Whisper Server

A lightweight MCP (Model Context Protocol) server for local audio transcription using whisper.cpp. There are several Whisper MCP implementations out there. This one is minimal and pairs with apple-voice-memo-mcp for a complete voice memo workflow.

Features

• Local transcription - All processing happens on your machine
• Multiple models - Choose from tiny, base, small, medium, or large models
• Various formats - Supports wav, mp3, m4a, and other audio formats
• Timestamps - Get transcriptions with or without timestamps

Requirements

• macOS (tested on Apple Silicon)
• Node.js 18+
• whisper-cpp: brew install whisper-cpp
• ffmpeg: brew install ffmpeg

Installation

npm install -g whisper-mcp

Or run directly:

npx whisper-mcp

Configuration

Claude Desktop

Add to your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "whisper-mcp": {
      "command": "npx",
      "args": ["-y", "whisper-mcp"]
    }
  }
}

After editing, restart Claude Desktop.

Claude Code (CLI)

For Claude Code, add to your project's .mcp.json file:

{
  "mcpServers": {
    "whisper-mcp": {
      "command": "npx",
      "args": ["-y", "whisper-mcp"]
    }
  }
}

Or for user-wide configuration, add to ~/.claude/settings.json:

{
  "mcpServers": {
    "whisper-mcp": {
      "command": "npx",
      "args": ["-y", "whisper-mcp"]
    }
  }
}

Tip: Use /mcp in Claude Code to verify the server is connected.

Local Development Setup

If running from source instead of npm:

{
  "mcpServers": {
    "whisper-mcp": {
      "command": "node",
      "args": ["/path/to/whisper-mcp/dist/index.js"]
    }
  }
}

With Apple Voice Memos MCP

For a complete voice memo workflow, use alongside apple-voice-memo-mcp:

{
  "mcpServers": {
    "apple-voice-memo-mcp": {
      "command": "npx",
      "args": ["-y", "apple-voice-memo-mcp"]
    },
    "whisper-mcp": {
      "command": "npx",
      "args": ["-y", "whisper-mcp"]
    }
  }
}

MCP Tools

transcribe_audio

Transcribe an audio file using Whisper.

Parameters:

• file_path (required): Absolute path to the audio file
• model (optional): Model to use (tiny.en, base.en, small.en, medium.en, large). Default: base.en
• language (optional): Language code. Default: en
• output_format (optional): text, timestamps, or json. Default: text

Example:

{
  "file_path": "/path/to/audio.m4a",
  "model": "medium.en",
  "output_format": "timestamps"
}

list_whisper_models

List available Whisper models and their download status.

Returns:

{
  "models": [
    {
      "name": "base.en",
      "size": "142 MB",
      "downloaded": true,
      "path": "/Users/you/.whisper/ggml-base.en.bin"
    }
  ]
}

download_whisper_model

Download a Whisper model for local use.

Parameters:

• model (required): Model to download (tiny.en, base.en, small.en, medium.en, large)

Models

Model	Size	Speed	Quality
tiny.en	75 MB	Fastest	Basic
base.en	142 MB	Fast	Good
small.en	466 MB	Medium	Better
medium.en	1.5 GB	Slow	Great
large	2.9 GB	Slowest	Best

Models are stored in ~/.whisper/.

Workflow Example

1. List your voice memos: list_voice_memos
2. Get audio path: get_audio with memo ID
3. Transcribe: transcribe_audio with the file path
4. Save to your vault

Development

# Clone and install
git clone https://github.com/jwulff/whisper-mcp.git
cd whisper-mcp
npm install

# Build
npm run build

# Test with MCP inspector
npm run inspector

License

MIT