transcribe-mlx 0.1.3

Audio transcription CLI using MLX Whisper

transcribe-mlx

Local audio transcription to JSON using MLX Whisper. Runs on Apple Silicon.

Install

pip install transcribe-mlx

Usage

transcribe-mlx "audio.mp3" "output.json"
transcribe-mlx "audio.mp3" "output.json" --model mlx-community/whisper-large-v3-mlx

Output is saved as a JSON file with segments, words, and timestamps.

First run: the model (~1–3 GB) is downloaded automatically from HuggingFace and cached locally. Subsequent runs use the cache.

Arguments

Argument	Description
audio	Path to the input audio file
output	Path where the output JSON will be saved

Options

Flag	Default	Description
--model / -m	mlx-community/whisper-large-v3-turbo	HuggingFace repo or local path to the Whisper MLX model

Requirements

• Python 3.11+
• Apple Silicon (MLX requires Metal)
• FFmpeg (brew install ffmpeg)

Development

git clone https://github.com/felipeinfantino/transcribe-mlx
cd transcribe-mlx
poetry install
poetry run transcribe-mlx "inputs/audio.mp3" "outputs/result.json"

Extending with a new package

poetry add [packagename]

# Check the CLI is still working
poetry run transcribe-mlx "inputs/audio.mp3" "outputs/result.json"

# Verify lockfile is clean
poetry lock

# Bump version
poetry version patch   # or minor / major

Git build versioning

Let x.y.z be the new bumped version:

git tag v[x.y.z]
git push origin main --tags

License

MIT