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",[244,245],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_212":253,"_247":254,"_248":255,"_250":251},{"_212":246,"_247":246,"_248":249,"_250":251},"en","value","filter",{"_252":251,"_247":246},"category","label","field","AI","soln_ai",{"_252":256,"_247":254},"technology",{"_111":1235,"_113":1236},"ngcApps",[1188],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1169":1170,"_1171":1172,"_1173":1174,"_1175":1176,"_1177":268,"_142":294,"_1178":1179,"_1180":55,"_1181":90,"_1182":90,"_1183":1184,"_1185":90,"_1186":1187},"amplitude",{"_1166":1167,"_807":-7,"_1168":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1160":1161,"_1162":1163,"_1164":1165},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787436526734,"1.12.0","defaultSearchSuggestions",{"_1047":172,"_1048":1049,"_1050":1051,"_1052":1053},"announcements",[],"consentState",{"_332":1037,"_1038":31,"_1039":31,"_1040":90,"_1041":1042},"consentConfig",{"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333},"showContactAdmin",[],[],[307,308,309,310,311,312],{"_45":58,"_47":68,"_49":106,"_51":-5,"_53":-5,"_54":55},{"_45":58,"_47":67,"_49":106,"_51":-5,"_53":-5,"_54":55},{"_45":58,"_47":65,"_49":106,"_51":-5,"_53":-5,"_54":55},{"_45":58,"_47":59,"_49":106,"_51":-5,"_53":-5,"_54":55},{"_45":56,"_47":57,"_49":50,"_51":52,"_53":313,"_54":55},{"_45":46,"_47":48,"_49":106,"_51":-5,"_53":-5,"_54":55},3,"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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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