---
license: mit
language:
- en
- af
- am
- ar
- as
- az
- ba
- be
- bg
- bn
- bo
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- eu
- fa
- fi
- fo
- fr
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- la
- lb
- ln
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- mt
- my
- ne
- nl
- nn
- 'no'
- oc
- pa
- pl
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- sn
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- tg
- th
- tk
- tl
- tr
- tt
- uk
- ur
- uz
- vi
- yi
- yo
- zh
tags:
- whisper
- core-ml
- apple
- Apple-Silicon
---
# Whisper.cpp Core ML Models for Apple Silicon

This repository provides **prebuilt Core ML models for Whisper.cpp**, optimized for **Apple Silicon (M1, M2, M3, M4, M5)** devices.  
These models enable **hardware-accelerated speech-to-text** using Apple’s Neural Engine via Core ML.

The repository is designed for **easy plug-and-play usage** with Whisper.cpp using a **prebuilt CLI binary**.

---

## 📦 Repository Contents

Each model directory contains **everything required** to run Whisper.cpp with Core ML acceleration:

- **`ggml-*.bin`** – Whisper model weights used by `whisper.cpp`
- **`*-encoder.mlmodelc/`** – Compiled Core ML encoder bundle

⚠️ **Important:** The `.mlmodelc` directories must remain intact. Do **not** modify, rename, or move their contents.

---

## 📁 Folder Structure

```
.
├── tiny/
├── tiny.en/
├── base/
├── base.en/
├── small/
├── small.en/
├── medium/
├── medium.en/
├── large-v1/
├── large-v2/
└── large-v3/
```

Each folder corresponds to a Whisper model variant and contains the matching `.bin` and `.mlmodelc` files.

---

## 🚀 Model Variants Overview

The table below summarizes the trade-offs between **speed**, **accuracy**, and **memory usage**.

| Folder       | Model Size | Speed        | Accuracy         | Notes |
|--------------|-----------|--------------|------------------|-------|
| `tiny`       | Very Small | ⚡ Fastest   | ⭐ Lowest        | Best for real-time, low-resource use |
| `tiny.en`    | Very Small | ⚡ Fastest   | ⭐ Lowest        | English-only |
| `base`       | Small      | ⚡ Fast      | ⭐⭐ Balanced     | Good default choice |
| `base.en`    | Small      | ⚡ Fast      | ⭐⭐ Balanced     | English-only |
| `small`      | Medium     | ⚡ Medium    | ⭐⭐⭐ Better      | Improved transcription quality |
| `small.en`   | Medium     | ⚡ Medium    | ⭐⭐⭐ Better      | English-only |
| `medium`     | Large      | 🐢 Slower   | ⭐⭐⭐⭐ High       | High accuracy, higher memory usage |
| `medium.en`  | Large      | 🐢 Slower   | ⭐⭐⭐⭐ High       | English-only |
| `large-v1`   | Very Large | 🐢 Slow     | ⭐⭐⭐⭐⭐ Best     | Maximum accuracy |
| `large-v2`   | Very Large | 🐢 Slow     | ⭐⭐⭐⭐⭐ Best     | Improved multilingual performance |
| `large-v3`   | Very Large | 🐢 Slow     | ⭐⭐⭐⭐⭐ Best     | Latest and most accurate |

---

## 🛠 Usage Instructions

### 1. Download the Prebuilt `whisper-cli` Binary

Download the Core ML–enabled `whisper-cli` binary directly from GitHub Releases:

```
https://github.com/aarush67/whisper-cli-for-core-ml/releases/download/v1.0.0/whisper-cli
```

Recommended directory structure:

```
.
├── bin/
│   └── whisper-cli
├── medium.en/
│   ├── ggml-medium.en.bin
│   └── medium.en-encoder.mlmodelc/
```

---

### 2. Make the Binary Executable

```bash
chmod +x bin/whisper-cli
```

---

### 3. Run Whisper with a Core ML Model

Place the `.bin` file and the matching `.mlmodelc` folder in the same directory.

#### Example (English-only transcription)

```bash
./bin/whisper-cli -m ggml-medium.en.bin -f sample.wav
```

Whisper.cpp will automatically detect and use the Core ML encoder when available.

---

## 🧠 Best Practices & Notes

- ✅ **Keep `.bin` and `.mlmodelc` files together** from the same model variant
- ❌ Do **not** rename, edit, or partially copy `.mlmodelc` directories
- 🧹 `.pt` cache files (if generated) are temporary and **safe to delete**
- 💾 Larger models require significantly more RAM and disk space
- ⚡ Best performance is achieved on Apple Silicon devices with a Neural Engine

---

## 📜 License

- **Repository structure & metadata:** MIT License
- **Model weights & Core ML artifacts:** Governed by the original  
  **Whisper.cpp / OpenAI Whisper licenses**

Please review upstream licenses before commercial or large-scale use.

---

## 🙏 Credits

- **OpenAI Whisper** – Original speech recognition models  
- **whisper.cpp** by Georgi Gerganov – High-performance C/C++ implementation  
- **Apple Core ML** – Hardware acceleration on Apple Silicon

---

## ⭐ Support

If this repository is useful to you:
- ⭐ Star the repository
- 🔗 Support the upstream Whisper.cpp project