Mini Whisper

MCP server for audio transcription using OpenAI Whisper.

Requirements

• Python 3.11+
• uv
• ffmpeg (apt install ffmpeg / brew install ffmpeg)

Install

uv sync

Run

stdio (for local agents)

uv run python -m mini_whisper_mcp --transport stdio

HTTP

uv run python -m mini_whisper_mcp --transport streamable-http --host 0.0.0.0 --port 8000

Docker

docker build -t mini-whisper-mcp .
docker run -p 8000:8000 mini-whisper-mcp

Docker Compose

Create a docker-compose.yml alongside your calling agent:

services:
  mini-whisper-mcp:
    image: mini-whisper-mcp
    build: ./mini-whisper-mcp   # path to this repo
    ports:
      - "8000:8000"
    environment:
      MCP_TRANSPORT: streamable-http
      MCP_HOST: 0.0.0.0
      MCP_PORT: "8000"
    restart: unless-stopped

  your-agent:
    build: ./your-agent
    environment:
      WHISPER_MCP_URL: http://mini-whisper-mcp:8000/mcp
    depends_on:
      - mini-whisper-mcp

docker compose up

The agent connects to the MCP server at http://mini-whisper-mcp:8000/mcp using the service name as hostname.

Configuration

Env var	Default	Description
MCP_TRANSPORT	streamable-http	stdio or streamable-http (Docker default)
MCP_HOST	0.0.0.0	Host for HTTP mode
MCP_PORT	8000	Port for HTTP mode

MCP Tools

health_check

Basic server health check. Returns "ok".

transcribe

Param	Type	Default	Description
audio_b64	string	—	Base64-encoded audio file content
model	string	base	tiny, base, small, medium, large
suffix	string	.mp3	File extension hint: .mp3, .wav, .m4a, etc.

Models are cached in memory after first load. Larger models are more accurate but slower.

Usage example (calling agent)

import base64

with open("audio.mp3", "rb") as f:
    audio_b64 = base64.b64encode(f.read()).decode()

result = await mcp_client.call_tool("transcribe", {
    "audio_b64": audio_b64,
    "model": "base",
    "suffix": ".mp3",
})

Testing with MCP Inspector

npx @modelcontextprotocol/inspector uv run python -m mini_whisper_mcp --transport stdio

For HTTP, start the server first then connect Inspector to http://localhost:8000/mcp.

Claude Desktop config (stdio)

{
  "mcpServers": {
    "whisper": {
      "command": "uv",
      "args": ["--directory", "/path/to/mini-whisper-mcp", "run", "python", "-m", "mini_whisper_mcp", "--transport", "stdio"]
    }
  }
}

Project structure

mini_whisper_mcp/
├── __main__.py   # CLI entrypoint (--transport, --host, --port)
├── server.py     # MCP tools
└── models.py     # Whisper model loader with CUDA fallback