awesome-repositories.com
博客
MCP
awesome-repositories.com

通过 AI 驱动的搜索,发现最优秀的开源仓库。

探索精选搜索开源替代品自托管软件博客网站地图
项目MCP 服务器关于排名机制媒体报道
法律隐私政策服务条款
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

14 个仓库

Awesome GitHub RepositoriesDevice Selection

Selecting hardware devices for computation.

Explore 14 awesome GitHub repositories matching operating systems & systems programming · Device Selection. Refine with filters or upvote what's useful.

Awesome Device Selection GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • ffmpeg/ffmpegFFmpeg 的头像

    FFmpeg/FFmpeg

    61,176在 GitHub 上查看↗

    FFmpeg is a cross-platform multimedia framework designed for the recording, conversion, and streaming of audio and video content. It functions as a comprehensive toolkit that provides both a command-line utility for direct media manipulation and a collection of low-level libraries for integration into custom applications. At its core, the project utilizes a packet-based stream engine and a format-agnostic abstraction layer to handle diverse media standards, containers, and network protocols. The framework distinguishes itself through a modular, graph-based filter execution model that allows f

    Sends processed audio or video streams to system playback devices, including sound cards, display windows, or specialized hardware.

    Caudiocffmpeg
    在 GitHub 上查看↗61,176
  • deepfakes/faceswapdeepfakes 的头像

    deepfakes/faceswap

    55,289在 GitHub 上查看↗

    Faceswap is a comprehensive framework for automated media manipulation and neural face synthesis. It provides a modular pipeline that manages the entire lifecycle of facial feature extraction, deep learning model training, and image conversion. By coordinating complex computer vision workflows, the system enables users to map facial identities between source and destination datasets while maintaining structural alignment and lighting consistency across video frames. The project distinguishes itself through a highly extensible plugin-based architecture that handles hardware-accelerated process

    Selects the optimal hardware device for computation, allowing users to toggle between graphics accelerators and CPU processing.

    Pythondeep-face-swapdeep-learningdeep-neural-networks
    在 GitHub 上查看↗55,289
  • microsoft/onnxruntimemicrosoft 的头像

    microsoft/onnxruntime

    19,347在 GitHub 上查看↗

    This project is a cross-platform machine learning inference engine designed to execute pre-trained models across diverse operating systems and hardware environments. It functions as a standardized execution framework that manages the entire lifecycle of model inference, from loading and graph optimization to hardware-accelerated execution and generative sequence management. The runtime distinguishes itself through a highly modular architecture that decouples model logic from hardware-specific kernels. By utilizing an execution provider abstraction, it enables developers to offload computation

    Defines policies for automatic or explicit hardware device selection to optimize inference performance.

    C++ai-frameworkdeep-learninghardware-acceleration
    在 GitHub 上查看↗19,347
  • anomalyco/opentuianomalyco 的头像

    anomalyco/opentui

    12,131在 GitHub 上查看↗

    Opentui is a terminal user interface framework for building interactive command line applications. It provides a component-based system featuring a flexbox layout engine, a virtual node component tree, and a low-level 2D cell array renderer. The project is distinguished by a sophisticated keyboard binding engine that maps complex multi-stroke sequences and chords to named commands using prioritized, reactive layers. It also implements a plugin architecture that allows external modules to inject custom UI components into designated layout slots and extend input logic at runtime. Its capabilit

    Lists available hardware output devices and assigns specific endpoints for audio playback.

    TypeScript
    在 GitHub 上查看↗12,131
  • file-new-project/eartrumpetFile-New-Project 的头像

    File-New-Project/EarTrumpet

    10,628在 GitHub 上查看↗

    EarTrumpet is a desktop utility for the Windows operating system that provides centralized control over system-wide audio. It functions as a taskbar-based interface for managing playback devices, monitoring active audio streams, and adjusting volume levels across the desktop environment. The application enables granular control by allowing users to adjust volume levels for individual running processes independently. It also supports multi-device audio routing, which permits the assignment of specific hardware output destinations to individual applications. These capabilities are facilitated t

    Enables the discovery of available playback hardware for audio routing and management.

    C#audioeartrumpetmicrosoft-store
    在 GitHub 上查看↗10,628
  • openvinotoolkit/openvinoopenvinotoolkit 的头像

    openvinotoolkit/openvino

    10,414在 GitHub 上查看↗

    OpenVINO is an AI inference engine and model serving platform designed to execute optimized deep learning models across CPUs, GPUs, and NPUs through a unified API. It includes a model optimization toolkit for converting, quantizing, and compressing models from various frameworks, alongside a specialized generative AI runtime for large language models. The project distinguishes itself through a plugin-based hardware acceleration layer that maps neural network operations to vendor-specific drivers. It features advanced execution mechanisms such as continuous batching, speculative decoding, and

    Automatically selects the optimal hardware device for a model with built-in fallback mechanisms.

    C++aicomputer-visiondeep-learning
    在 GitHub 上查看↗10,414
  • noisetorch/noisetorchnoisetorch 的头像

    noisetorch/NoiseTorch

    10,120在 GitHub 上查看↗

    NoiseTorch is a cross-platform audio processor and real-time noise filter designed to suppress ambient sound from audio streams. It functions as a virtual microphone noise suppressor and routing tool, capturing system audio sources and directing filtered signals into virtual input or output devices. The application uses a recurrent neural network to distinguish between human speech and ambient noise. It provides a virtual denoising microphone that removes background noise from a selected input, alongside tools for filtering audio output streams. The system includes capabilities for audio dev

    Identifies available system audio sources and sinks to locate specific device IDs for signal processing.

    Gohacktoberfesthacktoberfest2023linux
    在 GitHub 上查看↗10,120
  • microsoft/windows-universal-samplesmicrosoft 的头像

    microsoft/Windows-universal-samples

    9,696在 GitHub 上查看↗

    This repository is a comprehensive collection of reference implementations and sample libraries for the Universal Windows Platform. It provides practical examples of how to use Windows Runtime APIs to build cross-device applications, including detailed guidance on XAML-based declarative user interfaces and DirectX-integrated rendering. The project distinguishes itself by providing a wide array of hardware integration suites, covering low-level communication with USB, Serial, I2C, SPI, and GPIO peripherals. It includes specialized implementations for mixed reality holographic rendering, advanc

    Provides a standard user interface control for selecting a specific device from available hardware.

    JavaScript
    在 GitHub 上查看↗9,696
  • xuehaipan/nvitopXuehaiPan 的头像

    XuehaiPan/nvitop

    6,577在 GitHub 上查看↗

    Parses the CUDAVISIBLEDEVICES environment variable to select which GPUs to expose.

    Pythoncommand-line-toolconsolecuda
    在 GitHub 上查看↗6,577
  • naudio/naudionaudio 的头像

    naudio/NAudio

    6,094在 GitHub 上查看↗

    NAudio is a .NET audio library that provides playback, recording, format conversion, and signal processing capabilities for Windows applications. It serves as an audio format converter between common formats like WAV, MP3, AAC, and AIFF using ACM or Media Foundation codecs, and functions as an audio playback and recording API through Windows audio APIs including WaveOut, DirectSound, ASIO, and WASAPI. The library includes an audio stream processor that can mix, trim, loop, fade, resample, and adjust pitch or channel count of audio streams with a floating-point engine. It also provides an audi

    Lists all available audio output devices on the system for selection.

    C#
    在 GitHub 上查看↗6,094
  • plaidml/plaidmlplaidml 的头像

    plaidml/plaidml

    4,573在 GitHub 上查看↗

    PlaidML 是一个深度学习编译器框架和跨平台运行时,旨在在各种硬件目标上执行机器学习模型。它作为一个硬件无关的张量引擎,将张量模型翻译为可执行代码,从而允许深度学习网络在不同计算设备上运行,而无需特定的驱动程序依赖。 该系统通过使用 JSON 规范定义设备硬件,实现了在自定义或受限硬件上执行模型。它采用领域特定语言来描述张量计算,并提供中间层将各种机器学习框架与其硬件目标编译器集成。 该引擎支持一系列张量操作,包括带索引约束的张量收缩、带可配置步长和填充的多维卷积,以及使用广播的逐元素操作。它还包含一个测试套件,用于基准测试不同硬件组件和环境下的执行速度和效率。

    Defines device specifications in JSON files to run models on hardware with limited or custom software support.

    C++
    在 GitHub 上查看↗4,573
  • gqrx-sdr/gqrxgqrx-sdr 的头像

    gqrx-sdr/gqrx

    3,614在 GitHub 上查看↗

    Gqrx is a software defined radio application used to capture and demodulate radio signals across various frequencies to produce audio and visual data. It translates raw radio waves into audible sound using standard modes including AM, FM, SSB, and CW. The project provides tools for radio signal analysis through real-time frequency plots and waterfall displays. It enables the capture of raw baseband data and received audio for offline playback and technical analysis. The software includes capabilities for tuning radio frequencies, filtering signal noise via band-pass filters and gain control,

    Manages audio hardware configurations including sample rates and buffer sizes for SDR devices.

    C++airspyfftfuncube-dongle
    在 GitHub 上查看↗3,614
  • rustaudio/cpalRustAudio 的头像

    RustAudio/cpal

    3,551在 GitHub 上查看↗

    cpal is a low-level Rust audio hardware abstraction layer and cross-platform API. It serves as a unified interface for managing audio input and output streams across Windows, macOS, and Linux hardware, acting as a device driver wrapper to discover audio hosts and configure hardware devices. The project functions as a real-time audio streamer, utilizing a high-priority callback system to stream audio samples to hardware devices with minimal latency. The library covers audio hardware integration, including the discovery and configuration of sample rates and channel counts. It supports audio in

    Provides utilities for discovering and listing available audio hardware endpoints for system-wide routing.

    Rustaudiorustsound
    在 GitHub 上查看↗3,551
  • yichengchen/atv-bilibili-demoyichengchen 的头像

    yichengchen/ATV-Bilibili-demo

    2,996在 GitHub 上查看↗

    This project is an Apple TV video application and API client for Bilibili, designed to stream video content with support for HDR and subtitles. It serves as a specialized media player that integrates with Bilibili services to provide video discovery and user account management. The application features a real-time danmaku renderer that overlays scrolling user comments on top of video playback. It includes a plugin-based player system for adding custom overlays and behavioral modifications, as well as tools for adjusting playback speed and skipping sponsored segments. The system covers adapti

    Synchronizes playback state between mobile devices and television screens using remote projection and casting interfaces.

    Swift
    在 GitHub 上查看↗2,996
  1. Home
  2. Operating Systems & Systems Programming
  3. Hardware Interfacing and Drivers
  4. Hardware Acceleration
  5. Device Selection

探索子标签

  • Audio Device Enumerators3 个子标签Utilities for discovering and listing available audio hardware endpoints for system-wide routing. **Distinct from Device Selection:** Distinct from general hardware device selection: focuses specifically on audio-specific endpoint discovery.
  • CUDA Device Visibility ParsersParsers that read the CUDA_VISIBLE_DEVICES environment variable and normalize it into a list of NVML indices or UUIDs. **Distinct from Device Selection:** Distinct from Device Selection: focuses specifically on parsing the CUDA_VISIBLE_DEVICES environment variable rather than general hardware device selection.
  • Casting and ProjectionInterfaces for browsing and selecting remote hardware capable of receiving casted or projected media content. **Distinct from Device Selection:** Focuses on media projection devices specifically, unlike general computation hardware selection.
  • UIsStandardized user interface controls for selecting hardware devices. **Distinct from Device Selection:** Distinct from Device Selection: focuses on the UI control layer rather than the logical assignment of hardware.