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import torch | |
import spaces | |
from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer | |
import os | |
from huggingface_hub import login | |
max_length = 512 | |
auth_token = os.getenv('HF_SPACE_TOKEN') | |
login(token=auth_token) | |
def goai_traduction(text, src_lang, tgt_lang): | |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
if src_lang == "fra_Latn" and tgt_lang == "mos_Latn": | |
model_id = "ArissBandoss/nllb-200-distilled-600M-finetuned-fr-to-mos-V4" | |
elif src_lang == "mos_Latn" and tgt_lang == "fra_Latn": | |
model_id = "ArissBandoss/nllb-200-distilled-600M-finetuned-mos-to-fr-V5" | |
else: | |
model_id = "ArissBandoss/nllb-200-distilled-600M-finetuned-fr-to-mos-V4" | |
tokenizer = AutoTokenizer.from_pretrained(model_id, token=auth_token) | |
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, token=auth_token) | |
trans_pipe = pipeline("translation", | |
model=model, tokenizer=tokenizer, | |
src_lang=src_lang, tgt_lang=tgt_lang, | |
max_length=max_length, | |
device=device | |
) | |
return trans_pipe(text)[0]["translation_text"] | |