Generates structured notes from Bilibili videos by downloading audio, using Whisper for speech recognition, and leveraging LLMs to create comprehensive notes from video content.
Utilizes FFmpeg for audio processing, enabling the extraction and manipulation of audio streams from videos for transcription.
Uses OpenAI's services for LLM-based note generation from video content, requiring an API key to process and structure transcribed content.
BiliMind-MCP
功能特点
- 从B站视频生成结构化笔记
- 支持音频自动下载和转录
- 使用 Whisper 进行语音识别
- 使用 LLM 生成结构化笔记
- 提供 MCP 服务接口
环境准备
系统要求
- Python >= 3.10
- FFmpeg(用于音频处理)
1. 安装系统依赖
2. 安装 uv
3. 创建并激活虚拟环境
4. 安装项目依赖
5. 配置环境变量
使用方法
启动 MCP 服务器
API 调用示例
环境变量说明
OPENAI_API_KEY
: LLM API密钥API_BASE
: API基础URLMCP_PORT
: MCP服务器端口(默认8001)DEFAULT_OUTPUT_DIR
: 下载文件保存目录DEFAULT_MODEL_DIR
: 模型文件保存目录WHISPER_MODEL_SIZE
: Whisper模型大小(默认tiny)
注意事项
- 首次运行会自动下载 Whisper 模型文件
- 音频文件会在处理完成后自动删除
- 需要确保有足够的磁盘空间存储临时文件和模型文件
- API调用需要有效的 API 密钥
目录结构
致谢
本项目受到 BiliNote 项目的启发。BiliNote 是一个优秀的开源 AI 视频笔记助手,支持多平台视频内容的自动笔记生成。
License
MIT License
This server cannot be installed
An MCP server that generates structured notes from Bilibili videos by automatically downloading audio, transcribing with Whisper, and processing through LLM.
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