Audio-Text-to-Text
Transformers
Safetensors
step_audio_2
text-generation
audio-reasoning
chain-of-thought
multi-modal
step-audio-r1
custom_code
Instructions to use stepfun-ai/Step-Audio-R1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stepfun-ai/Step-Audio-R1.1 with Transformers:
# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("stepfun-ai/Step-Audio-R1.1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -18,6 +18,9 @@ Step-Audio R1.1 (Realtime) is a major upgrade to Step-Audio-R1, designed for int
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Unlike conventional streaming speech models that trade intelligence for latency, R1.1 enables *thinking while speaking*, achieving high intelligence without sacrificing speed.
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### Mind-Paced Speaking (Low Latency)
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Based on the research [*Mind-Paced Speaking*](MPS.pdf), the Realtime variant adopts a **Dual-Brain Architecture**:
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- A **Formulation Brain** responsible for high-level reasoning
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Unlike conventional streaming speech models that trade intelligence for latency, R1.1 enables *thinking while speaking*, achieving high intelligence without sacrificing speed.
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WeChat Developer Group
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### Mind-Paced Speaking (Low Latency)
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Based on the research [*Mind-Paced Speaking*](MPS.pdf), the Realtime variant adopts a **Dual-Brain Architecture**:
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- A **Formulation Brain** responsible for high-level reasoning
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