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Need for an AI-powered meeting assistant
This is where an innovative, generative AI-based app that can transcribe and summarize a meetings discussions would be helpful.
App for transcribing and summarizing discussions
Imagine an app that can transcribe the meeting discussions accurately and then provide a concise summary, highlighting the key points and decisions made. This is the power of combining automatic speech recognition, or ASR, and generative AI-based large language models or LLMs. You can use ASR technology to convert spoken language into readable text.
Then you can utilize an LLM to comprehend and summarize the text efficiently. LLM can also refine the speech to text output by correcting minor errors, ensuring a coherent and accurate result. This project will guide you through building such an app.
You'll utilize an ASR tool called OpenAI Whisper for speech to text conversion, and leverage Llama 2 LLM's capabilities to summarize and extract key points. Llama 2 is a robust open source language model by Meta.
Introduction to the project
The project includes step by step instructions on building and deploying the app in a serverless environment.
First, you will implement OpenAI Whisper to transcribe audio to text using a sample audio file.
Next, you build an intuitive and user-friendly interface for the app using Hugging Face Gradio.
Further, you will integrate the Llama 2 LLM hosted by IBM watsonx to summarize the transcribed audio effectively. IBM watsonx provides various generative AI models including Llama 2. You will learn to create a Python script to generate text using the model and some key parameters influencing the model's output.
Finally, you will learn to deploy the application online using IBM Code Engine, a serverless platform for running applications in the cloud.
For the project, you'll utilize Python to code different activities. You should have a basic knowledge of the programming language. Let's view the demo of the app you'll develop in this project.
This app output will be displayed in Gradio's app output textbox. The app interface displays the title Audio Transcription app. You can upload the recorded audio file using the click to upload icon. Click "Submit". The summary and key points of the content and the audio file are displayed as the output.
Learning objectives
By the end of this project, you will accomplish the following objectives.
- Explain how LLMs can help generate, refine, and summarize text.
- Implement automatic speech recognition technology for speech to text conversion.
- Design a user-friendly interface for an app.
- Deploy an application online using a cloud platform for hosting applications.
Get ready for the project!
By working on this project, you will lay a solid foundation for using LLMs for text generation and summarization tasks. The project provides an opportunity to demonstrate your Python programming skills to build and deploy an app leveraging the capabilities of AI speech to text conversion and generative AI LLMs. Get ready to apply and upgrade your skills.
