mcp-server-whisper 0.1.2

An MCP server for transcribing audio files using OpenAI Whisper and GPT-4o

MCP Server Whisper

A Model Context Protocol (MCP) server for advanced audio transcription and processing using OpenAI's Whisper and GPT-4o models.

Overview

MCP Server Whisper provides a standardized way to process audio files through OpenAI's transcription services. By implementing the Model Context Protocol, it enables AI assistants like Claude to seamlessly interact with audio processing capabilities.

Key features:

• 🔍 Advanced file searching with regex patterns, file metadata filtering, and sorting capabilities
• 🔄 Parallel batch processing for multiple audio files
• 🔄 Format conversion between supported audio types
• 📦 Automatic compression for oversized files
• ✏️ Enhanced transcription with specialized prompts
• 📊 Comprehensive metadata including duration, file size, and format support
• 🚀 High-performance caching for repeated operations

Installation

# Clone the repository
git clone https://github.com/arcaputo3/mcp-server-whisper.git
cd mcp-server-whisper

# Using uv (recommended)
uv sync -e .
uv sync -e ".[dev]"  # For development dependencies

# Using pip
pip install -e .
pip install -e ".[dev]"  # For development dependencies

# Set up pre-commit hooks
pre-commit install

Environment Setup

Create a .env file with the following variables:

OPENAI_API_KEY=your_openai_api_key
AUDIO_FILES_PATH=/path/to/your/audio/files

Usage

Starting the Server

To run the MCP server in development mode:

mcp dev src/mcp_server_whisper/server.py

To install the server for use with Claude Desktop or other MCP clients:

mcp install src/mcp_server_whisper/server.py

Exposed MCP Tools

Audio File Management

• list_audio_files - Lists audio files with comprehensive filtering and sorting options:
  • Filter by regex pattern matching on filenames
  • Filter by file size, duration, modification time, or format
  • Sort by name, size, duration, modification time, or format
  • All operations support parallelized batch processing
• get_latest_audio - Gets the most recently modified audio file with model support info

Audio Processing

• convert_audio - Converts audio files to supported formats (mp3 or wav)
• compress_audio - Compresses audio files that exceed size limits

Transcription

• transcribe_audio - Basic transcription using OpenAI's Whisper model
• transcribe_with_llm - Transcription with custom prompts using GPT-4o
• transcribe_with_enhancement - Enhanced transcription with specialized templates:
  • detailed - Includes tone, emotion, and background details
  • storytelling - Transforms the transcript into a narrative form
  • professional - Creates formal, business-appropriate transcriptions
  • analytical - Adds analysis of speech patterns and key points

Supported Audio Formats

Model	Supported Formats
Whisper	mp3, mp4, mpeg, mpga, m4a, wav, webm
GPT-4o	mp3, wav

Note: Files larger than 25MB are automatically compressed to meet API limits.

Example Usage with Claude

Basic Audio Transcription

Claude, please transcribe my latest audio file with detailed insights.

Claude will automatically:

1. Find the latest audio file using get_latest_audio
2. Determine the appropriate transcription method
3. Process the file with transcribe_with_enhancement using the "detailed" template
4. Return the enhanced transcription

Advanced Audio File Search and Filtering

Claude, list all my audio files that are longer than 5 minutes and were created after January 1st, 2024, sorted by size.

Claude will:

1. Convert the date to a timestamp
2. Use list_audio_files with appropriate filters:
   • min_duration_seconds: 300 (5 minutes)
   • min_modified_time: <timestamp for Jan 1, 2024>
   • sort_by: "size"
3. Return a sorted list of matching audio files with comprehensive metadata

Batch Processing Multiple Files

Claude, find all MP3 files with "interview" in the filename and create professional transcripts for each one.

Claude will:

1. Search for files using list_audio_files with:
   • pattern: ".*interview.*\\.mp3"
   • format: "mp3"
2. Process all matching files in parallel using transcribe_with_enhancement
   • enhancement_type: "professional"
3. Return all transcriptions in a well-formatted output

Development

This project uses modern Python development tools including uv, pytest, ruff, and mypy.

# Run tests
uv run pytest

# Run with coverage
uv run pytest --cov=src

# Format code
uv run ruff format src

# Lint code
uv run ruff check src

# Run type checking (strict mode)
uv run mypy --strict src

# Run the pre-commit hooks
pre-commit run --all-files

CI/CD Workflow

The project uses GitHub Actions for CI/CD:

1. Lint & Type Check: Ensures code quality with ruff and strict mypy type checking
2. Tests: Runs tests on multiple Python versions (3.10, 3.11)
3. Build: Creates distribution packages
4. Publish: Automatically publishes to PyPI when a new version tag is pushed

To create a new release version:

git checkout main
# Make sure everything is up to date
git pull
# Create a new version tag
git tag v0.1.1
# Push the tag
git push origin v0.1.1

How It Works

For detailed architecture information, see Architecture Documentation.

MCP Server Whisper is built on the Model Context Protocol, which standardizes how AI models interact with external tools and data sources. The server:

1. Exposes Audio Processing Capabilities: Through standardized MCP tool interfaces
2. Implements Parallel Processing: Using asyncio and batch operations for performance
3. Manages File Operations: Handles detection, validation, conversion, and compression
4. Provides Rich Transcription: Via different OpenAI models and enhancement templates
5. Optimizes Performance: With caching mechanisms for repeated operations

Under the hood, it uses:

• pydub for audio file manipulation
• asyncio for concurrent processing
• OpenAI's Whisper API for base transcription
• GPT-4o for enhanced audio understanding
• FastMCP for simplified MCP server implementation
• Type hints and strict mypy validation throughout the codebase

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a new branch for your feature (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run the tests and linting (uv run pytest && uv run ruff check src && uv run mypy --strict src)
5. Commit your changes (git commit -m 'Add some amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Model Context Protocol (MCP) - For the protocol specification
• pydub - For audio processing
• OpenAI Whisper - For audio transcription
• FastMCP - For MCP server implementation
• Anthropic Claude - For natural language interaction

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Made with ❤️ by Richie Caputo