  This study focuses on the development of Indonesian Automatic Speech
Recognition (ASR) using the XLSR-53 pre-trained model, the XLSR stands for
cross-lingual speech representations. The use of this XLSR-53 pre-trained model
is to significantly reduce the amount of training data in non-English languages
required to achieve a competitive Word Error Rate (WER). The total amount of
data used in this study is 24 hours, 18 minutes, and 1 second: (1) TITML-IDN 14
hours and 31 minutes; (2) Magic Data 3 hours and 33 minutes; and (3) Common
Voice 6 hours, 14 minutes, and 1 second. With a WER of 20%, the model built in
this study can compete with similar models using the Common Voice dataset split
test. WER can be decreased by around 8% using a language model, resulted in WER
from 20% to 12%. Thus, the results of this study have succeeded in perfecting
previous research in contributing to the creation of a better Indonesian ASR
with a smaller amount of data.
