Speech Synthesis: HifiGAN Model Card

Model overview

HifiGAN is a neural vocoder model for text-to-speech applications. It is intended as the second part of a two-stage speech synthesis\npipeline, with a mel-spectrogram generator such as FastPitch as the first stage.

Model architecture

HifiGAN is a neural vocoder based on a generative adversarial network framework, During training,\nthe model uses a powerful discriminator consisting of small sub-discriminators, each one focusing\non specific periodic parts of a raw waveform. The generator is very fast and has a small footprint,\nwhile producing high quality speech.

Training

Dataset

This model is trained on a proprietary dataset sampled at 44100Hz, and can be used to generate English\nvoices with an American accent. This model supports 1 male voice and 1 female voice that comes from the\nRiva Fastpitch model.

How to use this model

HifiGAN is intended to be used as the second part of a two stage speech synthesis pipeline. HifiGAN takes a\nmel spectrogram and returns audio.

Input: Mel spectrogram of shape (batch x mel_channels x time)

Output: Audio of shape (batch x time)

Limitations

N/A

References

HifiGAN paper: https://arxiv.org/abs/2010.05646

License

By downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the\nterms of the Riva license.

Ethical AI

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider\npotential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to\nensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and\ndocumentation are provided to understand error rates, confidence intervals, and results; and that the model is\nbeing used under the conditions and in the manner intended.

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It is intended as the second part of a two-stage speech synthesis pipeline, with a mel-spectrogram generator such as FastPitch as the first stage.","## Speech Synthesis: HifiGAN Model Card\n\n### Model overview\n\nHifiGAN is a neural vocoder model for text-to-speech applications. It is intended as the second part of a two-stage speech synthesis\npipeline, with a mel-spectrogram generator such as FastPitch as the first stage.\n\n### Model architecture\n\nHifiGAN is a neural vocoder based on a generative adversarial network framework, During training,\nthe model uses a powerful discriminator consisting of small sub-discriminators, each one focusing\non specific periodic parts of a raw waveform. The generator is very fast and has a small footprint,\nwhile producing high quality speech.\n\n### Training\n\n#### Dataset\n\nThis model is trained on a proprietary dataset sampled at 44100Hz, and can be used to generate English\nvoices with an American accent. This model supports 1 male voice and 1 female voice that comes from the\nRiva Fastpitch model.\n\n### How to use this model\n\nHifiGAN is intended to be used as the second part of a two stage speech synthesis pipeline. HifiGAN takes a\nmel spectrogram and returns audio.\n\n#### Input: Mel spectrogram of shape (batch x mel_channels x time)\n\n#### Output: Audio of shape (batch x time)\n\n### Limitations\n\nN/A\n\n### References\n\nHifiGAN paper: https://arxiv.org/abs/2010.05646\n\n### License\n\nBy downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the\nterms of the Riva license.\n\n### Ethical AI\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider\npotential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to\nensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and\ndocumentation are provided to understand error rates, confidence intervals, and results; and that the model is\nbeing used under the conditions and in the manner intended.","RIVA EnglishUS Hifigan",["D",1680636069683],"deployable_v1.0","/api/image/nvidia/riva/models/speechsynthesis_en_us_hifigan","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",55759177,"bias","explainability","privacy","safetyAndSecurity",[],[250,251,252,253],[],[],[],{"_92":258,"_128":258,"_129":259,"_131":132},{"_92":134,"_128":134,"_129":257,"_131":132},{"_92":255,"_128":255,"_129":256,"_131":132},{"_92":127,"_128":127,"_129":254,"_131":132},{"_133":132,"_128":127},"Text to speech",{"_133":132,"_128":255},{"_133":136,"_128":134},"HifiGan",{"_133":132,"_128":258},{"_171":1236,"_173":1237},"ngcApps",[1189],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1170":1171,"_1172":1173,"_1174":1175,"_1176":1177,"_1178":271,"_203":295,"_1179":1180,"_1181":51,"_1182":214,"_1183":214,"_1184":1185,"_1186":214,"_1187":1188},"amplitude",{"_1167":1168,"_809":-7,"_1169":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1161":1162,"_1163":1164,"_1165":1166},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1049":376,"_1050":1051,"_1052":1053,"_1054":1055},"announcements",[],"consentState",{"_333":1039,"_1040":214,"_1041":26,"_1042":26,"_1043":1044},"consentConfig",{"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334},"showContactAdmin",1785697873534,[],[],[309,310,311,312,313,314],{"_40":55,"_42":66,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":63,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":61,"_46":-5,"_48":-5,"_50":51},"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. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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