Speech Synthesis: FastPitch 1.1 Model Card

Model Overview

FastPitch is a mel-spectrogram generator, designed to be used as the first part of a neural text-to-speech system in\nconjunction with a neural vocoder. This model uses the International Phonetic Alphabet (IPA) for inference and training.

Model Architecture

FastPitch is a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours.\nThe model predicts pitch contours during inference. By altering these predictions, the generated speech can be more\nexpressive, better match the semantic of the utterance, and in the end more engaging to the listener.\nFastPitch is based on a fully-parallel Transformer architecture, with much higher real-time factor than Tacotron2\nfor mel-spectrogram synthesis of a typical utterance.

Training

Dataset

This model is trained on proprietary data sampled at 44100Hz, and can be used to generate a Spanish (US) voice. This model supports 1 female and 1 male voice.\nThe female voice comes with neutral, calm, angry and sad emotions. The male voice comes with neutral, calm, happy, and angry emotions.\nEach emotion is accessed as a speaker. For example Female-Calm, Male-Happy, and so on.

How to Use this Model

FastPitch is intended to be used as the first part of a two stage speech synthesis pipeline. FastPitch takes text\nand produces a mel-spectrogram. The second stage takes the generated mel-spectrogram and returns audio.

The encryption key for this model is tlt_encode

Input

Spanish text strings

Output

Mel-spectrogram of shape (batch x mel_channels x time)

References

[1] FastPitch: Parallel Text-to-speech with Pitch Prediction

License

By downloading and using the models and resources packaged with Riva Conversational AI, you accept the terms of the Riva license.

Ethical AI

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider\npotential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to\nensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and\ndocumentation are provided to understand error rates, confidence intervals, and results; and that the model is\nbeing used under the conditions and in the manner intended.

","resourceVersions","resourceVersionsTotal","hasMoreVersions","versionMetadata","versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/riva/models/speechsynthesis_es_us_fastpitch_ipa/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/riva/speechsynthesis_es_us_fastpitch_ipa:deployable_v1.3","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/riva/models/speechsynthesis_es_us_fastpitch_ipa/-/version-history","accessCheckResult",{"_188":189,"_190":26},"hasRelatedCollections",true,"context",{"_85":86,"_87":88},1787422765471,[],[],[221,222,223,224,225,226,227],{"_40":55,"_42":67,"_44":57,"_46":58,"_48":68,"_50":51},{"_40":55,"_42":64,"_44":45,"_46":65,"_48":66,"_50":51},{"_40":55,"_42":62,"_44":45,"_46":65,"_48":228,"_50":51},{"_40":55,"_42":60,"_44":57,"_46":58,"_48":61,"_50":51},{"_40":55,"_42":56,"_44":57,"_46":58,"_48":59,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},52,"nvidia/riva/speechsynthesis_es_us_fastpitch_ipa","nvidia/riva/models/speechsynthesis_es_us_fastpitch_ipa","speechsynthesis_es_us_fastpitch_ipa","Spanish US FastPitch model","## Speech Synthesis: FastPitch 1.1 Model Card\n\n### Model Overview\n\nFastPitch is a mel-spectrogram generator, designed to be used as the first part of a neural text-to-speech system in\nconjunction with a neural vocoder. This model uses the International Phonetic Alphabet (IPA) for inference and training.\n\n### Model Architecture\n\nFastPitch is a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours.\nThe model predicts pitch contours during inference. By altering these predictions, the generated speech can be more\nexpressive, better match the semantic of the utterance, and in the end more engaging to the listener.\nFastPitch is based on a fully-parallel Transformer architecture, with much higher real-time factor than Tacotron2\nfor mel-spectrogram synthesis of a typical utterance.\n\n### Training\n\n#### Dataset\n\nThis model is trained on proprietary data sampled at 44100Hz, and can be used to generate a Spanish (US) voice. This model supports 1 female and 1 male voice.\nThe female voice comes with neutral, calm, angry and sad emotions. The male voice comes with neutral, calm, happy, and angry emotions.\nEach emotion is accessed as a speaker. For example Female-Calm, Male-Happy, and so on.\n\n### How to Use this Model\n\nFastPitch is intended to be used as the first part of a two stage speech synthesis pipeline. FastPitch takes text\nand produces a mel-spectrogram. The second stage takes the generated mel-spectrogram and returns audio.\n\nThe encryption key for this model is tlt_encode\n\n#### Input\n\nSpanish text strings\n\n#### Output\n\nMel-spectrogram of shape (batch x mel_channels x time)\n\n### References\n\n[1] [FastPitch: Parallel Text-to-speech with Pitch Prediction](https://arxiv.org/abs/2006.06873)\n\n### Suggested Reading\n\nRefer to the [Riva documentation](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html) for more information.\n\n### License\n\nBy downloading and using the models and resources packaged with Riva Conversational AI, you accept the terms of the Riva [license](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\n### Ethical AI\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider\npotential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to\nensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and\ndocumentation are provided to understand error rates, confidence intervals, and results; and that the model is\nbeing used under the conditions and in the manner intended.","Riva TTS Spanish US FastPitch",["D",1719940119415],"deployable_v1.3","/api/image/nvidia/riva/models/speechsynthesis_es_us_fastpitch_ipa","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",179741792,"bias","Field | Response\n:------|:----------\nWhat is the language balance of the model validation data? | Latin American Spanish: 100%\nWhat is the geographic origin language balance of the model validation data? | Latin America: 100%\nWhat is the accent balance of the model validation data? | Latin American: 100%\nMeasures taken to mitigate against unwanted bias: | None\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None","explainability","Field | Response\n:------|:----------\nIntended Application & Domain: | Speech Synthesis\nModel Task | Speech Synthesis and Generative Adversarial Network\nIntended Users | This model is intended for developers building interactive call centers, virtual assistants, language learning assistants to improve pronunciation, automatically generate voice-overs, narrate or comment on videos, and provide audio alternatives for visually impaired users or people with light sensitivity.\nModel Output | Audio files (.wav)\nHow the model works | Model transcribes input text characters into audio representation.\nTechnical Limitations | Model only has the capacity to produce a voice in the language, dialect and gender(s) in which it is trained. This model makes no effort to moderate or modify input text. \nPerformance Metrics | % preference when compared with available alternatives
Pitch (mean)
Pitch_standard deviation (std) (mean)
Pitch_kurtosis (mean)
Pitch_skew (mean)
Fundamental Frequency Ratio (f0_ratio) (mean)
f0_ratio_std (mean)
f0_ratio_kurtosis (mean)
f0_ratio_skew (mean)
Pitch (median)
Pitch_std (median)
Pitch_kurtosis (median)
Pitch_skew (median)
f0_ratio (median)
f0_ratio_std (median)
f0_ratio_kurtosis (median)
f0_ratio_skew (median)\"\nPotential Known Risks | May unnaturally synthesize vocabulary not included in the pronunciation dictionary or omit phonetic symbols not used in training.\nLicensing: | [https://docs.nvidia.com/ai-foundation-models-community-license.pdf](https://docs.nvidia.com/ai-foundation-models-community-license.pdf)","privacy","Field | Response\n:------|:---------\nGeneratable or reverse engineerable personal information? | None\nHow often is dataset reviewed? | Before release (during training)\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf personal data collected for the development of the model, was it collected directly by NVIDIA? | Yes\nIf personal data collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Yes\nIf personal data collected for the development of this AI model, was it minimized to only what was required? | Yes\nIs there provenance for all datasets used in training? | Yes\nIs data in dataset traceable? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data.","safetyAndSecurity","Field | Response\n:------|:---------\nModel Application(s): | Speech synthesis\nDescribe the life critical impact (if present). | Not Applicable\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.\nUse case restrictions for the model. | Abide by [https://developer.nvidia.com/riva/ga/license](https://developer.nvidia.com/riva/ga/license)",[],[253,254],[],[],[],{"_92":256,"_128":256,"_129":257,"_131":132},{"_92":127,"_128":127,"_129":255,"_131":132},{"_133":132,"_128":127},"RMIR",{"_133":132,"_128":256},{"_171":1232,"_173":1233},"ngcApps",[1185],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1166":1167,"_1168":1169,"_1170":1171,"_1172":1173,"_1174":269,"_203":295,"_1175":1176,"_1177":51,"_1178":214,"_1179":214,"_1180":1181,"_1182":214,"_1183":1184},"amplitude",{"_1163":1164,"_809":-7,"_1165":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1157":1158,"_1159":1160,"_1161":1162},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787422765370,"1.12.0","defaultSearchSuggestions",{"_1049":377,"_1050":1051,"_1052":1053,"_1054":1055},"announcements",[],"consentState",{"_333":1039,"_1040":214,"_1041":26,"_1042":26,"_1043":1044},"consentConfig",{"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334},"showContactAdmin",[],[],[308,309,310,311,312,313],{"_40":55,"_42":67,"_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":314,"_50":51},4,"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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