Files
reflector/gpu/self_hosted/app/services/translator.py
Sergey Mankovsky ab859d65a6 feat: self-hosted gpu api (#636)
* Self-hosted gpu api

* Refactor self-hosted api

* Rename model api tests

* Use lifespan instead of startup event

* Fix self hosted imports

* Add newlines

* Add response models

* Move gpu dir to the root

* Add project description

* Refactor lifespan

* Update env var names for model api tests

* Preload diarizarion service

* Refactor uploaded file paths
2025-09-17 18:52:03 +02:00

45 lines
1.8 KiB
Python

import threading
from transformers import MarianMTModel, MarianTokenizer, pipeline
class TextTranslatorService:
"""Simple text-to-text translator using HuggingFace MarianMT models.
This mirrors the modal translator API shape but uses text translation only.
"""
def __init__(self):
self._pipeline = None
self._lock = threading.Lock()
def load(self, source_language: str = "en", target_language: str = "fr"):
# Pick a default MarianMT model pair if available; fall back to Helsinki-NLP en->fr
model_name = self._resolve_model_name(source_language, target_language)
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
self._pipeline = pipeline("translation", model=model, tokenizer=tokenizer)
def _resolve_model_name(self, src: str, tgt: str) -> str:
# Minimal mapping; extend as needed
pair = (src.lower(), tgt.lower())
mapping = {
("en", "fr"): "Helsinki-NLP/opus-mt-en-fr",
("fr", "en"): "Helsinki-NLP/opus-mt-fr-en",
("en", "es"): "Helsinki-NLP/opus-mt-en-es",
("es", "en"): "Helsinki-NLP/opus-mt-es-en",
("en", "de"): "Helsinki-NLP/opus-mt-en-de",
("de", "en"): "Helsinki-NLP/opus-mt-de-en",
}
return mapping.get(pair, "Helsinki-NLP/opus-mt-en-fr")
def translate(self, text: str, source_language: str, target_language: str) -> dict:
if self._pipeline is None:
self.load(source_language, target_language)
with self._lock:
results = self._pipeline(
text, src_lang=source_language, tgt_lang=target_language
)
translated = results[0]["translation_text"] if results else ""
return {"text": {source_language: text, target_language: translated}}