tiro-qwen3-asr-1.7b-v2

Tiro is a far-field fine-tune of Qwen3-ASR-1.7B, with better performance on reverberant and noisy speech, scaling as conditions get harder, while maintaining general ASR capabilities. See benchmarks below for more details.

Currently #1 on the FFASR leaderboard.

English only, bf16. Same architecture and parameter count as the base.

Benchmarks

Lower is better. ✅ marks the better result.

FFASR Leaderboard

Other benchmarks

Per-condition breakdown Open ASR Leaderboard

Base column is the published Qwen3-ASR-1.7B result on the 8 public English splits. The leaderboard's headline average also includes two private sets that are not publicly runnable, so the figure shown there is higher than this one.

Usage

Identical to the base model.

from transformers import AutoProcessor, Qwen3ASRForConditionalGeneration

model_id = "rohansheth/tiro-qwen3-asr-1.7b-v2"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3ASRForConditionalGeneration.from_pretrained(model_id, device_map="auto")

Citations

Trained by KL distillation from the frozen base model on noise-free audio, following:

@inproceedings{li2017large,
  title     = {Large-Scale Domain Adaptation via Teacher-Student Learning},
  author    = {Li, Jinyu and Seltzer, Michael L. and Wang, Xi and Zhao, Rui and Gong, Yifan},
  booktitle = {Interspeech},
  year      = {2017},
  eprint    = {1708.05466}
}

@inproceedings{mosner2019improving,
  title     = {Improving Noise Robustness of Automatic Speech Recognition via
               Parallel Data and Teacher-Student Learning},
  author    = {Mo{\v{s}}ner, Ladislav and Wu, Minhua and Raju, Anirudh and
               Parthasarathi, Sree Hari Krishnan and Kumatani, Kenichi and
               Sundaram, Shiva and Maas, Roland and Hoffmeister, Bj{\"o}rn},
  booktitle = {ICASSP},
  year      = {2019},
  eprint    = {1901.02348}
}

Base model:

@misc{qwen3asr,
  title  = {Qwen3-ASR},
  author = {Qwen Team},
  year   = {2026},
  url    = {https://huggingface.co/Qwen/Qwen3-ASR-1.7B-hf}
}