json_key_text],
+                g_json_key_path: d[g_json_key_path]
+            }
+        )
+    return output
+
+
+def b_change_index(index, batch):
+    global g_index, g_batch
+    g_index, g_batch = index, batch
+    datas = reload_data(index, batch)
+    output = []
+    for i , _ in enumerate(datas):
+        output.append(
+            # gr.Textbox(
+            #     label=f"Text {i+index}",
+            #     value=_[g_json_key_text]#text
+            # )
+            {
+                "__type__":"update",
+                "label":f"Text {i+index}",
+                "value":_[g_json_key_text]
+            }
+        )
+    for _ in range(g_batch - len(datas)):
+        output.append(
+            # gr.Textbox(
+            #     label=f"Text",
+            #     value=""
+            # )
+            {
+                "__type__": "update",
+                "label": f"Text",
+                "value": ""
+            }
+        )
+    for _ in datas:
+        output.append(_[g_json_key_path])
+    for _ in range(g_batch - len(datas)):
+        output.append(None)
+    for _ in range(g_batch):
+        output.append(False)
+    return output
+
+
+def b_next_index(index, batch):
+    if (index + batch) <= g_max_json_index:
+        return index + batch , *b_change_index(index + batch, batch)
+    else:
+        return index, *b_change_index(index, batch)
+
+
+def b_previous_index(index, batch):
+    if (index - batch) >= 0:
+        return index - batch , *b_change_index(index - batch, batch)
+    else:
+        return 0, *b_change_index(0, batch)
+
+
+def b_submit_change(*text_list):
+    global g_data_json
+    change = False
+    for i, new_text in enumerate(text_list):
+        if g_index + i <= g_max_json_index:
+            new_text = new_text.strip()+' '
+            if (g_data_json[g_index + i][g_json_key_text] != new_text):
+                g_data_json[g_index + i][g_json_key_text] = new_text
+                change = True
+    if change:
+        b_save_file()
+    return g_index, *b_change_index(g_index, g_batch)
+
+
+def b_delete_audio(*checkbox_list):
+    global g_data_json, g_index, g_max_json_index
+    change = False
+    for i, checkbox in reversed(list(enumerate(checkbox_list))):
+        if g_index + i < len(g_data_json):
+            if (checkbox == True):
+                g_data_json.pop(g_index + i)
+                change = True
+    
+    g_max_json_index = len(g_data_json)-1
+    if g_index > g_max_json_index:
+        g_index = g_max_json_index
+        g_index = g_index if g_index >= 0 else 0
+    # if change:
+    #     b_save_file()
+    # return gr.Slider(value=g_index, maximum=(g_max_json_index if g_max_json_index>=0 else 0)), *b_change_index(g_index, g_batch)
+    return {"value":g_index,"__type__":"update","maximum":(g_max_json_index if g_max_json_index>=0 else 0)},*b_change_index(g_index, g_batch)
+
+
+def b_invert_selection(*checkbox_list):
+    new_list = [not item if item is True else True for item in checkbox_list]
+    return new_list
+
+
+def get_next_path(filename):
+    base_dir = os.path.dirname(filename)
+    base_name = os.path.splitext(os.path.basename(filename))[0]
+    for i in range(100):
+        new_path = os.path.join(base_dir, f"{base_name}_{str(i).zfill(2)}.wav")
+        if not os.path.exists(new_path) :
+            return new_path
+    return os.path.join(base_dir, f'{str(uuid.uuid4())}.wav')
+
+
+def b_audio_split(audio_breakpoint, *checkbox_list):
+    global g_data_json , g_max_json_index
+    checked_index = []
+    for i, checkbox in enumerate(checkbox_list):
+        if (checkbox == True and g_index+i < len(g_data_json)):
+            checked_index.append(g_index + i)
+    if len(checked_index) == 1 :
+        index = checked_index[0]
+        audio_json = copy.deepcopy(g_data_json[index])
+        path = audio_json[g_json_key_path]
+        data, sample_rate = librosa.load(path, sr=None, mono=True)
+        audio_maxframe = len(data)
+        break_frame = int(audio_breakpoint * sample_rate)
+
+        if (break_frame >= 1 and break_frame < audio_maxframe):
+            audio_first = data[0:break_frame]
+            audio_second = data[break_frame:]
+            nextpath = get_next_path(path)
+            soundfile.write(nextpath, audio_second, sample_rate)
+            soundfile.write(path, audio_first, sample_rate)
+            g_data_json.insert(index + 1, audio_json)
+            g_data_json[index + 1][g_json_key_path] = nextpath
+            b_save_file()
+
+    g_max_json_index = len(g_data_json) - 1
+    # return gr.Slider(value=g_index, maximum=g_max_json_index), *b_change_index(g_index, g_batch)
+    return {"value":g_index,"maximum":g_max_json_index,"__type__":"update"}, *b_change_index(g_index, g_batch)
+
+def b_merge_audio(interval_r, *checkbox_list):
+    global g_data_json , g_max_json_index
+    checked_index = []
+    audios_path = []
+    audios_text = []
+    for i, checkbox in enumerate(checkbox_list):
+        if (checkbox == True and g_index+i < len(g_data_json)):
+            checked_index.append(g_index + i)
+            
+    if (len(checked_index)>1):
+        for i in checked_index:
+            audios_path.append(g_data_json[i][g_json_key_path])
+            audios_text.append(g_data_json[i][g_json_key_text])
+        for i in reversed(checked_index[1:]):
+            g_data_json.pop(i)
+
+        base_index = checked_index[0]
+        base_path = audios_path[0]
+        g_data_json[base_index][g_json_key_text] = "".join(audios_text)
+
+        audio_list = []
+        l_sample_rate = None
+        for i, path in enumerate(audios_path):
+            data, sample_rate = librosa.load(path, sr=l_sample_rate, mono=True)
+            l_sample_rate = sample_rate
+            if (i > 0):
+                silence = np.zeros(int(l_sample_rate * interval_r))
+                audio_list.append(silence)
+
+            audio_list.append(data)
+
+        audio_concat = np.concatenate(audio_list)
+
+        soundfile.write(base_path, audio_concat, l_sample_rate)
+
+        b_save_file()
+    
+    g_max_json_index = len(g_data_json) - 1
+    
+    # return gr.Slider(value=g_index, maximum=g_max_json_index), *b_change_index(g_index, g_batch)
+    return {"value":g_index,"maximum":g_max_json_index,"__type__":"update"}, *b_change_index(g_index, g_batch)
+
+
+def b_save_json():
+    with open(g_load_file,'w', encoding="utf-8") as file:
+        for data in g_data_json:
+            file.write(f'{json.dumps(data, ensure_ascii = False)}\n')
+
+
+def b_save_list():
+    with open(g_load_file,'w', encoding="utf-8") as file:
+        for data in g_data_json:
+            wav_path = data["wav_path"]
+            speaker_name = data["speaker_name"]
+            language = data["language"]
+            text = data["text"]
+            file.write(f"{wav_path}|{speaker_name}|{language}|{text}".strip()+'\n')
+
+
+def b_load_json():
+    global g_data_json, g_max_json_index
+    with open(g_load_file, 'r', encoding="utf-8") as file:
+        g_data_json = file.readlines()
+        g_data_json = [json.loads(line) for line in g_data_json]
+        g_max_json_index = len(g_data_json) - 1
+
+
+def b_load_list():
+    global g_data_json, g_max_json_index
+    with open(g_load_file, 'r', encoding="utf-8") as source:
+        data_list = source.readlines()
+        for _ in data_list:
+            data = _.split('|')
+            if (len(data) == 4):
+                wav_path, speaker_name, language, text = data
+                g_data_json.append(
+                        {
+                            'wav_path':wav_path,
+                            'speaker_name':speaker_name,
+                            'language':language,
+                            'text':text.strip()
+                        }
+                )
+            else:
+                print("error line:", data)
+        g_max_json_index = len(g_data_json) - 1
+
+
+def b_save_file():
+    if g_load_format == "json":
+        b_save_json()
+    elif g_load_format == "list":
+        b_save_list()
+
+
+def b_load_file():
+    if g_load_format == "json":
+        b_load_json()
+    elif g_load_format == "list":
+        b_load_list()
+
+
+def set_global(load_json, load_list, json_key_text, json_key_path, batch):
+    global g_json_key_text, g_json_key_path, g_load_file, g_load_format, g_batch
+
+    g_batch = int(batch)
+    
+    if (load_json != "None"):
+        g_load_format = "json"
+        g_load_file = load_json
+    elif (load_list != "None"):
+        g_load_format = "list"
+        g_load_file = load_list
+    else:
+        g_load_format = "list"
+        g_load_file = "demo.list"
+        
+    g_json_key_text = json_key_text
+    g_json_key_path = json_key_path
+
+    b_load_file()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Process some integers.')
+    parser.add_argument('--load_json', default="None", help='source file, like demo.json')
+    parser.add_argument('--load_list', default="None", help='source file, like demo.list')
+    parser.add_argument('--webui_port_subfix', default=9871, help='source file, like demo.list')
+    parser.add_argument('--json_key_text', default="text", help='the text key name in json, Default: text')
+    parser.add_argument('--json_key_path', default="wav_path", help='the path key name in json, Default: wav_path')
+    parser.add_argument('--g_batch', default=10, help='max number g_batch wav to display, Default: 10')
+
+    args = parser.parse_args()
+
+    set_global(args.load_json, args.load_list, args.json_key_text, args.json_key_path, args.g_batch)
+    
+    with gr.Blocks() as demo:
+
+        with gr.Row():
+            btn_change_index = gr.Button("Change Index")
+            btn_submit_change = gr.Button("Submit Text")
+            btn_merge_audio = gr.Button("Merge Audio")
+            btn_delete_audio = gr.Button("Delete Audio")
+            btn_previous_index = gr.Button("Previous Index")
+            btn_next_index = gr.Button("Next Index")
+            
+        with gr.Row():
+            index_slider = gr.Slider(
+                    minimum=0, maximum=g_max_json_index, value=g_index, step=1, label="Index", scale=3
+            )
+            splitpoint_slider = gr.Slider(
+                    minimum=0, maximum=120.0, value=0, step=0.1, label="Audio Split Point(s)", scale=3
+            )
+            btn_audio_split = gr.Button("Split Audio", scale=1)
+            btn_save_json = gr.Button("Save File", visible=True, scale=1)
+            btn_invert_selection = gr.Button("Invert Selection", scale=1)
+        
+        with gr.Row():
+            with gr.Column():
+                for _ in range(0,g_batch):
+                    with gr.Row():
+                        text = gr.Textbox(
+                            label = "Text",
+                            visible = True,
+                            scale=5
+                        )
+                        audio_output = gr.Audio(
+                            label="Output Audio",
+                            visible = True,
+                            scale=5
+                        )
+                        audio_check = gr.Checkbox(
+                            label="Yes",
+                            show_label = True,
+                            info = "Choose Audio",
+                            scale=1
+                        )
+                        g_text_list.append(text)
+                        g_audio_list.append(audio_output)
+                        g_checkbox_list.append(audio_check)
+
+
+
+        with gr.Row():
+            batchsize_slider = gr.Slider(
+                    minimum=1, maximum=g_batch, value=g_batch, step=1, label="Batch Size", scale=3, interactive=False
+            )
+            interval_slider = gr.Slider(
+                    minimum=0, maximum=2, value=0, step=0.01, label="Interval", scale=3
+            )
+            btn_theme_dark = gr.Button("Light Theme", link="?__theme=light", scale=1)
+            btn_theme_light = gr.Button("Dark Theme", link="?__theme=dark", scale=1)
+        
+        btn_change_index.click(
+            b_change_index,
+            inputs=[
+                index_slider,
+                batchsize_slider,
+            ],
+            outputs=[
+                *g_text_list,
+                *g_audio_list,
+                *g_checkbox_list
+            ],
+        )
+
+        
+        btn_submit_change.click(
+            b_submit_change,
+            inputs=[
+                *g_text_list,
+            ],
+            outputs=[
+                index_slider,
+                *g_text_list,
+                *g_audio_list,
+                *g_checkbox_list
+            ],
+        )
+
+        btn_previous_index.click(
+            b_previous_index,
+            inputs=[
+                index_slider,
+                batchsize_slider,
+            ],
+            outputs=[
+                index_slider,
+                *g_text_list,
+                *g_audio_list,
+                *g_checkbox_list
+            ],
+        )
+        
+        btn_next_index.click(
+            b_next_index,
+            inputs=[
+                index_slider,
+                batchsize_slider,
+            ],
+            outputs=[
+                index_slider,
+                *g_text_list,
+                *g_audio_list,
+                *g_checkbox_list
+            ],
+        )
+
+        btn_delete_audio.click(
+            b_delete_audio,
+            inputs=[
+                *g_checkbox_list
+            ],
+            outputs=[
+                index_slider,
+                *g_text_list,
+                *g_audio_list,
+                *g_checkbox_list
+            ]
+        )
+
+        btn_merge_audio.click(
+            b_merge_audio,
+            inputs=[
+                interval_slider,
+                *g_checkbox_list
+            ],
+            outputs=[
+                index_slider,
+                *g_text_list,
+                *g_audio_list,
+                *g_checkbox_list
+            ]
+        )
+
+        btn_audio_split.click(
+            b_audio_split,
+            inputs=[
+                splitpoint_slider,
+                *g_checkbox_list
+            ],
+            outputs=[
+                index_slider,
+                *g_text_list,
+                *g_audio_list,
+                *g_checkbox_list
+            ]
+        )
+
+        btn_invert_selection.click(
+            b_invert_selection,
+            inputs=[
+                *g_checkbox_list
+            ],
+            outputs=[
+                *g_checkbox_list
+            ]
+        )
+
+        btn_save_json.click(
+            b_save_file
+        )
+
+        demo.load(
+            b_change_index,
+            inputs=[
+                index_slider,
+                batchsize_slider,
+            ],
+            outputs=[
+                *g_text_list,
+                *g_audio_list,
+                *g_checkbox_list
+            ],
+        )
+        
+    demo.launch(
+        server_name="0.0.0.0",
+        inbrowser=True,
+        quiet=True,
+        server_port=int(args.webui_port_subfix)
+    )
\ No newline at end of file