Model Overview

parakeet-tdt_ctc-110m is an ASR model that transcribes speech with Punctuations and Capitalizations of the English alphabet. This model is jointly developed by NVIDIA NeMo and Suno.ai teams. It is a Large version of Hybrid FastConformer [1] TDT-CTC [2] (around 114M parameters) model. This model has been trained with fastconformer architecture with full attention hence this model can transcribe up to 20 minutes of audio in one single pass. This model achieves super fast RTFx values across all datasets, with an average RTFx of ~4000 on Hugging Face leaderboard evaluation sets. See the model architecture section and NeMo documentation for complete architecture details.

NVIDIA NeMo: Training

To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed latest PyTorch version.

pip install nemo_toolkit['all']\n

How to Use this Model

The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically instantiate the model

import nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.ASRModel.from_pretrained(model_name=\"parakeet-tdt_ctc-110m\")\n

Transcribing using Python

First, let's get a sample

wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav\n

Then simply do:

asr_model.transcribe(['2086-149220-0033.wav'])\n

Transcribing many audio files

By default model uses TDT to transcribe the audio files, to switch decoder to use CTC, use decoding_type='ctc'

"\n\">

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \n pretrained_name=\"parakeet-tdt_ctc-110m\" \n audio_dir=\"<DIRECTORY CONTAINING AUDIO FILES>\"\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.

Model Architecture

This model uses a Hybrid FastConformer-TDT-CTC architecture. FastConformer [1] is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. You may find more information on the details of FastConformer here: Fast-Conformer Model.

Training

The NeMo toolkit [3] was used for finetuning this model for 20,000 steps over parakeet-tdt-1.1 model. This model is trained with this example script and this base config.

The tokenizers for these models were built using the text transcripts of the train set with this script.

Datasets

The model was trained on 36K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams.

The training dataset consists of private subset with 27K hours of English speech plus 9k hours from the following public PnC datasets:

Librispeech 960 hours of English speech
Fisher Corpus
National Speech Corpus Part 1
VCTK
VoxPopuli (EN)
Europarl-ASR (EN)
Multilingual Librispeech (MLS EN) - 2,000 hour subset
Mozilla Common Voice (v7.0)

Performance

The performance of Automatic Speech Recognition models is measured using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.

The following table summarizes the performance of the available models in this collection with the Transducer decoder. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.

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

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

Version	Tokenizer	Vocabulary Size	AMI	Earnings-22	Giga Speech	LS test-clean	LS test-other	SPGI Speech	TEDLIUM-v3	Vox Populi
2.0	BPE	1024	15.88	12.42	10.52	2.4	5.2	2.54	4.16	6.91

These are greedy WER numbers without external LM. More details on evaluation can be found at HuggingFace ASR Leaderboard

NVIDIA Riva: Deployment

NVIDIA Riva, is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded.\nAdditionally, Riva provides:

World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support

Although this model isn’t supported yet by Riva, the list of supported models is here.
\nCheck out Riva live demo.

References

[1] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition

[2] Efficient Sequence Transduction by Jointly Predicting Tokens and Durations

[3] Google Sentencepiece Tokenizer

[4] NVIDIA NeMo Toolkit

[5] Suno.ai

[6] HuggingFace ASR Leaderboard

[7] Towards Measuring Fairness in AI: the Casual Conversations Dataset

Licence

License to use this model is covered by the CC-BY-4.0. By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-4.0 license.

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This model is jointly developed by NVIDIA NeMo and Suno.ai teams.","## Model Overview\n`parakeet-tdt_ctc-110m` is an ASR model that transcribes speech with Punctuations and Capitalizations of the English alphabet. This model is jointly developed by [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) and [Suno.ai](https://www.suno.ai/) teams. It is a Large version of Hybrid FastConformer [1] TDT-CTC [2] (around 114M parameters) model. This model has been trained with fastconformer architecture with full attention hence this model can transcribe up to 20 minutes of audio in one single pass. This model achieves super fast RTFx values across all datasets, with an average RTFx of ~4000 on Hugging Face leaderboard evaluation sets. See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer) for complete architecture details.\n\n## NVIDIA NeMo: Training\n\nTo train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version.\n```\npip install nemo_toolkit['all']\n``` \n\n## How to Use this Model\n\nThe model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.\n\n### Automatically instantiate the model\n\n```python\nimport nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.ASRModel.from_pretrained(model_name=\"parakeet-tdt_ctc-110m\")\n```\n\n### Transcribing using Python\nFirst, let's get a sample\n```\nwget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav\n```\nThen simply do:\n```\nasr_model.transcribe(['2086-149220-0033.wav'])\n```\n\n### Transcribing many audio files\n\nBy default model uses TDT to transcribe the audio files, to switch decoder to use CTC, use decoding_type='ctc'\n\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \n pretrained_name=\"parakeet-tdt_ctc-110m\" \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## Model Architecture\n\nThis model uses a Hybrid FastConformer-TDT-CTC architecture. FastConformer [1] is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. You may find more information on the details of FastConformer here: [Fast-Conformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer).\n\n\n## Training\n\nThe NeMo toolkit [3] was used for finetuning this model for 20,000 steps over `parakeet-tdt-1.1` model. This model is trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/hybrid_transducer_ctc/fastconformer_hybrid_transducer_ctc_bpe.yaml).\n\nThe tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).\n\n### Datasets\n\nThe model was trained on 36K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams.\n\nThe training dataset consists of private subset with 27K hours of English speech plus 9k hours from the following public PnC datasets:\n\n- Librispeech 960 hours of English speech\n- Fisher Corpus\n- National Speech Corpus Part 1\n- VCTK\n- VoxPopuli (EN)\n- Europarl-ASR (EN)\n- Multilingual Librispeech (MLS EN) - 2,000 hour subset\n- Mozilla Common Voice (v7.0)\n\n## Performance\n\nThe performance of Automatic Speech Recognition models is measured using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.\n\nThe following table summarizes the performance of the available models in this collection with the Transducer decoder. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. \n\n|**Version**|**Tokenizer**|**Vocabulary Size**|**AMI**|**Earnings-22**|**Giga Speech**|**LS test-clean**| **LS test-other** | **SPGI Speech**|**TEDLIUM-v3**|**Vox Populi**|\n|---------|-----------------------|-----------------|---------|---------------|------------|-----------|---------------|-------------|------|------|\n| 2.0 | BPE | 1024 | 15.88 | 12.42 | 10.52 | 2.4 | 5.2 | 2.54 | 4.16 | 6.91 | \n\nThese are greedy WER numbers without external LM. More details on evaluation can be found at [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)\n\n\n## NVIDIA Riva: Deployment\n\n[NVIDIA Riva](https://developer.nvidia.com/riva), is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. \nAdditionally, Riva provides: \n\n* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours \n* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization \n* Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support \n\nAlthough this model isn’t supported yet by Riva, the [list of supported models is here](https://huggingface.co/models?other=Riva). \nCheck out [Riva live demo](https://developer.nvidia.com/riva#demos). \n\n## References\n[1] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)\n\n[2] [Efficient Sequence Transduction by Jointly Predicting Tokens and Durations](https://arxiv.org/abs/2304.06795)\n\n[3] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\n\n[4] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n[5] [Suno.ai](https://suno.ai/)\n\n[6] [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)\n\n[7] [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://arxiv.org/abs/2104.02821)\n\n\n## Licence\n\nLicense to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.","displayName","Parakeet-TDT_CTC-110M",["D",1732745356780],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/parakeet-tdt_ctc-110m","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n|--------------------------------------------------------------------------------------------------------|----------|\n| If any special measures have been taken to mitigate against unwanted bias? | No |\n| Was model evaluated for bias? | No |\n| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | No |","explainability","| Field | Response |\n|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| High-level application domain: | ASR |\n| Describe the model input and output, e.g. input: streaming speech / file; output: Text with / without capitalization and punctuation. | This model accepts 16000 Hz Mono-channel Audio (wav files) as input and provides transcribed speech as a string for a given audio sample as output. |\n| Is this streaming / offline model? | Offline |\n| Explain model architecture e.g., encoder-decoder with RNNT loss / Tokenizer … | FastConformer is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. The model is trained in a multitask setup with joint TDT and CTC decoder loss. |\n| Number of parameters | 114M |\n| List the technical limitations of the model. | The model is non-streaming and outputs the speech as a string without capitalization and punctuation. Since this model was trained on publicly available speech datasets, performance might degrade for speech with untrained terms.|","privacy","| Field | Response |\n|--------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------|\n| Generatable or reverse engineerable personal data? | None |\n| If applicable, was a notice provided to the individuals prior to the collection of any personal data used? | Not Applicable |\n| If personal data was collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable |\n| Is there dataset provenance? (Do you know where the data came from?) | Yes |\n| If data is labeled, was it reviewed by legal on compliance privacy laws? Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |\n| Is a mechanism in place to honor data subject rights of access or deletion of personal data? | No |","safetyAndSecurity","| Field | Response |\n|--------------------------------------------------------------------------------------------------------|----------|\n| End user license agreement.? | CC-BY-4.0 |\n| Potential model risks | The model's performance may be sensitive to noise, and it may occasionally produce repeated words or omit some words in the expected transcript. 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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",[465],{"_77":466,"_432":463,"_433":34,"_208":467},"sp","Testing","google.com",[470],{"_77":471,"_432":468,"_433":34,"_208":472},"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",[475,476,477,478],{"_77":483,"_432":473,"_433":34,"_208":480},{"_77":482,"_432":473,"_433":34,"_208":480},{"_77":481,"_432":473,"_433":34,"_208":480},{"_77":479,"_432":473,"_433":34,"_208":480},"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",[486],{"_77":487,"_432":484,"_433":34,"_208":488},"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",[491,492,493,494],{"_77":500,"_432":489,"_433":34,"_208":496},{"_77":498,"_432":489,"_433":34,"_208":499},{"_77":497,"_432":489,"_433":34,"_208":496},{"_77":495,"_432":489,"_433":34,"_208":496},"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",[503,504,505,506],{"_77":512,"_432":501,"_433":34,"_208":510},{"_77":511,"_432":501,"_433":34,"_208":510},{"_77":509,"_432":501,"_433":34,"_208":510},{"_77":507,"_432":501,"_433":34,"_208":508},"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",[515],{"_77":516,"_432":513,"_433":34,"_208":517},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[520],{"_77":521,"_432":518,"_433":34,"_208":522},"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",[525],{"_77":526,"_432":523,"_433":34,"_208":467},"R","6sc.co",[529],{"_77":530,"_432":527,"_433":34,"_208":531},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679],[624,625,626,627,628],{"_425":518,"_236":518,"_208":427,"_398":650},{"_425":484,"_236":484,"_208":427,"_398":645},{"_425":638,"_236":638,"_208":427,"_398":639},{"_425":634,"_236":634,"_208":427,"_398":635},{"_425":629,"_236":629,"_208":427,"_398":630},"nvcrm.my.site.com",[631],{"_77":632,"_432":629,"_433":34,"_208":633},"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",[636],{"_77":637,"_432":634,"_433":34,"_208":467},"edge","github.com",[640,641],{"_77":644,"_432":638,"_433":34,"_208":643},{"_77":642,"_432":638,"_433":34,"_208":643},"logged_in","This domain is owned by Github. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website 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