feat: translate Step-Audio 2 project to English and add images

- Translate all content from Chinese to English
- Add three performance visualization images (Architecture, Audio Understanding, ASR Performance)
- Maintain professional academic tone and clear structure
- Position images at relevant sections for better readability

Author: FlyTia

content/projects/step-audio-2/ASR_performance.png

@@ -6,29 +6,57 @@ tags:
   - Speech Understanding
   - Chain-of-Thought
   - Reinforcement Learning
-summary: End-to-end multimodal large language model with audio reasoning capabilities, achieving SOTA performance on multiple benchmarks.
+summary: The world's first industrial-grade end-to-end audio LLM with deep thinking capabilities, achieving SOTA performance across multiple understanding and dialogue tasks.
 ---
 
-<div style="position: relative; width: 100%; padding-bottom: 75%; overflow: hidden;">
-  <iframe 
-    src="https://www.stepfun.com/research/en/step-audio2" 
-    style="position: absolute; top: 0; left: 0; width: 100%; height: 100%; border: none;"
-    allowfullscreen
-    loading="lazy">
-  </iframe>
-</div>
+## Project Resources
+
+- 📄 **Paper**: [arXiv:2507.16632](https://arxiv.org/abs/2507.16632)
+- 💻 **GitHub**: [stepfun-ai/Step-Audio2](https://github.com/stepfun-ai/Step-Audio2)
+- 🌐 **Tech Blog**: [中文](https://www.stepfun.com/research/zh/step-audio2) | [English](https://www.stepfun.com/research/en/step-audio2)
 
 ---
 
-**项目简介：**
+## Overview
+
+Step-Audio 2 is **the world's first end-to-end audio large language model with deep thinking capabilities designed for industrial applications**. This model innovatively combines a latent space audio encoder with audio reinforcement learning technology. It effectively captures paralinguistic information and speaking style features, and adopts a Chain-of-Thought (CoT) reasoning strategy combined with reinforcement learning optimization. Step-Audio 2 achieves high-performance speech dialogue capabilities across various scenarios. Experimental results demonstrate that the model achieves state-of-the-art (SOTA) performance on multiple understanding and dialogue tasks.
+
+## True End-to-End Architecture: Understanding Beyond Words
+
+Traditional AI voice systems have been criticized for lacking both intelligence and emotional understanding. First, they lack the knowledge base and reasoning capabilities comparable to text-based large models. Second, they sound "robotic" and fail to comprehend subtext, tone, emotions, and laughter—the "unspoken meanings." Step-Audio 2 solves these problems through innovative architectural design, achieving both cognitive and emotional intelligence.
+
+![Step-Audio 2 Architecture](Architecture.png)
+
+### Core Technical Features
+
+- **Genuine End-to-End Multimodal Architecture**: Step-Audio 2 breaks through the traditional ASR+LLM+TTS three-stage structure, achieving direct conversion from raw audio input to speech response output. The architecture is more concise with lower latency, and can effectively understand paralinguistic information and non-vocal signals.
+
+- **CoT Reasoning Combined with Reinforcement Learning**: Step-Audio 2 is the first to introduce Chain-of-Thought (CoT) reasoning combined with reinforcement learning optimization in end-to-end speech models. It can perform fine-grained understanding, reasoning, and natural response to paralinguistic and non-speech signals such as emotions, intonation, and music.
+
+- **Audio Knowledge Enhancement**: The model supports external tools including web search, helping to solve hallucination problems and enabling multi-scenario expansion capabilities.
+
+## SOTA Performance
+
+Step-Audio 2 achieves **SOTA results** across multiple key benchmarks, demonstrating outstanding performance in audio understanding, speech recognition, translation, and dialogue scenarios. The overall performance surpasses all open-source end-to-end speech models including Qwen-Omni and Kimi-Audio, and exceeds GPT-4o Audio in most tasks.
+
+![Audio Understanding Performance](Audio_understanding.png)
+
+### Key Performance Metrics
+
+- **MMAU (General Multimodal Audio Understanding)**: Ranks **#1** with a score of **78**
+
+- **URO Bench (Spoken Dialogue Capability)**: **#1 in Chinese, #2 in English**, demonstrating excellent dialogue understanding and expression abilities
 
-Step-Audio 2 is an end-to-end multimodal large language model engineered for industrial applications. This model innovatively integrates a latent space audio encoder with audio reinforcement learning.
+- **Machine Translation Tasks**:
+  - CoVoST 2 benchmark: **39.3**
+  - CVSS benchmark: **30.9**
+  - Significantly outperforms GPT-4o Audio and other open-source speech models
 
-**关键特性：**
-- Genuine end-to-end architecture eliminating traditional ASR+LLM+TTS pipelines
-- CoT-reinforcement learning fusion enabling audio reasoning capabilities
-- Acoustic knowledge enhancement via web search and audio retrieval
-- SOTA performance on MMAU, URO-Bench, and multiple ASR benchmarks
+- **Speech Recognition Tasks**: Achieves first place in multiple languages and dialects
+  - Average CER (Character Error Rate) on open-source Chinese test sets: **3.08**
+  - Average WER (Word Error Rate) on open-source English test sets: **3.14**
+  - Far ahead of other models
 
-[访问完整页面 →](https://www.stepfun.com/research/en/step-audio2)
+![ASR Performance](ASR_performance.png)
 
+- **Paralinguistic Understanding Tasks**: Ranks **#1** with a score of **83.1**