  We introduce VANI, a very lightweight multi-lingual accent controllable
speech synthesis system. Our model builds upon disentanglement strategies
proposed in RADMMM and supports explicit control of accent, language, speaker
and fine-grained $F_0$ and energy features for speech synthesis. We utilize the
Indic languages dataset, released for LIMMITS 2023 as part of ICASSP Signal
Processing Grand Challenge, to synthesize speech in 3 different languages. Our
model supports transferring the language of a speaker while retaining their
voice and the native accent of the target language. We utilize the
large-parameter RADMMM model for Track $1$ and lightweight VANI model for Track
$2$ and $3$ of the competition.
