← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-industry.146 · 记录 #1
ResoDiff-44k: High-Fidelity Cross-Lingual Speech and Singing Synthesis via Discrete Diffusion
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:06
摘要
While large-scale generative speech models have achieved remarkable semantic coherence, industrial deployment remains constrained by a fidelity ceiling typically capped at lower sampling rates. A fundamental limitation is the reliance on intermediate mel-spectrograms, a low-dimensional bottleneck that discards phase and high-frequency information, causing artifacts in expressive scenarios like singing. In this work, we introduce ResoDiff-44k, a production-grade generative foundation model designed for cinema-quality, 44.1kHz audio synthesis. Departing from standard masked audio modeling and mel-spectrogram inversion, ResoDiff-44k leverages Discrete Diffusion over a pure Descript Audio Codec latent space. We pre-train ResoDiff-44k on a massive 150K -hour multilingual dataset to establish a robust acoustic prior, followed by targeted fine-tuning on a curated regional mixed-language and singing corpus. Our experiments demonstrate that replacing the standard prediction head with a discrete diffusion trajectory significantly reduces misalignment in long sequences. We report a double-blind subjective evaluation showing that ResoDiff-44k achieves a 4.6 Mean Opinion Score in 44.1kHz singing synthesis and a 71% reduction in character error rate on regional mixed-language prompts compared to strong baselines. The proposed pipeline offers a viable path for deploying high-fidelity, culturally adaptive conversational agents.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026-07 · 最新观测版本
ResoDiff-44k: High-Fidelity Cross-Lingual Speech and Singing Synthesis via Discrete Diffusion
While large-scale generative speech models have achieved remarkable semantic coherence, industrial deployment remains constrained by a fidelity ceiling typically capped at lower sampling rates. A fundamental limitation is the reliance on intermediate mel-spectrograms, a low-dimensional bottleneck that discards phase and high-frequency information, causing artifacts in expressive scenarios like singing. In this work, we introduce ResoDiff-44k, a production-grade generative foundation model designed for cinema-quality, 44.1kHz audio synthesis. Departing from standard masked audio modeling and mel-spectrogram inversion, ResoDiff-44k leverages Discrete Diffusion over a pure Descript Audio Codec latent space. We pre-train ResoDiff-44k on a massive 150K -hour multilingual dataset to establish a robust acoustic prior, followed by targeted fine-tuning on a curated regional mixed-language and singing corpus. Our experiments demonstrate that replacing the standard prediction head with a discrete diffusion trajectory significantly reduces misalignment in long sequences. We report a double-blind subjective evaluation showing that ResoDiff-44k achieves a 4.6 Mean Opinion Score in 44.1kHz singing synthesis and a 71% reduction in character error rate on regional mixed-language prompts compared to strong baselines. The proposed pipeline offers a viable path for deploying high-fidelity, culturally adaptive conversational agents.
查看此版本解读 →查看此版本原文 ↗