185 lines
7.6 KiB
Python
185 lines
7.6 KiB
Python
from toolbox import CatchException, report_execption, select_api_key, update_ui, write_results_to_file, get_conf
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
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def split_audio_file(filename, split_duration=1000):
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"""
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根据给定的切割时长将音频文件切割成多个片段。
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Args:
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filename (str): 需要被切割的音频文件名。
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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。
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Returns:
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filelist (list): 一个包含所有切割音频片段文件路径的列表。
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"""
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from moviepy.editor import AudioFileClip
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import os
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os.makedirs('gpt_log/mp3/cut/', exist_ok=True) # 创建存储切割音频的文件夹
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# 读取音频文件
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audio = AudioFileClip(filename)
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# 计算文件总时长和切割点
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total_duration = audio.duration
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split_points = list(range(0, int(total_duration), split_duration))
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split_points.append(int(total_duration))
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filelist = []
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# 切割音频文件
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for i in range(len(split_points) - 1):
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start_time = split_points[i]
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end_time = split_points[i + 1]
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split_audio = audio.subclip(start_time, end_time)
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split_audio.write_audiofile(f"gpt_log/mp3/cut/{filename[0]}_{i}.mp3")
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filelist.append(f"gpt_log/mp3/cut/{filename[0]}_{i}.mp3")
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audio.close()
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return filelist
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def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
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import os, requests
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from moviepy.editor import AudioFileClip
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from request_llm.bridge_all import model_info
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# 设置OpenAI密钥和模型
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api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
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chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
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whisper_endpoint = chat_endpoint.replace('chat/completions', 'audio/transcriptions')
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url = whisper_endpoint
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headers = {
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'Authorization': f"Bearer {api_key}"
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}
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os.makedirs('gpt_log/mp3/', exist_ok=True)
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for index, fp in enumerate(file_manifest):
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audio_history = []
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# 提取文件扩展名
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ext = os.path.splitext(fp)[1]
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# 提取视频中的音频
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if ext not in [".mp3", ".wav", ".m4a", ".mpga"]:
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audio_clip = AudioFileClip(fp)
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audio_clip.write_audiofile(f'gpt_log/mp3/output{index}.mp3')
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fp = f'gpt_log/mp3/output{index}.mp3'
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# 调用whisper模型音频转文字
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voice = split_audio_file(fp)
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for j, i in enumerate(voice):
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with open(i, 'rb') as f:
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file_content = f.read() # 读取文件内容到内存
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files = {
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'file': (os.path.basename(i), file_content),
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}
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data = {
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"model": "whisper-1",
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"prompt": parse_prompt,
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'response_format': "text"
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}
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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 ..."])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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proxies, = get_conf('proxies')
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response = requests.post(url, headers=headers, files=files, data=data, proxies=proxies).text
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chatbot.append(["音频解析结果", response])
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history.extend(["音频解析结果", response])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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i_say = f'请对下面的音频片段做概述，音频内容是 ```{response}```'
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i_say_show_user = f'第{index + 1}段音频的第{j + 1} / {len(voice)}片段。'
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gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
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inputs=i_say,
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inputs_show_user=i_say_show_user,
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llm_kwargs=llm_kwargs,
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chatbot=chatbot,
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history=[],
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sys_prompt=f"总结音频。音频文件名{fp}"
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)
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chatbot[-1] = (i_say_show_user, gpt_say)
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history.extend([i_say_show_user, gpt_say])
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audio_history.extend([i_say_show_user, gpt_say])
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# 已经对该文章的所有片段总结完毕，如果文章被切分了
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result = "".join(audio_history)
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if len(audio_history) > 1:
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i_say = f"根据以上的对话，使用中文总结音频“{result}”的主要内容。"
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i_say_show_user = f'第{index + 1}段音频的主要内容：'
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gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
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inputs=i_say,
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inputs_show_user=i_say_show_user,
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llm_kwargs=llm_kwargs,
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chatbot=chatbot,
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history=audio_history,
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sys_prompt="总结文章。"
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)
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history.extend([i_say, gpt_say])
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audio_history.extend([i_say, gpt_say])
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res = write_results_to_file(history)
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chatbot.append((f"第{index + 1}段音频完成了吗？", res))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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# 删除中间文件夹
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import shutil
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shutil.rmtree('gpt_log/mp3')
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res = write_results_to_file(history)
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chatbot.append(("所有音频都总结完成了吗？", res))
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yield from update_ui(chatbot=chatbot, history=history)
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@CatchException
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def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, WEB_PORT):
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import glob, os
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# 基本信息：功能、贡献者
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chatbot.append([
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"函数插件功能？",
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"总结音视频内容，函数插件贡献者: dalvqw & BinaryHusky"])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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try:
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from moviepy.editor import AudioFileClip
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except:
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report_execption(chatbot, history,
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a=f"解析项目: {txt}",
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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```。")
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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# 清空历史，以免输入溢出
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history = []
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# 检测输入参数，如没有给定输入参数，直接退出
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if os.path.exists(txt):
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project_folder = txt
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else:
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if txt == "": txt = '空空如也的输入栏'
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report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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# 搜索需要处理的文件清单
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extensions = ['.mp4', '.m4a', '.wav', '.mpga', '.mpeg', '.mp3', '.avi', '.mkv', '.flac', '.aac']
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if txt.endswith(tuple(extensions)):
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file_manifest = [txt]
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else:
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file_manifest = []
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for extension in extensions:
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file_manifest.extend(glob.glob(f'{project_folder}/**/*{extension}', recursive=True))
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# 如果没找到任何文件
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if len(file_manifest) == 0:
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report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何音频或视频文件: {txt}")
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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# 开始正式执行任务
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if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
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parse_prompt = plugin_kwargs.get("advanced_arg", '将音频解析为简体中文')
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yield from AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history)
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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