File size: 1,947 Bytes
ae7562a f22e097 463f1ed f22e097 463f1ed 2245c7a 463f1ed c01fd75 463f1ed 2245c7a 463f1ed 2245c7a 463f1ed b20f45d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
---
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](https://github.com/aiola-lab/drax) and our [paper](https://arxiv.org/abs/2510.04162).
## Usage
See [https://github.com/aiola-lab/drax](https://github.com/aiola-lab/drax) for installation instructions.
```python
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.
```python
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:
```python
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
```bibtex
@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}
}
``` |