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In this case study we trained and published a state-of-the-art open-sourcemodel for Automatic Speech Recognition (ASR) for German to evaluate the currentpotential of this technology for the use in the larger context of DigitalHumanities and cultural heritage indexation. Along with this paper we publishour wav2vec2 based speech to text model while we evaluate its performance on acorpus of historical recordings we assembled compared against commercialcloud-based and proprietary services. While our model achieves moderateresults, we see that proprietary cloud services fare significantly better. Asour results show, recognition rates over 90 percent can currently be achieved,however, these numbers drop quickly once the recordings feature limited audioquality or use of non-every day or outworn language. A big issue is the highvariety of different dialects and accents in the German language. Nevertheless,this paper highlights that the currently available quality of recognition ishigh enough to address various use cases in the Digital Humanities. We arguethat ASR will become a key technology for the documentation and analysis ofaudio-visual sources and identify an array of important questions that the DHcommunity and cultural heritage stakeholders will have to address in the nearfuture.

wav2vec and its current potential to Automatic Speech Recognition in  German for the usage in Digital History: A comparative assessment of  available ASR-technologies for the use in cultural heritage contexts