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.

本研究使用土耳其语准备的 Mozilla Common Voice 11.0 开源数据集，对两个预训练的多语言语音到文本模型 Whisper-Small 和 Wav2Vec2-XLS-R-300M 进行性能测试和比较，WER 值分别为 0.28 和 0.16。此外，还使用呼叫中心记录准备了测试数据集以检验模型的性能。