REDIT: Resilient Distributed Text-to-Speech at Edge Networks.

Existing deep learning-based Text-to-Speech (TTS) mechanisms are computationally intensive, which puts a strain in their practical applications especially over edge networks comprised of resource constrained devices. Our focus is on distributing TTS tasks across multiple devices (i.e., workers) at edge networks and providing TTSaware resiliency against straggling workers. In particular, we design a REsilient DIstributed Tts (REDIT) framework by exploiting the text summarization as redundancy to provide resiliency for distributed TTS. We show analytically that REDIT improves the task completion time as compared to the distributed TTS without resiliency. We determine the optimum amount of redundancy/summary based on our task completion time analysis. We implement our REDIT framework in a real testbed consisting of NVIDIA Jetson Nano cards, and show that our REDIT algorithm improves the task completion delay as compared to baselines.