autrainer: A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917981702848512 |
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| author | Rampp, Simon Triantafyllopoulos, Andreas Milling, Manuel Schuller, Björn W. |
| author_facet | Rampp, Simon Triantafyllopoulos, Andreas Milling, Manuel Schuller, Björn W. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11943 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | autrainer: A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks Rampp, Simon Triantafyllopoulos, Andreas Milling, Manuel Schuller, Björn W. Sound Artificial Intelligence Machine Learning Audio and Speech Processing 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. |
| title | autrainer: A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks |
| topic | Sound Artificial Intelligence Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2412.11943 |