clipwright-transcribe

MCP tool to transcribe audio/video files and generate SRT/VTT captions and OTIO timeline.

External Binaries / Files

This tool requires the following external binaries/files to exist in the execution environment. They are not installed via pip, so obtain them separately.

whisper.cpp Binary

Used for transcription.

• Place whisper-cli (or the binary name appropriate for your environment) on PATH, or specify the full path in the CLIPWRIGHT_WHISPER environment variable.
• Obtain: Build from https://github.com/ggerganov/whisper.cpp, or use release binaries.

export CLIPWRIGHT_WHISPER=/path/to/whisper-cli

ggml Model File

Speech recognition model (.bin file) used by whisper.cpp.

• Specify the full path to the model file in the CLIPWRIGHT_WHISPER_MODEL environment variable. Can be overridden by the model_path parameter at tool invocation.
• Obtain: Download from https://huggingface.co/ggerganov/whisper.cpp etc.

export CLIPWRIGHT_WHISPER_MODEL=/path/to/ggml-base.bin

ffmpeg

Required to convert audio to 16kHz mono WAV (input format for whisper.cpp).

• Place ffmpeg on PATH, or specify the full path in the CLIPWRIGHT_FFMPEG environment variable.

export CLIPWRIGHT_FFMPEG=/path/to/ffmpeg

Environment Variables Summary

GPU / CUDA Acceleration

clipwright-transcribe supports GPU-accelerated transcription transparently: simply point CLIPWRIGHT_WHISPER at a CUDA or Metal build of whisper.cpp — no code or parameter changes are required.

Obtaining a CUDA / Metal Binary

# Windows CUDA example
export CLIPWRIGHT_WHISPER=/path/to/whisper-cublas/whisper-cli.exe

# macOS Metal example (after brew install whisper-cpp)
export CLIPWRIGHT_WHISPER=/opt/homebrew/bin/whisper-cli

Confirming GPU / Backend Usage

The tool envelope includes data.backend and data.realtime_factor so you can verify the device actually used at runtime:

{
  "data": {
    "backend": {
      "device": "cuda",
      "detail": "CUDA"
    },
    "realtime_factor": 12.5,
    "whisper_wall_seconds": 14.2
  }
}

• data.backend.device — one of cuda, metal, cpu, or unknown.
• data.backend.detail — sanitized fixed device label (CWE-209: no raw stderr / model path). Values: "CUDA" (cuda), "Metal" (metal), "cpu" (cpu), "" (unknown).
• data.realtime_factor — audio_duration_sec / whisper_wall_seconds. Values above 1.0 mean faster than realtime (e.g. 12.5 means 12.5× faster than realtime); a GPU build typically yields values well above 1.0 while a slow CPU build may fall below 1.0.
• data.whisper_wall_seconds — raw wall-clock seconds spent in the whisper subprocess.

summary also reports the backend used (e.g. " Backend: cuda (12.5x realtime)."), so the information is visible in the one-line MCP response without unpacking data.

Note on Python GPU Libraries

clipwright-transcribe does not import faster-whisper, CTranslate2, or any CUDA Python library. Transcription is always invoked as an external subprocess (CLIPWRIGHT_WHISPER), keeping GPU acceleration completely separate from the package install and preserving license independence. Any whisper-cli-compatible binary — CPU, CUDA, Metal, ROCm — can be used by updating the environment variable alone.

MCP Tool

clipwright_transcribe(media, output, options?) — Transcribe audio/video file and generate output.otio / output.srt / output.vtt.