Update README.md

README.md

@@ -15,6 +15,13 @@ OmniAudio is the world's fastest and most efficient audio-language model for on-
 
 Unlike traditional approaches that chain ASR and LLM models together, OmniAudio-2.6B unifies both capabilities in a single efficient architecture for minimal latency and resource overhead.
 
+## Demo
+
+<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/6618e0424dbef6bd3c72f89a/538_aQ2hRexTlXFL-cYhW.mp4"></video>
+
+## Performance Benchmarks on Consumer Hardware
+On a 2024 Mac Mini M4 Pro, **Qwen2-Audio-7B-Instruct** running on 🤗 Transformers achieves an average decoding speed of 6.38 tokens/second, while **Omni-Audio-2.6B** through Nexa SDK reaches 35.23 tokens/second in FP16 GGUF version and 66 tokens/second in Q4_K_M quantized GGUF version - delivering **5.5x to 10.3x faster performance** on consumer hardware.
+
 ## Use Cases
 * **Voice QA without Internet**: Process offline voice queries like "I am at camping, how do I start a fire without fire starter?" OmniAudio provides practical guidance even without network connectivity.
 * **Voice-in Conversation**: Have conversations about personal experiences. When you say "I am having a rough day at work," OmniAudio engages in supportive talk and active listening.
@@ -22,15 +29,12 @@ Unlike traditional approaches that chain ASR and LLM models together, OmniAudio-
 * **Recording Summary**: Simply ask "Can you summarize this meeting note?" to convert lengthy recordings into concise, actionable summaries.
 * **Voice Tone Modification**: Transform casual voice memos into professional communications. When you request "Can you make this voice memo more professional?" OmniAudio adjusts the tone while preserving the core message.
 
-## Performance Benchmarks on Consumer Hardware
-On a 2024 Mac Mini M4 Pro, **Qwen2-Audio-7B-Instruct** running on 🤗 Transformers achieves an average decoding speed of 6.38 tokens/second, while **Omni-Audio-2.6B** through Nexa SDK reaches 35.23 tokens/second in FP16 GGUF version and 66 tokens/second in Q4_K_M quantized GGUF version - delivering **5.5x to 10.3x faster performance** on consumer hardware.
-
 ## Quick Links
-1. Interactive Demo in our [HuggingFace Space]().
-2. [Quickstart for local setup]()
-3. Learn more in our [Blogs]()
+1. Interactive Demo in our [HuggingFace Space](https://huggingface.co/spaces/NexaAIDev/omni-audio-demo)
+2. [Quickstart for local setup](#How-to-Use-On-Device)
+3. Learn more in our [Blogs](https://nexa.ai/blogs/OmniAudio-2.6B)
 
-## Run OmniAudio-2.6B on Your Device
+## How to Use On Device
 Step 1: Install Nexa-SDK (local on-device inference framework)
 
 [🚀 Install Nexa-SDK](https://github.com/NexaAI/nexa-sdk?tab=readme-ov-file#install-option-1-executable-installer)