FireRedASR2S
A SOTA Industrial-Grade All-in-One ASR System
[Code] [Paper] [Model] [Blog] [Demo]
FireRedASR2S is a state-of-the-art (SOTA), industrial-grade, all-in-one ASR system presented in the paper FireRedASR2S: A State-of-the-Art Industrial-Grade All-in-One Automatic Speech Recognition System. It integrates four modules into a unified pipeline: ASR, Voice Activity Detection (VAD), Spoken Language Identification (LID), and Punctuation Prediction (Punc).
Key Features
- FireRedASR2: Supports speech and singing transcription for Mandarin, Chinese dialects/accents, English, and code-switching.
- FireRedVAD: Ultra-lightweight module (0.6M parameters) supporting streaming and multi-label VAD (speech/singing/music).
- FireRedLID: Supports Spoken Language Identification for 100+ languages and 20+ Chinese dialects.
- FireRedPunc: BERT-style punctuation prediction for Chinese and English.
Sample Usage
To use the system, first clone the official repository and install the dependencies. Then you can use the following Python API:
from fireredasr2s import FireRedAsr2System, FireRedAsr2SystemConfig
# Initialize the system with default config
asr_system_config = FireRedAsr2SystemConfig()
asr_system = FireRedAsr2System(asr_system_config)
# Process an audio file (16kHz 16-bit mono PCM)
result = asr_system.process("assets/hello_zh.wav")
print(result['text'])
# Output: 你好世界。
🔥 News
- [2026.03.12] 🔥 We release FireRedASR2S technical report. See arXiv.
- [2026.02.25] 🔥 We release FireRedASR2-LLM model weights. 🤗
- [2026.02.12] 🔥 We release FireRedASR2S (FireRedASR2-AED, FireRedVAD, FireRedLID, and FireRedPunc) with model weights and inference code.
Evaluation
FireRedASR2-LLM achieves 2.89% average CER on 4 public Mandarin benchmarks and 11.55% on 19 public Chinese dialects and accents benchmarks, outperforming competitive baselines including Doubao-ASR, Qwen3-ASR, and Fun-ASR.
| Model | Mandarin (Avg CER%) | Dialects (Avg CER%) |
|---|---|---|
| FireRedASR2-LLM | 2.89 | 11.55 |
| FireRedASR2-AED | 3.05 | 11.67 |
| Doubao-ASR | 3.69 | 15.39 |
| Qwen3-ASR | 3.76 | 11.85 |
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}
}