---
id: mlx-whisper
version: "0.4.3"
license: MIT
license_treatment: permissive
maintenance: active
---

# mlx-whisper — OpenAI Whisper on Apple silicon with MLX and the Hugging Face Hub

License: permissive · Maintenance: active · Downloads: 337.0K/mo

## What it is and what it does

mlx-whisper brings OpenAI's Whisper speech recognition to Apple silicon Macs by wrapping the models in MLX, a machine learning framework optimized for Apple hardware. It offers both a command-line interface for simple transcription and a Python API for programmatic use. The package can load models from the Hugging Face Hub or local paths, supporting models at various parameter scales and offering features like word-level timestamps.

The package depends on torch, mlx, numpy, scipy, and several utility libraries for audio processing and model management. It requires ffmpeg as a system dependency for audio handling. Transcription is performed locally on-device, which means no network calls to external APIs and full control over model selection and quantization.

Use it for:

- Transcribe audio files to text on macOS without sending data to external APIs.
- Build command-line tools that convert speech recordings (MP3, WAV, etc.) to text files.
- Integrate speech-to-text into Python applications with control over model size and precision.
- Process audio streams piped from other programs directly into transcription.
- Generate word-level timestamps for audio segments to enable precise subtitle generation.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Runs OpenAI's Whisper speech recognition models on Apple silicon using MLX, with support for models ranging from 39 million to 1.5 billion parameters.

Yes, if you are on Apple silicon and need local speech recognition. The package is actively maintained, has low install friction, and MIT licensing poses no restrictions. Verify that torch and mlx dependencies resolve cleanly on your target hardware, and ensure ffmpeg is available before installation.

## Install

pip install mlx-whisper
uv add mlx-whisper
poetry add mlx-whisper

## Installing mlx-whisper

Before you install: Low install friction with a pure Python wheel. Active maintenance with recent commits and 8879 repository stars. Requires ffmpeg as a system dependency and nine runtime packages including torch, mlx, and numpy.

License in practice: MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects.

Quickstart:

pip install mlx-whisper

import mlx_whisper

text = mlx_whisper.transcribe("audio_file.mp3")["text"]

Requires ffmpeg to be installed on the system (e.g., `brew install ffmpeg` on macOS) and torch/mlx dependencies which are platform-specific.

Verify before relying:

- Whether MLX and torch dependencies install smoothly on non-Apple-silicon systems or if they are strictly required for Apple hardware.
- Performance characteristics and latency for different model sizes on typical Apple silicon devices.
- Whether pre-converted models from Hugging Face Hub cover all use cases or if manual conversion is often needed.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 337.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

speech recognition apple silicon, whisper transcription mlx, audio to text macos, openai whisper mlx, on-device speech recognition, whisper model inference, audio transcription apple, speech-recognition, apple-silicon, offline-inference

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