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