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Works with

Claude CodeClaude DesktopCursorVS CodeClineCodex CLIOpenClaw+ any MCP client

Install to Claude Code

This server doesn't publish a one-line install command. Follow the setup in the source repository.

Summary

Transcribe audio/video files using Groq-hosted Whisper with word-level timestamps, automatic caching, and cost estimation, offering up to 9x savings over OpenAI Whisper.

README.md

groq-whisper-mcp

MCP server for audio/video transcription via Groq-hosted Whisper. Word-level timestamps, automatic caching, cost estimation.

9x cheaper than OpenAI Whisper — same model, different API.

| Provider | Model | Cost/hour | |----------|-------|-----------| | Groq | whisper-large-v3-turbo | $0.04 | | Groq | whisper-large-v3 | $0.111 | | Groq | distil-whisper | $0.02 | | OpenAI | whisper-1 | $0.36 |

Prerequisites

Installation

git clone https://github.com/bis-code/groq-whisper-mcp.git
cd groq-whisper-mcp
python3 -m venv venv
venv/bin/pip install mcp openai

Claude Code Configuration

Add to your project's .mcp.json:

{
  "mcpServers": {
    "whisper": {
      "command": "/path/to/groq-whisper-mcp/venv/bin/python",
      "args": ["-m", "server"],
      "cwd": "/path/to/groq-whisper-mcp/src",
      "env": {
        "GROQ_API_KEY": "your-groq-api-key"
      }
    }
  }
}

Tools

transcribe_video

Transcribe a video file with word-level timestamps.

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | video_path | string | yes | Path to video file | | model | string | no | Whisper model (default: whisper-large-v3-turbo) | | force_retranscribe | boolean | no | Bypass cache (default: false) |

Returns full text, word-level timestamps [{word, start, end}], and duration. Results are cached per-project.

estimate_transcription_cost

Estimate cost before transcribing.

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | video_path | string | yes | Path to video file | | model | string | no | Whisper model (default: whisper-large-v3-turbo) |

Returns duration, estimated cost, and comparison with OpenAI pricing.

How It Works

  1. Extracts audio from video via ffmpeg (128kbps MP3, falls back to 64kbps if >25MB)
  2. Sends to Groq's OpenAI-compatible API (same openai SDK, different base_url)
  3. Returns word-level timestamps with ~0.1s precision
  4. Caches results as whisper_words.json alongside the video

Running Tests

PYTHONPATH=src venv/bin/pytest tests/ -v

License

MIT

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