whisper-transcribe-mcp 0.2.0

MCP server for audio transcription via faster-whisper (local) or OpenAI Whisper API

whisper-transcribe-mcp

MCP server for audio transcription using faster-whisper (local, free, offline) or OpenAI Whisper API (cloud, requires API key).

Installation

Option A — uvx (no install needed, recommended)

# Local backend (downloads model on first use):
uvx "whisper-transcribe-mcp[local]"

# OpenAI backend:
uvx "whisper-transcribe-mcp[openai]"

# Both backends:
uvx "whisper-transcribe-mcp[all]"

Option B — pip

# Local backend:
pip install "whisper-transcribe-mcp[local]"

# OpenAI backend:
pip install "whisper-transcribe-mcp[openai]"

# Both:
pip install "whisper-transcribe-mcp[all]"

Configuration

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json)

Local backend (default):

{
  "mcpServers": {
    "whisper-transcribe": {
      "command": "uvx",
      "args": ["whisper-transcribe-mcp[local]"],
      "env": {
        "WHISPER_MODEL": "base"
      }
    }
  }
}

OpenAI backend:

{
  "mcpServers": {
    "whisper-transcribe": {
      "command": "uvx",
      "args": ["whisper-transcribe-mcp[openai]"],
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

Claude Code

claude mcp add whisper-transcribe uvx -- "whisper-transcribe-mcp[local]"

Environment Variables

Variable	Default	Description
WHISPER_MODEL	base	Local model size: tiny, base, small, medium, large-v3
OPENAI_API_KEY	—	If set, uses OpenAI Whisper API instead of local model

Tools

transcribe_file

Transcribe an audio file by path (mp3, wav, m4a, ogg, flac, etc.).

transcribe_base64

Transcribe audio provided as a base64-encoded string.

list_models

Show available models and current backend configuration.

Local Model Sizes

Model	Size	Speed	Notes
tiny	39M	~32x	Fastest
base	74M	~16x	Good balance (default)
small	244M	~6x	Better accuracy
medium	769M	~2x	High accuracy
large-v3	1.5G	~1x	Best accuracy

Models are downloaded automatically from HuggingFace on first use and cached locally.

License

MIT