Kashmiri, an Indo-Aryan language written in a modified Perso-Arabic script, frequently omits diacritic marks in digital text, creating ambiguity and challenging downstream NLP applications. We present Koshur Diacritizer, a ByT5-small byte-level sequence-to-sequence model for restoring diacritics in Kashmiri text. To support this task, we release a publicly available dataset of 23.7k aligned undiacritized diacritized Kashmiri sentence pairs. The proposed framework combines script-aware normalization, alignment validation, and skeleton-preserving inference to ensure reliable restoration while maintaining the original base-letter sequence. Experimental results on a held-out test set achieve a DERm of 0.2012 and a WER of 0.2159. Additionally, evaluation by a native Kashmiri linguistic expert yields a mean accuracy of 77.5%. The dataset, model, and source code are publicly released to provide a reproducible baseline for Kashmiri diacritic restoration and future low-resource language research.

翻译：克什米尔语是一种采用改良波斯-阿拉伯字母书写的印度-雅利安语言，其数字文本常省略变音符号，导致语义歧义并对下游自然语言处理应用构成挑战。我们提出Koshur Diacritizer——一种基于ByT5-small字节级序列到序列模型，用于恢复克什米尔语文本中的变音符号。为支持该任务，我们发布了包含23.7k对齐的未标记/标记克什米尔语句子对的公开数据集。所提出的框架结合了脚本感知规范化、对齐验证和骨架保留推理，确保在保持原始基本字母序列的同时实现可靠的恢复。在保留测试集上的实验结果表明，其DERm指标为0.2012，WER指标为0.2159。此外，由克什米尔语母语语言学专家评估的平均准确率达77.5%。我们公开了数据集、模型和源代码，以提供克什米尔语变音符号恢复及未来低资源语言研究的可复现基线。