Text-to-speech (TTS) technology enhances human-computer interaction and increases content accessibility. Tacotron and other deep learning models have enhanced the naturalness of text-to-speech systems. The vocoder, which transforms mel-spectrograms into audio waveforms, significantly influences voice quality. This study evaluates Tacotron2 vocoders for Nepali text-to speech synthesis. While English language vocoders have been thoroughly examined, Nepali language vocoders remain underexplored. The study utilizes the WaveNet and MelGAN vocoders to generate speech from mel-spectrograms produced by Tacotron2 for Nepali text. In order to assess the quality of voice synthesis, this paper study the mel-cepstral distortion (MCD) and Mean Opinion Score (MOS) for speech produced by both vocoders. The comparative investigation of the Tacotron2 + MelGAN and Tacotron2 + WaveNet models, utilizing the Nepali OpenSLR and News male voice datasets, consistently reveals the advantage of Tacotron2 + MelGAN in terms of naturalness and accuracy. The Tacotron2 + MelGAN model achieved an average MOS score of 4.245 on the Nepali OpenSLR dataset and 2.885 on the male voice dataset.