AI meeting companion: From voice to insight
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Build an AI-powered meeting companion that converts voice conversations into actionable insights. Learn how to integrate speech recognition and natural language processing for efficient meetings.
At a Glance
Create an app to capture audio (like lectures) and summarize it. Build the app using OpenAI Whisper (text to speech), then summarize it with an open source LLAMA 2 LLM hosted by IBM watsonx. You deploy the app in a serverless environment using IBM Cloud Code Engine.
In this project, you use OpenAI’s Whisper to transform speech into text. Then, you use IBM watsonx AI to summarize and find key points. This stage couples with prompt engineering through PromptTemplate in Langchain. You’ll make an app with HuggingFace Gradio as the user interface.
The output from the LLM not only summarizes and highlights key points, it also corrects minor mistakes made by the speech-to-text model, ensuring a coherent and accurate result.
A look at the project ahead
- Speech-to-text conversion: Use OpenAI’s Whisper technology to convert lecture recordings into text, accurately.
- Content summarization: Implement IBM watsonx AI to effectively summarize the transcribed lectures and extract key points.
- User interface development: Create an intuitive and user-friendly interface using HuggingFace Gradio, ensuring ease of use for students and educators.
- App deployment: Learn and apply the skills necessary to deploy the application online by using IBM Code Engine, making the tool accessible to a wider audience.
What you’ll need
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