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In this paper, we present our progress in pretraining Czech monolingual audiotransformers from a large dataset containing more than 80 thousand hours ofunlabeled speech, and subsequently fine-tuning the model on automatic speechrecognition tasks using a combination of in-domain data and almost 6 thousandhours of out-of-domain transcribed speech. We are presenting a large palette ofexperiments with various fine-tuning setups evaluated on two public datasets(CommonVoice and VoxPopuli) and one extremely challenging dataset from theMALACH project. Our results show that monolingual Wav2Vec 2.0 models are robustASR systems, which can take advantage of large labeled and unlabeled datasetsand successfully compete with state-of-the-art LVCSR systems. Moreover, Wav2Vecmodels proved to be good zero-shot learners when no training data are availablefor the target ASR task.

Exploring Capabilities of Monolingual Audio Transformers using Large Datasets in Automatic Speech Recognition of Czech