LibriWASN is a data set whose design is based on the LibriCSS data set. The main difference is that the data was recorded by distributed devices of an acoustic sensor network, randomly positioned on a meeting table. Thus, the microphone channels between the devices show a sampling rate offset. The data set with a total length of 20 hours was recorded in two acoustically different rooms. An acoustics lab with a room reverberation time of about 200ms and a lab room with about 800ms reverberation time. Nine different devices with different numbers of channels are available: Five smartphones with a single recording channel, 2 compact microphone arrays with 6 channels, 1 compact microphone array with 4 channels, and 1 circular microphone array with 8 channels. A total of 29 channels are available in the recordings. The same LibriSpeech sentences and speakers of the LibriCSS dataset were re-recorded and the directory structures of LibriCSS were kept. The data set is organized into subsets with different percentages of speech overlap (0% - 40%). LibriWASN can be used for various research purposes, e.g., as a test set for synchronization algorithms, speech separation, diarization, and meeting transcription systems in wireless acoustic ad-hoc sensor networks. Visit https://github.com/fgnt/libriwasn for tools and scripts. To cite this dataset please refer to @InProceedings{SchTgbHaeb2023, Title = {LibriWASN: A Data Set for Meeting Separation, Diarization, and Recognition with Asynchronous Recording Devices}, Author = {Joerg Schmalenstroeer and Tobias Gburrek and Reinhold Haeb-Umbach}, Booktitle = {ITG conference on Speech Communication (ITG 2023)}, Year = {2023}, Month = {Sep}, } A preview of the paper is available from here: http://arxiv.org/abs/2308.10682