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 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.
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).
| 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 |
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-BaseWe highly recommend using our vLLM backend for faster and streaming inference, also deploying across multiple GPUs.
…python examples.py
# python examples-base.py
# python examples-vllm.py
# python examples-think.pypip install gradio
python web_demo.py
# python web_demo_vllm.pyYou can scan the following QR code to join our WeChat group for communication and discussion.
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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