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Step-Audio-2-mini

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Introduction

Step-Audio 2 is an end-to-end multi-modal large language model designed for industry-strength audio understanding and speech conversation.

  • Advanced Speech and Audio Understanding: Promising performance in ASR and audio understanding by comprehending and reasoning semantic information, para-linguistic and non-vocal information.

  • Intelligent Speech Conversation: Achieving natural and intelligent interactions that are contextually appropriate for various conversational scenarios and paralinguistic information.

  • Tool Calling and Multimodal RAG: By leveraging tool calling and RAG to access real-world knowledge (both textual and acoustic), Step-Audio 2 can generate responses with fewer hallucinations for diverse scenarios, while also having the ability to switch timbres based on retrieved speech.

  • State-of-the-Art Performance: Achieving state-of-the-art performance on various audio understanding and conversational benchmarks compared to other open-source and commercial solutions. (See Evaluation and Technical Report).

Model Download

Huggingface

Models 🤗 Hugging Face
Step-Audio 2 mini stepfun-ai/Step-Audio-2-mini
Step-Audio 2 mini Base stepfun-ai/Step-Audio-2-mini-Base

Model Usage

🔧 Dependencies and Installation 

conda create -n stepaudio2 python=3.10
conda activate stepaudio2
pip install transformers==4.49.0 torchaudio librosa onnxruntime s3tokenizer diffusers hyperpyyaml

git clone https://github.com/stepfun-ai/Step-Audio2.git
cd Step-Audio2
git lfs install
git clone https://huggingface.co/stepfun-ai/Step-Audio-2-mini

🚀 Inference Scripts 

python examples.py

🚀 Local web demonstration 

pip install gradio
python web_demo.py

Online demonstration

StepFun realtime console

StepFun AI Assistant

  • Step-Audio 2 is also available in our StepFun AI Assistant mobile App with both web and audio search tools enabled.
  • Please scan the following QR code to download it from your app store then tap the phone icon in the top-right corner.
QR code

WeChat group

You can scan the following QR code to join our WeChat group for communication and discussion.

QR code

Evaluation

Architecture

Automatic speech recognition

CER for Chinese, Cantonese and Japanese and WER for Arabian and English. N/A indicates that the language is not supported.

Category Test set Doubao LLM ASR GPT-4o Transcribe Kimi-Audio Qwen-Omni Step-Audio 2 Step-Audio 2 mini
English Common Voice 9.20 9.30 7.83 8.33 5.95 6.76
FLEURS English 7.22 2.71 4.47 5.05 3.03 3.05
LibriSpeech clean 2.92 1.75 1.49 2.93 1.17 1.33
LibriSpeech other 5.32 4.23 2.91 5.07 2.42 2.86
Average 6.17 4.50 4.18 5.35 3.14 3.50
Chinese AISHELL 0.98 3.52 0.64 1.17 0.63 0.78
AISHELL-2 3.10 4.26 2.67 2.40 2.10 2.16
FLEURS Chinese 2.92 2.62 2.91 7.01 2.68 2.53
KeSpeech phase1 6.48 26.80 5.11 6.45 3.63 3.97
WenetSpeech meeting 4.90 31.40 5.21 6.61 4.75 4.87
WenetSpeech net 4.46 15.71 5.93 5.24 4.67 4.82
Average 3.81 14.05 3.75 4.81 3.08 3.19
Multilingual FLEURS Arabian N/A 11.72 N/A 25.13 14.22 16.46
Common Voice yue 9.20 11.10 38.90 7.89 7.90 8.32
FLEURS Japanese N/A 3.27 N/A 10.49 3.18 4.67
In-house Anhui accent 8.83 50.55 22.17 18.73 10.61 11.65
Guangdong accent 4.99 7.83 3.76 4.03 3.81 4.44
Guangxi accent 3.37 7.09 4.29 3.35 4.11 3.51
Shanxi accent 20.26 55.03 34.71 25.95 12.44 15.60
Sichuan dialect 3.01 32.85 5.26 5.61 4.35 4.57
Shanghai dialect 47.49 89.58 82.90 58.74 17.77 19.30
Average 14.66 40.49 25.52 19.40 8.85 9.85

Paralinguistic information understanding

StepEval-Audio-Paralinguistic

Model Avg. Gender Age Timbre Scenario Event Emotion Pitch Rhythm Speed Style Vocal
GPT-4o Audio 43.45 18 42 34 22 14 82 40 60 58 64 44
Kimi-Audio 49.64 94 50 10 30 48 66 56 40 44 54 54
Qwen-Omni 44.18 40 50 16 28 42 76 32 54 50 50 48
Step-Audio-AQAA 36.91 70 66 18 14 14 40 38 48 54 44 0
Step-Audio 2 83.09 100 96 82 78 60 86 82 86 88 88 68
Step-Audio 2 mini 80.00 100 94 80 78 60 82 82 68 74 86 76

Audio understanding and reasoning

MMAU

Model Avg. Sound Speech Music
Audio Flamingo 3 73.1 76.9 66.1 73.9
Gemini 2.5 Pro 71.6 75.1 71.5 68.3
GPT-4o Audio 58.1 58.0 64.6 51.8
Kimi-Audio 69.6 79.0 65.5 64.4
Omni-R1 77.0 81.7 76.0 73.4
Qwen2.5-Omni 71.5 78.1 70.6 65.9
Step-Audio-AQAA 49.7 50.5 51.4 47.3
Step-Audio 2 78.0 83.5 76.9 73.7
Step-Audio 2 mini 73.2 76.6 71.5 71.6

Speech translation

Model CoVoST 2 (S2TT)
Avg. English-to-Chinese Chinese-to-English
GPT-4o Audio 29.61 40.20 19.01
Qwen2.5-Omni 35.40 41.40 29.40
Step-Audio-AQAA 28.57 37.71 19.43
Step-Audio 2 39.26 49.01 29.51
Step-Audio 2 mini 39.29 49.12 29.47
Model CVSS (S2ST)
Avg. English-to-Chinese Chinese-to-English
GPT-4o Audio 23.68 20.07 27.29
Qwen-Omni 15.35 8.04 22.66
Step-Audio-AQAA 27.36 30.74 23.98
Step-Audio 2 30.87 34.83 26.92
Step-Audio 2 mini 29.08 32.81 25.35

Tool calling

StepEval-Audio-Toolcall. Date and time tools have no parameter.

Model Objective Metric Audio search Date & Time Weather Web search
Qwen3-32B† Trigger Precision / Recall 67.5 / 98.5 98.4 / 100.0 90.1 / 100.0 86.8 / 98.5
Type Accuracy 100.0 100.0 98.5 98.5
Parameter Accuracy 100.0 N/A 100.0 100.0
Step-Audio 2 Trigger Precision / Recall 86.8 / 99.5 96.9 / 98.4 92.2 / 100.0 88.4 / 95.5
Type Accuracy 100.0 100.0 90.5 98.4
Parameter Accuracy 100.0 N/A 100.0 100.0

Speech-to-speech conversation

URO-Bench. U. R. O. stands for understanding, reasoning, and oral conversation, respectively.

Model Language Basic Pro
Avg. U. R. O. Avg. U. R. O.
GPT-4o Audio Chinese 78.59 89.40 65.48 85.24 67.10 70.60 57.22 70.20
Kimi-Audio 73.59 79.34 64.66 79.75 66.07 60.44 59.29 76.21
Qwen-Omni 68.98 59.66 69.74 77.27 59.11 59.01 59.82 58.74
Step-Audio-AQAA 74.71 87.61 59.63 81.93 65.61 74.76 47.29 68.97
Step-Audio 2 83.32 91.05 75.45 86.08 68.25 74.78 63.18 65.10
Step-Audio 2 mini 77.81 89.19 64.53 84.12 69.57 76.84 58.90 69.42
GPT-4o Audio English 84.54 90.18 75.90 90.41 67.51 60.65 64.36 78.46
Kimi-Audio 60.04 83.36 42.31 60.36 49.79 50.32 40.59 56.04
Qwen-Omni 70.58 66.29 69.62 76.16 50.99 44.51 63.88 49.41
Step-Audio-AQAA 71.11 90.15 56.12 72.06 52.01 44.25 54.54 59.81
Step-Audio 2 83.90 92.72 76.51 84.92 66.07 64.86 67.75 66.33
Step-Audio 2 mini 74.36 90.07 60.12 77.65 61.25 58.79 61.94 63.80

License

The model and code in the repository is licensed under Apache 2.0 License.

Citation 

@misc{wu2025stepaudio2technicalreport,
      title={Step-Audio 2 Technical Report},
      author={Boyong Wu and Chao Yan and Chen Hu and Cheng Yi and Chengli Feng and Fei Tian and Feiyu Shen and Gang Yu and Haoyang Zhang and Jingbei Li and Mingrui Chen and Peng Liu and Wang You and Xiangyu Tony Zhang and Xingyuan Li and Xuerui Yang and Yayue Deng and Yechang Huang and Yuxin Li and Yuxin Zhang and Zhao You and Brian Li and Changyi Wan and Hanpeng Hu and Jiangjie Zhen and Siyu Chen and Song Yuan and Xuelin Zhang and Yimin Jiang and Yu Zhou and Yuxiang Yang and Bingxin Li and Buyun Ma and Changhe Song and Dongqing Pang and Guoqiang Hu and Haiyang Sun and Kang An and Na Wang and Shuli Gao and Wei Ji and Wen Li and Wen Sun and Xuan Wen and Yong Ren and Yuankai Ma and Yufan Lu and Bin Wang and Bo Li and Changxin Miao and Che Liu and Chen Xu and Dapeng Shi and Dingyuan Hu and Donghang Wu and Enle Liu and Guanzhe Huang and Gulin Yan and Han Zhang and Hao Nie and Haonan Jia and Hongyu Zhou and Jianjian Sun and Jiaoren Wu and Jie Wu and Jie Yang and Jin Yang and Junzhe Lin and Kaixiang Li and Lei Yang and Liying Shi and Li Zhou and Longlong Gu and Ming Li and Mingliang Li and Mingxiao Li and Nan Wu and Qi Han and Qinyuan Tan and Shaoliang Pang and Shengjie Fan and Siqi Liu and Tiancheng Cao and Wanying Lu and Wenqing He and Wuxun Xie and Xu Zhao and Xueqi Li and Yanbo Yu and Yang Yang and Yi Liu and Yifan Lu and Yilei Wang and Yuanhao Ding and Yuanwei Liang and Yuanwei Lu and Yuchu Luo and Yuhe Yin and Yumeng Zhan and Yuxiang Zhang and Zidong Yang and Zixin Zhang and Binxing Jiao and Daxin Jiang and Heung-Yeung Shum and Jiansheng Chen and Jing Li and Xiangyu Zhang and Yibo Zhu},
      year={2025},
      eprint={2507.16632},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.16632},
}
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