Model Overview

The FastPitch model generates mel-spectrograms from raw input text and allows to exert additional control over the synthesized utterances.

Model Architecture

FastPitch is a fully feedforward Transformer model that predicts mel-spectrograms\nfrom raw text (Figure 1). The entire process is parallel, which means that all input letters are processed simultaneously to produce a full mel-spectrogram in a single forward pass.

\n $\"FastPitch$ \n

\n Figure 1. Architecture of FastPitch (source). The model is composed of a bidirectional Transformer backbone (also known as a Transformer encoder), a pitch predictor, and a duration predictor. After passing through the first *N* Transformer blocks, encoding, the signal is augmented with pitch information and discretely upsampled. Then it goes through another set of *N* Transformer blocks, with the goal of\nsmoothing out the upsampled signal, and constructing a mel-spectrogram.\n \n

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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\n $\"FastPitch$ \n

\n\n\n \n## Training\n\nThis model was trained using script available on [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:fastpitch_for_pytorch) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/FastPitch)\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://ngc.nvidia.com/catalog/resources/nvidia:fastpitch_for_pytorch/performance)\n\n## References\n- [original paper](https://arxiv.org/abs/2006.06873)\n- [NVIDIA model implementation in NGC](https://ngc.nvidia.com/catalog/resources/nvidia:fastpitch_for_pytorch)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/FastPitch)\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/FastPitch) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","FastPitch checkpoint (PyTorch, AMP, LJSpeech-1.1)",["D",1680636873124],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/models/fastpitch_pyt_amp_ckpt_v1","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[268,269,270,271],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_237":286,"_113":287,"_273":288,"_275":276},{"_237":282,"_113":283,"_273":284,"_275":276},{"_237":278,"_113":279,"_273":280,"_275":276},{"_237":272,"_113":272,"_273":274,"_275":276},"TTS","filter",{"_277":276,"_113":272},"category","label","field","Text to Speech","uscs_text_to_speech",{"_277":281,"_113":279},"application","Deep Learning Examples","pltfm_deep_learning_examples",{"_277":285,"_113":283},"platform","Conversational AI","soln_conversational_ai",{"_277":289,"_113":287},"technology",{"_151":1464,"_153":1465},"ngcApps",[1417],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1398":1399,"_1400":1401,"_1402":1403,"_1404":1405,"_1406":301,"_182":325,"_1407":1408,"_1409":59,"_1410":130,"_1411":130,"_1412":1413,"_1414":130,"_1415":1416},"amplitude",{"_1395":1396,"_997":-7,"_1397":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1389":1390,"_1391":1392,"_1393":1394},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1272":210,"_1273":1274,"_1275":1276,"_1277":1278},"announcements",[],"consentState",{"_363":1262,"_1263":130,"_1264":34,"_1265":34,"_1266":1267},"consentConfig",{"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358,"_359":360,"_361":362,"_363":364},"showContactAdmin",1784327518957,[],[],[339,340,341,342,343,344],{"_48":63,"_50":74,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":68,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":146,"_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. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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Key	Value
dataset	LJSpeech-1.1
epochs	1500
training_precision	AMP