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“Waveglow: a flow-based generative network for speech synthesis,”
Open accessAbstract
In this paper we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms. WaveGlow combines insights from Glow [1] and WaveNet [2] in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable. Our PyTorch implementation produces audio samples at a rate of more than 500 kHz on an NVIDIA V100 GPU. Mean Opinion Scores show that it delivers audio quality as good as the best publicly available WaveNet implementation. All code will be made publicly available online [3].
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“When the sampling frequency (sampling rate) of the audio deepfakes is less than 16 kHz, perceived speech quality of audio deepfakes drops significantly, and the higher sampling rate may give way to higher audio quality (Prenger et al., 2019).”
From Audio deepfakes: A survey (2023) · cites this paper as Prenger et al., 2019
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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
Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions
Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Zhang, Y., Wang, Y., Skerrv-Ryan, R., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461368 · Cited by 2,791
Variational Inference with Normalizing FlowsPreprint
Rezende, D. J., & Mohamed, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.05770 · Cited by 2,583
Signal estimation from modified short-time Fourier transform
Griffin, D. W., & Lim, J. S. (1984) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1984.1164317 · Cited by 2,085
Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2016) · arXiv · Cited by 2,067
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural NetworksPreprint
Salimans, T., & Kingma, D. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.07868 · Cited by 1,551
NICE: Non-linear Independent Components Estimation
Dinh, L., Krueger, D., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1410.8516 · Cited by 1,493
Improving Variational Inference with Inverse Autoregressive FlowPreprint
Kingma, D., Salimans, T., Józefowicz, R., Chen, X., Sutskever, I., & Welling, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04934 · Cited by 1,168
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, Ł., Shazeer, N., Ku, A., & Tran, D. (2018) · arXiv · doi:10.48550/arxiv.1802.05751 · Cited by 1,085
Research works printed but not linked: 2
- ito, 2017, The LJ speech dataset
- yamamoto, 2018, Wavenet vocoder
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Chen, C., Gao, R., Calamia, P. T., & Grauman, K. L. (2022) · 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/cvpr52688.2022.01829 · Cited by 48
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SingGAN: Generative Adversarial Network For High-Fidelity Singing Voice Generation
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Serrà, J., Pascual, S., & Segura, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.00794 · Cited by 46
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Sainburg, T., & Gentner, T. Q. (2021) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2021.811737 · Cited by 48
JDI-T: Jointly Trained Duration Informed Transformer for Text-To-Speech without Explicit Alignment
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iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compression
Zhang, S., Zhang, C., Kang, N., & Li, Z. (2021) · 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/cvpr46437.2021.00068 · Cited by 44
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Wang, X., & Yamagishi, J. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094779 · Cited by 41
Universal Neural Vocoding with Parallel Wavenet
Jiao, Y., Gabryś, A., Tinchev, G., Putrycz, B., Korzekwa, D., & Klimkov, V. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414444 · Cited by 40
Liz-López, H., Keïta, M., Ahmed, A. T., Hadid, A., Huertas‐Tato, J., & Camacho, D. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102103 · Cited by 46
Many-to-Many Voice Transformer Network
Kameoka, H., Huang, W.-C., Tanaka, K., Kaneko, T., Hojo, N., & Toda, T. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3047262 · Cited by 33
A Spectral Energy Distance for Parallel Speech SynthesisPreprint
Gritsenko, A. A., Salimans, T., van den Berg, R., Snoek, J., & Kalchbrenner, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.01160 · Cited by 40
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Ardizzone, L., Kruse, J., Lüth, C. T., Bracher, N., Rother, C., & Köthe, U. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-71278-5_27 · Cited by 39
Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis
Yang, K., Markovic, D., Krenn, S., Agrawal, V., & Richard, A. (2022) · 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/cvpr52688.2022.00805 · Cited by 39
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Qu, L., Weber, C., & Wermter, S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3191677 · Cited by 38
A review of differentiable digital signal processing for music and speech synthesis
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Audio representations for deep learning in sound synthesis: A review
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Ai, Y., & Ling, Z.-H. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.2970241 · Cited by 35
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A comprehensive overview of Generative AI (GAI): Technologies, applications, and challenges
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RobuTrans: A Robust Transformer-Based Text-to-Speech Model
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Azizah, K., Adriani, M., & Jatmiko, W. (2020) · IEEE Access · doi:10.1109/access.2020.3027619 · Cited by 33
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Xue, L., Pan, S., He, L., Xie, L., & Soong, F. K. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.03.005 · Cited by 31
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Wang, X., Delgado, H., Tak, H., Jung, J.-W., Shim, H.-J., Todisco, M., Kukanov, I., Liu, X., Sahidullah, M., Kinnunen, T., et al. (2026) · Computer Speech & Language · doi:10.1016/j.csl.2025.101825 · Cited by 30
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Zhang, X., Xue, L., Gu, Y., Wang, Y., Li, J., He, H., Wang, C., Liu, S., Chen, X., Zhang, J. S., et al. (2024) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt61566.2024.10832255 · Cited by 26
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Puchtler, P., Wirth, J., & Peinl, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87626-5_15 · Cited by 25
Prosody-TTS: An End-to-End Speech Synthesis System with Prosody Control
Pamisetty, G., & Murty, K. S. R. (2022) · Circuits Systems and Signal Processing · doi:10.1007/s00034-022-02126-z · Cited by 25
EditSpeech: A Text Based Speech Editing System Using Partial Inference and Bidirectional Fusion
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Learning selection channels for image steganalysis in spatial domain
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Tan, D., & Lee, T. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1129 · Cited by 24
Wu, X., Wellington, S., Fu, Z., & Zhang, D. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad593a · Cited by 24
Parametric Resynthesis With Neural Vocoders
Maiti, S., & Mandel, M. (2019) · IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) · doi:10.1109/waspaa.2019.8937165 · Cited by 21
WavThruVec: Latent speech representation as intermediate features for neural speech synthesis
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Jain, R., Yiwere, M. Y., Bigioi, D., Corcoran, P., & Cucu, H. (2022) · IEEE Access · doi:10.1109/access.2022.3170836 · Cited by 26
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Deep Long Audio InpaintingPreprint
Chang, Y.-L., Lee, K.-Y., Wu, P.-Y., Lee, H.-Y., & Hsu, W. H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.06476 · Cited by 23
An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep LearningPreprint
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NU-GAN: High resolution neural upsampling with GANPreprint
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Metadata
- Authors
- Prenger, R., Valle, R., & Catanzaro, B.
- Affiliations
- Nvidia (United States) (US)
- ORCID
- Catanzaro, Bryan
- Venue
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 3617-3621
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2898847420
- Cited by
- 1,004
- References
- 22
- Citations by year
- 2026: 39 · 2025: 86 · 2024: 111 · 2023: 156 · 2022: 161 · 2021: 220 · 2020: 169 · 2019: 58
- FWCI
- 58.16
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Signal Processing › Music and Audio Processing
- Keywords
- WaveGlow, speech synthesis, flow-based generative models, glow, WaveNet, audio generation, likelihood maximization, high-quality audio
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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