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Automatic sung speech recognition is a relatively understudied topic that has been held back by a lack of large and freely available datasets. This has recently changed thanks to the release of the DAMP Sing! dataset, a 1100 hour karaoke dataset originating from the social music-making company, Smule. This paper presents work undertaken to define an easily replicable, automatic speech recognition benchmark for this data. In particular, we describe how transcripts and alignments have been recovered from Karaoke prompts and timings; how suitable training, development and test sets have been defined with varying degrees of accent variability; and how language models have been developed using lyric data from the LyricWikia website. Initial recognition experiments have been performed using factored-layer TDNN acoustic models with lattice-free MMI training using Kaldi. The best WER is 19.60% — a new state-of-the-art for this type of data. The paper concludes with a discussion of the many challenging problems that remain to be solved. Dataset definitions and Kaldi scripts have been made available so that the benchmark is easily replicable.