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Introduction

<aside> 🧑‍💻 The objective is to generate original, coherent 3-minute musical compositions with vocals using Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE). Secretly hoped GANs could create a fusion of music.

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• Speed Boost: Python's interpreted nature and memory management can slow down high-performance tasks, especially with deep learning models like VAEs and GANs for music generation. Rust offers the speed and efficiency needed for these resource-intensive applications.
• Memory Efficiency: Python libraries like librosa, pydub, and scipy struggle with memory-heavy tasks. Rust handles memory management more efficiently, allowing for smoother processing without hitting limits.
• AI Performance: Developing AI applications in Python can be like running a marathon in flip-flops due to inefficiencies. Rust provides the necessary boost, like high-tech running shoes, to excel in high-performance and memory-intensive AI tasks.

Public Repository

To share my progress and collaborate with the community, I have created a public repository on GitHub containing the Rust-based audio processing tools I have developed so far:

https://github.com/ShaliniAnandaPhD/Audioprocess_CUDA_Rust

Here is also a repository on some of the things I tried with Librosa:

https://github.com/ShaliniAnandaPhD/PIXEL-PIONEERS-TUTORIALS/blob/main/Testing_Librosa_Library_for_Music_Generation.ipynb

Music created with GANs and VAEs in Pytorch:

https://soundcloud.com/shalini-anandamide

Python Challenges in Audio Processing and AI

<aside> 🧑‍💻 Disclaimer: The numerical values, performance metrics, and benchmarks presented in this document are for illustrative purposes only and may not reflect actual performance in real-world scenarios, so it is essential to conduct thorough benchmarking and testing within the context of individual projects to obtain accurate and reliable performance measurements.

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