This model is deprecated, please use the RIVA EnglishUS Fastpitch

Speech Synthesis: FastPitch 1.1 Model Card

Model overview

FastPitch is a mel-spectrogram generator, designed to be used as the first part of a neural text-to-speech system in\nconjunction with a neural vocoder.

Model architecture

FastPitch is a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours.\nThe model predicts pitch contours during inference. By altering these predictions, the generated speech can be more\nexpressive, better match the semantic of the utterance, and in the end more engaging to the listener.\nFastPitch is based on a fully-parallel Transformer architecture, with much higher real-time factor than Tacotron2\nfor mel-spectrogram synthesis of a typical utterance.

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

FastPitch is intended to be used as the first part of a two stage speech synthesis pipeline. FastPitch takes text\nand produces a mel spectrogram. The second stage takes the generated mel spectrogram and returns audio.

Input

English text strings

Output

Mel spectrogram of shape (batch x mel_channels x time)

References

FastPitch paper: https://arxiv.org/abs/2006.06873

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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By altering these predictions, the generated speech can be more\nexpressive, better match the semantic of the utterance, and in the end more engaging to the listener.\nFastPitch is based on a fully-parallel Transformer architecture, with much higher real-time factor than Tacotron2\nfor mel-spectrogram synthesis of a typical utterance.\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\nFastPitch is intended to be used as the first part of a two stage speech synthesis pipeline. FastPitch takes text\nand produces a mel spectrogram. The second stage takes the generated mel spectrogram and returns audio.\n\n##### Input\n\nEnglish text strings\n\n##### Output\n\nMel spectrogram of shape (batch x mel_channels x time)\n\n#### References\n\nFastPitch paper: https://arxiv.org/abs/2006.06873\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 English Fastpitch Female 1",["D",1696600372020],"/api/image/nvidia/tao/models/speechsynthesis_en_us_fastpitch_female_1","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO-RIVA.png","bias","explainability","privacy","safetyAndSecurity",[],[288,289,290],[],[],[],{"_93":294,"_129":294,"_130":295,"_132":133},{"_93":292,"_129":292,"_130":293,"_132":133},{"_93":135,"_129":135,"_130":291,"_132":133},{"_134":137,"_129":135},"Fastpitch",{"_134":133,"_129":292},"English",{"_134":296,"_129":294},"language",{"_186":1465,"_188":1466},"ngcApps",[1418],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1400":1401,"_1402":1403,"_1404":1405,"_1406":1407,"_1408":308,"_213":332,"_1409":1410,"_1398":51,"_1411":165,"_1412":165,"_1413":1414,"_1415":165,"_1416":1417},"amplitude",{"_1396":1397,"_1398":51,"_1004":-7,"_1399":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1390":1391,"_1392":1393,"_1394":1395},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1279":241,"_1280":1281,"_1282":1283,"_1284":1285},"announcements",[],"consentState",{"_369":1269,"_1270":165,"_1271":26,"_1272":26,"_1273":1274},"consentConfig",{"_351":352,"_353":354,"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370},"showContactAdmin",1783371580679,[],[],[346,347,348,349,350],{"_40":54,"_42":65,"_44":181,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":63,"_44":181,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":181,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":181,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":181,"_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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