  In this paper, we present several baselines for automatic speech recognition
(ASR) models for the two official written languages in Norway: Bokm{\aa}l and
Nynorsk. We compare the performance of models of varying sizes and pre-training
approaches on multiple Norwegian speech datasets. Additionally, we measure the
performance of these models against previous state-of-the-art ASR models, as
well as on out-of-domain datasets. We improve the state of the art on the
Norwegian Parliamentary Speech Corpus (NPSC) from a word error rate (WER) of
17.10\% to 7.60\%, with models achieving 5.81\% for Bokm{\aa}l and 11.54\% for
Nynorsk. We also discuss the challenges and potential solutions for further
improving ASR models for Norwegian.
