Model Overview

HiFi-GAN model implements a spectrogram inversion model that allows to synthesize speech waveforms from mel-spectrograms.

Model Architecture

The entire model is composed of a generator and two discriminators. Both discriminators can be further divided into smaller sub-networks, that work at different resolutions.\nThe loss functions take as inputs intermediate feature maps and outputs of those sub-networks.\nAfter training, the generator is used for synthesis, and the discriminators are discarded.\nAll three components are convolutional networks with different architectures.

\n $\"HiFi-GAN$ \n

\n Figure 1. The architecture of HiFi-GAN\n

Training

This model was trained using script available in GitHub repo.

Dataset

The following datasets were used to train this model:

LJSpeech-1.1 - Dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.

Performance

Performance numbers for this model are available in GitHub readme performance section.

References

Original paper
NVIDIA model implementation on GitHub

License

This model was trained using open-source software available in Deep Learning Examples repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.

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\n $\"HiFi-GAN$ \n

\n Figure 1. The architecture of HiFi-GAN\n

\n\n\n \n## Training\n\nThis model was trained using script available in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/HiFi-GAN).\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [LJSpeech-1.1](https://keithito.com/LJ-Speech-Dataset/) - Dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.\n\n\n## Performance\n\nPerformance numbers for this model are available in [GitHub readme performance section](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/HiFi-GAN#performance).\n\n## References\n- [Original paper](TBA)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/HiFi-GAN)\n\n## License\n\nThis model was trained using open-source software available in [Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/HiFi-GAN) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","HiFi-GAN PyT checkpoint (22kHz, AMP)",["D",1680636975009],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/hifigan__pyt_ckpt_ds-ljs22khz","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[238,239,240],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":252,"_242":253,"_243":254,"_245":246},{"_206":248,"_242":249,"_243":250,"_245":246},{"_206":241,"_242":241,"_243":244,"_245":246},"TTS","value","filter",{"_247":246,"_242":241},"category","label","field","Text to Speech","uscs_text_to_speech",{"_247":251,"_242":249},"application","Deep Learning Examples","pltfm_deep_learning_examples",{"_247":255,"_242":253},"platform",{"_107":1232,"_109":1233},"ngcApps",[1185],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1166":1167,"_1168":1169,"_1170":1171,"_1172":1173,"_1174":267,"_138":293,"_1175":1176,"_1177":56,"_1178":86,"_1179":86,"_1180":1181,"_1182":86,"_1183":1184},"amplitude",{"_1163":1164,"_803":-7,"_1165":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1157":1158,"_1159":1160,"_1161":1162},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787520886923,"1.12.0","defaultSearchSuggestions",{"_1043":168,"_1044":1045,"_1046":1047,"_1048":1049},"announcements",[],"consentState",{"_329":1033,"_1034":86,"_1035":31,"_1036":31,"_1037":1038},"consentConfig",{"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330},"showContactAdmin",[],[],[306,307,308,309,310],{"_45":60,"_47":71,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":102,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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