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Parakeet CTC 0.6B Taiwanese Mandarin (zh-TW) - Speech-to-Text Collection

Overview

Automatic Speech Recognition (ASR) systems are widely used in applications such as voice command interfaces, automated subtitle generation for online media, and transcription of customer interactions for archival. Recent advances in deep learning have enabled speech-to-text models to transcribe audio to text in real time, achieving high accuracy even in adverse acoustic conditions, with robustness to noise and accents, and low word error rates (WER).

The Parakeet CTC 0.6B Taiwanese Mandarin model represents a significant advancement in conversational AI, offering state-of-the-art Taiwanese Mandarin (Traditional Chinese) speech recognition with 600 million parameters. Integrated within the NeMo framework, it provides developers with a highly accurate and efficient solution for building robust voice-enabled applications.

In this collection

This collection provides a comprehensive set of finetunable artifacts and a ready-to-use Parakeet NIM container, enabling flexible deployment and customization of the Parakeet CTC 0.6B Taiwanese Mandarin model as a NVIDIA Inference Microservice (NIM). The collection includes:

Acoustic Model - Parakeet-CTC-XL (FastConformer-CTC) base model for Taiwanese Mandarin audio-to-text transcription
Language Model - N-gram language model for contextual word sequence prediction in Taiwanese Mandarin with punctuation support
Inverse Text Normalization (ITN) - Converts spoken form text to written form in Taiwanese Mandarin
Voice Activity Detection (VAD) - Detects speech segments in audio streams
Speaker Diarization Model (Sortformer) - Identifies and labels different speakers in audio (who spoke when)

Each component can be used as provided or further finetuned to suit custom deployment needs, enabling domain-specific customization for various industries and use cases.

How does Parakeet CTC work?

The Parakeet CTC pipeline processes audio through multiple specialized components that work together to produce accurate, properly formatted transcriptions with speaker labels:

1. Voice Activity Detection (VAD)

Before processing begins, the Silero VAD model identifies segments of the audio that contain speech versus silence or background noise. This preprocessing step:

Reduces computational overhead by filtering non-speech segments
Improves noise robustness by focusing processing on relevant audio
Enhances the ability to detect the beginning and end of speech segments for acurate endpointing.

2. Feature Extraction

The audio preprocessing stage converts raw analog signals into a machine-understandable format:

Resampling: Converts audio to the appropriate sample rate
Signal Preprocessing: Applies standardization and windowing techniques
Spectrogram Transformation: Converts time-domain audio signals into frequency-domain representations that capture acoustic features

3. Acoustic Modeling with Parakeet-CTC

The core of the system is the Parakeet-CTC-XL (FastConformer-CTC) acoustic model, which:

Architecture: Uses an optimized Conformer architecture with 8x depthwise-separable convolutional downsampling
Scale: 600 million parameters optimized for Taiwanese Mandarin (Traditional Chinese)
Input Processing: Accepts spectrogram features and processes them through self-attention and convolution layers
CTC Loss: Employs Connectionist Temporal Classification to align audio frames with text outputs
Output: Produces log probability scores for vocabulary tokens at each time step

The FastConformer architecture combines the strengths of Transformers (global context via self-attention) with CNNs (local feature extraction), achieving superior accuracy with improved efficiency through linearly scalable attention mechanisms.

4. Language Modeling

The n-gram language model adds contextual understanding for Taiwanese Mandarin:

Statistical Context: Calculates probability distributions over Taiwanese Mandarin character sequences
Punctuation Support: Includes punctuation symbols (period, comma, question mark) through LM decoding
Error Correction: Helps correct acoustic model mistakes by considering likely character combinations
Domain Adaptation: Can be customized with domain-specific vocabulary and patterns
Architecture: 4-gram model trained with Kneser-Ney smoothing

5. Inverse Text Normalization (ITN)

ITN converts spoken-form Taiwanese Mandarin text to written form using weighted finite-state transducers (WFST):

Numbers: Converts spoken numbers to written form
Measurements: Standardizes measurement expressions
Currency: Formats currency amounts appropriately
Dates and times: Converts spoken dates to standard format

The ITN model uses OpenFST finite state archives (.far) within the Sparrowhawk normalization engine, specifically designed for Mandarin (zh-TW) text.

6. Speaker Diarization with Sortformer

The Sortformer diarization model identifies and labels different speakers in multi-speaker audio:

Speaker Segmentation: Determines when different speakers are talking
Speaker Labeling: Assigns consistent labels to each speaker throughout the audio
Streaming Support: Provides real-time speaker diarization with low latency
Multi-speaker Support: Handles up to 4 speakers simultaneously

Sortformer uses an innovative arrival-time ordering approach to resolve speaker permutation problems, making it particularly effective for conversational audio, meetings, and interviews. The model employs a FastConformer encoder with Transformer layers trained with Sort-Loss and Permutation Invariant Loss objectives.

Where to Get Started

Prerequisites

Runtime Environment:

Riva 2.19.0 or higher
Linux operating system (Linux 4 Tegra also supported)
NVIDIA GPU (H100, A100, or L40 recommended)

Development Tools:

NeMo Framework for model finetuning
NGC CLI for downloading artifacts
Docker for containerized deployment

Deployment Workflow

\n
Download the artifacts: Use NGC CLI to download all artifacts from this collection
\n
\n\">
```
ngc registry model download-version <artifact>\n
```
\n
\n
Finetune for Your Domain (Optional): Customize models for your specific use case
\n
- Acoustic Model: Finetune on domain-specific Taiwanese Mandarin audio (regional variations, terminology, acoustics) Tutorial
- Language Model: Train on domain-specific text corpus for better contextual predictions Tutorial
- ITN Rules: Customize normalization rules for domain-specific entities Tutorial
\n
\n
Optimize and Deploy: Package customized models into a NIM
\n
- Use Riva build and deploy flow to convert and optimize models
- Deploy as a microservice with Riva NIMs
- Scale horizontally for production workloads
\n

References

Academic Papers

Software & Tools

NeMo Framework - Training and finetuning toolkit
KenLM - Language model training
Google Sparrowhawk - Text normalization engine
Riva - Deployment and inference platform

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Terms of Use

By using this software or microservice, you are agreeing to the terms and conditions of the license and acceptable use policy.

Governing Terms

This trial is governed by the NVIDIA API Trial Terms of Service (found at https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). The use of this model is governed by the AI Foundation Models Community License Agreement.

You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.

Disclaimer

AI models generate responses and outputs based on complex algorithms and machine learning techniques, and those responses or outputs may be inaccurate or indecent. By testing this model, you assume the risk of any harm caused by any response or output of the model. Please do not upload any confidential information or personal data. Your use is logged for security.

","resourceVersions",[],"resourceVersionsTotal","hasMoreVersions","versionMetadata","versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/-/collections/parakeet-ctc-0.6b-zh-tw/-","version","-","imageReference","cliDownloadCommand","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/-/collections/parakeet-ctc-0.6b-zh-tw/-/version-history","accessCheckResult",{"_134":63},"hasRelatedCollections","context",{"_131":132,"_133":-7},[],[],[128,129,130],{"_34":49,"_36":60,"_38":51,"_40":52,"_42":61,"_44":45},{"_34":46,"_36":47,"_38":76,"_40":-5,"_42":-5,"_44":45},{"_34":35,"_36":37,"_38":39,"_40":41,"_42":43,"_44":45},"orgName","nvidia","teamName","allowed","id","nvidia/parakeet-ctc-0.6b-zh-tw","key","nvidia/collections/parakeet-ctc-0.6b-zh-tw","resourceType","collections","resourceId","parakeet-ctc-0.6b-zh-tw","shortDescription","A collection of easy to use, highly optimized Deep Learning Models for Speech Recognition. The Parakeet collection provides Data Scientists and Software Engineers with recipes to train, fine-tune, and deploy state-of-the-art ASR models.","description","# Parakeet CTC 0.6B Taiwanese Mandarin (zh-TW) - Speech-to-Text Collection\n\n## Overview\n\nAutomatic Speech Recognition (ASR) systems are widely used in applications such as voice command interfaces, automated subtitle generation for online media, and transcription of customer interactions for archival. Recent advances in deep learning have enabled speech-to-text models to transcribe audio to text in real time, achieving high accuracy even in adverse acoustic conditions, with robustness to noise and accents, and low word error rates (WER).\n\nThe Parakeet CTC 0.6B Taiwanese Mandarin model represents a significant advancement in conversational AI, offering state-of-the-art Taiwanese Mandarin (Traditional Chinese) speech recognition with 600 million parameters. Integrated within the NeMo framework, it provides developers with a highly accurate and efficient solution for building robust voice-enabled applications.\n\n## In this collection\n\nThis collection provides a comprehensive set of finetunable artifacts and a ready-to-use Parakeet NIM container, enabling flexible deployment and customization of the Parakeet CTC 0.6B Taiwanese Mandarin model as a NVIDIA Inference Microservice (NIM). The collection includes:\n\n1. **Acoustic Model** - Parakeet-CTC-XL (FastConformer-CTC) base model for Taiwanese Mandarin audio-to-text transcription\n2. **Language Model** - N-gram language model for contextual word sequence prediction in Taiwanese Mandarin with punctuation support\n3. **Inverse Text Normalization (ITN)** - Converts spoken form text to written form in Taiwanese Mandarin\n4. **Voice Activity Detection (VAD)** - Detects speech segments in audio streams\n5. **Speaker Diarization Model (Sortformer)** - Identifies and labels different speakers in audio (who spoke when)\n\nEach component can be used as provided or further finetuned to suit custom deployment needs, enabling domain-specific customization for various industries and use cases.\n\n## How does Parakeet CTC work?\n\nThe Parakeet CTC pipeline processes audio through multiple specialized components that work together to produce accurate, properly formatted transcriptions with speaker labels:\n\n### 1. Voice Activity Detection (VAD)\n\nBefore processing begins, the Silero VAD model identifies segments of the audio that contain speech versus silence or background noise. This preprocessing step:\n- Reduces computational overhead by filtering non-speech segments\n- Improves noise robustness by focusing processing on relevant audio\n- Enhances the ability to detect the beginning and end of speech segments for acurate endpointing.\n\n\n### 2. Feature Extraction\n\nThe audio preprocessing stage converts raw analog signals into a machine-understandable format:\n- **Resampling**: Converts audio to the appropriate sample rate\n- **Signal Preprocessing**: Applies standardization and windowing techniques\n- **Spectrogram Transformation**: Converts time-domain audio signals into frequency-domain representations that capture acoustic features\n\n### 3. Acoustic Modeling with Parakeet-CTC\n\nThe core of the system is the Parakeet-CTC-XL (FastConformer-CTC) acoustic model, which:\n- **Architecture**: Uses an optimized Conformer architecture with 8x depthwise-separable convolutional downsampling\n- **Scale**: 600 million parameters optimized for Taiwanese Mandarin (Traditional Chinese)\n- **Input Processing**: Accepts spectrogram features and processes them through self-attention and convolution layers\n- **CTC Loss**: Employs Connectionist Temporal Classification to align audio frames with text outputs\n- **Output**: Produces log probability scores for vocabulary tokens at each time step\n\nThe FastConformer architecture combines the strengths of Transformers (global context via self-attention) with CNNs (local feature extraction), achieving superior accuracy with improved efficiency through linearly scalable attention mechanisms.\n\n### 4. Language Modeling\n\nThe n-gram language model adds contextual understanding for Taiwanese Mandarin:\n- **Statistical Context**: Calculates probability distributions over Taiwanese Mandarin character sequences\n- **Punctuation Support**: Includes punctuation symbols (period, comma, question mark) through LM decoding\n- **Error Correction**: Helps correct acoustic model mistakes by considering likely character combinations\n- **Domain Adaptation**: Can be customized with domain-specific vocabulary and patterns\n- **Architecture**: 4-gram model trained with Kneser-Ney smoothing\n\n### 5. Inverse Text Normalization (ITN)\n\nITN converts spoken-form Taiwanese Mandarin text to written form using weighted finite-state transducers (WFST):\n- Numbers: Converts spoken numbers to written form\n- Measurements: Standardizes measurement expressions\n- Currency: Formats currency amounts appropriately\n- Dates and times: Converts spoken dates to standard format\n\nThe ITN model uses OpenFST finite state archives (.far) within the Sparrowhawk normalization engine, specifically designed for Mandarin (zh-TW) text.\n\n### 6. Speaker Diarization with Sortformer\n\nThe Sortformer diarization model identifies and labels different speakers in multi-speaker audio:\n- **Speaker Segmentation**: Determines when different speakers are talking\n- **Speaker Labeling**: Assigns consistent labels to each speaker throughout the audio\n- **Streaming Support**: Provides real-time speaker diarization with low latency\n- **Multi-speaker Support**: Handles up to 4 speakers simultaneously\n\nSortformer uses an innovative arrival-time ordering approach to resolve speaker permutation problems, making it particularly effective for conversational audio, meetings, and interviews. The model employs a FastConformer encoder with Transformer layers trained with Sort-Loss and Permutation Invariant Loss objectives.\n\n## Where to Get Started\n\n### Prerequisites\n\n**Runtime Environment:**\n- Riva 2.19.0 or higher\n- Linux operating system (Linux 4 Tegra also supported)\n- NVIDIA GPU (H100, A100, or L40 recommended)\n\n**Development Tools:**\n- NeMo Framework for model finetuning\n- NGC CLI for downloading artifacts\n- Docker for containerized deployment\n\n### Deployment Workflow\n\n1. **Download the artifacts**: Use NGC CLI to download all artifacts from this collection\n ```bash\n ngc registry model download-version \n ```\n\n2. **Finetune for Your Domain** (Optional): Customize models for your specific use case\n - **Acoustic Model**: Finetune on domain-specific Taiwanese Mandarin audio (regional variations, terminology, acoustics) [Tutorial](https://github.com/nvidia-riva/tutorials/blob/main/asr-finetune-parakeet-nemo.ipynb)\n - **Language Model**: Train on domain-specific text corpus for better contextual predictions [Tutorial](https://github.com/nvidia-riva/tutorials/blob/main/asr-python-advanced-nemo-ngram-training-and-finetuning.ipynb)\n - **ITN Rules**: Customize normalization rules for domain-specific entities [Tutorial](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/text_normalization/intro.html)\n\n4. **Optimize and Deploy**: Package customized models into a NIM\n - Use Riva [build and deploy flow](https://docs.nvidia.com/nim/riva/asr/latest/custom-deployment.html) to convert and optimize models\n - Deploy as a microservice with Riva NIMs\n - Scale horizontally for production workloads\n\n## References\n\n### Academic Papers\n\n1. [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)\n2. [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)\n3. [NeMo Inverse Text Normalization: From Development To Production](https://arxiv.org/abs/2104.05055)\n\n### Software & Tools\n\n1. [NeMo Framework](https://github.com/NVIDIA/NeMo) - Training and finetuning toolkit\n2. [KenLM](https://github.com/kpu/kenlm) - Language model training\n3. [Google Sparrowhawk](https://github.com/google/sparrowhawk) - Text normalization engine\n4. [Riva](https://developer.nvidia.com/riva) - Deployment and inference platform\n\n## Ethical Considerations\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\n\n\n## Terms of Use\n\nBy using this software or microservice, you are agreeing to the [terms and conditions](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) of the license and acceptable use policy.\n\n### Governing Terms\n\nThis trial is governed by the NVIDIA API Trial Terms of Service (found at https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). The use of this model is governed by the AI Foundation Models Community License Agreement.\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n## Disclaimer\n\nAI models generate responses and outputs based on complex algorithms and machine learning techniques, and those responses or outputs may be inaccurate or indecent. By testing this model, you assume the risk of any harm caused by any response or output of the model. Please do not upload any confidential information or personal data. 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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,[455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476],[339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355],{"_356":450,"_147":450,"_145":358,"_329":451},{"_356":446,"_147":446,"_145":358,"_329":447},{"_356":441,"_147":441,"_145":358,"_329":442},{"_356":437,"_147":437,"_145":358,"_329":438},{"_356":432,"_147":432,"_145":358,"_329":433},{"_356":427,"_147":427,"_145":358,"_329":428},{"_356":419,"_147":419,"_145":358,"_329":420},{"_356":411,"_147":411,"_145":358,"_329":412},{"_356":406,"_147":406,"_145":358,"_329":407},{"_356":401,"_147":401,"_145":358,"_329":402},{"_356":396,"_147":396,"_145":358,"_329":397},{"_356":391,"_147":391,"_145":358,"_329":392},{"_356":386,"_147":386,"_145":358,"_329":387},{"_356":379,"_147":379,"_145":358,"_329":380},{"_356":374,"_147":374,"_145":358,"_329":375},{"_356":367,"_147":367,"_145":358,"_329":368},{"_356":357,"_147":357,"_145":358,"_329":359},"hostName","amazon-adsystem.com","",[360,361],{"_183":366,"_363":357,"_364":20,"_145":365},{"_183":362,"_363":357,"_364":20,"_145":365},"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",[369,370],{"_183":373,"_363":367,"_364":20,"_145":372},{"_183":371,"_363":367,"_364":20,"_145":372},"IDE","This domain is owned by Doubleclick (Google). 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.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.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","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. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","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,[548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572],[519,520,521,522,523],{"_356":432,"_147":432,"_145":358,"_329":543},{"_356":406,"_147":406,"_145":358,"_329":538},{"_356":533,"_147":533,"_145":358,"_329":534},{"_356":528,"_147":528,"_145":358,"_329":529},{"_356":524,"_147":524,"_145":358,"_329":525},"nvidia.partners",[526],{"_183":527,"_363":524,"_364":20,"_145":400},"edge","app.hushly.com",[530],{"_183":531,"_363":528,"_364":20,"_145":532},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[535],{"_183":536,"_363":533,"_364":20,"_145":537},"_octo","This domain is owned by Github. 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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 performance.",[631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659],[611,612,613,614],{"_356":626,"_147":626,"_145":358,"_329":627},{"_356":621,"_147":621,"_145":358,"_329":622},{"_356":419,"_147":419,"_145":358,"_329":618},{"_356":533,"_147":533,"_145":358,"_329":615},[616],{"_183":617,"_363":533,"_364":63,"_145":537},"_gh_sess",[619],{"_183":620,"_363":419,"_364":63,"_145":426},"YSC","bizible.com",[623],{"_183":624,"_363":621,"_364":20,"_145":625},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[628],{"_183":629,"_363":626,"_364":20,"_145":630},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_183":701,"_363":478,"_364":20,"_145":702},{"_183":686,"_363":700,"_364":63,"_145":687},{"_183":669,"_363":481,"_364":20,"_145":594},{"_183":669,"_363":582,"_364":20,"_145":594},{"_183":698,"_363":478,"_364":20,"_145":699},{"_183":686,"_363":484,"_364":63,"_145":687},{"_183":696,"_363":478,"_364":20,"_145":697},{"_183":686,"_363":481,"_364":63,"_145":687},{"_183":694,"_363":581,"_364":20,"_145":695},{"_183":686,"_363":603,"_364":63,"_145":687},{"_183":666,"_363":481,"_364":20,"_145":668},{"_183":692,"_363":478,"_364":63,"_145":693},{"_183":690,"_363":478,"_364":20,"_145":691},{"_183":688,"_363":478,"_364":63,"_145":689},{"_183":686,"_363":577,"_364":63,"_145":687},{"_183":684,"_363":478,"_364":20,"_145":685},{"_183":682,"_363":478,"_364":20,"_145":683},{"_183":680,"_363":478,"_364":63,"_145":681},{"_183":678,"_363":586,"_364":20,"_145":679},{"_183":666,"_363":577,"_364":20,"_145":668},{"_183":676,"_363":478,"_364":20,"_145":677},{"_183":674,"_363":478,"_364":63,"_145":675},{"_183":672,"_363":478,"_364":20,"_145":673},{"_183":670,"_363":478,"_364":20,"_145":671},{"_183":669,"_363":478,"_364":20,"_145":594},{"_183":666,"_363":667,"_364":20,"_145":668},{"_183":664,"_363":478,"_364":20,"_145":665},{"_183":662,"_363":478,"_364":63,"_145":663},{"_183":660,"_363":478,"_364":20,"_145":661},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","_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.","_session_id","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.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. 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