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weemed/Breeze-ASR-26-ONNX
Breeze-ASR-26-ONNX is a automatic speech recognition model from weemed. Use it when you need speech turned into text. The card lists the license as apache-2.0.
The only build that runs on Android, iOS, and in the browser (WASM). Encoder and decoder are single ONNX files each. Slower on CPU (RTF 1.3) than CT2/GGML — use this only where you need mobile or WASM; otherwise prefe…
Downloads · 30 days
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Updated Jul 17, 2026
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.onnx1.8 GB · 100%
From the Hugging Face model README
The only build that runs on Android, iOS, and in the browser (WASM). Encoder and decoder are single ONNX files each. Slower on CPU (RTF 1.3) than CT2/GGML — use this only where you need mobile or WASM; otherwise prefer CT2 (speed) or GGML (low RAM).
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).
import sherpa_onnx
rec = sherpa_onnx.OfflineRecognizer.from_whisper(
encoder="breeze-asr-26-large-v2-encoder.int8.onnx",
decoder="breeze-asr-26-large-v2-decoder.int8.onnx",
tokens="breeze-asr-26-large-v2-tokens.txt",
language="zh", task="transcribe", num_threads=4)