In the field of automatic speech recognition (ASR), state-of-the-art results are achieved by end-to-end models. These models are sequence-to-sequence models and are trained using pairs of speech and corresponding texts, which implies that additional finetuning of underlying language models is not possible. In this paper we demonstrate that the performance of Serbian Whisper-based ASR can be improved by leveraging data generation with a high quality text-to-speech (TTS) system in Serbian. Synthetic speech is produced based on text extracted from Serbian web-scale text corpus, SrWAC, using data curation and large language model (LLM)-based normalization to mitigate problems in rendering Serbian pronounciation. A total quantity of 1500 h of speech is generated exploiting 9 text-to-speech voices based on deep-neural architectures and neural vocoding. The experiments are conducted on the medium Whisper model. The baseline model is initially finetuned using 1300 h of transcribed data and then additionally finetuned by synthetic speech, during which process the encoder section of the system is kept frozen. The experimental results confirm the character and word-error rate improvements on the CommonVoice database, as well as on real-life recordings.