ManaTTS 波斯语:为低资源语言创建 TTS 数据集的秘诀

Mahta Fetrat Qharabagh, Zahra Dehghanian, Hamid R. Rabiee
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引用次数: 0

摘要

在本研究中,我们介绍了 ManaTTS,它是最广泛的可公开访问的单人波斯语语料库,也是收集波斯语转录语音数据集的综合框架。ManaTTS 在开放的 CC-0 许可下发布,包含约 86 小时的音频,采样率为 44.1 kHz。除 ManaTTS 外,我们还生成了 VirgoolInformal 数据集,以评估用于强制对齐的波斯语语音识别模型,该数据集的音频时长超过 5 小时。这些数据集由麻省理工学院授权的完全透明的管道提供支持,证明了该领域的创新。它包括用于句子标记化、边界音频分割的独特工具,以及一种新颖的强制对齐方法。这种对齐技术专为低资源语言设计,满足了该领域的关键需求。利用该数据集,我们训练了一个基于 Tacotron2 的 TTS 模型,其平均意见得分(MOS)达到了 3.76,与相同声码器和自然频谱图生成的语篇的 MOS 3.86 和自然波形的 MOS 4.01 非常接近,证明了该语料库的卓越质量和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ManaTTS Persian: a recipe for creating TTS datasets for lower resource languages
In this study, we introduce ManaTTS, the most extensive publicly accessible single-speaker Persian corpus, and a comprehensive framework for collecting transcribed speech datasets for the Persian language. ManaTTS, released under the open CC-0 license, comprises approximately 86 hours of audio with a sampling rate of 44.1 kHz. Alongside ManaTTS, we also generated the VirgoolInformal dataset to evaluate Persian speech recognition models used for forced alignment, extending over 5 hours of audio. The datasets are supported by a fully transparent, MIT-licensed pipeline, a testament to innovation in the field. It includes unique tools for sentence tokenization, bounded audio segmentation, and a novel forced alignment method. This alignment technique is specifically designed for low-resource languages, addressing a crucial need in the field. With this dataset, we trained a Tacotron2-based TTS model, achieving a Mean Opinion Score (MOS) of 3.76, which is remarkably close to the MOS of 3.86 for the utterances generated by the same vocoder and natural spectrogram, and the MOS of 4.01 for the natural waveform, demonstrating the exceptional quality and effectiveness of the corpus.
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