autrainer: A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks

2024/12/16 by Simon Rampp, Andreas Triantafyllopoulos, Rampp, Simon +5 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Neural Networks and Applications #Sound (cs.SD) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2412.11943

openalex publication_date 2024/12/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

This work introduces the key operating principles for autrainer, our new deep learning training framework for computer audition tasks. autrainer is a PyTorch-based toolkit that allows for rapid, reproducible, and easily extensible training on a variety of different computer audition tasks. Concretely, autrainer offers low-code training and supports a wide range of neural networks as well as preprocessing routines. In this work, we present an overview of its inner workings and key capabilities.

Cited by

• Charting 15 years of progress in deep learning for speech emotion recognition: A replication study

Related

• Deep Learning Tools for Audacity: Helping Researchers Expand the\n Artist's Toolkit
• ODAS: Open embeddeD Audition System
• Auden-Voice: General-Purpose Voice Encoder for Speech and Language Understanding
• Computer Audition: From Task-Specific Machine Learning to Foundation Models
• AAT: Adapting Audio Transformer for Various Acoustics Recognition Tasks