Transliterations to/from Indian languages are still generally low quality. One problem is access to data. Another is that there is no standard transliteration. For Hindi--English, we build novel dataset for names using the ESPNcricinfo. For instance, see here for hindi version of the english scorecard. We also create a dataset from election affidavits We also exploit the Google Dakshina dataset.
To overcome the fact that there isn't one standard way of transliteration, we provide k-best transliterations.
We strongly recommend installing indicate inside a Python virtual environment (see venv documentation)
pip install indicate
- transliterate.hindi2english will take Hindi text and translate into English.
from indicate import transliterate
english_translated = transliterate.hindi2english("हिंदी")
print(english_translated)
output - hindi
We expose 1 function, which will take Hindi text and transliterate it to English.
- transliterate.hindi2english(input)
- What it does:
- Converts given hindi text into English alphabet
- Output
- Returns text in English
- What it does:
The datasets used to train the model:
- Indian Election affidavits
- Google Dakshina dataset
- ESPN Cric Info for hindi version of the english scorecard.
- IIT Bombay English-Hindi Corpus
Model was evaluated on test dataset of Google Dakshina dataset, Model predicted 73.64% exact matches. Indic-trans predicted 63.12% exact matches on Google Dakshina dataset. Below is the edit distance metrics on test dataset (0.0 mean exact match, the farther away from 0.0, the difference is more between predicted text and actual text)
Rajashekar Chintalapati and Gaurav Sood
The project welcomes contributions from everyone! In fact, it depends on it. To maintain this welcoming atmosphere, and to collaborate in a fun and productive way, we expect contributors to the project to abide by the Contributor Code of Conduct.
The package is released under the MIT License.