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","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",[277,278,279],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_246":288,"_114":289,"_281":290,"_283":284},{"_246":286,"_114":286,"_281":287,"_283":284},{"_246":280,"_114":280,"_281":282,"_283":284},"TTS","filter",{"_285":284,"_114":280},"category","label","field","Text-to-Speech",{"_285":284,"_114":286},"Text to Speech","uscs_text_to_speech",{"_285":291,"_114":289},"application",{"_158":1465,"_160":1466},"ngcApps",[1418],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1399":1400,"_1401":1402,"_1403":1404,"_1405":1406,"_1407":303,"_189":327,"_1408":1409,"_1410":59,"_1411":137,"_1412":137,"_1413":1414,"_1415":137,"_1416":1417},"amplitude",{"_1396":1397,"_999":-7,"_1398":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1390":1391,"_1392":1393,"_1394":1395},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1273":220,"_1274":1275,"_1276":1277,"_1278":1279},"announcements",[],"consentState",{"_364":1263,"_1264":137,"_1265":34,"_1266":34,"_1267":1268},"consentConfig",{"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365},"showContactAdmin",1783947106280,[],[],[341,342,343,344,345],{"_48":62,"_50":73,"_52":153,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":153,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":153,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":153,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":153,"_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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Key	Value
batch_size	10
LR	0.0004
training_precision	AMP
dataset	LJSpeech-1.1
epochs	3000
iterations	468156