Create README.md

README.md

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+# Qwen2-Audio
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/6618e0424dbef6bd3c72f89a/ThcKJj7LcWCZPwN1So05f.png" alt="Example" style="width:700px;"/>
+
+Qwen2-Audio is a SOTA small-scale multimodal model that handles audio and text inputs, allowing you to have voice interactions without ASR modules. Qwen2-Audio supports English, Chinese, and major European languages,and also provides robust audio analysis for local use cases like:
+- Speaker identification and response
+- Speech translation and transcription
+- Mixed audio and noise detection
+- Music and sound analysis
+
+## We're bringing Qwen2-Audio to edge devices with Nexa SDK, offering various quantization options.
+- Voice Chat: Users can freely engage in voice interactions with Qwen2-Audio without text input.
+- Audio Analysis: Users can provide both audio and text instructions for analysis during the interaction.
+### Demo
+
+<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/6618e0424dbef6bd3c72f89a/02XDwJe3bhZHYptor-b2_.mp4"></video>
+
+## How to Run Locally On-Device
+
+In the following, we demonstrate how to run Qwen2-Audio locally 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)
+
+> 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 to run with local streamlit UI**
+
+```bash
+nexa run qwen2audio -st
+```
+
+**or to use in terminal**:
+
+```bash
+nexa run qwen2audio
+```
+
+### Usage Instructions
+
+For terminal:
+1. Drag and drop your audio file into the terminal (or enter file path on Linux)
+2. Add text prompt to guide analysis or leave empty for direct voice input
+
+### System Requirements
+
+💻 **RAM Requirements**:
+- Default q4_K_M version requires 4.2GB of RAM
+- Check the RAM requirements table for different quantization versions
+
+🎵 **Audio Format**:
+- Optimal: 16kHz `.wav` format
+- Other formats and sample rates are supported with automatic conversion
+
+## Use Cases
+
+### Voice Chat
+- Answer daily questions
+- Offer suggestions
+- Speaker identification and response
+- Speech translation
+- Detecting background noise and responding accordingly
+
+### Audio Analysis
+- Information Extraction
+- Audio summary
+- Speech Transcription and Expansion
+- Mixed audio and noise detection
+- Music and sound analysis
+
+## Performance Benchmark
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/6618e0424dbef6bd3c72f89a/lax8bLpR5uK2_Za0G6G3j.png" alt="Example" style="width:700px;"/>
+
+Results demonstrate that Qwen2-Audio significantly outperforms either previous SOTAs or Qwen-Audio across all tasks.
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/6618e0424dbef6bd3c72f89a/2vACK_gD_MAuZ7Hn4Yfiv.png" alt="Example" style="width:700px;"/>
+
+To learn more about Qwen2-Audio's capability, please refer to their [Blog], [GitHub], and [Report].
+
+## Follow Nexa AI to run more models on-device
+[Website](https://nexa.ai/)