  In this paper, we describe the TTS models developed by NVIDIA for the
MMITS-VC (Multi-speaker, Multi-lingual Indic TTS with Voice Cloning) 2024
Challenge. In Tracks 1 and 2, we utilize RAD-MMM to perform few-shot TTS by
training additionally on 5 minutes of target speaker data. In Track 3, we
utilize P-Flow to perform zero-shot TTS by training on the challenge dataset as
well as external datasets. We use HiFi-GAN vocoders for all submissions.
RAD-MMM performs competitively on Tracks 1 and 2, while P-Flow ranks first on
Track 3, with mean opinion score (MOS) 4.4 and speaker similarity score (SMOS)
of 3.62.
