Showing 3 open source projects for "frame rate"

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    VibeVoice

    VibeVoice

    Open-source multi-speaker long-form text-to-speech model

    ...Unlike traditional TTS systems, it excels in scalability, speaker consistency, and natural turn-taking for up to 90 minutes of continuous speech with as many as four distinct speakers. A key innovation is its use of continuous acoustic and semantic speech tokenizers operating at an ultra-low frame rate of 7.5 Hz, enabling high audio fidelity with efficient processing of long sequences. The model integrates a Qwen2.5-based large language model with a diffusion head to produce realistic acoustic details and capture conversational context. Training involved curriculum learning with increasing sequence lengths up to 65K tokens, allowing VibeVoice to handle very long dialogues effectively. ...
    Downloads: 16 This Week
    Last Update:
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  • 2
    Qwen-2.5-VL

    Qwen-2.5-VL

    Qwen2.5-VL is the multimodal large language model series

    Qwen2.5 is a series of large language models developed by the Qwen team at Alibaba Cloud, designed to enhance natural language understanding and generation across multiple languages. The models are available in various sizes, including 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B parameters, catering to diverse computational requirements. Trained on a comprehensive dataset of up to 18 trillion tokens, Qwen2.5 models exhibit significant improvements in instruction following, long-text generation...
    Downloads: 4 This Week
    Last Update:
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  • 3
    Qwen2.5-VL-3B-Instruct

    Qwen2.5-VL-3B-Instruct

    Qwen2.5-VL-3B-Instruct: Multimodal model for chat, vision & video

    ...The model can serve as an intelligent visual agent capable of interacting with digital interfaces and understanding long-form videos by dynamically sampling resolution and frame rate. It uses a SwiGLU and RMSNorm-enhanced ViT architecture and introduces mRoPE updates for robust temporal and spatial understanding. The model supports flexible image input (file path, URL, base64) and outputs structured responses like bounding boxes or JSON, making it highly versatile in commercial and research settings. It excels in a wide range of benchmarks such as DocVQA, InfoVQA, and AndroidWorld control tasks.
    Downloads: 0 This Week
    Last Update:
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