Virtual music composition collaborations

TEXT_INPUT:User inputs their musical idea;;CHATGPT:Prompt ChatGPT with the user's input to generate suggestions for collaboration;;TEXT_INPUT:User selects a suggestion or provides additional input;;HTTP_REQUEST:Fetch relevant musical samples or loops from an API;;DISPLAY_OUTPUT:Display the fetched musical samples or loops for the user

Generative AI models such as LLMs (Large Language Models) and image generators can revolutionize virtual music composition collaborations. For example, using OpenAI's MuseNet, musicians can generate unique compositions in various styles. They can input their melodies and let the AI complete them, infusing new creative ideas into the composition process.

Another application could be using AI models like DALL-E to convert textual descriptions into specific album artwork or visualizers for the compositions. Musicians could input descriptions of their desired visuals and the AI will generate corresponding images.

ChatGPT could also be used for writing song lyrics. Once given the base context or a few lines, it could generate a song's complete lyrics. Through these, generative AI models could enhance how musicians collaborate on virtual platforms, leading to richer and dynamic compositions.

How to build with Clevis

This is an example of an application that you can build using Clevis, a popular tool for developing AI applications. The application, known as 'Virtual Music Collaborator', is designed to aid in the music composition process through digital collaborations.

The workflow of the application consists of several steps. Initially, the user enters their musical idea through a text input. This idea is then passed into ChatGPT, OpenAI's powerful language model, to generate suggestions for further musical development or collaborative ideas. The user can then react to these suggestions by providing additional input or choosing a suggestion they find interesting.

Next, an HTTP request is initiated to fetch applicable musical samples or loops from an API. These HTTP calls allow the application to reach out to a vast library of musical resources, retrieving samples that are relevant to the user's input. Finally, these fetched musical loops or samples are displayed for the user. They can listen and choose one or several that resonates with their original idea.

This application demonstrates the streamlined and efficient workflow that can be developed for the music domain using Clevis. Similar music-related apps can also be built using this tool.