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511KeV
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Parent(s):
Duplicate from 511KeV/nllb-translation-demo
Browse files- .gitattributes +27 -0
- README.md +13 -0
- app.py +88 -0
- flores200_codes.py +210 -0
- imp.js +2 -0
- requirements.txt +3 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: Nllb Translation Demo
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emoji: 👀
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colorFrom: indigo
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colorTo: green
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sdk: gradio
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sdk_version: 3.0.26
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app_file: app.py
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pinned: false
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duplicated_from: 511KeV/nllb-translation-demo
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import os
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import torch
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import gradio as gr
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import time
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
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from flores200_codes import flores_codes
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#print(f"Is CUDA available: {torch.cuda.is_available()}")
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# True
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#print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
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def load_models():
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# build model and tokenizer
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model_name_dict = {'nllb-distilled-600M': 'facebook/nllb-200-distilled-600M',
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#'nllb-1.3B': 'facebook/nllb-200-1.3B',
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#'nllb-distilled-1.3B': 'facebook/nllb-200-distilled-1.3B',
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#'nllb-3.3B': 'facebook/nllb-200-3.3B',
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}
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model_dict = {}
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for call_name, real_name in model_name_dict.items():
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print('\tLoading model: %s' % call_name)
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model = AutoModelForSeq2SeqLM.from_pretrained(real_name)
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tokenizer = AutoTokenizer.from_pretrained(real_name)
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model_dict[call_name+'_model'] = model
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model_dict[call_name+'_tokenizer'] = tokenizer
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return model_dict
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def translation(source, target, text):
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if len(model_dict) == 2:
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model_name = 'nllb-distilled-600M'
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start_time = time.time()
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source = flores_codes[source]
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target = flores_codes[target]
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model = model_dict[model_name + '_model']
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tokenizer = model_dict[model_name + '_tokenizer']
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translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=source, tgt_lang=target)
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output = translator(text, max_length=4098)
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end_time = time.time()
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output = output[0]['translation_text']
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result = {'inference_time': end_time - start_time,
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'source': source,
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'target': target,
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'result': output}
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return result
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if __name__ == '__main__':
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print('\tinit models')
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global model_dict
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model_dict = load_models()
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# define gradio demo
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lang_codes = list(flores_codes.keys())
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#inputs = [gr.inputs.Radio(['nllb-distilled-600M', 'nllb-1.3B', 'nllb-distilled-1.3B'], label='NLLB Model'),
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inputs = [gr.inputs.Dropdown(lang_codes, default='English', label='Source'),
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gr.inputs.Dropdown(lang_codes, default='Kashmiri', label='Target'),
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gr.inputs.Textbox(lines=5, label="Input text"),
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]
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outputs = gr.outputs.JSON()
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title = "NLLB distilled 600M demo"
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demo_status = "Demo is running on CPU"
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description = f"Details: https://github.com/facebookresearch/fairseq/tree/nllb. {demo_status}"
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examples = [
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['English', 'Kashmiri', 'Hi. nice to meet you']
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]
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gr.Interface(translation,
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inputs,
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outputs,
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title=title,
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description=description,
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).launch()
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flores200_codes.py
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codes_as_string = '''Acehnese (Arabic script) ace_Arab
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Acehnese (Latin script) ace_Latn
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Mesopotamian Arabic acm_Arab
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Ta’izzi-Adeni Arabic acq_Arab
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Tunisian Arabic aeb_Arab
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Afrikaans afr_Latn
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South Levantine Arabic ajp_Arab
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Akan aka_Latn
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Amharic amh_Ethi
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North Levantine Arabic apc_Arab
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Modern Standard Arabic arb_Arab
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Modern Standard Arabic (Romanized) arb_Latn
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Najdi Arabic ars_Arab
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Moroccan Arabic ary_Arab
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Egyptian Arabic arz_Arab
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Assamese asm_Beng
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Asturian ast_Latn
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Awadhi awa_Deva
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Central Aymara ayr_Latn
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South Azerbaijani azb_Arab
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North Azerbaijani azj_Latn
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Bashkir bak_Cyrl
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Bambara bam_Latn
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Balinese ban_Latn
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Belarusian bel_Cyrl
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Bemba bem_Latn
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Bengali ben_Beng
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Bhojpuri bho_Deva
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Banjar (Arabic script) bjn_Arab
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Banjar (Latin script) bjn_Latn
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Standard Tibetan bod_Tibt
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Bosnian bos_Latn
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Buginese bug_Latn
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Bulgarian bul_Cyrl
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Catalan cat_Latn
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Cebuano ceb_Latn
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Czech ces_Latn
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Chokwe cjk_Latn
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Central Kurdish ckb_Arab
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Crimean Tatar crh_Latn
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Welsh cym_Latn
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Danish dan_Latn
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German deu_Latn
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Southwestern Dinka dik_Latn
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Dyula dyu_Latn
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Dzongkha dzo_Tibt
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Greek ell_Grek
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English eng_Latn
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Esperanto epo_Latn
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Estonian est_Latn
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Basque eus_Latn
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Ewe ewe_Latn
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Faroese fao_Latn
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Fijian fij_Latn
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Finnish fin_Latn
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Fon fon_Latn
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French fra_Latn
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Friulian fur_Latn
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Nigerian Fulfulde fuv_Latn
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Scottish Gaelic gla_Latn
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Irish gle_Latn
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Galician glg_Latn
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Guarani grn_Latn
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Gujarati guj_Gujr
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Haitian Creole hat_Latn
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Hausa hau_Latn
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Hebrew heb_Hebr
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Hindi hin_Deva
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Chhattisgarhi hne_Deva
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Croatian hrv_Latn
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Hungarian hun_Latn
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Armenian hye_Armn
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Igbo ibo_Latn
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74 |
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Ilocano ilo_Latn
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Indonesian ind_Latn
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Icelandic isl_Latn
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Italian ita_Latn
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Javanese jav_Latn
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Japanese jpn_Jpan
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Kabyle kab_Latn
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Jingpho kac_Latn
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Kamba kam_Latn
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Kannada kan_Knda
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Kashmiri kas_Arab
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Georgian kat_Geor
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Central Kanuri (Arabic script) knc_Arab
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Central Kanuri (Latin script) knc_Latn
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Kazakh kaz_Cyrl
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Kabiyè kbp_Latn
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Kabuverdianu kea_Latn
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Khmer khm_Khmr
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92 |
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Kikuyu kik_Latn
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Kinyarwanda kin_Latn
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Kyrgyz kir_Cyrl
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Kimbundu kmb_Latn
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Northern Kurdish kmr_Latn
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Kikongo kon_Latn
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Korean kor_Hang
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Lao lao_Laoo
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Ligurian lij_Latn
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Limburgish lim_Latn
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Lingala lin_Latn
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Lithuanian lit_Latn
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Lombard lmo_Latn
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Latgalian ltg_Latn
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Luxembourgish ltz_Latn
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Luba-Kasai lua_Latn
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Ganda lug_Latn
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Luo luo_Latn
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Mizo lus_Latn
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Standard Latvian lvs_Latn
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Magahi mag_Deva
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Maithili mai_Deva
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Malayalam mal_Mlym
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Marathi mar_Deva
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Minangkabau (Arabic script) min_Arab
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Minangkabau (Latin script) min_Latn
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Macedonian mkd_Cyrl
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Plateau Malagasy plt_Latn
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Maltese mlt_Latn
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Meitei (Bengali script) mni_Beng
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Halh Mongolian khk_Cyrl
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Mossi mos_Latn
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Maori mri_Latn
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Burmese mya_Mymr
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Dutch nld_Latn
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Norwegian Nynorsk nno_Latn
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Norwegian Bokmål nob_Latn
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Nepali npi_Deva
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Northern Sotho nso_Latn
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Nuer nus_Latn
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Nyanja nya_Latn
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Occitan oci_Latn
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West Central Oromo gaz_Latn
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Odia ory_Orya
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Pangasinan pag_Latn
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Eastern Panjabi pan_Guru
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Papiamento pap_Latn
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Western Persian pes_Arab
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Polish pol_Latn
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Portuguese por_Latn
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Dari prs_Arab
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Southern Pashto pbt_Arab
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Ayacucho Quechua quy_Latn
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Romanian ron_Latn
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Rundi run_Latn
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Russian rus_Cyrl
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Sango sag_Latn
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Sanskrit san_Deva
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Santali sat_Olck
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Sicilian scn_Latn
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Shan shn_Mymr
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Sinhala sin_Sinh
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Slovak slk_Latn
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155 |
+
Slovenian slv_Latn
|
156 |
+
Samoan smo_Latn
|
157 |
+
Shona sna_Latn
|
158 |
+
Sindhi snd_Arab
|
159 |
+
Somali som_Latn
|
160 |
+
Southern Sotho sot_Latn
|
161 |
+
Spanish spa_Latn
|
162 |
+
Tosk Albanian als_Latn
|
163 |
+
Sardinian srd_Latn
|
164 |
+
Serbian srp_Cyrl
|
165 |
+
Swati ssw_Latn
|
166 |
+
Sundanese sun_Latn
|
167 |
+
Swedish swe_Latn
|
168 |
+
Swahili swh_Latn
|
169 |
+
Silesian szl_Latn
|
170 |
+
Tamil tam_Taml
|
171 |
+
Tatar tat_Cyrl
|
172 |
+
Telugu tel_Telu
|
173 |
+
Tajik tgk_Cyrl
|
174 |
+
Tagalog tgl_Latn
|
175 |
+
Thai tha_Thai
|
176 |
+
Tigrinya tir_Ethi
|
177 |
+
Tamasheq (Latin script) taq_Latn
|
178 |
+
Tamasheq (Tifinagh script) taq_Tfng
|
179 |
+
Tok Pisin tpi_Latn
|
180 |
+
Tswana tsn_Latn
|
181 |
+
Tsonga tso_Latn
|
182 |
+
Turkmen tuk_Latn
|
183 |
+
Tumbuka tum_Latn
|
184 |
+
Turkish tur_Latn
|
185 |
+
Twi twi_Latn
|
186 |
+
Central Atlas Tamazight tzm_Tfng
|
187 |
+
Uyghur uig_Arab
|
188 |
+
Ukrainian ukr_Cyrl
|
189 |
+
Umbundu umb_Latn
|
190 |
+
Urdu urd_Arab
|
191 |
+
Northern Uzbek uzn_Latn
|
192 |
+
Venetian vec_Latn
|
193 |
+
Vietnamese vie_Latn
|
194 |
+
Waray war_Latn
|
195 |
+
Wolof wol_Latn
|
196 |
+
Xhosa xho_Latn
|
197 |
+
Eastern Yiddish ydd_Hebr
|
198 |
+
Yoruba yor_Latn
|
199 |
+
Yue Chinese yue_Hant
|
200 |
+
Chinese (Simplified) zho_Hans
|
201 |
+
Chinese (Traditional) zho_Hant
|
202 |
+
Standard Malay zsm_Latn
|
203 |
+
Zulu zul_Latn'''
|
204 |
+
|
205 |
+
codes_as_string = codes_as_string.split('\n')
|
206 |
+
|
207 |
+
flores_codes = {}
|
208 |
+
for code in codes_as_string:
|
209 |
+
lang, lang_code = code.split('\t')
|
210 |
+
flores_codes[lang] = lang_code
|
imp.js
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
import nllb-200-distilled-600M
|
2 |
+
import nllb-200-3.3B
|
requirements.txt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
git+https://github.com/huggingface/transformers
|
2 |
+
gradio
|
3 |
+
torch
|