  In this work, we showcase a cost-effective method for generating training
data for speech processing tasks. First, we transcribe unlabeled speech using a
state-of-the-art Automatic Speech Recognition (ASR) model. Next, we align
generated transcripts with the audio and apply segmentation on short
utterances. Our focus is on ASR for low-resource languages, such as Ukrainian,
using podcasts as a source of unlabeled speech.
  We release a new dataset UK-PODS that features modern conversational
Ukrainian language. It contains over 50 hours of text audio-pairs as well as
uk-pods-conformer, a 121 M parameters ASR model that is trained on MCV-10 and
UK-PODS and achieves 3x reduction of Word Error Rate (WER) on podcasts
comparing to publically available uk-nvidia-citrinet while maintaining
comparable WER on MCV-10 test split. Both dataset UK-PODS
https://huggingface.co/datasets/taras-sereda/uk-pods and ASR uk-pods-conformer
https://huggingface.co/taras-sereda/uk-pods-conformer are available on the
hugging-face hub.
