Update README.md

README.md

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 # OmniAudio-2.6B
 OmniAudio is the world's fastest and most efficient audio-language model for on-device deployment - a 2.6B-parameter multimodal model that processes both text and audio inputs. It integrates three components: Gemma-2-2b, Whisper turbo, and a custom projector module, enabling secure, responsive audio-text processing directly on edge devices.
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 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.
-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]()
+
 ## 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.
 * **Creative Content Generation**: Transform voice prompts into creative pieces. Ask "Write a haiku about autumn leaves" and receive poetic responses inspired by your voice input.
 * **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.
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+## 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]()
+
 ## Run OmniAudio-2.6B on Your 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)
+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)
+
 > ***Nexa-SDK is a open-sourced, local on-device inference framework, supporting text generation, image generation, vision-language models (VLM), audio-language models, speech-to-text (ASR), and text-to-speech (TTS) capabilities. Installable via Python Package or Executable Installer.***
-**Step 2: Then run the following code in your terminal**
+
+Step 2: Then run the following code in your terminal
 ```bash
 nexa run omniaudio -st
 ```
+
 💻 OmniAudio-2.6B q4_K_M version requires 1.30GB RAM and 1.60GB storage space.
+
 ## Training
 We developed OmniAudio through a three-stage training pipeline:
-**Pretraining:** The initial stage focuses on core audio-text alignment using MLS English 10k transcription dataset. We introduced a special <|transcribe|> token to enable the model to distinguish between transcription and completion tasks, ensuring consistent performance across use cases.
-**Supervised Fine-tuning (SFT):** We enhance the model's conversation capabilities using synthetic datasets derived from MLS English 10k transcription. This stage leverages a proprietary model to generate contextually appropriate responses, creating rich audio-text pairs for effective dialogue understanding.
-**Direct Preference Optimization (DPO):** The final stage refines model quality using GPT-4o API as a reference. The process identifies and corrects inaccurate responses while maintaining semantic alignment. We additionally leverage Gemma2's text responses as a gold standard to ensure consistent quality across both audio and text inputs.
+* **Pretraining:** The initial stage focuses on core audio-text alignment using MLS English 10k transcription dataset. We introduced a special <|transcribe|> token to enable the model to distinguish between transcription and completion tasks, ensuring consistent performance across use cases.
+* **Supervised Fine-tuning (SFT):** We enhance the model's conversation capabilities using synthetic datasets derived from MLS English 10k transcription. This stage leverages a proprietary model to generate contextually appropriate responses, creating rich audio-text pairs for effective dialogue understanding.
+* **Direct Preference Optimization (DPO):** The final stage refines model quality using GPT-4o API as a reference. The process identifies and corrects inaccurate responses while maintaining semantic alignment. We additionally leverage Gemma2's text responses as a gold standard to ensure consistent quality across both audio and text inputs.
+
 ## What's Next for OmniAudio?
 OmniAudio is in active development and we are working to advance its capabilities:
 * Building direct audio generation for two-way voice communication
 * Implementing function calling support via [Octopus_v2](https://huggingface.co/NexaAIDev/Octopus-v2) integration
+  
 In the long term, we aim to establish OmniAudio as a comprehensive solution for edge-based audio-language processing.
 
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