Naru AI 🤖✨ - A Voice-Activated Conversational Assistant

Naru is a highly responsive, voice-activated AI assistant with a unique Hinglish personality. Built with Python, Groq, and state-of-the-art speech technologies, Naru is designed to be a helpful, context-aware, and engaging companion.

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🚀 Features

• Voice-Activated: Hands-free operation using wake words ("Hey Naru").
• High-Speed Responses: Powered by the Groq LPU™ Inference Engine with the Llama 3.1 model for near-instantaneous replies.
• Unique Personality: A custom "Hinglish" persona that makes interactions feel natural and fun, not robotic.
• Context-Aware: Knows the current time, date, and your location to provide relevant answers.
• Real-time Weather: Integrated with Open-Meteo for live weather forecasts.
• Noise Reduction: Cleans up microphone input for better speech recognition in noisy environments.
• Switchable Voices: Choose between a male or female voice on the fly.
• Conversation Memory: Remembers the recent parts of your conversation for follow-up questions.
• Extensible & Versatile: Capable of handling a wide range of tasks:
  • Movie Recommendations
  • Finding Places
  • Human Emotion Improvement (empathetic conversation)
  • Weather Forecasts
  • Fashion Sensing and Advice
  • General Talk & Knowledge Q&A

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🛠️ How It Works

Naru operates on a simple yet effective loop: Listen -> Process -> Think -> Respond.

1. Wake Word Detection: Listens passively for a wake word (e.g., "Hey Naru").
2. Command Recording: Upon activation, it records the user's command.
3. Audio Processing: The recorded audio is cleaned using a noise reduction algorithm.
4. Speech-to-Text (STT): The clean audio is transcribed into text using Google's STT engine.
5. Context Assembly: The system gathers the user's query, conversation history, time, and real-time weather data.
6. LLM Inference: The context-rich prompt is sent to the Groq API to generate a smart, in-character response.
7. Text-to-Speech (TTS): The response text is converted into high-quality, natural-sounding audio using Microsoft Edge's neural voices.
8. Audio Playback: The final audio is played back to the user.

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🔧 Tech Stack

• LLM Engine: Groq (Llama 3.1 8B Instant)
• Speech-to-Text: speech_recognition (Google STT)
• Text-to-Speech: edge-tts
• Audio Processing: pyaudio, pydub, sounddevice, noisereduce
• APIs: Open-Meteo (Weather), TMDB (Movies)
• Core Language: Python 3.9+
• Concurrency: asyncio

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⚙️ Setup and Installation

Prerequisites

• Python 3.9+
• API Keys for:
  • GroqCloud
  • TheMovieDB (TMDB) (Optional but recommended)
• System dependencies for PyAudio.
  • On Debian/Ubuntu/Raspberry Pi OS:

    sudo apt-get update && sudo apt-get install portaudio19-dev

  • On macOS:

    brew install portaudio

Installation Steps

1. Clone the repository:

   git clone [https://github.com/your-username/naru-ai.git](https://github.com/your-username/naru-ai.git)
   cd naru-ai

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate

3. Install the required packages:

   pip install -r requirements.txt

4. Set up your environment variables:

   • Create a file named .env in the root directory.
   • Add your API keys to this file.

   .env file:

   GROQ_API_KEY="gsk_YourGroqApiKeyHere"
   TMDB_API_KEY="YourTmdbApiKeyHere"

requirements.txt

Your requirements.txt file should contain:

groq
requests
speechrecognition
edge-tts
python-dotenv
pydub
numpy
sounddevice
pyaudio
scipy
noisereduce