Breeze ASR 25 — WhisperKit CoreML (4-bit Palettized)

The first CoreML conversion of MediaTek's Breeze ASR 25, optimized for on-device inference on Apple devices via WhisperKit.

Breeze ASR 25 is fine-tuned from Whisper large-v2 with significantly better performance on Taiwanese Mandarin and Mandarin-English code-switching — up to 56% lower WER compared to the original Whisper large-v2.

This repo provides the 4-bit palettized CoreML version with outlier decomposition, ready for iPhone, iPad, Mac, and potentially Vision Pro.

Model Details

File Sizes

Performance (from Breeze ASR 25 paper)

Short-form Audio

Long-form Audio

Usage with WhisperKit

Swift (iOS / macOS / visionOS)

import WhisperKit

// Download and extract model files to a local directory
let config = WhisperKitConfig(
    modelFolder: "/path/to/breeze-asr-25-whisperkit-coreml",
    download: false
)
let whisperKit = try await WhisperKit(config)
let result = try await whisperKit.transcribe(audioPath: "audio.wav")
print(result.text)

Required Files

Place these files in your model folder:

breeze-asr-25-whisperkit-coreml/
├── AudioEncoder.mlmodelc/
├── TextDecoder.mlmodelc/
└── MelSpectrogram.mlmodelc/

Conversion Details

• Method: 4-bit palettization via whisperkit-generate-model
• Outlier Decomposition: Enabled — outlier weights preserved at higher precision for quality
• PSNR: AudioEncoder torch2coreml = 58.6 dB (excellent)
• Conversion Time: ~4.5 hours on M1 Max (single-threaded CoreML compilation)

Conversion Command

whisperkit-generate-model \
    --model-version MediaTek-Research/Breeze-ASR-25 \
    --output-dir ./output \
    --generate-quantized-variants \
    --allowed-nbits 4 \
    --force-recipe-nbits \
    --outlier-decomp \
    --generate-decoder-context-prefill-data

Expected Device Performance

Limitations

• Not real-time: At ~1 GB, this model is best for batch transcription, not live dictation. For real-time keyboard use, consider smaller models (whisper-base or whisper-small).
• First load time: ANE compilation on first use takes ~10-12 minutes per component. Subsequent loads are cached.
• visionOS: CoreML is available on visionOS but WhisperKit visionOS support needs verification.

License

This model is released under the Apache 2.0 License, same as the original Breeze ASR 25 model.

Credits

• MediaTek Research for Breeze ASR 25
• Argmax (WhisperKit) for whisperkittools and the on-device ASR framework
• Apple for coremltools

Citation

If you use this model, please cite the original Breeze ASR 25 paper:

@article{chou2025selfrefiningframeworkenhancingasr,
  title={A Self-Refining Framework for Enhancing ASR Using TTS-Synthesized Data},
  author={Cheng Kang Chou and Chan-Jan Hsu and Ho-Lam Chung and Liang-Hsuan Tseng and Hsi-Chun Cheng and Yu-Kuan Fu and Kuan Po Huang and Hung-Yi Lee},
  journal={arXiv preprint arXiv:2506.11130},
  year={2025}
}

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Breeze ASR 25 — WhisperKit CoreML（4-bit 量化版）

首個 MediaTek Breeze ASR 25 的 CoreML 轉換版本，針對 Apple 裝置上透過 WhisperKit 進行本地推理優化。

Breeze ASR 25 基於 Whisper large-v2 微調，在繁體中文和中英混用場景的表現大幅超越原始模型 — WER 最高降低 56%。

本 repo 提供 4-bit palettization + outlier decomposition 的 CoreML 版本，可在 iPhone、iPad、Mac 及 Vision Pro 上直接使用。

模型資訊

檔案大小

使用方式

Swift（iOS / macOS / visionOS）

import WhisperKit

// 下載並解壓模型檔案到本地目錄
let config = WhisperKitConfig(
    modelFolder: "/path/to/breeze-asr-25-whisperkit-coreml",
    download: false
)
let whisperKit = try await WhisperKit(config)
let result = try await whisperKit.transcribe(audioPath: "audio.wav")
print(result.text)

裝置效能預估

限制

• 非即時辨識：模型約 1 GB，適合批次轉錄而非即時聽寫。即時場景建議使用較小模型（whisper-base 或 whisper-small）。
• 首次載入時間：ANE 編譯首次使用時每個元件約需 10-12 分鐘，後續使用會快取。
• visionOS：CoreML 在 visionOS 可用，但 WhisperKit 的 visionOS 支援仍需驗證。

授權

本模型採用 Apache 2.0 授權，與原始 Breeze ASR 25 模型相同。