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