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Step-Audio2

> 编程语言
Open source

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

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

Step-Audio 2

News!!

  • Sep 15, 2025: We release Step-Audio 2 mini Think and its corresponding examples.
  • Sep 3, 2025: We release our vLLM backend and corresponding examples.
  • Aug 29, 2025: We are pleased to open-source Step-Audio 2 mini, Step-Audio 2 mini Base and their corresponding inference examples. Technical report is also updated.
  • Jul 24, 2025: We release demonstration videos for Step-Audio 2.
  • Jul 23, 2025: We release our benchmark for paralinguistic information understanding, StepEval-Audio-Paralinguistic.
  • Jul 23, 2025: We release our benchmark for tool calling, StepEval-Audio-Toolcall.
  • Jul 23, 2025: We release the technical report of Step-Audio 2.

WeChat Developer Group

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.

  • Emotional Reasoning: Analyzing user's paralinguistic information such as age and emotion, leading to more accurate and intelligent interpretation of the audio context.

  • 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).

  • Open-source: Step-Audio 2 mini, Step-Audio 2 mini Base and Step-Audio 2 mini Think are released under Apache 2.0 license.

Model Download

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

Model Usage

Dependencies and Installation

  • Python >= 3.10
  • PyTorch >= 2.3-cu121
  • CUDA Toolkit
bash
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
# git clone https://huggingface.co/stepfun-ai/Step-Audio-2-mini-Base

vLLM docker image

We highly recommend using our vLLM backend for faster and streaming inference, also deploying across multiple GPUs.

…

Inference Scripts

bash
python examples.py
# python examples-base.py
# python examples-vllm.py
# python examples-think.py

Local web demonstration

bash
pip install gradio
python web_demo.py
# python web_demo_vllm.py

Online demonstration

StepFun realtime console

  • Both Step-Audio 2 and Step-Audio 2 mini are available in our StepFun realtime console with web search tool enabled.
  • You will need an API key from the StepFun Open Platform.

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.

WeChat group

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

Evaluation

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

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> Details

PublishedAug 1, 2026
UpdatedSep 17, 2026
Category编程语言
PricingOpen source

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