Step-by-Step Guide to Building an AI Voice Assistant with Streamlit & OpenAI

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Description

This comprehensive course introduces you to the fascinating world of AI-driven technology, focusing on building a voice-operated assistant using Python, Streamlit, and OpenAI's cutting-edge APIs. With a hands-on approach, you will learn how to integrate the Whisper API for accurate speech recognition, utilize the Chat Completion for intelligent dialogue generation, and employ the Text to Speech API to produce realistic voice responses. By the end of this course, you'll have a fully functional AI voice assistant that can interact intelligently in spoken English.

What You will Learn?

Understanding OpenAI's APIs: Gain a solid foundation in how the Whisper, Chat Completion, and TTS APIs function and their practical applications.
Developing a Voice Assistant: Learn the step-by-step process of constructing a voice-operated AI using Streamlit, from setup to full deployment.
Integration of Speech-to-Text and Text-to-Speech: Explore how to accurately convert spoken language into text and vice versa, enhancing user interaction.
Creating Dynamic AI Responses: Implement AI to generate context-aware responses, simulating intelligent conversations.
Real-world Application and Testing: Test and refine your application to ensure reliability and user-friendly experience.

Pre-Requisites

Basic knowledge of Python programming.
Familiarity with API usage and integration.
Some experience with web application development, particularly using Streamlit, is helpful but not required.

Skills

Proficiency in Streamlit for web applications.
Expertise in implementing and leveraging OpenAI's APIs.
Advanced techniques in speech recognition and synthesis.
Ability to design and deploy interactive AI systems.

SECTION 1: INTEGRATING OPENAI'S WHISPER API
This crucial section of the course focuses on integrating the Whisper API to convert spoken language into text. You will start by setting up your development environment, followed by installing the necessary Python libraries. Through hands-on exercises, you will learn to capture audio input, process it using the Whisper API, and handle the textual output for further processing. This section not only lays the groundwork for speech recognition but also ensures you understand the nuances of handling audio data in a web application.
- Introduction to Voice-Assistant Technology and Setup
  00:15:46
- Implementing Speech to Text with Whisper API
  00:23:09
- Generating AI Responses Using Chat Completion API
  00:13:47
- Adding Realism with Text to Speech
  00:16:06

Tutor: Bertha Kgokong

Software Programmer And Tech Entrepreneur

Software Programmer and Tech Entrepreneur, i have extensive experience in Software Development - end-to-end in most platforms, Business Processes and Entrepreneurship. I am a fully qualified Engineer, with a Bachelors Degree and Masters in Business Administration - with over 17 years of professional experience. I am also an entrepreneur with a couple of award winning ventures and projects in Software Development.