Recent Speech-to-Text models often require a large amount of hardware resources and are mostly trained in English. This paper presents Speech-to-Text models for German, as well as for Spanish and French with special features: (a) They are small and run in real-time on microcontrollers like a RaspberryPi. (b) Using a pretrained English model, they can be trained on consumer-grade hardware with a relatively small dataset. (c) The models are competitive with other solutions and outperform them in German. In this respect, the models combine advantages of other approaches, which only include a subset of the presented features. Furthermore, the paper provides a new library for handling datasets, which is focused on easy extension with additional datasets and shows an optimized way for transfer-learning new languages using a pretrained model from another language with a similar alphabet.
@article{arxiv.2110.07982,
title = {Scribosermo: Fast Speech-to-Text models for German and other Languages},
author = {Daniel Bermuth and Alexander Poeppel and Wolfgang Reif},
journal= {arXiv preprint arXiv:2110.07982},
year = {2021}
}