  We present ASR Bundestag, a dataset for automatic speech recognition in
German, consisting of 610 hours of aligned audio-transcript pairs for
supervised training as well as 1,038 hours of unlabeled audio snippets for
self-supervised learning, based on raw audio data and transcriptions from
plenary sessions and committee meetings of the German parliament. In addition,
we discuss utilized approaches for the automated creation of speech datasets
and assess the quality of the resulting dataset based on evaluations and
finetuning of a pre-trained state of the art model. We make the dataset
publicly available, including all subsets.
