license: apache-2.0
library_name: mlx
tags:
- mlx
- speech-to-text
- asr
- robust-asr
- qwen3-asr
base_model:
- zhifeixie/Mega-ASR
- Qwen/Qwen3-ASR-1.7B
language:
- en
- zh
pipeline_tag: automatic-speech-recognition
Mega-ASR-bf16
This model was converted to MLX format from zhifeixie/Mega-ASR (built on Qwen/Qwen3-ASR-1.7B) using mlx-audio.
Mega-ASR is a robustness layer over Qwen3-ASR-1.7B: a tiny audio-quality router classifies each utterance as clean or degraded and switches a dense LoRA adapter in/out of the base weights at inference — degraded audio runs the LoRA (robust) path, clean audio runs the unmodified base path. This recovers large WER gains on noisy/far-field speech while leaving clean-speech accuracy unchanged.
The base weights are stored as dense bf16 on purpose: Mega-ASR adds fp32 LoRA deltas to the base at inference, so the base cannot be quantized without losing the runtime router/LoRA switching.
Use with mlx-audio
pip install mlx-audio
from mlx_audio.stt import load
model = load("mlx-community/Mega-ASR-bf16")
result = model.generate("audio.wav", language="en")
print(result.text)
CLI:
python -m mlx_audio.stt.generate --model mlx-community/Mega-ASR-bf16 --audio audio.wav
The router decides per-utterance automatically; no flags needed.
Validation
Reproduces the paper's published robustness gains. Word Error Rate on the real NOIZEUS corpus (8 noise types × 4 SNR × 30 utterances, Apple Silicon):
| SNR | base (Qwen3-ASR) | Mega-ASR (robust) | paper base | paper robust |
|---|---|---|---|---|
| 0 dB | 23.35 | 20.61 | 23.97 | 19.80 |
| 5 dB | 8.47 | 6.51 | — | — |
| 10 dB | 3.31 | 2.17 | 3.41 | 2.79 |
| 15 dB | 2.12 | 0.83 | — | — |
| overall | 9.31 | 7.53 | 9.45 | 7.52 |
Overall robust WER 7.53 vs the paper's 7.52 — a ~20% relative reduction over the Qwen3-ASR baseline, reproduced. On clean read speech (FLEURS) the model matches plain Qwen3-ASR, as intended.
License & attribution
Apache-2.0. Built on zhifeixie/Mega-ASR (adapter + router) and Qwen/Qwen3-ASR-1.7B (base).