Groq Whisper

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

• Python 3.10+
• ffmpeg (for audio extraction from video)
• Groq API key

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