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aitytech
/
Whisper-Base-MLX-FP16

Automatic Speech Recognition
MLX
Safetensors
Core ML
whisper
speech-recognition
fp16
apple-silicon
ios
Model card Files
xet
Community
Whisper-Base-MLX-FP16
File size: 2,294 Bytes
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---
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-base
---

# Whisper Base - MLX FP16

This is the [OpenAI Whisper Base](https://huggingface.co/openai/whisper-base) model converted to [MLX](https://github.com/ml-explore/mlx) format with FP16 precision, optimized for Apple Silicon inference.

## Model Details

| Property | Value |
|---|---|
| Base Model | openai/whisper-base |
| Parameters | ~74M |
| Format | MLX SafeTensors (FP16) |
| Model Size | 137.02 MB |
| Sample Rate | 16,000 Hz |
| Audio Layers | 6 |
| Text Layers | 6 |
| Hidden Size | 512 |
| Attention Heads | 8 |
| 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](https://github.com/argmaxinc/WhisperKit) or [MLX](https://github.com/ml-explore/mlx) frameworks.

## Files

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

## Usage

```python
import mlx_whisper

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

## Original Model

- **Paper:** [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356)
- **Authors:** OpenAI
- **License:** Apache-2.0