πŸ“„
FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis paper by Rongjie Huang, Max W. Y. Lam, J. Wang, Dan Su, Dong Yu, Yi Ren, Zhou Zhao
⭐ 59.3
πŸ“„
Paper

FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis

by Rongjie Huang, Max W. Y. Lam, J. Wang, Dan Su, Dong Yu, Yi Ren, Zhou Zhao 2204.09934
Free2AITools Nexus Index
59.3
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 89
P: Popularity 67
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hindered their applications to speech synthesis. This paper proposes FastDiff, a fast conditional diffusion model for high-quality speech synthesis. FastDiff employs a stack of time-aware location-variable convolutions of diverse receptive field patterns to efficiently model long-term time dependencies with adaptive cond...

Source β†’
Semantic Scholar 212 Citations
Paper Information Summary
Entity Passport
Registry ID 2204.09934
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{arxiv_2204_09934,
  author = {Rongjie Huang, Max W. Y. Lam, J. Wang, Dan Su, Dong Yu, Yi Ren, Zhou Zhao},
  title = {FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2204.09934}},
  note = {Accessed via Free2AITools.}
}
APA Style
Rongjie Huang, Max W. Y. Lam, J. Wang, Dan Su, Dong Yu, Yi Ren, Zhou Zhao. (2026). FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis [Paper]. Free2AITools. https://arxiv.org/abs/2204.09934

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Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 89
Popularity (P) 67
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis: Authority (A:89), Popularity (P:67), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

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HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
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πŸ“ Executive Summary

"Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hindered their applications to speech synthesis. This paper proposes FastDiff, a fast conditional diffusion model for high-quality speech synthesis. FastDiff employs a stack of time-aware location-variable convolutions of diverse receptive field patterns to efficiently model long-term time dependencies with adaptive cond..."

❝ Cite Node 

@article{Huang2026FastDiff:,
  title={FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis},
  author={Rongjie Huang and Max W. Y. Lam and J. Wang and Dan Su and Dong Yu and Yi Ren and Zhou Zhao},
  journal={arXiv preprint arXiv:2204.09934},
  year={2026}
}

πŸ‘₯ Collaborating Minds

Rongjie Huang Max W. Y. Lam J. Wang Dan Su Dong Yu Yi Ren Zhou Zhao

πŸ”— Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

πŸ“Š Research Signals

πŸ“ˆ212CitationsSemantic Scholar
πŸ›οΈ89AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈtext generationField

🏷️ Research Topics

speech models
πŸ“¦Data Source: semantic_scholar
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Open Metadata

πŸ†” Identity & Source

id
2204.09934
slug
2204.09934
source
semantic_scholar
author
Rongjie Huang, Max W. Y. Lam, J. Wang, Dan Su, Dong Yu, Yi Ren, Zhou Zhao
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

πŸ“Š Engagement & Metrics

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citations
212

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