Sample-Efficient Diffusion for Text-To-Speech Synthesis

• URL: http://arxiv.org/abs/2409.03717v1
• Date: Sun, 1 Sep 2024 20:34:36 GMT
• Title: Sample-Efficient Diffusion for Text-To-Speech Synthesis
• Authors: Justin Lovelace, Soham Ray, Kwangyoun Kim, Kilian Q. Weinberger, Felix Wu,
• Abstract summary: It is based on a novel diffusion architecture, that we call U-Audio Transformer (U-AT) SESD achieves impressive results despite training on less than 1k hours of speech. It synthesizes more intelligible speech than the state-of-the-art auto-regressive model, VALL-E, while using less than 2% the training data.
• Score: 31.372486998377966
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces Sample-Efficient Speech Diffusion (SESD), an algorithm for effective speech synthesis in modest data regimes through latent diffusion. It is based on a novel diffusion architecture, that we call U-Audio Transformer (U-AT), that efficiently scales to long sequences and operates in the latent space of a pre-trained audio autoencoder. Conditioned on character-aware language model representations, SESD achieves impressive results despite training on less than 1k hours of speech - far less than current state-of-the-art systems. In fact, it synthesizes more intelligible speech than the state-of-the-art auto-regressive model, VALL-E, while using less than 2% the training data.

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