We developed Drax, a novel discrete flow matching framework for ASR that achieves state-of-the-art recognition accuracy while enabling highly efficient parallel decoding. Our approach uses an audio-conditioned path to better align training and inference, proving that discrete flow matching is a critical advancement for Non-Autoregressive (NAR) ASR.