The previous relative attention paper used an algorithm that was overly Which is not possible with the original Transformer model. Relative self-attentionĪlso allows the model to generalize beyond the length of the training examples, The model is able to focus more on relational features. Which explicitly modulates attention based on how far apart two tokens are, We found that by using relative attention, ![]() Track of regularity that is based on relative distances, event orderings, and periodicity. While the original Transformer allows us to capture self-reference throughĪttention, it relies on absolute timing signals and thus has a hard time keeping Our recent Wave2Midi2Wave project also uses Music Transformer-based model that has direct access to all earlier In contrast to an LSTM-based model like Performance RNN thatĬompresses earlier events into a fixed-size hidden state, here we use a That allows us to generate expressive performances directly (i.e. Similar to Performance RNN, we use an event-based representation
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