646 lines
21 KiB
Python
646 lines
21 KiB
Python
import os
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import time
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import logging
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import re
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from typing import List, Optional
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import psycopg2
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import psycopg2.extras
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from psycopg2.extras import execute_values
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import ctranslate2
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from transformers import AutoTokenizer
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from langdetect import detect, DetectorFactory
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DetectorFactory.seed = 0
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
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LOG = logging.getLogger("translator")
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# =========================
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# DB CONFIG
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# =========================
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DB_CONFIG = {
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"host": os.environ.get("DB_HOST", "localhost"),
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"port": int(os.environ.get("DB_PORT", 5432)),
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"dbname": os.environ.get("DB_NAME", "rss"),
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"user": os.environ.get("DB_USER", "rss"),
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"password": os.environ.get("DB_PASS", "x"),
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}
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# =========================
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# ENV HELPERS
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# =========================
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def _env_list(name: str, default="es"):
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raw = os.environ.get(name)
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if raw:
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return [s.strip() for s in raw.split(",") if s.strip()]
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return [default]
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def _env_int(name: str, default: int = 8):
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v = os.environ.get(name)
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try:
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return int(v)
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except Exception:
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return default
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def _env_float(name: str, default: float = 5.0):
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v = os.environ.get(name)
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try:
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return float(v)
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except Exception:
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return default
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def _env_str(name: str, default=None):
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v = os.environ.get(name)
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return v if v else default
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# =========================
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# CONFIG
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# =========================
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TARGET_LANGS = _env_list("TARGET_LANGS") # por defecto ["es"]
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BATCH_SIZE = _env_int("TRANSLATOR_BATCH", 8)
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ENQUEUE_MAX = _env_int("ENQUEUE", 200)
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SLEEP_IDLE = _env_float("TRANSLATOR_SLEEP_IDLE", 5.0)
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# CTranslate2 Configuration
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CT2_MODEL_PATH = _env_str("CT2_MODEL_PATH", "./models/nllb-ct2")
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CT2_DEVICE = _env_str("CT2_DEVICE", "auto") # auto, cpu, cuda
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CT2_COMPUTE_TYPE = _env_str("CT2_COMPUTE_TYPE", "auto") # auto, int8, float16, int8_float16
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MAX_SRC_TOKENS = _env_int("MAX_SRC_TOKENS", 512)
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MAX_NEW_TOKENS_TITLE = _env_int("MAX_NEW_TOKENS_TITLE", 96)
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MAX_NEW_TOKENS_BODY = _env_int("MAX_NEW_TOKENS_BODY", 512)
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NUM_BEAMS_TITLE = _env_int("NUM_BEAMS_TITLE", 2)
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NUM_BEAMS_BODY = _env_int("NUM_BEAMS_BODY", 2)
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# HuggingFace model name (used for tokenizer)
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UNIVERSAL_MODEL = _env_str("UNIVERSAL_MODEL", "facebook/nllb-200-distilled-600M")
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IDENTITY_PAISES_ES = _env_int("IDENTITY_PAISES_ES", 58)
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BODY_CHARS_CHUNK = _env_int("BODY_CHARS_CHUNK", 900)
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# =========================
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# LANG MAP
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# =========================
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NLLB_LANG = {
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"es": "spa_Latn", "en": "eng_Latn", "fr": "fra_Latn", "de": "deu_Latn",
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"it": "ita_Latn", "pt": "por_Latn", "nl": "nld_Latn", "sv": "swe_Latn",
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"da": "dan_Latn", "fi": "fin_Latn", "no": "nob_Latn",
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"pl": "pol_Latn", "cs": "ces_Latn", "sk": "slk_Latn",
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"sl": "slv_Latn", "hu": "hun_Latn", "ro": "ron_Latn",
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"el": "ell_Grek", "ru": "rus_Cyrl", "uk": "ukr_Cyrl",
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"tr": "tur_Latn", "ar": "arb_Arab", "fa": "pes_Arab",
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"he": "heb_Hebr", "zh": "zho_Hans", "ja": "jpn_Jpan",
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"ko": "kor_Hang", "vi": "vie_Latn",
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}
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def map_to_nllb(code: Optional[str]):
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if not code:
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return None
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c = code.strip().lower()
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return NLLB_LANG.get(c, f"{c}_Latn")
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def normalize_lang(code: Optional[str], default=None):
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return (code or default).strip().lower() if code else default
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def _norm(s: str) -> str:
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return re.sub(r"\W+", "", (s or "").lower()).strip()
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def _is_repetitive_output(text: str, threshold: float = 0.25) -> bool:
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"""Detect if translation output is repetitive/low quality.
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Args:
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text: The translated text to check
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threshold: Minimum unique word ratio (default 0.25 = 25% unique words)
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Returns:
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True if text appears to be repetitive/low quality
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"""
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if not text or len(text) < 50:
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return False
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# Check for obvious repetitive patterns
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repetitive_patterns = [
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r'(\b\w+\b)( \1){3,}', # Same word repeated 4+ times
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r'(\b\w+ \w+\b)( \1){2,}', # Same 2-word phrase repeated 3+ times
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r'de la la ',
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r'la línea de la línea',
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r'de Internet de Internet',
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]
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for pattern in repetitive_patterns:
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if re.search(pattern, text, re.IGNORECASE):
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LOG.warning(f"Detected repetitive pattern: {pattern}")
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return True
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# Check word diversity
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words = text.lower().split()
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if len(words) < 10:
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return False
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unique_ratio = len(set(words)) / len(words)
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if unique_ratio < threshold:
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LOG.warning(f"Low word diversity: {unique_ratio:.2%} (threshold: {threshold:.2%})")
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return True
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return False
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# =========================
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# DB
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# =========================
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def get_conn():
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return psycopg2.connect(**DB_CONFIG)
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def ensure_indexes(conn):
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with conn.cursor() as cur:
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cur.execute("CREATE INDEX IF NOT EXISTS traducciones_lang_to_status_idx ON traducciones (lang_to, status);")
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cur.execute("CREATE INDEX IF NOT EXISTS traducciones_status_idx ON traducciones (status);")
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conn.commit()
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pass # Moved to translation_ops.py
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pass # Moved to translation_ops.py
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def fetch_pending_batch(conn, lang_to: str, batch: int):
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"""Fetch pending translations with row locking to support multiple workers."""
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if batch <= 0:
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return []
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# Use FOR UPDATE SKIP LOCKED to allow multiple workers
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# Each worker will get different rows without conflicts
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with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
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cur.execute(
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"""
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SELECT t.id AS tr_id, t.noticia_id, t.lang_from, t.lang_to,
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n.titulo, n.resumen
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FROM traducciones t
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JOIN noticias n ON n.id=t.noticia_id
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WHERE t.lang_to=%s AND t.status='pending'
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ORDER BY t.id
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LIMIT %s
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FOR UPDATE OF t SKIP LOCKED;
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""",
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(lang_to, batch),
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)
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rows = cur.fetchall()
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# Update status within the same transaction while rows are locked
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if rows:
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ids = [r["tr_id"] for r in rows]
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cur.execute("UPDATE traducciones SET status='processing' WHERE id = ANY(%s)", (ids,))
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conn.commit()
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return rows
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# =========================
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# LANGUAGE DETECTION
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# =========================
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def detect_lang(text1: str, text2: str):
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txt = (text1 or "").strip() or (text2 or "").strip()
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if not txt:
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return None
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try:
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return detect(txt)
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except Exception:
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return None
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# =========================
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# MODEL LOADING (CTranslate2)
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# =========================
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_TOKENIZER = None
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_TRANSLATOR = None
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_DEVICE = None
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def _resolve_device():
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if CT2_DEVICE == "cpu":
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return "cpu"
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if CT2_DEVICE == "cuda":
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return "cuda"
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# auto
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return "cuda" if ctranslate2.get_cuda_device_count() > 0 else "cpu"
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def _ensure_ct2_model():
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"""Convert HuggingFace model to CTranslate2 format if not exists."""
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import os
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import subprocess
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model_dir = CT2_MODEL_PATH
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# Check if model already exists
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if os.path.isdir(model_dir) and os.path.exists(os.path.join(model_dir, "model.bin")):
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LOG.info("CTranslate2 model already exists at %s", model_dir)
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return True
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LOG.info("CTranslate2 model not found, converting from %s...", UNIVERSAL_MODEL)
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LOG.info("This may take 5-10 minutes on first run...")
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# Create directory if needed
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os.makedirs(os.path.dirname(model_dir) or ".", exist_ok=True)
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# Convert the model
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quantization = CT2_COMPUTE_TYPE if CT2_COMPUTE_TYPE != "auto" else "int8_float16"
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cmd = [
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"ct2-transformers-converter",
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"--model", UNIVERSAL_MODEL,
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"--output_dir", model_dir,
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"--quantization", quantization,
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"--force"
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]
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try:
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LOG.info("Running: %s", " ".join(cmd))
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result = subprocess.run(cmd, capture_output=True, text=True, timeout=1800)
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if result.returncode != 0:
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LOG.error("Model conversion failed: %s", result.stderr)
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return False
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LOG.info("Model conversion completed successfully")
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return True
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except subprocess.TimeoutExpired:
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LOG.error("Model conversion timed out after 30 minutes")
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return False
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except Exception as e:
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LOG.error("Model conversion error: %s", e)
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return False
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def get_universal_components():
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global _TOKENIZER, _TRANSLATOR, _DEVICE
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if _TRANSLATOR:
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return _TOKENIZER, _TRANSLATOR
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# Ensure CT2 model exists (convert if needed)
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if not _ensure_ct2_model():
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raise RuntimeError(f"Failed to load/convert CTranslate2 model at {CT2_MODEL_PATH}")
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device = _resolve_device()
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LOG.info("Loading CTranslate2 model from %s on %s", CT2_MODEL_PATH, device)
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_TRANSLATOR = ctranslate2.Translator(
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CT2_MODEL_PATH,
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device=device,
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compute_type=CT2_COMPUTE_TYPE,
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)
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_TOKENIZER = AutoTokenizer.from_pretrained(UNIVERSAL_MODEL)
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_DEVICE = device
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LOG.info("CTranslate2 model loaded successfully")
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return _TOKENIZER, _TRANSLATOR
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# =========================
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# TRANSLATION (CTranslate2)
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# =========================
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def _safe_src_len(tokenizer):
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max_len = getattr(tokenizer, "model_max_length", 1024) or 1024
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if max_len > 100000:
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max_len = 1024
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return min(MAX_SRC_TOKENS, max_len - 16)
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def _translate_texts(src, tgt, texts, beams, max_new_tokens):
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"""Translate texts using CTranslate2."""
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if not texts:
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return []
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clean = [(t or "").strip() for t in texts]
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if all(not t for t in clean):
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return ["" for _ in clean]
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tok, translator = get_universal_components()
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src_code = map_to_nllb(src)
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tgt_code = map_to_nllb(tgt)
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# Set source language on tokenizer
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try:
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tok.src_lang = src_code
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except Exception:
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pass
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safe_len = _safe_src_len(tok)
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max_new = max(16, min(int(max_new_tokens), MAX_NEW_TOKENS_BODY))
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# Tokenize: convert text to tokens
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sources = []
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for t in clean:
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if t:
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ids = tok.encode(t, truncation=True, max_length=safe_len)
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tokens = tok.convert_ids_to_tokens(ids)
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sources.append(tokens)
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else:
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sources.append([])
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# Target language prefix for NLLB
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target_prefix = [[tgt_code]] * len(sources)
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# Translate with CTranslate2
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start = time.time()
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results = translator.translate_batch(
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sources,
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target_prefix=target_prefix,
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beam_size=beams,
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max_decoding_length=max_new,
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repetition_penalty=2.5, # Increased from 1.2 to prevent loops
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no_repeat_ngram_size=3, # Prevent 3-gram repetition
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)
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dt = time.time() - start
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# Decode results
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translated = []
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total_tokens = 0
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for result, src_tokens in zip(results, sources):
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if result.hypotheses:
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# Skip the first token (language prefix)
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tokens = result.hypotheses[0][1:]
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total_tokens += len(tokens) + len(src_tokens)
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text = tok.decode(tok.convert_tokens_to_ids(tokens))
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translated.append(text.strip())
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else:
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translated.append("")
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if total_tokens > 0:
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LOG.info(" → tokens=%d tiempo=%.2fs velocidad=%d tok/s",
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total_tokens, dt, int(total_tokens / dt) if dt > 0 else 0)
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return translated
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def _split_body_into_chunks(text: str) -> List[str]:
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text = (text or "").strip()
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if len(text) <= BODY_CHARS_CHUNK:
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return [text] if text else []
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parts = re.split(r'(\n\n+|(?<=[\.\!\?؛؟。])\s+)', text)
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chunks = []
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current = ""
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for part in parts:
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if not part:
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continue
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if len(current) + len(part) <= BODY_CHARS_CHUNK:
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current += part
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else:
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if current.strip():
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chunks.append(current.strip())
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current = part
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if current.strip():
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chunks.append(current.strip())
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if not chunks:
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return [text]
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return chunks
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def translate_body_long(src: str, tgt: str, body: str) -> str:
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body = (body or "").strip()
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if not body:
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return ""
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chunks = _split_body_into_chunks(body)
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if len(chunks) == 1:
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translated = _translate_texts(src, tgt, [body], NUM_BEAMS_BODY, MAX_NEW_TOKENS_BODY)[0]
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return translated.strip()
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translated_chunks = []
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for ch in chunks:
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tr = _translate_texts(src, tgt, [ch], NUM_BEAMS_BODY, MAX_NEW_TOKENS_BODY)[0]
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translated_chunks.append(tr.strip())
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return "\n\n".join(c for c in translated_chunks if c)
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# =========================
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# BATCH PROCESS
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# =========================
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def process_batch(conn, rows):
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todo = []
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done = []
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errors = []
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for r in rows:
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lang_to = normalize_lang(r["lang_to"], "es") or "es"
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lang_from = (
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normalize_lang(r["lang_from"])
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or detect_lang(r["titulo"], r["resumen"])
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or "en"
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)
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titulo = (r["titulo"] or "").strip()
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resumen = (r["resumen"] or "").strip()
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if map_to_nllb(lang_from) == map_to_nllb(lang_to):
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done.append((titulo, resumen, lang_from, r["tr_id"]))
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else:
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todo.append({
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"tr_id": r["tr_id"],
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"lang_from": lang_from,
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"lang_to": lang_to,
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"titulo": titulo,
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"resumen": resumen,
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})
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from collections import defaultdict
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groups = defaultdict(list)
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for item in todo:
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key = (item["lang_from"], item["lang_to"])
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groups[key].append(item)
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for (lang_from, lang_to), items in groups.items():
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LOG.info("Translating %s -> %s (%d items)", lang_from, lang_to, len(items))
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titles = [i["titulo"] for i in items]
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try:
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tt = _translate_texts(
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lang_from,
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lang_to,
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titles,
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NUM_BEAMS_TITLE,
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MAX_NEW_TOKENS_TITLE,
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)
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bodies_translated: List[str] = []
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for i in items:
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bodies_translated.append(
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translate_body_long(lang_from, lang_to, i["resumen"])
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)
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for i, ttr, btr in zip(items, tt, bodies_translated):
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ttr = (ttr or "").strip()
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btr = (btr or "").strip()
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if not ttr or _norm(ttr) == _norm(i["titulo"]):
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ttr = i["titulo"]
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if not btr or _norm(btr) == _norm(i["resumen"]):
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btr = i["resumen"]
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# CLEANING: Remove <unk> tokens
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if ttr:
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ttr = ttr.replace("<unk>", "").replace(" ", " ").strip()
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if btr:
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btr = btr.replace("<unk>", "").replace(" ", " ").strip()
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# VALIDATION: Check for repetitive output
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if _is_repetitive_output(ttr) or _is_repetitive_output(btr):
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LOG.warning(f"Rejecting repetitive translation for tr_id={i['tr_id']}")
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errors.append(("Repetitive output detected", i["tr_id"]))
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continue
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done.append((ttr, btr, lang_from, i["tr_id"]))
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except Exception as e:
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err = str(e)[:800]
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LOG.exception("Error translating %s -> %s: %s", lang_from, lang_to, err)
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for i in items:
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errors.append((err, i["tr_id"]))
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with conn.cursor() as cur:
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if done:
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execute_values(
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cur,
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"""
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UPDATE traducciones AS t
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SET titulo_trad=v.titulo_trad,
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resumen_trad=v.resumen_trad,
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lang_from=COALESCE(t.lang_from, v.lang_from),
|
|
status='done',
|
|
error=NULL
|
|
FROM (VALUES %s) AS v(titulo_trad,resumen_trad,lang_from,id)
|
|
WHERE t.id=v.id;
|
|
""",
|
|
done,
|
|
)
|
|
|
|
# --- NEW: Persist stats ---
|
|
# Insert a record for each translated item into translation_stats
|
|
# We need the language 'lang_to'. In this batch, lang_to is uniform for the group usually,
|
|
# but let's extract it from the 'done' items structure if we had it, or pass it down.
|
|
# In process_batch, we iterate groups.
|
|
# 'done' list here is flattened from multiple groups?
|
|
# process_batch logic:
|
|
# 1. 'done' checks map_to_nllb identity (already done?) -> these have lang_to from row?
|
|
# 2. 'groups' loop -> translates -> appends to 'done' with lang_from.
|
|
#
|
|
# Wait, 'done' list doesn't have lang_to in the tuple: (titulo, resumen, lang_from, tr_id).
|
|
# We need to change the 'done' collection to include lang_to OR we insert based on tr_id.
|
|
|
|
# Let's verify process_batch logic.
|
|
# rows has all info.
|
|
# define a mapping tr_id -> lang_to
|
|
tr_map = {r["tr_id"]: r["lang_to"] for r in rows}
|
|
|
|
stats_data = []
|
|
for item in done:
|
|
# item is (titulo, resumen, lang_from, tr_id)
|
|
lang_from = item[2]
|
|
lang_to = tr_map.get(item[3], "es")
|
|
stats_data.append((lang_from, lang_to))
|
|
|
|
execute_values(
|
|
cur,
|
|
"INSERT INTO translation_stats (lang_from, lang_to) VALUES %s",
|
|
stats_data
|
|
)
|
|
# --------------------------
|
|
|
|
if errors:
|
|
execute_values(
|
|
cur,
|
|
"""
|
|
UPDATE traducciones AS t
|
|
SET status='error', error=v.error
|
|
FROM (VALUES %s) AS v(error,id)
|
|
WHERE t.id=v.id;
|
|
""",
|
|
errors,
|
|
)
|
|
|
|
conn.commit()
|
|
|
|
def process_entity_summaries(conn):
|
|
"""Translate pending entity summaries from Wikipedia."""
|
|
from cache import cache_del
|
|
|
|
LOG.info("DEBUG: Checking for pending entity summaries...")
|
|
|
|
with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
|
|
cur.execute("""
|
|
SELECT id, entity_name, summary, summary_en
|
|
FROM entity_images
|
|
WHERE status_es = 'pending'
|
|
LIMIT 20
|
|
FOR UPDATE SKIP LOCKED;
|
|
""")
|
|
rows = cur.fetchall()
|
|
|
|
if not rows:
|
|
return False
|
|
|
|
LOG.info("DEBUG: Found %d pending entity summaries to process", len(rows))
|
|
|
|
for r in rows:
|
|
entity_id = r["id"]
|
|
name = r["entity_name"]
|
|
text = r["summary_en"] or r["summary"]
|
|
|
|
if not text:
|
|
cur.execute("UPDATE entity_images SET status_es = 'none' WHERE id = %s", (entity_id,))
|
|
continue
|
|
|
|
try:
|
|
# English -> Spanish
|
|
translated = translate_body_long('en', 'es', text)
|
|
if translated:
|
|
cur.execute("""
|
|
UPDATE entity_images
|
|
SET summary_es = %s, status_es = 'done'
|
|
WHERE id = %s
|
|
""", (translated, entity_id))
|
|
# Invalidate cache
|
|
cache_del(f"wiki:data:{name.lower()}")
|
|
LOG.info(" → Translated entity summary: %s", name)
|
|
else:
|
|
cur.execute("UPDATE entity_images SET status_es = 'error' WHERE id = %s", (entity_id,))
|
|
except Exception as e:
|
|
LOG.error("Error translating entity summary [%s]: %s", name, e)
|
|
cur.execute("UPDATE entity_images SET status_es = 'error' WHERE id = %s", (entity_id,))
|
|
|
|
conn.commit()
|
|
return True
|
|
|
|
# =========================
|
|
# MAIN LOOP
|
|
# =========================
|
|
def main():
|
|
LOG.info("Translator worker iniciado (CTranslate2)")
|
|
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
|
|
get_universal_components()
|
|
|
|
while True:
|
|
any_work = False
|
|
with get_conn() as conn:
|
|
ensure_indexes(conn)
|
|
|
|
# 1. Process entity summaries (Wikipedia) -> REMOVED per user request
|
|
# Logic moved out to keep translator focused on news ONLY.
|
|
# try:
|
|
# if process_entity_summaries(conn):
|
|
# any_work = True
|
|
# except Exception as e:
|
|
# LOG.error("Error in process_entity_summaries: %s", e)
|
|
|
|
# 2. Process news translations
|
|
for tgt in TARGET_LANGS:
|
|
while True:
|
|
rows = fetch_pending_batch(conn, tgt, BATCH_SIZE)
|
|
if not rows:
|
|
break
|
|
any_work = True
|
|
LOG.info("[%s] %d elementos", tgt, len(rows))
|
|
process_batch(conn, rows)
|
|
|
|
if not any_work:
|
|
time.sleep(SLEEP_IDLE)
|
|
|
|
if __name__ == "__main__":
|
|
main()
|
|
|