FireRedASR2-AED

FireRedASR2S A SOTA Industrial-Grade All-in-One ASR System

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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.

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}
}