Audio Diffusion Model Project

This project focuses on the development and understanding of an audio diffusion model using PyTorch. Audio diffusion models are a type of generative model that learns to generate complex audio signals by gradually denoising a random signal. This README aggregates all the key information, setup instructions, and components needed to work with and explore audio diffusion models.

Project Overview

The goal of this project is to implement and experiment with a simplified audio diffusion model framework for generating synthetic audio signals. This includes creating a custom dataset, defining the model architecture, computing the loss during training, and synthesizing new audio samples from noise.

Getting Started

Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.8 or newer
• PyTorch
• Librosa
• NumPy

You can install the necessary libraries using pip:

pip install torch librosa numpy

Dataset

The MySyntheticAudioDataset class is designed to generate synthetic sine wave audio signals for training. It's a placeholder for experimenting when specific audio data is not available.

Model

The AudioDiffusionModel provides a conceptual framework for the diffusion model. It outlines the core components and steps for setting up such a model, including initializing the model, setting up a training loop, and computing the loss function.

Training

The training process involves using the compute_loss function to calculate the difference between the model's predictions and the actual data at various stages of the diffusion process. The goal is to train the model to predict the noise added at each timestep and reverse this process to generate clean audio signals.

Synthesis

The model includes a sample method to synthesize new audio signals from random noise by iteratively applying the reverse diffusion process.

Usage

To use this project for training an audio diffusion model, follow these steps:

1. Define the Dataset:

Implement the MySyntheticAudioDataset class to provide synthetic or real audio data for training.

2. Model Setup:

Initialize the AudioDiffusionModel with the desired configuration.

3. Training Loop:

Use the compute_loss function within a training loop to optimize the model's parameters.

4. Audio Generation:

After training, use the model's sample method to generate new audio samples from noise.

Code Structure

• my_dataset.py: Contains the MySyntheticAudioDataset class for generating synthetic audio data.
• diffusion_model.py: Defines the AudioDiffusionModel class, outlining the model architecture.
• train.py: Includes the training loop and the compute_loss function.

Contributing

Contributions to this project are welcome. You can contribute by improving the model architecture, adding new dataset support, or refining the training process. Please open an issue or pull request to discuss your changes.

License

This project is open-sourced under the MIT License. See the LICENSE file for more details.

Conclusion

This project provides a starting point for exploring and implementing audio diffusion models. It's designed to help understand the basics of diffusion models in the audio domain and serves as a foundation for more complex and detailed implementations.