FireRedTeam/FireRedLID

<div align="center"> <h1> FireRedASR2S - FireRedLID <br> A SOTA Industrial-Grade Spoken Language Identification System </h1>

</div>

[[Paper]](https://huggingface.co/papers/2603.10420) [[Code]](https://github.com/FireRedTeam/FireRedASR2S) [[Blog]](https://fireredteam.github.io/demos/firered_asr/) [[Demo]](https://huggingface.co/spaces/FireRedTeam/FireRedASR)

FireRedLID is the Spoken Language Identification (LID) module of FireRedASR2S, a state-of-the-art (SOTA), industrial-grade, all-in-one ASR system. It supports 100+ languages and 20+ Chinese dialects/accents, achieving 97.18% accuracy on the FLEURS benchmark, outperforming Whisper and SpeechBrain-LID.

This model was introduced in the paper FireRedASR2S: A State-of-the-Art Industrial-Grade All-in-One Automatic Speech Recognition System.

🔥 News

• —[2026.02.12] We release FireRedASR2S (FireRedASR2-AED, FireRedVAD, FireRedLID, and FireRedPunc) with model weights and inference code.

Evaluation

FireRedLID

Metric: Utterance-level LID Accuracy (%). Higher is better.

Sample Usage

To use this module independently, first clone the GitHub repository and install the dependencies.

Python API Usage

from fireredasr2s.fireredlid import FireRedLid, FireRedLidConfig

batch_uttid = ["hello_zh", "hello_en"]
batch_wav_path = ["assets/hello_zh.wav", "assets/hello_en.wav"]

config = FireRedLidConfig(use_gpu=True, use_half=False)
model = FireRedLid.from_pretrained("FireRedTeam/FireRedLID", config)

results = model.process(batch_uttid, batch_wav_path)
print(results)
# [{'uttid': 'hello_zh', 'lang': 'zh mandarin', 'confidence': 0.996, 'dur_s': 2.32, 'rtf': '0.0741', 'wav': 'assets/hello_zh.wav'}, {'uttid': 'hello_en', 'lang': 'en', 'confidence': 0.996, 'dur_s': 2.24, 'rtf': '0.0741', 'wav': 'assets/hello_en.wav'}]

Citation

@article{xu2026fireredasr2s,
  title={FireRedASR2S: A State-of-the-Art Industrial-Grade All-in-One Automatic Speech Recognition System},
  author={Xu, Kaituo and Jia, Yan and Huang, Kai and Chen, Junjie and Li, Wenpeng and Liu, Kun and Xie, Feng-Long and Tang, Xu and Hu, Yao},
  journal={arXiv preprint arXiv:2603.10420},
  year={2026}
}