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