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Model Overview

ContexNet-1024 model which was fine-tuned from English language to Spanish. We took a model trained on over 42,000 hours of English speech. Then we changed the model's decoder to output characters from the Spanish alphabet and fine-tuned this model using Spanish portions of the Mozilla CommonVoice (MCV7.0) [2], Multilingual LibriSpeech (MLS) [3], and Voxpopuli [4] training sets, as well as a subset of the Spanish Fisher dataset [5, 6]. This amounted to 1,340 hours of Spanish training data.

It utilizes a Google SentencePiece [7] tokenizer with vocabulary size 1024, and transcribes text in lower case Spanish alphabet characters.

Model Architecture

ContextNet [1] model is an autoregressive, transducer based Automatic Speech Recognition model. You may find more info on the detail of this model here: ContextNet Model.

Training

The NeMo toolkit [8] was used for training this model over several hundred epochs on multiple GPUs.

Datasets

While finetuning this model, we used the following datasets:

Mozilla Common Voice 7.0 (Spanish) - 289 hours of training data after data cleaning
Multilingual LibriSpeech (Spanish) - 801 hours of training data after data cleaning
Voxpopuli transcribed subset (Spanish) - 110 hours of training data after data cleaning
Fisher dataset (Spanish) - 140 hours of training data after data cleaning

Tokenizer Construction

The tokenizer for this model was built using the text that was in the training set.

We build a Google Sentencepiece Tokenizer with the following script:

" \\\n --vocab_size=1024 \\\n --tokenizer="spe" \\\n --spe_type="unigram" \\\n --spe_character_coverage=1.0 \\\n --no_lower_case \\\n --log\n\">

python [NEMO_GIT_FOLDER]/scripts/process_asr_text_tokenizer.py \\\n  --manifest=\"train_manifest.json\" \\\n  --data_root=\"<OUTPUT DIRECTORY FOR TOKENIZER>\" \\\n  --vocab_size=1024 \\\n  --tokenizer=\"spe\" \\\n  --spe_type=\"unigram\" \\\n  --spe_character_coverage=1.0 \\\n  --no_lower_case \\\n  --log\n

Performance

The performance of Automatic Speech Recognition models is measuring using Word Error Rate.

The model obtains the following greedy scores on the following evaluation datasets:

4.8 % WER on MCV7.0 dev set
3.1 % WER on MLS dev set
5.1 % WER on Voxpopuli dev set
15.3 % WER on Fisher dev set (manually created)

\n\n

5.2 % WER on MCV7.0 test set
3.5 % WER on MLS test set
6.2 % WER on Voxpopuli test set
15.1 % WER on Fisher test set (manually created)

\n\n

18.2 % WER on Spanish CALLHOME train set [9,10] (model was not trained on this data)
19.1 % WER on Spanish CALLHOME devtest set [9,10] (model was not trained on this data)
20.7 % WER on Spanish CALLHOME evltest set [9,10] (model was not trained on this data)

How to Use this Model

The model is available for use in the NeMo toolkit [5], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically load the model from NGC

import nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=\"stt_es_contextnet_1024\")\n

Transcribing text with this model

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n  pretrained_name=\"stt_es_contextnet_1024\" \\\n  audio_dir=\"\"\n

Input

This model accepts 16000 Hz Mono-channel Audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

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.

References

[1] ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

[2] Mozilla CommonVoice (MCV7.0)

[3] Multilingual LibriSpeech (MLS)

[4] VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation

[5] Fisher Spanish - Transcripts

[6] Fisher Spanish Speech

[7] Google Sentencepiece Tokenizer

[8] NVIDIA NeMo Toolkit

[9] CALLHOME Spanish Transcripts

[10] CALLHOME Spanish Speech

Licence

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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We took a [model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_contextnet_1024_mls) trained on over 42,000 hours of English speech. Then we changed the model's decoder to output characters from the Spanish alphabet and fine-tuned this model using Spanish portions of the Mozilla CommonVoice (MCV7.0) [2], Multilingual LibriSpeech (MLS) [3], and Voxpopuli [4] training sets, as well as a subset of the Spanish Fisher dataset [5, 6]. This amounted to 1,340 hours of Spanish training data.\n\nIt utilizes a Google SentencePiece [7] tokenizer with vocabulary size 1024, and transcribes text in lower case Spanish alphabet characters.\n\n# Model Architecture\n\nContextNet [1] model is an autoregressive, transducer based Automatic Speech Recognition model. You may find more info on the detail of this model here: [ContextNet Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html).\n\n# Training\nThe NeMo toolkit [8] was used for training this model over several hundred epochs on multiple GPUs.\n\n## Datasets\n\nWhile finetuning this model, we used the following datasets:\n* Mozilla Common Voice 7.0 (Spanish) - 289 hours of training data after data cleaning\n* Multilingual LibriSpeech (Spanish) - 801 hours of training data after data cleaning\n* Voxpopuli transcribed subset (Spanish) - 110 hours of training data after data cleaning\n* Fisher dataset (Spanish) - 140 hours of training data after data cleaning\n\n## Tokenizer Construction\nThe tokenizer for this model was built using the text that was in the training set.\n\nWe build a Google Sentencepiece Tokenizer with the following script:\n```bash\npython [NEMO_GIT_FOLDER]/scripts/process_asr_text_tokenizer.py \\\n --manifest=\"train_manifest.json\" \\\n --data_root=\"\" \\\n --vocab_size=1024 \\\n --tokenizer=\"spe\" \\\n --spe_type=\"unigram\" \\\n --spe_character_coverage=1.0 \\\n --no_lower_case \\\n --log\n ```\n\n# Performance\nThe performance of Automatic Speech Recognition models is measuring using Word Error Rate. \n\nThe model obtains the following greedy scores on the following evaluation datasets:\n\n* 4.8 % WER on MCV7.0 dev set\n* 3.1 % WER on MLS dev set\n* 5.1 % WER on Voxpopuli dev set\n* 15.3 % WER on Fisher dev set (manually created)\n\n* 5.2 % WER on MCV7.0 test set\n* 3.5 % WER on MLS test set\n* 6.2 % WER on Voxpopuli test set\n* 15.1 % WER on Fisher test set (manually created)\n\n* 18.2 % WER on Spanish CALLHOME train set [9,10] (model was not trained on this data)\n* 19.1 % WER on Spanish CALLHOME devtest set [9,10] (model was not trained on this data)\n* 20.7 % WER on Spanish CALLHOME evltest set [9,10] (model was not trained on this data)\n\n# How to Use this Model\n\nThe model is available for use in the NeMo toolkit [5], 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```python\nimport nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=\"stt_es_contextnet_1024\")\n```\n\n## Transcribing text with this model\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_es_contextnet_1024\" \\\n audio_dir=\"\"\n```\n\n## Input\n\nThis model accepts 16000 Hz Mono-channel Audio (wav files) as input.\n\n## Output\n\nThis model provides transcribed speech as a string for a given audio sample.\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.\n\n# References\n[1] [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://arxiv.org/abs/2005.03191)\n\n[2] [Mozilla CommonVoice (MCV7.0)](https://commonvoice.mozilla.org/en)\n\n[3] [Multilingual LibriSpeech (MLS)](https://arxiv.org/abs/2012.03411)\n\n[4] [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)\n\n[5] [Fisher Spanish - Transcripts](https://catalog.ldc.upenn.edu/LDC2010T04)\n\n[6] [Fisher Spanish Speech](https://catalog.ldc.upenn.edu/LDC2010S01)\n\n[7] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\n\n[8] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n[9] [CALLHOME Spanish Transcripts](https://catalog.ldc.upenn.edu/LDC96T17)\n\n[10] [CALLHOME Spanish Speech](https://catalog.ldc.upenn.edu/LDC96S35)\n\n# Licence\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","STT Es ContextNet 1024",["D",1680635207169],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_es_contextnet_1024","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239,240,241,242,243],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_207":260,"_245":261,"_246":262,"_248":249},{"_207":258,"_245":258,"_246":259,"_248":249},{"_207":254,"_245":255,"_246":256,"_248":249},{"_207":252,"_245":252,"_246":253,"_248":249},{"_207":244,"_245":244,"_246":247,"_248":249},"NeMo","value","filter",{"_250":251,"_245":244},"category","label","field","framework","Deep-Learning",{"_250":249,"_245":252},"Conversational AI","soln_conversational_ai",{"_250":257,"_245":255},"technology","ContextNet",{"_250":249,"_245":258},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_250":263,"_245":261},"application",{"_108":1437,"_110":1438},"ngcApps",[1390],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1371":1372,"_1373":1374,"_1375":1376,"_1377":1378,"_1379":275,"_139":299,"_1380":1381,"_1382":56,"_1383":87,"_1384":87,"_1385":1386,"_1387":87,"_1388":1389},"amplitude",{"_1368":1369,"_970":-7,"_1370":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1362":1363,"_1364":1365,"_1366":1367},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1245":169,"_1246":1247,"_1248":1249,"_1250":1251},"announcements",[],"consentState",{"_337":1235,"_1236":87,"_1237":31,"_1238":31,"_1239":1240},"consentConfig",{"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338},"showContactAdmin",1783998379435,[],[],[313,314,315,316,317],{"_45":60,"_47":71,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":318,"_55":56},5,"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.",[487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521],[360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376],{"_377":483,"_207":483,"_168":169,"_351":484},{"_377":478,"_207":478,"_168":169,"_351":479},{"_377":474,"_207":474,"_168":169,"_351":475},{"_377":469,"_207":469,"_168":169,"_351":470},{"_377":464,"_207":464,"_168":169,"_351":465},{"_377":452,"_207":452,"_168":169,"_351":453},{"_377":440,"_207":440,"_168":169,"_351":441},{"_377":435,"_207":435,"_168":169,"_351":436},{"_377":424,"_207":424,"_168":169,"_351":425},{"_377":419,"_207":419,"_168":169,"_351":420},{"_377":414,"_207":414,"_168":169,"_351":415},{"_377":409,"_207":409,"_168":169,"_351":410},{"_377":404,"_207":404,"_168":169,"_351":405},{"_377":399,"_207":399,"_168":169,"_351":400},{"_377":394,"_207":394,"_168":169,"_351":395},{"_377":387,"_207":387,"_168":169,"_351":388},{"_377":378,"_207":378,"_168":169,"_351":379},"hostName","amazon-adsystem.com",[380,381],{"_79":386,"_383":378,"_384":31,"_168":385},{"_79":382,"_383":378,"_384":31,"_168":385},"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",[389,390],{"_79":393,"_383":387,"_384":31,"_168":392},{"_79":391,"_383":387,"_384":31,"_168":392},"IDE","This domain is owned by Doubleclick (Google). 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[416],{"_79":417,"_383":414,"_384":31,"_168":418},"sp","Testing","google.com",[421],{"_79":422,"_383":419,"_384":31,"_168":423},"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",[426,427,428,429],{"_79":434,"_383":424,"_384":31,"_168":431},{"_79":433,"_383":424,"_384":31,"_168":431},{"_79":432,"_383":424,"_384":31,"_168":431},{"_79":430,"_383":424,"_384":31,"_168":431},"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",[437],{"_79":438,"_383":435,"_384":31,"_168":439},"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",[442,443,444,445],{"_79":451,"_383":440,"_384":31,"_168":447},{"_79":449,"_383":440,"_384":31,"_168":450},{"_79":448,"_383":440,"_384":31,"_168":447},{"_79":446,"_383":440,"_384":31,"_168":447},"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",[454,455,456,457],{"_79":463,"_383":452,"_384":31,"_168":461},{"_79":462,"_383":452,"_384":31,"_168":461},{"_79":460,"_383":452,"_384":31,"_168":461},{"_79":458,"_383":452,"_384":31,"_168":459},"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",[466],{"_79":467,"_383":464,"_384":31,"_168":468},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[471],{"_79":472,"_383":469,"_384":31,"_168":473},"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",[476],{"_79":477,"_383":474,"_384":31,"_168":418},"R","6sc.co",[480],{"_79":481,"_383":478,"_384":31,"_168":482},"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.",[609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629],[574,575,576,577,578],{"_377":469,"_207":469,"_168":169,"_351":600},{"_377":435,"_207":435,"_168":169,"_351":595},{"_377":588,"_207":588,"_168":169,"_351":589},{"_377":584,"_207":584,"_168":169,"_351":585},{"_377":579,"_207":579,"_168":169,"_351":580},"nvcrm.my.site.com",[581],{"_79":582,"_383":579,"_384":31,"_168":583},"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",[586],{"_79":587,"_383":584,"_384":31,"_168":418},"edge","github.com",[590,591],{"_79":594,"_383":588,"_384":31,"_168":593},{"_79":592,"_383":588,"_384":31,"_168":593},"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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