Showing 4 open source projects for "format low level"

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  • 1
    whisper.cpp

    whisper.cpp

    Port of OpenAI's Whisper model in C/C++

    whisper.cpp is a lightweight, C/C++ reimplementation of OpenAI’s Whisper automatic speech recognition (ASR) model—designed for efficient, standalone transcription without external dependencies. The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples. whisper.cpp supports integer quantization of the Whisper ggml models. ...
    Downloads: 556 This Week
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  • 2
    TensorFlow Serving

    TensorFlow Serving

    Serving system for machine learning models

    ...In order to serve a Tensorflow model, simply export a SavedModel from your Tensorflow program. SavedModel is a language-neutral, recoverable, hermetic serialization format that enables higher-level systems and tools to produce, consume, and transform TensorFlow models.
    Downloads: 0 This Week
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  • 3
    Gen.jl

    Gen.jl

    A general-purpose probabilistic programming system

    ...Gen’s inference library gives users building blocks for writing efficient probabilistic inference algorithms that are tailored to their models, while automating the tricky math and the low-level implementation details. Gen helps users write hybrid algorithms that combine neural networks, variational inference, sequential Monte Carlo samplers, and Markov chain Monte Carlo. Gen features an easy-to-use modeling language for writing down generative models, inference models, variational families, and proposal distributions using ordinary code. ...
    Downloads: 0 This Week
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  • 4
    EvaDB

    EvaDB

    Database system for building simpler and faster AI-powered application

    Over the last decade, AI models have radically changed the world of natural language processing and computer vision. They are accurate on various tasks ranging from question answering to object tracking in videos. To use an AI model, the user needs to program against multiple low-level libraries, like PyTorch, Hugging Face, Open AI, etc. This tedious process often leads to a complex AI app that glues together these libraries to accomplish the given task. This programming complexity prevents people who are experts in other domains from benefiting from these models. Running these deep learning models on large document or video datasets is costly and time-consuming. ...
    Downloads: 0 This Week
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