Model Overview

FastSpeech2HifiGanE2E is an end-to-end, non-autoregressive model that generates audio from text. It combines FastSpeech2 and HiFiGan into one model and is traned jointly in an end-to-end manner.

Model Architecture

The FastSpeech2 portion consists of the same transformer-based encoder, and a 1D-convolution-based variance adaptor as the original FastSpeech2 model.\nThe HiFiGan portion takes the discriminator from HiFiGan and uses it to generate audio from the output of the fastspeech2 portion.\nNo spectrograms are used in the training of the model. All losses are taken from HiFiGan plus additional losses for the variance adaptor.

Training

This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent. Supplementary data (durations, pitches, energies) were calculated using dataset preprocessing scripts that can be found in the NeMo library.

Performance

No performance information available at this time.

How to Use this Model

This model can be automatically loaded from NGC.

import soundfile as sf\nfrom nemo.collections.tts.models import FastSpeech2HifiGanE2EModel\n\n# Load the model from NGC\nmodel = FastSpeech2HifiGanE2EModel.from_pretrained(model_name=\"tts_en_e2e_fastspeech2hifigan\")\n\n# Run inference\ntokens = model.parse(\"Hey, I can speak!\")\naudio = model.convert_text_to_waveform(tokens=tokens)\n\n# Save the audio to disk in a file called speech.wav\nsf.write(\"speech.wav\", audio.to('cpu').numpy(), 22050)\n

Input

This model accepts batches of text.

Output

This model generates audio.

Limitations

This model outputs audio at 22050Hz.

Versions

1.0.0 (current): The original version released with NeMo 1.0.0.

References

FastSpeech 2/2s paper: https://arxiv.org/abs/2006.04558

LJSpeech preprocessing scripts: https://github.com/NVIDIA/NeMo/tree/v1.0.0/scripts/dataset_processing/ljspeech

Licence

License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.

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It combines FastSpeech2 and HiFiGan into one model and is traned jointly in an end-to-end manner.\n\n## Model Architecture\n\nThe FastSpeech2 portion consists of the same transformer-based encoder, and a 1D-convolution-based variance adaptor as the original FastSpeech2 model.\nThe HiFiGan portion takes the discriminator from HiFiGan and uses it to generate audio from the output of the fastspeech2 portion.\nNo spectrograms are used in the training of the model. All losses are taken from HiFiGan plus additional losses for the variance adaptor.\n\n## Training\n\nThis model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent. Supplementary data (durations, pitches, energies) were calculated using dataset preprocessing scripts that can be found in the NeMo library.\n\n## Performance\nNo performance information available at this time.\n\n## How to Use this Model\n\nThis model can be automatically loaded from NGC.\n\n```python\nimport soundfile as sf\nfrom nemo.collections.tts.models import FastSpeech2HifiGanE2EModel\n\n# Load the model from NGC\nmodel = FastSpeech2HifiGanE2EModel.from_pretrained(model_name=\"tts_en_e2e_fastspeech2hifigan\")\n\n# Run inference\ntokens = model.parse(\"Hey, I can speak!\")\naudio = model.convert_text_to_waveform(tokens=tokens)\n\n# Save the audio to disk in a file called speech.wav\nsf.write(\"speech.wav\", audio.to('cpu').numpy(), 22050)\n```\n\n### Input\nThis model accepts batches of text.\n\n### Output\nThis model generates audio.\n\n## Limitations\nThis model outputs audio at 22050Hz.\n\n## Versions\n1.0.0 (current): The original version released with NeMo 1.0.0.\n\n## References\nFastSpeech 2/2s paper: https://arxiv.org/abs/2006.04558\n\nLJSpeech preprocessing scripts: https://github.com/NVIDIA/NeMo/tree/v1.0.0/scripts/dataset_processing/ljspeech\n\n## Licence\nLicense to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms).","displayName","TTS En E2E Fastspeech2 Hifigan",["D",1680637391666],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/tts_en_e2e_fastspeech2hifigan","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[237,238,239,240,241,242,243],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_205":262,"_245":262,"_246":263,"_248":249},{"_205":260,"_245":260,"_246":261,"_248":249},{"_205":258,"_245":258,"_246":259,"_248":249},{"_205":256,"_245":256,"_246":257,"_248":249},{"_205":253,"_245":253,"_246":254,"_248":249},{"_205":251,"_245":251,"_246":252,"_248":249},{"_205":244,"_245":244,"_246":247,"_248":249},"TTS","value","filter",{"_250":249,"_245":244},"category","label","field","PytorchLightning",{"_250":249,"_245":251},"NeMo",{"_250":255,"_245":253},"framework","Hifigan",{"_250":249,"_245":256},"FastSpeech2",{"_250":249,"_245":258},"End-To-End",{"_250":249,"_245":260},"En",{"_250":249,"_245":262},{"_105":1439,"_107":1440},"ngcApps",[1392],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1373":1374,"_1375":1376,"_1377":1378,"_1379":1380,"_1381":275,"_136":299,"_1382":1383,"_1384":55,"_1385":84,"_1386":84,"_1387":1388,"_1389":84,"_1390":1391},"amplitude",{"_1370":1371,"_972":-7,"_1372":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1364":1365,"_1366":1367,"_1368":1369},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1247":166,"_1248":1249,"_1250":1251,"_1252":1253},"announcements",[],"consentState",{"_337":1237,"_1238":84,"_1239":30,"_1240":30,"_1241":1242},"consentConfig",{"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338},"showContactAdmin",1784255850953,[],[],[313,314,315,316,317,318],{"_44":58,"_46":68,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":67,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":65,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":59,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":100,"_50":-5,"_52":-5,"_54":55},"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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