OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models

William Chen, Jinchuan Tian, Yifan Peng, Brian Yan, Chao-Han Huck Yang, Shinji Watanabe
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:9121-9145, 2025.

Abstract

Neural scaling laws offer valuable insights for designing robust sequence processing architectures. While these laws have been extensively characterized in other modalities, their behavior in speech remains comparatively underexplored. In this work, we introduce OWLS, an open-access, reproducible suite of multilingual speech recognition and translation models spanning 0.25B to 18B parameters, with the 18B version being the largest speech model, to the best of our knowledge. OWLS leverages up to 360K hours of public speech data across 150 languages, enabling a systematic investigation into how data, model, and compute scaling each influence performance in multilingual speech tasks. We use OWLS to derive neural scaling laws, showing how final performance can be reliably predicted when scaling. Scaling to larger models can improve ASR performance across the board, in both low and high resource languages, improving the accessibility of speech technologies. Finally, we show how OWLS can be used to power new research directions by discovering emergent abilities in large-scale speech models. Model checkpoints will be released on https://huggingface.co/collections/espnet/owls-scaling-laws-for-speech-recognition-and-translation-67ab7f991c194065f057ce8d for future studies.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-chen25bj, title = {{OWLS}: Scaling Laws for Multilingual Speech Recognition and Translation Models}, author = {Chen, William and Tian, Jinchuan and Peng, Yifan and Yan, Brian and Yang, Chao-Han Huck and Watanabe, Shinji}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {9121--9145}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/chen25bj/chen25bj.pdf}, url = {https://proceedings.mlr.press/v267/chen25bj.html}, abstract = {Neural scaling laws offer valuable insights for designing robust sequence processing architectures. While these laws have been extensively characterized in other modalities, their behavior in speech remains comparatively underexplored. In this work, we introduce OWLS, an open-access, reproducible suite of multilingual speech recognition and translation models spanning 0.25B to 18B parameters, with the 18B version being the largest speech model, to the best of our knowledge. OWLS leverages up to 360K hours of public speech data across 150 languages, enabling a systematic investigation into how data, model, and compute scaling each influence performance in multilingual speech tasks. We use OWLS to derive neural scaling laws, showing how final performance can be reliably predicted when scaling. Scaling to larger models can improve ASR performance across the board, in both low and high resource languages, improving the accessibility of speech technologies. Finally, we show how OWLS can be used to power new research directions by discovering emergent abilities in large-scale speech models. Model checkpoints will be released on https://huggingface.co/collections/espnet/owls-scaling-laws-for-speech-recognition-and-translation-67ab7f991c194065f057ce8d for future studies.} }
Endnote
%0 Conference Paper %T OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models %A William Chen %A Jinchuan Tian %A Yifan Peng %A Brian Yan %A Chao-Han Huck Yang %A Shinji Watanabe %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-chen25bj %I PMLR %P 9121--9145 %U https://proceedings.mlr.press/v267/chen25bj.html %V 267 %X Neural scaling laws offer valuable insights for designing robust sequence processing architectures. While these laws have been extensively characterized in other modalities, their behavior in speech remains comparatively underexplored. In this work, we introduce OWLS, an open-access, reproducible suite of multilingual speech recognition and translation models spanning 0.25B to 18B parameters, with the 18B version being the largest speech model, to the best of our knowledge. OWLS leverages up to 360K hours of public speech data across 150 languages, enabling a systematic investigation into how data, model, and compute scaling each influence performance in multilingual speech tasks. We use OWLS to derive neural scaling laws, showing how final performance can be reliably predicted when scaling. Scaling to larger models can improve ASR performance across the board, in both low and high resource languages, improving the accessibility of speech technologies. Finally, we show how OWLS can be used to power new research directions by discovering emergent abilities in large-scale speech models. Model checkpoints will be released on https://huggingface.co/collections/espnet/owls-scaling-laws-for-speech-recognition-and-translation-67ab7f991c194065f057ce8d for future studies.
APA
Chen, W., Tian, J., Peng, Y., Yan, B., Yang, C.H. & Watanabe, S.. (2025). OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:9121-9145 Available from https://proceedings.mlr.press/v267/chen25bj.html.

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