2 citations · 3 across the 7 of their papers we have counts for
6 papers · 1 filter
MSVAE: Rethinking Musical Semantics in Continuous Latent Spaces
Guobin Ma, Yuepeng Jiang, Dake Guo +15
Continuous latent representations underlie recent diffusion-based lyrics-to-song and text-to-music systems, yet aligning lyric conditions with these latents remains difficult: exis…
Rethinking Music Tokenization: A Semantic Codec toward High-Fidelity LLM Music Generation
Huakang Chen, Guobin Ma, Yuepeng Jiang +10
Discrete audio tokenization has become the critical interface between raw waveforms and autoregressive modeling in recent music generation. As a result, music tokenizers must simul…
SoulX-Singer: Towards High-Quality Zero-Shot Singing Voice Synthesis
Jiale Qian, Hao Meng, Tian Zheng +17
While recent years have witnessed rapid progress in speech synthesis, open-source singing voice synthesis (SVS) systems still face significant barriers to industrial deployment, pa…
Semantic-VAE: Semantic-Alignment Latent Representation for Better Speech Synthesis
Zhikang Niu, Shujie Hu, Jeongsoo Choi +8
Mel-spectrograms have been widely used in zero-shot text-to-speech (TTS); their inherent redundancy leads to inefficiency in text-speech alignment. Compact VAE-based latent represe…
F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching
Yushen Chen, Zhikang Niu, Ziyang Ma +5
This paper introduces F5-TTS, a fully non-autoregressive text-to-speech system based on flow matching with Diffusion Transformer (DiT). Without requiring complex designs such as du…
InstructSing: High-Fidelity Singing Voice Generation via Instructing Yourself
Chang Zeng, Chunhui Wang, Xiaoxiao Miao +3
It is challenging to accelerate the training process while ensuring both high-quality generated voices and acceptable inference speed. In this paper, we propose a novel neural voco…