Video Transcriber MCP server

Video Transcriber MCP 🚀

High-performance video transcription MCP server using whisper.cpp (Rust)

A Model Context Protocol (MCP) server that transcribes videos from 1000+ platforms using whisper.cpp. Built with Rust for maximum performance and efficiency.

📦 Installation

Homebrew (macOS/Linux) - Recommended

The easiest way to install with all dependencies:

brew install nhatvu148/tap/video-transcriber-mcp

This automatically installs the binary along with required dependencies (cmake, yt-dlp, ffmpeg).

Cargo Install

If you have Rust installed:

cargo install video-transcriber-mcp

Note: You'll need to manually install dependencies: yt-dlp, ffmpeg, cmake

Pre-built Binaries

Download from GitHub Releases:

# macOS (Intel)
curl -L https://github.com/nhatvu148/video-transcriber-mcp-rs/releases/latest/download/video-transcriber-mcp-x86_64-apple-darwin.tar.gz | tar xz
sudo mv video-transcriber-mcp /usr/local/bin/

# macOS (Apple Silicon)
curl -L https://github.com/nhatvu148/video-transcriber-mcp-rs/releases/latest/download/video-transcriber-mcp-aarch64-apple-darwin.tar.gz | tar xz
sudo mv video-transcriber-mcp /usr/local/bin/

# Linux (x86_64) — no ARM64 Linux build, see issue #13; use `cargo install`
curl -L https://github.com/nhatvu148/video-transcriber-mcp-rs/releases/latest/download/video-transcriber-mcp-x86_64-unknown-linux-gnu.tar.gz | tar xz
sudo mv video-transcriber-mcp /usr/local/bin/

# Windows: Download .zip from releases page

Note: You'll need to manually install dependencies: yt-dlp, ffmpeg

Claude Code plugin

Installs the MCP server and a /transcribe skill in one step:

/plugin marketplace add nhatvu148/video-transcriber-mcp-rs
/plugin install video-transcriber@nhatvu148-tools

The plugin registers the MCP server for you, but it does not install the binary — run one of the install commands above first, so video-transcriber-mcp is on your PATH.

🎯 Why Rust?

This version uses whisper.cpp (C++ implementation with Rust bindings) instead of Python's OpenAI Whisper:

Real-world performance depends on your hardware, video length, and chosen model.

✨ Features

• 🚀 High performance transcription using whisper.cpp (C++ with Rust bindings)
• 🎥 Download from 1000+ platforms (YouTube, Vimeo, TikTok, Twitter, etc.)
• 📂 Transcribe local video files (mp4, avi, mov, mkv, etc.)
• 🎤 100% offline transcription (privacy-first)
• 🎛️ 5 model sizes (tiny, base, small, medium, large)
• 🌐 90+ languages supported
• 📝 Multiple output formats (TXT, JSON, Markdown)
• 🔌 MCP integration for Claude Code
• 🌐 Dual transport - stdio (local) and Streamable HTTP (remote)
• ⚡ Native binary - no Python or Node.js required
• 💾 Low memory footprint compared to Python implementations

⚡ Quick Start (Using Taskfile)

The fastest way to get started:

# 1. Install Task (if not already installed)
brew install go-task/tap/go-task

# 2. Complete setup (build + download model)
task setup

# 3. Run a quick test
task test:quick

# Done! 🎉

Shortened. The full README is on GitHub.