Search Results for "device to device" - Page 2
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Showing 71 open source projects for "device to device"
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Intel Extension for PyTorch
A Python package for extending the official PyTorch
...Optimizations take advantage of Intel® Advanced Vector Extensions 512 (Intel® AVX-512) Vector Neural Network Instructions (VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs through the PyTorch* xpu device. -
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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. -
3
macai
All-in-one native macOS AI chat application
...It includes advanced features such as multimodal capabilities, image generation, search integration, and reasoning workflows, making it more than just a simple chat client. The application also emphasizes privacy by avoiding telemetry and offering optional iCloud synchronization for cross-device continuity. With its combination of native performance, multi-provider flexibility, and user-friendly design, macai serves as a comprehensive AI hub for macOS users. -
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WhisperLive
A nearly-live implementation of OpenAI's Whisper
WhisperLive is a “nearly live” implementation of OpenAI’s Whisper model focused on real-time transcription. It runs as a server–client system in which the server hosts a Whisper backend and clients stream audio to be transcribed with very low delay. The project supports multiple inference backends, including Faster-Whisper, NVIDIA TensorRT, and OpenVINO, allowing you to target GPUs and different CPU architectures efficiently. It can handle microphone input, pre-recorded audio files, and... -
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5
NErlNet
Nerlnet is a framework for research and development
NErlNet is a research-grade framework for distributed machine learning over IoT and edge devices. Built with Erlang (Cowboy HTTP), OpenNN, and Python (Flask), it enables simulation of clusters on a single machine or real deployment across heterogeneous devices. -
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TorchAudio
Data manipulation and transformation for audio signal processing
The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal processing library. The benefits of PyTorch can be seen in torchaudio through having all the computations be through PyTorch... -
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AndroidEnv
RL research on Android devices
android_env is a reinforcement learning (RL) environment developed by Google DeepMind that enables agents to interact with Android applications directly as a learning environment. It provides a standardized API for training agents to perform tasks on Android apps, supporting tasks ranging from games to productivity apps, making it suitable for research in real-world RL settings. -
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PML
The easiest way to use deep metric learning in your application
This library contains 9 modules, each of which can be used independently within your existing codebase, or combined together for a complete train/test workflow. To compute the loss in your training loop, pass in the embeddings computed by your model, and the corresponding labels. The embeddings should have size (N, embedding_size), and the labels should have size (N), where N is the batch size. The TripletMarginLoss computes all possible triplets within the batch, based on the labels you... -
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YandexStation
Management of Yandex Station and other smart home devices
YandexStation is a Home Assistant custom component that integrates Yandex-branded smart speakers and other devices with Alice into a unified smart home automation environment. It supports both local and cloud control, depending on the device type, with Yandex speakers often supporting both modes and third-party speakers typically limited to cloud control. The integration exposes playback and volume controls, as well as text-to-speech capabilities that send spoken messages in Alice’s voice directly to the speakers. It also lets you send arbitrary text commands as if you were talking to Alice, enabling scenarios such as “play my music,” launching routines, or querying information via Home Assistant automations. ... -
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OpenVoiceOS
OpenVoiceOS Core, the FOSS Artificial Intelligence platform
OpenVoiceOS is the central component of OpenVoiceOS, an open-source platform for smart speakers and voice-centric devices. It continues the spirit of Mycroft by giving developers and enthusiasts a hackable voice assistant foundation. The core package can be installed standalone for custom assistant development or as part of a fuller OVOS system through installer, Raspberry Pi, or embedded Linux workflows. OVOS is powered by skills, including OpenVoiceOS skills and many classic... -
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OpenRecall
OpenRecall is a fully open-source, privacy-first alternative
...This data is then indexed into a searchable database, allowing users to retrieve past information quickly using natural language queries. Unlike proprietary alternatives, OpenRecall operates entirely locally, ensuring that all captured data remains on the user’s device and is never transmitted to external servers. The platform supports multiple operating systems, including Windows, macOS, and Linux, making it widely accessible across different environments. -
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Android Use
Automate native Android apps with AI using accessibility APIs
...It fills a gap in automation tooling by focusing on mobile-first workflows where traditional browser or desktop-based automation doesn’t work; such as logistics, gig work, field operations, and other industries reliant on phones or tablets. The project works by using Android’s accessibility API to extract structured UI state (as XML) from the device, which is then fed to a large language model (LLM) like OpenAI’s models for decision-making, and actions are executed via the Android Debug Bridge (ADB). This approach bypasses expensive vision-based models and provides faster, cheaper automation with fine-grained interaction capabilities (for example, tapping buttons, typing text, navigating screens). -
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MLX-Audio
A text-to-speech, speech-to-text and speech-to-speech library
...It focuses on text-to-speech and speech-to-speech workflows, with APIs and a command-line interface that make it easy to generate high-quality audio from text. Because it uses MLX and targets Apple Silicon, inference is fast and can take advantage of hardware acceleration and quantization for efficient on-device performance. The project provides a straightforward CLI (mlx_audio.tts.generate) as well as a Python API for programmatic generation of audio, including parameters for voice choice, speed, language hints, output format, and sample rate. It includes examples such as audiobook generation to demonstrate long-form synthesis and joined audio segments. ... -
14
TorchMetrics
Machine learning metrics for distributed, scalable PyTorch application
TorchMetrics is a collection of 80+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. Your data will always be placed on the same device as your metrics. You can log Metric objects directly in Lightning to reduce even more boilerplate. The module-based metrics contain internal metric states (similar to the parameters of the PyTorch module) that automate accumulation and synchronization across devices! Automatic accumulation over multiple batches. Automatic synchronization between multiple devices. ... -
15
Cactus Needle
26m function call model that runs on incredibly small devices
Needle is an experimental 26-million-parameter function-calling model designed to run on extremely small devices such as phones, watches, glasses, and low-power personal AI hardware. It is based on a Simple Attention Network architecture and was distilled from a much larger model to focus on fast, compact tool-use behavior. The project provides open weights, training details, dataset generation resources, and a playground for testing the model with custom tools. Needle is optimized for... -
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MiniCPM4
Ultra-Efficient LLMs on End Device
MiniCPM4 is part of the MiniCPM family of ultra-efficient large language models designed specifically for high performance on edge devices and resource-constrained environments. Unlike traditional large-scale models that require extensive computational resources, MiniCPM4 focuses on delivering competitive reasoning and language capabilities while maintaining significantly lower latency and higher efficiency. It achieves this through optimized architectures, scalable training strategies, and... -
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OpenHome Abilities
Open-source abilities for OpenHome agents
...Each ability is intentionally simple in structure, centering on a single main.py file that contains the core Python logic, which lowers the barrier to building and sharing custom behaviors. The system is meant to support a wide range of voice-driven actions, from API calls and media playback to quiz flows, device control, and multi-turn conversations, so it functions as a practical extension framework rather than a narrow template library. The repository includes official abilities maintained by the OpenHome team as well as community-contributed ones, creating both a stable baseline and a path for outside developers to publish their own work. -
18
HY-MT
Hunyuan Translation Model Version 1.5
HY-MT (Hunyuan Translation) is a high-quality multilingual machine translation model suite developed to support mutual translation across dozens of languages with strong performance even at smaller model scales. It ships with both an 1.8 B parameter model and a larger 7 B model, the latter optimized not only for direct translation but also for formatted and contextualized output, allowing better handling of terminology and mixed-language content. The project emphasizes both speed and... -
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SDGym
Benchmarking synthetic data generation methods
...In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device. -
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Jaaz
Open source multimodal creative AI assistant with infinite canvas tool
...Jaaz supports multiple AI models and can integrate both local and cloud-based inference systems, enabling flexible creative workflows. Jaaz emphasizes privacy and local-first operation, allowing creators to run AI models locally so that their data does not leave their device. It also includes collaborative planning tools such as visual layouts and storyboard organization to support complex creative projects. By combining generative AI with a canvas-based interface, the project aims to provide a creative platform. -
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TensorFlow Model Garden
Models and examples built with TensorFlow
...A flexible and lightweight library that users can easily use or fork when writing customized training loop code in TensorFlow 2.x. It seamlessly integrates with tf.distribute and supports running on different device types (CPU, GPU, and TPU). -
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UI-TARS
UI-TARS-desktop version that can operate on your local personal device
UI-TARS is an open-source multimodal “GUI agent” created by ByteDance: a model designed to perceive raw screenshots (or rendered UI frames), reason about what needs to be done, and then perform real interactions with graphical user interfaces (GUIs) — like clicking, typing, navigating menus — across desktop, browser, mobile, or game environments. Rather than relying on rigid, manually scripted UI automation, UI-TARS uses a unified vision-language model (VLM) that integrates perception,... -
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Dough
Dough is a open source tool for steering AI animations with precision
We believe that AI has the potential to allow billions to experience creative fulfillment this century. However, in order to reach this potential, artistic control is key - it's the difference between something feeling like it was made by you rather than for you. To unlock the multitude of control types and artistic flows possible with AI, we want to build tooling and infrastructure to empower a community of tool-builders, who in turn empower a world of budding artists. If you have a... -
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Intel Extension for Transformers
Build your chatbot within minutes on your favorite device
Intel Extension for Transformers is an innovative toolkit designed to accelerate Transformer-based models on Intel platforms, including CPUs and GPUs. It offers state-of-the-art compression techniques for Large Language Models (LLMs) and provides tools to build chatbots within minutes on various devices. The extension aims to optimize the performance of Transformer-based models, making them more efficient and accessible. -
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Insanely Fast Whisper
An opinionated CLI to transcribe Audio files w/ Whisper on-device
Insanely Fast Whisper is a high-performance command-line tool designed to dramatically accelerate speech-to-text transcription using OpenAI’s Whisper models on local hardware. It leverages modern optimizations such as batch processing, mixed precision, and advanced attention mechanisms like Flash Attention to significantly reduce inference time while maintaining high transcription accuracy. The project is built on top of the Transformers ecosystem and integrates with libraries such as...