#Voice Assistant

This project delivers a voice-activated assistant integrating Deepgram's voice-to-text and ElevenLabs' text-to-speech with OpenAI's GPT-3 for dynamic responses. Accessible via a web interface, it appeals to AI enthusiasts and developers. Requires Python 3.8-3.11 and API keys for setup, offering straightforward installation for efficient voice interaction.

Aura is a smart voice assistant that utilizes advanced technologies like Whisper Speech Recognition, GPT-4o, and Eleven Labs TTS streaming to deliver low-latency interactions in web browsers. With the support of Vercel Edge Functions, Aura efficiently tackles traditional latency challenges, providing developers with a robust foundation for creating fast and tailor-made voice solutions without noticeable delays. This positions Aura as an innovative tool in enhancing digital user experiences through timely audio responses.

The GLaDOS voice assistant is a home automation tool inspired by the GLaDOS character from the Portal series. It offers features such as local trigger detection via PocketSphinx and a custom Text-to-Speech engine. Enjoy seamless integration with Home Assistant to control lights, read sensor data, and deliver weather updates. Current development focuses on optimizing for select hardware, requiring Python, PyAudio, and PocketSphinx. Detailed tutorials and a dedicated software integration process are available. Raspberry Pi support is presently limited, with solutions available in the Raspberry branch.

The AI-driven voice assistant utilizes advanced AI models for diverse functions such as voice input, transcription, text-to-speech conversion, and image processing. Supporting tools like GPT-4 Vision and LLava-Next enhance its intelligent response capabilities, akin to innovative AI devices. Customizable interfaces and optional features are designed for versatile user experiences. The installation involves setting up API keys for essential AI services, allowing flexible deployment options. Contributions from the community are welcomed to further improve this comprehensive AI solution.

This guide provides instructions to construct a Python-based voice assistant with offline capabilities. Utilizing technologies such as Whisper for speech recognition, Bark for text-to-speech synthesis, and Ollama for managing LLMs, it details the setup of essential libraries like Rich and langchain. Additionally, the guide discusses performance optimization strategies and suggests improvements like customizable prompts and multimodal capabilities for enhanced functionality.