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",[260,261,262,263],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_235":210,"_89":-5,"_44":236,"_237":-5,"_238":239,"_92":240,"_241":242,"_243":244,"_245":26,"_246":-5,"_247":26,"_248":-5,"_249":26,"_250":-5,"_251":26,"_252":-5,"_253":26,"_254":-5,"_255":26,"_256":-5,"_257":26,"_258":259},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/nemo/models/tts_en_multispeaker_fastpitchhifigan/-","version","1.0.1","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/nemo/tts_en_multispeaker_fastpitchhifigan:1.0.1","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/nemo/models/tts_en_multispeaker_fastpitchhifigan/1.0.1/version-history","accessCheckResult",{"_234":157},"hasRelatedCollections","context",{"_84":85,"_86":233},1784049587652,[],[],[226,227,228,229,230,231,232],{"_40":54,"_42":65,"_44":56,"_46":57,"_48":66,"_50":51},{"_40":54,"_42":63,"_44":56,"_46":57,"_48":64,"_50":51},{"_40":54,"_42":61,"_44":56,"_46":57,"_48":62,"_50":51},{"_40":54,"_42":59,"_44":56,"_46":57,"_48":60,"_50":51},{"_40":54,"_42":55,"_44":173,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":49,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},"nemo","allowed","versionId","UPLOAD_COMPLETE","releaseType","createdDate",["D",1656445636564],["D",1740798505876],"totalSizeInBytes",546406400,"totalFileCount",2,"isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_77":270,"_265":271,"_237":-5,"_245":26,"_247":26,"_258":269,"_249":26},{"_77":268,"_265":268,"_237":-5,"_245":26,"_247":26,"_258":269,"_249":26},{"_77":210,"_265":210,"_237":-5,"_245":26,"_247":26,"_258":267,"_249":26},{"_77":264,"_265":264,"_237":-5,"_245":157,"_247":26,"_258":266,"_249":26},"1.0.0","name",[],[],"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","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.",[],[294,295],[],[],[],{"_90":298,"_130":298,"_131":299,"_133":134},{"_90":296,"_130":296,"_131":297,"_133":134},"PytorchLightning",{"_135":134,"_130":296},"En",{"_135":134,"_130":298},{"_178":1472,"_180":1473},"ngcApps",[1425],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1406":1407,"_1408":1409,"_1410":1411,"_1412":1413,"_1414":311,"_209":335,"_1415":1416,"_1417":51,"_1418":157,"_1419":157,"_1420":1421,"_1422":157,"_1423":1424},"amplitude",{"_1403":1404,"_1009":-7,"_1405":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1397":1398,"_1399":1400,"_1401":1402},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1284":417,"_1285":1286,"_1287":1288,"_1289":1290},"announcements",[],"consentState",{"_374":1274,"_1275":157,"_1276":26,"_1277":26,"_1278":1279},"consentConfig",{"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375},"showContactAdmin",1784049587580,[],[],[349,350,351,352,353,354],{"_40":54,"_42":65,"_44":173,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":63,"_44":173,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":61,"_44":173,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":173,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":173,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":355,"_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. 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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. 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,[526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560],[398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414],{"_415":522,"_90":522,"_89":417,"_388":523},{"_415":517,"_90":517,"_89":417,"_388":518},{"_415":513,"_90":513,"_89":417,"_388":514},{"_415":508,"_90":508,"_89":417,"_388":509},{"_415":503,"_90":503,"_89":417,"_388":504},{"_415":491,"_90":491,"_89":417,"_388":492},{"_415":479,"_90":479,"_89":417,"_388":480},{"_415":474,"_90":474,"_89":417,"_388":475},{"_415":463,"_90":463,"_89":417,"_388":464},{"_415":458,"_90":458,"_89":417,"_388":459},{"_415":453,"_90":453,"_89":417,"_388":454},{"_415":448,"_90":448,"_89":417,"_388":449},{"_415":443,"_90":443,"_89":417,"_388":444},{"_415":438,"_90":438,"_89":417,"_388":439},{"_415":433,"_90":433,"_89":417,"_388":434},{"_415":426,"_90":426,"_89":417,"_388":427},{"_415":416,"_90":416,"_89":417,"_388":418},"hostName","amazon-adsystem.com","",[419,420],{"_265":425,"_422":416,"_423":26,"_89":424},{"_265":421,"_422":416,"_423":26,"_89":424},"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",[428,429],{"_265":432,"_422":426,"_423":26,"_89":431},{"_265":430,"_422":426,"_423":26,"_89":431},"IDE","This domain is owned by Doubleclick (Google). 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[455],{"_265":456,"_422":453,"_423":26,"_89":457},"sp","Testing","google.com",[460],{"_265":461,"_422":458,"_423":26,"_89":462},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[465,466,467,468],{"_265":473,"_422":463,"_423":26,"_89":470},{"_265":472,"_422":463,"_423":26,"_89":470},{"_265":471,"_422":463,"_423":26,"_89":470},{"_265":469,"_422":463,"_423":26,"_89":470},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[476],{"_265":477,"_422":474,"_423":26,"_89":478},"bcookie","This domain is owned by LinkedIn, the business networking platform. 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Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[493,494,495,496],{"_265":502,"_422":491,"_423":26,"_89":500},{"_265":501,"_422":491,"_423":26,"_89":500},{"_265":499,"_422":491,"_423":26,"_89":500},{"_265":497,"_422":491,"_423":26,"_89":498},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[505],{"_265":506,"_422":503,"_423":26,"_89":507},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[510],{"_265":511,"_422":508,"_423":26,"_89":512},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[515],{"_265":516,"_422":513,"_423":26,"_89":457},"R","6sc.co",[519],{"_265":520,"_422":517,"_423":26,"_89":521},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. 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. If you do not allow these cookies, then some or all of these services may not function properly.",[648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668],[613,614,615,616,617],{"_415":508,"_90":508,"_89":417,"_388":639},{"_415":474,"_90":474,"_89":417,"_388":634},{"_415":627,"_90":627,"_89":417,"_388":628},{"_415":623,"_90":623,"_89":417,"_388":624},{"_415":618,"_90":618,"_89":417,"_388":619},"nvcrm.my.site.com",[620],{"_265":621,"_422":618,"_423":26,"_89":622},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[625],{"_265":626,"_422":623,"_423":26,"_89":457},"edge","github.com",[629,630],{"_265":633,"_422":627,"_423":26,"_89":632},{"_265":631,"_422":627,"_423":26,"_89":632},"logged_in","This domain is owned by Github. 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The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website 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