Model Overview

WaveGlow is a flow-based network capable of generating high quality speech from mel-spectrograms. WaveGlow combines insights from Glow [1] and WaveNet [2] in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable.

The details of this model can be seen in this paper: \"WAVEGLOW: A FLOW-BASED GENERATIVE NETWORK FOR SPEECH SYNTHESIS\" at this address: https://arxiv.org/abs/1811.00002 .

Model Architecture

WaveGlow is a generative model that generates audio by sampling from a distribution. To use a neural network as a generative model, samples are taken from a simple distribution, in this case, a zero mean spherical Gaussian with the same number of dimensions as our desired output, and those samples are put through a series of layers that transforms the simple distribution to one which has the desired distribution. In this case, the distribution of audio samples conditioned on a mel-spectrogram is modeled.

Training

The WaveGlow network we use has 12 coupling layers and 12 invertible 1x1 convolutions. The coupling layer networks (WN) each have 8 layers of dilated convolutions , with 512 channels used as residual connections and 256 channels in the skip connections. We also output 2 of the channels after every 4 coupling layers.

The WaveGlow network was trained on 8 Nvidia GV100 GPU’s using randomly chosen clips of 16,000 samples for 580,000 iterations using weight normalization and the Adam optimizer, with a batch size of 24 and a step size of 0.0001 When training appeared to plateau, the learning rate was further reduced to 5*0.00005.

Dataset

We trained the model on the LJ speech data [6]. This data set consists of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books.

The data consists of roughly 24 hours of speech data recorded on a MacBook Pro using its built-in microphone in a home environment. We use a sampling rate of 22,050kHz.

Performance

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 40 volume normalized utterances disjoint from the training set for evaluation, and randomly chose the utterances for each subject. After completing the rating, each rater was excluded from further tests to avoid anchoring effects. The MOS scores are shown in Table 1 with 95% confidence intervals. Though MOS scores of synthesized samples are close on an absolute scale, none of the methods reach the MOS score of real audio. Though WaveGlow has the highest MOS, all the methods have similar scores with only 1,000 samples. This roughly matches our subjective qualitative assessment. The larger advantage of WaveGlow is in training simplicity and inference speed.

\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	Mean Opinion Score (MOS)
Griffin-Lim	3.823∓0.1349
WaveNet	3.885∓0.1238
WaveGlow	3.961∓0.1343
GroundTruth	4.274∓0.1340

How to Use this Model

Clone our repo and initialize submodule

git clone https://github.com/NVIDIA/waveglow.git\ncd waveglow\ngit submodule init\ngit submodule update\n

\n
Install requirements pip3 install -r requirements.txt
\n
\n
Install Apex
\n

Generate audio with our pre-existing model

Download this model
Download mel-spectrograms
Generate audio python3 inference.py -f <(ls mel_spectrograms/*.pt) -w waveglow_256channels.pt -o . --is_fp16 -s 0.6

N.b. use convert_model.py to convert your older models to the current model\nwith fused residual and skip connections.

Train your own model

\n
Download LJ Speech Data. In this example it's in data/
\n

Make a list of the file names to use for training/testing

ls data/*.wav | tail -n+10 &gt; train_files.txt\nls data/*.wav | head -n10 &gt; test_files.txt\n

\n
Train your WaveGlow networks
\n
```
mkdir checkpoints\npython train.py -c config.json\n
```
\n
For multi-GPU training replace train.py with distributed.py. Only tested with single node and NCCL.
\n
For mixed precision training set \"fp16_run\": true on config.json.
\n
\n
Make test set mel-spectrograms
\n
python mel2samp.py -f test_files.txt -o . -c config.json
\n

Do inference with your network

ls *.pt &gt; mel_files.txt\npython3 inference.py -f mel_files.txt -w checkpoints/waveglow_10000 -o . --is_fp16 -s 0.6\n

Input

We use the mel-spectrogram of the original audio of the dataset as the input to the WaveNet and WaveGlow networks. For WaveGlow, we use mel-spectrograms with 80 bins using librosa mel filter defaults, i.e. each bin is normalized by the filter length and the scale is the same as HTK. The parameters of the melspectrograms are FFT size 1024, hop size 256, and window size 1024.

Output

The model produces audio samples at a rate of 1200 kHz on an NVIDIA V100 GPU.

References

[1] Diederik P Kingma and Prafulla Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” arXiv preprint arXiv:1807.03039, 2018.

[2] Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal\nKalchbrenner, Andrew Senior, and Koray Kavukcuoglu,“Wavenet: A generative model for raw audio,” arXiv preprint arXiv:1609.03499, 2016.

[3]Laurent Dinh, David Krueger, and Yoshua Bengio,“Nice: Non-linear independent components estimation,”\narXiv preprint arXiv:1410.8516, 2014.

[4] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio,“Density estimation using real nvp,” arXiv preprint arXiv:1605.08803, 2016.

[5]Danilo Jimenez Rezende and Shakir Mohamed, “Variational inference with normalizing flows,” arXiv preprint arXiv:1505.05770, 2015.

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

License

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WaveGlow combines insights from Glow [1] and WaveNet [2] in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable. \n\nThe details of this model can be seen in this paper: \"WAVEGLOW: A FLOW-BASED GENERATIVE NETWORK FOR SPEECH SYNTHESIS\" at this address: https://arxiv.org/abs/1811.00002 .\n\n## Model Architecture\n\nWaveGlow is a generative model that generates audio by sampling from a distribution. To use a neural network as a generative model, samples are taken from a simple distribution, in this case, a zero mean spherical Gaussian with the same number of dimensions as our desired output, and those samples are put through a series of layers that transforms the simple distribution to one which has the desired distribution. In this case, the distribution of audio samples conditioned on a mel-spectrogram is modeled.\n\n\n## Training\nThe WaveGlow network we use has 12 coupling layers and 12 invertible 1x1 convolutions. The coupling layer networks (WN) each have 8 layers of dilated convolutions , with 512 channels used as residual connections and 256 channels in the skip connections. We also output 2 of the channels after every 4 coupling layers. \n\nThe WaveGlow network was trained on 8 Nvidia GV100 GPU’s using randomly chosen clips of 16,000 samples for 580,000 iterations using weight normalization and the Adam optimizer, with a batch size of 24 and a step size of 0.0001 When training appeared to plateau, the learning rate was further reduced to 5*0.00005.\n\n#### Dataset\n\nWe trained the model on the LJ speech data [6]. This data set consists of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. \n\nThe data consists of roughly 24 hours of speech data recorded on a MacBook Pro using its built-in microphone in a home environment. We use a sampling rate of 22,050kHz.\n\n\n## Performance\n\nWe 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 40 volume normalized utterances disjoint from the training set for evaluation, and randomly chose the utterances for each subject. After completing the rating, each rater was excluded from further tests to avoid anchoring effects. The MOS scores are shown in Table 1 with 95% confidence intervals. Though MOS scores of synthesized samples are close on an absolute scale, none of the methods reach the MOS score of real audio. Though WaveGlow has the highest MOS, all the methods have similar scores with only 1,000 samples. This roughly matches our subjective qualitative assessment. The larger advantage of WaveGlow is in training simplicity and inference speed.\n\n\n| Model | Mean Opinion Score (MOS) |\n|----------------------------|---------------------------------|\n| Griffin-Lim | 3.823∓0.1349 |\n| WaveNet | 3.885∓0.1238 |\n| WaveGlow | 3.961∓0.1343 |\n| GroundTruth | 4.274∓0.1340 |\n\n\n## How to Use this Model\n\n1. Clone our repo and initialize submodule\n\n ```command\n git clone https://github.com/NVIDIA/waveglow.git\n cd waveglow\n git submodule init\n git submodule update\n ```\n\n2. Install requirements `pip3 install -r requirements.txt`\n\n3. Install [Apex](https://github.com/nvidia/apex)\n\n\n *Generate audio with our pre-existing model*\n\n1. Download this model\n2. Download [mel-spectrograms]\n3. Generate audio `python3 inference.py -f <(ls mel_spectrograms/*.pt) -w waveglow_256channels.pt -o . --is_fp16 -s 0.6`\n\nN.b. use `convert_model.py` to convert your older models to the current model\nwith fused residual and skip connections.\n\n*Train your own model*\n\n1. Download [LJ Speech Data](https://keithito.com/LJ-Speech-Dataset). In this example it's in `data/`\n\n2. Make a list of the file names to use for training/testing\n\n ```command\n ls data/*.wav | tail -n+10 > train_files.txt\n ls data/*.wav | head -n10 > test_files.txt\n ```\n\n3. Train your WaveGlow networks\n\n ```command\n mkdir checkpoints\n python train.py -c config.json\n ```\n\n For multi-GPU training replace `train.py` with `distributed.py`. Only tested with single node and NCCL.\n\n For mixed precision training set `\"fp16_run\": true` on `config.json`.\n\n4. Make test set mel-spectrograms\n\n `python mel2samp.py -f test_files.txt -o . -c config.json`\n\n5. Do inference with your network\n\n ```command\n ls *.pt > mel_files.txt\n python3 inference.py -f mel_files.txt -w checkpoints/waveglow_10000 -o . --is_fp16 -s 0.6\n ```\n\n\n#### Input\n\nWe use the mel-spectrogram of the original audio of the dataset as the input to the WaveNet and WaveGlow networks. For WaveGlow, we use mel-spectrograms with 80 bins using librosa mel filter defaults, i.e. each bin is normalized by the filter length and the scale is the same as HTK. The parameters of the melspectrograms are FFT size 1024, hop size 256, and window size 1024.\n\n\n#### Output\n\nThe model produces audio samples at a rate of 1200 kHz on an NVIDIA V100 GPU.\n\n## References\n\n[1] Diederik P Kingma and Prafulla Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” arXiv preprint arXiv:1807.03039, 2018.\n\n[2] Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal\nKalchbrenner, Andrew Senior, and Koray Kavukcuoglu,“Wavenet: A generative model for raw audio,” arXiv preprint arXiv:1609.03499, 2016.\n\n[3]Laurent Dinh, David Krueger, and Yoshua Bengio,“Nice: Non-linear independent components estimation,”\narXiv preprint arXiv:1410.8516, 2014.\n\n[4] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio,“Density estimation using real nvp,” arXiv preprint arXiv:1605.08803, 2016.\n\n[5]Danilo Jimenez Rezende and Shakir Mohamed, “Variational inference with normalizing flows,” arXiv preprint arXiv:1505.05770, 2015.\n\n[6] Keith Ito et al., “The LJ speech dataset,” 2017.\n\n## License\n\n[License](https://github.com/NVIDIA/waveglow/blob/master/LICENSE)\n\n[//]: # (TODO)\n[//]: # (PROVIDE INSTRUCTIONS FOR DOWNLOADING LJS)\n[pytorch 1.0]: https://github.com/pytorch/pytorch#installation\n[website]: https://nv-adlr.github.io/WaveGlow\n[paper]: https://arxiv.org/abs/1811.00002\n[WaveNet implementation]: https://github.com/r9y9/wavenet_vocoder\n[Glow]: https://blog.openai.com/glow/\n[WaveNet]: https://deepmind.com/blog/wavenet-generative-model-raw-audio/\n[PyTorch]: http://pytorch.org\n[published model]: https://drive.google.com/open?id=1rpK8CzAAirq9sWZhe9nlfvxMF1dRgFbF\n[mel-spectrograms]: https://drive.google.com/file/d/1g_VXK2lpP9J25dQFhQwx7doWl_p20fXA/view?usp=sharing\n[LJ Speech Data]: https://keithito.com/LJ-Speech-Dataset\n[Apex]: https://github.com/nvidia/apex","displayName","WaveGlow",["D",1680636934528],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/adlr/models/waveglow","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-and-AI.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[240,241,242,243,244,245],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_209":266,"_247":267,"_248":268,"_250":251},{"_209":263,"_247":264,"_248":265,"_250":251},{"_209":259,"_247":260,"_248":261,"_250":251},{"_209":257,"_247":257,"_248":258,"_250":251},{"_209":253,"_247":254,"_248":255,"_250":251},{"_209":246,"_247":246,"_248":249,"_250":251},"TTS","value","filter",{"_252":251,"_247":246},"category","label","field","Text to Speech","uscs_text_to_speech",{"_252":256,"_247":254},"application","Speech",{"_252":251,"_247":257},"DL","soln_dl",{"_252":262,"_247":260},"technology","Conversational AI","soln_conversational_ai",{"_252":262,"_247":264},"AI","soln_ai",{"_252":262,"_247":267},{"_119":1441,"_121":1442},"ngcApps",[1394],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1375":1376,"_1377":1378,"_1379":1380,"_1381":1382,"_1383":280,"_150":304,"_1384":1385,"_1386":59,"_1387":98,"_1388":98,"_1389":1390,"_1391":98,"_1392":1393},"amplitude",{"_1372":1373,"_975":-7,"_1374":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1366":1367,"_1368":1369,"_1370":1371},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1250":181,"_1251":1252,"_1253":1254,"_1255":1256},"announcements",[],"consentState",{"_341":1240,"_1241":98,"_1242":34,"_1243":34,"_1244":1245},"consentConfig",{"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342},"showContactAdmin",1783955614410,[],[],[318,319,320,321,322],{"_48":63,"_50":73,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":114,"_54":-5,"_56":-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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This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_hly_vid","This cookie _hly_vid 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.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. 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