README.md

---
license: cc-by-4.0
datasets:
- bene-ges/spellmapper_en_train_v1
language:
- en
library_name: nemo
---
# SpellMapper - Spellchecking ASR Customization Model 

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| [![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets)

This model is an alternative to word boosting/shallow fusion approaches:

- does not require retraining ASR model;
- does not require beam-search/language model (LM);
- can be applied on top of any English ASR model output;

Paper: [SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings](https://arxiv.org/abs/2306.02317)

[Documentation page](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/spellchecking_asr_customization.html).

## How to Use this Model

To use this model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo).

See [Bash-script](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/spellchecking_asr_customization/run_infer.sh) with example of inference pipeline.

Or play with [Tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/SpellMapper_English_ASR_Customization.ipynb).


## Citation

```bibtex
    @inproceedings{inproceedings,
        author = {Antonova, Alexandra and Bakhturina, Evelina and Ginsburg, Boris},
        year = {2023},
        month = {08},
        pages = {1404-1408},
        title = {SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings},
        doi = {10.21437/Interspeech.2023-768}
    }
```