Update README.md
Browse files# English G2P token classification model
This is a non-autoregressive model for English grapheme-to-phoneme (G2P) conversion based on BERT architecture. It predicts phonemes in CMU format.
Initial data was built using CMUdict v0.07.
## Intended uses & limitations
The input is expected to consist of single words, consisting of English letters and apostrophe, all separated by space.
### How to use
Install NeMo.
Download en_g2p.nemo (this model)
```bash
git lfs install
git clone https://huggingface.co/bene-ges/en_g2p_cmu_bert_large
```
Run
```bash
python ${NEMO_ROOT}/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py \
pretrained_model=en_g2p_cmu_bert_large/en_g2p.nemo \
inference.from_file=input.txt \
inference.out_file=output.txt \
model.max_sequence_len=64 \
inference.batch_size=128 \
lang=en
```
Example of input file:
```
g e f f e r t
p r o s c r i b e d
p r o m i n e n t l y
j o c e l y n
m a r c e c a ' s
s t a n k o w s k i
m u f f l e
```
Example of output file:
```
G EH1 F ER0 T g e f f e r t G EH1 <DELETE> F <DELETE> ER0 T G EH1 <DELETE> F <DELETE> ER0 T PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
P R OW0 S K R AY1 B D p r o s c r i b e d P R OW0 S K R AY1 B <DELETE> D P R OW0 S K R AY1 B <DELETE> D PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
P R AA1 M AH0 N AH0 N T L IY0 p r o m i n e n t l y P R AA1 M AH0 N AH0 N T L IY0 P R AA1 M AH0 N AH0 N T L IY0 PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
JH AO1 S L IH0 N j o c e l y n JH AO1 S <DELETE> L IH0 N JH AO1 S <DELETE> L IH0 N PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
M AA0 R S EH1 K AH0 Z m a r c e c a ' s M AA0 R S EH1 K AH0 <DELETE> Z M AA0 R S EH1 K AH0 <DELETE> Z PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
S T AH0 NG K AO1 F S K IY0 s t a n k o w s k i S T AH0 NG K AO1 F S K IY0 S T AH0 NG K AO1 F S K IY0 PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
M AH1 F AH0L m u f f l e M AH1 <DELETE> F AH0_L <DELETE> M AH1 <DELETE> F AH0_L <DELETE> 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 `_`, you should get CMU-like transcription.
### How to use for TTS