{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/parallel-wavenet-fast-high-fidelity-speech","title":"Parallel WaveNet: Fast High-Fidelity Speech Synthesis","arxiv_id":"1711.10433","date":"2017-11-28","proceeding":"ICML 2018 7","authors":["Aaron van den Oord","Yazhe Li","Igor Babuschkin","Karen Simonyan","Oriol Vinyals","Koray Kavukcuoglu","George van den Driessche","Edward Lockhart","Luis C. Cobo","Florian Stimberg","Norman Casagrande","Dominik Grewe","Seb Noury","Sander Dieleman","Erich Elsen","Nal Kalchbrenner","Heiga Zen","Alex Graves","Helen King","Tom Walters","Dan Belov","Demis Hassabis"],"abstract":"The recently-developed WaveNet architecture is the current state of the art\nin realistic speech synthesis, consistently rated as more natural sounding for\nmany different languages than any previous system. However, because WaveNet\nrelies on sequential generation of one audio sample at a time, it is poorly\nsuited to today's massively parallel computers, and therefore hard to deploy in\na real-time production setting. This paper introduces Probability Density\nDistillation, a new method for training a parallel feed-forward network from a\ntrained WaveNet with no significant difference in quality. The resulting system\nis capable of generating high-fidelity speech samples at more than 20 times\nfaster than real-time, and is deployed online by Google Assistant, including\nserving multiple English and Japanese voices.","url_abs":"http://arxiv.org/abs/1711.10433v1","url_pdf":"http://arxiv.org/pdf/1711.10433v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"parallel-wavenet-fast-high-fidelity-speech","repo_url":"https://github.com/HaiFengZeng/clari_wavenet_vocoder","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"parallel-wavenet-fast-high-fidelity-speech","repo_url":"https://github.com/PhilippeNguyen/keras_wavenet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"speech-synthesis","task_name":"Speech Synthesis"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[{"method_slug":"dilated-causal-convolution","method_name":"Dilated Causal Convolution"},{"method_slug":"mixture-of-logistic-distributions","method_name":"Mixture of Logistic Distributions"},{"method_slug":"wavenet","method_name":"WaveNet"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.10433","atlas_url":"https://app.syntology.ai/?focus=1711.10433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.10433"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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