OpenAI Stack Hack

Join us to find out how developers can interact with Redis, a real-time data platform with its blazing-fast vector database

Redis Talk

Paweł Czech and Mathias Asberg, co-founders of New Native will talk about lablab.ai's mission and vision. https://www.twitch.tv/lablabai

Start

We will shortly introduce you to the OpenAI Whisper, GPT3, Codex & DALL-E 2 Hackathon, Challenge, lablab.ai platform, Discord server.

Introduction to the Challenge

Short intro from lablab.ai's partner - Redis, the leading provider of enterprise-class solutions for vector search, data storage, and ML feature workloads

Redis Introduction

Startup lessons especially when it comes to actually applied NLP AI

Useful tips from lablab.ai developer Fabian

ChatGPT for developers/ how are for what to use

Whisper Tutorial for developers

Go-to-market strategy, basics of pricing, funding and valuation for AI startups

GPT-3 Live Coding Session

Under the hood: Building a QA app using Redis

Time's up! Submit your solution before the deadline

I was going to use Whisper to take texts from a podcast and enable user to ask particular question about it, but for some reason there was a bug which was unresolve-able so I had to improvise last minute

Podsearch

Vashistha_Pandya

Whisper ASR is a speech recognition model that can convert speech input to text. TTS (Text-to-Speech) is a technology that can convert text to speech. GPT 3.5 turbo is a language model that can generate human-like text.

By using these models together, it is possible to create an AI voice assistant that can understand spoken input, generate a response in natural language, and then convert that response to speech.

Here's how the process would work:

The user speaks into a microphone, and the speech is captured as an audio file.

The audio file is passed to the Whisper ASR model, which converts the speech to text.

The text is then passed to the GPT 3.5 turbo language model, which generates a response.

The response generated by GPT is then passed to the TTS model, which converts the text to speech.

The resulting speech is played back to the user through the speakers or headphones.

Overall, this process allows for a natural, conversational interaction between the user and the AI assistant. The user can speak to the AI in the same way they would speak to another person, and the AI can respond in a way that is both accurate and human-like.

I have used Whisper ASR , a TTS model and GPT 3.5 turbo to take a voice as input and work on that by GPT and give the answer in a speech format

"Podsearch team on OpenAI Stack Hack Hackathon"

Team Idea

Submission