Search Results for "machine"
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Showing 17 open source projects for "machine"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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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. Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently. -
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Porcupine
On-device wake word detection powered by deep learning
Build always-listening yet private voice applications. Porcupine is a highly-accurate and lightweight wake word engine. It enables building always-listening voice-enabled applications. It is using deep neural networks trained in real-world environments. Compact and computationally-efficient. It is perfect for IoT. Cross-platform. Arm Cortex-M, STM32, PSoC, Arduino, and i.MX RT. Raspberry Pi, NVIDIA Jetson Nano, and BeagleBone. Android and iOS. Chrome, Safari, Firefox, and Edge. Linux... -
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quadplay
The quadplay fantasy console
quadplay✜ is a fantasy console by CasualEffects for creating and playing retro-style video games on any device. Create and play games on any laptop, desktop, tablet, or phone. Or, build your own programmable arcade machine from a Raspberry Pi, Tegra, or old computer. Create games on Windows, macOS, Linux. Play your games in any modern web browser on a laptop, desktop, tablet, phone, Raspberry Pi 4, or Jetson Nano. 60 fps @ 384 x 224 pixels = 12:7 aspect ≈ 16:9.3. 4096 sRGB (4:4:4) colors. Four players with virtual controls for D-pad and eight buttons. ... -
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Raspberry Digital Signage
Digital signage for the Raspberry Pi (all models)
Raspberry Digital Signage is an operating system designed for digital signage installations on the Raspberry Pi: it displays a full-screen browser view restricted to the resource specified. It shows web pages from an Internet, local area network or internal (SD-card-contained) resource; there is no way to escape this view but rebooting the machine. It is a hacker-proof secure operating systems for indoor and outdoor use. It features wired/wireless networking, proxy, on-screen keyboard and more. SSH logins. -
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Tria Sistema Operatiu
Helps you find, download & burn the best Operating System for any PC
...REQUIREMENTS: The Linux EDITION need Gambas 3.3 or later, so you have to install the 'gambas3' package before installing. 90's EDITION needs 160 MB (192 MB on a virtual machine) of RAM and it runs on Windows 95 and Windows NT 4.0. BRONTOSAURUS EDITION needs DirectX 9.0c and .Net Framework 2.0; runs on Windows 98, Windows 98 SE, Windows ME & Windows 2000. XP EDITION requires .Net Framework 4.0 or newer. Runs on Windows XP, Vista, WindowsServer 2003, 2003 R2, Home Server & Server 2008. STANDARD EDITION needs .Net Framework 4.8 or 4.8.1 and PowerShell 5.1. ... -
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OnnxStream
Lightweight inference library for ONNX files, written in C++
The challenge is to run Stable Diffusion 1.5, which includes a large transformer model with almost 1 billion parameters, on a Raspberry Pi Zero 2, which is a microcomputer with 512MB of RAM, without adding more swap space and without offloading intermediate results on disk. The recommended minimum RAM/VRAM for Stable Diffusion 1.5 is typically 8GB. Generally, major machine learning frameworks and libraries are focused on minimizing inference latency and/or maximizing throughput, all of which at the cost of RAM usage. So I decided to write a super small and hackable inference library specifically focused on minimizing memory consumption: OnnxStream. OnnxStream is based on the idea of decoupling the inference engine from the component responsible for providing the model weights, which is a class derived from WeightsProvider. ... -
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TensorFlow Documentation
TensorFlow documentation
An end-to-end platform for machine learning. TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples. -
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Face Mask Detection
Face Mask Detection system based on computer vision and deep learning
Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras. Face Mask Detection System built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in real-time video streams. Amid the ongoing COVID-19 pandemic, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential... -
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DeepSpeech
Open source embedded speech-to-text engine
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers. DeepSpeech is an open-source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier. A pre-trained English model is available for use and can be downloaded following the instructions in the usage docs. If you want to use the pre-trained English model for performing speech-to-text, you can download it (along with other important inference material) from the DeepSpeech releases page. -
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BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events... -
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image-builder-rpi
SD card image for Raspberry Pi with Docker: HypriotOS
This repo builds the SD card image with HypriotOS for the Raspberry Pi 1, 2, 3, 4 and Zero. You can find released versions of the SD card image here in the GitHub releases page. To build this SD card image we have to take the files for the root filesystem from os-rootfs. -
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GPUImage 2
Framework for GPU-accelerated video and image processing
...The original GPUImage framework was written in Objective-C and targeted Mac and iOS, but this latest version is written entirely in Swift and can also target Linux and future platforms that support Swift code. The objective of the framework is to make it as easy as possible to set up and perform realtime video processing or machine vision against image or video sources. By relying on the GPU to run these operations, performance improvements of 100X or more over CPU-bound code can be realized. This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS. -
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haikuVM
A Java VM for ARDUINO and other micros using the leJOS runtime.
HaikuVM has been started for hobbyists who develop applications for ARDUINO to open the world of JAVA and leJOS. Yes, you can program an ARDUINO with JAVA! HaikuVM is so small that it even runs on an atmega8 (and the ASURO robot). And yes, you can program an ASURO robot with JAVA! And since version 1.4.0 the RCX lego brick. -
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Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a folder of images from the command line. ... -
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TensorFlow on Raspberry Pi
TensorFlow for Raspberry Pi
TensorFlow on Raspberry Pi. -
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SummitDB
In-memory NoSQL database with ACID transactions, Raft consensus, etc.
...SummitDB can be compiled and used on Linux, OSX, Windows, FreeBSD, ARM (Raspberry PI) and probably others since the codebase is 100% Go. We support both 32-bit and 64-bit systems. Go must be installed on the build machine. It may be worth noting that while SummitDB supports many Redis features, it is not a strict Redis clone. SummitDB provides one key space that is a large B-tree. An ordered key space allows for stable paging through keys using the KEYS command. Redis uses an unordered dictionary structure and provides a specialized SCAN command for iterating through keys. -
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RPi-64
computer simulation for my raspberry pi
Pi64 is not an emulator, it just mimics the 80's classic 8bit computers with SDL based graphics and RtAudio based sound. The fullscreen editor for shell and the built-in program editor help you create your own programs and games. Programming language is LUA, but Pi64 is open for any library plugins written in your favourite language. Source was written in pure C, so should compile on Windows, Mac OS X and Linux without issues.
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