title: Tango 2: Aligning Diffusion-based Text-to-Audio Generations through
    Direct Preference Optimization
summary: Generative multimodal content is increasingly prevalent in much of the
  content creation arena, as it has the potential to allow artists and media
  personnel to create pre-production mockups by quickly bringing their ideas to
  life. The generation of audio from text prompts is an important aspect of such
  processes in the music and film industry. Many of the recent diffusion-based
  text-to-audio models focus on training increasingly sophisticated diffusion
  models on a large set of datasets of prompt-audio pairs. These models do not
  explicitly focus on the presence of concepts or events and their temporal
  ordering in the output audio with respect to the input prompt. Our hypothesis
  is focusing on how these aspects of audio generation could improve audio
  generation performance in the presence of limited data. As such, in this work,
  using an existing text-to-audio model Tango, we synthetically create a
  preference dataset where each prompt has a winner audio output and some loser
  audio outputs for the diffusion model to learn from. The loser outputs, in
  theory, have some concepts from the prompt missing or in an incorrect order. We
  fine-tune the publicly available Tango text-to-audio model using diffusion-DPO
  (direct preference optimization) loss on our preference dataset and show that
  it leads to improved audio output over Tango and AudioLDM2, in terms of both
  automatic- and manual-evaluation metrics.
paper_authors._id: 661de5560a9d678b6f653cea
paper_authors.name: Navonil Majumder
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paper_authors.fullname: Navonil Majumder
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paper_authors.name: Chia-Yu Hung
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paper_authors.fullname: Hung Chia Yu
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paper_authors.name: Deepanway Ghosal
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paper_authors.fullname: Deepanway
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paper_authors.user: deepanway
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paper_authors.name: Wei-Ning Hsu
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paper_authors.fullname: Wei-Ning Hsu
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paper_authors.user: wnhsu
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paper_authors.name: Rada Mihalcea
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paper_authors._id: 64153d8f95fb6f824b22d960
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paper_authors.fullname: Rada Mihalcea
paper_authors.type: user
paper_authors.user: mihalcea
paper_authors._id: 661de5560a9d678b6f653cef
paper_authors.name: Soujanya Poria
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-16T12:44:26.953Z
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paper_authors.fullname: Soujanya Poria
paper_authors.type: user
paper_authors.user: soujanyaporia
paper_title: Tango 2: Aligning Diffusion-based Text-to-Audio Generations through
    Direct Preference Optimization
paper_summary: Generative multimodal content is increasingly prevalent in much of the
  content creation arena, as it has the potential to allow artists and media
  personnel to create pre-production mockups by quickly bringing their ideas to
  life. The generation of audio from text prompts is an important aspect of such
  processes in the music and film industry. Many of the recent diffusion-based
  text-to-audio models focus on training increasingly sophisticated diffusion
  models on a large set of datasets of prompt-audio pairs. These models do not
  explicitly focus on the presence of concepts or events and their temporal
  ordering in the output audio with respect to the input prompt. Our hypothesis
  is focusing on how these aspects of audio generation could improve audio
  generation performance in the presence of limited data. As such, in this work,
  using an existing text-to-audio model Tango, we synthetically create a
  preference dataset where each prompt has a winner audio output and some loser
  audio outputs for the diffusion model to learn from. The loser outputs, in
  theory, have some concepts from the prompt missing or in an incorrect order. We
  fine-tune the publicly available Tango text-to-audio model using diffusion-DPO
  (direct preference optimization) loss on our preference dataset and show that
  it leads to improved audio output over Tango and AudioLDM2, in terms of both
  automatic- and manual-evaluation metrics.
paper_ai_summary: Improving text-to-audio generation by fine-tuning the Tango model using diffusion-DPO on a preference dataset enhances audio quality in terms of automatic and manual evaluations.
paper_ai_keywords: diffusion models preference dataset text-to-audio models Tango diffusion-DPO direct preference optimization audio generation automatic-evaluation metrics manual-evaluation metrics AudioLDM2