YouTube Transcriber MCP

A Model Context Protocol (MCP) server that enables intelligent transcription of YouTube videos with automatic optimization for any video length. This tool integrates with desktop applications to provide high-quality, local transcription capabilities using OpenAI Whisper with smart processing strategies.

Features

• Automatic Strategy Selection: Intelligently chooses optimal processing method based on video duration

• Long Video Support: Efficiently handles videos from minutes to hours with smart sampling

• Local Processing: All transcription happens on your machine - no external APIs required

• Speaker Identification: Automatically detects and labels different speakers in videos using local diarization

• High Accuracy: Leverages OpenAI Whisper for state-of-the-art transcription quality

• MCP Integration: Seamlessly works with MCP-compatible applications

• Automatic Cleanup: Downloaded files are automatically removed after processing

• Multiple Model Sizes: Choose from tiny to large models based on your accuracy/speed needs

Related MCP server: YouTube Transcript MCP Server

Installation

Prerequisites

• Python 3.8 or higher

• FFmpeg installed on your system

• MCP-compatible application (e.g., Claude Desktop)

Install FFmpeg

macOS:

brew install ffmpeg

Ubuntu/Debian:

sudo apt update
sudo apt install ffmpeg

Windows: Download from FFmpeg website

Setup

1. Clone the repository:

git clone https://github.com/StevenGeller/youtube-transcriber-mcp.git
cd youtube-transcriber-mcp

2. Create a virtual environment:

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Configuration

For Claude Desktop

1. Open Claude Desktop settings

2. Navigate to the "Developer" section

3. Under "Edit Config", add the YouTube transcriber to your MCP servers:

{
  "mcpServers": {
    "youtube-transcriber": {
      "command": "/path/to/youtube-transcriber-mcp/venv/bin/python",
      "args": ["/path/to/youtube-transcriber-mcp/youtube_mcp_server.py"],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}

Important: Replace /path/to/youtube-transcriber-mcp with the actual path where you cloned the repository.

Example for macOS:

{
  "mcpServers": {
    "youtube-transcriber": {
      "command": "/Users/yourusername/youtube-transcriber-mcp/venv/bin/python",
      "args": ["/Users/yourusername/youtube-transcriber-mcp/youtube_mcp_server.py"],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}

4. Save the configuration

5. Restart Claude Desktop

For Other MCP Clients

The server follows the MCP standard and can be used with any MCP-compatible client. The key configuration elements are:

• Command: Path to the Python interpreter in your virtual environment

• Arguments: Path to youtube_mcp_server.py

• Environment: Set PYTHONUNBUFFERED=1 for proper output handling

Usage

Once configured, you can transcribe YouTube videos by asking:

• "Transcribe this YouTube video: [URL]"

• "Get the transcript from: [URL]"

• "Transcribe [URL] without timestamps"

The server automatically optimizes processing based on video length:

Automatic Strategy Selection

Model Sizes

• tiny: Fastest, least accurate (~39M parameters)

• base: Good balance (default for short videos, ~74M parameters)

• small: Better accuracy (~244M parameters)

• medium: High accuracy (~769M parameters)

• large: Best accuracy (~1550M parameters)

Note: The server automatically selects appropriate model sizes based on video duration to optimize performance.

Advanced Features

Long Video Optimization

The transcriber automatically handles long videos efficiently:

• Automatic Detection: Analyzes video duration and selects optimal strategy

• Chunked Processing: For medium videos (10-60 min), splits into chunks for parallel processing

• Smart Sampling: For long videos (>60 min), intelligently samples key sections:

  • Introduction (first 2 minutes)

  • Key points at 25%, 50%, 75% marks

  • Conclusion (last 2 minutes)

• Performance: ~90% time savings on long videos while capturing essential content

Speaker Diarization

The transcriber includes built-in local speaker diarization that works completely offline:

• Detects the number of speakers in the video

• Segments the audio by speaker

• Labels each transcript segment with the appropriate speaker

• Uses MFCC features and clustering for voice identification

Project Structure

youtube-transcriber-mcp/
├── youtube_mcp_server.py     # Main MCP server
├── transcriber.py            # WhisperX transcription engine
├── local_diarization.py      # Local speaker diarization
├── quiet_transcriber.py      # Fallback transcriber
├── requirements.txt          # Python dependencies
└── README.md                # This file

Troubleshooting

"Server disconnected" error

• Ensure FFmpeg is installed and in your PATH

• Check that all Python dependencies are installed

• Verify the file paths in your MCP configuration

Memory issues

• Try using a smaller model size

• Ensure you have sufficient RAM (4GB+ recommended)

Speaker identification issues

• The local diarization should work automatically

• If speaker detection fails, all speech will be labeled as SPEAKER_00

• Check the logs for any error messages

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is released into the public domain under The Unlicense - see the LICENSE file for details.

Acknowledgments

• Built with WhisperX for enhanced transcription

• Uses yt-dlp for reliable YouTube downloads

• Implements the Model Context Protocol specification