stepfun-ai / Step-Audio-R1.1

Overview of Step-Audio-R1.1

Introduction

Step-Audio R1.1 (Realtime) is a major upgrade to Step-Audio-R1, designed for interactive spoken dialogue with both real-time responsiveness and strong reasoning capability .

Unlike conventional streaming speech models that trade intelligence for latency, R1.1 enables thinking while speaking , achieving high intelligence without sacrificing speed.

Mind-Paced Speaking (Low Latency)

Based on the research Mind-Paced Speaking , the Realtime variant adopts a Dual-Brain Architecture :

• A Formulation Brain responsible for high-level reasoning
• An Articulation Brain dedicated to speech generation

This decoupling allows the model to perform Chain-of-Thought reasoning during speech output , maintaining ultra-low latency while handling complex tasks in real time.

Acoustic-Grounded Reasoning (High Intelligence)

To address the inverted scaling issue—where reasoning over transcripts can degrade performance—Step-Audio R1.1 grounds its reasoning directly in acoustic representations rather than text alone.

Through iterative self-distillation, extended deliberation becomes a strength instead of a liability. This enables effective test-time compute scaling and leads to state-of-the-art performance , including top-ranking results on the AA benchmark.

Model Usage

📜 Requirements

• GPU : NVIDIA GPUs with CUDA support (tested on 4×L40S/H100/H800/H20).
• Operating System : Linux.
• Python : >= 3.10.0.

⬇️ Download Model

First, you need to download the Step-Audio-R1 model weights.

Method A · Git LFS

git lfs install
git clone https://huggingface.co/stepfun-ai/Step-Audio-R1.1

Method B · Hugging Face CLI

hf download stepfun-ai/Step-Audio-R1.1 --local-dir ./Step-Audio-R1.1

🚀 Deployment and Execution

We provide two ways to serve the model: Docker (recommended) or compiling the customized vLLM backend.

🐳 Method 1 · Run with Docker (Recommended)

A customized vLLM image is required.

1. Pull the image :

docker pull stepfun2025/vllm:step-audio-2-v20250909

1. Start the service : Assuming the model is downloaded in the Step-Audio-R1 folder in the current directory.

   docker run --rm -ti --gpus all \
       -v $(pwd)/Step-Audio-R1.1:/Step-Audio-R1.1 \
       -p 9999:9999 \
       stepfun2025/vllm:step-audio-2-v20250909 \
       -- vllm serve /Step-Audio-R1.1 \
       --served-model-name Step-Audio-R1.1 \
       --port 9999 \
       --max-model-len 16384 \
       --max-num-seqs 32 \
       --tensor-parallel-size 4 \
       --chat-template '{%- macro render_content(content) -%}{%- if content is string -%}{{- content.replace("<audio_patch>\n", "<audio_patch>") -}}{%- elif content is mapping -%}{{- content['"'"'value'"'"'] if '"'"'value'"'"' in content else content['"'"'text'"'"'] -}}{%- elif content is iterable -%}{%- for item in content -%}{%- if item.type == '"'"'text'"'"' -%}{{- item['"'"'value'"'"'] if '"'"'value'"'"' in item else item['"'"'text'"'"'] -}}{%- elif item.type == '"'"'audio'"'"' -%}<audio_patch>{%- endif -%}{%- endfor -%}{%- endif -%}{%- endmacro -%}{%- if tools -%}{{- '"'"'<|BOT|>system\n'"'"' -}}{%- if messages[0]['"'"'role'"'"'] == '"'"'system'"'"' -%}{{- render_content(messages[0]['"'"'content'"'"']) + '"'"'<|EOT|>'"'"' -}}{%- endif -%}{{- '"'"'<|BOT|>tool_json_schemas\n'"'"' + tools|tojson + '"'"'<|EOT|>'"'"' -}}{%- else -%}{%- if messages[0]['"'"'role'"'"'] == '"'"'system'"'"' -%}{{- '"'"'<|BOT|>system\n'"'"' + render_content(messages[0]['"'"'content'"'"']) + '"'"'<|EOT|>'"'"' -}}{%- endif -%}{%- endif -%}{%- for message in messages -%}{%- if message["role"] == "user" -%}{{- '"'"'<|BOT|>human\n'"'"' + render_content(message["content"]) + '"'"'<|EOT|>'"'"' -}}{%- elif message["role"] == "assistant" -%}{{- '"'"'<|BOT|>assistant\n'"'"' + (render_content(message["content"]) if message["content"] else '"'"''"'"') -}}{%- set is_last_assistant = true -%}{%- for m in messages[loop.index:] -%}{%- if m["role"] == "assistant" -%}{%- set is_last_assistant = false -%}{%- endif -%}{%- endfor -%}{%- if not is_last_assistant -%}{{- '"'"'<|EOT|>'"'"' -}}{%- endif -%}{%- elif message["role"] == "function_output" -%}{%- else -%}{%- if not (loop.first and message["role"] == "system") -%}{{- '"'"'<|BOT|>'"'"' + message["role"] + '"'"'\n'"'"' + render_content(message["content"]) + '"'"'<|EOT|>'"'"' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- '"'"'<|BOT|>assistant\n<think>\n'"'"' -}}{%- endif -%}' \
       --enable-log-requests \
       --interleave-mm-strings \
       --trust-remote-code
   

After the service starts, it will listen on localhost:9999 .

🐳 Method 2 · Run from Source (Compile vLLM)

Step-Audio-R1 requires a customized vLLM backend.

1. Download Source Code :

   git clone https://github.com/stepfun-ai/vllm.git
   cd vllm
   

2. Prepare Environment :

   python3 -m venv .venv
   source .venv/bin/activate
   

3. Install and Compile : vLLM contains both C++ and Python code. We mainly modified the Python code, so the C++ part can use the pre-compiled version to speed up the process.

   # Use pre-compiled C++ extensions (Recommended)
   VLLM_USE_PRECOMPILED=1 pip install -e .
   

4. Switch Branch : After compilation, switch to the branch that supports Step-Audio.

   git checkout feat/step-audio-support
   

5. Start the Service :

   # Ensure you are in the vllm directory and the virtual environment is activated
   source .venv/bin/activate
   
   python3 -m vllm.entrypoints.openai.api_server \
       --model ../Step-Audio-R1.1 \
       --served-model-name Step-Audio-R1.1 \
       --port 9999 \
       --host 0.0.0.0 \
       --max-model-len 65536 \
       --max-num-seqs 128 \
       --tensor-parallel-size 4 \
       --gpu-memory-utilization 0.85 \
       --trust-remote-code \
       --enable-log-requests \
       --interleave-mm-strings \
       --chat-template '{%- macro render_content(content) -%}{%- if content is string -%}{{- content.replace("<audio_patch>\n", "<audio_patch>") -}}{%- elif content is mapping -%}{{- content['"'"'value'"'"'] if '"'"'value'"'"' in content else content['"'"'text'"'"'] -}}{%- elif content is iterable -%}{%- for item in content -%}{%- if item.type == '"'"'text'"'"' -%}{{- item['"'"'value'"'"'] if '"'"'value'"'"' in item else item['"'"'text'"'"'] -}}{%- elif item.type == '"'"'audio'"'"' -%}<audio_patch>{%- endif -%}{%- endfor -%}{%- endif -%}{%- endmacro -%}{%- if tools -%}{{- '"'"'<|BOT|>system\n'"'"' -}}{%- if messages[0]['"'"'role'"'"'] == '"'"'system'"'"' -%}{{- render_content(messages[0]['"'"'content'"'"']) + '"'"'<|EOT|>'"'"' -}}{%- endif -%}{{- '"'"'<|BOT|>tool_json_schemas\n'"'"' + tools|tojson + '"'"'<|EOT|>'"'"' -}}{%- else -%}{%- if messages[0]['"'"'role'"'"'] == '"'"'system'"'"' -%}{{- '"'"'<|BOT|>system\n'"'"' + render_content(messages[0]['"'"'content'"'"']) + '"'"'<|EOT|>'"'"' -}}{%- endif -%}{%- endif -%}{%- for message in messages -%}{%- if message["role"] == "user" -%}{{- '"'"'<|BOT|>human\n'"'"' + render_content(message["content"]) + '"'"'<|EOT|>'"'"' -}}{%- elif message["role"] == "assistant" -%}{{- '"'"'<|BOT|>assistant\n'"'"' + (render_content(message["content"]) if message["content"] else '"'"''"'"') -}}{%- set is_last_assistant = true -%}{%- for m in messages[loop.index:] -%}{%- if m["role"] == "assistant" -%}{%- set is_last_assistant = false -%}{%- endif -%}{%- endfor -%}{%- if not is_last_assistant -%}{{- '"'"'<|EOT|>'"'"' -}}{%- endif -%}{%- elif message["role"] == "function_output" -%}{%- else -%}{%- if not (loop.first and message["role"] == "system") -%}{{- '"'"'<|BOT|>'"'"' + message["role"] + '"'"'\n'"'"' + render_content(message["content"]) + '"'"'<|EOT|>'"'"' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- '"'"'<|BOT|>assistant\n<think>\n'"'"' -}}{%- endif -%}'
   

After the service starts, it will listen on localhost:9999 .