MCP Video & Audio Text Extraction Server

An MCP server that provides text extraction capabilities from various video platforms and audio files. This server implements the Model Context Protocol (MCP) to provide standardized access to audio transcription services.

Supported Platforms

This service supports downloading videos and extracting audio from various platforms, including but not limited to:

YouTube
Bilibili
TikTok
Instagram
Twitter/X
Facebook
Vimeo
Dailymotion
SoundCloud

For a complete list of supported platforms, please visit yt-dlp supported sites.

Core Technology

This project utilizes OpenAI's Whisper model for audio-to-text processing through MCP tools. The server exposes four main tools:

Video download: Download videos from supported platforms
Audio download: Extract audio from videos on supported platforms
Video text extraction: Extract text from videos (download and transcribe)
Audio file text extraction: Extract text from audio files

MCP Integration

This server is built using the Model Context Protocol, which provides:

Standardized way to expose tools to LLMs
Secure access to video content and audio files
Integration with MCP clients like Claude Desktop

Features

High-quality speech recognition based on Whisper
Multi-language text recognition
Support for various audio formats (mp3, wav, m4a, etc.)
MCP-compliant tools interface
Asynchronous processing for large files

Tech Stack

Python 3.10+
Model Context Protocol (MCP) Python SDK
yt-dlp (YouTube video download)
openai-whisper (Core audio-to-text engine)
pydantic

System Requirements

FFmpeg (Required for audio processing)
Minimum 8GB RAM
Recommended GPU acceleration (NVIDIA GPU + CUDA)
Sufficient disk space (for model download and temporary files)

Important First Run Notice

Important: On first run, the system will automatically download the Whisper model file (approximately 1GB). This process may take several minutes to tens of minutes, depending on your network conditions. The model file will be cached locally and won't need to be downloaded again for subsequent runs.

Installation

Using uv (recommended)

When using uv no specific installation is needed. We will use uvx to directly run the video extraction server:

curl -LsSf https://astral.sh/uv/install.sh | sh

Install FFmpeg

FFmpeg is required for audio processing. You can install it through various methods:

# Ubuntu or Debian
sudo apt update && sudo apt install ffmpeg

# Arch Linux
sudo pacman -S ffmpeg

# MacOS
brew install ffmpeg

# Windows (using Chocolatey)
choco install ffmpeg

# Windows (using Scoop)
scoop install ffmpeg

Usage

Configure for Claude/Cursor

Add to your Claude/Cursor settings:

"mcpServers": {
  "video-extraction": {
    "command": "uvx",
    "args": ["mcp-video-extraction"]
  }
}

Available MCP Tools

Video download: Download videos from supported platforms
Audio download: Extract audio from videos on supported platforms
Video text extraction: Extract text from videos (download and transcribe)
Audio file text extraction: Extract text from audio files

Configuration

The service can be configured through environment variables:

Whisper Configuration

WHISPER_MODEL: Whisper model size (tiny/base/small/medium/large), default: 'base'
WHISPER_LANGUAGE: Language setting for transcription, default: 'auto'

YouTube Download Configuration

YOUTUBE_FORMAT: Video format for download, default: 'bestaudio'
AUDIO_FORMAT: Audio format for extraction, default: 'mp3'
AUDIO_QUALITY: Audio quality setting, default: '192'

Storage Configuration

TEMP_DIR: Temporary file storage location, default: '/tmp/mcp-video'

Download Settings

DOWNLOAD_RETRIES: Number of download retries, default: 10
FRAGMENT_RETRIES: Number of fragment download retries, default: 10
SOCKET_TIMEOUT: Socket timeout in seconds, default: 30

Performance Optimization Tips

GPU Acceleration:
- Install CUDA and cuDNN
- Ensure GPU version of PyTorch is installed
Model Size Adjustment:
- tiny: Fastest but lower accuracy
- base: Balanced speed and accuracy
- large: Highest accuracy but requires more resources
Use SSD storage for temporary files to improve I/O performance

Notes

Whisper model (approximately 1GB) needs to be downloaded on first run
Ensure sufficient disk space for temporary audio files
Stable network connection required for YouTube video downloads
GPU recommended for faster audio processing
Processing long videos may take considerable time

MCP Integration Guide

This server can be used with any MCP-compatible client, such as:

Claude Desktop
Custom MCP clients
Other MCP-enabled applications

For more information about MCP, visit Model Context Protocol.

Documentation

For Chinese version of this documentation, please refer to README_zh.md

License

MIT

MCP Video & Audio Text Extraction Server

Integrations