This article presents SSR7000, a corpus of synchronized ultrasound tongue and lip images designed for end-to-end silent speech recognition (SSR). Although neural end-to-end models are successfully updating the state-of-the-art technology in the field of automatic speech recognition, SSR research based on ultrasound tongue imaging has still not evolved past cascaded DNN-HMM models due to the absence of a large dataset. In this study, we constructed a large dataset, namely SSR7000, to exploit the performance of the end-to-end models. The SSR7000 dataset contains ultrasound tongue and lip images of 7484 utterances by a single speaker. It contains more utterances per person than any other SSR corpus based on ultrasound imaging. We also describe preprocessing techniques to tackle data variances that are inevitable when collecting a large dataset and present benchmark results using an end-to-end model. The SSR7000 corpus is publicly available under the CC BY-NC 4.0 license.