🗓️ 48 hours of building (24-26th March)
💻 Create innovative new apps with OpenAI's latest ChatGPT and Whisper APIs
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⚒️ Combine new GPT 3,5 Turbo and Whisper APIs to build your AI app
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ChatGPT API & Whisper API Global Hackathon

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StableCode 24-hours Hackathon

No-rank

Apprentice

marinarose12

GptHelpLine

Kajal_sabu

xrg360

aswin21

AURA-the chatbot is a chatgpt-integrated speech assistant that converts user audio input into text and feeds it to chatgpt.Chatgpt then discovers a solution to our query which is  converted back into audio files by Auro for the users. The two main tools used for this project are whisper and chatgpt API. Whisper can accurately recognize speech in a multitude of languages, accents, and environments. It can handle technical language and background noise and perform at par with human capabilities. While ChatGPT is a specialized variant of the GPT-3 language model designed to generate human-like responses in conversational contexts. We combine both of these cutting-edge technologies to develop an API that is useful to everyone.

Using the chatgpt API, we are creating a speech assistant. The idea is to record our speech and feed it into chatgpt, which will find the answer and convert it back to audio for the user.


Aura the GPT assistant

Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.

Whisper

ChatGPT is a large language model trained by OpenAI to generate human-like text in a conversational style. It is a variant of the GPT-3 model, which was specifically designed to be used to generate text in response to user input. The ChatGPT model has been trained on a vast amount of text data, including conversations and other types of human-generated text, which allows it to generate text that is similar in style and content to human conversation. ChatGPT can be used to generate responses to questions, code, make suggestions, or provide information in a conversational manner, and it is able to do so in a way that is often indistinguishable from human-generated text. The initial model has been trained using Reinforcement Learning from Human Feedback (RLHF), using methods similar to InstructGPT, but with slight differences in the data collection setup. The model is trained using supervised fine-tuning, where human AI trainers provided conversations in which they played both sides—the user and an AI assistant. The trainers would have had access to model-written suggestions to help them compose their responses.

Marina Rose@marinarose12

Events attended

Submissions made

I built with

Socials

🤝 Top Collabolators

Kajal Sabu

Rohit George

Aswin A S

🤓 Submissions

Aura the GPT assistant

👌 Attended Hackathons

ChatGPT API & Whisper API Global Hackathon

StableCode 24-hours Hackathon

📝 Certificates

ChatGPT API & Whisper API Global Hackathon | Certificate