Introduction
In this tutorial, you'll learn how to create a simple AI voice assistant that can handle phone calls using text-to-speech and speech recognition technologies. This is similar to the technology being deployed by Deutsche Telekom and ElevenLabs, but we'll build a basic version that you can experiment with on your own computer. You'll learn how to process voice input, generate AI responses, and simulate a phone call experience using Python and popular libraries.
Prerequisites
Before starting this tutorial, you'll need:
- A computer with Python 3.7 or higher installed
- Basic understanding of Python programming concepts
- Internet connection for downloading packages
- Microphone and speakers or headphones for testing
Step-by-Step Instructions
1. Set Up Your Python Environment
First, we need to create a virtual environment to keep our project dependencies organized. This prevents conflicts with other Python projects on your system.
python -m venv ai_call_assistant
source ai_call_assistant/bin/activate # On Windows: ai_call_assistant\Scripts\activateWhy this step? Virtual environments isolate your project's dependencies, ensuring that package installations don't interfere with your system's Python setup.
2. Install Required Libraries
We'll need several Python packages to handle speech recognition, text-to-speech, and audio processing:
pip install SpeechRecognition pyttsx3 pyaudioWhy this step? These libraries provide the core functionality for capturing voice input, converting text to speech, and managing audio streams - all essential for our AI assistant.
3. Create the Basic AI Assistant Class
Now, let's create a Python file called ai_assistant.py that will contain our main assistant logic:
import speech_recognition as sr
import pyttsx3
import time
class PhoneCallAssistant:
def __init__(self):
# Initialize speech recognition
self.recognizer = sr.Recognizer()
self.microphone = sr.Microphone()
# Initialize text-to-speech
self.tts_engine = pyttsx3.init()
# Set up microphone
with self.microphone as source:
self.recognizer.adjust_for_ambient_noise(source)
print("AI Assistant ready for phone call simulation")
def listen_for_speech(self):
"""Listen for speech input from the microphone"""
try:
with self.microphone as source:
print("Listening...")
audio = self.recognizer.listen(source, timeout=5)
# Convert speech to text
text = self.recognizer.recognize_google(audio)
print(f"You said: {text}")
return text
except sr.WaitTimeoutError:
print("No speech detected")
return None
except sr.UnknownValueError:
print("Could not understand audio")
return None
def speak_response(self, text):
"""Convert text to speech and play it"""
print(f"AI Assistant: {text}")
self.tts_engine.say(text)
self.tts_engine.runAndWait()
def process_call(self):
"""Simulate a phone call interaction"""
print("Starting phone call simulation...")
self.speak_response("Hello, this is your AI assistant. How can I help you today?")
while True:
user_input = self.listen_for_speech()
if user_input:
# Simple response logic
if 'hello' in user_input.lower() or 'hi' in user_input.lower():
response = "Hello there! How can I assist you today?"
elif 'help' in user_input.lower():
response = "I can help you with basic information. What do you need?"
elif 'bye' in user_input.lower() or 'goodbye' in user_input.lower():
response = "Goodbye! Have a great day!"
break
else:
response = "I'm not sure I understand. Can you rephrase that?"
self.speak_response(response)
else:
print("No input received. Try again.")
print("Call ended")Why this step? This class structure organizes our functionality into logical components - listening for speech, speaking responses, and processing the conversation flow.
4. Create a Main Script to Run the Assistant
Create a file called main.py with the following code:
from ai_assistant import PhoneCallAssistant
def main():
assistant = PhoneCallAssistant()
assistant.process_call()
if __name__ == "__main__":
main()Why this step? This script serves as the entry point for our application, creating an instance of our assistant and starting the call simulation.
5. Test Your AI Assistant
Run your assistant by executing:
python main.pyWhen prompted, speak into your microphone. Try saying phrases like:
- Hello
- Help me
- What can you do
- Goodbye
Why this step? Testing helps you verify that all components work together correctly and gives you hands-on experience with the speech recognition and text-to-speech functionality.
6. Enhance Your Assistant with More Features
Let's improve our assistant by adding a simple knowledge base:
# Add this to your PhoneCallAssistant class
def get_knowledge_base_response(self, query):
"""Simple knowledge base for common questions"""
knowledge_base = {
"what is your name": "I am your AI phone assistant.",
"how are you": "I'm doing well, thank you for asking.",
"what can you do": "I can answer basic questions and provide information.",
"tell me a joke": "Why don't scientists trust atoms? Because they make up everything!",
"what time is it": "I don't have access to real-time information, but I'm here to help!"
}
for key, response in knowledge_base.items():
if key in query.lower():
return response
return None
def process_call(self):
"""Enhanced phone call interaction with knowledge base"""
print("Starting phone call simulation...")
self.speak_response("Hello, this is your AI assistant. How can I help you today?")
while True:
user_input = self.listen_for_speech()
if user_input:
# Check knowledge base first
response = self.get_knowledge_base_response(user_input)
if not response:
# Fallback to basic responses
if 'hello' in user_input.lower() or 'hi' in user_input.lower():
response = "Hello there! How can I assist you today?"
elif 'help' in user_input.lower():
response = "I can help you with basic information. What do you need?"
elif 'bye' in user_input.lower() or 'goodbye' in user_input.lower():
response = "Goodbye! Have a great day!"
break
else:
response = "I'm not sure I understand. Can you rephrase that?"
self.speak_response(response)
else:
print("No input received. Try again.")
print("Call ended")Why this step? Adding a knowledge base makes your assistant more useful by providing specific responses to common questions, simulating how real AI assistants work with predefined knowledge.
Summary
In this tutorial, you've built a basic AI voice assistant that can simulate phone call interactions. You learned how to:
- Set up a Python virtual environment
- Install and use speech recognition and text-to-speech libraries
- Create a class-based structure for handling voice input and output
- Implement basic conversation logic
- Enhance your assistant with a knowledge base
This foundation demonstrates the core technologies used in the Deutsche Telekom and ElevenLabs partnership. While this is a simplified version, it shows how AI can process voice calls without requiring a dedicated app, similar to what's being deployed in Germany.
For future enhancements, you could integrate with cloud APIs like Google Cloud Speech-to-Text or ElevenLabs' voice synthesis to improve accuracy and voice quality, or add more sophisticated natural language processing capabilities.
