Showing 4 open source projects for "iphone is"

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  • 1
    Kitten TTS

    Kitten TTS

    State-of-the-art TTS model under 25MB

    KittenTTS is an open-source, ultra-lightweight, and high-quality text-to-speech model featuring just 15 million parameters and a binary size under 25 MB. It is designed for real-time CPU-based deployment across diverse platforms. Ultra-lightweight, model size less than 25MB. CPU-optimized, runs without GPU on any device. High-quality voices, several premium voice options available. Fast inference, optimized for real-time speech synthesis.
    Downloads: 3 This Week
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  • 2
    MiniCPM-o

    MiniCPM-o

    A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming

    MiniCPM-o 2.6 is a cutting-edge multimodal large language model (MLLM) designed for high-performance tasks across vision, speech, and video. Capable of running on end-side devices such as smartphones and tablets, it provides powerful features like real-time speech conversation, video understanding, and multimodal live streaming. With 8 billion parameters, MiniCPM-o 2.6 surpasses its predecessors in versatility and efficiency, making it one of the most robust models available. It supports...
    Downloads: 0 This Week
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  • 3
    MobileCLIP

    MobileCLIP

    Implementation of "MobileCLIP" CVPR 2024

    ...The repo provides training, inference, and evaluation code for MobileCLIP models trained on DataCompDR, and for newer MobileCLIP2 models trained on DFNDR. It includes an iOS demo app and Core ML artifacts to showcase practical, offline photo search and classification on iPhone-class hardware. Project notes highlight latency/accuracy trade-offs, with MobileCLIP2 variants matching or surpassing larger baselines at notably lower parameter counts and runtime on mobile devices. A companion “mobileclip-dr” repository details large-scale, distributed data-generation pipelines used to reinforce datasets across billions of samples on thousands of GPUs. ...
    Downloads: 0 This Week
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  • 4
    Apple Neural Engine (ANE) Transformers

    Apple Neural Engine (ANE) Transformers

    Reference implementation of the Transformer architecture optimized

    ANE Transformers is a reference PyTorch implementation of Transformer components optimized for Apple Neural Engine on devices with A14 or newer and on Macs with M1 or newer chips. It demonstrates how to structure attention and related layers to achieve substantial speedups and lower peak memory compared to baseline implementations when deployed to ANE. The repository targets practitioners who want to keep familiar PyTorch modeling while preparing models for Core ML/ANE execution paths....
    Downloads: 0 This Week
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