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Model Overview

The Tacotron 2 and WaveGlow model form a text-to-speech system that enables user to synthesise a natural sounding speech from raw transcripts.

Model Architecture

The Tacotron 2 model is a recurrent sequence-to-sequence model with attention that\npredicts mel-spectrograms from text. The encoder (blue blocks in the figure\nbelow) transforms the whole text into a fixed-size hidden feature\nrepresentation. This feature representation is then consumed by the\nautoregressive decoder (orange blocks) that produces one spectrogram frame at\na time. In our implementation, the autoregressive WaveNet (green block) is\nreplaced by the flow-based generative WaveGlow.

$\"\"$

Figure 1. Architecture of the Tacotron 2 model. Taken from the\nTacotron 2 paper.

The WaveGlow model is a flow-based generative model that generates audio\nsamples from Gaussian distribution using mel-spectrogram conditioning (Figure\n2). During training, the model learns to transform the dataset distribution\ninto spherical Gaussian distribution through a series of flows. One step of a\nflow consists of an invertible convolution, followed by a modified WaveNet\narchitecture that serves as an affine coupling layer. During inference, the\nnetwork is inverted and audio samples are generated from the Gaussian\ndistribution. Our implementation uses 512 residual channels in the coupling layer.

$\"\"$

Figure 2. Architecture of the WaveGlow model. Taken from the\nWaveGlow paper.

Training

This model was trained using script available on NGC and in GitHub repo.

Dataset

The following datasets were used to train this model:

LJSpeech-1.1 - Dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.

Performance

Performance numbers for this model are available in NGC.

References

License

This model was trained using open-source software available in Deep Learning Examples repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.

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The encoder (blue blocks in the figure\nbelow) transforms the whole text into a fixed-size hidden feature\nrepresentation. This feature representation is then consumed by the\nautoregressive decoder (orange blocks) that produces one spectrogram frame at\na time. In our implementation, the autoregressive WaveNet (green block) is\nreplaced by the flow-based generative WaveGlow.\n\n![](https://github.com/NVIDIA/DeepLearningExamples/raw/master/PyTorch/SpeechSynthesis/Tacotron2/img/tacotron2_arch.png \"Tacotron 2 architecture\")\n\n\nFigure 1. Architecture of the Tacotron 2 model. Taken from the\n[Tacotron 2](https://arxiv.org/abs/1712.05884) paper.\n\nThe WaveGlow model is a flow-based generative model that generates audio\nsamples from Gaussian distribution using mel-spectrogram conditioning (Figure\n2). During training, the model learns to transform the dataset distribution\ninto spherical Gaussian distribution through a series of flows. One step of a\nflow consists of an invertible convolution, followed by a modified WaveNet\narchitecture that serves as an affine coupling layer. During inference, the\nnetwork is inverted and audio samples are generated from the Gaussian\ndistribution. Our implementation uses 512 residual channels in the coupling layer.\n\n![](https://github.com/NVIDIA/DeepLearningExamples/raw/master/PyTorch/SpeechSynthesis/Tacotron2/img/waveglow_arch.png \"WaveGlow architecture\")\n\n\nFigure 2. Architecture of the WaveGlow model. Taken from the\n[WaveGlow](https://arxiv.org/abs/1811.00002) paper.\n\n\n \n## Training\n\nThis model was trained using script available on [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/tts_taco2-wg_pyt) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2).\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [LJSpeech-1.1](https://keithito.com/LJ-Speech-Dataset/) - Dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.\n\n\n## Performance\n\nPerformance numbers for this model are available in [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/tts_taco2-wg_pyt/performance).\n\n## References\n- [Original paper](https://arxiv.org/abs/1712.05884)\n- [NVIDIA model implementation in NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/tts_taco2-wg_pyt)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2)\n\n## License\n\nThis model was trained using open-source software available in [Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","Waveglow PyTorch checkpoint",["D",1680635065157],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/waveglow__pyt_ckpt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[237,238,239],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_205":249,"_241":250,"_242":251,"_244":245},{"_205":247,"_241":247,"_242":248,"_244":245},{"_205":240,"_241":240,"_242":243,"_244":245},"TTS","value","filter",{"_246":245,"_241":240},"category","label","field","Text-to-Speech",{"_246":245,"_241":247},"Text to Speech","uscs_text_to_speech",{"_246":252,"_241":250},"application",{"_106":1429,"_108":1430},"ngcApps",[1382],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1363":1364,"_1365":1366,"_1367":1368,"_1369":1370,"_1371":264,"_137":288,"_1372":1373,"_1374":55,"_1375":85,"_1376":85,"_1377":1378,"_1379":85,"_1380":1381},"amplitude",{"_1360":1361,"_961":-7,"_1362":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1354":1355,"_1356":1357,"_1358":1359},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1236":166,"_1237":1238,"_1239":1240,"_1241":1242},"announcements",[],"consentState",{"_326":1226,"_1227":85,"_1228":30,"_1229":30,"_1230":1231},"consentConfig",{"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327},"showContactAdmin",1784274608989,[],[],[302,303,304,305,306,307],{"_44":59,"_46":69,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":58,"_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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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