Local Faster-Whisper MCP Server

A high-performance, fully local Model Context Protocol (MCP) server powered by faster-whisper (CTranslate2 INT8 engine) and yt-dlp. Built specifically for content creators, YouTube strategists, and autonomous AI agents, it transcribes local audio/video files, automatically downloads and transcribes YouTube competitor URLs, generates production-ready subtitles (SRT / WebVTT), and audits script pacing and audience retention risk.

🚀 Key Features

1. whisper_transcribe_file

   • Transcribes any local video or audio format (.mp4, .mp3, .wav, .m4a, .mkv, etc.).

   • Optimized for CPU execution using CTranslate2 INT8 quantization (fast inference with minimal memory footprint).

   • Supports all standard Whisper models (tiny, base, small, medium, large-v3).

   • Word-level timestamps, automatic language detection, and segment-level probability scores.

2. whisper_generate_subtitles

   • Automatically exports formatted subtitles in SRT or WebVTT (.vtt) format.

   • Saves subtitle files alongside media files or returns the raw formatted string.

   • Ideal for automating video editing workflows, Premiere Pro / DaVinci Resolve caption imports, or web video players.

3. whisper_analyze_retention_pacing

   • Analyzes spoken pacing and cadence for YouTube audience retention optimization.

   • Computes:

     • Words Per Minute (WPM): Classifies speech rate (Slow, Optimal, Fast, Rushed).

     • Dead Air & Long Pauses: Detects silence gaps longer than a customizable threshold (default: 1.5s) that risk audience drop-off.

     • Active Speech Ratio: Percentage of total video runtime occupied by active voice.

     • Actionable Script Recommendations: Concrete feedback to improve hook speed, pacing, and retention.

4. whisper_transcribe_youtube_url

   • Accepts any public YouTube URL (youtube.com/watch?v=... or youtu.be/...).

   • Automatically downloads the highest-quality audio stream in the background using yt-dlp.

   • Transcribes content and provides both full text, segment breakdown, and spoken pacing analysis.

   • Automatically cleans up temporary media files to preserve disk space.

Related MCP server: faster-whisper-mcp

🛠️ Architecture & Tech Stack

• Speech-to-Text Engine: faster-whisper with CTranslate2.

• Quantization: int8 on CPU (or float16 on CUDA if GPU is available).

• Audio Extraction & FFmpeg: Integrated statically via imageio-ffmpeg (zero PATH configuration needed on Windows).

• YouTube Ingestion: yt-dlp.

• MCP Framework: FastMCP (Python Model Context Protocol SDK).

• 100% Local & Private: No audio leaves your machine. Zero cloud API costs.

📦 Installation & Setup

Prerequisites

• Python 3.10+ (Python 3.12 recommended)

• Windows / Linux / macOS

Installation

# Clone the repository
git clone https://github.com/ramedina-ia/faster-whisper-mcp.git
cd faster-whisper-mcp

# Install dependencies
pip install -r requirements.txt

# Run automated tests
python test/test_whisper.py

# Run MCP server
python -m src.server

Antigravity & Claude Desktop Configuration

Add to your mcp_config.json:

{
  "mcpServers": {
    "faster-whisper-mcp": {
      "command": "cmd.exe",
      "args": ["/c", "path\\to\\faster-whisper-mcp\\run.cmd"]
    }
  }
}

📄 License

MIT License. Developed by Ramiro Medina.