---
license: cc-by-4.0
language:
- ru
library_name: nemo
pipeline_tag: token-classification
tags:
- G2P
- Grapheme-to-Phoneme
---

# Russian G2P token classification model

This is a non-autoregressive model for Russian grapheme-to-phoneme (G2P) conversion based on BERT architecture. It predicts phonemes in IPA format. 
Initial data was built using Wiktionary json from https://kaikki.org/dictionary/Russian/index.html 


## Intended uses & limitations

The input is expected to consist of cyrillic letters separated by space. Real space should be replaced to underscore(_).
Note that the model was trained on single words and some short phrases.
Though it can accept longer phrases its accuracy may degrade on them.

### How to use

Install NeMo.

Download ru_g2p.nemo (this model)
```bash
git lfs install
git clone https://huggingface.co/bene-ges/ru_g2p_ipa_bert_large
```

Run

```bash
python ${NEMO_ROOT}/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py \
  pretrained_model=ru_g2p_ipa_bert_large/ru_g2p.nemo \
  inference.from_file=input.txt \
  inference.out_file=output.txt \
  model.max_sequence_len=512 \
  inference.batch_size=128 \
  lang=ru
```

Example of input file:
```
и с х о д
т р а н с н е п т у н о в ы х
т е л я т н и к о в с к о е
ц а р с к о г о
к р о с х о ф
г а н с - ю р г е н
д а р д а н е л л
```

Example of output file:
```
ɪ s x 'o t      и с х о д       ɪ s x 'o t      ɪ s x 'o t      PLAIN PLAIN PLAIN PLAIN PLAIN
t r a nʲ sʲ nʲ ɪ p t 'u n ə v ɨ x       т р а н с н е п т у н о в ы х   t r a nʲ sʲ nʲ ɪ p t 'u n ə v ɨ x       t r a nʲ sʲ nʲ ɪ p t 'u n ə v ɨ x       PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
tʲ ɪ lʲ 'æ tʲ nʲ ɪ k ə f s k ə jə       т е л я т н и к о в с к о е     tʲ ɪ lʲ 'æ tʲ nʲ ɪ k ə f s k ə jə       tʲ ɪ lʲ 'æ tʲ nʲ ɪ k ə f s k ə jə       PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
t~s 'a r s k ə v ə      ц а р с к о г о t~s 'a r s k ə v ə      t~s 'a r s k ə v ə      PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
k r ɐ s x 'o f  к р о с х о ф   k r ɐ s x 'o f  k r ɐ s x 'o f  PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
ɡ a n s 'ju r ɡʲ ɪ n    г а н с - ю р г е н     ɡ a n s _ 'ju r ɡʲ ɪ n  ɡ a n s _ 'ju r ɡʲ ɪ n  PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
d ə r d ɐ n 'ɛ ɫ        д а р д а н е л л       d ə r d ɐ n 'ɛ <DELETE> ɫ       d ə r d ɐ n 'ɛ <DELETE> ɫ       PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
```

Note that the correct output tags are in the **third** column, input is in the second column.
Tags correspond to input letters in a one-to-one fashion. If you remove `<DELETE>` tag, `+`, and spaces, you should get IPA-like transcription.
The model does not predict secondary stress. The primary stress is put directly before the stressed vowel. In some cases stress can be missing.