ManaTTS Persian：面向低资源语言的TTS数据集构建方案
ManaTTS Persian: a recipe for creating TTS datasets for lower resource languages
AI总结：
本研究推出大规模波斯语单说话人TTS语料库ManaTTS及配套低资源语言语音数据处理流水线，训练的Tacotron2模型MOS得分接近自然语音水平。
摘要：
本研究推出了目前公开可用的规模最大的波斯语单说话人语料库ManaTTS，以及一套用于收集波斯语转录语音数据集的综合框架。ManaTTS以开放CC-0许可协议发布，包含约86小时、采样率为44.1kHz的音频数据。除ManaTTS外，我们还生成了VirgoolInformal数据集，用于评估执行强制对齐的波斯语语音识别模型，该数据集包含超过5小时的音频。这些数据集配套有完全透明、采用MIT许可的处理流水线，是该领域的创新成果，流水线包含句子分词、有界音频分割的专属工具，以及一种专为低资源语言设计的新型强制对齐方法，解决了该领域的一项关键需求。基于该数据集，我们训练了一个基于Tacotron2的TTS模型，其平均意见得分（MOS）达到3.76，这一得分与使用同一声码器和自然频谱图生成的语音的3.86分、以及自然波形的4.01分十分接近，证明了该语料库出色的质量与有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
