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Crafting Advanced Voice & Smart Assistant Applications

Master real-time AI applications for business automation, including meeting planning, transcription, and building AI assistants to handle emails and interactive tasks.

Lesson 1

Real-Time Transcription with Open Source Language Models

In this tutorial, you'll learn how to build an AI-powered real-time transcription system using open-source language models and Replicate. We'll guide you through creating a web server that records and transcribes audio in real time, perfect for meetings and podcasts. By utilizing Incredibly Fast Whisper, this project offers a faster alternative to traditional transcription tools.

You'll learn how to:

  • Set up a Python environment with Flask for server-side processing
  • Record and segment audio via a web interface
  • Send audio chunks for transcription using Replicate and AWS S3
  • Display real-time transcriptions on the webpage

By the end of this guide, you'll have a fully functional AI transcription co-pilot capable of real-time speech-to-text processing.

Full Video & Source Code
 

Lesson 2

Refining Real-Time Transcriptions and Eliminating Errors

In this tutorial, we build on our real-time transcription service by addressing common issues like punctuation errors, misplaced words, and silence being transcribed as unwanted phrases. Using a small, efficient language model, we introduce an endpoint that improves transcript accuracy while allowing for prompt-based corrections and even translations.

You'll learn how to:

  • Create a new server endpoint for improving transcriptions with a small language model
  • Refine punctuation and replace incorrect words in transcriptions
  • Implement JavaScript to trigger transcript improvements on the frontend
  • Use prompts to enhance, translate, and correct transcripts
  • Detect and handle silent segments in audio to prevent transcription errors

By the end, you'll have a robust transcription service capable of delivering more accurate and contextually aware results.

Full Video & Source Code
 

Lesson 3

Building a Meeting Transcription and Summarization Web App

In this tutorial, we’ll build a web application that transcribes entire meetings from audio files and uses speaker diarization to identify individual speakers. By integrating the Whisper diarization model for accurate speaker tracking and Llama2 to summarize discussions, we’ll create concise meeting protocols.

You'll learn how to:

  • Set up a Flask web app for uploading and transcribing audio files
  • Implement speaker diarization with the Whisper model using Replicate
  • Upload and manage audio files in AWS S3 via Boto3
  • Generate meeting summaries and follow-up actions using Llama2
  • Display results in a clean UI with JSON-formatted outputs for better readability

By the end, you’ll have a powerful tool for real-time meeting transcriptions and structured summaries, all within a user-friendly web interface.

Full Video & Source Code
 

Lesson 4

Building Your Own AI Jarvis with Groq

In this tutorial, you'll learn how to create your own voice-activated AI assistant, inspired by Jarvis from Iron Man. We'll use Groq, a lightning-fast alternative to GPT, to process voice commands in real-time. You'll record your voice, convert it to text with DeepGram, and use Groq to generate responses that will be spoken back to you.

You'll learn how to:

  • Set up a Python web app with Flask to handle voice input
  • Record and transcribe voice data using DeepGram for speech-to-text
  • Leverage Groq’s fast processing for answering questions and translating text
  • Convert AI-generated responses back to speech
  • Build a full cycle for an interactive AI assistant, capable of real-time conversation

By the end, you’ll have built a responsive AI assistant that listens, responds, and performs tasks like a true digital co-pilot.

Full Video & Source Code
 

Lesson 5

Creating a Real-Time Terminal-Based Jarvis with Voice Commands

In this video, we enhance the Jarvis experience by moving beyond buttons and creating a terminal-based, real-time voice assistant. We'll focus on capturing and processing audio continuously, making Jarvis feel more like the intuitive AI we know from the movies. Using Python, pyaudio, and a clever combination of silence detection and speech-to-text processing, we simulate real-time conversations with your virtual assistant.

You'll learn how to:

  • Set up a virtual recording studio with pyaudio for real-time voice capture
  • Implement silence detection to manage audio input and determine when the user has finished speaking
  • Convert speech into text, process the text, and generate a response
  • Convert text back to speech and play it in real-time, creating a fluid interaction loop
  • Build a continuous, terminal-based dialogue system that listens, processes, and responds without manual input

By the end, you'll have a fully functional, voice-activated AI assistant that operates seamlessly from the terminal, creating a more immersive and hands-free experience.

Full Video & Source Code
 

Lesson 6

Integrating Gmail with Your Jarvis Using GPT and Langchain

In this video, we'll show you how to enable your Jarvis to access and interact with Gmail, allowing it to summarize emails and perform inbox-related tasks. By integrating GPT's function-calling capabilities, we'll guide the language model to handle email queries and return structured JSON responses for actions like fetching emails or summarizing the latest message.

You'll learn how to:

  • Set up Gmail API credentials and connect to your inbox
  • Use Langchain to fetch and process emails from Gmail
  • Write a service to automate email retrieval and integrate it with your AI assistant
  • Design system prompts to trigger specific actions, such as email fetching
  • Handle the JSON responses and execute email-related tasks in real-time

By the end, your JarvisTool will be able to fetch and summarize emails based on natural language prompts, laying the foundation for more advanced email management capabilities.

Full Video & Source Code