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Model Overview

Flowtron is an autoregressive flow-based generative network for text-to-speech synthesis with control over speech variation and style transfer. Flowtron borrows insights from Autoregressive Flows and revamps Tacotron in order to provide high-quality and expressive mel-spectrogram synthesis. Flowtron is optimized by maximizing the likelihood of the training data, which makes training simple and stable. Flowtron learns an invertible mapping of data to a latent space that can be manipulated to control many aspects of speech synthesis (pitch, tone, speech rate, cadence, accent).

Our mean opinion scores (MOS) show that Flowtron matches state-of-the-art TTS models in terms of speech quality. In addition, we provide results on control of speech variation, interpolation between samples and style transfer between speakers seen and unseen during training.

Model Architecture

The model published as \"Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis\" at this address \"https://arxiv.org/abs/2005.05957\".

Our text encoder modifies Tacotron’s by replacing batchnorm with instance-norm. Our decoder and NN architecture removes the essential Prenet and Postnet layers from Tacotron. We use a single speaker embedding that is channel-wise concatenated with the encoder outputs at every token. We use a fixed dummy speaker embedding for models not conditioned on speaker id. Finally, we add a dense layer with a sigmoid output the flow step closest to z. This provides the model with a gating mechanism as early as possible during inference to avoid extra computation.

Training

Flowtron was trained using a dataset that combines the LJSpeech (LJS) dataset[1] with two proprietary single speaker datasets with 20 and 10 hours each. Flowtron also was trained on the train-clean-100 subset of LibriTTS [2] with 123 speakers and 25 minutes on average per speaker. Speakers with less than 5 minutes of data and files that are larger than 10 seconds are filtered out. For each dataset, at least 180 randomly chosen samples was used for the validation set and the remainder for the training set. The models are trained on uniformly sampled normalized text and ARPAbet encodings obtained from the CMU Pronouncing Dictionary. A sampling rate of 22050 Hz and mel-spectrograms with 80 bins using librosa mel filter defaults were used in training. We apply the STFT with a FFT size of 1024, window size of 1024 samples and hop size of 256 samples. We use the ADAM optimizer with default parameters, 1e-4 learning rate and 1e-6 weight decay. We anneal the learning rate once the generalization error starts to plateau and stop training once the the generalization error stops significantly decreasing or starts increasing. The Flowtron models with 2 steps of flow were trained on the LSH dataset for approximately 1000 epochs and then fine-tuned on LibriTTS for 500 epochs.

The model is trained on a single NVIDIA DGX-1 with 8 GPUs.

Dataset

The model trained using a dataset that combines the LJSpeech (LJS) dataset with two proprietary single speaker datasets with 20 and 10 hours each (Sally and Helen). We called this combined dataset as LSH. LJS is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.\nThe model also was trained on the train-clean-100 subset of LibriTTS with 123 speakers and 25 minutes on average per speaker. LibriTTS is a multi-speaker English corpus of approximately 585 hours of read English speech at 24 kHz sampling rate. Speakers with less than 5 minutes of data and files that are larger than 10 seconds are filtered out. For each dataset we use at least 180 randomly chosen samples for the validation set and the remainder for the training set.

Performance

We provide results that compare mean opinion scores a Flowtron with 2 steps of flow and samples from our implementation of Tacotron both trained on LSH. Although the models evaluated are multi-speaker, we only compute mean opinion scores on LJS. We crowd-sourced mean opinion score (MOS) tests on Amazon Mechanical Turk. Raters first had to pass a hearing test to be eligible. Then they listened to an utterance, after which they rated pleasantness on a five-point scale. We used 30 volume normalized utterances from all speakers disjoint from the training set for evaluation, and randomly chose the utterances for each subject.

The mean opinion scores are shown in Table 1 with 95% confidence intervals computed over approximately 250 scores per source. The results roughly match our subjective qualitative assessment. The larger advantage of Flowtron is in the control over the amount of speech variation and the manipulation of the latent space.

\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\">

Model	Flows	Mean Opinion Score (MOS)
Real	-	4.274∓0.1340
Flowtron	3	3.665∓0.1634
Tacotron	-	3.521∓0.1721

How to Use this Model

In oeder to run this model your system needs to have Nvidia GPU, Cuda and cuDNN.

To test this model check \"fine-tuning Flowtron model\" resource in NGC Catalog at this address: https://ngc.nvidia.com/catalog/resources/nvidia:flowtron. It shows how to fine-tune and test the model.

Input

The input of model in training step is a set of audio files and their transcripts. In inferencing step the input of the model is a text that we want to have its audio version.

Output

The output of inferencing script is an audio file that you can find it in results directory. it is the audio file of the text that you gave to the model and the sound is similar to the audio file that you used for fine tuning the model.

Limitations

The audio files that are used for finetuning need to have high quality.

References

[1] Keith Ito et al., “The LJ speech dataset,” 2017.

[2] Hsu, W.-N., Zhang, Y., Weiss, R. J., Zen, H., Wu, Y., Wang, Y., Cao, Y., Jia, Y., Chen, Z., Shen, J., et al. Hierarchical generative modeling for controllable speech synthesis. arXiv preprint arXiv:1810.07217, 2018.

License

This implementation uses code from the following repos: Keith\nIto, Prem Seetharaman and Liyuan Liu as described in our code.

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Flowtron borrows insights from Autoregressive Flows and revamps Tacotron in order to provide high-quality and expressive mel-spectrogram synthesis. Flowtron is optimized by maximizing the likelihood of the training data, which makes training simple and stable. Flowtron learns an invertible mapping of data to a latent space that can be manipulated to control many aspects of speech synthesis (pitch, tone, speech rate, cadence, accent).\n\nOur mean opinion scores (MOS) show that Flowtron matches state-of-the-art TTS models in terms of speech quality. In addition, we provide results on control of speech variation, interpolation between samples and style transfer between speakers seen and unseen during training.\n\n## Model Architecture\n\nThe model published as \"Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis\" at this address \"https://arxiv.org/abs/2005.05957\". \n\nOur text encoder modifies Tacotron’s by replacing batchnorm with instance-norm. Our decoder and NN architecture removes the essential Prenet and Postnet layers from Tacotron. We use a single speaker embedding that is channel-wise concatenated with the encoder outputs at every token. We use a fixed dummy speaker embedding for models not conditioned on speaker id. Finally, we add a dense layer with a sigmoid output the flow step closest to z. This provides the model with a gating mechanism as early as possible during inference to avoid extra computation.\n\n## Training\n\nFlowtron was trained using a dataset that combines the LJSpeech (LJS) dataset[1] with two proprietary single speaker datasets with 20 and 10 hours each. Flowtron also was trained on the train-clean-100 subset of LibriTTS [2] with 123 speakers and 25 minutes on average per speaker. Speakers with less than 5 minutes of data and files that are larger than 10 seconds are filtered out. For each dataset, at least 180 randomly chosen samples was used for the validation set and the remainder for the training set. The models are trained on uniformly sampled normalized text and ARPAbet encodings obtained from the CMU Pronouncing Dictionary. A sampling rate of 22050 Hz and mel-spectrograms with 80 bins using librosa mel filter defaults were used in training. We apply the STFT with a FFT size of 1024, window size of 1024 samples and hop size of 256 samples. We use the ADAM optimizer with default parameters, 1e-4 learning rate and 1e-6 weight decay. We anneal the learning rate once the generalization error starts to plateau and stop training once the the generalization error stops significantly decreasing or starts increasing. The Flowtron models with 2 steps of flow were trained on the LSH dataset for approximately 1000 epochs and then fine-tuned on LibriTTS for 500 epochs. \n\nThe model is trained on a single NVIDIA DGX-1 with 8 GPUs.\n\n#### Dataset\n\nThe model trained using a dataset that combines the LJSpeech (LJS) dataset with two proprietary single speaker datasets with 20 and 10 hours each (Sally and Helen). We called this combined dataset as LSH. LJS is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.\nThe model also was trained on the train-clean-100 subset of LibriTTS with 123 speakers and 25 minutes on average per speaker. LibriTTS is a multi-speaker English corpus of approximately 585 hours of read English speech at 24 kHz sampling rate. Speakers with less than 5 minutes of data and files that are larger than 10 seconds are filtered out. For each dataset we use at least 180 randomly chosen samples for the validation set and the remainder for the training set.\n\n## Performance\n\nWe provide results that compare mean opinion scores a Flowtron with 2 steps of flow and samples from our implementation of Tacotron both trained on LSH. Although the models evaluated are multi-speaker, we only compute mean opinion scores on LJS. We crowd-sourced mean opinion score (MOS) tests on Amazon Mechanical Turk. Raters first had to pass a hearing test to be eligible. Then they listened to an utterance, after which they rated pleasantness on a five-point scale. We used 30 volume normalized utterances from all speakers disjoint from the training set for evaluation, and randomly chose the utterances for each subject.\n\nThe mean opinion scores are shown in Table 1 with 95% confidence intervals computed over approximately 250 scores per source. The results roughly match our subjective qualitative assessment. The larger advantage of Flowtron is in the control over the amount of speech variation and the manipulation of the latent space.\n\n\n| Model | Flows | Mean Opinion Score (MOS) |\n|-----------|----------------|---------------------------|\n| Real | - | 4.274∓0.1340 |\n| Flowtron | 3 | 3.665∓0.1634 |\n| Tacotron | - | 3.521∓0.1721 |\n\n## How to Use this Model\n\nIn oeder to run this model your system needs to have Nvidia GPU, Cuda and cuDNN. \n \n \nTo test this model check \"fine-tuning Flowtron model\" resource in NGC Catalog at this address: https://ngc.nvidia.com/catalog/resources/nvidia:flowtron. It shows how to fine-tune and test the model.\n\n#### Input\n\nThe input of model in training step is a set of audio files and their transcripts. In inferencing step the input of the model is a text that we want to have its audio version.\n\n#### Output\n The output of inferencing script is an audio file that you can find it in results directory. it is the audio file of the text that you gave to the model and the sound is similar to the audio file that you used for fine tuning the model.\n\n## Limitations\nThe audio files that are used for finetuning need to have high quality.\n\n## References\n[1] Keith Ito et al., “The LJ speech dataset,” 2017.\n\n[2] Hsu, W.-N., Zhang, Y., Weiss, R. J., Zen, H., Wu, Y., Wang, Y., Cao, Y., Jia, Y., Chen, Z., Shen, J., et al. Hierarchical generative modeling for controllable speech synthesis. arXiv preprint arXiv:1810.07217, 2018.\n\n## License\n[License](https://github.com/NVIDIA/flowtron/blob/master/LICENSE)\n\nThis implementation uses code from the following repos: [Keith\nIto](https://github.com/keithito/tacotron/), [Prem Seetharaman](https://github.com/pseeth/pytorch-stft) and [Liyuan Liu](https://github.com/LiyuanLucasLiu/RAdam) as described in our code.\n\n[ignored]: https://github.com/NVIDIA/flowtron/config.json#L12\n[paper]: https://arxiv.org/abs/2005.05957\n[Flowtron LJS]: https://drive.google.com/open?id=1Cjd6dK_eFz6DE0PKXKgKxrzTUqzzUDW-\n[Flowtron LibriTTS]: https://drive.google.com/open?id=1KhJcPawFgmfvwV7tQAOeC253rYstLrs8\n[Flowtron LibriTTS2K]: https://drive.google.com/open?id=1sKTImKkU0Cmlhjc_OeUDLrOLIXvUPwnO\n[WaveGlow]: https://drive.google.com/open?id=1rpK8CzAAirq9sWZhe9nlfvxMF1dRgFbF\n[PyTorch]: https://github.com/pytorch/pytorch#installation\n[website]: https://nv-adlr.github.io/Flowtron\n[AMP]: https://github.com/NVIDIA/apex/tree/master/apex/amp\n[Tacotron]: https://arxiv.org/abs/1712.05884","displayName","Flowtron",["D",1680635049727],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/adlr/models/flowtron","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-and-AI.png","canGuestDownload","latestVersionSizeInBytes",243920959,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[246,247,248,249,250,251,252],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_213":276,"_254":277,"_255":278,"_257":258},{"_213":273,"_254":274,"_255":275,"_257":258},{"_213":269,"_254":270,"_255":271,"_257":258},{"_213":266,"_254":267,"_255":268,"_257":258},{"_213":264,"_254":264,"_255":265,"_257":258},{"_213":260,"_254":261,"_255":262,"_257":258},{"_213":253,"_254":253,"_255":256,"_257":258},"TTS","value","filter",{"_259":258,"_254":253},"category","label","field","Text to Speech","uscs_text_to_speech",{"_259":263,"_254":261},"application","NLP",{"_259":258,"_254":264},"Natural Language Processing","uscs_natural_language_processing",{"_259":263,"_254":267},"DL","soln_dl",{"_259":272,"_254":270},"technology","Conversational AI","soln_conversational_ai",{"_259":272,"_254":274},"AI","soln_ai",{"_259":272,"_254":277},{"_105":1453,"_107":1454},"ngcApps",[1406],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1387":1388,"_1389":1390,"_1391":1392,"_1393":1394,"_1395":290,"_137":314,"_1396":1397,"_1398":55,"_1399":84,"_1400":84,"_1401":1402,"_1403":84,"_1404":1405},"amplitude",{"_1384":1385,"_986":-7,"_1386":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1378":1379,"_1380":1381,"_1382":1383},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1261":165,"_1262":1263,"_1264":1265,"_1266":1267},"announcements",[],"consentState",{"_352":1251,"_1252":84,"_1253":30,"_1254":30,"_1255":1256},"consentConfig",{"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353},"showContactAdmin",1784144550879,[],[],[328,329,330,331,332,333],{"_44":58,"_46":68,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":66,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":64,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":59,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":129,"_54":55},"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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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1243":1244,"_1245":1246,"_1247":1248,"_1249":1250},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[354,355,356,357,358],{"_359":895,"_76":896,"_164":897,"_363":84,"_364":16,"_366":898,"_368":899},{"_359":685,"_76":686,"_164":687,"_363":30,"_364":53,"_366":688,"_368":689},{"_359":585,"_76":586,"_164":587,"_363":30,"_364":129,"_366":588,"_368":589},{"_359":370,"_76":371,"_164":372,"_363":30,"_364":373,"_366":374,"_368":375},{"_359":360,"_76":361,"_164":362,"_363":30,"_364":365,"_366":367,"_368":369},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. 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,[503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537],[376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392],{"_393":499,"_213":499,"_164":165,"_366":500},{"_393":494,"_213":494,"_164":165,"_366":495},{"_393":490,"_213":490,"_164":165,"_366":491},{"_393":485,"_213":485,"_164":165,"_366":486},{"_393":480,"_213":480,"_164":165,"_366":481},{"_393":468,"_213":468,"_164":165,"_366":469},{"_393":456,"_213":456,"_164":165,"_366":457},{"_393":451,"_213":451,"_164":165,"_366":452},{"_393":440,"_213":440,"_164":165,"_366":441},{"_393":435,"_213":435,"_164":165,"_366":436},{"_393":430,"_213":430,"_164":165,"_366":431},{"_393":425,"_213":425,"_164":165,"_366":426},{"_393":420,"_213":420,"_164":165,"_366":421},{"_393":415,"_213":415,"_164":165,"_366":416},{"_393":410,"_213":410,"_164":165,"_366":411},{"_393":403,"_213":403,"_164":165,"_366":404},{"_393":394,"_213":394,"_164":165,"_366":395},"hostName","amazon-adsystem.com",[396,397],{"_76":402,"_399":394,"_400":30,"_164":401},{"_76":398,"_399":394,"_400":30,"_164":401},"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",[405,406],{"_76":409,"_399":403,"_400":30,"_164":408},{"_76":407,"_399":403,"_400":30,"_164":408},"IDE","This domain is owned by Doubleclick (Google). 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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.",[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],{"_393":485,"_213":485,"_164":165,"_366":616},{"_393":451,"_213":451,"_164":165,"_366":611},{"_393":604,"_213":604,"_164":165,"_366":605},{"_393":600,"_213":600,"_164":165,"_366":601},{"_393":595,"_213":595,"_164":165,"_366":596},"nvcrm.my.site.com",[597],{"_76":598,"_399":595,"_400":30,"_164":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],{"_76":603,"_399":600,"_400":30,"_164":434},"edge","github.com",[606,607],{"_76":610,"_399":604,"_400":30,"_164":609},{"_76":608,"_399":604,"_400":30,"_164":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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