Title: Encoding Musical Style with Transformer Autoencoders. Abstract: We consider the problem of learning high-level controls over the global structure of sequence generation, particularly in the context of symbolic music generation with complex language models. In this work, we present the Transformer autoencoder, which aggregates encodings of the input data across time to obtain a global representation of style from a given performance.  We show it is possible to combine this global embedding with other temporally distributed embeddings, enabling improved control over the separate aspects of performance style and  and melody. Empirically, we demonstrate the effectiveness of our method on a variety of music generation tasks on the MAESTRO dataset and an internal, 10,000+ hour dataset of piano performances, where we achieve improvements in terms of log-likelihood and mean listening scores as compared to relevant baselines.