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

ASR Bundestag: A Large-Scale political debate dataset in German