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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- audio-text-to-text |
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- chat |
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- audio |
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- GGUF |
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--- |
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# Qwen2-Audio |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6618e0424dbef6bd3c72f89a/ThcKJj7LcWCZPwN1So05f.png" alt="Example" style="width:700px;"/> |
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## We're bringing Qwen2-Audio to run locally on edge devices with Nexa-SDK, offering various GGUF quantization options. |
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Qwen2-Audio is a SOTA small-scale multimodal model (AudioLM) 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 provides voice chat and audio analysis capabilities for local use cases like: |
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- Speaker identification and response |
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- Speech translation and transcription |
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- Mixed audio and noise detection |
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- Music and sound analysis |
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### Demo |
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/6618e0424dbef6bd3c72f89a/02XDwJe3bhZHYptor-b2_.mp4"></video> |
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See more demos in our [blogs](https://nexa.ai/blogs/qwen2-audio) |
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## How to Run Locally On Device |
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In the following, we demonstrate how to run Qwen2-Audio locally on your device. |
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**Step 1: Install Nexa-SDK (local on-device inference framework)** |
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[Install Nexa-SDK](https://github.com/NexaAI/nexa-sdk?tab=readme-ov-file#install-option-1-executable-installer) |
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> 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. |
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**Step 2: Then run the following code in your terminal** |
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```bash |
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nexa run qwen2audio |
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``` |
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This will run default q4_K_M quantization. |
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For terminal: |
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1. Drag and drop your audio file into the terminal (or enter file path on Linux) |
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2. Add text prompt to guide analysis or leave empty for direct voice input |
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**or to use with local UI (streamlit)**: |
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```bash |
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nexa run qwen2audio -st |
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``` |
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## Choose Quantizations for your device |
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Run [different quantization versions here](https://nexa.ai/Qwen/Qwen2-Audio-7.8B-Instruct/gguf-q4_K_M/readme) and check RAM requirements in our list. |
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> The default q4_K_M version requires 4.2GB of RAM. |
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## Use Cases |
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### Voice Chat |
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- Answer daily questions |
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- Offer suggestions |
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- Speaker identification and response |
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- Speech translation |
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- Detecting background noise and responding accordingly |
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### Audio Analysis |
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- Information Extraction |
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- Audio summary |
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- Speech Transcription and Expansion |
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- Mixed audio and noise detection |
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- Music and sound analysis |
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## Performance Benchmark |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6618e0424dbef6bd3c72f89a/lax8bLpR5uK2_Za0G6G3j.png" alt="Example" style="width:700px;"/> |
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Results demonstrate that Qwen2-Audio significantly outperforms either previous SOTAs or Qwen-Audio across all tasks. |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6618e0424dbef6bd3c72f89a/2vACK_gD_MAuZ7Hn4Yfiv.png" alt="Example" style="width:700px;"/> |
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## Blog |
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Learn more in our [blogs](https://nexa.ai/blogs/qwen2-audio) |
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## Join Community |
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[Discord](https://discord.gg/nexa-ai) | [X(Twitter)](https://x.com/nexa_ai) |
