Model Overview

This collection contains two models:

\n
FastPitch (around 50M parameters) trained on OpenSLR neutral German dataset with over 23 hours of German speech and 1 speaker.
\n
\n
HiFi-GAN trained on mel spectrograms produced by the FastPitch model in (1).
\n

Model Architecture

FastPitch

FastPitch is non-autoregressive model for mel-spectrogram generation based on FastSpeech, conditioned on fundamental frequency contours. For more details about model, please refer to the original paper. Original FastPitch model uses an external Tacotron 2 model trained on LJSpeech-1.1 to extract training alignments and estimate durations of input symbols. This implementation of FastPitch is based on Deep Learning Examples, which uses an alignment mechanism proposed in RAD-TTS and extended in TTS Aligner.

For more information on training a basic FastPitch model, please refer to FastPitch_MixerTTS_Training.ipynb tutorial.

HiFi-GAN

HiFi-GAN is a generative adversarial network (GAN) model that generates audio from mel spectrograms. The generator uses transposed convolutions to upsample mel spectrograms to audio. For more details about the model, please refer to the original paper. NeMo re-implementation of HiFi-GAN can be found here.

Training

Datasets

FastPitch: This model is trained from scratch on one male speaker named Thorsten Müller from OpenSLR - German Neutral-TTS dataset sampled at 22050Hz. Link here.

HiFi-GAN: This model is derived after finetuning TTS Vocoder Hifigan v1.0.0rc1 (pretrained on English dataset) on predicted mel spectrograms from FastPitch above.

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.

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_de_fastpitch\")\n\n# Load Vocoder\nfrom nemo.collections.tts.models import HifiGanModel\nmodel = HifiGanModel.from_pretrained(model_name=\"tts_de_openslr_hifigan_ft_fastpitch\")\n\n# Generate audio\nimport soundfile as sf\nparsed = spec_generator.parse(\"Sie können Ihren Satz hier eingeben, um Nemo dazu zu bringen, Sprache zu produzieren.\")\nspectrogram = spec_generator.generate_spectrogram(tokens=parsed)\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(), 22050)\n

Input

This model accepts batches of text.

Output

This model generates mel spectrograms.

Limitations

Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. This checkpoint only works well with vocoders that were trained on 22050Hz data. Otherwise, the generated audio may be scratchy or choppy-sounding.

Versions

1.15.0 (latest): This version applies grapheme-based tokenizer with grapheme-only symbol set, where the capitalization of each word is preserved. It includes FastPitch and HifiGan models trained on Thorsten's Neutral Datasets 21.02 and 22.10, respectively. There are four model checkpoints available.
1.10.0: This version applies phoneme-based tokenizer with the symbol set mixed with both phonemes and graphemes on Thorsten's Neural 21.02 dataset, where sentenc-level grapheme sequence and phoneme sequence for each transcript are considered as valid inputs. The version of FastPitch 1.1 without energy conditioning, see details here.

References

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

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

[3] HiFi-GAN paper: https://arxiv.org/abs/2010.05646

[4] NVIDIA NeMo Toolkit

NVIDIA License

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","resourceVersions",[203,204,205],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_186":29,"_187":-5,"_53":83,"_80":-5,"_84":188,"_86":189,"_190":99,"_92":62,"_89":35,"_191":-5,"_88":35,"_192":-5,"_193":35,"_194":-5,"_195":35,"_196":-5,"_197":35,"_198":-5,"_199":35,"_200":-5,"_201":35,"_90":202},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/nemo/models/tts_de_fastpitchhifigan/-","version","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/nemo/tts_de_fastpitchhifigan:1.15.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/nemo/models/tts_de_fastpitchhifigan/1.15.0/version-history","accessCheckResult",{"_185":107},"hasRelatedCollections","context",{"_181":182,"_183":184},1784104457000,[],[],[175,176,177,178,179,180],{"_49":63,"_51":73,"_53":65,"_55":66,"_57":74,"_58":59},{"_49":63,"_51":70,"_53":65,"_55":66,"_57":71,"_58":59},{"_49":63,"_51":68,"_53":65,"_55":66,"_57":69,"_58":59},{"_49":63,"_51":64,"_53":65,"_55":66,"_57":67,"_58":59},{"_49":60,"_51":61,"_53":123,"_55":-5,"_57":-5,"_58":59},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":16,"_58":59},"orgName","nvidia","teamName","nemo","allowed","versionId","description",["D",1675290928041],["D",1740798427598],"totalSizeInBytes","malwareScanStatus","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance",[],{"_77":208,"_79":209,"_80":-5,"_89":35,"_88":35,"_90":207,"_193":35},{"_77":29,"_79":29,"_80":-5,"_89":35,"_88":35,"_90":207,"_193":35},{"_77":78,"_79":78,"_80":-5,"_89":35,"_88":35,"_90":206,"_193":35},[],[],"-","Latest (1.15.0)","nvidia/nemo/tts_de_fastpitchhifigan","key","nvidia/nemo/models/tts_de_fastpitchhifigan","resourceId","tts_de_fastpitchhifigan","shortDescription","This collection contains two models: 1) FastPitch (around 50M parameters) trained on OpenSLR neutral German dataset with over 23 hours of German speech and 1 speaker. 2) HiFi-GAN trained on mel spectrograms produced by the FastPitch model in (1).","## Model Overview\n\nThis collection contains two models:\n\n1) FastPitch (around 50M parameters) trained on OpenSLR neutral German dataset with over 23 hours of German speech and 1 speaker.\n\n2) HiFi-GAN trained on mel spectrograms produced by the FastPitch model in (1).\n\n## Model Architecture\n\n### FastPitch\n\nFastPitch is non-autoregressive model for mel-spectrogram generation based on FastSpeech, conditioned on fundamental frequency contours. For more details about model, please refer to the original [paper](https://ieeexplore.ieee.org/abstract/document/9413889). Original [FastPitch model](https://ieeexplore.ieee.org/abstract/document/9413889) uses an external Tacotron 2 model trained on LJSpeech-1.1 to extract training alignments and estimate durations of input symbols. This implementation of FastPitch is based on [Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/FastPitch), which uses an alignment mechanism proposed in [RAD-TTS](https://openreview.net/pdf?id=0NQwnnwAORi) and extended in [TTS Aligner](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9747707).\n\nFor more information on training a basic FastPitch model, please refer to [FastPitch_MixerTTS_Training.ipynb](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/FastPitch_MixerTTS_Training.ipynb) tutorial.\n\n### HiFi-GAN\n\nHiFi-GAN is a generative adversarial network (GAN) model that generates audio from mel spectrograms. The generator uses transposed convolutions to upsample mel spectrograms to audio. For more details about the model, please refer to the original [paper](https://arxiv.org/abs/2010.05646). NeMo re-implementation of HiFi-GAN can be found [here](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/models/hifigan.py).\n\n## Training\n\n### Datasets\n\nFastPitch: This model is trained from scratch on one male speaker named Thorsten Müller from OpenSLR - German Neutral-TTS dataset sampled at 22050Hz. Link [here](https://www.openslr.org/95).\n\nHiFi-GAN: This model is derived after finetuning [TTS Vocoder Hifigan v1.0.0rc1](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_hifigan) (pretrained on English dataset) on predicted mel spectrograms from FastPitch above.\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\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_de_fastpitch\")\n\n# Load Vocoder\nfrom nemo.collections.tts.models import HifiGanModel\nmodel = HifiGanModel.from_pretrained(model_name=\"tts_de_openslr_hifigan_ft_fastpitch\")\n\n# Generate audio\nimport soundfile as sf\nparsed = spec_generator.parse(\"Sie können Ihren Satz hier eingeben, um Nemo dazu zu bringen, Sprache zu produzieren.\")\nspectrogram = spec_generator.generate_spectrogram(tokens=parsed)\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(), 22050)\n```\n\n### Input\n\nThis model accepts batches of text.\n\n### Output\n\nThis model generates mel spectrograms.\n\n## Limitations\n\nSince this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. This checkpoint only works well with vocoders that were trained on 22050Hz data. Otherwise, the generated audio may be scratchy or choppy-sounding.\n\n## Versions\n* 1.15.0 (latest): This version applies grapheme-based tokenizer with grapheme-only symbol set, where the capitalization of each word is preserved. It includes FastPitch and HifiGan models trained on Thorsten's Neutral Datasets 21.02 and 22.10, respectively. There are four model checkpoints available.\n* 1.10.0: This version applies phoneme-based tokenizer with the symbol set mixed with both phonemes and graphemes on Thorsten's Neural 21.02 dataset, where sentenc-level grapheme sequence and phoneme sequence for each transcript are considered as valid inputs. The version of FastPitch 1.1 without energy conditioning, see details [here](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/FastPitch).\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] HiFi-GAN 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### 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 De FastPitch HiFi-GAN",["D",1680636722805],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/tts_de_fastpitchhifigan","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[249,250,251,252,253,254],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_218":274,"_256":274,"_257":275,"_259":260},{"_218":270,"_256":271,"_257":272,"_259":260},{"_218":267,"_256":267,"_257":268,"_259":260},{"_218":265,"_256":265,"_257":266,"_259":260},{"_218":262,"_256":262,"_257":263,"_259":260},{"_218":255,"_256":255,"_257":258,"_259":260},"PytorchLightning","value","filter",{"_261":260,"_256":255},"category","label","field","PyTorch",{"_261":264,"_256":262},"framework","NeMo",{"_261":264,"_256":265},"German",{"_261":269,"_256":267},"language","DL","soln_dl",{"_261":273,"_256":271},"technology","De",{"_261":260,"_256":274},{"_128":1450,"_130":1451},"ngcApps",[1403],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1384":1385,"_1386":1387,"_1388":1389,"_1390":1391,"_1392":287,"_159":311,"_1393":1394,"_1395":59,"_1396":107,"_1397":107,"_1398":1399,"_1400":107,"_1401":1402},"amplitude",{"_1381":1382,"_985":-7,"_1383":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1375":1376,"_1377":1378,"_1379":1380},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1260":392,"_1261":1262,"_1263":1264,"_1265":1266},"announcements",[],"consentState",{"_349":1250,"_1251":107,"_1252":35,"_1253":35,"_1254":1255},"consentConfig",{"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350},"showContactAdmin",1784104456923,[],[],[325,326,327,328,329,330],{"_49":63,"_51":73,"_53":123,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":70,"_53":123,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":68,"_53":123,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":64,"_53":123,"_55":-5,"_57":-5,"_58":59},{"_49":60,"_51":61,"_53":123,"_55":-5,"_57":-5,"_58":59},{"_49":50,"_51":52,"_53":123,"_55":-5,"_57":-5,"_58":59},"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,[501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535],[373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389],{"_390":497,"_218":497,"_187":392,"_363":498},{"_390":492,"_218":492,"_187":392,"_363":493},{"_390":488,"_218":488,"_187":392,"_363":489},{"_390":483,"_218":483,"_187":392,"_363":484},{"_390":478,"_218":478,"_187":392,"_363":479},{"_390":466,"_218":466,"_187":392,"_363":467},{"_390":454,"_218":454,"_187":392,"_363":455},{"_390":449,"_218":449,"_187":392,"_363":450},{"_390":438,"_218":438,"_187":392,"_363":439},{"_390":433,"_218":433,"_187":392,"_363":434},{"_390":428,"_218":428,"_187":392,"_363":429},{"_390":423,"_218":423,"_187":392,"_363":424},{"_390":418,"_218":418,"_187":392,"_363":419},{"_390":413,"_218":413,"_187":392,"_363":414},{"_390":408,"_218":408,"_187":392,"_363":409},{"_390":401,"_218":401,"_187":392,"_363":402},{"_390":391,"_218":391,"_187":392,"_363":393},"hostName","amazon-adsystem.com","",[394,395],{"_79":400,"_397":391,"_398":35,"_187":399},{"_79":396,"_397":391,"_398":35,"_187":399},"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",[403,404],{"_79":407,"_397":401,"_398":35,"_187":406},{"_79":405,"_397":401,"_398":35,"_187":406},"IDE","This domain is owned by Doubleclick (Google). 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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",[440,441,442,443],{"_79":448,"_397":438,"_398":35,"_187":445},{"_79":447,"_397":438,"_398":35,"_187":445},{"_79":446,"_397":438,"_398":35,"_187":445},{"_79":444,"_397":438,"_398":35,"_187":445},"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",[451],{"_79":452,"_397":449,"_398":35,"_187":453},"bcookie","This domain is owned by LinkedIn, the business networking platform. 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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.",[623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643],[588,589,590,591,592],{"_390":483,"_218":483,"_187":392,"_363":614},{"_390":449,"_218":449,"_187":392,"_363":609},{"_390":602,"_218":602,"_187":392,"_363":603},{"_390":598,"_218":598,"_187":392,"_363":599},{"_390":593,"_218":593,"_187":392,"_363":594},"nvcrm.my.site.com",[595],{"_79":596,"_397":593,"_398":35,"_187":597},"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",[600],{"_79":601,"_397":598,"_398":35,"_187":432},"edge","github.com",[604,605],{"_79":608,"_397":602,"_398":35,"_187":607},{"_79":606,"_397":602,"_398":35,"_187":607},"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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