Abstract
Arabic Text-to-Speech (TTS) research has been hindered by the availability of both publicly available training data and accurate Arabic diacritization models. In this paper, we address the limitation by exploring Arabic TTS training on large automatically annotated data. Namely, we built a robust pipeline for collecting Arabic recordings and processing them automatically using voice activity detection, speech recognition, automatic diacritization, and noise filtering, resulting in around 4,000 hours of Arabic TTS training data. We then trained several robust TTS models with voice cloning using varying amounts of data, namely 100, 1,000, and 4,000 hours with and without diacritization. We show that though models trained on diacritized data are generally better, larger amounts of training data compensate for the lack of diacritics to a significant degree. We plan to release a public Arabic TTS model that works without the need for diacritization.
Abstract (translated)
阿拉伯语文本到语音（TTS）的研究一直受到公开可用的训练数据和准确的阿拉伯语标点模型稀缺性的限制。在本文中，我们通过探索使用大量自动注释的数据进行阿拉伯语TTS训练来解决这一问题。具体来说，我们建立了一个稳健的管道，用于收集阿拉伯语录音，并利用语音活动检测、语音识别、自动标音和噪声过滤等技术对其进行自动处理，最终产生了约4,000小时的阿拉伯语TTS训练数据。然后，我们在不同数量的数据（即100小时、1,000小时和4,000小时）上使用声纹克隆技术训练了几个稳健的TTS模型，并分别在有标点和无标点的情况下进行了测试。我们发现，虽然基于标点数据训练出来的模型通常表现更好，但大量的训练数据可以在很大程度上弥补缺少标音的问题。我们的计划是发布一个无需依赖标音即可工作的公共阿拉伯语TTS模型。
URL
https://arxiv.org/abs/2603.01622
