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weiren119/Breeze-ASR-26-coreml-4bit-palette
Breeze-ASR-26-coreml-4bit-palette is a automatic speech recognition model from weiren119. Use it when you need speech turned into text. It is set up for whisperkit. The card lists the license as apache-2.0.
This repository contains the Apple CoreML version of MediaTek-Research/Breeze-ASR-26, quantized to 4-bit using palette (lookup table) quantization for efficient on-device inference.
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From the Hugging Face model README
This repository contains the Apple CoreML version of MediaTek-Research/Breeze-ASR-26, quantized to 4-bit using palette (lookup table) quantization for efficient on-device inference.
| Property | Value |
|---|---|
| Base Model | MediaTek-Research/Breeze-ASR-26 |
| Architecture | Whisper (large-v2 based) |
| Format | Apple CoreML (.mlmodelc) |
| Quantization | 4-bit palette (mixedBitPalettized 4.0-bit) |
| Model Size | ~890 MB |
| Languages | Chinese (zh), English (en) |
├── AudioEncoder.mlmodelc/ # Mel spectrogram → encoder hidden states (~317 MB)
├── MelSpectrogram.mlmodelc/ # Audio waveform → Mel spectrogram (~372 KB)
├── TextDecoder.mlmodelc/ # Encoder states → token predictions (~533 MB)
├── config.json # Model configuration
└── generation_config.json # Generation/decoding parameters
This model is designed to run with WhisperKit on Apple devices (iPhone, iPad, Mac).
import WhisperKit
let pipe = try await WhisperKit(
model: "weiren119/Breeze-ASR-26-coreml-4bit-palette"
)
let result = try await pipe.transcribe(audioPath: "audio.wav")
print(result.text)
4-bit palette quantization compresses model weights by mapping them to a lookup table of 16 representative values (2⁴ = 16). This significantly reduces model size while maintaining reasonable accuracy, making it suitable for on-device deployment where memory is constrained.