  This paper describes the joint effort of Brno University of Technology (BUT),
AGH University of Krakow and University of Buenos Aires on the development of
Automatic Speech Recognition systems for the CHiME-7 Challenge. We train and
evaluate various end-to-end models with several toolkits. We heavily relied on
Guided Source Separation (GSS) to convert multi-channel audio to single
channel. The ASR is leveraging speech representations from models pre-trained
by self-supervised learning, and we do a fusion of several ASR systems. In
addition, we modified external data from the LibriSpeech corpus to become a
close domain and added it to the training. Our efforts were focused on the
far-field acoustic robustness sub-track of Task 1 - Distant Automatic Speech
Recognition (DASR), our systems use oracle segmentation.
