Text to Song Generation (With Vocals + Music) App using Generative AI
TLDRThis video from the AI Anytime channel introduces an innovative project: a Text to Song Generation application that leverages Generative AI to convert text prompts into short songs complete with music and vocals. The project combines two generative models, GPT 3.5 Turbo by OpenAI for text generation and a model called Bark by Sunno AI, to first create lyrics and then generate a song. The process is demonstrated through a web app where users can input text, and the system produces a song in seconds. The video also covers the technical aspects of setting up the application, including using the Replicate platform to manage the infrastructure for the AI models. The host provides a live demonstration of the app, showcasing its ability to generate songs in various styles, and discusses the potential for further development and the ethical considerations of AI in music creation.
Takeaways
- 🎵 The project aims to generate a song from a text prompt, including both music and vocals.
- 🤖 The process involves using two generative models: GPT 3.5 turbo by Open AI for text and a model called BArk by Sunno AI for music.
- 💻 The application is end-to-end, with a front end and back end, and is built using FastAPI and deployed on Render.
- 📝 Users can input text descriptions or prompts, and the system will generate a song of about 5 to 10 seconds.
- 🔗 The video provides a link to Sunno AI's BArk model and discusses its capabilities.
- 📈 The project demonstrates the potential of generative AI in creating music and vocals from text, with the potential for further development.
- 🛠️ The code and application are open-sourced on GitHub for others to use, extend, and build upon.
- 🌐 The use of APIs, such as Open AI and Replicate, allows for the integration of different models without relying on open-source solutions.
- 📱 The application is responsive and designed to work on different screen sizes using media queries and web kit.
- 🎧 The generated songs can be previewed and downloaded by users, showcasing the practical application of text-to-music generation.
- 🚀 The project serves as a proof of concept for text-to-song generation, hinting at future possibilities with advancements in AI and machine learning.
Q & A
What is the main focus of the project discussed in the video?
-The main focus of the project is to create a Text to Song Generation application that uses Generative AI to convert text prompts into songs with both music and vocals.
Which two generative models are combined to achieve the project's goal?
-The project combines GPT 3.5 Turbo by Open AI for text generation and a model called BArk by Sunno AI for generating melodies and audio.
What is the name of the web app developed for this project?
-The web app developed for this project is named 'Harmonics'.
How long does it typically take for the system to generate a song after receiving a text prompt?
-It typically takes around 10 to 15 seconds for the system to generate a song after receiving a text prompt.
What is the significance of using the emoji in the formatted lyrics when passing it to Sunno's BArk model?
-The emoji is significant because it is a required format for Sunno's BArk model to understand that the input is intended to generate a song with vocals, rather than just an audio clip.
How is the application deployed in the project?
-The application is deployed as a service on Render, which allows for easy deployment and hosting of the web app.
What is the role of the 'Replicate' platform in the project?
-Replicate is used to manage the infrastructure for deploying and running the generative models. It allows the use of these models through an API key, simplifying the process of integrating them into the application.
What are the key dependencies required to build the backend of the application?
-The key dependencies for the backend include Fast API for creating the web server, Uvicorn for running the Fast API server, and the 'requests' library for making HTTP requests.
How does the application handle the user's input to generate music?
-The application uses the user's input as a prompt for the GPT 3.5 Turbo model to generate lyrics. Then, it formats these lyrics according to the requirements of Sunno's BArk model and uses it to generate a song.
What is the purpose of the 'generate_music' function in the application?
-The 'generate_music' function is responsible for taking the user's prompt, generating lyrics with the help of the Open AI model, and then using these lyrics to generate a song through the BArk model.
What are the potential applications of this Text to Song Generation technology?
-The technology can be used for hobby projects, music industry experimentation, and exploring the capabilities of generative AI in creating music. It can also serve as a foundation for building more advanced text-to-music applications in the future.
How can one access the source code and further details of the project?
-The source code and further details of the project can be accessed through the GitHub repository mentioned in the video. The presenter also encourages viewers to extend the project for their own use.
Outlines
🎵 Introducing the Text-to-Song Generation Project
The video introduces a project that aims to generate songs from text prompts. The process involves not only creating music but also incorporating vocals. The project will utilize two generative models: GPT 3.5 Turbo by Open AI for text generation and a model called BARK by Sunno AI for generating melodies and audio. The speaker plans to combine these models to first generate lyrics and then create a short song. The project's goal is to demonstrate the potential of AI in creating music from textual descriptions, and a quick demo of the web application is shown where users can input text and receive a generated song.
🛠️ Building the Text-to-Song Application
The speaker outlines the process of building the application, which includes setting up a web app where users can input text prompts to generate songs. The app is built using FastAPI for the backend and incorporates various dependencies like Uvicorn and Open AI. The speaker also discusses using Replicate to access the BARK model and demonstrates how to write the Python code to interact with the model. The front-end code is also briefly mentioned, which includes HTML and CSS styling using Bootstrap, and a responsive design using media queries.
📝 Generating Lyrics with Open AI
The paragraph details the function to generate lyrics using the Open AI model GPT 3.5 Turbo. The process involves role-based prompting, where the model is instructed to act as a lyricist. The speaker provides a code snippet that includes setting up the Open AI client, defining the system's role, and passing the user's prompt to generate lyrics. The output is then cleaned and formatted before being passed to the next stage of song generation.
🎶 Generating Music with Sunno's BARK Model
The speaker explains how to generate music using the Sunno BARK model. The process requires a specific format that includes an emoji to indicate the generation of vocals. The speaker shows how to use the BARK model through the Replicate API, passing the formatted lyrics to generate an audio output. The output is then formatted as a URL that can be used to access the generated music.
🚀 Deploying and Testing the Application
The video demonstrates deploying the application using Uvicorn and testing it by generating a song with a user-provided prompt. The speaker discusses the potential for the application to be used as a proof of concept or a hobby project for those interested in music and AI. The testing phase includes generating different styles of music, such as hip-hop and Bollywood-style songs, and sharing the results, which vary in quality due to the limitations of the current models.
📚 Conclusion and Future Work
The speaker concludes by stating that the project's repository will be available on GitHub for anyone interested in extending or using the application. They encourage feedback and comments, and provide information on how to reach out via social media channels. The speaker also encourages viewers to like and subscribe to their channel for more content on similar topics, and they share a screenshot of their YouTube content related to music generation.
Mindmap
Keywords
💡Text to Song Generation
💡Generative AI
💡GPT 3.5 Turbo
💡Sunno's BARK Model
💡Front End and Back End
💡FastAPI
💡Replicate
💡API Key
💡Web App
💡Render
💡GitHub
Highlights
The project aims to generate a song from a text prompt, including both music and vocals.
The process involves using two generative models: GPT 3.5 Turbo by OpenAI for text generation and a model called BArk by Sunno AI for music generation.
The system is designed as an end-to-end project with a front end and back end, creating a web app for users to input text and receive a generated song.
The video provides a live demonstration of the web app, showcasing how users can input a description and generate a song.
The text prompt is sent to OpenAI, which generates lyrics, then passed to Sunno's BARK model to generate a short song.
The project utilizes the FastAPI framework for the backend and incorporates the use of the Replicate platform to leverage AI models.
The application is deployed as a service on Render, demonstrating how to deploy the app for free.
The video explains the process of using the Replicate API to run the Sunno BARK model and generate music.
The code for the project is provided, including the use of FastAPI, GJA2 templates, and the integration with OpenAI and Replicate.
The project includes a function to format the generated lyrics into a format that the BARK model can interpret for vocal generation.
The video demonstrates the process of generating a song from a text prompt, including handling API responses and generating URLs for the music.
The application allows users to input the length of the music they desire, from 0 to 20 seconds.
The project is described as a proof of concept rather than a finished product, suitable for hobbyists and those interested in exploring generative AI in music.
The video includes a demonstration of generating different styles of music, such as hip-hop and romantic songs, using the application.
The creator discusses the potential for improvement and the need for more data and better models to refine the text-to-song generation process.
The GitHub repository for the project, named 'Harmonics', is mentioned for those interested in extending the application or using it as a base for their projects.
The video concludes with an invitation for feedback and comments, encouraging the community to engage with the project.