FDSoundActivatedRecorder is a Swift library for iOS that implements sound-activated audio recording. It automatically starts recording when sound exceeds a dynamic threshold and stops when silence is detected, saving the output as an m4a file with fade-in/fade-out to prevent clicks. The core logic uses a moving average of audio power levels to dynamically adjust trigger thresholds, making it robust to varying noise environments. Built with modern Swift concurrency (actor) for thread safety, it provides a delegate protocol for callbacks and metering updates. The library is self-contained, depends only on standard iOS frameworks, and includes an example app demonstrating integration via SwiftUI.
Silero VAD is a pre-trained enterprise-grade Voice Activity Detector library that provides speech detection capabilities for audio signals. The system consists of a core PyTorch model (also available in ONNX format) with Python utilities for audio processing, speech timestamp extraction, and streaming applications. It supports 8kHz and 16kHz sampling rates and includes a VADIterator class for real-time processing. The repository offers multi-language examples (C++, C#, Go, Haskell, Java, Rust) demonstrating integration, along with tools for model tuning and sequence-based ONNX optimization for improved performance on long audio files.
Entropic is a local-first desktop AI workspace built with Tauri and React. It provides a full desktop environment including chat with an AI agent, file management (Finder), web browsing, office document editing (OnlyOffice integration), voice control, task board with cron jobs, and an integration marketplace (OAuth for Google, Outlook, Slack, etc.). The application runs a sandboxed runtime (OpenClaw) inside Docker/Colima on macOS/Linux or WSL2 on Windows, ensuring secure execution of agent actions. Communication with the agent gateway happens via WebSocket with structured RPC frames. The system supports two modes: local API keys (user provides LLM keys) and managed proxy mode with hosted authentication and billing via Supabase/Stripe. The architecture is a monolith with separate microservices for browser automation (Playwright) and skill scanning (Cisco AI Defense). The frontend uses React 18, TypeScript, Vite 5, and Tailwind CSS 4, while the backend is Rust with Tauri v2 plugins for deep-link, updater, store, stronghold, and shell access. The codebase demonstrates strong engineering practices with reusable hooks for audio recording/transcription, a custom markdown renderer with workspace link support, and a resilient WebSocket client. However, some components are monolithic (Chat.tsx ~4000 lines, Files.tsx ~3000 lines) and there are hardcoded model lists and pricing that may drift over time.
This repository is a collection of Home Assistant blueprints (YAML automation and script templates) that enable voice-controlled music playback via the Music Assistant integration. It provides three distinct options: a fully local intent-based automation using custom conversation triggers, an LLM-enhanced automation that leverages an external LLM conversation agent to parse natural language queries, and an LLM script that can be used as a tool by LLM conversation agents for more flexible music requests. The blueprints are well-structured with multi-language support (English, Dutch, French, German, Hungarian, Italian, Portuguese, Slovak, Spanish, Finnish) and demonstrate advanced use of Home Assistant's conversation triggers, Jinja2 templating, and integration with Music Assistant's play_media service. The repository is community-driven and actively maintained, with detailed documentation and changelogs. While tightly coupled to Home Assistant and Music Assistant, the blueprints exhibit good craftsmanship in handling target resolution, shuffle logic, and LLM prompt engineering.
IBM TJBot is a Raspberry Pi-based robotics application that interacts with IBM Watson AI services through various 'recipes'. Each recipe demonstrates a different AI capability: conversation (chatbot using Watson Assistant, Speech to Text, and Text to Speech), sentiment analysis (monitoring Twitter tone and changing LED color), speech to text (voice-controlled LED), and translation (speech-to-speech language translation). The architecture consists of a core TJBot library that abstracts hardware and service interactions, with each recipe being a standalone Node.js application. Key entry points are the recipe-specific JavaScript files (conversation.js, sentiment.js, stt.js, translator.js) and the bootstrap script for Raspberry Pi setup. Data flows involve capturing input (audio, Twitter stream), processing through Watson services, and triggering hardware actions (LED, servo, speaker) based on results.