Hugging Face's logo

Spaces:
Gnib
/
TecGPT
Runtime error

App Files Community
1
TecGPT
File size: 8,133 Bytes 
444f09e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
 
from toolbox import CatchException, report_exception, select_api_key, update_ui, get_conf
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
from toolbox import write_history_to_file, promote_file_to_downloadzone, get_log_folder

def split_audio_file(filename, split_duration=1000):
    """
    根据给定的切割时长将音频文件切割成多个片段。

    Args:
        filename (str): 需要被切割的音频文件名。
        split_duration (int, optional): 每个切割音频片段的时长（以秒为单位）。默认值为1000。

    Returns:
        filelist (list): 一个包含所有切割音频片段文件路径的列表。

    """
    from moviepy.editor import AudioFileClip
    import os
    os.makedirs(f"{get_log_folder(plugin_name='audio')}/mp3/cut/", exist_ok=True)  # 创建存储切割音频的文件夹

    # 读取音频文件
    audio = AudioFileClip(filename)

    # 计算文件总时长和切割点
    total_duration = audio.duration
    split_points = list(range(0, int(total_duration), split_duration))
    split_points.append(int(total_duration))
    filelist = []

    # 切割音频文件
    for i in range(len(split_points) - 1):
        start_time = split_points[i]
        end_time = split_points[i + 1]
        split_audio = audio.subclip(start_time, end_time)
        split_audio.write_audiofile(f"{get_log_folder(plugin_name='audio')}/mp3/cut/{filename[0]}_{i}.mp3")
        filelist.append(f"{get_log_folder(plugin_name='audio')}/mp3/cut/{filename[0]}_{i}.mp3")

    audio.close()
    return filelist

def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
    import os, requests
    from moviepy.editor import AudioFileClip
    from request_llms.bridge_all import model_info

    # 设置OpenAI密钥和模型
    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
    chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']

    whisper_endpoint = chat_endpoint.replace('chat/completions', 'audio/transcriptions')
    url = whisper_endpoint
    headers = {
        'Authorization': f"Bearer {api_key}"
    }

    os.makedirs(f"{get_log_folder(plugin_name='audio')}/mp3/", exist_ok=True)
    for index, fp in enumerate(file_manifest):
        audio_history = []
        # 提取文件扩展名
        ext = os.path.splitext(fp)[1]
        # 提取视频中的音频
        if ext not in [".mp3", ".wav", ".m4a", ".mpga"]:
            audio_clip = AudioFileClip(fp)
            audio_clip.write_audiofile(f"{get_log_folder(plugin_name='audio')}/mp3/output{index}.mp3")
            fp = f"{get_log_folder(plugin_name='audio')}/mp3/output{index}.mp3"
        # 调用whisper模型音频转文字
        voice = split_audio_file(fp)
        for j, i in enumerate(voice):
            with open(i, 'rb') as f:
                file_content = f.read()  # 读取文件内容到内存
                files = {
                    'file': (os.path.basename(i), file_content),
                }
                data = {
                    "model": "whisper-1",
                    "prompt": parse_prompt,
                    'response_format': "text"
                }

            chatbot.append([f"将 {i} 发送到openai音频解析终端 (whisper)，当前参数：{parse_prompt}", "正在处理 ..."])
            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
            proxies = get_conf('proxies')
            response = requests.post(url, headers=headers, files=files, data=data, proxies=proxies).text

            chatbot.append(["音频解析结果", response])
            history.extend(["音频解析结果", response])
            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面

            i_say = f'请对下面的音频片段做概述，音频内容是 ```{response}```'
            i_say_show_user = f'第{index + 1}段音频的第{j + 1} / {len(voice)}片段。'
            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
                inputs=i_say,
                inputs_show_user=i_say_show_user,
                llm_kwargs=llm_kwargs,
                chatbot=chatbot,
                history=[],
                sys_prompt=f"总结音频。音频文件名{fp}"
            )

            chatbot[-1] = (i_say_show_user, gpt_say)
            history.extend([i_say_show_user, gpt_say])
            audio_history.extend([i_say_show_user, gpt_say])

        # 已经对该文章的所有片段总结完毕，如果文章被切分了
        result = "".join(audio_history)
        if len(audio_history) > 1:
            i_say = f"根据以上的对话，使用中文总结音频“{result}”的主要内容。"
            i_say_show_user = f'第{index + 1}段音频的主要内容：'
            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
                inputs=i_say,
                inputs_show_user=i_say_show_user,
                llm_kwargs=llm_kwargs,
                chatbot=chatbot,
                history=audio_history,
                sys_prompt="总结文章。"
            )
            history.extend([i_say, gpt_say])
            audio_history.extend([i_say, gpt_say])

        res = write_history_to_file(history)
        promote_file_to_downloadzone(res, chatbot=chatbot)
        chatbot.append((f"第{index + 1}段音频完成了吗？", res))
        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面

    # 删除中间文件夹
    import shutil
    shutil.rmtree(f"{get_log_folder(plugin_name='audio')}/mp3")
    res = write_history_to_file(history)
    promote_file_to_downloadzone(res, chatbot=chatbot)
    chatbot.append(("所有音频都总结完成了吗？", res))
    yield from update_ui(chatbot=chatbot, history=history)


@CatchException
def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, WEB_PORT):
    import glob, os

    # 基本信息：功能、贡献者
    chatbot.append([
        "函数插件功能？",
        "总结音视频内容，函数插件贡献者: dalvqw & BinaryHusky"])
    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面

    try:
        from moviepy.editor import AudioFileClip
    except:
        report_exception(chatbot, history,
                         a=f"解析项目: {txt}",
                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade moviepy```。")
        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
        return

    # 清空历史，以免输入溢出
    history = []

    # 检测输入参数，如没有给定输入参数，直接退出
    if os.path.exists(txt):
        project_folder = txt
    else:
        if txt == "": txt = '空空如也的输入栏'
        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
        return

    # 搜索需要处理的文件清单
    extensions = ['.mp4', '.m4a', '.wav', '.mpga', '.mpeg', '.mp3', '.avi', '.mkv', '.flac', '.aac']

    if txt.endswith(tuple(extensions)):
        file_manifest = [txt]
    else:
        file_manifest = []
        for extension in extensions:
            file_manifest.extend(glob.glob(f'{project_folder}/**/*{extension}', recursive=True))

    # 如果没找到任何文件
    if len(file_manifest) == 0:
        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何音频或视频文件: {txt}")
        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
        return

    # 开始正式执行任务
    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
    parse_prompt = plugin_kwargs.get("advanced_arg", '将音频解析为简体中文')
    yield from AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history)

    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面