Add dual model support: M2M100 and NLLB-200
- Added optional 'model' parameter to translation request (default: m2m100)
- M2M100: 105 languages, Apache 2.0 License (commercial OK)
- NLLB-200: 200 languages, CC-BY-NC 4.0 License (non-commercial only)
- Updated /api/translate endpoint to accept model selection
- Updated /api/supported-languages to show languages per model
- Added comprehensive language name mappings for all NLLB-200 languages
- Both models can be used independently with automatic model loading
- Model information includes license and commercial use status
Example usage:
- Default (M2M100): {"text": "Hello", "source_lang": "en", "target_lang": "ko"}
- NLLB-200: {"text": "Hello", "source_lang": "en", "target_lang": "ko", "model": "nllb200"}
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
jungwoo choi
@ -1,4 +1,4 @@
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from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
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from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer, AutoModelForSeq2SeqLM, AutoTokenizer
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import torch
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from typing import Dict, Optional
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import logging
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@ -10,8 +10,9 @@ logger = logging.getLogger(__name__)
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class TranslationService:
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"""
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Service for handling multilingual translation
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Uses M2M100 model (Apache 2.0 License - Commercial use allowed)
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Supports 100 languages for many-to-many translation
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Supports two models:
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- M2M100 (105 languages, Apache 2.0 License - Commercial use allowed)
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- NLLB-200 (200 languages, CC-BY-NC License - Non-commercial only)
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"""
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def __init__(self):
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@ -19,9 +20,9 @@ class TranslationService:
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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logger.info(f"Using device: {self.device}")
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# M2M100 supported language codes (100 languages)
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# M2M100 supported language codes (105 languages)
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# Full list: https://huggingface.co/facebook/m2m100_418M
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self.lang_codes = {
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self.m2m100_lang_codes = {
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# Major languages
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"en": "en", # English
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"zh": "zh", # Chinese
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@ -100,7 +101,7 @@ class TranslationService:
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"uz": "uz", # Uzbek
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"mn": "mn", # Mongolian
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# Additional languages (completing 100)
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# Additional languages
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"sq": "sq", # Albanian
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"hy": "hy", # Armenian
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"be": "be", # Belarusian
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@ -120,7 +121,6 @@ class TranslationService:
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"ht": "ht", # Haitian Creole
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"is": "is", # Icelandic
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"jv": "jv", # Javanese
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"kn": "kn", # Kannada
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"ku": "ku", # Kurdish
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"ky": "ky", # Kyrgyz
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"la": "la", # Latin
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@ -143,21 +143,238 @@ class TranslationService:
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"yi": "yi", # Yiddish
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}
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def _get_model_info(self, source_lang: str, target_lang: str) -> tuple[str, str, str]:
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# NLLB-200 supported language codes (200 languages)
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# NLLB uses different format: xxx_Yyyy (language_Script)
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# Full list: https://huggingface.co/facebook/nllb-200-distilled-600M
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self.nllb200_lang_codes = {
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# Major languages
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"en": "eng_Latn", # English
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"zh": "zho_Hans", # Chinese (Simplified)
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"es": "spa_Latn", # Spanish
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"ar": "arb_Arab", # Arabic (Standard)
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"hi": "hin_Deva", # Hindi
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"bn": "ben_Beng", # Bengali
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"pt": "por_Latn", # Portuguese
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"ru": "rus_Cyrl", # Russian
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"ja": "jpn_Jpan", # Japanese
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"de": "deu_Latn", # German
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"fr": "fra_Latn", # French
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"ko": "kor_Hang", # Korean
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"it": "ita_Latn", # Italian
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"tr": "tur_Latn", # Turkish
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"vi": "vie_Latn", # Vietnamese
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"th": "tha_Thai", # Thai
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"pl": "pol_Latn", # Polish
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"nl": "nld_Latn", # Dutch
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"uk": "ukr_Cyrl", # Ukrainian
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"ro": "ron_Latn", # Romanian
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# Southeast Asian languages
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"ms": "zsm_Latn", # Malay (Standard)
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"id": "ind_Latn", # Indonesian
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"tl": "tgl_Latn", # Tagalog
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"my": "mya_Mymr", # Burmese
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"km": "khm_Khmr", # Khmer
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"lo": "lao_Laoo", # Lao
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# South Asian languages
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"ur": "urd_Arab", # Urdu
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"ta": "tam_Taml", # Tamil
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"te": "tel_Telu", # Telugu
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"mr": "mar_Deva", # Marathi
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"gu": "guj_Gujr", # Gujarati
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"kn": "kan_Knda", # Kannada
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"ml": "mal_Mlym", # Malayalam
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"pa": "pan_Guru", # Punjabi
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"ne": "npi_Deva", # Nepali
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"si": "sin_Sinh", # Sinhala
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# European languages
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"sv": "swe_Latn", # Swedish
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"da": "dan_Latn", # Danish
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"fi": "fin_Latn", # Finnish
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"no": "nob_Latn", # Norwegian (Bokmål)
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"cs": "ces_Latn", # Czech
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"sk": "slk_Latn", # Slovak
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"hu": "hun_Latn", # Hungarian
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"bg": "bul_Cyrl", # Bulgarian
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"sr": "srp_Cyrl", # Serbian
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"hr": "hrv_Latn", # Croatian
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"sl": "slv_Latn", # Slovenian
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"et": "est_Latn", # Estonian
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"lv": "lvs_Latn", # Latvian
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"lt": "lit_Latn", # Lithuanian
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"el": "ell_Grek", # Greek
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"he": "heb_Hebr", # Hebrew
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"fa": "pes_Arab", # Persian
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# African languages
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"sw": "swh_Latn", # Swahili
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"am": "amh_Ethi", # Amharic
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"ha": "hau_Latn", # Hausa
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"ig": "ibo_Latn", # Igbo
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"yo": "yor_Latn", # Yoruba
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"zu": "zul_Latn", # Zulu
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"xh": "xho_Latn", # Xhosa
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"af": "afr_Latn", # Afrikaans
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"sn": "sna_Latn", # Shona
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"so": "som_Latn", # Somali
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# Other languages
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"az": "azj_Latn", # Azerbaijani (North)
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"ka": "kat_Geor", # Georgian
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"kk": "kaz_Cyrl", # Kazakh
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"uz": "uzn_Latn", # Uzbek (Northern)
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"mn": "khk_Cyrl", # Mongolian (Halh)
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"sq": "als_Latn", # Albanian
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"hy": "hye_Armn", # Armenian
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"be": "bel_Cyrl", # Belarusian
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"bs": "bos_Latn", # Bosnian
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"ca": "cat_Latn", # Catalan
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"ceb": "ceb_Latn", # Cebuano
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"cy": "cym_Latn", # Welsh
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"eo": "epo_Latn", # Esperanto
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"eu": "eus_Latn", # Basque
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"gl": "glg_Latn", # Galician
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"is": "isl_Latn", # Icelandic
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"jv": "jav_Latn", # Javanese
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"ku": "kmr_Latn", # Kurdish (Kurmanji)
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"ky": "kir_Cyrl", # Kyrgyz
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"la": "lat_Latn", # Latin
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"lb": "ltz_Latn", # Luxembourgish
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"lg": "lug_Latn", # Luganda
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"mg": "plt_Latn", # Malagasy
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"mk": "mkd_Cyrl", # Macedonian
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"mt": "mlt_Latn", # Maltese
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"ny": "nya_Latn", # Chichewa
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"ps": "pbt_Arab", # Pashto (Southern)
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"st": "sot_Latn", # Sesotho
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"su": "sun_Latn", # Sundanese
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"tg": "tgk_Cyrl", # Tajik
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"tk": "tuk_Latn", # Turkmen
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"ug": "uig_Arab", # Uyghur
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# Additional NLLB-200 exclusive languages (examples, 95 more)
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"ace": "ace_Latn", # Acehnese
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"acm": "acm_Arab", # Mesopotamian Arabic
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"acq": "acq_Arab", # Ta'izzi-Adeni Arabic
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"aeb": "aeb_Arab", # Tunisian Arabic
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"ajp": "ajp_Arab", # South Levantine Arabic
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"als": "als_Latn", # Tosk Albanian
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"ars": "ars_Arab", # Najdi Arabic
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"ary": "ary_Arab", # Moroccan Arabic
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"arz": "arz_Arab", # Egyptian Arabic
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"asm": "asm_Beng", # Assamese
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"ast": "ast_Latn", # Asturian
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"awa": "awa_Deva", # Awadhi
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"ayr": "ayr_Latn", # Central Aymara
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"azb": "azb_Arab", # South Azerbaijani
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"bak": "bak_Cyrl", # Bashkir
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"bam": "bam_Latn", # Bambara
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"ban": "ban_Latn", # Balinese
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"bho": "bho_Deva", # Bhojpuri
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"bjn": "bjn_Latn", # Banjar
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"bod": "bod_Tibt", # Tibetan
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"bug": "bug_Latn", # Buginese
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"crh": "crh_Latn", # Crimean Tatar
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"cjk": "cjk_Latn", # Chokwe
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"ckb": "ckb_Arab", # Central Kurdish
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"dik": "dik_Latn", # Southwestern Dinka
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"dyu": "dyu_Latn", # Dyula
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"dzo": "dzo_Tibt", # Dzongkha
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"fur": "fur_Latn", # Friulian
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"fuv": "fuv_Latn", # Nigerian Fulfulde
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"gaz": "gaz_Latn", # West Central Oromo
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"grn": "grn_Latn", # Guarani
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"hne": "hne_Deva", # Chhattisgarhi
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"ilo": "ilo_Latn", # Iloko
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"kab": "kab_Latn", # Kabyle
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"kac": "kac_Latn", # Jingpho
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"kam": "kam_Latn", # Kamba
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"kas": "kas_Arab", # Kashmiri
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"kea": "kea_Latn", # Kabuverdianu
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"khk": "khk_Cyrl", # Halh Mongolian
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"kin": "kin_Latn", # Kinyarwanda
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"lij": "lij_Latn", # Ligurian
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"lim": "lim_Latn", # Limburgish
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"lin": "lin_Latn", # Lingala
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"lmo": "lmo_Latn", # Lombard
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"ltg": "ltg_Latn", # Latgalian
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"luo": "luo_Latn", # Luo
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"lus": "lus_Latn", # Mizo
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"mag": "mag_Deva", # Magahi
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"mai": "mai_Deva", # Maithili
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"min": "min_Latn", # Minangkabau
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"mni": "mni_Beng", # Meitei
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"mos": "mos_Latn", # Mossi
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"mri": "mri_Latn", # Maori
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"nus": "nus_Latn", # Nuer
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"ory": "ory_Orya", # Odia
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"pag": "pag_Latn", # Pangasinan
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"pap": "pap_Latn", # Papiamento
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"prs": "prs_Arab", # Dari
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"quy": "quy_Latn", # Ayacucho Quechua
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"run": "run_Latn", # Rundi
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"sag": "sag_Latn", # Sango
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"san": "san_Deva", # Sanskrit
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"sat": "sat_Beng", # Santali
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"scn": "scn_Latn", # Sicilian
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"shn": "shn_Mymr", # Shan
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"srd": "srd_Latn", # Sardinian
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"szl": "szl_Latn", # Silesian
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"taq": "taq_Latn", # Tamasheq
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"tat": "tat_Cyrl", # Tatar
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"tir": "tir_Ethi", # Tigrinya
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"tpi": "tpi_Latn", # Tok Pisin
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"tsn": "tsn_Latn", # Tswana
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"tso": "tso_Latn", # Tsonga
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"tum": "tum_Latn", # Tumbuka
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"twi": "twi_Latn", # Twi
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"tzm": "tzm_Tfng", # Central Atlas Tamazight
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"uig": "uig_Arab", # Uyghur
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"vec": "vec_Latn", # Venetian
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"war": "war_Latn", # Waray
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"wol": "wol_Latn", # Wolof
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"xho": "xho_Latn", # Xhosa
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"ydd": "ydd_Hebr", # Eastern Yiddish
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"yor": "yor_Latn", # Yoruba
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"yue": "yue_Hant", # Cantonese
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"zho_hant": "zho_Hant", # Chinese (Traditional)
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}
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def get_supported_languages(self, model_type: str = "m2m100") -> Dict[str, str]:
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"""Get supported language codes for the specified model"""
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if model_type == "m2m100":
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return self.m2m100_lang_codes
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elif model_type == "nllb200":
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return self.nllb200_lang_codes
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else:
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raise ValueError(f"Unknown model type: {model_type}")
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def _get_model_info(self, source_lang: str, target_lang: str, model_type: str) -> tuple[str, str, str, str]:
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"""Get the model name and language codes for translation"""
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# Using M2M100 418M model (smaller, faster, commercial-friendly)
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model_name = "facebook/m2m100_418M"
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src_code = self.lang_codes.get(source_lang)
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tgt_code = self.lang_codes.get(target_lang)
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if model_type == "m2m100":
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model_name = "facebook/m2m100_418M"
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lang_codes = self.m2m100_lang_codes
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elif model_type == "nllb200":
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model_name = "facebook/nllb-200-distilled-600M"
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lang_codes = self.nllb200_lang_codes
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else:
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raise ValueError(f"Unknown model type: {model_type}")
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if not src_code or not tgt_code:
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raise ValueError(f"Unsupported language pair: {source_lang} -> {target_lang}")
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src_code = lang_codes.get(source_lang)
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tgt_code = lang_codes.get(target_lang)
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return model_name, src_code, tgt_code
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if not src_code:
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raise ValueError(f"Source language '{source_lang}' not supported by {model_type}. Use /api/supported-languages?model={model_type} to see available languages.")
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if not tgt_code:
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raise ValueError(f"Target language '{target_lang}' not supported by {model_type}. Use /api/supported-languages?model={model_type} to see available languages.")
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def load_model(self, source_lang: str, target_lang: str) -> None:
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return model_name, src_code, tgt_code, model_type
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def load_model(self, source_lang: str, target_lang: str, model_type: str = "m2m100") -> None:
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"""Load translation model for specific language pair"""
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model_name, _, _ = self._get_model_info(source_lang, target_lang)
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model_name, _, _, _ = self._get_model_info(source_lang, target_lang, model_type)
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if model_name in self.models:
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logger.info(f"Model {model_name} already loaded")
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@ -165,18 +382,30 @@ class TranslationService:
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try:
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logger.info(f"Loading model: {model_name}")
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tokenizer = M2M100Tokenizer.from_pretrained(
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model_name,
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cache_dir=settings.model_cache_dir
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)
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model = M2M100ForConditionalGeneration.from_pretrained(
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model_name,
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cache_dir=settings.model_cache_dir
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).to(self.device)
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if model_type == "m2m100":
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tokenizer = M2M100Tokenizer.from_pretrained(
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model_name,
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cache_dir=settings.model_cache_dir
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)
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model = M2M100ForConditionalGeneration.from_pretrained(
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model_name,
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cache_dir=settings.model_cache_dir
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).to(self.device)
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elif model_type == "nllb200":
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tokenizer = AutoTokenizer.from_pretrained(
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model_name,
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cache_dir=settings.model_cache_dir
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)
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model = AutoModelForSeq2SeqLM.from_pretrained(
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model_name,
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cache_dir=settings.model_cache_dir
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).to(self.device)
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self.models[model_name] = {
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"tokenizer": tokenizer,
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"model": model
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"model": model,
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"type": model_type
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}
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logger.info(f"Successfully loaded model: {model_name}")
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@ -184,7 +413,7 @@ class TranslationService:
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logger.error(f"Error loading model {model_name}: {str(e)}")
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raise
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def translate(self, text: str, source_lang: str, target_lang: str) -> tuple[str, str]:
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def translate(self, text: str, source_lang: str, target_lang: str, model_type: str = "m2m100") -> tuple[str, str]:
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"""
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Translate text from source language to target language
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@ -192,41 +421,66 @@ class TranslationService:
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text: Text to translate
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source_lang: Source language code
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target_lang: Target language code
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model_type: Model to use ('m2m100' or 'nllb200')
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Returns:
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Tuple of (translated_text, model_name)
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"""
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model_name, src_code, tgt_code = self._get_model_info(source_lang, target_lang)
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model_name, src_code, tgt_code, _ = self._get_model_info(source_lang, target_lang, model_type)
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# Load model if not already loaded
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if model_name not in self.models:
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self.load_model(source_lang, target_lang)
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self.load_model(source_lang, target_lang, model_type)
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try:
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tokenizer = self.models[model_name]["tokenizer"]
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model = self.models[model_name]["model"]
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# Set source language for tokenizer
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tokenizer.src_lang = src_code
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if model_type == "m2m100":
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# M2M100 uses src_lang attribute
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tokenizer.src_lang = src_code
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# Tokenize input
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inputs = tokenizer(
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text,
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return_tensors="pt",
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padding=True,
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truncation=True,
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max_length=settings.max_length
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).to(self.device)
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# Generate translation - M2M100 uses target language token
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generated_tokens = tokenizer.get_lang_id(tgt_code)
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|
||||
with torch.no_grad():
|
||||
translated = model.generate(
|
||||
**inputs,
|
||||
forced_bos_token_id=generated_tokens,
|
||||
# Tokenize input
|
||||
inputs = tokenizer(
|
||||
text,
|
||||
return_tensors="pt",
|
||||
padding=True,
|
||||
truncation=True,
|
||||
max_length=settings.max_length
|
||||
)
|
||||
).to(self.device)
|
||||
|
||||
# Generate translation
|
||||
generated_tokens = tokenizer.get_lang_id(tgt_code)
|
||||
|
||||
with torch.no_grad():
|
||||
translated = model.generate(
|
||||
**inputs,
|
||||
forced_bos_token_id=generated_tokens,
|
||||
max_length=settings.max_length
|
||||
)
|
||||
|
||||
elif model_type == "nllb200":
|
||||
# NLLB uses different approach with src_lang in tokenizer call
|
||||
tokenizer.src_lang = src_code
|
||||
|
||||
# Tokenize input
|
||||
inputs = tokenizer(
|
||||
text,
|
||||
return_tensors="pt",
|
||||
padding=True,
|
||||
truncation=True,
|
||||
max_length=settings.max_length
|
||||
).to(self.device)
|
||||
|
||||
# Generate translation - NLLB uses forced_bos_token_id
|
||||
forced_bos_token_id = tokenizer.lang_code_to_id[tgt_code]
|
||||
|
||||
with torch.no_grad():
|
||||
translated = model.generate(
|
||||
**inputs,
|
||||
forced_bos_token_id=forced_bos_token_id,
|
||||
max_length=settings.max_length
|
||||
)
|
||||
|
||||
# Decode output
|
||||
translated_text = tokenizer.batch_decode(translated, skip_special_tokens=True)[0]
|
||||
@ -238,17 +492,18 @@ class TranslationService:
|
||||
raise
|
||||
|
||||
def preload_all_models(self) -> None:
|
||||
"""Preload all supported translation models"""
|
||||
"""Preload commonly used translation models"""
|
||||
# Only preload M2M100 by default (commercial-friendly)
|
||||
language_pairs = [
|
||||
("ms", "en"),
|
||||
("en", "ms")
|
||||
("ms", "en", "m2m100"),
|
||||
("en", "ms", "m2m100")
|
||||
]
|
||||
|
||||
for source, target in language_pairs:
|
||||
for source, target, model_type in language_pairs:
|
||||
try:
|
||||
self.load_model(source, target)
|
||||
self.load_model(source, target, model_type)
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not preload model for {source}->{target}: {str(e)}")
|
||||
logger.warning(f"Could not preload model for {source}->{target} ({model_type}): {str(e)}")
|
||||
|
||||
def is_ready(self) -> bool:
|
||||
"""Check if at least one model is loaded"""
|
||||
|
||||
Reference in New Issue
Block a user