Model Overview

This collection contains two models:

\n
Multi-speaker 44100Hz FastPitch (around 50M parameters) trained on approximately 20 hours of Latin American Spanish speech from 174 speakers (1 to 10 minutes of audio per speaker) spread across 6 locales (es-AR, es-CL, es-CO, es-PE, es-PR, es-VE) from OpenSLR.
\n
\n
HiFiGAN trained on mel spectrograms produced by the Multi-speaker FastPitch in (1).
\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]. It is trained used a mixed text representation of graphemes and IPA phonemes [3].

HiFiGAN [4], 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.

Dataset

The original OpenSLR dataset contains approximately 38 hours of audio sampled at 48000Hz. For training we downsample to 44100Hz, and trim the silence from the beginning and end of each file (totaling about 18 hours of silence), reducing the dataset size from 38 hours to 20 hours.

https://www.openslr.org/61

https://www.openslr.org/71

https://www.openslr.org/72

https://www.openslr.org/73

https://www.openslr.org/74

https://www.openslr.org/75

https://research.google/pubs/pub49150/

Performance

No performance information available at this time.

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 0 to 173.

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

import soundfile as sf\nfrom nemo.collections.tts.models import FastPitchModel\nfrom nemo.collections.tts.models import HifiGanModel\n\nfastpitch_name = \"tts_es_fastpitch_multispeaker\"\nhifigan_name = \"tts_es_hifigan_ft_fastpitch_multispeaker\"\n\n# Load spectrogram generator\nspec_generator = FastPitchModel.from_pretrained(fastpitch_name)\n\n# Load Vocoder\nmodel = HifiGanModel.from_pretrained(hifigan_name)\n\n# Generate audio\ntext = \"Escribe tu texto aquí.\"\n# Optionally, provide custom IPA input escaped with |\n# text = \"|e s k ɾ ˈ i β e| tu |t ˈ e k s t o| aquí.\"\n\nparsed = spec_generator.parse(text, normalize=False)\nspeaker = 5\nspectrogram = spec_generator.generate_spectrogram(tokens=parsed, speaker=speaker)\naudio = model.convert_spectrogram_to_audio(spec=spectrogram)\naudio = audio.detach().cpu().numpy()\n\n# Save the audio to disk in a file called speech.wav\nsample_rate = 44100\nsf.write(\"speech.wav\", audio, sample_rate)\n

Input/Output

The FastPitch model accepts batches of text and speaker IDs and outputs mel spectrograms. The HifiGan model accepts batches of mel spectrograms and outputs audio.

Limitations

This model was trained on a limited amount of publically available speech data. The performance might degrade for speech which includes terms or vernacular that the model has not been trained on. The model might also perform worse on certain locales, regional dialects, or accents.

Versions

1.15.0 (current): Added IPA support. Contains built in pronunciation dictionary and accepts mixed inputs of graphemes and IPA phonemes. Trained with speaker level pitch normalization.\n1.14.0: The original version released with NeMo 1.14.0 which supports Spanish grapheme input.

References

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

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

[3] Mixed representation training: https://arxiv.org/abs/1811.07240

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

[5] NVIDIA NeMo Toolkit: https://github.com/NVIDIA/NeMo

[6] OpenSLR data paper: https://arxiv.org/abs/2010.05646

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","resourceVersions",[201,202,203],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_184":29,"_185":-5,"_53":82,"_79":-5,"_83":186,"_85":187,"_188":81,"_91":16,"_88":35,"_189":-5,"_87":35,"_190":-5,"_191":35,"_192":-5,"_193":35,"_194":-5,"_195":35,"_196":-5,"_197":35,"_198":-5,"_199":35,"_89":200},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/nemo/models/tts_es_multispeaker_fastpitchhifigan/-","version","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/nemo/tts_es_multispeaker_fastpitchhifigan:1.14.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/nemo/models/tts_es_multispeaker_fastpitchhifigan/1.14.0/version-history","accessCheckResult",{"_183":104},"hasRelatedCollections","context",{"_179":180,"_181":182},1783598154247,[],[],[172,173,174,175,176,177,178],{"_49":63,"_51":74,"_53":65,"_55":66,"_57":71,"_59":60},{"_49":63,"_51":72,"_53":120,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":65,"_55":66,"_57":71,"_59":60},{"_49":63,"_51":68,"_53":65,"_55":66,"_57":69,"_59":60},{"_49":63,"_51":64,"_53":65,"_55":66,"_57":67,"_59":60},{"_49":61,"_51":62,"_53":54,"_55":56,"_57":58,"_59":60},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":58,"_59":60},"orgName","nvidia","teamName","nemo","allowed","versionId","description",["D",1670022680347],["D",1740798540952],"totalSizeInBytes","malwareScanStatus","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance",[],{"_77":206,"_78":207,"_79":-5,"_88":35,"_87":35,"_89":205,"_191":35},{"_77":98,"_78":98,"_79":-5,"_88":35,"_87":35,"_89":205,"_191":35},{"_77":29,"_78":29,"_79":-5,"_88":35,"_87":35,"_89":204,"_191":35},[],[],"-","Latest (1.15.0)","nvidia/nemo/tts_es_multispeaker_fastpitchhifigan","key","nvidia/nemo/models/tts_es_multispeaker_fastpitchhifigan","resourceId","tts_es_multispeaker_fastpitchhifigan","shortDescription","This collection contains two models. 1) Multi-speaker 44100Hz FastPitch trained on approximately 20 hours of Latin American Spanish speech from 174 speakers. 2) HiFiGAN trained on mel spectrograms produced by the Multi-speaker FastPitch in (1).","## Model Overview\n\nThis collection contains two models:\n\n1) Multi-speaker 44100Hz FastPitch (around 50M parameters) trained on approximately 20 hours of Latin American Spanish speech from 174 speakers (1 to 10 minutes of audio per speaker) spread across 6 locales (es-AR, es-CL, es-CO, es-PE, es-PR, es-VE) from OpenSLR.\n\n2) HiFiGAN trained on mel spectrograms produced by the Multi-speaker FastPitch in (1).\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]. It is trained used a mixed text representation of graphemes and IPA phonemes [3].\n\nHiFiGAN [4], 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.\n\n## Dataset\n\nThe original OpenSLR dataset contains approximately 38 hours of audio sampled at 48000Hz. For training we downsample to 44100Hz, and trim the silence from the beginning and end of each file (totaling about 18 hours of silence), reducing the dataset size from 38 hours to 20 hours.\n\nhttps://www.openslr.org/61\n\nhttps://www.openslr.org/71\n\nhttps://www.openslr.org/72\n\nhttps://www.openslr.org/73\n\nhttps://www.openslr.org/74\n\nhttps://www.openslr.org/75\n\nhttps://research.google/pubs/pub49150/\n\n## Performance\n\nNo performance information available at this time.\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 0 to 173.\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```python\nimport soundfile as sf\nfrom nemo.collections.tts.models import FastPitchModel\nfrom nemo.collections.tts.models import HifiGanModel\n\nfastpitch_name = \"tts_es_fastpitch_multispeaker\"\nhifigan_name = \"tts_es_hifigan_ft_fastpitch_multispeaker\"\n\n# Load spectrogram generator\nspec_generator = FastPitchModel.from_pretrained(fastpitch_name)\n\n# Load Vocoder\nmodel = HifiGanModel.from_pretrained(hifigan_name)\n\n# Generate audio\ntext = \"Escribe tu texto aquí.\"\n# Optionally, provide custom IPA input escaped with |\n# text = \"|e s k ɾ ˈ i β e| tu |t ˈ e k s t o| aquí.\"\n\nparsed = spec_generator.parse(text, normalize=False)\nspeaker = 5\nspectrogram = spec_generator.generate_spectrogram(tokens=parsed, speaker=speaker)\naudio = model.convert_spectrogram_to_audio(spec=spectrogram)\naudio = audio.detach().cpu().numpy()\n\n# Save the audio to disk in a file called speech.wav\nsample_rate = 44100\nsf.write(\"speech.wav\", audio, sample_rate)\n```\n\n## Input/Output\n\nThe FastPitch model accepts batches of text and speaker IDs and outputs mel spectrograms. The HifiGan model accepts batches of mel spectrograms and outputs audio.\n\n## Limitations\n\nThis model was trained on a limited amount of publically available speech data. The performance might degrade for speech which includes terms or vernacular that the model has not been trained on. The model might also perform worse on certain locales, regional dialects, or accents.\n\n## Versions\n\n1.15.0 (current): Added IPA support. Contains built in pronunciation dictionary and accepts mixed inputs of graphemes and IPA phonemes. Trained with speaker level pitch normalization.\n1.14.0: The original version released with NeMo 1.14.0 which supports Spanish grapheme input. \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] Mixed representation training: [https://arxiv.org/abs/1811.07240](https://arxiv.org/abs/1811.07240)\n\n[4] HiFiGan paper: [https://arxiv.org/abs/2010.05646](https://arxiv.org/abs/2010.05646)\n\n[5] NVIDIA NeMo Toolkit: [https://github.com/NVIDIA/NeMo](https://github.com/NVIDIA/NeMo)\n\n[6] OpenSLR data paper: [https://arxiv.org/abs/2010.05646](https://research.google/pubs/pub49150/)\n\n\n### NVIDIA License\n\n#### 1. Definitions\n\n"Licensor" means any person or entity that distributes its Work.\n\n"Work" means (a) the original work of authorship made available under this license, which may include software, documentation, or other files, and (b) any additions to or derivative works thereof that are made available under this license.\n\nThe terms "reproduce," "reproduction," "derivative works," and "distribution" have the meaning as provided under U.S. copyright law; provided, however, that for the purposes of this license, derivative works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work.\n\nWorks are "made available" under this license by including in or with the Work either (a) a copyright notice referencing the applicability of this license to the Work, or (b) a copy of this license.\n\n#### 2. License Grant\n\n2.1 Copyright Grant. Subject to the terms and conditions of this license, each Licensor grants to you a perpetual, worldwide, non-exclusive, royalty-free, copyright license to use, reproduce, prepare derivative works of, publicly display, publicly perform, sublicense and distribute its Work and any resulting derivative works in any form.\n\n#### 3. Limitations\n\n3.1 Redistribution. You may reproduce or distribute the Work only if (a) you do so under this license, (b) you include a complete copy of this license with your distribution, and (c) you retain without modification any copyright, patent, trademark, or attribution notices that are present in the Work.\n\n3.2 Derivative Works. You may specify that additional or different terms apply to the use, reproduction, and distribution of your derivative works of the Work ("Your Terms") only if (a) Your Terms provide that the use limitation in Section 3.3 applies to your derivative works, and (b) you identify the specific derivative works that are subject to Your Terms. Notwithstanding Your Terms, this license (including the redistribution requirements in Section 3.1) will continue to apply to the Work itself.\n\n3.3 Use Limitation. The Work and any derivative works thereof only may be used or intended for use non-commercially. Notwithstanding the foregoing, NVIDIA Corporation and its affiliates may use the Work and any derivative works commercially. As used herein, "non-commercially" means for research or evaluation purposes only.\n\n3.4 Patent Claims. If you bring or threaten to bring a patent claim against any Licensor (including any claim, cross-claim or counterclaim in a lawsuit) to enforce any patents that you allege are infringed by any Work, then your rights under this license from such Licensor (including the grant in Section 2.1) will terminate immediately.\n\n3.5 Trademarks. This license does not grant any rights to use any Licensor's or its affiliates' names, logos, or trademarks, except as necessary to reproduce the notices described in this license.\n\n3.6 Termination. If you violate any term of this license, then your rights under this license (including the grant in Section 2.1) will terminate immediately.\n\n#### 4. Disclaimer of Warranty.\n\nTHE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF\n\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER THIS LICENSE.\n\n#### 5. Limitation of Liability.\n\nEXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.","displayName","TTS Es Multispeaker FastPitch HiFiGAN",["D",1680637416062],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/tts_es_multispeaker_fastpitchhifigan","rawImageUrl","","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[247,248,249,250,251,252,253],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_216":275,"_255":276,"_256":277,"_258":259},{"_216":271,"_255":272,"_256":273,"_258":259},{"_216":269,"_255":269,"_256":270,"_258":259},{"_216":267,"_255":267,"_256":268,"_258":259},{"_216":264,"_255":264,"_256":265,"_258":259},{"_216":262,"_255":262,"_256":263,"_258":259},{"_216":254,"_255":254,"_256":257,"_258":259},"Spanish","value","filter",{"_260":261,"_255":254},"category","label","field","language","PytorchLightning",{"_260":259,"_255":262},"PyTorch",{"_260":266,"_255":264},"framework","NeMo",{"_260":266,"_255":267},"Es",{"_260":259,"_255":269},"DL","soln_dl",{"_260":274,"_255":272},"technology","AI","soln_ai",{"_260":274,"_255":276},{"_125":1451,"_127":1452},"ngcApps",[1404],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1385":1386,"_1387":1388,"_1389":1390,"_1391":1392,"_1393":289,"_156":313,"_1394":1395,"_1396":60,"_1397":104,"_1398":104,"_1399":1400,"_1401":104,"_1402":1403},"amplitude",{"_1382":1383,"_986":-7,"_1384":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1376":1377,"_1378":1379,"_1380":1381},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1261":226,"_1262":1263,"_1264":1265,"_1266":1267},"announcements",[],"consentState",{"_351":1251,"_1252":104,"_1253":35,"_1254":35,"_1255":1256},"consentConfig",{"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352},"showContactAdmin",1783598154176,[],[],[327,328,329,330,331,332],{"_49":63,"_51":74,"_53":120,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":120,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":68,"_53":120,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":120,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":120,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":120,"_55":-5,"_57":-5,"_59":60},"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,[502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536],[375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391],{"_392":498,"_216":498,"_185":226,"_365":499},{"_392":493,"_216":493,"_185":226,"_365":494},{"_392":489,"_216":489,"_185":226,"_365":490},{"_392":484,"_216":484,"_185":226,"_365":485},{"_392":479,"_216":479,"_185":226,"_365":480},{"_392":467,"_216":467,"_185":226,"_365":468},{"_392":455,"_216":455,"_185":226,"_365":456},{"_392":450,"_216":450,"_185":226,"_365":451},{"_392":439,"_216":439,"_185":226,"_365":440},{"_392":434,"_216":434,"_185":226,"_365":435},{"_392":429,"_216":429,"_185":226,"_365":430},{"_392":424,"_216":424,"_185":226,"_365":425},{"_392":419,"_216":419,"_185":226,"_365":420},{"_392":414,"_216":414,"_185":226,"_365":415},{"_392":409,"_216":409,"_185":226,"_365":410},{"_392":402,"_216":402,"_185":226,"_365":403},{"_392":393,"_216":393,"_185":226,"_365":394},"hostName","amazon-adsystem.com",[395,396],{"_78":401,"_398":393,"_399":35,"_185":400},{"_78":397,"_398":393,"_399":35,"_185":400},"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",[404,405],{"_78":408,"_398":402,"_399":35,"_185":407},{"_78":406,"_398":402,"_399":35,"_185":407},"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",[431],{"_78":432,"_398":429,"_399":35,"_185":433},"sp","Testing","google.com",[436],{"_78":437,"_398":434,"_399":35,"_185":438},"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",[441,442,443,444],{"_78":449,"_398":439,"_399":35,"_185":446},{"_78":448,"_398":439,"_399":35,"_185":446},{"_78":447,"_398":439,"_399":35,"_185":446},{"_78":445,"_398":439,"_399":35,"_185":446},"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",[452],{"_78":453,"_398":450,"_399":35,"_185":454},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[457,458,459,460],{"_78":466,"_398":455,"_399":35,"_185":462},{"_78":464,"_398":455,"_399":35,"_185":465},{"_78":463,"_398":455,"_399":35,"_185":462},{"_78":461,"_398":455,"_399":35,"_185":462},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. 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",[469,470,471,472],{"_78":478,"_398":467,"_399":35,"_185":476},{"_78":477,"_398":467,"_399":35,"_185":476},{"_78":475,"_398":467,"_399":35,"_185":476},{"_78":473,"_398":467,"_399":35,"_185":474},"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",[481],{"_78":482,"_398":479,"_399":35,"_185":483},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[486],{"_78":487,"_398":484,"_399":35,"_185":488},"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",[491],{"_78":492,"_398":489,"_399":35,"_185":433},"R","6sc.co",[495],{"_78":496,"_398":493,"_399":35,"_185":497},"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.",4,[625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645],[590,591,592,593,594],{"_392":484,"_216":484,"_185":226,"_365":616},{"_392":450,"_216":450,"_185":226,"_365":611},{"_392":604,"_216":604,"_185":226,"_365":605},{"_392":600,"_216":600,"_185":226,"_365":601},{"_392":595,"_216":595,"_185":226,"_365":596},"nvcrm.my.site.com",[597],{"_78":598,"_398":595,"_399":35,"_185":599},"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",[602],{"_78":603,"_398":600,"_399":35,"_185":433},"edge","github.com",[606,607],{"_78":610,"_398":604,"_399":35,"_185":609},{"_78":608,"_398":604,"_399":35,"_185":609},"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. 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