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

Reproducibility, code quality, and development speed constitute the'impossible trinity' of contemporary experimental artificial intelligence (AI) research. Of the three, the first has attracted the most attention in recent literature [1], as reproducibility of findings is a cornerstone of science. However, the impact of the other two should not be underestimated. Development speed allows the quick iteration of ideas - a necessary prerequisite in experimental sciences and a prominent feature of AI research, as asserted by "The Bitter Lesson" of R. Sutton [2]. Similarly, code quality can be the key differentiating factor when it comes to "standing on the shoulders of giants", as shaky foundations can lead to a spectacular collapse. This is why toolkits that are easy-to-use and provide pre-baked reproducibility are critical for the proliferation and adaptation of new ideas. The not-so-recent renaissance of deep learning (DL) has been largely driven by the creation of such toolkits.