A YouTube MCP Server for video information and transcription
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# YouTube MCP Server A powerful Model Context Protocol (MCP) server for YouTube video transcription and metadata extraction. This server provides advanced tools for AI agents to retrieve video metadata and generate high-quality transcriptions with native language support. ## 🌟 Features - **Metadata Extraction**: Retrieve comprehensive video details (title, description, views, duration, etc.) without downloading the video. - **Smart Transcription**: - **In-Memory Processing**: fast, efficient, and disk-I/O free pipeline. - **VAD (Voice Activity Detection)**: uses Silero VAD for precise segmentation. - **Multilingual Support**: supports **99 languages**. - **Translation**: Transcribe to any supported language. - **Caching**: Intelligent file-based caching to avoid redundant processing. - **Optimized Performance**: - Uses `yt-dlp` for robust extraction. - Hardware acceleration (MPS/CUDA) for Whisper inference. - Parallel processing for transcription segments. --- ## 🛠️ Prerequisites - **Python 3.10+** - **ffmpeg**: Required for audio processing. - Mac: `brew install ffmpeg` - Linux: `sudo apt install ffmpeg` - Windows: Download and add to PATH. ## 📦 Installation 1. **Clone the repository**: ```bash git clone https://github.com/mourad-ghafiri/youtube-mcp-server cd youtube-mcp-server ``` 2. **Install dependencies**: Using `uv` (recommended): ```bash uv sync ``` ## ⚙️ Configuration The server configuration is located in `src/youtube_mcp_server/config.py`. You can adjust the following parameters: ### Directories - **`TRANSCRIPTIONS_DIR`**: Directory where transcription JSON files are cached (default: `"transcriptions"`). ### Models - **`WHISPER_MODEL_NAME`**: OpenAI Whisper model to use. Options: `"tiny"`, `"base"`, `"small"`, `"medium"`, `"large"`, `"turbo"`. (default: `"tiny"`). > **Note**: Larger models require more RAM and a GPU (CUDA/MPS). - **`SILERO_REPO` / `SILERO_MODEL`**: VAD model repository and ID. …
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