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import gradio as gr |
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import torch |
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from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer |
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model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_1.2B") |
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tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_1.2B") |
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langs = """Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian (ca), Cebuano (ceb), Czech (cs), Welsh (cy), Danish (da), German (de), Greeek (el), English (en), Spanish (es), Estonian (et), Persian (fa), Fulah (ff), Finnish (fi), French (fr), Western Frisian (fy), Irish (ga), Gaelic; Scottish Gaelic (gd), Galician (gl), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (ht), Hungarian (hu), Armenian (hy), Indonesian (id), Igbo (ig), Iloko (ilo), Icelandic (is), Italian (it), Japanese (ja), Javanese (jv), Georgian (ka), Kazakh (kk), Central Khmer (km), Kannada (kn), |
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Korean (ko), Luxembourgish; Letzeburgesch (lb), Ganda (lg), Lingala (ln), Lao (lo), Lithuanian (lt), Latvian (lv), Malagasy (mg), Macedonian (mk), Malayalam (ml), Mongolian (mn), Marathi (mr), Malay (ms), Burmese (my), Nepali (ne), Dutch; Flemish (nl), Norwegian (no), Northern Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto; Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd), Sinhala; Sinhalese (si), Slovak (sk), |
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Slovenian (sl), Somali (so), Albanian (sq), Serbian (sr), Swati (ss), Sundanese (su), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), Tagalog (tl), Tswana (tn), |
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Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi), Wolof (wo), Xhosa (xh), Yiddish (yi), Yoruba (yo), Chinese (zh), Zulu (zu)""" |
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lang_list = [lang.strip() for lang in langs.split(',')] |
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def translate(src, tgt, text): |
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src = src.split(" ")[-1][1:-1] |
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tgt = tgt.split(" ")[-1][1:-1] |
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tokenizer.src_lang = src |
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encoded_src = tokenizer(text, return_tensors="pt") |
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generated_tokens = model.generate(**encoded_src, forced_bos_token_id=tokenizer.get_lang_id(tgt)) |
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) |
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return result |
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output_text = gr.outputs.Textbox() |
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gr.Interface(translate, inputs=[gr.inputs.Dropdown(lang_list, label="Source Language"), gr.inputs.Dropdown(lang_list, label="Target Language"), 'text'], outputs=output_text, title="M2M100", |
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description="100개국어 번역").launch() |
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