Showing 5 open source projects for "runtime library"

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  • 1
    shadowhook

    shadowhook

    Android inline hook library which supports thumb, arm32 and arm64

    shadowhook is an open-source native code hooking library for Android — designed to let developers intercept and override native (C/C++) functions inside Android apps at runtime. It supports both ARM32 and ARM64 architectures (and the respective “thumb” instruction set) and works across a wide range of Android OS versions. The library allows you to specify hook targets either by function address or by library name + function name, and it automatically handles newly loaded shared libraries (ELFs), ensuring hooks remain effective even when code is dynamically loaded at runtime. ...
    Downloads: 4 This Week
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  • 2
    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....
    Downloads: 641 This Week
    Last Update:
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  • 3
    React Native Voice

    React Native Voice

    React Native Voice Recognition library for iOS and Android

    A speech-to-text library for React Native. Manually or automatically link the NativeModule. Drag the Voice.xcodeproj from the @react-native-voice/voice/ios folder to the Libraries group on Xcode in your project. Click on your main project file (the one that represents the .xcodeproj) select Build Phases and drag the static library, lib.Voice.a, from the Libraries/Voice.xcodeproj/Products folder to Link Binary With Libraries.
    Downloads: 0 This Week
    Last Update:
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  • 4
    MACE

    MACE

    Deep learning inference framework optimized for mobile platforms

    ...Mechanism like automatically breaking OpenCL kernel into small units is introduced to allow better preemption for the UI rendering task. Graph level memory allocation optimization and buffer reuse are supported. The core library tries to keep minimum external dependencies to keep the library footprint small.
    Downloads: 0 This Week
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  • 5
    BoostMultiDex

    BoostMultiDex

    Load multiple dex files on low Android version devices

    ...It adopts a multi-stage loading approach (DEX bytecode → DEX file → ODEX file) to pick the fastest viable route at runtime, and optionally performs optimization (OPT) in a separate process with interruption/recovery support — minimizing startup time and preserving stability.
    Downloads: 0 This Week
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