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from toolbox import CatchException, report_execption, 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_llm.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_execption(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_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}")
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_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}")
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
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