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English(EN) SwiftAudio: Data-Efficient Caption-Only Distillation for One-Step Text-to-Audio Diffusion-based Generation

SwiftAudio 使用仅字幕蒸馏实现高效文本到音频生成

研究人员开发了 SwiftAudio,一种新颖的一步式文本到音频扩散模型,在蒸馏过程中无需配对音频数据。该方法仅使用文本字幕和预训练的扩散教师模型,将数据需求显著降低到约 45,000 个字幕。SwiftAudio 在一步式方法中取得了最先进的结果,并缩小了与更复杂的步进式扩散系统的性能差距。 AI

影响 该方法可能导致更高效的文本到音频模型训练,减少对大型配对音频数据集的依赖。

排序理由 该集群包含一篇详细介绍文本到音频生成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

SwiftAudio 使用仅字幕蒸馏实现高效文本到音频生成

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该集群包含一篇详细介绍文本到音频生成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Binh Mai, Tran Quoc Bao Le, Hung Dinh, Cong Tran ·

    SwiftAudio:数据高效的仅文本蒸馏,用于一步式文本到音频的扩散生成

    arXiv:2606.31259v1 Announce Type: cross Abstract: Diffusion-based text-to-audio (TTA) models achieve impressive synthesis quality but suffer from high inference latency due to iterative multi-step denoising. Existing one-step approaches alleviate this issue but still rely on pair…