Model Overview

The English-US Multispeaker FastPitch-HifiGAN model transcribes text into audio representations using two model components: FastPitch and HifiGAN. This model is ready for commercial use.

This collection contains two models:

\n
Multi-speaker FastPitch is a mel-spectrogram generator used as the first part of a neural text-to-speech system with a neural vocoder. This model's weights are conditioned to the energy of input audios and it uses the International Phonetic Alphabet (IPA) for inference and training.
\n
\n
HiFiGAN is a neural vocoder model for text-to-speech applications. It is trained on mel spectrograms produced by the Multi-speaker FastPitch in (1). It is the second part of a two-stage speech synthesis pipeline.
\n

Model Architecture

FastPitch [1] is a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours. The model predicts pitch contours during inference. By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and in the end more engaging to the listener. FastPitch is based on a fully-parallel Transformer architecture, with much higher real-time factor than Tacotron2 for mel-spectrogram synthesis of a typical utterance. Additionally it uses unsupervised speech-text aligner [2].

HiFiGAN [3], a generative adversarial network (GAN) model that generates audio from mel spectrograms produced by the Multi-speaker FastPitch in (1). The generator uses transposed convolutions to upsample mel spectrograms to audio. During training, the model uses a powerful discriminator consisting of small sub-discriminators, each one focusing on specific periodic parts of a raw waveform.

How to Use this Model

The model is available for use in the NeMo toolkit [4], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

In order to generate spectrogram specific to a particular speaker you will need to provide speaker ID to FastPitch. The speaker IDs go from 1 to 20. For security purposes the model generates spectrograms for synthetic speakers generated by interpolating original HiFiTTS speakers.

NOTE: For best results you should use the vocoder (HiFiGAN) checkpoint in this model card along with the mel spectrogram generator (FastPitch) checkpoint.

Automatically load the model from NGC

# Load spectrogram generator\nfrom nemo.collections.tts.models import FastPitchModel\nspec_generator = FastPitchModel.from_pretrained(\"tts_en_fastpitch_multispeaker\")\n\n# Load Vocoder\nfrom nemo.collections.tts.models import HifiGanModel\nmodel = HifiGanModel.from_pretrained(model_name=\"tts_en_hifitts_hifigan_ft_fastpitch\")\n\n# Generate audio\nimport soundfile as sf\nparsed = spec_generator.parse(\"You can type your sentence here to get nemo to produce speech.\")\nspeaker_id = 10\nspectrogram = spec_generator.generate_spectrogram(tokens=parsed, speaker=10)\naudio = model.convert_spectrogram_to_audio(spec=spectrogram)\n\n# Save the audio to disk in a file called speech.wav\nsf.write(\"speech.wav\", audio.to('cpu').numpy(), 44100)\n

References

[1] Fastpitch: https://arxiv.org/abs/2006.06873

[2] One TTS Alignment To Rule Them All: https://arxiv.org/abs/2108.10447

[3] HiFiGan paper: https://arxiv.org/abs/2010.05646

[4] NVIDIA NeMo Toolkit

Input: (Enter \"None\" As Needed)

For FastPitch (1st Stage): Text Strings in English

Other Properties Related to Input: [400 Character Text String Limit]

Output: (Enter \"None\" As Needed)

For HifiGAN (2nd Stage): Audio of shape (batch x time) in wav format

Output Parameters Related to Output: Mono, Encoded 16 bit audio; 20 Second Maximum Length; Depending on input, this model can output a female or a male voice for American English with six (6) emotions for the female voice and four (4) emotions for male voices. The female voice is classified as “neutral, calm, happy, angry, fearful, and sad.” The male voice is classified as “neutral,” “calm,” “happy,” and “angry.”

Other Properties Related to Output: [20 Second Maximum Length]

Software Integration:

Runtime Engine(s): [Riva 2.13.0]

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):

tts-FastPitch_44k_EnglishUS_IPA_v1.10.0

Training & Evaluation:

Training Dataset:

** Data Collection Method by dataset

[Human]

Properties (Quantity, Dataset Descriptions, Sensor(s)): This model is trained on a proprietary dataset of audio-text pairs sampled at 44100 Hz, which contains one Female and one Male voice speaking US English. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. The dataset also contains a subset of sentences with different words emphasized.

Evaluation Dataset:

** Data Collection Method by dataset

[Human]

Properties (Quantity, Dataset Descriptions, Sensor(s)): This model is trained on a proprietary dataset sampled at 44100 Hz, which contains one Female and one Male voice speaking US English. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. The dataset also contains a subset of sentences with different words emphasized.

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

Ethical Considerations:

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 Subcard. Please report security vulnerabilities or NVIDIA AI Concerns here.

","resourceVersions",[265,266,267,268],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_241":212,"_89":-5,"_44":242,"_243":-5,"_244":245,"_92":246,"_247":51,"_248":51,"_249":158,"_250":-5,"_251":-5,"_252":26,"_253":-5,"_254":26,"_255":-5,"_256":26,"_257":-5,"_258":26,"_259":-5,"_260":26,"_261":-5,"_262":26,"_263":264},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/nemo/models/tts_en_multispeaker_fastpitchhifigan/-","version","1.0.0","resolvedVersion","imageReference","registry","targetPath","cliDownloadCommand","ngc registry model download-version nvidia/nemo/tts_en_multispeaker_fastpitchhifigan:1.0.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/nemo/models/tts_en_multispeaker_fastpitchhifigan/1.0.0/version-history","accessCheckResult",{"_240":158},"hasRelatedCollections","showNimMetadataTab","context",{"_84":85,"_86":239},1794623458780,[],[],[232,233,234,235,236,237,238],{"_40":55,"_42":66,"_44":57,"_46":58,"_48":67,"_50":51},{"_40":55,"_42":64,"_44":57,"_46":58,"_48":65,"_50":51},{"_40":55,"_42":62,"_44":57,"_46":58,"_48":63,"_50":51},{"_40":55,"_42":60,"_44":57,"_46":58,"_48":61,"_50":51},{"_40":55,"_42":56,"_44":175,"_46":-5,"_48":-5,"_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},"nemo","allowed","versionId","UPLOAD_COMPLETE","releaseType","createdDate",["D",1656445620567],["D",1740798478276],"totalSizeInBytes","totalFileCount","isSigned","digest","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_77":275,"_269":276,"_243":-5,"_249":26,"_252":26,"_263":274,"_254":26},{"_77":273,"_269":273,"_243":-5,"_249":26,"_252":26,"_263":274,"_254":26},{"_77":271,"_269":271,"_243":-5,"_249":26,"_252":26,"_263":272,"_254":26},{"_77":212,"_269":212,"_243":-5,"_249":158,"_252":26,"_263":270,"_254":26},"name",[],"1.0.1",[],"1.10.0",[],"-","Latest (1.10.0)","nvidia/nemo/tts_en_multispeaker_fastpitchhifigan","nvidia/nemo/models/tts_en_multispeaker_fastpitchhifigan","tts_en_multispeaker_fastpitchhifigan","This collection contains two models:\n1) Multi-speaker FastPitch (around 50M parameters) trained on HiFiTTS with over 291.6 hours of english speech and 10 speakers.\n2) HiFiGAN trained on mel spectrograms produced by the Multi-speaker FastPitch in (1).","## Model Overview\n\nThe English-US Multispeaker FastPitch-HifiGAN model transcribes text into audio representations using two model components: FastPitch and HifiGAN. This model is ready for commercial use. \n\nThis collection contains two models:\n\n1) Multi-speaker FastPitch is a mel-spectrogram generator used as the first part of a neural text-to-speech system with a neural vocoder. This model's weights are conditioned to the energy of input audios and it uses the International Phonetic Alphabet (IPA) for inference and training. \n\n2) HiFiGAN is a neural vocoder model for text-to-speech applications. It is trained on mel spectrograms produced by the Multi-speaker FastPitch in (1). It is the second part of a two-stage speech synthesis pipeline.\n\n\n## Model Architecture\n\nFastPitch [1] is a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours. The model predicts pitch contours during inference. By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and in the end more engaging to the listener. FastPitch is based on a fully-parallel Transformer architecture, with much higher real-time factor than Tacotron2 for mel-spectrogram synthesis of a typical utterance. Additionally it uses unsupervised speech-text aligner [2].\n\nHiFiGAN [3], a generative adversarial network (GAN) model that generates audio from mel spectrograms produced by the Multi-speaker FastPitch in (1). The generator uses transposed convolutions to upsample mel spectrograms to audio. During training, the model uses a powerful discriminator consisting of small sub-discriminators, each one focusing on specific periodic parts of a raw waveform.\n\n\n## How to Use this Model\n\nThe model is available for use in the NeMo toolkit [4], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.\n\nIn order to generate spectrogram specific to a particular speaker you will need to provide speaker ID to FastPitch. The speaker IDs go from 1 to 20. For security purposes the model generates spectrograms for synthetic speakers generated by interpolating original HiFiTTS speakers.\n\nNOTE: For best results you should use the vocoder (HiFiGAN) checkpoint in this model card along with the mel spectrogram generator (FastPitch) checkpoint.\n\n### Automatically load the model from NGC\n\n```python\n# Load spectrogram generator\nfrom nemo.collections.tts.models import FastPitchModel\nspec_generator = FastPitchModel.from_pretrained(\"tts_en_fastpitch_multispeaker\")\n\n# Load Vocoder\nfrom nemo.collections.tts.models import HifiGanModel\nmodel = HifiGanModel.from_pretrained(model_name=\"tts_en_hifitts_hifigan_ft_fastpitch\")\n\n# Generate audio\nimport soundfile as sf\nparsed = spec_generator.parse(\"You can type your sentence here to get nemo to produce speech.\")\nspeaker_id = 10\nspectrogram = spec_generator.generate_spectrogram(tokens=parsed, speaker=10)\naudio = model.convert_spectrogram_to_audio(spec=spectrogram)\n\n# Save the audio to disk in a file called speech.wav\nsf.write(\"speech.wav\", audio.to('cpu').numpy(), 44100)\n```\n\n## References\n\n[1] Fastpitch: [https://arxiv.org/abs/2006.06873](https://arxiv.org/abs/2006.06873)\n\n[2] One TTS Alignment To Rule Them All: [https://arxiv.org/abs/2108.10447](https://arxiv.org/abs/2108.10447)\n\n[3] HiFiGan paper: [https://arxiv.org/abs/2010.05646](https://arxiv.org/abs/2010.05646)\n\n[4] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n## Input: (Enter \"None\" As Needed)\nFor FastPitch (1st Stage): Text Strings in English\n\n**Other Properties Related to Input:** [400 Character Text String Limit]
\n\n## Output: (Enter \"None\" As Needed)\nFor HifiGAN (2nd Stage): Audio of shape (batch x time) in wav format\n\nOutput Parameters Related to Output: Mono, Encoded 16 bit audio; 20 Second Maximum Length; Depending on input, this model can output a female or a male voice for American English with six (6) emotions for the female voice and four (4) emotions for male voices. The female voice is classified as “neutral, calm, happy, angry, fearful, and sad.” The male voice is classified as “neutral,” “calm,” “happy,” and “angry.” \n\n**Other Properties Related to Output:** [20 Second Maximum Length]
\n\n## Software Integration:\n**Runtime Engine(s):** [Riva 2.13.0]
\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): \ntts-FastPitch_44k_EnglishUS_IPA_v1.10.0
\n\n# Training & Evaluation: \n\n## Training Dataset:\n\n** Data Collection Method by dataset
\n* [Human]
\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** This model is trained on a proprietary dataset of audio-text pairs sampled at 44100 Hz, which contains one Female and one Male voice speaking US English. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. The dataset also contains a subset of sentences with different words emphasized.
\n\n## Evaluation Dataset:\n** Data Collection Method by dataset
\n* [Human]
\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** This model is trained on a proprietary dataset sampled at 44100 Hz, which contains one Female and one Male voice speaking US English. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. The dataset also contains a subset of sentences with different words emphasized.
\n\n## Inference:\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## Ethical Considerations:\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 Subcard. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","TTS En Multispeaker FastPitch HiFiGAN",["D",1706124995441],"/api/image/nvidia/nemo/models/tts_en_multispeaker_fastpitchhifigan","https://assets.nvidiagrid.net/ngc/logos/Nemo.png",546406400,"bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------\nParticipation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None of the Above\nMeasures taken to mitigate against unwanted bias: | None","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------\nIntended Application(s) & Domain(s): | Speech Synthesis\nModel Type: | 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. \nOutput: | Audio (.wav)\nDescribe how 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 including speech that includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. This model makes no effort to moderate or modify input text. \nVerified to have met prescribed NVIDIA standards: | Yes\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 pronunciation dictionary or omit phonetic symbols not used in training.\nLicensing: | https://www.developer.nvidia.com/riva/ga/license","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nProtected classes used to create this model? | Not Applicable\nWas consent obtained for any PII used? | Yes\nHow often is dataset reviewed? | When Acquired\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | No\nIf PII collected for the development of the model, was it collected directly by NVIDIA? | PII Not Collected for Development of This Model.\nIf PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf PII 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\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? | Yes, for data collected by NVIDIA. No, for all externally-sourced data.\nApplicable NVIDIA Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:------------------------------------------------------------------\nModel Application(s): | Speech Synthesis\nDescribe the life-critical application (if present).| Not Applicable\nUse Case Restrictions: | Abide by https://developer.nvidia.com/riva/ga/license \nDescribe access restrictions (if any): | 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.",[],[300,301],[],[],[],{"_90":304,"_131":304,"_132":305,"_134":135},{"_90":302,"_131":302,"_132":303,"_134":135},"PytorchLightning",{"_136":135,"_131":302},"En",{"_136":135,"_131":304},{"_180":1439,"_182":1440},"ngcApps",[1392],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1373":1374,"_1375":1376,"_1377":1378,"_1379":1380,"_1381":317,"_211":343,"_1382":1383,"_1384":51,"_1385":158,"_1386":158,"_1387":1388,"_1389":158,"_1390":1391},"amplitude",{"_1370":1371,"_981":-7,"_1372":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1364":1365,"_1366":1367,"_1368":1369},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1794623458726,"1.31.0","defaultSearchSuggestions",{"_1251":426,"_1252":1253,"_1254":1255,"_1256":1257},"announcements",[],"consentState",{"_382":1241,"_1242":26,"_1243":26,"_1244":158,"_1245":1246},"consentConfig",{"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379,"_380":381,"_382":383},"showContactAdmin",1794623458727,[],[],[357,358,359,360,361,362,363],{"_40":55,"_42":66,"_44":175,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":175,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":175,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":175,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":175,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":175,"_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. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567],[406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423],{"_424":525,"_90":525,"_89":426,"_396":526},{"_424":521,"_90":521,"_89":426,"_396":522},{"_424":516,"_90":516,"_89":426,"_396":517},{"_424":512,"_90":512,"_89":426,"_396":513},{"_424":507,"_90":507,"_89":426,"_396":508},{"_424":502,"_90":502,"_89":426,"_396":503},{"_424":495,"_90":495,"_89":426,"_396":496},{"_424":487,"_90":487,"_89":426,"_396":488},{"_424":482,"_90":482,"_89":426,"_396":483},{"_424":477,"_90":477,"_89":426,"_396":478},{"_424":469,"_90":469,"_89":426,"_396":470},{"_424":464,"_90":464,"_89":426,"_396":465},{"_424":459,"_90":459,"_89":426,"_396":460},{"_424":454,"_90":454,"_89":426,"_396":455},{"_424":447,"_90":447,"_89":426,"_396":448},{"_424":442,"_90":442,"_89":426,"_396":443},{"_424":435,"_90":435,"_89":426,"_396":436},{"_424":425,"_90":425,"_89":426,"_396":427},"hostName","amazon-adsystem.com","",[428,429],{"_269":434,"_431":425,"_432":26,"_89":433},{"_269":430,"_431":425,"_432":26,"_89":433},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[437,438],{"_269":441,"_431":435,"_432":26,"_89":440},{"_269":439,"_431":435,"_432":26,"_89":440},"IDE","This domain is owned by Doubleclick (Google). 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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. 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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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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","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.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.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.\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.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","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","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.","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.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed 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.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","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.","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. 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