TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow
  Matching and Clap-Ranked Preference Optimization
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.
67735d5d4a4e0c546461a90d
Chia-Yu Hung
claimed_verified
2025-01-01T20:13:06.951Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/62864cdf011a13fe5f84d746/5yPYgAtGJTaUq4fHhh9Pr.jpeg
Hung Chia Yu
user
hungchiayu
67735d5d4a4e0c546461a90e
Navonil Majumder
67735d5d4a4e0c546461a90f
Zhifeng Kong
67735d5d4a4e0c546461a910
Ambuj Mehrish
67735d5d4a4e0c546461a911
Rafael Valle
67735d5d4a4e0c546461a912
Bryan Catanzaro
67735d5d4a4e0c546461a913
Soujanya Poria
TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow
  Matching and Clap-Ranked Preference Optimization
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.
TangoFlux is an efficient Text-to-Audio (TTA) generative model that uses CLAP-Ranked Preference Optimization (CRPO) to enhance audio alignment and achieve state-of-the-art performance.
Text-to-Audio generative model enhancement CLAP-Ranked Preference Optimization preference data alignment objective benchmarks subjective benchmarks