  Deep learning techniques have been shown to be efficient in various tasks,
especially in the development of speech recognition systems, that is, systems
that aim to transcribe an audio sentence in a sequence of written words.
Despite the progress in the area, speech recognition can still be considered
difficult, especially for languages lacking available data, such as Brazilian
Portuguese (BP). In this sense, this work presents the development of an public
Automatic Speech Recognition (ASR) system using only open available audio data,
from the fine-tuning of the Wav2vec 2.0 XLSR-53 model pre-trained in many
languages, over BP data. The final model presents an average word error rate of
12.4% over 7 different datasets (10.5% when applying a language model).
According to our knowledge, the obtained error is the lowest among open
end-to-end (E2E) ASR models for BP.
