Voice Agent Core

A flexible, conversational voice companion bot framework for Python. Easily build your own AI assistant by plugging in any LLM (OpenAI, Gemini, etc.) and using built-in voice tools. Great for personal productivity, home automation, or just having a friendly AI to talk to!

Features

• Conversational AI: Integrate any LLM (OpenAI, Gemini, etc.) for smart, natural conversations.
• Speech Recognition: Uses Whisper and SpeechRecognition for accurate voice input.
• Text-to-Speech: Responds with high-quality voice using TTS APIs and local fallback.
• Extensible Tools: Add your own Python functions as tools (play music, check weather, control apps, etc.).
• Easy API: Just provide an LLM handler and start your bot!

Requirements

• Python 3.8+
• System dependencies for audio:
  • Linux: sudo apt-get install portaudio19-dev ffmpeg
  • macOS: brew install portaudio ffmpeg
  • Windows: Install FFmpeg and ensure it's in your PATH.

Installation

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install voice-agent-core

Quick Start: Your Own Companion Bot

Create a Python file (e.g., my_bot.py):

from voice_agent_core import VoiceCompanionBot
import datetime

def my_llm_handler(text):
    if "date" in text or "time" in text:
        now = datetime.datetime.now()
        return {"type": "text_response", "content": f"The current date and time is: {now}"}
    else:
        return {"type": "text_response", "content": "I am your companion bot! You said: " + text}

bot = VoiceCompanionBot(llm_handler=my_llm_handler)
bot.listen_and_respond()

Run it:

python my_bot.py

Speak to your bot! It will respond with the date/time or echo your message.

Advanced: Use Any LLM (OpenAI Example)

from voice_agent_core import VoiceCompanionBot
import openai

def openai_llm_handler(text):
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{"role": "system", "content": "You are a helpful, friendly voice companion."},
                  {"role": "user", "content": text}]
    )
    return {"type": "text_response", "content": response.choices[0].message['content']}

bot = VoiceCompanionBot(llm_handler=openai_llm_handler)
bot.listen_and_respond()

Adding Custom Tools

You can add your own Python functions as tools. For example:

def get_weather(location):
    # Your weather API logic here
    return f"Weather in {location}: Sunny!"

tools = {"get_weather": get_weather}
bot = VoiceCompanionBot(llm_handler=my_llm_handler, tools=tools)

Your LLM handler should return:

{"type": "function_call", "call": {"name": "get_weather", "args": {"location": "London"}}}

The bot will call your tool and speak the result.

API Overview

• VoiceCompanionBot(llm_handler, tools=None, speak_func=None, listen_func=None)
  • llm_handler(text): function that takes user speech and returns a dict:
    • { "type": "function_call", "call": {"name": ..., "args": {...}} }
    • { "type": "text_response", "content": ... }
  • tools: dict of tool name to function (optional)
  • speak_func: custom TTS function (optional)
  • listen_func: custom speech recognition function (optional)
• bot.listen_and_respond(): starts the main loop

License

MIT

For more details, see the API reference and examples above. Enjoy building your own AI companion!