metadata
license: cc-by-4.0
language:
- en
- it
- pt
- de
- fr
- es
- ja
- zh
tags:
- automatic-speech-recognition
- speech
- audio
- Transformer
- flow-matching
- discrete-flow-matching
- pytorch
- hf-asr-leaderboard
Drax: Speech Recognition with Discrete Flow Matching
Model Overview
The Drax model family provides speech recognition models based on discrete flow matching.
The drax-v1 model supports eight languages: English, Spanish, French, Portuguese, German, Italian, Japanese and Chinese.
It is an encoder-decoder model consists of a Whisper-large-v3 encoder, and a DiT based decoder, with a total of ~1.2B parameters.
More details on usage in our GitHub repo, https://github.com/aiola-lab/drax and our paper.
Usage
See https://github.com/aiola-lab/drax for installation instructions.
from drax import Transcriber
asr = Transcriber(model_path="aiola/drax-v1")
result = asr.transcribe("/path/to/audio.wav", language="en")
print(result[0].transcript)
Control sampling steps, temperature etc.
from drax import Transcriber
asr = Transcriber(model_path="aiola/drax-v1")
result = asr.transcribe("/path/to/audio.wav", language="en", sampling_steps=32, temperature=1e-2)
print(result[0].transcript)
Batch inference:
from drax import Transcriber
asr = Transcriber(model_path="aiola/drax-v1")
audio_paths = ["/path/to/audio1.wav", "/path/to/audio2.wav"]
languages = ["en", "de"]
result = asr.transcribe(audio_paths, language=languages)
print(result.transcript)
Citation
@article{navon2025drax,
title={Drax: Speech Recognition with Discrete Flow Matching},
author={Navon, Aviv and Shamsian, Aviv and Glazer, Neta and Segal-Feldman, Yael and Hetz, Gill and Keshet, Joseph and Fetaya, Ethan},
journal={arXiv preprint arXiv:2510.04162},
year={2025}
}