Video Transcriber

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! 🎉

Available Commands:

task setup           # Complete project setup

task test:quick # Test with short video task benchmark # Run performance benchmark task deps:check # Check dependencies task download:base # Download base model task help # Show all commands

See Taskfile.yml for all available tasks.

---

🌐 Transport Modes

The server supports two transport modes:

Stdio Transport (Default)

Standard I/O transport for local CLI usage with Claude Code. This is the default mode.

video-transcriber-mcp

or explicitly:

video-transcriber-mcp --transport stdio

Streamable HTTP Transport

HTTP transport for remote access. Allows the MCP server to be accessed over the network.

# Start HTTP server on default port (8080)

video-transcriber-mcp --transport http

Custom host and port

video-transcriber-mcp --transport http --host 0.0.0.0 --port 3000

Remote MCP Client Configuration:

For HTTP transport, configure your MCP client with the URL:

{

"mcpServers": { "video-transcriber-mcp": { "url": "http://localhost:8080/mcp" } } }

Benefits of HTTP Transport:

• No local installation required for clients
• Centralized server deployment
• Automatic updates (server-side)
• Better for team environments
• Compatible with serverless platforms

CLI Options

video-transcriber-mcp --help

Options: -t, --transport Transport mode [default: stdio] [possible values: stdio, http] --host Host address for HTTP transport [default: 127.0.0.1] -p, --port Port for HTTP transport [default: 8080] -h, --help Print help -V, --version Print version

---

📦 Manual Build from Source

Prerequisites

1. Rust (1.85+ for Rust 2024 edition)

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

1. yt-dlp (for downloading videos)

# macOS

brew install yt-dlp

Linux

pip install yt-dlp

Windows

winget install yt-dlp.yt-dlp

1. FFmpeg (for audio processing)

# macOS

brew install ffmpeg

Linux

sudo apt install ffmpeg # Debian/Ubuntu sudo dnf install ffmpeg # Fedora

Windows

choco install ffmpeg

1. clang/libclang (whisper-rs-sys uses bindgen to build whisper.cpp, which needs libclang at build time)

# macOS: bundled with Xcode Command Line Tools

Linux

sudo apt install libclang-dev # Debian/Ubuntu sudo dnf install clang-devel # Fedora

Windows: install LLVM (https://releases.llvm.org) and set LIBCLANG_PATH

Build from Source

# Clone the repository

git clone https://github.com/nhatvu148/video-transcriber-mcp-rs.git cd video-transcriber-mcp-rs

Build the project

cargo build --release

The binary will be at: target/release/video-transcriber-mcp-rs

Download Whisper Models

# Download base model (recommended for testing)

bash scripts/download-models.sh base

Or download all models

bash scripts/download-models.sh all

Models are stored in ~/.cache/video-transcriber-mcp/models/

🚀 Quick Start

MCP Server (for Claude Code)

Add to ~/.claude/settings.json:

Option 1: If installed via GitHub Release or cargo install:

{

"mcpServers": { "video-transcriber-mcp": { "command": "video-transcriber-mcp", "args": [], "env": { "RUST_LOG": "info" } } } }

Option 2: If built from source:

{

"mcpServers": { "video-transcriber-mcp": { "command": "/absolute/path/to/video-transcriber-mcp-rs/target/release/video-transcriber-mcp", "args": [], "env": { "RUST_LOG": "info" } } } }

Then use in Claude Code:

Basic transcription (uses base model by default):

Please transcribe this YouTube video: https://www.youtube.com/watch?v=VIDEO_ID

Transcribe with specific model:

Transcribe this video using the large model for best accuracy:

https://www.youtube.com/watch?v=VIDEO_ID

Transcribe local video file:

Transcribe this local video file: /Users/myname/Videos/meeting.mp4

Transcribe in specific language:

Transcribe this Spanish video: https://www.youtube.com/watch?v=VIDEO_ID

(language: es, model: medium)

📊 Performance

Expected Performance Characteristics

Based on whisper.cpp vs OpenAI Whisper benchmarks from the community:

Transcription Speed (approximate, varies by hardware):

• whisper.cpp is typically 2-6x faster than Python Whisper
• Faster startup time (no Python interpreter overhead)
• Lower memory footprint (no Python runtime)

Real-world factors that affect performance:

• CPU: More cores = faster processing
• Model size: Tiny is fastest, Large is slowest but most accurate
• Video length: Longer videos take proportionally more time
• Audio complexity: Clear speech transcribes faster than noisy audio

Want to help?

We're collecting real benchmark data! If you run both versions, please share your results:

• Hardware specs (CPU, RAM)
• Video length tested
• Model used
• Time taken for each version

Open an issue with your benchmark results to help improve this section!

🎛️ Model Comparison

🌍 Supported Platforms

Thanks to yt-dlp, this tool supports 1000+ video platforms including:

• Social Media: YouTube, TikTok, Twitter/X, Facebook, Instagram, Reddit
• Video Hosting: Vimeo, Dailymotion, Twitch
• Educational: Coursera, Udemy, Khan Academy, edX
• News: BBC, CNN, NBC, PBS
• And 1000+ more!

📝 Output Format

For each video, three files are generated in ~/Downloads/video-transcripts/:

video-id-title.txt   # Plain text transcript

video-id-title.json # JSON with metadata and timestamps video-id-title.md # Markdown with video info

Example Output

# How to Build Fast Software

Video: https://www.youtube.com/watch?v=example Platform: YouTube Channel: Tech Channel Duration: 600s

---

Transcript

The key to building fast software is understanding...

---

*Transcribed using whisper.cpp (Rust) - Model: base*

🔧 Configuration

Environment Variables

All environment variables are optional. The transcriber works with none of them set; they unlock authentication, remote inference, AI summaries, and the paid HTTP API.

💡 The transcript output directory is not an env var — pass output_dir to the transcribe_video tool (defaults to ~/Downloads/video-transcripts). Output files are named - .{txt,json,md}.

Remote MCP access (--transport http)

The HTTP transport only answers requests whose Host header is on an allowlist. It defaults to loopback (localhost, 127.0.0.1, ::1) as protection against DNS rebinding, which means a deployed instance rejects its own public hostname with 403 until you name it:

# Comma-separated. Added on top of the loopback defaults, so local

development and health checks keep working.

export MCP_ALLOWED_HOSTS=mcp.example.com,mcp.example.com:8080

On Fly:

fly secrets set MCP_ALLOWED_HOSTS=your-app.fly.dev

Leave it unset for local use — the server logs which hosts it accepts at startup, so a 403 from a remote client is easy to diagnose.

⚠️ This controls reachability, not authorization. Anyone who can reach

the URL can call the tools, including transcribe_video, which spends real

money when remote Whisper / OpenRouter are configured. Put an

authenticating proxy in front of a public deplo