### Qwen2-Audio-7B-GGUF Open - Source Multimodal Model: Achieve Audio - Text Interaction without Speech Recognition Module

Qwen2 Audio 7B GGUF

Developed by NexaAIDev

Qwen2-Audio is an advanced small-scale multimodal model that supports audio and text input, enabling voice interaction without relying on speech recognition modules.

Audio-to-Text EnglishOpen Source License:Apache-2.0 #Voice-free interaction #Multilingual audio analysis #Edge device deployment

Downloads 5,001

Release Time : 10/23/2024

Model Overview

Qwen2-Audio is a multimodal model capable of processing audio and text input, supporting Chinese, English, and major European languages, suitable for various scenarios such as voice conversations and audio analysis.

Model Features

Multimodal processing

Supports audio and text input, enabling voice interaction without relying on speech recognition modules.

Multilingual support

Supports Chinese, English, and major European languages, providing voice conversation and audio analysis capabilities for localized scenarios.

GGUF quantization

Offers various GGUF quantization schemes, suitable for local operation on edge devices.

High performance

Significantly outperforms previous SOTA models and Qwen-Audio in all tasks.

Model Capabilities

Speaker recognition and response

Speech translation and transcription

Mixed audio and noise detection

Music and sound analysis

Daily Q&A

Suggestion provision

Real-time speech translation

Environmental noise recognition and response

Key information extraction

Audio content summarization

Speech transcription and expansion

Mixed audio separation and detection

Music feature analysis

Use Cases

Voice interaction

Daily Q&A

Engage in daily question-and-answer interactions via voice.

Speaker recognition and response

Recognize the speaker and provide corresponding responses.

Real-time speech translation

Translate speech into other languages in real-time.

Audio analysis

Key information extraction

Extract key information from audio.

Audio content summarization

Generate summaries of audio content.

Music feature analysis

Analyze the features and attributes of music.

🚀 Qwen2-Audio

We're bringing Qwen2-Audio to run locally on edge devices with Nexa-SDK, offering various GGUF quantization options. Qwen2-Audio is a SOTA small-scale multimodal model (AudioLM) that handles audio and text inputs, allowing voice interactions without ASR modules.

🚀 Quick Start

Qwen2-Audio supports English, Chinese, and major European languages, and provides voice chat and audio analysis capabilities for local use cases.

Demo

See more demos in our blogs

✨ Features

Speaker identification and response
Speech translation and transcription
Mixed audio and noise detection
Music and sound analysis

📦 Installation

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

⚠️ Important Note

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

nexa run qwen2audio

This will run default q4_K_M quantization.

💡 Usage Tip

For terminal:

Drag and drop your audio file into the terminal (or enter file path on Linux)

Add text prompt to guide analysis or leave empty for direct voice input

or to use with local UI (streamlit):