title: Stack-and-Delay: a new codebook pattern for music generation
summary: In language modeling based music generation, a generated waveform is
  represented by a sequence of hierarchical token stacks that can be decoded
  either in an auto-regressive manner or in parallel, depending on the codebook
  patterns. In particular, flattening the codebooks represents the highest
  quality decoding strategy, while being notoriously slow. To this end, we
  propose a novel stack-and-delay style of decoding strategy to improve upon the
  flat pattern decoding where generation speed is four times faster as opposed to
  vanilla flat decoding. This brings the inference time close to that of the
  delay decoding strategy, and allows for faster inference on GPU for small batch
  sizes. For the same inference efficiency budget as the delay pattern, we show
  that the proposed approach performs better in objective evaluations, almost
  closing the gap with the flat pattern in terms of quality. The results are
  corroborated by subjective evaluations which show that samples generated by the
  new model are slightly more often preferred to samples generated by the
  competing model given the same text prompts.
paper_authors._id: 6509155c7b68c4a6f65f3f3b
paper_authors.name: Gael Le Lan
paper_authors._id: 6509155c7b68c4a6f65f3f3c
paper_authors.name: Varun Nagaraja
paper_authors._id: 6509155c7b68c4a6f65f3f3d
paper_authors.name: Ernie Chang
paper_authors._id: 6509155c7b68c4a6f65f3f3e
paper_authors.name: David Kant
paper_authors._id: 6509155c7b68c4a6f65f3f3f
paper_authors.name: Zhaoheng Ni
paper_authors._id: 6509155c7b68c4a6f65f3f40
paper_authors.name: Yangyang Shi
paper_authors._id: 6509155c7b68c4a6f65f3f41
paper_authors.name: Forrest Iandola
paper_authors._id: 6509155c7b68c4a6f65f3f42
paper_authors.name: Vikas Chandra
paper_title: Stack-and-Delay: a new codebook pattern for music generation
paper_summary: In language modeling based music generation, a generated waveform is
  represented by a sequence of hierarchical token stacks that can be decoded
  either in an auto-regressive manner or in parallel, depending on the codebook
  patterns. In particular, flattening the codebooks represents the highest
  quality decoding strategy, while being notoriously slow. To this end, we
  propose a novel stack-and-delay style of decoding strategy to improve upon the
  flat pattern decoding where generation speed is four times faster as opposed to
  vanilla flat decoding. This brings the inference time close to that of the
  delay decoding strategy, and allows for faster inference on GPU for small batch
  sizes. For the same inference efficiency budget as the delay pattern, we show
  that the proposed approach performs better in objective evaluations, almost
  closing the gap with the flat pattern in terms of quality. The results are
  corroborated by subjective evaluations which show that samples generated by the
  new model are slightly more often preferred to samples generated by the
  competing model given the same text prompts.
paper_ai_keywords: hierarchical token stacks auto-regressive codebook patterns flattening the codebooks stack-and-delay style decoding strategy delay decoding strategy inference time inference efficiency budget objective evaluations subjective evaluations