LocalVocal is a Speech AI assistant OBS Plugin that enables users to transcribe speech into text and translate it into any language locally on their machine. The plugin runs OpenAI's Whisper for real-time speech processing and prediction. It supports features like transcribing audio in real-time, displaying captions on screen, sending captions to files, syncing captions with recordings, and translating captions to major languages. Users can bring their own Whisper model, filter or replace captions, and experience partial transcriptions for streaming. The plugin is privacy-focused, requiring no GPU, cloud costs, network, or downtime.

LocalVocal is a live-streaming AI assistant plugin for OBS that allows you to transcribe audio speech into text and perform various language processing functions on the text using AI / LLMs (Large Language Models). It's privacy-first, with all data staying on your machine, and requires no GPU, cloud costs, network, or downtime.

The URL/API Source is a plugin for OBS Studio that allows users to add a media source fetching data from a URL or API endpoint and displaying it as text. It supports input and output templating, various request types, output parsing (JSON, XML/HTML, Regex, CSS selectors), live data updating, output styling, and formatting. Future features include authentication, websocket support, more parsing options, request types, and output formats. The plugin is cross-platform compatible and actively maintained by the developer. Users can support the project on GitHub.

Bit is a build system that organizes source code into composable components, enabling the creation of reliable, scalable, and consistent applications. It supports the creation of reusable UI components, standard building blocks, shell applications, and atomic deployments. Bit is compatible with various tools in the JavaScript ecosystem and offers official dev environments for popular frameworks. It can be used in different codebase structures like monorepos or polyrepos, and even without repositories. Users can install Bit, create shell applications, compose components, release and deploy components, and modernize existing projects using Bit Cloud or self-hosted scopes.

The AI Toolkit by Ostris is a collection of tools for machine learning, specifically designed for image generation, LoRA (latent representations of attributes) extraction and manipulation, and model training. It provides a user-friendly interface and extensive documentation to make it accessible to both developers and non-developers. The toolkit is actively under development, with new features and improvements being added regularly. Some of the key features of the AI Toolkit include: - Batch Image Generation: Allows users to generate a batch of images based on prompts or text files, using a configuration file to specify the desired settings. - LoRA (lierla), LoCON (LyCORIS) Extractor: Facilitates the extraction of LoRA and LoCON representations from pre-trained models, enabling users to modify and manipulate these representations for various purposes. - LoRA Rescale: Provides a tool to rescale LoRA weights, allowing users to adjust the influence of specific attributes in the generated images. - LoRA Slider Trainer: Enables the training of LoRA sliders, which can be used to control and adjust specific attributes in the generated images, offering a powerful tool for fine-tuning and customization. - Extensions: Supports the creation and sharing of custom extensions, allowing users to extend the functionality of the toolkit with their own tools and scripts. - VAE (Variational Auto Encoder) Trainer: Facilitates the training of VAEs for image generation, providing users with a tool to explore and improve the quality of generated images. The AI Toolkit is a valuable resource for anyone interested in exploring and utilizing machine learning for image generation and manipulation. Its user-friendly interface, extensive documentation, and active development make it an accessible and powerful tool for both beginners and experienced users.

Habitat-Sim is a high-performance physics-enabled 3D simulator with support for 3D scans of indoor/outdoor spaces, CAD models of spaces and piecewise-rigid objects, configurable sensors, robots described via URDF, and rigid-body mechanics. It prioritizes simulation speed over the breadth of simulation capabilities, achieving several thousand frames per second (FPS) running single-threaded and over 10,000 FPS multi-process on a single GPU when rendering a scene from the Matterport3D dataset. Habitat-Sim simulates a Fetch robot interacting in ReplicaCAD scenes at over 8,000 steps per second (SPS), where each ‘step’ involves rendering 1 RGBD observation (128×128 pixels) and rigid-body dynamics for 1/30sec.

node-llama-cpp is a tool that allows users to run AI models locally on their machines. It provides pre-built bindings with the option to build from source using cmake. Users can interact with text generation models, chat with models using a chat wrapper, and force models to generate output in a parseable format like JSON. The tool supports Metal and CUDA, offers CLI functionality for chatting with models without coding, and ensures up-to-date compatibility with the latest version of llama.cpp. Installation includes pre-built binaries for macOS, Linux, and Windows, with the option to build from source if binaries are not available for the platform.

FlexFlow Serve is an open-source compiler and distributed system for **low latency**, **high performance** LLM serving. FlexFlow Serve outperforms existing systems by 1.3-2.0x for single-node, multi-GPU inference and by 1.4-2.4x for multi-node, multi-GPU inference.

FinalRip is a distributed video processing tool based on FFmpeg and VapourSynth. It cuts the original video into multiple clips, processes each clip in parallel, and merges them into the final video. Users can deploy the system in a distributed way, configure settings via environment variables or remote config files, and develop/test scripts in the vs-playground environment. It supports Nvidia GPU, AMD GPU with ROCm support, and provides a dashboard for selecting compatible scripts to process videos.

Tock is an open conversational AI platform for building bots. It offers a natural language processing open source stack compatible with various tools, a user interface for building stories and analytics, a conversational DSL for different programming languages, built-in connectors for text/voice channels, toolkits for custom web/mobile integration, and the ability to deploy anywhere in the cloud or on-premise with Docker.

RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite. It offers configurable options for choosing LLM providers, database types, and rerankers. The toolkit is fast and permissive, utilizing lightweight dependencies and hardware acceleration. RAGLite provides features like PDF to Markdown conversion, multi-vector chunk embedding, optimal semantic chunking, hybrid search capabilities, adaptive retrieval, and improved output quality. It is extensible with a built-in Model Context Protocol server, customizable ChatGPT-like frontend, document conversion to Markdown, and evaluation tools. Users can configure RAGLite for various tasks like configuring, inserting documents, running RAG pipelines, computing query adapters, evaluating performance, running MCP servers, and serving frontends.

SciPIP is a scientific paper idea generation tool powered by a large language model (LLM) designed to assist researchers in quickly generating novel research ideas. It conducts a literature review based on user-provided background information and generates fresh ideas for potential studies. The tool is designed to help researchers in various fields by providing a GUI environment for idea generation, supporting NLP, multimodal, and CV fields, and allowing users to interact with the tool through a web app or terminal. SciPIP uses Neo4j as its database and provides functionalities for generating new ideas, fetching papers, and constructing the database.

aiohttp-client-cache is an asynchronous persistent cache for aiohttp client requests, based on requests-cache. It is easy to use, customizable, and persistent, with several storage backends available, including SQLite, DynamoDB, MongoDB, DragonflyDB, and Redis.

Airframe is a set of essential building blocks for developing applications in Scala. It includes logging, object serialization using JSON or MessagePack, dependency injection, http server/client with RPC support, functional testing with AirSpec, and more.

KanTV is an open-source project that focuses on studying and practicing state-of-the-art AI technology in real applications and scenarios, such as online TV playback, transcription, translation, and video/audio recording. It is derived from the original ijkplayer project and includes many enhancements and new features, including: * Watching online TV and local media using a customized FFmpeg 6.1. * Recording online TV to automatically generate videos. * Studying ASR (Automatic Speech Recognition) using whisper.cpp. * Studying LLM (Large Language Model) using llama.cpp. * Studying SD (Text to Image by Stable Diffusion) using stablediffusion.cpp. * Generating real-time English subtitles for English online TV using whisper.cpp. * Running/experiencing LLM on Xiaomi 14 using llama.cpp. * Setting up a customized playlist and using the software to watch the content for R&D activity. * Refactoring the UI to be closer to a real commercial Android application (currently only supports English). Some goals of this project are: * To provide a well-maintained "workbench" for ASR researchers interested in practicing state-of-the-art AI technology in real scenarios on mobile devices (currently focusing on Android). * To provide a well-maintained "workbench" for LLM researchers interested in practicing state-of-the-art AI technology in real scenarios on mobile devices (currently focusing on Android). * To create an Android "turn-key project" for AI experts/researchers (who may not be familiar with regular Android software development) to focus on device-side AI R&D activity, where part of the AI R&D activity (algorithm improvement, model training, model generation, algorithm validation, model validation, performance benchmark, etc.) can be done very easily using Android Studio IDE and a powerful Android phone.

Lunary is an open-source observability and prompt platform for Large Language Models (LLMs). It provides a suite of features to help AI developers take their applications into production, including analytics, monitoring, prompt templates, fine-tuning dataset creation, chat and feedback tracking, and evaluations. Lunary is designed to be usable with any model, not just OpenAI, and is easy to integrate and self-host.

LocalVocal is a Speech AI assistant OBS Plugin that enables users to transcribe speech into text and translate it into any language locally on their machine. The plugin runs OpenAI's Whisper for real-time speech processing and prediction. It supports features like transcribing audio in real-time, displaying captions on screen, sending captions to files, syncing captions with recordings, and translating captions to major languages. Users can bring their own Whisper model, filter or replace captions, and experience partial transcriptions for streaming. The plugin is privacy-focused, requiring no GPU, cloud costs, network, or downtime.

LocalAI is a free and open-source OpenAI alternative that acts as a drop-in replacement REST API compatible with OpenAI (Elevenlabs, Anthropic, etc.) API specifications for local AI inferencing. It allows users to run LLMs, generate images, audio, and more locally or on-premises with consumer-grade hardware, supporting multiple model families and not requiring a GPU. LocalAI offers features such as text generation with GPTs, text-to-audio, audio-to-text transcription, image generation with stable diffusion, OpenAI functions, embeddings generation for vector databases, constrained grammars, downloading models directly from Huggingface, and a Vision API. It provides a detailed step-by-step introduction in its Getting Started guide and supports community integrations such as custom containers, WebUIs, model galleries, and various bots for Discord, Slack, and Telegram. LocalAI also offers resources like an LLM fine-tuning guide, instructions for local building and Kubernetes installation, projects integrating LocalAI, and a how-tos section curated by the community. It encourages users to cite the repository when utilizing it in downstream projects and acknowledges the contributions of various software from the community.

Local Multimodal AI Chat is a hands-on project that teaches you how to build a multimodal chat application. It integrates different AI models to handle audio, images, and PDFs in a single chat interface. This project is perfect for anyone interested in AI and software development who wants to gain practical experience with these technologies.

OpenAI for Workers AI is a simple, quick, and dirty implementation of OpenAI's API on Cloudflare's new Workers AI platform. It allows developers to use the OpenAI SDKs with the new LLMs without having to rewrite all of their code. The API currently supports completions, chat completions, audio transcription, embeddings, audio translation, and image generation. It is not production ready but will be semi-regularly updated with new features as they roll out to Workers AI.

Use the OpenAI API with Ruby! 🤖🩵 Stream text with GPT-4, transcribe and translate audio with Whisper, or create images with DALL·E... Hire me | 🎮 Ruby AI Builders Discord | 🐦 Twitter | 🧠 Anthropic Gem | 🚂 Midjourney Gem ## Table of Contents * Ruby OpenAI * Table of Contents * Installation * Bundler * Gem install * Usage * Quickstart * With Config * Custom timeout or base URI * Extra Headers per Client * Logging * Errors * Faraday middleware * Azure * Ollama * Counting Tokens * Models * Examples * Chat * Streaming Chat * Vision * JSON Mode * Functions * Edits * Embeddings * Batches * Files * Finetunes * Assistants * Threads and Messages * Runs * Runs involving function tools * Image Generation * DALL·E 2 * DALL·E 3 * Image Edit * Image Variations * Moderations * Whisper * Translate * Transcribe * Speech * Errors * Development * Release * Contributing * License * Code of Conduct

Deepgram JavaScript SDK. Power your apps with world-class speech and Language AI models.

Whisper-WebUI is a Gradio-based browser interface for Whisper, serving as an Easy Subtitle Generator. It supports generating subtitles from various sources such as files, YouTube, and microphone. The tool also offers speech-to-text and text-to-text translation features, utilizing Facebook NLLB models and DeepL API. Users can translate subtitle files from other languages to English and vice versa. The project integrates faster-whisper for improved VRAM usage and transcription speed, providing efficiency metrics for optimized whisper models. Additionally, users can choose from different Whisper models based on size and language requirements.

Edgen is a local GenAI API server that serves as a drop-in replacement for OpenAI's API. It provides multi-endpoint support for chat completions and speech-to-text, is model agnostic, offers optimized inference, and features model caching. Built in Rust, Edgen is natively compiled for Windows, MacOS, and Linux, eliminating the need for Docker. It allows users to utilize GenAI locally on their devices for free and with data privacy. With features like session caching, GPU support, and support for various endpoints, Edgen offers a scalable, reliable, and cost-effective solution for running GenAI applications locally.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.