Model Overview

Automatic Speech Recognition (ASR) systems typically generate text with no punctuation and capitalization of the words. Besides being hard to read, the ASR output could be an input to named entity recognition, machine translation or text-to-speech models. If the input text has punctuation and words are capitalized correctly, this could potentially boost the performance of such models.

For each word in the input text, the model:

predicts a punctuation mark that should follow the word (if any). The model supports commas, periods and question marks.
predicts if the word should be capitalized or not.

Model Architecture

The Punctuation and Capitalization model consists of the pre-trained DistilBERT[1] followed by two token classification heads. One classification head is responsible for the punctuation task, the other one handles the capitalization task. Both token level classification heads take the DistilBERT encoded representation of the [CLS] token as input. Such architecture allows this model to solve two tasks at once with only a single pass through the DistilBERT. Finally, all the parameters are fine-tuned on this joint task.

Training

The model was trained with Huggingface DistilBERT base uncased checkpoint.

Datasets

The model was trained on a subset of data from the following sources:

Tatoeba sentences
Books from the Project Gutenberg that were used as part of the LibriSpeech corpus
Transcripts from Fisher English Training Speech

Performance

Each word in the input sequence could be split into one or more tokens, as a result, there are two possible ways of the model evaluation:

marking the whole entity as a single label
perform an evaluation on the sub token level

During training, the first approach was applied, and the predictions for the first token of the input were used to label the whole word. Each task is evaluated separately. Due to the high class unbalancing, the suggested metric for this model is F1 score (with macro averaging).

This model was evaluated on an internal dataset, and it reached the F1 score of 77%.

How to use this model

The model is available for use in the NeMo toolkit [2], 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\nimport nemo.collections.nlp as nemo_nlp\nmodel = nemo_nlp.models.PunctuationCapitalizationModel.from_pretrained(model_name=\"punctuation_en_distilbert\")\n

Use the model to add punctuation and capitalization

model.add_punctuation_capitalization(['how are you', 'great how about you'])\n

Input

The model accepts lower cased English text without punctuation marks.

Output

Text with punctuation and capitalization restored.

Limitations

The length of the input text is currently constrained by the maximum sequence length of the DistilBERT base uncased model, which is 512 tokens after tokenization. The punctuation model supports commas, periods and question marks.

References

[1] Sanh, Victor, et al. \"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.\" arXiv preprint arXiv:1910.01108 (2019).

[2] NVIDIA NeMo Toolkit

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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Besides being hard to read, the ASR output could be an input to named entity recognition, machine translation or text-to-speech models. If the input text has punctuation and words are capitalized correctly, this could potentially boost the performance of such models.\n\nFor each word in the input text, the model:\n\n- predicts a punctuation mark that should follow the word (if any). The model supports commas, periods and question marks.\n- predicts if the word should be capitalized or not.\n\n## Model Architecture\n\nThe Punctuation and Capitalization model consists of the pre-trained [DistilBERT](https://arxiv.org/abs/1910.01108)[1] followed by two token classification heads. One classification head is responsible for the punctuation task, the other one handles the capitalization task. Both token level classification heads take the DistilBERT encoded representation of the [CLS] token as input. Such architecture allows this model to solve two tasks at once with only a single pass through the DistilBERT. Finally, all the parameters are fine-tuned on this joint task.\n\n## Training\n\nThe model was trained with [Huggingface DistilBERT base uncased checkpoint](https://huggingface.co/transformers/model_doc/distilbert.html).\n\n### Datasets\n\nThe model was trained on a subset of data from the following sources: \n- [Tatoeba sentences](https://tatoeba.org/eng/downloads)\n- Books from the Project Gutenberg that were used as part of [the LibriSpeech corpus](http://www.openslr.org/12)\n- Transcripts from [Fisher English Training Speech](https://catalog.ldc.upenn.edu/LDC2004S13)\n\n## Performance\n\nEach word in the input sequence could be split into one or more tokens, as a result, there are two possible ways of the model evaluation: \n- marking the whole entity as a single label \n- perform an evaluation on the sub token level\n\nDuring training, the first approach was applied, and the predictions for the first token of the input were used to label the whole word. Each task is evaluated separately. Due to the high class unbalancing, the suggested metric for this model is F1 score (with macro averaging).\n\nThis model was evaluated on an internal dataset, and it reached the F1 score of 77%.\n\n## How to use this model\n\nThe model is available for use in the NeMo toolkit [2], 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\n\timport nemo\n\timport nemo.collections.nlp as nemo_nlp\n\tmodel = nemo_nlp.models.PunctuationCapitalizationModel.from_pretrained(model_name=\"punctuation_en_distilbert\")\n \n### Use the model to add punctuation and capitalization\n\n\tmodel.add_punctuation_capitalization(['how are you', 'great how about you'])\n\n### Input\n\nThe model accepts lower cased English text without punctuation marks.\n\n### Output\n\nText with punctuation and capitalization restored.\n\n## Limitations\n\nThe length of the input text is currently constrained by the maximum sequence length of the DistilBERT base uncased model, which is 512 tokens after tokenization. The punctuation model supports commas, periods and question marks.\n\n## References\n\n[1] Sanh, Victor, et al. \"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.\" arXiv preprint arXiv:1910.01108 (2019).\n\n[2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\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","Punctuation En Distilbert",["D",1680636613025],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/punctuation_en_distilbert","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":258,"_245":258,"_246":259,"_248":249},{"_207":256,"_245":256,"_246":257,"_248":249},{"_207":253,"_245":253,"_246":254,"_248":249},{"_207":251,"_245":251,"_246":252,"_248":249},{"_207":244,"_245":244,"_246":247,"_248":249},"PytorchLightning","value","filter",{"_250":249,"_245":244},"category","label","field","Punctuation",{"_250":249,"_245":251},"NeMo",{"_250":255,"_245":253},"framework","En",{"_250":249,"_245":256},"DistilBert",{"_250":249,"_245":258},{"_108":1242,"_110":1243},"ngcApps",[1195],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1176":1177,"_1178":1179,"_1180":1181,"_1182":1183,"_1184":271,"_139":299,"_1185":1186,"_1187":56,"_1188":87,"_1189":87,"_1190":1191,"_1192":87,"_1193":1194},"amplitude",{"_1173":1174,"_810":-7,"_1175":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1167":1168,"_1169":1170,"_1171":1172},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1785994693582,"1.11.0","defaultSearchSuggestions",{"_1050":169,"_1051":1052,"_1053":1054,"_1055":1056},"announcements",[],"consentState",{"_336":1040,"_1041":87,"_1042":31,"_1043":31,"_1044":1045},"consentConfig",{"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337},"showContactAdmin",1785994693583,[],[],[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":103,"_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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