We present CS-YODAS, a Creative Commons-licensed dataset of in-the-wild code-switched speech mined from multilingual YouTube data. Code-switching (CS), or the alternation between languages within an utterance or conversation, is common in multilingual settings but remains underrepresented in existing CS speech resources, which are typically small, domain-specific, or artificially constructed. Building on the YODAS corpus, we develop a scalable, human-in-the-loop pipeline for identifying and validating naturally occurring code-switching. The resulting dataset, which totals 313 hours and spans 7 matrix languages, provides diverse, real-world examples of spontaneous code-switched speech. We further analyze the distribution and characteristics of code-switching in the wild, examining language-pair frequencies and switching patterns, and report baseline results for spoken language identification. We hope that CS-YODAS will encourage broader and more comprehensive research on code-switched speech. Dataset link: https://huggingface.co/datasets/byan/cs-yodas.

翻译：我们提出CS-YODAS，这是一个基于知识共享许可协议的数据集，包含从多语言YouTube数据中挖掘的自然场景语码转换语音。语码转换（Code-Switching，CS）指在话语或对话中交替使用不同语言的现象，在多语环境中十分常见，但在现有的CS语音资源中仍缺乏代表性——这些资源通常规模较小、领域特定或人为构建。基于YODAS语料库，我们开发了一种可扩展的人机协同流水线，用于识别和验证自然发生的语码转换。最终数据集总计313小时，涵盖7种基质语言，提供了多样化的真实世界自发性语码转换语音示例。我们进一步分析了自然场景下语码转换的分布与特征，考察了语言对频率和转换模式，并报告了口语语言识别的基线结果。希望CS-YODAS能推动对语码转换语音的更广泛、更全面研究。数据集链接：https://huggingface.co/datasets/byan/cs-yodas。