weemed/Breeze-ASR-26-ct2

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 | CTranslate2 / faster-whisper | ~2.9 GB | 0.21 | servers, 8 GB+ hosts, GPU | | Breeze-ASR-26-GGML | whisper.cpp / MacWhisper | 1.85 GB | 0.40 | 4 GB hosts, desktop apps | | Breeze-ASR-26-ONNX | sherpa-onnx / onnxruntime | — | 1.3 | Android / iOS / WASM | All Apache-2.0, derived from 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

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.