LiteFocus: Accelerated Diffusion Inference for Long Audio Synthesis

P. Taylor, Text-to-speech synthesis . Cambridge university press, 2009

2009

Earlier work this paper cites.

F. R. Sharma and S. G. Wasson, “Speech recognition and synthesis tool: assistive technology for physically disabled persons,” International Journal of Computer Science and Telecommunications , vol. 3, no. 4, pp. 86–91, 2012

2012

Earlier work this paper cites.

D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013

Original

2013

Earlier work this paper cites.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 . Springer, 2015, pp. 234–241

2015

Earlier work this paper cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017

2017

Earlier work this paper cites.

A. Van Den Oord, O. Vinyals et al. , “Neural discrete representation learning,” Advances in neural information processing systems , vol. 30, 2017

2017

Earlier work this paper cites.

H.-W. Dong, W.-Y. Hsiao, L.-C. Yang, and Y.-H. Yang, “Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 32, no. 1, 2018

2018

Earlier work this paper cites.

K. Kilgour, M. Zuluaga, D. Roblek, and M. Sharifi, “Fr \ \backslash ’echet audio distance: A metric for evaluating music enhancement algorithms,” arXiv preprint arXiv:1812.08466 , 2018

Original

2018

Earlier work this paper cites.

C. D. Kim, B. Kim, H. Lee, and G. Kim, “Audiocaps: Generating captions for audios in the wild,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , 2019, pp. 119–132

2019

Earlier work this paper cites.

Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro, “Diffwave: A versatile diffusion model for audio synthesis,” arXiv preprint arXiv:2009.09761 , 2020

Original

2020

Earlier work this paper cites.

M. Jeong, H. Kim, S. J. Cheon, B. J. Choi, and N. S. Kim, “Diff-tts: A denoising diffusion model for text-to-speech,” arXiv preprint arXiv:2104.01409 , 2021

Original

2021

Earlier work this paper cites.

F. Kreuk, G. Synnaeve, A. Polyak, U. Singer, A. Défossez, J. Copet, D. Parikh, Y. Taigman, and Y. Adi, “Audiogen: Textually guided audio generation,” arXiv preprint arXiv:2209.15352 , 2022

Original

2022

Earlier work this paper cites.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695

2022

Cited alongside, same era.

R. Huang, Z. Zhao, H. Liu, J. Liu, C. Cui, and Y. Ren, “Prodiff: Progressive fast diffusion model for high-quality text-to-speech,” in Proceedings of the 30th ACM International Conference on Multimedia , 2022, pp. 2595–2605

2022

Cited alongside, same era.

I. Vovk, T. Sadekova, V. Gogoryan, V. Popov, M. A. Kudinov, and J. Wei, “Fast grad-tts: Towards efficient diffusion-based speech generation on cpu.” in Interspeech , 2022, pp. 838–842

2022

Cited alongside, same era.

C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in neural information processing systems , vol. 35, pp. 36 479–36 494, 2022

Y. Bai, T. Dang, D. Tran, K. Koishida, and S. Sojoudi, “Accelerating diffusion-based text-to-audio generation with consistency distillation,” arXiv preprint arXiv:2309.10740 , 2023

Original

2023

Later among the works it cites.

J. Chen, X. Song, Z. Peng, B. Zhang, F. Pan, and Z. Wu, “Lightgrad: Lightweight diffusion probabilistic model for text-to-speech,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5

2023

Later among the works it cites.

Y. Guo, C. Du, Z. Ma, X. Chen, and K. Yu, “Voiceflow: Efficient text-to-speech with rectified flow matching,” arXiv preprint arXiv:2309.05027 , 2023

Original

2023

Later among the works it cites.

N. Ruiz, Y. Li, V. Jampani, Y. Pritch, M. Rubinstein, and K. Aberman, “Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 22 500–22 510