---
license: apache-2.0
base_model: MediaTek-Research/Breeze-ASR-26
language: [zh, nan]
pipeline_tag: automatic-speech-recognition
library_name: ctranslate2
tags: [whisper, taiwanese-hokkien, taigi, ctranslate2, faster-whisper, int8, real-time, speech-recognition]
---

# Breeze-ASR-26 — CTranslate2 / faster-whisper (INT8)

The **fastest** edge build: RTF 0.21 on CPU, real-time with headroom. Use this on
servers, 8 GB+ hosts, or GPU. For 4 GB hosts or desktop apps use the GGML repo;
for mobile/WASM use ONNX.

> Part of the **Breeze-ASR-26 edge family** — the same MediaTek model in every runtime, pick by your constraint:
>
> | Repo | Runtime | RSS | RTF (CPU 4-thread) | Best for |
> |---|---|---|---|---|
> | [Breeze-ASR-26-ct2](https://huggingface.co/weemed/Breeze-ASR-26-ct2) | CTranslate2 / faster-whisper | ~2.9 GB | **0.21** | servers, 8 GB+ hosts, GPU |
> | [Breeze-ASR-26-GGML](https://huggingface.co/weemed/Breeze-ASR-26-GGML) | whisper.cpp / MacWhisper | **1.85 GB** | 0.40 | 4 GB hosts, desktop apps |
> | [Breeze-ASR-26-ONNX](https://huggingface.co/weemed/Breeze-ASR-26-ONNX) | sherpa-onnx / onnxruntime | — | 1.3 | Android / iOS / WASM |
>
> All Apache-2.0, derived from [MediaTek-Research/Breeze-ASR-26](https://huggingface.co/MediaTek-Research/Breeze-ASR-26). Measured on real multi-speaker Mandarin meeting audio. Mandarin does not regress; Taigi is transcribed as Mandarin meaning (not verbatim Taigi characters).

## Usage

```python
from faster_whisper import WhisperModel
m = WhisperModel("weemed/Breeze-ASR-26-ct2", device="cpu", compute_type="int8", cpu_threads=4)
segments, _ = m.transcribe("meeting.wav", language="zh", beam_size=1)
print("".join(s.text for s in segments))
```

INT8 vs FP32: CER 4.69% (function-word/segmentation jitter only), 2.4x faster, quarter the size.