This model is deprecated, please use the RIVA EnglishUS Hifigan

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 female English voices with an American accent.

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\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 female English voices with an American accent.\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 Hifigan Female 1",["D",1696600603127],"/api/image/nvidia/tao/models/speechsynthesis_en_us_hifigan_female_1","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO-RIVA.png","bias","explainability","privacy","safetyAndSecurity",[],[247,248,249],[],[],[],{"_89":259,"_251":259,"_252":260,"_254":255},{"_89":257,"_251":257,"_252":258,"_254":255},{"_89":250,"_251":250,"_252":253,"_254":255},"tts","value","filter",{"_256":255,"_251":250},"category","label","field","riva",{"_256":255,"_251":257},"hifigan",{"_256":255,"_251":259},{"_145":1429,"_147":1430},"ngcApps",[1382],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1364":1365,"_1366":1367,"_1368":1369,"_1370":1371,"_1372":272,"_172":296,"_1373":1374,"_1362":51,"_1375":124,"_1376":124,"_1377":1378,"_1379":124,"_1380":1381},"amplitude",{"_1360":1361,"_1362":51,"_968":-7,"_1363":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1354":1355,"_1356":1357,"_1358":1359},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1243":200,"_1244":1245,"_1246":1247,"_1248":1249},"announcements",[],"consentState",{"_333":1233,"_1234":124,"_1235":26,"_1236":26,"_1237":1238},"consentConfig",{"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334},"showContactAdmin",1783371632501,[],[],[310,311,312,313,314],{"_40":54,"_42":65,"_44":140,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":63,"_44":140,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":140,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":140,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":140,"_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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