Audio data loading and augmentations in JAX

Pitch-shift audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others.

This repository contains the code and methodology used for the BirdCLEF 2024 Kaggle competition, where I achieved a rank of 55th out of 974 participants, earning a bronze medal. The goal of this competition was to build a model that can accurately classify bird sounds.

⚡ Blazing fast audio augmentation in Python, powered by GPU for high-efficiency processing in machine learning and audio analysis tasks.

Converting text to audio and applying audio augmentation

A ready-to-use pytorch dataloader for audio classification, speech classification, speaker recognition, etc. with in-GPU augmentations

Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

A python library for generating different permutations of audible segments from audio files.

Implementation of audio, image, and spectrogram augmentation techniques provided by the librosa, Keras and audiomentations

A Convolutional Neural Network that distinguishes between the speakers emotions. Comes with multiple preprocessors to improve the models performance.

SoundScaper is an audio augmented reality mobile application that allows users to author, save and reload virtual, and spatially interactive, three-dimensional binaural soundscapes within physical, real world spaces.

A tool/script for batch speech data enhancement with speed/volume/RIRS/MUSAN