# mlx\_whisper local transcription benchmark & usage

> Fast local audio transcription benchmark and Python snippet for Apple Silicon Macs.

This note provides quick benchmark figures and minimal Python code for running OpenAI Whisper models locally on Apple Silicon using the mlx\_whisper library. It demonstrates that three hours of audio can be transcribed in under five minutes on high-end chips like M2 Ultra and M3 Max using the whisper-large-v3-turbo checkpoint. It serves as a handy reference for setting up efficient, private speech-to-text without cloud APIs.

## How it works

Question: how long to transcribe 3 hours of video locally on macOS with mlx\_whisper?

- M2 Ultra: 4:34 mins

- M3 Max: 4:56 mins

Install: pip install mlx\_whisper

Usage:

import mlx\_whisper

text = mlx\_whisper.transcribe(

"path/to/audio",

path\_or\_hf\_repo="mlx-community/whisper-large-v3-turbo")\["text"\]

print(text)

Caveat from the author: some repetitions appear in the transcription, but this is due to the model itself, not the tool.

## What it is for

- Transcribe long audio recordings locally without cloud API costs
- Estimate speech-to-text performance when sizing hardware on Apple Silicon
- Integrate private transcription workflows into local Python scripts

## Key points

- Speed benchmarks across M2 Ultra and M3 Max hardware
- Minimal two-line Python snippet using the mlx\_whisper package
- Direct compatibility with Hugging Face MLX community models
- Note on model-level repetition quirks with whisper-large-v3-turbo

## Pricing

Open source. Free and open source

## Details

- Type: Tip
- Category: [AI](https://xiry.app/topics/ai.md)
- Topic: [Text to Speech](https://xiry.app/topics/ai/text-to-speech.md)
- Open source: yes
- Platforms: macOS, Code library
- For: Developers and macOS users needing fast, local speech-to-text transcription.
- Alternative to: whisper.cpp, OpenAI Whisper API, faster-whisper
- Tags: whisper, mlx, apple silicon, speech to text, audio, transcription, python

## Why it was saved

- [@ivanfioravanti, 28 October 2024](https://x.com/ivanfioravanti/status/1850959426985082965): Shows mlx\_whisper can transcribe 3 hours of video locally in under 5 minutes on Apple Silicon, and gives the minimal code to do it.

## Related

- [ElevenLabs](https://xiry.app/resources/elevenlabs): AI voice generator, voice cloning, and conversational agent platform (Web app, Freemium)
- [Cartesia Sonic](https://xiry.app/resources/cartesia-sonic): Ultra-low-latency streaming text-to-speech and instant voice cloning model. (AI model, Freemium)
- [Dia](https://xiry.app/resources/dia): Open-source 1.6B text-to-speech model generating multi-speaker dialogue in one pass. (AI model, Open source)
- [Airy](https://xiry.app/resources/airy): Fast, low-cost text-to-speech API and studio for creators and developers (AI model, Freemium)
- [AudioPen](https://xiry.app/resources/audiopen): Converts spoken thoughts into structured, polished writing in customizable styles. (Web app, Freemium)
- [cartesia-python](https://xiry.app/resources/cartesia-python): Official Python library for Cartesia's real-time text-to-speech API (GitHub repo, Paid)

Updated 11 October 2026. Page: https://xiry.app/resources/mlx-whisper-local-transcription-benchmark-and-usage

Make your own library: Xiry takes your saved reels, tweets, Reddit bookmarks and Watch Later videos, makes them searchable and pulls the useful stuff out, like this library. It's invite-only for now; join the waitlist at https://xiry.app/#waitlist.