  TTS, or text-to-speech, is a complicated process that can be accomplished
through appropriate modeling using deep learning methods. In order to implement
deep learning models, a suitable dataset is required. Since there is a scarce
amount of work done in this field for the Persian language, this paper will
introduce the single speaker dataset: ArmanTTS. We compared the characteristics
of this dataset with those of various prevalent datasets to prove that ArmanTTS
meets the necessary standards for teaching a Persian text-to-speech conversion
model. We also combined the Tacotron 2 and HiFi GAN to design a model that can
receive phonemes as input, with the output being the corresponding speech. 4.0
value of MOS was obtained from real speech, 3.87 value was obtained by the
vocoder prediction and 2.98 value was reached with the synthetic speech
generated by the TTS model.
