Speech Synthesis: WaveGlow Model Card

Model Overview

WaveGlow is a Glow-based (alternatively flow-based) model that generates audio conditioned on mel spectrograms. WaveGlow\nis a reversible neural network. It can be run in two modes: the first mode takes audio and transforms it to samples\ndrawn from a normal distribution, and the second mode takes samples from a normal distribution and transforms it to\naudio. Both modes are conditioned on a mel spectrogram.

Intended Use

WaveGlow is intended to be used as the second part of a two stage speech synthesis pipeline. WaveGlow takes a mel\nspectrogram and returns audio.

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

Output\nAudio of shape (batch x time)

How to Use This Model

The provided .ejrvs checkpoint can be used, in junction with a Tacotron2 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 most voices in most languages without\nretraining. We have observed this trained WaveGlow to generate English audio and Mandarin audio.

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

WaveGlow paper: https://arxiv.org/abs/1811.00002

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WaveGlow\nis a reversible neural network. It can be run in two modes: the first mode takes audio and transforms it to samples\ndrawn from a normal distribution, and the second mode takes samples from a normal distribution and transforms it to\naudio. Both modes are conditioned on a mel spectrogram.\n\nIntended Use\n------------\nWaveGlow is intended to be used as the second part of a two stage speech synthesis pipeline. WaveGlow takes a mel\nspectrogram and returns audio.\n\nInput\nMel spectrogram of shape (batch x mel_channels x time)\n\nOutput\nAudio of shape (batch x time)\n\nHow to Use This Model\n---------------------\nThe provided .ejrvs checkpoint can be used, in junction with a Tacotron2 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 most voices in most languages without\nretraining. We have observed this trained WaveGlow to generate English audio and Mandarin audio.\n\nLicense\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=============================\nWaveGlow paper: https://arxiv.org/abs/1811.00002","Speech Synthesis Waveglow",["D",1680636584463],"/api/image/nvidia/tlt-jarvis/models/speechsynthesis_waveglow","https://raw.githubusercontent.com/kbojo/images/master/TLT-Jarvis.png","bias","explainability","privacy","safetyAndSecurity",[],[303,304],[],[],[],{"_93":307,"_131":308,"_132":309,"_134":135},{"_93":305,"_131":305,"_132":306,"_134":135},"Jarvis",{"_136":135,"_131":305},"Conversational AI","soln_conversational_ai",{"_136":310,"_131":308},"technology",{"_196":1286,"_198":1287},"ngcApps",[1239],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1220":1221,"_1222":1223,"_1224":1225,"_1226":1227,"_1228":322,"_227":346,"_1229":1230,"_1231":51,"_1232":175,"_1233":175,"_1234":1235,"_1236":175,"_1237":1238},"amplitude",{"_1217":1218,"_858":-7,"_1219":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1211":1212,"_1213":1214,"_1215":1216},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1098":256,"_1099":1100,"_1101":1102,"_1103":1104},"announcements",[],"consentState",{"_385":1088,"_1089":175,"_1090":26,"_1091":26,"_1092":1093},"consentConfig",{"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378,"_379":380,"_381":382,"_383":384,"_385":386},"showContactAdmin",1785093131014,[],[],[360,361,362,363,364,365],{"_40":55,"_42":66,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":366,"_50":51},5,"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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