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arXiv · 2505.07701

Lightweight End-to-end Text-to-speech Synthesis for low resource on-device applications

Biel Tura Vecino·Adam Gabryś·Daniel Mątwicki·Andrzej Pomirski·Tom Iddon·Marius Cotescu·Jaime Lorenzo-Trueba
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Abstract

Recent works have shown that modelling raw waveform directly from text in an end-to-end (E2E) fashion produces more natural-sounding speech than traditional neural text-to-speech (TTS) systems based on a cascade or two-stage approach. However, current E2E state-of-the-art models are computationally complex and memory-consuming, making them unsuitable for real-time offline on-device applications in low-resource scenarios. To address this issue, we propose a Lightweight E2E-TTS (LE2E) model that generates high-quality speech requiring minimal computational resources. We evaluate the proposed model on the LJSpeech dataset and show that it achieves state-of-the-art performance while being up to $90\%$ smaller in terms of model parameters and $10\times$ faster in real-time-factor. Furthermore, we demonstrate that the proposed E2E training paradigm achieves better quality compared to an equivalent architecture trained in a two-stage approach. Our results suggest that LE2E is a promising approach for developing real-time, high quality, low-resource TTS applications for on-device applications.

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BibTeXRIS

Biel Tura Vecino, Adam Gabryś, Daniel Mątwicki, Andrzej Pomirski, Tom Iddon, Marius Cotescu, Jaime Lorenzo-Trueba. 2025-05-12. Lightweight End-to-end Text-to-speech Synthesis for low resource on-device applications. https://doi.org/10.21437/ssw.2023-35

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