title: TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow
    Matching and Clap-Ranked Preference Optimization
summary: We introduce TangoFlux, an efficient Text-to-Audio (TTA) generative model
  with 515M parameters, capable of generating up to 30 seconds of 44.1kHz audio
  in just 3.7 seconds on a single A40 GPU. A key challenge in aligning TTA models
  lies in the difficulty of creating preference pairs, as TTA lacks structured
  mechanisms like verifiable rewards or gold-standard answers available for Large
  Language Models (LLMs). To address this, we propose CLAP-Ranked Preference
  Optimization (CRPO), a novel framework that iteratively generates and optimizes
  preference data to enhance TTA alignment. We demonstrate that the audio
  preference dataset generated using CRPO outperforms existing alternatives. With
  this framework, TangoFlux achieves state-of-the-art performance across both
  objective and subjective benchmarks. We open source all code and models to
  support further research in TTA generation.
paper_authors._id: 67735d5d4a4e0c546461a90d
paper_authors.name: Chia-Yu Hung
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-01T20:13:06.951Z
paper_authors._id: 62864cdf011a13fe5f84d746
paper_authors.avatarUrl: /avatars/d01d3f2261572a625949e6b9dee2aa47.svg
paper_authors.fullname: Hung Chia Yu
paper_authors.type: user
paper_authors.user: hungchiayu
paper_authors._id: 67735d5d4a4e0c546461a90e
paper_authors.name: Navonil Majumder
paper_authors._id: 67735d5d4a4e0c546461a90f
paper_authors.name: Zhifeng Kong
paper_authors._id: 67735d5d4a4e0c546461a910
paper_authors.name: Ambuj Mehrish
paper_authors._id: 67735d5d4a4e0c546461a911
paper_authors.name: Rafael Valle
paper_authors._id: 67735d5d4a4e0c546461a912
paper_authors.name: Bryan Catanzaro
paper_authors._id: 67735d5d4a4e0c546461a913
paper_authors.name: Soujanya Poria
paper_title: TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow
    Matching and Clap-Ranked Preference Optimization
paper_summary: We introduce TangoFlux, an efficient Text-to-Audio (TTA) generative model
  with 515M parameters, capable of generating up to 30 seconds of 44.1kHz audio
  in just 3.7 seconds on a single A40 GPU. A key challenge in aligning TTA models
  lies in the difficulty of creating preference pairs, as TTA lacks structured
  mechanisms like verifiable rewards or gold-standard answers available for Large
  Language Models (LLMs). To address this, we propose CLAP-Ranked Preference
  Optimization (CRPO), a novel framework that iteratively generates and optimizes
  preference data to enhance TTA alignment. We demonstrate that the audio
  preference dataset generated using CRPO outperforms existing alternatives. With
  this framework, TangoFlux achieves state-of-the-art performance across both
  objective and subjective benchmarks. We open source all code and models to
  support further research in TTA generation.
paper_ai_keywords: Text-to-Audio (TTA) TangoFlux Text-to-Audio (TTA) TTAs Large Language Models (LLMs) CLAP-Ranked Preference Optimization (CRPO) preference data audio generation