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import time
import nltk
from transformers import pipeline
nltk.download('punkt')
from nltk.tokenize import sent_tokenize
def detect_language(text,LID):
predictions = LID.predict(text)
detected_lang_code = predictions[0][0].replace("__label__", "")
return detected_lang_code
def translation(model_name,
sentence_mode, selection_mode,
source, target,
text,
flores_codes,
model_dict, device):
start_time = time.time()
# Determine the source language
if selection_mode == "Auto-detect":
detected_lang_code = detect_language(text)
flores_source_code = detected_lang_code
source_code = flores_source_code
else:
if source == "Auto-detect": # Make sure we don't use "Auto-detect" as a key
return {'error': "Source language cannot be 'Auto-detect' when selection mode is manual."}
source_code = flores_codes.get(source)
if not source_code:
return {'error': f"Source language {source} not found in flores_codes."}
target_code = flores_codes[target]
model = model_dict[model_name + '_model']
tokenizer = model_dict[model_name + '_tokenizer']
translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=source_code, tgt_lang=target_code, device=device)
if sentence_mode == "Sentence-wise":
sentences = sent_tokenize(text)
translated_sentences = []
for sentence in sentences:
translated_sentence = translator(sentence, max_length=400)[0]['translation_text']
translated_sentences.append(translated_sentence)
output = ' '.join(translated_sentences)
else:
output = translator(text, max_length=400)[0]['translation_text']
end_time = time.time()
result = {
'inference_time': end_time - start_time,
'source_language': source_code,
'target_language': target_code,
'result': output
}
return result |