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.

通过集成特定语言的音位分布、自动化录制过程、自动化和人机协同质量保证、以及记录的处理以符合指定格式，本文引入了一种端到端工具以生成文本到语音 (TTS) 模型的高质量数据集，以满足对高质量数据的重要需求，从而简化 TTS 模型的数据集创建过程，促进基于语音的技术的发展。