Local Speech-to-Text MCP Server

The Local Speech‑to‑Text MCP Server is a purpose‑built, high‑performance transcription engine that runs entirely on the user’s machine. By leveraging whisper.cpp and Apple Silicon’s Neural Engine, it delivers real‑time audio transcription without the latency or privacy concerns of cloud APIs. Developers can integrate this server into AI workflows to provide instant, on‑device speech understanding for chatbots, voice assistants, or any application that needs reliable text output from audio input.

This server solves the common bottleneck of external transcription services: dependency on internet connectivity, data privacy risks, and unpredictable costs. It also addresses the need for speaker diarization—the ability to distinguish between multiple speakers in a single recording—by incorporating the pyannote speaker‑diarization model. The result is a comprehensive tool that can transcribe long audio files, automatically convert various media formats (MP3, M4A, FLAC, etc.) to the 16 kHz mono waveform required by whisper.cpp, and output results in multiple human‑readable formats such as plain text, JSON, VTT, SRT, and CSV.

Key capabilities include:

• 100 % local processing for end‑to‑end privacy and zero external dependencies after initial setup.
• Apple Silicon optimization that achieves over 15× real‑time speed, outperforming many GPU‑based solutions while keeping memory usage below 2 GB.
• Automatic audio format detection and conversion powered by ffmpeg, allowing developers to accept any common media file without manual preprocessing.
• Speaker diarization that tags each utterance with speaker identifiers, essential for meeting transcripts, podcast editing, or multi‑party conversational AI.
• Multiple output formats that fit diverse downstream needs—from simple text for search indexing to VTT/SRT for captioning services.

In real‑world scenarios, the server is invaluable for developers building voice‑enabled applications that must operate offline or in privacy‑sensitive environments, such as medical transcription tools, legal dictation software, or on‑device personal assistants. It also serves as a backbone for AI pipelines that require quick, accurate transcriptions before feeding the text to language models, summarization engines, or analytics modules. By exposing its functionality through MCP tools like , , and , the server can be seamlessly invoked from any MCP‑compatible client, making it a plug‑and‑play component in sophisticated AI ecosystems.