AudioNotes

AudioNotes

快速提取音视频内容,整理成一份结构化的markdown笔记

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AudioNotes is a system built on FunASR and Qwen2 that can quickly extract content from audio and video, and organize it using large models into structured markdown notes for easy reading. Users can interact with the audio and video content, install Ollama, pull models, and deploy services using Docker or locally with a PostgreSQL database. The system provides a seamless way to convert audio and video into structured notes for efficient consumption.

README:

AudioNotes

基于 FunASR 和 Qwen2 构建的音视频转结构化笔记系统

能够快速提取音视频的内容,并且调用大模型进行整理,成为一份结构化的markdown笔记,方便快速阅读

FunASR: https://github.com/modelscope/FunASR

Qwen2: https://ollama.com/library/qwen2

效果展示

音视频识别和整理

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与音视频内容对话

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使用方法

① 安装 Ollama

下载对应系统的 Ollama 安装包进行安装

https://ollama.com/download

② 拉取模型

我以 阿里的千问2 7b 为例 https://ollama.com/library/qwen2

ollama pull qwen2:7b

③ 部署服务

有两种部署方式,一种是使用 Docker 部署,另一种是本地部署

Docker部署(推荐)🐳

curl -fsSL https://github.com/harry0703/AudioNotes/raw/main/docker-compose.yml -o docker-compose.yml
docker-compose up

docker 启动后,访问 http://localhost:15433/

本地部署 📦

需要有可访问的 postgresql 数据库

conda create -n AudioNotes python=3.10 -y
conda activate AudioNotes
git clone https://github.com/harry0703/AudioNotes.git
cd AudioNotes
pip install -r requirements.txt

.env.example 重命名为 .env,修改相关配置信息

chainlit run main.py

服务启动后,访问 http://localhost:8000/

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