Unlock Creative Possibilities: AI-Generated Tiny Audio Diffusion

Unlock Creative Possibilities: AI-Generated Tiny Audio Diffusion

Table of Contents:

1. Introduction
2. Prerequisites
   1. Linux System Requirement
   2. Good GPU with Nvidia Toolkit
   3. Anaconda or Mini Conda Installation
3. Setting up the Environment
   1. Cloning the Repository
   2. Creating a Virtual Environment
   3. Activating the Environment
   4. Installing the Python Kernel for Jupyter Notebook
4. Defining Environment Variables
5. Pre-Trained Models
   1. Kick Model
   2. Percussion Model
6. Generating Samples
   1. Kick Samples
   2. Percussion Samples
7. Style Transfer
   1. Using Snare Drum as Input
   2. Using Guitar Sample as Input
8. Training Your Own Models
9. Conclusion

Introduction

In this tutorial, we will explore the setup and usage of Tiny Audio Diffusion, a recent project focused on performing waveform audio diffusion on limited hardware. We will guide You through the steps of setting up the environment, generating samples with pre-trained models, and demonstrating interesting possibilities with different sounds.

Prerequisites

Before we dive into the tutorial, there are a few prerequisites that need to be fulfilled:

1. Linux System Requirement

   • Tiny Audio Diffusion is designed to run on a Linux system. If you are using Windows, you can use the Windows Subsystem for Linux (WSL).

2. Good GPU with Nvidia Toolkit

   • To run Tiny Audio Diffusion efficiently, you need to have a capable GPU and the Nvidia toolkit installed. Ensure that the GPU meets the minimum requirements for optimal performance.

3. Anaconda or Mini Conda Installation

   • Anaconda or Mini Conda needs to be installed on your system. This will help us set up the virtual environment and manage the necessary packages easily.

Setting up the Environment

To start using Tiny Audio Diffusion, follow these steps:

1. Clone the Repository

   • Go to the repository page and clone the code using the git clone command. Alternatively, you can download the repository as a ZIP file and extract it.

2. Create a Virtual Environment

   • Navigate to the cloned repository folder and create a virtual environment using either Anaconda or Mini Conda. This environment will ensure that all dependencies are isolated and do not interfere with other packages on your system.

3. Activate the Environment

   • Once the virtual environment is created, activate it using the conda activate command. This will enable us to run the required commands from within the environment.

4. Install the Python Kernel for Jupyter Notebook

   • Inside the repository folder, locate the setup folder and execute the command Mentioned in the README file. This step will install the Python kernel required for Jupyter Notebook.

Defining Environment Variables

Next, we need to define the environment variables:

1. Rename the end.tmp file

   • Navigate to the repository folder and locate the end.tmp file. Rename it to .env.

2. Replace the placeholder information

   • Open the .env file and replace the placeholder information with your own username for weights and biases, your API key, and a project name of your choice. If you do not have a weights and biases account, sign up for one.

Pre-Trained Models

Tiny Audio Diffusion comes with pre-trained models that can be used for generating samples. We will explore two models: the Kick Model and the Percussion Model.

1. Kick Model

   • Download the Kick Model from the provided link and save it in the "saved_models/kicks" folder within the repository.

2. Percussion Model

   • Similarly, download the Percussion Model and save it in the "saved_models/percussion" folder within the repository.

Generating Samples

Now, we can start generating samples using the pre-trained models. Let's begin with the Kick Model.

1. Kick Samples

   • Using the Inference Notebook, open it in Jupyter Notebook and execute each block of code step by step.
   • Ensure that the checkpoint and config files for the Kick Model are correctly defined.
   • Adjust the sample length, number of samples, and denoising steps according to your requirements.
   • Run the code to generate kick samples Based on the model.

2. Percussion Samples

   • Similarly, follow the same steps for the Percussion Model.
   • Define the necessary variables, such as sample length, number of samples, and denoising steps.
   • Generate percussion samples based on the model.

Style Transfer

Tiny Audio Diffusion also supports style transfer, where you can use existing audio samples as input to generate new sounds. Let's explore this feature using a Snare Drum and a Guitar Sample.

1. Using Snare Drum as Input

   • Prepare a Snare Drum sample and add it to the repository folder.
   • In the Inference Notebook, define the necessary variables and enable noise and trimming if desired.
   • Run the code to generate a transformed kick sample based on the Snare Drum input.

2. Using Guitar Sample as Input

   • Repeat the same steps as above but this time, use a Guitar Sample as input.
   • Observe the transformation of the sample into a drum sound.

Training Your Own Models

The Tiny Audio Diffusion repository also provides information on training your own models. You can find detailed instructions on how to train models using your CPU or GPU and using checkpoints for saving the trained weights.

Conclusion

In this tutorial, we have explored the setup and usage of Tiny Audio Diffusion, a powerful tool for waveform audio diffusion. We have covered the prerequisites, environment setup, generating samples with pre-trained models, style transfer using different input samples, and even training your own models. We hope this tutorial has sparked your creativity and Curiosity to experiment with audio AI and create unique sounds. If you have any questions or encounter any issues, feel free to leave a comment and we will assist you. Happy experimenting!

Highlights:

• Tiny Audio Diffusion is a recent project focused on waveform audio diffusion on limited hardware.
• Prerequisites include a Linux system, a good GPU with Nvidia Toolkit, and the installation of Anaconda or Mini Conda.
• Setting up the environment involves cloning the repository, creating a virtual environment, and activating it.
• Pre-trained models, such as Kick Model and Percussion Model, can be used to generate samples.
• Style transfer allows using existing audio samples as input to create unique sounds.
• Tiny Audio Diffusion also provides information on training your own models.

FAQs:

Q: Can Tiny Audio Diffusion be used on a Windows system? A: Yes, it can be used on a Windows system by using the Windows Subsystem for Linux (WSL).

Q: Is a powerful GPU necessary? A: While a good GPU is recommended for optimal performance, Tiny Audio Diffusion is designed to work on limited hardware as well.

Q: Can I train my own models using Tiny Audio Diffusion? A: Yes, the repository provides instructions on training your own models using either CPU or GPU.