MLX Whisper MCP is a standalone Python-based server that provides audio transcription capabilities, supporting direct file, base64 data, and YouTube video inputs. It leverages the high-quality MLX Whisper model and is optimized for Apple Silicon Macs, automating dependency management and offering a rich console for debugging. It is ideal for integrating speech-to-text features into local workflows or applications.
Added on:
Created by:
Apr 11 2025
MLX Whisper MCP is a standalone Python-based server that provides audio transcription capabilities, supporting direct file, base64 data, and YouTube video inputs. It leverages the high-quality MLX Whisper model and is optimized for Apple Silicon Macs, automating dependency management and offering a rich console for debugging. It is ideal for integrating speech-to-text features into local workflows or applications.
Added on:
Created by:
Apr 11 2025
Kachi O
Featured
What is MLX Whisper MCP?
This MCP (Model Context Protocol) server enables high-quality audio transcription using MLX Whisper on Apple Silicon Macs. It supports multiple input methods, including direct audio file paths, base64-encoded audio data, and YouTube videos, making it versatile for various transcription needs. The server automates dependency installation via uv, manages temporary files, and saves transcriptions alongside original audio. It utilizes the advanced MLX Whisper large-v3-turbo model for accurate transcription, providing a seamless and efficient solution for developers requiring local speech recognition capabilities, especially on Mac environments.
Who will use MLX Whisper MCP?
- Developers requiring local speech-to-text solutions
- Researchers working on audio transcription
- Mac users using Apple Silicon Macs for AI projects
- Teams integrating transcription into workflows
- Content creators needing transcriptions of videos
How to use the MLX Whisper MCP?
- Step1: Install Python 3.12 or higher on your Mac.
- Step2: Run the server using the command: `uv run mlx_whisper_mcp.py`.
- Step3: Use supported tools like `transcribe_file`, `transcribe_audio`, or `transcribe_youtube` via API calls or client integrations.
- Step4: Provide the required input parameters such as file path, base64 audio data, or YouTube URL.
- Step5: Receive the transcription output, which is also saved as a text file alongside the input.
- Step6: Stop or restart the server as needed for updates or changes.
MLX Whisper MCP's Core Features & Benefits
The Core Features
- transcribe_file: Transcribes an audio file from disk
- transcribe_audio: Transcribes base64-encoded audio data
- download_youtube: Downloads a YouTube video
- transcribe_youtube: Downloads and transcribes a YouTube video
The Benefits
- Supports multiple input formats for flexibility
- Optimized for Apple Silicon Macs
- Automated dependency management
- High-quality transcription using MLX Whisper large-v3-turbo model
- Rich console output for debugging
MLX Whisper MCP's Main Use Cases & Applications
- Transcribing podcasts or interviews locally
- Automating transcription of video content from YouTube
- Integrating speech recognition into Mac-based AI workflows
- Research projects requiring high-accuracy transcriptions
- Content creators generating subtitles or transcripts
FAQs of MLX Whisper MCP
Developer
- kachiO
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