Speech Synthesis: RAD-TTS Model Card

Model Overview

RAD-TTS 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, and it can output a female or a male voice for US English. It is also trained for six (6) emotions for the female voice, and four (4) emotions for the male voice, which can be mixed and regulated through SSML tags.

Model Architecture

RAD-TTS is a parallel flow-based generative network for text-to-speech synthesis. It extends prior parallel approaches by additionally modeling speech rhythm as a separate generative distribution to facilitate variable token duration during inference.

Input:

Text Strings in English

Other Properties Related to Input: 400 Character Text String Limit

Output:

Mel spectrogram of shape (batch x mel_channels x time)

Software Integration:

Runtime Engine(s): Riva 2.13.0 or greater

Supported Hardware Platform(s):

NVIDIA Volta V100
NVIDIA Turing T4
NVIDIA A100 GPU
NVIDIA A30 GPU
NVIDIA A10 GPU
NVIDIA H100 GPU
NVIDIA L4 GPU
NVIDIA L40 GPU
NVIDIA Jetson Orin
NVIDIA Jetson AGX Xavier
NVIDIA Jetson NX Xavier

Supported Operating System(s):

Linux
Linux 4 Tegra

Model Version(s):

RadTTSpp_Emo_mix_44k_EnglishUS_Emotion_IPA v2.16.0

Training & Evaluation:

Training Dataset:

** Data Collection Method by dataset

Human
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): This model is trained on a proprietary dataset sampled at 44100Hz, and can be used to generate English voices with an American accent. This model supports one (1) male voice and one (1) female voice. The female voice comes with neutral, calm, happy, angry, sad, and fearful emotions. The male voice comes with neutral, calm, happy, and angry emotions. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. Each emotion is accessed as a speaker. For example Female-Sad, Male-Happy, and so on. The dataset also contains a subset of sentences with different words emphasized.

Evaluation Dataset:

** Data Collection Method by dataset

Human
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): This model is trained on a proprietary dataset sampled at 44100Hz, and can be used to generate English voices with an American accent. This model supports one (1) male voice and one (1) female voice. The female voice comes with neutral, calm, happy, angry, sad, and fearful emotions. The male voice comes with neutral, calm, happy, and angry emotions. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. Each emotion is accessed as a speaker. For example Female-Sad, Male-Happy, and so on. The dataset also contains a subset of sentences with different words emphasized.

How to Use this Model

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

Inference:

Engine: Triton
\nTest Hardware:

NVIDIA Volta V100
NVIDIA Turing T4
NVIDIA A100 GPU
NVIDIA A30 GPU
NVIDIA A10 GPU
NVIDIA H100 GPU
NVIDIA L4 GPU
NVIDIA L40 GPU
NVIDIA Jetson Orin
NVIDIA Jetson AGX Xavier
NVIDIA Jetson NX Xavier

References

[1] RAD-TTS: Parallel Flow-Based TTS with Robust Alignment Learning and Diverse Synthesis

Ethical AI

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns here.

License

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

","resourceVersions","resourceVersionsTotal","hasMoreVersions","versionMetadata","versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/riva/models/speechsynthesis_en_us_radttspp_ipa/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/riva/speechsynthesis_en_us_radttspp_ipa:deployable_v1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/riva/models/speechsynthesis_en_us_radttspp_ipa/-/version-history","accessCheckResult",{"_187":188,"_189":26},"hasRelatedCollections",true,"context",{"_84":85,"_86":87},1787560938668,[],[],[220,221,222,223,224,225,226],{"_40":55,"_42":66,"_44":57,"_46":58,"_48":67,"_50":51},{"_40":55,"_42":63,"_44":45,"_46":64,"_48":65,"_50":51},{"_40":55,"_42":62,"_44":45,"_46":64,"_48":228,"_50":51},{"_40":55,"_42":60,"_44":57,"_46":58,"_48":227,"_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},85,56,"nvidia/riva/speechsynthesis_en_us_radttspp_ipa","nvidia/riva/models/speechsynthesis_en_us_radttspp_ipa","speechsynthesis_en_us_radttspp_ipa","Riva multisepaker with IPA for G2P","## Speech Synthesis: RAD-TTS Model Card\n\n### Model Overview\n\nRAD-TTS 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, and it can output a female or a male voice for US English. It is also trained for six (6) emotions for the female voice, and four (4) emotions for the male voice, which can be mixed and regulated through SSML tags.\n\n### Model Architecture\n\nRAD-TTS is a parallel flow-based generative network for text-to-speech synthesis. It extends prior parallel approaches by additionally modeling speech rhythm as a separate generative distribution to facilitate variable token duration during inference.\n\n### Input:\n\nText Strings in English\n\n**Other Properties Related to Input:** 400 Character Text String Limit
\n\n### Output:\n\nMel spectrogram of shape (batch x mel_channels x time)\n\n### Software Integration:\n\n**Runtime Engine(s):** Riva 2.13.0 or greater
\n\n**Supported Hardware Platform(s):**
\n* NVIDIA Volta V100
\n* NVIDIA Turing T4
\n* NVIDIA A100 GPU
\n* NVIDIA A30 GPU
\n* NVIDIA A10 GPU
\n* NVIDIA H100 GPU
\n* NVIDIA L4 GPU
\n* NVIDIA L40 GPU
\n* NVIDIA Jetson Orin
\n* NVIDIA Jetson AGX Xavier
\n* NVIDIA Jetson NX Xavier
\n\n**Supported Operating System(s):**
\n* Linux
\n* Linux 4 Tegra
\n\n### Model Version(s):\n\nRadTTSpp_Emo_mix_44k_EnglishUS_Emotion_IPA v2.16.0
\n\n## Training & Evaluation:\n\n### Training Dataset:\n\n** Data Collection Method by dataset
\n* Human
\n **Properties (Quantity, Dataset Descriptions, Sensor(s)):** This model is trained on a proprietary dataset sampled at 44100Hz, and can be used to generate English voices with an American accent. This model supports one (1) male voice and one (1) female voice. The female voice comes with neutral, calm, happy, angry, sad, and fearful emotions. The male voice comes with neutral, calm, happy, and angry emotions. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. Each emotion is accessed as a speaker. For example Female-Sad, Male-Happy, and so on. The dataset also contains a subset of sentences with different words emphasized.\n\n### Evaluation Dataset:\n\n** Data Collection Method by dataset
\n* Human
\n **Properties (Quantity, Dataset Descriptions, Sensor(s)):** This model is trained on a proprietary dataset sampled at 44100Hz, and can be used to generate English voices with an American accent. This model supports one (1) male voice and one (1) female voice. The female voice comes with neutral, calm, happy, angry, sad, and fearful emotions. The male voice comes with neutral, calm, happy, and angry emotions. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. Each emotion is accessed as a speaker. For example Female-Sad, Male-Happy, and so on. The dataset also contains a subset of sentences with different words emphasized.\n\n### How to Use this Model\n\nRAD-TTS is intended to be used as the first part of a two stage speech synthesis pipeline. RAD-TTS takes text\nand produces a mel-spectrogram. The second stage takes the generated mel-spectrogram and returns audio.\n\n### Inference:\n\n**Engine:** Triton
\n**Test Hardware:**
\n* NVIDIA Volta V100\n* NVIDIA Turing T4\n* NVIDIA A100 GPU\n* NVIDIA A30 GPU\n* NVIDIA A10 GPU\n* NVIDIA H100 GPU\n* NVIDIA L4 GPU\n* NVIDIA L40 GPU\n* NVIDIA Jetson Orin\n* NVIDIA Jetson AGX Xavier\n* NVIDIA Jetson NX Xavier\n\n### References\n\n[1] [RAD-TTS: Parallel Flow-Based TTS with Robust Alignment Learning and Diverse Synthesis](https://openreview.net/pdf?id=0NQwnnwAORi)\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### Ethical AI\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\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://docs.nvidia.com/ai-foundation-models-community-license.pdf).","RIVA EnglishUS RADTTSPP with emotion mixing.",["D",1719859328573],"deployable_v1.0","/api/image/nvidia/riva/models/speechsynthesis_en_us_radttspp_ipa","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",1436038705,"bias","Field | Response\n:------|:----------\nWhat is the language balance of the model validation data? | English: 100%\nWhat is the geographic origin language balance of the model validation data? | United States: 100%\nWhat is the accent balance of the model validation data? | American English: 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, 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)
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f0_ratio_kurtosis (median)
f0_ratio_skew (median)\"\nPotential Known Risks | May unnaturally synthesize vocabulary not included in 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? | When ingested\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)",[],[],[],[],[],{"_170":1227,"_172":1228},"ngcApps",[1180],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1161":1162,"_1163":1164,"_1165":1166,"_1167":1168,"_1169":264,"_202":290,"_1170":1171,"_1172":51,"_1173":213,"_1174":213,"_1175":1176,"_1177":213,"_1178":1179},"amplitude",{"_1158":1159,"_804":-7,"_1160":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1152":1153,"_1154":1155,"_1156":1157},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787560938551,"1.12.0","defaultSearchSuggestions",{"_1044":372,"_1045":1046,"_1047":1048,"_1049":1050},"announcements",[],"consentState",{"_328":1034,"_1035":213,"_1036":26,"_1037":26,"_1038":1039},"consentConfig",{"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329},"showContactAdmin",1787560938552,[],[],[304,305,306,307,308,309],{"_40":55,"_42":66,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":63,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":54,"_50":51},"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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These cookies may store a unique ID so that our system will remember you when you return. 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