Whisper MCP 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 filemodel(optional): Model to use (tiny.en, base.en, small.en, medium.en, large). Default: base.enlanguage(optional): Language code. Default: enoutput_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
Models are stored in ~/.whisper/.
Workflow Example
- List your voice memos:
list_voice_memos - Get audio path:
get_audiowith memo ID - Transcribe:
transcribe_audiowith the file path - 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
