FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis
Open accessAbstract
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 conditions. A noise schedule predictor is also adopted to reduce the sampling steps without sacrificing the generation quality. Based on FastDiff, we design an end-to-end text-to-speech synthesizer, FastDiff-TTS, which generates high-fidelity speech waveforms without any intermediate feature (e.g., Mel-spectrogram). Our evaluation of FastDiff demonstrates the state-of-the-art results with higher-quality (MOS 4.28) speech samples. Also, FastDiff enables a sampling speed of 58x faster than real-time on a V100 GPU, making diffusion models practically applicable to speech synthesis deployment for the first time. We further show that FastDiff generalized well to the mel-spectrogram inversion of unseen speakers, and FastDiff-TTS outperformed other competing methods in end-to-end text-to-speech synthesis. Audio samples are available at https://FastDiff.github.io/.
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Recent research has continued to refine speech synthesis technologies, enabling synthesised speech to sound increasingly natural while improving synthesis speed.
“In recent years, research [44,45,46,47,48,49,50,51] has continued to refine these technologies, enabling synthesised speech to sound increasingly natural while also improving synthesis speed.”
From Audio Deepfake Detection: What Has Been Achieved and What Lies Ahead (2025) · cites this paper as [45]
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works
Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712 · see citing works
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911 · see citing works
Generative Adversarial Networks: An Overview
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540 · see citing works
Rix, A. W., Beerends, J. G., Hollier, M. P., & Hekstra, A. P. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2001.941023 · Cited by 3,248 · see citing works
Denoising Diffusion Implicit Models
Song, J., Meng, C., & Ermon, S. (2020) · arXiv · doi:10.48550/arxiv.2010.02502 · Cited by 2,507 · see citing works
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech SynthesisPreprint
Kong, J., Kim, J., & Bae, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.05646 · Cited by 1,188 · see citing works
A short-time objective intelligibility measure for time-frequency weighted noisy speech
Taal, C., Hendriks, R. C., Heusdens, R. R., & Jensen, J. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5495701 · Cited by 1,111 · see citing works
“Waveglow: a flow-based generative network for speech synthesis,”
Prenger, R., Valle, R., & Catanzaro, B. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683143 · Cited by 1,004 · see citing works
Tackling the Generative Learning Trilemma with Denoising Diffusion GANsPreprint
Xiao, Z., Kreis, K., & Vahdat, A. (2021) · International Conference on Learning Representations · doi:10.48550/arxiv.2112.07804 · Cited by 806 · see citing works
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
Ren, Y., Hu, C., Tan, X., Qin, T., Zhao, S., Zhao, Z., & Liu, T.-Y. (2020) · arXiv · doi:10.48550/arxiv.2006.04558 · Cited by 580 · see citing works
Self-citationDiffWave: A Versatile Diffusion Model for Audio Synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., & Catanzaro, B. (2020) · arXiv · doi:10.48550/arxiv.2009.09761 · Cited by 569 · see citing works
WaveGrad: Estimating Gradients for Waveform GenerationPreprint
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., & Chan, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.00713 · Cited by 410 · see citing works
Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-SpeechPreprint
Kim, J., Kong, J., & Son, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06103 · Cited by 389 · see citing works
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment SearchPreprint
Kim, J., Kim, S., Kong, J., & Yoon, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.11129 · Cited by 286 · see citing works
DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism
Liu, J., Li, C., Ren, Y., Chen, F., & Zhao, Z. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21350 · Cited by 236 · see citing works
Self-citationEnd-to-End Adversarial Text-to-SpeechPreprint
Donahue, J., Dieleman, S., Binkowski, M., Elsen, E., & Simonyan, K. (2020) · International Conference on Learning Representations · doi:10.48550/arxiv.2006.03575 · Cited by 200 · see citing works
Jang, W., Lim, D., Yoon, J., Kim, B., & Kim, J. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1016 · Cited by 110 · see citing works
Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus
Huang, R., Chen, F., Ren, Y., Liu, J., Cui, C., & Zhao, Z. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475437 · Cited by 76 · see citing works
Self-citationWaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., Dehak, N., & Chan, W. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1897 · Cited by 46 · see citing works
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech SynthesisPreprint
Lam, M. W. Y., Wang, J., Su, D., & Yu, D. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.13508 · Cited by 43 · see citing works
Self-citationDiffSinger: Singing Voice Synthesis via Shallow Diffusion MechanismPreprint
Liu, J., Li, C., Ren, Y., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.02446 · Cited by 34 · see citing works
Self-citationLVCNet: Efficient Condition-Dependent Modeling Network for Waveform Generation
Zeng, Z., Wang, J., Cheng, N., & Xiao, J. Z. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414710 · Cited by 16 · see citing works
Self-citationWaveGrad 2: Iterative Refinement for Text-to-Speech SynthesisPreprint
Chen, N., Zhang, Y., Zen, H., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09660 · Cited by 5 · see citing works
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Research works printed but not linked: 2
- Dhariwal and Nichol, 2021] Prafulla Dhariwal and Alexan- der Nichol. Diffusion models beat gans on image synthe- sis. In Proc. of NeurIPS, volume 34, 2021.
- Yi Ren, Jinglin Liu, and Zhou Zhao. Por- taspeech: Portable and high-quality generative text-to- speech. In Proc. of NeurIPS, volume 34, 2021.
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Bansal, A., Chu, H.-M., Schwarzschild, A., Sengupta, S., Goldblum, M., Geiping, J., & Goldstein, T. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00091 · Cited by 173 · see citing works
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Chou, S.-Y., Chen, P., & Ho, T.-Y. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00391 · Cited by 88 · see citing works
HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models
Pang, L., Rui, X., Cui, L., Hóngzhōng, W., Meng, D., & Cao, X. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00290 · Cited by 67 · see citing works
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Li, T., Hu, C., Cong, J., Zhu, X., Li, J., Tian, Q., Wang, Y., & Xie, L. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3313413 · Cited by 24 · see citing works
Cheng, X., Jin, T., Huang, R., Li, L., Wang, L., Wang, Z., Wang, Y., Liu, H., Yin, A., & Zhao, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01442 · Cited by 24 · see citing works
Self-citationXiong, B., Chen, W., Li, H., Niu, Y., Zeng, N., Gan, Z., & Xu, Y. N. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3450124 · Cited by 22 · see citing works
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Foti, P., & Brendel, A. (2025) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco63237.2025.11226135 · Cited by 1 · see citing works
Parasite: A Steganography-based Backdoor Attack Framework for Diffusion Models
Chen, J., Pan, Y., Wang, L., & Du, Y. (2025) · International Conference on Communication and Network Security (ICCNS) · doi:10.1145/3789456.3789470 · Cited by 1 · see citing works
Huang, W., & Zhan, F. (2023) · Mathematics · doi:10.3390/math11153345 · Cited by 1 · see citing works
A Synthetic Corpus Generation Method for Neural Vocoder Training
Wang, Z., Liu, P., Chen, J., Li, S., Bai, J., He, G., Wu, Z., & Meng, H. M. L. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094786
Bi-LSTM Model for Speech Recognition
Sohal, J. (2023) · International Conference on Data Science and Network Security (ICDSNS) · doi:10.1109/icdsns58469.2023.10245403
Neutral TTS Female Voice Corpus in Brazilian Portuguese
Leite, P. H. L., Hoyle, E., Antelo, Á., Kruszielski, L. F., & Biscainho, L. W. P. (2023) · Anais de XXXV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais · doi:10.14209/sbrt.2023.1570917697
SWave: Improving Vocoder Efficiency by Straightening the Waveform Generation Path
Liu, P., Zhou, J., Tian, X., & Lin, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78172-8_26
Li, J., Tan, Y., Fan, S., Li, F., Liu, X., Liu, R., Li, Y., & Meng, W. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3516352
PTQ4ADM: Post-Training Quantization for Efficient Text Conditional Audio Diffusion Models
Vora, J., Krishnan, A., Bouacida, N., Shankar, P. R. V., & Mohapatra, P. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890341
MixDiff-TTS: Mixture Alignment and Diffusion Model for Text-to-Speech
Long, Y., Yang, K., Ma, Y., & Yang, Y. (2025) · Applied Sciences · doi:10.3390/app15094810
DiffAugment: Diffusion Based Long-Tailed Visual Relationship Recognition
Gupta, P., Nguyen, T. T., Dhall, A., Hayat, M., Le, T., & Do, T.-T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91907-7_3
Backdoor Risks in Diffusion Models
Chen, P., & Liu, S. (2025) · Springer eBooks · doi:10.1007/978-3-031-76770-8_17
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Matsumoto, T., MIURA, T., Okamura, S., Zolfaghari, B., & Yanai, N. (2025) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2024icp0005
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Kumar, S., Trivedi, N., Purohit, R. M., & Patil, H. A. (2025) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc65261.2025.11249045
SynSpeech: A Dataset and Benchmark for Fake Speech Detection
Qiu, Q., Li, Y., Lee, L., Wang, F. L., & Yang, Z. (2025) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3743093.3771030
Invisible Stealthy Backdoor Attack on Diffusion Models
Chen, Z., Luo, Q., Zhou, X., & Ma, J. (2025) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11322912
CacheNoise: Accomplishing More Denoising Steps with Less Noise
Liu, F., Li, L., Qiu, Q., Jin, Y., Hu, X., Guo, J., & Cheng, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5693-9_32
Bad-PoseDiff: Pose-Guided Backdoor Triggering in Diffusion Models
Chen, J., Pan, Y. C., Wang, L., & Du, Y. (2025) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom66490.2025.00303
UniDiff-TTS: Aligner-Free Diffusion Speech Synthesis with Duration Guidance
Chen, Y., Qi, Y., & Chen, W. (2025) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice68195.2025.11382398
AC-Refiner: Efficient Arithmetic Circuit Optimization Using Conditional Diffusion Models
Xue, C., Li, K., Zhang, J., Ren, Y., Shi, Z., Zhang, C., Lin, Y., Zhang, L., Xu, Q., & Sun, G. (2026) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/asp-dac66049.2026.11420458
Self-citationDiffusion-Based Brain-to-Speech Synthesis: Decoding Intracranial EEG into Natural Speech
Akaaboune, F. E., & Al-Radhi, M. S. (2026) · Journal of Physics Conference Series · doi:10.1088/1742-6596/3190/1/012008
UniDiff-TTS: Aligner-Free Diffusion Speech Synthesis with Duration Guidance
Chen, Y., Qi, Y., & Chen, W. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461388
Zhou, W., Okamoto, T., Ohtani, Y., Sakti, S., & Kawai, H. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461371
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Fujita, N., Nakamura, H., & Itakura, K. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462589
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Kong, W., Wu, J., & Zhao, W. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00042
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Zhou, L., Chen, Y., & Wang, J. (2026) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2026.104889
Self-citationHong, Z., Xia, Y., & Wang, H. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134683
Li, R., Shen, L., Ma, L., Lu, H., Zhang, H., Feng, Y., Liu, J., Deng, Y., Qian, K., Hu, B., et al. (2026) · Circuits Systems and Signal Processing · doi:10.1007/s00034-026-03720-1
Kohli, C., Chug, A., & Singh, A. P. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01257-9
BadRSSD: Backdoor Attacks on Regularized Self-Supervised Diffusion Models
Wang, J., Zhang, Y., Hasan, M. M., Lei, X., Zhang, J., Zhu, J., Wu, Q., & Zhao, D. (2026) · doi:10.1109/cvprf71909.2026.00072
Self-citation
- Authors
- Huang, R., Lam, M. W. Y., Wang, J., Su, D., Yu, D., Ren, Y., & Zhao, Z.
- Affiliations
- Zhejiang University (CN)
- Tencent (China) (CN)
- Venue
- Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
- Published
- 2022-07
- Pages
- 4157-4163
- OpenAlex
- W4285605725
- Cited by
- 96
- References
- 22
- Citations by year
- 2026: 14 · 2025: 34 · 2024: 27 · 2023: 21
- FWCI
- 11.34
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Zhejiang Province
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- conditional diffusion models, speech synthesis, text-to-speech, denoising diffusion probabilistic models, high-speed sampling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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