def __init__(self, lang_code):
"""
Initialize deeptranslit
Parameters:
lang_code (str): Name or code of the language. (Currently supported: hindi/hi)
"""
if lang_code in lang_code_mapping:
lang_code = lang_code_mapping[lang_code]
if lang_code not in model_links:
print("DeepTranslit doesn't support '" + lang_code + "' yet.")
print(
"Please raise a issue at https://github.com/bedapudi6788/deeptranslit to add this language into future checklist."
)
return None
# loading the model
home = os.path.expanduser("~")
lang_path = os.path.join(home, '.DeepTranslit_' + lang_code)
checkpoint_path = os.path.join(lang_path, 'checkpoint')
params_path = os.path.join(lang_path, 'params')
if not os.path.exists(lang_path):
os.mkdir(lang_path)
if not os.path.exists(checkpoint_path):
print('Downloading checkpoint',
model_links[lang_code]['checkpoint'], 'to', checkpoint_path)
pydload.dload(url=model_links[lang_code]['checkpoint'],
save_to_path=checkpoint_path,
max_time=None)
if not os.path.exists(params_path):
print('Downloading model params', model_links[lang_code]['params'],
'to', params_path)
pydload.dload(url=model_links[lang_code]['params'],
save_to_path=params_path,
max_time=None)
self.model, self.params = build_model(params_path=params_path,
enc_lstm_units=64,
use_gru=True,
display_summary=False)
self.model.load_weights(checkpoint_path)
def __init__(self, lang_code, rank='auto'):
"""
Initialize deeptranslit
Parameters:
lang_code (str): Name or code of the language. (Currently supported: hindi/hi)
rank (str): Mode of ranking. In default mode ('auto') kenlm will be used if available. (simple|kenlm|auto are the supported options)
"""
if lang_code in lang_code_mapping:
lang_code = lang_code_mapping[lang_code]
if lang_code not in model_links:
print("DeepTranslit doesn't support '" + lang_code + "' yet.")
print(
"Please raise a issue at https://github.com/bedapudi6788/deeptranslit to add this language into future checklist."
)
return None
# loading the model
home = os.path.expanduser("~")
lang_path = os.path.join(home, '.DeepTranslit_' + lang_code)
checkpoint_path = os.path.join(lang_path, 'checkpoint')
params_path = os.path.join(lang_path, 'params')
words_path = os.path.join(lang_path, 'words')
lm_path = os.path.join(lang_path, 'lm')
if not os.path.exists(lang_path):
os.mkdir(lang_path)
if not os.path.exists(checkpoint_path):
print('Downloading checkpoint',
model_links[lang_code]['checkpoint'], 'to', checkpoint_path)
pydload.dload(url=model_links[lang_code]['checkpoint'],
save_to_path=checkpoint_path,
max_time=None)
if not os.path.exists(params_path):
print('Downloading model params', model_links[lang_code]['params'],
'to', params_path)
pydload.dload(url=model_links[lang_code]['params'],
save_to_path=params_path,
max_time=None)
if not os.path.exists(words_path):
print('Downloading words', model_links[lang_code]['words'], 'to',
words_path)
pydload.dload(url=model_links[lang_code]['words'],
save_to_path=words_path,
max_time=None)
if not os.path.exists(lm_path):
print('Downloading lm', model_links[lang_code]['lm'], 'to',
lm_path)
pydload.dload(url=model_links[lang_code]['lm'],
save_to_path=lm_path,
max_time=None)
DeepTranslit.model, DeepTranslit.params = build_model(
params_path=params_path,
enc_lstm_units=64,
use_gru=True,
display_summary=False)
DeepTranslit.model.load_weights(checkpoint_path)
DeepTranslit.words = pickle.load(open(words_path, 'rb'))
if kenlm_available and rank in {'auto', 'kenlm'}:
logging.warn('Loading KenLM.')
DeepTranslit.lm = kenlm.Model(lm_path)
DeepTranslit.rank = rank
for i in range(len(train_text[j])):
#continue
if train_text[j][i] not in chars:
train_text[j]=train_text[j].replace(train_text[j][i], " ")
#print(train_text[j])
train_text[3]
from txt2txt import build_params, build_model, convert_training_data
from keras.callbacks import ModelCheckpoint
input_data=train_text
output_data=train_text
build_params(input_data = input_data, output_data = output_data, params_path = 'params', max_lenghts=(10, 10))
model, params = build_model(params_path='params')
input_data, output_data = convert_training_data(input_data, output_data, params)
checkpoint = ModelCheckpoint('checkpoint', monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
model.fit(input_data, output_data, validation_data=(input_data, output_data), batch_size=2, epochs=2, callbacks=callbacks_list)
from txt2txt import build_model, infer
model, params = build_model(params_path='params')
model.load_weights('checkpoint')
#infer('nmae', model, params)
infer('nmae', model, params)
def __init__(self, params_path, checkpoint_path):
# loading the model
DeepCorrect.deepcorrect_model = build_model(params_path)
DeepCorrect.deepcorrect_model[0].load_weights(checkpoint_path)
from txt2txt import build_model, infer
model, params = build_model(params_path='params', enc_lstm_units=256)
model.load_weights('checkpoint')
while 1:
print('Enter input')
print(infer(input(), model, params))