title: SoundStorm: Efficient Parallel Audio Generation
summary: We present SoundStorm, a model for efficient, non-autoregressive audio
  generation. SoundStorm receives as input the semantic tokens of AudioLM, and
  relies on bidirectional attention and confidence-based parallel decoding to
  generate the tokens of a neural audio codec. Compared to the autoregressive
  generation approach of AudioLM, our model produces audio of the same quality
  and with higher consistency in voice and acoustic conditions, while being two
  orders of magnitude faster. SoundStorm generates 30 seconds of audio in 0.5
  seconds on a TPU-v4. We demonstrate the ability of our model to scale audio
  generation to longer sequences by synthesizing high-quality, natural dialogue
  segments, given a transcript annotated with speaker turns and a short prompt
  with the speakers' voices.
paper_authors._id: 64642490823c7f880550a724
paper_authors.name: Zalán Borsos
paper_authors._id: 64642490823c7f880550a725
paper_authors.name: Matt Sharifi
paper_authors._id: 64642490823c7f880550a726
paper_authors.name: Damien Vincent
paper_authors._id: 64642490823c7f880550a727
paper_authors.name: Eugene Kharitonov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-26T09:30:03.266Z
paper_authors._id: 64509292fc5e5ff919b4a97d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64509292fc5e5ff919b4a97d/OOn5EaCXw39ePmgKptOuT.png
paper_authors.fullname: Eugene Kharitonov
paper_authors.type: user
paper_authors.user: ekharitonov
paper_authors._id: 64642490823c7f880550a728
paper_authors.name: Neil Zeghidour
paper_authors._id: 64642490823c7f880550a729
paper_authors.name: Marco Tagliasacchi
paper_title: SoundStorm: Efficient Parallel Audio Generation
paper_summary: We present SoundStorm, a model for efficient, non-autoregressive audio
  generation. SoundStorm receives as input the semantic tokens of AudioLM, and
  relies on bidirectional attention and confidence-based parallel decoding to
  generate the tokens of a neural audio codec. Compared to the autoregressive
  generation approach of AudioLM, our model produces audio of the same quality
  and with higher consistency in voice and acoustic conditions, while being two
  orders of magnitude faster. SoundStorm generates 30 seconds of audio in 0.5
  seconds on a TPU-v4. We demonstrate the ability of our model to scale audio
  generation to longer sequences by synthesizing high-quality, natural dialogue
  segments, given a transcript annotated with speaker turns and a short prompt
  with the speakers' voices.
paper_ai_keywords: bidirectional attention confidence-based parallel decoding neural audio codec autoregressive generation TPU-v4 dialogue synthesis speaker turns prompt-based generation voice consistency acoustic conditions