Changes have been made to a forked branch in order to transliterate large datasets more efficiently. As transliteration works on a word-by-word basis, it is not efficient to repeatedly transliterate the same words within the dataset. Therefore, the following changes have been implemented:

1. Transliteration caching
2. Batch processing
3. Usage examples.

IndicXlit is a transformer-based multilingual transliteration model (~11M) that supports 21 Indic languages for Roman to native script and native to Roman script conversions. It is trained on Aksharantar dataset which is the largest publicly available parallel corpus containing 26 million word pairs spanning 20 Indic languages at the time of writing (5 May 2022). It supports the following 21 Indic languages:

IndicXlit is evaluated on Dakshina benchmark and Aksharantar benchmark. IndicXlit achieves state-of-the-art results on the Dakshina testset and also provides baseline results on the new Aksharantar testset. The Top-1 results are summarized below. For more details, refer our paper.

Aksharantar (native words): 37.72

• Table of contents
• Resources
  • Download IndicXlit model
  • Using hosted APIs
  • Accessing on ULCA
• Running inference
  • Command line interface
  • Python interface
• Training model
  • Setting up your environment
  • Details of models and hyperparameters
  • Training procedure and code
  • WandB plots
  • Evaluating trained model
  • Detailed benchmarking results
• Finetuning model on your data
• Mining details
• Directory structure
• Citing
  • License
  • Contributors
  • Contact
• Acknowledgements

Roman to Indic model v1.0

Indic to Roman model v1.0

Aksharantar-Dataset: Huggingface

Transliteration-word-pairs

Transliteration-sentence-pairs

Roman to Indic Interface

Indic to Roman Interface

You can try out our model at ULCA under Transliteration Models, and the Aksharantar dataset under Transliteration Benchmark Datasets.

The model is trained on words as inputs. Hence, users need to split sentences into words before running the transliteration model when using our command line interface.

Follow the Colab notebook to setup the environment, download the trained IndicXlit model and transliterate your own text. GPU support is given in command line interface.

Command line interface -->

Python interface -->

The python interface is useful in case you want to reuse the model for multiple transliterations and do not want to reinitialize the model each time.

• Architecture: IndicXlit uses 6 encoder and decoder layers, input embeddings of size 256 with 4 attention heads and feedforward dimension of 1024, with a total of 11M parameters.
• Loss: Cross-Entropy loss
• Optimizer: Adam
• Adam-betas: (0.9, 0.98)
• Peak-learning-rate: 0.001
• Learning-rate-scheduler: inverse-sqrt
• Temperature-sampling (T): 1.5
• Warmup-steps: 4000

Please refer to section 6 of our paper for more details on training setup.

# Clone IndicXlit repository
git clone https://github.com/AI4Bharat/IndicXlit.git

# Install required libraries
pip install indic-nlp-library

# Install Fairseq from source
git clone https://github.com/pytorch/fairseq.git
cd fairseq
pip install --editable ./

The high level steps we follow for training are as follows:

• Organize the train/test/valid data in corpus directory such that it has all the files containing parallel data for en-X (English-X) language pairs in the following format:
  • train_x.en for training file of en-X language pair which contains the space separated Roman characters in each line.
  • train_x.x for training file of en-X language pair which contains the space separated Indic language characters in each line.

# corpus/
# ├── train_bn.bn
# ├── train_bn.en
# ├── train_gu.gu
# ├── train_gu.en
# ├── ....
# ├── valid_bn.bn
# ├── valid_bn.en
# ├── valid_gu.gu
# ├── valid_gu.en
# ├── ....
# ├── test_bn.bn
# ├── test_bn.en
# ├── test_gu.gu
# ├── test_gu.en
# └── ....

• Combine the training files (joint training) across all languages.

# corpus/
# ├── train_combine.cmb
# └── train_combine.en

• Create the joint vocabulary using all the combined training data.

fairseq-preprocess \
   --trainpref corpus/train_combine \
   --source-lang en --target-lang cmb \
   --workers 256 \
   --destdir corpus-bin

• Create the binarized data required for Fairseq for each language separately using joint vocabulary.

for lang_abr in bn gu hi kn ml mr pa sd si ta te ur
do
   fairseq-preprocess \
   --trainpref corpus/train_$lang_abr --validpref corpus/valid_$lang_abr --testpref corpus/test_$lang_abr \
   --srcdict corpus-bin/dict.en.txt \
   --tgtdict corpus-bin/dict.cmb.txt \
   --source-lang en --target-lang $lang_abr \
   --workers 32 \
   --destdir corpus-bin 
done

• Add all language codes to lang_list.txt file and save it in the same directory.

• Start training with fairseq-train command. Please refer to Fairseq documentaion to know more about each of these options.

• Please refer https://github.com/facebookresearch/fairseq/tree/main/examples/multilingual to know more about the 'translation_multi_simple_epoch' task.

# training script
fairseq-train corpus-bin \
  --save-dir transformer \
  --arch transformer --layernorm-embedding \
  --task translation_multi_simple_epoch \
  --sampling-method "temperature" \
  --sampling-temperature 1.5 \
  --encoder-langtok "tgt" \
  --lang-dict lang_list.txt \
  --lang-pairs en-bn,en-gu,en-hi,en-kn,en-ml,en-mr,en-pa,en-sd,en-si,en-ta,en-te,en-ur  \
  --decoder-normalize-before --encoder-normalize-before \
  --activation-fn gelu --adam-betas "(0.9, 0.98)"  \
  --batch-size 1024 \
  --decoder-attention-heads 4 --decoder-embed-dim 256 --decoder-ffn-embed-dim 1024 --decoder-layers 6 \
  --dropout 0.5 \
  --encoder-attention-heads 4 --encoder-embed-dim 256 --encoder-ffn-embed-dim 1024 --encoder-layers 6 \
  --lr 0.001 --lr-scheduler inverse_sqrt \
  --max-epoch 51 \
  --optimizer adam  \
  --num-workers 32 \
  --warmup-init-lr 0 --warmup-updates 4000

The above steps are further documented in our Colab notebook -->

Please refer to section 6 of our paper for more details of our training hyperparameters.

IndicXlit en-indic model.

• The trained model is saved in the transformer directory. It will have the following files:

# transformer/
# └── checkpoint_best.pt

• To generate outputs after training, use the following generation script to predict outputs which will be saved in the output directory.

for lang_abr in bn gu hi kn ml mr pa sd si ta te ur
do
source_lang=en
target_lang=$lang_abr
fairseq-generate corpus-bin \
  --path transformer/checkpoint_best.pt \
  --task translation_multi_simple_epoch \
  --gen-subset test \
  --beam 4 \
  --nbest 4 \
  --source-lang $source_lang \
  --target-lang $target_lang \
  --batch-size 2048 \
  --encoder-langtok "tgt" \
  --lang-dict lang_list.txt \
  --num-workers 64 \
  --lang-pairs en-bn,en-gu,en-hi,en-kn,en-ml,en-mr,en-pa,en-sd,en-si,en-ta,en-te,en-ur  > output/${source_lang}_${target_lang}.txt
done

• To test the models post training, use generate_result_files.py to convert the Fairseq output file into XML files and evaluate_result_with_rescore_option.py to compute accuracies.

• evaluate_result_with_rescore_option.py can be downloaded using the following link:

wget https://github.com/AI4Bharat/IndicXlit/releases/download/v1.0/evaluate_result_with_rescore_option.py

The above evaluation steps and code for generate_result_files.py are further documented in the Colab notebook -->

Refer to Evaluation Results for results of IndicXlit model on Dakshina and Aksharantar benchmarks. Please refer to section 7 of our paper for a detailed discussion of the results.

The high level steps for finetuning on your own dataset are:

• Organize the train/test/valid data in corpus directory such that it has all the files containing parallel data for en-X language pair in the following format:

  • train_x.en for training file of en-X language pair which contains the space separated Roman characters in each line.

  • train_x.x for training file of en-X language pair which contains the space separated Indic characters in each line.

# corpus/
# ├── train_bn.bn
# ├── train_bn.en
# ├── train_gu.gu
# ├── train_gu.en
# ├── ....
# ├── valid_bn.bn
# ├── valid_bn.en
# ├── valid_gu.gu
# ├── valid_gu.en
# ├── ....
# ├── test_bn.bn
# ├── test_bn.en
# ├── test_gu.gu
# ├── test_gu.en
# └── ....

• To download and decompress the model file and joint vocabulary files use the following command.

# download the IndicXlit models
wget https://github.com/AI4Bharat/IndicXlit/releases/download/v1.0/indicxlit-en-indic-v1.0.zip
unzip indicxlit-en-indic-v1.0.zip

• Binarizing the files using the joint dictionaries.

for lang_abr in bn gu hi kn ml mr pa sd si ta te ur
do
   fairseq-preprocess \
   --trainpref corpus/train_$lang_abr --validpref corpus/valid_$lang_abr --testpref corpus/test_$lang_abr \
   --srcdict corpus-bin/dict.en.txt \
   --tgtdict corpus-bin/dict.mlt.txt \
   --source-lang en --target-lang $lang_abr \
   --destdir corpus-bin 
done

• Add all language codes that models supports to lang_list.txt file and save it in the same directory.

• Please refer to Fairseq documentation to know more about each of these options.

# We will use fairseq-train to finetune the model
# Some notable args:
# --lr                  -> Learning Rate. From our limited experiments, we find that lower learning rates like 3e-5 works best for finetuning.
# --restore-file        -> Reload the pretrained checkpoint and start training from here (change this path for Indic-en; currently it is set to en-Indic).
# --reset-*             -> Reset and not use lr scheduler, dataloader, optimizer etc of the older checkpoint.

fairseq-train corpus-bin \
    --save-dir transformer \
    --arch transformer --layernorm-embedding \
    --task translation_multi_simple_epoch \
    --sampling-method "temperature" \
    --sampling-temperature 1.5 \
    --encoder-langtok "tgt" \
    --lang-dict lang_list.txt \
    --lang-pairs en-bn,en-gu,en-hi,en-kn,en-ml,en-mr,en-pa,en-sd,en-si,en-ta,en-te,en-ur \
    --decoder-normalize-before --encoder-normalize-before \
    --activation-fn gelu --adam-betas "(0.9, 0.98)"  \
    --batch-size 1024 \
    --decoder-attention-heads 4 --decoder-embed-dim 256 --decoder-ffn-embed-dim 1024 --decoder-layers 6 \
    --dropout 0.5 \
    --encoder-attention-heads 4 --encoder-embed-dim 256 --encoder-ffn-embed-dim 1024 --encoder-layers 6 \
    --lr 0.001 --lr-scheduler inverse_sqrt \
    --max-epoch 51 \
    --optimizer adam  \
    --num-workers 32 \
    --warmup-init-lr 0 --warmup-updates 4000 \
    --keep-last-epochs 5 \
    --patience 5 \
    --restore-file transformer/indicxlit.pt \
    --reset-lr-scheduler \
    --reset-meters \
    --reset-dataloader \
    --reset-optimizer

The above steps (setting up the environment, downloading the trained IndicXlit model and preparing your custom dataset for finetuning) are further documented in our Colab notebook -->

Following links provide a detailed description of mining from various resources:

• Samanantar: https://github.com/AI4Bharat/IndicXlit/tree/master/data_mining/transliteration_mining_samanantar
• IndicCorp: https://github.com/AI4Bharat/IndicXlit/tree/master/data_mining/IndicCorp/skeleton/en_dict_workplace

IndicXlit
├── Checker
│   ├── README.md
│   ├── Transliteration_Checker.java
│   └── Transliteration_Checker.py
├── Dataset_Format
│   ├── Create_Aksharantar_JSONL.py
│   └── README.md
├── LICENSE
├── README.md
├── ULCA_Format
│   ├── README.md
│   └── ULCA_dataset.py
├── ablation_study
│   ├── data_filteration
│   │   ├── data_filteration_with_benchmark_test_dakshina_test_valid
│   │   └── data_filteration_with_dakshina_test_valid
│   └── model
│       ├── monolingual_model
│       ├── multilingual_model_(same for_singlescript_model)
│       ├── north_model
│       ├── preprocessing_for_rescoring
│       ├── south_model
│       └── specific_to_E_because_(differ_across_dataset_E_has_specific_langs)
├── app
│   ├── Caddyfile
│   ├── Hosting.md
│   ├── MANIFEST.in
│   ├── README.md
│   ├── ai4bharat
│   │   ├── __init__.py
│   │   └── transliteration
│   ├── api_expose.py
│   ├── auto_certif_renew.py
│   ├── dependencies.txt
│   ├── setup.py
│   └── start_server.py
├── corpus_preprocessing
│   ├── Analysis
│   │   ├── GIT_analysis.py
│   │   ├── README.md
│   │   └── len_stats.py
│   ├── Benchmark_data_from_JSONS(Karya)
│   │   ├── Benchmark_Named_entities.py
│   │   ├── Benchmark_Transliteration_data.py
│   │   └── README.md
│   ├── Collating_existing_dataset
│   │   ├── collate_data.ipynb
│   │   ├── dataset_info.csv
│   │   └── stats_detail.txt
│   ├── Create_Unique_list_from_datasets
│   │   ├── IndicCorp
│   │   ├── LDCIL
│   │   ├── README.md
│   │   └── Words_freq_probability_after_kenlm
│   └── Pre_process_arabic_scripts
│       ├── README.md
│       └── clean_urdu.py
├── data_mining
│   ├── IndicCorp
│   │   ├── preprocess_data
│   │   └── skeleton
│   ├── readme.md
│   └── transliteration_mining_samanantar
│       ├── align_data.sh
│       ├── convert_csv.py
│       ├── extract_translit_pairs.sh
│       ├── install_tools.txt
│       ├── model_run_steps.txt
│       ├── preprocess_data.py
│       ├── readme.md
│       ├── samanantar_pairs_count.xlsx
│       └── validation_script.py
├── inference
│   ├── cli
│   │   ├── en-indic
│   │   │	├── generate_result_files.py
│   │ 	│	├── interactive.sh
│   │   │	├── lang_list.txt
│   │   │	└── transliterate_word.sh
│   │   └── indic-en
│   │   	├── generate_result_files.py
│   │ 		├── interactive.sh
│   │   	├── lang_list.txt
│   │   	└── transliterate_word.sh
│   └── python
│       ├── custom_interactive.py
│       ├── lang_list.txt
│       ├── test_api_inference.py
│       └── xlit_translit.py
├── model_training_scripts
│   ├── README.md
│   ├── binarizing
│   │   └── preprocess_all_lang.sh
│   ├── data_filtration
│   │   ├── combining_data_acrooss_lang.py
│   │   ├── refresh_data_train_all_test_valid.py
│   │   └── refresh_test_valid_data.py
│   ├── evaluate
│   │   ├── evaluate_result_with_rescore_option.py
│   │   ├── final_result.sh
│   │   └── final_result_without_rescoring.sh
│   ├── generation
│   │   ├── generate.sh
│   │   └── generate_result_files.py
│   ├── skeleton
│   │   ├── blank_file.txt
│   │   ├── creating_dir_struct.sh
│   │   ├── indiccorp
│   │   ├── mined_data
│   │   ├── multi_lang
│   │   ├── preprocess_data
│   │   └── working
│   ├── training
│   │   ├── lang_list.txt
│   │   └── train.sh
│   └── vocab_creation
│       └── preprocess.sh
└── sample_images
    ├── main_page.png
    ├── select_language.png
    └── transliterate_sentence.png

If you are using any of the resources, please cite the following article:

@article{Madhani2022AksharantarTB,
  title={Aksharantar: Towards building open transliteration tools for the next billion users},
  author={Yash Madhani and Sushane Parthan and Priyanka A. Bedekar and Ruchi Khapra and Vivek Seshadri and Anoop Kunchukuttan and Pratyush Kumar and Mitesh M. Khapra},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.03018}
}

We would like to hear from you if:

• You are using our resources. Please let us know how you are putting these resources to use.
• You have any feedback on these resources.

The IndicXlit code (and models) are released under the MIT License.

• Yash Madhani _{(AI4Bharat, IITM)}
• Sushane Parthan _{(AI4Bharat, IITM)}
• Priyanka Bedakar _{(AI4Bharat, IITM)}
• Ruchi Khapra _(AI4Bharat)
• Gokul NC _(AI4Bharat)
• Anoop Kunchukuttan _{(AI4Bharat, Microsoft)}
• Pratyush Kumar _{(AI4Bharat, Microsoft, IITM)}
• Mitesh M. Khapra _{(AI4Bharat, IITM)}

• Anoop Kunchukuttan ([email protected])
• Mitesh Khapra ([email protected])
• Pratyush Kumar ([email protected])

We would like to thank EkStep Foundation for their generous grant which helped in setting up the Centre for AI4Bharat at IIT Madras to support our students, research staff, data and computational requirements. We would like to thank The Ministry of Electronics and Information Technology (NLTM) for its grant to support the creation of datasets and models for Indian languages under its ambitious Bhashini project. We would like to thank Vivek Sheshadri and Karya team for their contribution to the data collection process. We would also like to thank the Centre for Development of Advanced Computing, India (C-DAC) for providing access to the Param Siddhi supercomputer for training our models. Lastly, we would like to thank Microsoft for its grant to create datasets, tools and resources for Indian languages.