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--- |
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license: cc-by-4.0 |
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datasets: |
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- bene-ges/spellmapper_en_train_v1 |
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language: |
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- en |
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library_name: nemo |
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--- |
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# SpellMapper - Spellchecking ASR Customization Model |
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<style> |
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img { |
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display: inline; |
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} |
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</style> |
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| [![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets) |
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This model is an alternative to word boosting/shallow fusion approaches: |
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- does not require retraining ASR model; |
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- does not require beam-search/language model (LM); |
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- can be applied on top of any English ASR model output; |
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Paper: [SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings](https://arxiv.org/abs/2306.02317) |
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[Documentation page](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/spellchecking_asr_customization.html). |
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## How to Use this Model |
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To use this model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). |
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See [Bash-script](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/spellchecking_asr_customization/run_infer.sh) with example of inference pipeline. |
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Or play with [Tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/SpellMapper_English_ASR_Customization.ipynb). |
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## Citation |
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```bibtex |
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@inproceedings{inproceedings, |
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author = {Antonova, Alexandra and Bakhturina, Evelina and Ginsburg, Boris}, |
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year = {2023}, |
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month = {08}, |
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pages = {1404-1408}, |
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title = {SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings}, |
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doi = {10.21437/Interspeech.2023-768} |
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} |
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``` |
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