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Model Overview

The NeMo Mel Codec is a spectrogram-based audio codec which compresses audio into a quantized representation by encoding mel-spectrogram features. Compared to standard audio codecs, the mel codec produces better audio quality when used with speech synthesis models.

The model works with full-bandwidth 22.05kHz audio. The quantized features (tokens) are encoded with token rate of 86.1 tokens per second and 80 bits per token, resulting in 6.9 kbps bitrate.

Model Architecture

$\"\"$

The NeMo Mel Codec model is a non-autoregressive encoder-quantizer-decoder model for audio token extraction. This 64M parameter model is trained end-to-end using mel and STFT reconstruction losses, and adversarial training with a multi-period descriminator and multi-scale complex STFT discriminator.

The model uses the multi-band mel-spectrogram FSQ configuration described in [1].

Training

The model was trained using the NVIDIA NeMo toolkit [2] for 100k steps on 64 NVIDIA A100 GPUs with a batch size of 128 per GPU.

Datasets

The model was trained on an internal dataset containining 25.5k hours of English LibriVox data with 4.3k speakers, and the Common Voice 13 [3] dataset containing 3.2k hours of multi-lingual data with 105 languages and 100k speakers. All training data used was full-bandwidth 22.05kHz.

Performance

We evaluate our codec using several objective audio quality metrics. We evaluate ViSQOL [4] and PESQ [5] for perception quality, ESTOI [6] for intelligbility, and the mel spectrogram and STFT distances to for reconstruction accuracy. Metrics are reported on the test set for both the LibriVox and CommonVoice data. The model has not been trained or evaluated on non-speech audio.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Dataset	ViSQOL	PESQ	ESTOI	Mel Distance	STFT Distance
LibriVox	4.56	3.25	0.91	0.066	0.034
CommonVoice	4.60	2.94	0.90	0.096	0.057

How to Use This Model

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

Automatically load the model from NGC

from nemo.collections.tts.models import AudioCodecModel\nnemo_codec_model = AudioCodecModel.from_pretrained('mel_codec_22khz_medium')\n

Getting discrete tokens from Audio

", sr=nemo_codec_model.sample_rate)\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\naudio_tensor = torch.from_numpy(audio).unsqueeze(dim=0).to(device)\naudio_len = torch.tensor([audio_tensor[0].shape[0]]).to(device)\nnemo_codec_model.freeze()\nencoded_tokens, encoded_len = nemo_codec_model.encode(audio=audio_tensor, audio_len=audio_len)\n\">

import librosa\nimport torch\naudio, _ = librosa.load(\"<path_to_audio>\", sr=nemo_codec_model.sample_rate)\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\naudio_tensor = torch.from_numpy(audio).unsqueeze(dim=0).to(device)\naudio_len = torch.tensor([audio_tensor[0].shape[0]]).to(device)\nnemo_codec_model.freeze()\nencoded_tokens, encoded_len = nemo_codec_model.encode(audio=audio_tensor, audio_len=audio_len)\n

Reconstructing audio from discrete tokens

reconstructed_audio, _ = nemo_codec_model.decode(tokens=encoded_tokens, tokens_len=encoded_len)\n

Listen to audio

", output_audio, samplerate=nemo_codec_model.sample_rate)\n\">

import soundfile as sf\noutput_audio = reconstructed_audio.cpu().numpy().squeeze()\nsf.write(\"<path_to_output_audio>\", output_audio, samplerate=nemo_codec_model.sample_rate)\n

Input

The model accepts single-channel audio sampled at 22050 Hz as input.

Output

The model encodes audio to discrete tokens and decodes the discrete tokens to reconstruct the original audio.

Limitations

The model is trained on 22.05 kHz speech data. The reconstructed audio might not be accurate for low-bandwidth speech (e.g. 16kHz speech upsampled to 22.05kHz) or for non-speech audio.

References

\n
Spectral Codecs
\n
\n
NVIDIA NeMo Toolkit
\n
\n
Common Voice
\n
\n
ViSQOL
\n
\n
PESQ
\n
\n
ESTOI
\n

License

License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.

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Compared to standard audio codecs, the mel codec produces better audio quality when used with speech synthesis models.\n\nThe model works with full-bandwidth 22.05kHz audio. The quantized features (tokens) are encoded with token rate of 86.1 tokens per second and 80 bits per token, resulting in 6.9 kbps bitrate.\n\n## Model Architecture\n\n![](https://github.com/NVIDIA/NeMo/releases/download/v1.23.0/spectral_codec_architecture_multiband.png)\n\nThe NeMo Mel Codec model is a non-autoregressive encoder-quantizer-decoder model for audio token extraction. This 64M parameter model is trained end-to-end using mel and STFT reconstruction losses, and adversarial training with a multi-period descriminator and multi-scale complex STFT discriminator.\n\nThe model uses the multi-band mel-spectrogram FSQ configuration described in [1].\n\n## Training\n\nThe model was trained using the NVIDIA NeMo toolkit [2] for 100k steps on 64 NVIDIA A100 GPUs with a batch size of 128 per GPU.\n\n## Datasets\n\nThe model was trained on an internal dataset containining 25.5k hours of English LibriVox data with 4.3k speakers, and the Common Voice 13 [3] dataset containing 3.2k hours of multi-lingual data with 105 languages and 100k speakers. All training data used was full-bandwidth 22.05kHz.\n\n## Performance\n\nWe evaluate our codec using several objective audio quality metrics. We evaluate ViSQOL [4] and PESQ [5] for perception quality, ESTOI [6] for intelligbility, and the mel spectrogram and STFT distances to for reconstruction accuracy. Metrics are reported on the test set for both the LibriVox and CommonVoice data. The model has not been trained or evaluated on non-speech audio.\n\n| Dataset | ViSQOL |PESQ |ESTOI |Mel Distance |STFT Distance|\n|:-----------:|:----------:|:----------:|:----------:|:-----------:|:-----------:|\n| LibriVox | 4.56 | 3.25 | 0.91 | 0.066 | 0.034 |\n| CommonVoice | 4.60 | 2.94 | 0.90 | 0.096 | 0.057 |\n\n## How to Use This Model\n\nThe model is available for use in the NVIDIA NeMo toolkit, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.\n\n### Automatically load the model from NGC\n\n```python\nfrom nemo.collections.tts.models import AudioCodecModel\nnemo_codec_model = AudioCodecModel.from_pretrained('mel_codec_22khz_medium')\n```\n\n### Getting discrete tokens from Audio\n\n```python\nimport librosa\nimport torch\naudio, _ = librosa.load(\"\", sr=nemo_codec_model.sample_rate)\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\naudio_tensor = torch.from_numpy(audio).unsqueeze(dim=0).to(device)\naudio_len = torch.tensor([audio_tensor[0].shape[0]]).to(device)\nnemo_codec_model.freeze()\nencoded_tokens, encoded_len = nemo_codec_model.encode(audio=audio_tensor, audio_len=audio_len)\n```\n\n### Reconstructing audio from discrete tokens\n\n```python\nreconstructed_audio, _ = nemo_codec_model.decode(tokens=encoded_tokens, tokens_len=encoded_len)\n```\n\n### Listen to audio\n\n```python\nimport soundfile as sf\noutput_audio = reconstructed_audio.cpu().numpy().squeeze()\nsf.write(\"\", output_audio, samplerate=nemo_codec_model.sample_rate)\n```\n\n## Input\n\nThe model accepts single-channel audio sampled at 22050 Hz as input.\n\n## Output\n\nThe model encodes audio to discrete tokens and decodes the discrete tokens to reconstruct the original audio.\n\n## Limitations\n\nThe model is trained on 22.05 kHz speech data. The reconstructed audio might not be accurate for low-bandwidth speech (e.g. 16kHz speech upsampled to 22.05kHz) or for non-speech audio.\n\n## References\n\n1. [Spectral Codecs](https://arxiv.org/abs/2406.05298)\n\n2. [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n3. [Common Voice](https://commonvoice.mozilla.org/en)\n\n4. [ViSQOL](https://github.com/google/visqol)\n\n5. [PESQ](https://lightning.ai/docs/torchmetrics/stable/audio/perceptual_evaluation_speech_quality.html)\n\n6. [ESTOI](https://ieeexplore.ieee.org/document/7539284)\n\n## License\n\nLicense to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms).","displayName","Mel Codec 22kHz medium",["D",1720197705322],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/mel_codec_22khz_medium","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[238,239,240],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_205":253,"_242":254,"_244":255,"_246":247},{"_205":250,"_242":250,"_244":251,"_246":247},{"_205":241,"_242":243,"_244":245,"_246":247},"Text to Speech","value","uscs_text_to_speech","filter",{"_248":249,"_242":243},"category","label","field","application","NeMo",{"_248":252,"_242":250},"framework","Audio Synthesis","uscs_audio_synthesis",{"_248":249,"_242":254},{"_107":1430,"_109":1431},"ngcApps",[1383],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1364":1365,"_1366":1367,"_1368":1369,"_1370":1371,"_1372":267,"_138":291,"_1373":1374,"_1375":56,"_1376":86,"_1377":86,"_1378":1379,"_1380":86,"_1381":1382},"amplitude",{"_1361":1362,"_964":-7,"_1363":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1355":1356,"_1357":1358,"_1359":1360},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1239":223,"_1240":1241,"_1242":1243,"_1244":1245},"announcements",[],"consentState",{"_329":1229,"_1230":86,"_1231":31,"_1232":31,"_1233":1234},"consentConfig",{"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330},"showContactAdmin",1784097052893,[],[],[305,306,307,308,309],{"_45":59,"_47":70,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":310,"_55":56},9,"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,[480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514],[353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369],{"_370":476,"_205":476,"_167":223,"_343":477},{"_370":471,"_205":471,"_167":223,"_343":472},{"_370":467,"_205":467,"_167":223,"_343":468},{"_370":462,"_205":462,"_167":223,"_343":463},{"_370":457,"_205":457,"_167":223,"_343":458},{"_370":445,"_205":445,"_167":223,"_343":446},{"_370":433,"_205":433,"_167":223,"_343":434},{"_370":428,"_205":428,"_167":223,"_343":429},{"_370":417,"_205":417,"_167":223,"_343":418},{"_370":412,"_205":412,"_167":223,"_343":413},{"_370":407,"_205":407,"_167":223,"_343":408},{"_370":402,"_205":402,"_167":223,"_343":403},{"_370":397,"_205":397,"_167":223,"_343":398},{"_370":392,"_205":392,"_167":223,"_343":393},{"_370":387,"_205":387,"_167":223,"_343":388},{"_370":380,"_205":380,"_167":223,"_343":381},{"_370":371,"_205":371,"_167":223,"_343":372},"hostName","amazon-adsystem.com",[373,374],{"_78":379,"_376":371,"_377":31,"_167":378},{"_78":375,"_376":371,"_377":31,"_167":378},"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",[382,383],{"_78":386,"_376":380,"_377":31,"_167":385},{"_78":384,"_376":380,"_377":31,"_167":385},"IDE","This domain is owned by Doubleclick (Google). The main business activity is: Doubleclick is Googles real time bidding advertising exchange","test_cookie","bing.com",[389],{"_78":390,"_376":387,"_377":31,"_167":391},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[394],{"_78":395,"_376":392,"_377":31,"_167":396},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","If you know anything about this host and its uses please add your contributions here. ","tiktok.com",[399],{"_78":400,"_376":397,"_377":31,"_167":401},"_ttp","TikTok is a social media platform owned by ByteDance, known for short-form video sharing. The service is used to create and share videos, and it utilizes cookies for various purposes including advertising and analytics.","t.co",[404],{"_78":405,"_376":402,"_377":31,"_167":406},"muc_ads","This domain is owned by Twitter. It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[409],{"_78":410,"_376":407,"_377":31,"_167":411},"sp","Testing","google.com",[414],{"_78":415,"_376":412,"_377":31,"_167":416},"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",[419,420,421,422],{"_78":427,"_376":417,"_377":31,"_167":424},{"_78":426,"_376":417,"_377":31,"_167":424},{"_78":425,"_376":417,"_377":31,"_167":424},{"_78":423,"_376":417,"_377":31,"_167":424},"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",[430],{"_78":431,"_376":428,"_377":31,"_167":432},"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",[435,436,437,438],{"_78":444,"_376":433,"_377":31,"_167":440},{"_78":442,"_376":433,"_377":31,"_167":443},{"_78":441,"_376":433,"_377":31,"_167":440},{"_78":439,"_376":433,"_377":31,"_167":440},"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",[447,448,449,450],{"_78":456,"_376":445,"_377":31,"_167":454},{"_78":455,"_376":445,"_377":31,"_167":454},{"_78":453,"_376":445,"_377":31,"_167":454},{"_78":451,"_376":445,"_377":31,"_167":452},"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",[459],{"_78":460,"_376":457,"_377":31,"_167":461},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[464],{"_78":465,"_376":462,"_377":31,"_167":466},"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",[469],{"_78":470,"_376":467,"_377":31,"_167":411},"R","6sc.co",[473],{"_78":474,"_376":471,"_377":31,"_167":475},"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,[603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623],[568,569,570,571,572],{"_370":462,"_205":462,"_167":223,"_343":594},{"_370":428,"_205":428,"_167":223,"_343":589},{"_370":582,"_205":582,"_167":223,"_343":583},{"_370":578,"_205":578,"_167":223,"_343":579},{"_370":573,"_205":573,"_167":223,"_343":574},"nvcrm.my.site.com",[575],{"_78":576,"_376":573,"_377":31,"_167":577},"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",[580],{"_78":581,"_376":578,"_377":31,"_167":411},"edge","github.com",[584,585],{"_78":588,"_376":582,"_377":31,"_167":587},{"_78":586,"_376":582,"_377":31,"_167":587},"logged_in","This domain is owned by Github. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie is used to record website and server performance with each visit to the site. 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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