An Automated End-to-End Open-Source Software for High-Quality
  Text-to-Speech Dataset Generation
Ahmet Gunduz Kamer Ali Yuksel Kareem Darwish Golara Javadi Fabio Minazzi Nicola Sobieski Sebastien Bratieres
Data availability is crucial for advancing artificial intelligence
applications, including voice-based technologies. As content creation,
particularly in social media, experiences increasing demand, translation and
text-to-speech (TTS) technologies have become essential tools. Notably, the
performance of these TTS technologies is highly dependent on the quality of the
training data, emphasizing the mutual dependence of data availability and
technological progress. This paper introduces an end-to-end tool to generate
high-quality datasets for text-to-speech (TTS) models to address this critical
need for high-quality data. The contributions of this work are manifold and
include: the integration of language-specific phoneme distribution into sample
selection, automation of the recording process, automated and human-in-the-loop
quality assurance of recordings, and processing of recordings to meet specified
formats. The proposed application aims to streamline the dataset creation
process for TTS models through these features, thereby facilitating
advancements in voice-based technologies.