Title: HAPPIER: Hierarchical Polyphonic Music Generative RNN. Abstract: Generating polyphonic music with coherent global structure is a major challenge for automatic composition algorithms. The primary difficulty arises due to the inefficiency of models to recognize underlying patterns beneath music notes across different levels of time scales and remain long-term consistency while composing. Hierarchical architectures can capture and represent learned patterns in different temporal scales and maintain consistency over long time spans, and this corresponds to the hierarchical structure in music. Motivated by this, focusing on leveraging the idea of hierarchical models and improve them to fit the sequence modeling problem, our paper proposes HAPPIER: a novel HierArchical PolyPhonic musIc gEnerative RNN. In HAPPIER, A higher `measure level' learns correlations across measures and patterns for chord progressions, and a lower `note level' learns a conditional distribution over the notes to generate within a measure. The two hierarchies operate at different clock rates: the higher one operates on a longer timescale and updates every measure, while the lower one operates on a shorter timescale and updates every unit duration. The two levels communicate with each other, and thus the entire architecture is trained jointly end-to-end by back-propagation. HAPPIER, profited from the strength of the hierarchical structure, generates polyphonic music with long-term dependencies compared to the state-of-the-art methods.