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MCP Server Whisper

@arcaputo3

代码仓库

基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

arcaputo3

关于 MCP Server Whisper

An MCP Server for audio transcription using OpenAI

MCP Server Whisper 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

MCP Server Whisper 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

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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