Performance Comparison of Pre-trained Models for Speech-to-Text in
  Turkish: Whisper-Small and Wav2Vec2-XLS-R-300M
Oyku Berfin Mercan Sercan Cepni Davut Emre Tasar Sukru Ozan
In this study, the performances of the Whisper-Small and Wav2Vec2-XLS-R-300M
models which are two pre-trained multilingual models for speech to text were
examined for the Turkish language. Mozilla Common Voice version 11.0 which is
prepared in Turkish language and is an open-source data set, was used in the
study. The multilingual models, Whisper- Small and Wav2Vec2-XLS-R-300M were
fine-tuned with this data set which contains a small amount of data. The speech
to text performance of the two models was compared. WER values are calculated
as 0.28 and 0.16 for the Wav2Vec2-XLS- R-300M and the Whisper-Small models
respectively. In addition, the performances of the models were examined with
the test data prepared with call center records that were not included in the
training and validation dataset.