f586f930b6
- Implemented REST API for 105+ language translation - Used Facebook M2M100 model (Apache 2.0 License - Commercial use allowed) - Supports any-to-any translation between 105 languages - Major languages: English, Chinese, Spanish, Arabic, Russian, Japanese, Korean, etc. - Southeast Asian: Malay, Indonesian, Thai, Vietnamese, Tagalog, Burmese, Khmer, Lao - South Asian: Bengali, Hindi, Urdu, Tamil, Telugu, Marathi, Gujarati, etc. - European: German, French, Italian, Spanish, Portuguese, Russian, etc. - African: Swahili, Amharic, Hausa, Igbo, Yoruba, Zulu, Xhosa - And many more languages Tech Stack: - FastAPI for REST API - Transformers (Hugging Face) for ML model - PyTorch for inference - Docker for containerization - M2M100 418M parameter model Features: - Health check endpoint - Supported languages listing - Dynamic language validation - Model caching for performance - GPU support (auto-detection) - CORS enabled for web clients 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
260 lines
8.6 KiB
Python
260 lines
8.6 KiB
Python
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
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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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from .config import settings
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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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"""
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def __init__(self):
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self.models: Dict[str, Dict] = {}
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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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# Full list: https://huggingface.co/facebook/m2m100_418M
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self.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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"es": "es", # Spanish
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"ar": "ar", # Arabic
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"hi": "hi", # Hindi
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"bn": "bn", # Bengali
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"pt": "pt", # Portuguese
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"ru": "ru", # Russian
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"ja": "ja", # Japanese
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"de": "de", # German
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"fr": "fr", # French
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"ko": "ko", # Korean
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"it": "it", # Italian
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"tr": "tr", # Turkish
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"vi": "vi", # Vietnamese
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"th": "th", # Thai
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"pl": "pl", # Polish
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"nl": "nl", # Dutch
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"uk": "uk", # Ukrainian
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"ro": "ro", # Romanian
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# Southeast Asian languages
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"ms": "ms", # Malay
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"id": "id", # Indonesian
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"tl": "tl", # Tagalog
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"my": "my", # Burmese
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"km": "km", # Khmer
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"lo": "lo", # Lao
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# South Asian languages
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"ur": "ur", # Urdu
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"ta": "ta", # Tamil
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"te": "te", # Telugu
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"mr": "mr", # Marathi
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"gu": "gu", # Gujarati
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"kn": "kn", # Kannada
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"ml": "ml", # Malayalam
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"pa": "pa", # Punjabi
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"ne": "ne", # Nepali
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"si": "si", # Sinhala
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# European languages
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"sv": "sv", # Swedish
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"da": "da", # Danish
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"fi": "fi", # Finnish
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"no": "no", # Norwegian
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"cs": "cs", # Czech
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"sk": "sk", # Slovak
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"hu": "hu", # Hungarian
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"bg": "bg", # Bulgarian
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"sr": "sr", # Serbian
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"hr": "hr", # Croatian
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"sl": "sl", # Slovenian
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"et": "et", # Estonian
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"lv": "lv", # Latvian
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"lt": "lt", # Lithuanian
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"el": "el", # Greek
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"he": "he", # Hebrew
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"fa": "fa", # Persian
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# African languages
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"sw": "sw", # Swahili
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"am": "am", # Amharic
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"ha": "ha", # Hausa
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"ig": "ig", # Igbo
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"yo": "yo", # Yoruba
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"zu": "zu", # Zulu
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"xh": "xh", # Xhosa
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"af": "af", # Afrikaans
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# Other major languages
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"az": "az", # Azerbaijani
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"ka": "ka", # Georgian
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"kk": "kk", # Kazakh
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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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"sq": "sq", # Albanian
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"hy": "hy", # Armenian
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"be": "be", # Belarusian
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"bs": "bs", # Bosnian
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"ca": "ca", # Catalan
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"ceb": "ceb", # Cebuano
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"cy": "cy", # Welsh
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"eo": "eo", # Esperanto
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"eu": "eu", # Basque
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"fil": "fil", # Filipino
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"fy": "fy", # Frisian
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"ga": "ga", # Irish
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"gd": "gd", # Scottish Gaelic
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"gl": "gl", # Galician
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"haw": "haw", # Hawaiian
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"hmn": "hmn", # Hmong
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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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"lb": "lb", # Luxembourgish
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"lg": "lg", # Luganda
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"ln": "ln", # Lingala
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"mg": "mg", # Malagasy
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"mi": "mi", # Maori
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"mk": "mk", # Macedonian
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"mt": "mt", # Maltese
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"ny": "ny", # Chichewa
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"ps": "ps", # Pashto
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"sn": "sn", # Shona
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"so": "so", # Somali
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"st": "st", # Sesotho
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"su": "su", # Sundanese
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"tg": "tg", # Tajik
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"tk": "tk", # Turkmen
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"ug": "ug", # Uyghur
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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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"""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 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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return model_name, src_code, tgt_code
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def load_model(self, source_lang: str, target_lang: str) -> 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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if model_name in self.models:
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logger.info(f"Model {model_name} already loaded")
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return
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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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self.models[model_name] = {
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"tokenizer": tokenizer,
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"model": model
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}
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logger.info(f"Successfully loaded model: {model_name}")
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except Exception as e:
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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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"""
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Translate text from source language to target language
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Args:
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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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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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# 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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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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# 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():
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translated = model.generate(
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**inputs,
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forced_bos_token_id=generated_tokens,
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max_length=settings.max_length
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)
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# Decode output
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translated_text = tokenizer.batch_decode(translated, skip_special_tokens=True)[0]
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return translated_text, model_name
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except Exception as e:
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logger.error(f"Translation error: {str(e)}")
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raise
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def preload_all_models(self) -> None:
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"""Preload all supported translation models"""
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language_pairs = [
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("ms", "en"),
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("en", "ms")
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]
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for source, target in language_pairs:
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try:
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self.load_model(source, target)
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except Exception as e:
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logger.warning(f"Could not preload model for {source}->{target}: {str(e)}")
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def is_ready(self) -> bool:
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"""Check if at least one model is loaded"""
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return len(self.models) > 0
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# Global translator instance
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translator = TranslationService()
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