Speech Synthesis: Tacotron 2 Model Card

Model Overview

Tacotron 2 is a LSTM-based Encoder-Attention-Decoder model that converts text to mel spectrograms. The encoder network\nThe encoder network first embeds either characters or phonemes. The embedding is sent through a convolution stack, and\nthen sent through a bidirectional LSTM. The decoder is an autoregressive LSTM: it generates one time slice of the\nmel spectrogram on each call. The decoder is connected the encoder via the attention module which tells the decoder\nwhich part of the encoded text to use to generate each slice of the spectrogram.

Intended Use

Tacotron 2 is intended to be used as the first part of a two stage speech synthesis pipeline. Tacotron 2 takes text\nand produces a mel spectrogram. The second stage takes the generated mel spectrogram and returns audio.

Input\nEnglish text strings

Output\nMel spectrogram of shape (batch x mel_channels x time)

How to Use This Model

The provided .nemo checkpoint can be used, in junction with a WaveGlow checkpoint, to generate speech via Jarvis. To\ndeploy a TTS service via Jarvis, please refer to the Jarvis documentation

Training Information

This model is trained on LJSpeech sampled at 22050Hz, and can be used to generate female English voices with an American accent.

License

By downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Jarvis license

Ethical AI

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

Citations and Further Reading

Tacotron 2 paper: https://arxiv.org/abs/1712.05884

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The encoder network\nThe encoder network first embeds either characters or phonemes. The embedding is sent through a convolution stack, and\nthen sent through a bidirectional LSTM. The decoder is an autoregressive LSTM: it generates one time slice of the\nmel spectrogram on each call. The decoder is connected the encoder via the attention module which tells the decoder\nwhich part of the encoded text to use to generate each slice of the spectrogram.\n\nIntended Use\n------------\nTacotron 2 is intended to be used as the first part of a two stage speech synthesis pipeline. Tacotron 2 takes text\nand produces a mel spectrogram. The second stage takes the generated mel spectrogram and returns audio.\n\nInput\nEnglish text strings\n\nOutput\nMel spectrogram of shape (batch x mel_channels x time)\n\nHow to Use This Model\n---------------------\nThe provided .nemo checkpoint can be used, in junction with a WaveGlow checkpoint, to generate speech via Jarvis. To\ndeploy a TTS service via Jarvis, please refer to the [Jarvis documentation](http://docs.jarvis-ai.nvidia.com/release-1-0/service-tts.html)\n\nTraining Information\n--------------------\nThis model is trained on LJSpeech sampled at 22050Hz, and can be used to generate female English voices with an American accent.\n\nLicense\n-------\n\nBy downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Jarvis [license](https://developer.nvidia.com/ngc/nvidia-jarvis-license)\n\n## Ethical AI\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications.\nConsider potential algorithmic bias when choosing or creating the models being deployed.\nWork with the model’s developer to ensure that it meets the requirements for the relevant industry and use case;\nthat the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.\n\nCitations and Further Reading\n=============================\nTacotron 2 paper: https://arxiv.org/abs/1712.05884","displayName","Speech Synthesis English Tacotron2",["D",1680637783097],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tlt-jarvis/models/speechsynthesis_english_tacotron2","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/TLT-Jarvis.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[243,244],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_211":252,"_246":253,"_247":254,"_249":250},{"_211":245,"_246":245,"_247":248,"_249":250},"Jarvis","value","filter",{"_251":250,"_246":245},"category","label","field","Conversational AI","soln_conversational_ai",{"_251":255,"_246":253},"technology",{"_108":1234,"_110":1235},"ngcApps",[1187],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1168":1169,"_1170":1171,"_1172":1173,"_1174":1175,"_1176":267,"_140":291,"_1177":1178,"_1179":56,"_1180":87,"_1181":87,"_1182":1183,"_1184":87,"_1185":1186},"amplitude",{"_1165":1166,"_802":-7,"_1167":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1159":1160,"_1161":1162,"_1163":1164},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1042":170,"_1043":1044,"_1045":1046,"_1047":1048},"announcements",[],"consentState",{"_328":1032,"_1033":87,"_1034":31,"_1035":31,"_1036":1037},"consentConfig",{"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329},"showContactAdmin",1785091194839,[],[],[305,306,307,308,309],{"_45":60,"_47":71,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":103,"_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. 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