Qwen2.5-Omni
Qwen2.5-Omni enables all-round processing of text, images, audio and video, and supports real-time voice and video chat.
English
English
Qwen2.5-Omni is a new flagship end-to-end multimodal AI model in the Qwen series designed for comprehensive multimodal perception. It not only handles inputs including text, images, audio and video, but also provides real-time streaming responses through text generation and natural speech synthesis.
This model adopts the Thinker-Talker architecture, combined with the innovative TMRoPE (Time-aligned Multimodal RoPE) position embedding technology, effectively synchronizes the timestamps of video and audio, providing users with an accurate multimodal interactive experience.
Text processing: Supports natural language dialogue, instructions and long text processing, and supports multilingual.
Image recognition: recognize and understand image content.
Audio processing: perform voice recognition, understand voice commands and generate smooth voice.
Video understanding: Analyze video content, support video Q&A and other functions.
Real-time voice and video chat: supports real-time interaction between voice and video streams.
Thinker-Talker architecture: divided into two parts: "Thinker" (understand multimodal information) and "Talker" (generate voice output).
TMRoPE technology: Time-aligned multimodal position embedding method to ensure video and audio synchronization.
Streaming processing: block processing of multimodal data, supporting real-time response.
Training stage: including visual and audio encoder training, full parameter training, and long sequence data training.
Intelligent customer service: Provide real-time voice and text customer service.
Virtual assistant: helps users to manage schedules, query, etc.
Educational field: voice explanation, interactive question and answer functions.
Entertainment field: voice interaction, character dubbing, content recommendation, etc.
Smart office: voice conference records and work efficiency improvement.
ModelScope : Suitable for mainland Chinese users, providing more stable model download and deployment support.
vLLM Deployment : It is recommended to use vLLM to quickly deploy Qwen2.5-Omni , which supports streaming inference.
Docker image : In order to simplify the deployment process, Qwen2.5-Omni provides an official Docker image, where users only need to download the model file and start the demo. Qwen2.5-Omni provides powerful multimodal processing capabilities, is suitable for various industry scenarios, and supports open source downloads, which facilitates developers and enterprises to conduct secondary development and commercial deployment.
