Tango 2：通过直接偏好优化对齐基于扩散的文本到音频生成
Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization
AI总结：
本文提出Tango 2，通过合成偏好数据集和扩散DPO损失微调Tango模型，改进文本到音频生成中概念存在与时间顺序，在自动和人工评估中优于Tango和AudioLDM2。
摘要：
生成式多模态内容在内容创作领域日益普及，因为它有潜力让艺术家和媒体人员通过快速将想法变为现实来创建预制作模型。从文本提示生成音频是音乐和电影行业此类过程中的一个重要方面。许多最近的基于扩散的文本到音频模型专注于在大量提示-音频对数据集上训练日益复杂的扩散模型。这些模型并未明确关注输出音频中概念或事件的存在及其相对于输入提示的时间顺序。我们的假设是，关注音频生成的这些方面可以在数据有限的情况下提高音频生成性能。因此，在这项工作中，我们使用现有的文本到音频模型Tango，合成创建了一个偏好数据集，其中每个提示都有一个获胜的音频输出和一些失败的音频输出供扩散模型学习。理论上，失败的输出缺少提示中的某些概念或顺序不正确。我们使用扩散DPO（直接偏好优化）损失在我们的偏好数据集上微调公开可用的Tango文本到音频模型，并表明它在自动和手动评估指标方面都优于Tango和AudioLDM2的音频输出。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
