Scaling NVIDIA's Multi-speaker Multi-lingual TTS Systems with Zero-Shot TTS to Indic Languages

Akshit Arora, Rohan Badlani, Sungwon Kim, Rafael Valle, Bryan Catanzaro
1/24/2024
cs.SDcs.LGeess.AS

No confident code implementation found for this paper. 5 possible matches can be shown below.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 5 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model

82% match1 repo452024

A multi-speaker multi-lingual voice cloning system based on vits2 for limmits 2024 challenge

82% match2024

IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTS

81% match1 repo652024

MParrotTTS: Multilingual Multi-speaker Text to Speech Synthesis in Low Resource Setting

80% match2023

Zero-Shot vs. Few-Shot Multi-Speaker TTS Using Pre-trained Czech SpeechT5 Model

79% match2024

A2TTS: TTS for Low Resource Indian Languages

79% match2025

Cite this paper

@article{arora2024scaling,
  title  = {Scaling NVIDIA's Multi-speaker Multi-lingual TTS Systems with Zero-Shot TTS to Indic Languages},
  author = {Akshit Arora and Rohan Badlani and Sungwon Kim and Rafael Valle and Bryan Catanzaro},
  year   = {2024},
  eprint = {2401.13851},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.13851v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2401.13851.svg)](https://codeofpaper.com/paper/2401.13851)

Discussion