Overview

NVIDIA NeMo toolkit supports Text To Speech (TTS) which is also referred to as Speech Synthesis via a two step procedure. First, a model is used to generate a mel spectrogram from text. Second, a model is used to generate audio from a mel spectrogram.\nIn this collection, Mel Spectrogram Generators Tacotron 2 and Glow-TTS are included.In the audio Generators (Vocoders) section, WaveGlow is included.\nUsing the scripts in the TTS directory, train any of these models for domain specific data. Note: Transfer learning is currently a research area in TTS.

Usage

You can instantiate all these models automatically directly from NGC. To do so, start your script with:

import nemo\nimport nemo.collections.tts as nemo_tts\n

Then chose what type of model you would like to instantiate. See table below for the list of model base classes. Then use base_class.from_pretrained(...) method.\nFor example:

# Spectrogram generator which takes text as an input and produces spectrogram\nspectrogram_generator = nemo_tts.models.Tacotron2Model.from_pretrained(model_name=\"Tacotron2-22050Hz\")\n# Vocoder model which takes spectrogram and produces actual audio\nvocoder = nemo_tts.models.WaveGlowModel.from_pretrained(model_name=\"WaveGlow-22050Hz\")\n

Note that you can also list all available models using API by calling base_class.list_available_models(...) method.

You can also download all models' \".nemo\" files in the \"File Browser\" tab and then instantiate those models with base_class.restore_from(PATH_TO_DOTNEMO_FILE) method. In this case, make sure you are matching NeMo and models' versions.

Here is a list of currently available models together with their base classes and short descriptions.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model name	Model Base Class	Description
Tacotron2-22050Hz	`Tacotron2Model`	This model is trained on LJSpeech sampled at 22050Hz, and can be used to generate female English voices with an American accent.
WaveGlow-22050Hz	`WaveGlowModel`	This model is trained on LJSpeech sampled at 22050Hz, and can be used as an universal vocoder.
SqueezeWave-22050Hz	`SqueezeWaveModel`	This model is trained on LJSpeech sampled at 22050Hz, and can be used as an universal vocoder.
GlowTTS-22050Hz	`GlowTTSModel`	This model is trained on LJSpeech sampled at 22050Hz, and can be used to generate female English voices with an American accent.

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First, a model is used to generate a mel spectrogram from text. Second, a model is used to generate audio from a mel spectrogram.\nIn this collection, Mel Spectrogram Generators Tacotron 2 and Glow-TTS are included.In the audio Generators (Vocoders) section, WaveGlow is included.\nUsing the scripts in the [TTS](https://github.com/NVIDIA/NeMo/tree/main/examples/tts) directory, train any of these models for domain specific data. Note: Transfer learning is currently a research area in TTS.\n\n## Usage\n\nYou can instantiate all these models automatically directly from NGC. To do so, start your script with:\n```\nimport nemo\nimport nemo.collections.tts as nemo_tts\n```\nThen chose what type of model you would like to instantiate. See table below for the list of model base classes. Then use ```base_class.from_pretrained(...)``` method.\nFor example:\n```\n# Spectrogram generator which takes text as an input and produces spectrogram\nspectrogram_generator = nemo_tts.models.Tacotron2Model.from_pretrained(model_name=\"Tacotron2-22050Hz\")\n# Vocoder model which takes spectrogram and produces actual audio\nvocoder = nemo_tts.models.WaveGlowModel.from_pretrained(model_name=\"WaveGlow-22050Hz\")\n```\n\nNote that you can also list all available models using API by calling ```base_class.list_available_models(...)``` method.\n\nYou can also download all models' \".nemo\" files in the \"File Browser\" tab and then instantiate those models with ```base_class.restore_from(PATH_TO_DOTNEMO_FILE)``` method. In this case, make sure you are matching NeMo and models' versions.\n\n\nHere is a list of currently available models together with their base classes and short descriptions.\n\n| Model name | Model Base Class | Description |\n|-----------------------------------|---------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Tacotron2-22050Hz | ```Tacotron2Model``` | This model is trained on LJSpeech sampled at 22050Hz, and can be used to generate female English voices with an American accent. |\n| WaveGlow-22050Hz | ```WaveGlowModel```\t| This model is trained on LJSpeech sampled at 22050Hz, and can be used as an universal vocoder. |\n| SqueezeWave-22050Hz |\t```SqueezeWaveModel``` | This model is trained on LJSpeech sampled at 22050Hz, and can be used as an universal vocoder. |\n| GlowTTS-22050Hz |\t```GlowTTSModel``` | This model is trained on LJSpeech sampled at 22050Hz, and can be used to generate female English voices with an American accent.|","displayName","NeMo Speech Synthesis models",["D",1680634687316],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/models/nemottsmodels","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239,240],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_208":248,"_242":249,"_243":250,"_245":246},{"_208":241,"_242":241,"_243":244,"_245":246},"TTS","value","filter",{"_247":246,"_242":241},"category","label","field","Conversational AI","soln_conversational_ai",{"_247":251,"_242":249},"technology",{"_119":1230,"_121":1231},"ngcApps",[1183],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1164":1165,"_1166":1167,"_1168":1169,"_1170":1171,"_1172":263,"_150":287,"_1173":1174,"_1175":59,"_1176":98,"_1177":98,"_1178":1179,"_1180":98,"_1181":1182},"amplitude",{"_1161":1162,"_799":-7,"_1163":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1155":1156,"_1157":1158,"_1159":1160},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1039":179,"_1040":1041,"_1042":1043,"_1044":1045},"announcements",[],"consentState",{"_325":1029,"_1030":34,"_1031":34,"_1032":98,"_1033":1034},"consentConfig",{"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326},"showContactAdmin",1785406404920,[],[],[301,302,303,304,305,306],{"_48":62,"_50":72,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":67,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":90,"_58":59},{"_48":49,"_50":51,"_52":114,"_54":-5,"_56":-5,"_58":59},"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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