Poor quality sleep can be characterized by the occurrence of events ranging from body movement to breathing impairment.Utilizing widely-available earbuds equipped with a sleep event detection algorithm, it is possible to offer a convenient and efficient alternative to laborious clinical diagnoses for individuals suffering from sleep disorders. Although various solutions utilizing wearables have been proposed to detect these events, they ignore the fact that individuals often share sleeping spaces with others; roommates or couples, for example (henceforth referred to as wear-aware). To address this issue, we introduce DreamCatcher, the first publicly available dataset for wearer-aware sleep event detection on earables. DreamCatcher encompasses eight distinct sleep events, including synchronous two-channel audio and motion data collected from 12 pairs (24 participants) totaling 210 hours (420 hour.person) with fine-grained label.We further tested multiple benchmark models on three tasks, demonstrating the usability and unique challenge of DreamCatcher.We hope that the proposed DreamCatcher can inspireother researchers to further explore efficient wearer-aware human vocal activity sensing on earables. DreamCatcher was made open-source at site https://anonymous.4open.science/r/open-earsleep-D369.