MobileSpeech: A Fast and High-Fidelity Framework for Mobile Zero-Shot Text-to-Speech

TL;DR

MobileSpeech is a novel, fast, lightweight, and robust zero-shot TTS framework optimized for mobile devices, achieving high speech quality and inference speed with state-of-the-art results.

Contribution

It introduces a mobile-friendly zero-shot TTS system with a novel speech mask decoder and probabilistic masking, enabling real-time high-quality speech synthesis on mobile devices.

Findings

Achieves RTF of 0.09 on A100 GPU

Demonstrates effective multilingual speech synthesis

Successfully deployed on mobile devices

Abstract

Zero-shot text-to-speech (TTS) has gained significant attention due to its powerful voice cloning capabilities, requiring only a few seconds of unseen speaker voice prompts. However, all previous work has been developed for cloud-based systems. Taking autoregressive models as an example, although these approaches achieve high-fidelity voice cloning, they fall short in terms of inference speed, model size, and robustness. Therefore, we propose MobileSpeech, which is a fast, lightweight, and robust zero-shot text-to-speech system based on mobile devices for the first time. Specifically: 1) leveraging discrete codec, we design a parallel speech mask decoder module called SMD, which incorporates hierarchical information from the speech codec and weight mechanisms across different codec layers during the generation process. Moreover, to bridge the gap between text and speech, we introduce a high-level probabilistic mask that simulates the progression of information flow from less to more during speech generation. 2) For speaker prompts, we extract fine-grained prompt duration from the prompt speech and incorporate text, prompt speech by cross attention in SMD. We demonstrate the effectiveness of MobileSpeech on multilingual datasets at different levels, achieving state-of-the-art results in terms of generating speed and speech quality. MobileSpeech achieves RTF of 0.09 on a single A100 GPU and we have successfully deployed MobileSpeech on mobile devices. Audio samples are available at \url{https://mobilespeech.github.io/} .