Audio Summary with local LLM

This tool is designed to provide a quick and concise summary of audio and video files. It supports summarizing content either from a local file or directly from YouTube. The tool uses Whisper for transcription and a local version of Mistral AI (Ollama) for generating summaries.

Tip

It is possible to change the model you wish to use. To do this, change the OLLAMA_MODEL variable, and download the associated model via ollama

Features

YouTube Integration: Download and summarize content directly from YouTube.
Local File Support: Summarize audio files available on your local disk.
Transcription: Converts audio content to text using Whisper.
Summarization: Generates a concise summary using Mistral AI (Ollama).

Prerequisites

Before you start using this tool, you need to install the following dependencies:

Python 3.8 or higher
pytube for downloading videos from YouTube.
pathlibfor local file
openai-whisper for audio transcription.
Ollama for LLM model management.
ffmpeg (required for whisper)

Installation

Python Requirements

Clone the repository and install the required Python packages:

git clone https://github.com/damienarnodo/audio-summary-with-local-LLM.git
cd audio-summary-with-local-LLM
pip install -r src/requirements.txt

LLM Requierement

Download and install Ollama to carry out LLM Management More details about LLM model supported can be discribe on the Ollama github.

Download and use Mistral model :

ollama pull mistral

## Test the access :
ollama run mistral "tell me a joke"

Usage

The tool can be executed with the following command line options:

--from-youtube: To download and summarize a video from YouTube.
--from-local: To load and summarize an audio or video file from the local disk.

Examples

Summarizing a YouTube video:

python src/summary.py --from-youtube <YouTube-Video-URL>

Summarizing a local audio file:

python src/summary.py --from-local <path-to-audio-file>

The output summary will be saved in a markdown file in the specified output directory.

Output

The summarized content is saved as a markdown file named summary.md in the current working directory. This file includes the transcribed text and its corresponding summary.

2.8 KiB Raw Blame History