transly 0.1.3

Pronunciation and Transliteration module trained on CMU pronouncing dictionary, IIT Bombay and IIT Kharagpur text corpora

Transly

Transly is a sequence to sequence Bi-directional LSTM Encoder-Decoder model with Bahdanau Attention that’s trained on the CMU pronouncing dictionary, IIT Bombay English-Hindi Parallel Corpus and IIT Kharagpur transliteration corpus.

The pronunciation module in Transly can predict pronunciation of any given word (with an American accent of course!)

Take any word of any language - just transliterate the word in English (all capitals) and you are good to go. Be it a new or old, seen or unseen, sensible or insensible word - Transly can catch’em all!

Another module in Transly is the transliteration module. It currently supports Hindi to English and English to Hindi transliterations.

Pre-trained models can be found inside the respective trained_models folders. New models can also be trained on custom data.

Installation

Use the package manager pip to install transly

pip install transly

Usage

Pronunciation

Using the pre-trained pronunciation model

import transly.pronunciation as tp

# let's try a hindi word
# the prediction accent would be American
QUERY = 'MAKAAN'
a = tp.load_model(model_path='cmu')
a.infer(QUERY, separator=" ")
# use infer_batch function to infer batches
# use beamsearch function to perform a beam search

>> 'M AH0 K AA1 N'

Training a new model on custom data

from transly.seq2seq.config import SConfig
from transly.seq2seq.version0 import Seq2Seq

config = SConfig(training_data_path=training_data_path, input_mode='character_level', output_mode='word_level')
s2s = Seq2Seq(config)
s2s.fit()
s2s.save_model(path_to_model=model_path, model_file_name=model_file_name)

Training data file should be a csv with two columns, the input and the output

Input	Output
AA	AA1
AABERG	AA1 B ER0 G
AACHEN	AA1 K AH0 N
AACHENER	AA1 K AH0 N ER0

Transliteration

Hindi to English

Using the pre-trained model

import transly.transliteration as tl

QUERY = 'निखिल'
a = tl.load_model(model_path='hi2en')
a.infer(QUERY)
# use infer_batch function to infer batches
# use beamsearch function to perform a beam search

>> 'NIKHIL'

English to Hindi

Using the pre-trained model

import transly.transliteration as tl

QUERY = 'NIKHIL'
a = tl.load_model(model_path='en2hi')
a.infer(QUERY)
# use infer_batch function to infer batches
# use beamsearch function to perform a beam search

>> 'निखिल'

Training a new model on custom data

from transly.seq2seq.config import SConfig
from transly.seq2seq.version0 import Seq2Seq

config = SConfig(training_data_path=training_data_path)
s2s = Seq2Seq(config)
s2s.fit()
s2s.save_model(path_to_model=model_path, model_file_name=model_file_name)

License

The Python code in this module is distributed with Apache License 2.0