Awesome GitHub RepositoriesAudio Machine Learning Frameworks

Audiocraft is a deep learning audio library and machine learning framework designed for training, fine-tuning, and evaluating generative models for music and sound effects. It functions as a text-to-music generative model and a neural audio codec, providing the tools necessary to compress audio signals into discrete representations and synthesize high-fidelity waveforms from textual descriptions. The framework is distinguished by its ability to combine multiple conditioning signals, allowing for the generation of audio based on text prompts, melodic excerpts, or style-based audio clips. It al

pyAudioAnalysis is a Python library and framework for audio signal processing and analysis. It provides tools for extracting mathematical representations of sound, such as spectrograms, and implements a system for training and evaluating machine learning models to classify audio segments based on acoustic patterns. The project includes dedicated utilities for audio segmentation, which allow for the removal of silence and the detection of specific audio events to divide recordings into meaningful sections. It also provides data visualization capabilities that use dimensionality reduction to ma

Stable-audio-tools is a toolkit for training and deploying latent diffusion models for high-fidelity audio synthesis. It provides a framework for generating audio by iteratively refining noise within a compressed latent space, using specialized encoders to preserve temporal and spectral features of the audio signal. The project features a system for adapting pre-trained audio checkpoints to new datasets through modular initialization and configuration files. It includes utilities for weight extraction and inference model export, which remove training metadata and optimizer states to create li