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MCP Server Whisper

@arcaputo3

リポジトリ

基本情報

カテゴリ

その他

ライセンス

MIT

トランスポート

stdio

作者

arcaputo3

MCP Server Whisper について

An MCP Server for audio transcription using OpenAI

MCP Server Whisper の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-server-whisper": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

MCP Server Whisper のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is MCP Server Whisper?

MCP Server Whisper is a Model Context Protocol (MCP) server for advanced audio transcription and processing using OpenAI's Whisper and GPT‑4o models. It enables AI assistants like Claude to search, transcribe, chat with, and generate audio through a standardized interface.

How to use MCP Server Whisper?

Install by cloning the repository and running uv sync. Configure a .env file with OPENAI_API_KEY and AUDIO_FILES_PATH. Launch with bunx dotenv-cli -- claude for local development or add the server to your Claude Desktop configuration. The server exposes tools such as list_audio_files, transcribe_audio, chat_with_audio, and create_audio.

Key features of MCP Server Whisper

  • Advanced file searching with regex and metadata filters
  • Multi‑model transcription (Whisper, GPT‑4o transcribe)
  • Interactive audio chat with GPT‑4o audio models
  • Text‑to‑speech with customizable voices and speed
  • Automatic compression of files over 25 MB
  • Enhanced transcription with specialized templates

Use cases of MCP Server Whisper

  • Transcribe the latest audio file with detailed insights
  • Search and filter audio files by name, size, duration, or date
  • Batch transcribe multiple files in parallel using native MCP concurrency
  • Generate text‑to‑speech audio from a script with custom voice and style
  • Ask conversational questions about audio content using GPT‑4o

よくある質問

1

What models does MCP Server Whisper support?

It supports `whisper-1`, `gpt-4o-transcribe`, `gpt-4o-mini-transcribe` for transcription; `gpt-4o-audio-preview` for audio chat; and `gpt-4o-mini-tts` for text‑to‑speech.

2

What are the runtime requirements?

Python 3.10 or later and an OpenAI API key set in the `OPENAI_API_KEY` environment variable. The recommended package manager is `uv`.

3

Where are audio files and outputs stored?

Audio files are read from the path specified in `AUDIO_FILES_PATH`. Transcriptions, converted files, and generated speech are saved to the same directory.

4

How are large files handled?

Files larger than 25 MB are automatically compressed to meet OpenAI API limits before processing

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