Model Overview

This is an English inverse text normalization model based on Albert Base v2 [1] and T5-small [2].\nInverse text normalization is the task of converting a spoken-domain text into its written form. For example, \"one hundred twenty three dollars\" should be converted to \"$123\", while \"one twenty three king avenue\" should be converted to \"123 King Ave\".

Model Architecture

This model uses a two stage process: 1. transformer-based NER model for identifying \"semiotic\" spans in the input (e.g., spans that are about times, dates, or monetary amounts) 2. transformer-based seq2seq model for decoding the semiotic spans into spoken form.

The tagger model first uses a Transformer encoder (e.g., Albert-Base-v2) to build a contextualized representation for each input token. It then uses a classification head to predict the tag for each token (e.g., if a token should stay the same, its tag should be SAME). The decoder model then takes the semiotic spans identified by the tagger and transforms them into the written form. The decoder model is essentially a Transformer-based encoder-decoder seq2seq model (e.g., the example training script uses the T5-small model by default). Overall, our design is partly inspired by the RNN-based sliding window model proposed in the paper Neural Models of Text Normalization for Speech Applications [3].

Training

The model was trained with pretrained Albert-Base-v2 and T5-small.

Dataset

The model is trained on a processed and upsampled version of the English Google Text Normalization dataset [4].

Performance

Sentence level accuracy on the English Google Text Normalization dataset [4]:

The performance of ITN models can be measured using Word Error Rate(WER), and Sentence Accuracy. We measure Sentence Accuracy w.r.t. multi-variant reference and subdivide the errors into \"digit\" and \"other\".

The model obtains the following scores on the following evaluation datasets

Default test set\n    WER:  2.9%\n    Sentence accuracy:  97.31%\n        digit errors:    0.35%\n        other errors:    2.34%\n\n\nHard test set\n    WER:  9.34%\n    Sentence accuracy:  85.34%\n        digit errors:    3.12%\n        other errors:   11.54%\n\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\nimport nemo.collections.nlp as nemo_nlp\ntagger = nemo_nlp.models.duplex_text_normalization.DuplexTaggerModel.from_pretrained(model_name=\"itn_en_t5\")\ndecoder = nemo_nlp.models.duplex_text_normalization.DuplexDecoderModel.from_pretrained(model_name=\"itn_en_t5\")\nnormalizer = nemo_nlp.models.duplex_text_normalization.DuplexTextNormalizationModel(tagger, decoder, lang='en')\n

Inference

python [NEMO_GIT_FOLDER]/examples/nlp/duplex_text_normalization/duplex_text_normalization_infer.py lang=en mode=itn tagger_pretrained_model=itn_en_t5 decoder_pretrained_model=itn_en_t5 inference.interactive=True\n

To use inference from data file, set inference.interactive=False inference.from_file=[DATA_FILE]

Input

Both the DuplexTaggerModel model and the DuplexDecoderModel model use the same simple text format as the dataset. The data needs to be stored in TAB separated files (.tsv) with three columns. The first of which is the \"semiotic class\" (e.g., numbers, times, dates), the second is the token in written form, and the third is the spoken form. It is expected that a complete dataset contains three files: train.tsv, dev.tsv, and test.tsv.

Output

The model outputs either the transformed sentence in interactive mode or the evaluation metrics in test mode.

Limitations

The length of the input text is currently constrained by the maximum sequence length of the tagger and decoder model, which is 512 tokens after tokenization.

References

[1] https://huggingface.co/albert-base-v2

[2] https://huggingface.co/t5-small

[3] https://research.fb.com/publications/neural-models-of-text-normalization-for-speech-applications

[4] https://arxiv.org/abs/1611.00068

[5] NVIDIA NeMo Toolkit

License

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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For example, \"one hundred twenty three dollars\" should be converted to \"$123\", while \"one twenty three king avenue\" should be converted to \"123 King Ave\". \n\n\n## Model Architecture\n\nThis model uses a two stage process: 1. transformer-based NER model for identifying \"semiotic\" spans in the input (e.g., spans that are about times, dates, or monetary amounts) 2. transformer-based seq2seq model for decoding the semiotic spans into spoken form.\n\nThe tagger model first uses a Transformer encoder (e.g., Albert-Base-v2) to build a contextualized representation for each input token. It then uses a classification head to predict the tag for each token (e.g., if a token should stay the same, its tag should be SAME). The decoder model then takes the semiotic spans identified by the tagger and transforms them into the written form. The decoder model is essentially a Transformer-based encoder-decoder seq2seq model (e.g., the example training script uses the T5-small model by default). Overall, our design is partly inspired by the RNN-based sliding window model proposed in the paper Neural Models of Text Normalization for Speech Applications [3].\n\n## Training\n\nThe model was trained with pretrained Albert-Base-v2 and T5-small.\n\n\n### Dataset\n\nThe model is trained on a processed and upsampled version of the English Google Text Normalization dataset [4]. \n\n## Performance\n\nSentence level accuracy on the English Google Text Normalization dataset [4]:\n \nThe performance of ITN models can be measured using Word Error Rate(WER), and Sentence Accuracy. We measure Sentence Accuracy w.r.t. multi-variant reference and subdivide the errors into \"digit\" and \"other\".\n\nThe model obtains the following scores on the following evaluation datasets\n\n```\nDefault test set\n WER: 2.9%\n Sentence accuracy: 97.31%\n digit errors: 0.35%\n other errors: 2.34%\n\n\nHard test set\n WER: 9.34%\n Sentence accuracy: 85.34%\n digit errors: 3.12%\n other errors: 11.54%\n\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\nimport nemo.collections.nlp as nemo_nlp\ntagger = nemo_nlp.models.duplex_text_normalization.DuplexTaggerModel.from_pretrained(model_name=\"itn_en_t5\")\ndecoder = nemo_nlp.models.duplex_text_normalization.DuplexDecoderModel.from_pretrained(model_name=\"itn_en_t5\")\nnormalizer = nemo_nlp.models.duplex_text_normalization.DuplexTextNormalizationModel(tagger, decoder, lang='en')\n```\n\n### Inference\n```bash\npython [NEMO_GIT_FOLDER]/examples/nlp/duplex_text_normalization/duplex_text_normalization_infer.py lang=en mode=itn tagger_pretrained_model=itn_en_t5 decoder_pretrained_model=itn_en_t5 inference.interactive=True\n```\n\nTo use inference from data file, set `inference.interactive=False inference.from_file=[DATA_FILE]`\n\n\n### Input\n\nBoth the DuplexTaggerModel model and the DuplexDecoderModel model use the same simple text format as the dataset. The data needs to be stored in TAB separated files (.tsv) with three columns. The first of which is the \"semiotic class\" (e.g., numbers, times, dates), the second is the token in written form, and the third is the spoken form. It is expected that a complete dataset contains three files: train.tsv, dev.tsv, and test.tsv.\n\n### Output\n\nThe model outputs either the transformed sentence in interactive mode or the evaluation metrics in test mode.\n\n## Limitations\n\nThe length of the input text is currently constrained by the maximum sequence length of the tagger and decoder model, which is 512 tokens after tokenization. \n\n## References\n\n\n\n[1] https://huggingface.co/albert-base-v2\n\n[2] https://huggingface.co/t5-small\n\n[3] https://research.fb.com/publications/neural-models-of-text-normalization-for-speech-applications\n\n[4] https://arxiv.org/abs/1611.00068\n\n[5] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n## License\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","English T5-based Inverse Text Normalization",["D",1680635171730],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/itn_en_t5","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[245,246],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_213":254,"_248":255,"_249":256,"_251":252},{"_213":247,"_248":247,"_249":250,"_251":252},"en","value","filter",{"_253":252,"_248":247},"category","label","field","AI","soln_ai",{"_253":257,"_248":255},"technology",{"_114":1431,"_116":1432},"ngcApps",[1384],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1365":1366,"_1367":1368,"_1369":1370,"_1371":1372,"_1373":269,"_145":292,"_1374":1375,"_1376":56,"_1377":93,"_1378":93,"_1379":1380,"_1381":93,"_1382":1383},"amplitude",{"_1362":1363,"_965":-7,"_1364":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1356":1357,"_1358":1359,"_1360":1361},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1240":173,"_1241":1242,"_1243":1244,"_1245":1246},"announcements",[],"consentState",{"_330":1230,"_1231":93,"_1232":31,"_1233":31,"_1234":1235},"consentConfig",{"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331},"showContactAdmin",1784025206409,[],[],[306,307,308,309,310,311],{"_45":60,"_47":71,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":109,"_51":-5,"_53":-5,"_55":56},"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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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.",[603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623],[568,569,570,571,572],{"_371":463,"_213":463,"_172":173,"_344":594},{"_371":429,"_213":429,"_172":173,"_344":589},{"_371":582,"_213":582,"_172":173,"_344":583},{"_371":578,"_213":578,"_172":173,"_344":579},{"_371":573,"_213":573,"_172":173,"_344":574},"nvcrm.my.site.com",[575],{"_80":576,"_377":573,"_378":31,"_172":577},"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",[580],{"_80":581,"_377":578,"_378":31,"_172":412},"edge","github.com",[584,585],{"_80":588,"_377":582,"_378":31,"_172":587},{"_80":586,"_377":582,"_378":31,"_172":587},"logged_in","This domain is owned by Github. 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by Google Analytics to identify and track an individual session with your device.","__sec__token","__sec__cid","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","_gd_visitor","_biz_dfsA","This cookie is associated with CloudFlare, used to speed up page load times. It is not used to track users but rather to limit the number of requests that have to be made to doubleclick.net.","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","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).","__sec_tid","__sec__fid","crowdsource.nvidia.com","This cookie name is associated with 6sense which is used to collect visitor data related to the user’s visit to the website, such as number of visits, average time spent on the website and which pages have been loaded, for the purpose of serving targeted advertisements.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","__sec__ghost","_gd_svisitor","info.nvidia.com","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.","research.nvidia.com","_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.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. It is either correlated to the 6suuid or a unique value, and can be changed during the lifetime of this cookie.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","s_ips","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_gd_session","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.","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. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","nv.referrerHref","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","__sec_crid","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). To determine this, we try to store the _hjTLDTest cookie for different URL substring alternatives until it fails. After this check, the cookie is removed.","s_tp","s_plt","This cookie is used to measure the time taken for pages to load, allowing website owners to identify slow loading pages and improve user experience.","This _gd_session cookie is used to collect visitor data related to the user's visit to the website, such as number of visits, average time spent on the website and which pages have been loaded, for the purpose of serving targeted advertisements.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. 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Name	Size	Updated	Actions
itn_en_t5_decoder.nemo	230.89 MB	February 28, 2025 UTC	curl wget Download
itn_en_t5_tagger.nemo	42.36 MB	February 28, 2025 UTC	curl wget Download