README.md

metadata

license: apache-2.0
library_name: mlx
tags:
  - mlx
  - whisper
  - speech-recognition
  - automatic-speech-recognition
  - fp16
  - apple-silicon
  - ios
  - coreml
language:
  - en
  - zh
  - de
  - es
  - ru
  - ko
  - fr
  - ja
  - pt
  - tr
  - pl
  - ca
  - nl
  - ar
  - sv
  - it
  - id
  - hi
  - fi
  - vi
  - he
  - uk
  - el
  - ms
  - cs
  - ro
  - da
  - hu
  - ta
  - 'no'
  - th
  - ur
  - hr
  - bg
  - lt
  - la
  - mi
  - ml
  - cy
  - sk
  - te
  - fa
  - lv
  - bn
  - sr
  - az
  - sl
  - kn
  - et
  - mk
  - br
  - eu
  - is
  - hy
  - ne
  - mn
  - bs
  - kk
  - sq
  - sw
  - gl
  - mr
  - pa
  - si
  - km
  - sn
  - yo
  - so
  - af
  - oc
  - ka
  - be
  - tg
  - sd
  - gu
  - am
  - yi
  - lo
  - uz
  - fo
  - ht
  - ps
  - tk
  - nn
  - mt
  - sa
  - lb
  - my
  - bo
  - tl
  - mg
  - as
  - tt
  - haw
  - ln
  - ha
  - ba
  - jw
  - su
  - yue
pipeline_tag: automatic-speech-recognition
base_model: openai/whisper-tiny

Whisper Tiny - MLX FP16

This is the OpenAI Whisper Tiny model converted to MLX format with FP16 precision, optimized for Apple Silicon inference.

Model Details

Property	Value
Base Model	openai/whisper-tiny
Parameters	~39M
Format	MLX SafeTensors (FP16)
Model Size	70.94 MB
Sample Rate	16,000 Hz
Audio Layers	4
Text Layers	4
Hidden Size	384
Attention Heads	6
Vocabulary Size	51,865

Intended Use

This model is optimized for on-device automatic speech recognition (ASR) on Apple Silicon devices (Mac, iPhone, iPad). It is designed for use with the WhisperKit or MLX frameworks.

Files

• config.json - Model configuration
• model.safetensors - Model weights in SafeTensors format (FP16)
• multilingual.tiktoken - Tokenizer

Usage

import mlx_whisper

result = mlx_whisper.transcribe(
    "audio.mp3",
    path_or_hf_repo="aitytech/Whisper-Tiny-MLX-FP16",
)
print(result["text"])

Original Model

• Paper: Robust Speech Recognition via Large-Scale Weak Supervision
• Authors: OpenAI
• License: Apache-2.0