Adolfo Reyna
134 lines
5.0 KiB
Python
134 lines
5.0 KiB
Python
import sys
|
|
from unittest.mock import MagicMock
|
|
|
|
# Comprehensive workaround for missing _lzma in some Python builds
|
|
try:
|
|
import lzma
|
|
except ImportError:
|
|
mock_lzma = MagicMock()
|
|
mock_lzma.FORMAT_XZ = 1
|
|
mock_lzma.FORMAT_ALONE = 2
|
|
mock_lzma.FORMAT_RAW = 3
|
|
mock_lzma.CHECK_NONE = 0
|
|
mock_lzma.CHECK_CRC32 = 1
|
|
mock_lzma.CHECK_CRC64 = 4
|
|
mock_lzma.CHECK_SHA256 = 10
|
|
sys.modules["_lzma"] = MagicMock()
|
|
sys.modules["lzma"] = mock_lzma
|
|
|
|
import torch
|
|
from transformers import MarianMTModel, MarianTokenizer
|
|
import re
|
|
import argparse
|
|
import multiprocessing
|
|
import time
|
|
import requests
|
|
|
|
TARGET_LANGS = {
|
|
"es": "Helsinki-NLP/opus-mt-en-es",
|
|
"fr": "Helsinki-NLP/opus-mt-en-fr",
|
|
"ar": "Helsinki-NLP/opus-mt-en-ar"
|
|
}
|
|
|
|
def split_text_for_translation(text, max_chars=250):
|
|
normalized = " ".join(text.split()).strip()
|
|
if not normalized or len(normalized) <= max_chars:
|
|
return [normalized] if normalized else []
|
|
chunks = []
|
|
current = ""
|
|
sentences = [s for s in re.split(r"(?<=[.!?])\s+", normalized) if s]
|
|
for sentence in sentences:
|
|
if len(sentence) > max_chars:
|
|
words = sentence.split()
|
|
word_chunk = ""
|
|
for word in words:
|
|
candidate = f"{word_chunk} {word}".strip()
|
|
if len(candidate) <= max_chars: word_chunk = candidate
|
|
else:
|
|
if word_chunk: chunks.append(word_chunk)
|
|
word_chunk = word
|
|
if word_chunk: chunks.append(word_chunk)
|
|
continue
|
|
candidate = f"{current} {sentence}".strip()
|
|
if candidate and len(candidate) <= max_chars: current = candidate
|
|
else:
|
|
if current: chunks.append(current)
|
|
current = sentence
|
|
if current: chunks.append(current)
|
|
return chunks
|
|
|
|
def call_ollama_generate(args, prompt):
|
|
payload = {"model": args.post_correct_model, "prompt": prompt, "stream": False}
|
|
try:
|
|
resp = requests.post("http://127.0.0.1:11434/api/generate", json=payload, timeout=10)
|
|
return resp.json().get("response", "").strip()
|
|
except: return ""
|
|
|
|
def structure_paragraph_with_llm(text, args):
|
|
prompt = f"Format the following transcribed segments into a coherent paragraph. Output ONLY the paragraph:\n{text}"
|
|
return call_ollama_generate(args, prompt) or text
|
|
|
|
def post_correct_with_llm(text, args):
|
|
prompt = f"Correct grammar and typos in this caption. Output ONLY the corrected text:\n{text}"
|
|
return call_ollama_generate(args, prompt) or text
|
|
|
|
def run_translation(in_queue, out_queue, args):
|
|
device = "mps" if torch.backends.mps.is_available() else "cpu"
|
|
translation_engines = {}
|
|
for lang_key, model_id in TARGET_LANGS.items():
|
|
if getattr(args, lang_key, False):
|
|
print(f"[Translate] Loading {lang_key} model...")
|
|
translation_engines[lang_key] = (
|
|
MarianMTModel.from_pretrained(model_id).to(device),
|
|
MarianTokenizer.from_pretrained(model_id)
|
|
)
|
|
|
|
paragraph_buffer = []
|
|
|
|
while True:
|
|
try:
|
|
item = in_queue.get()
|
|
if item is None: break
|
|
|
|
# Draft support
|
|
if "draft" in item:
|
|
out_queue.put({"draft": item["draft"]})
|
|
continue
|
|
|
|
text = item.get("original", "").strip()
|
|
detected_lang = item.get("detected_lang", "en")
|
|
|
|
if args.post_correct and args.post_correct_llm:
|
|
text = post_correct_with_llm(text, args)
|
|
|
|
paragraph_buffer.append(text)
|
|
|
|
# Paragraph logic
|
|
if not args.llm_paragraph or text.endswith((".", "?", "!")):
|
|
full_text = " ".join(paragraph_buffer)
|
|
|
|
if args.llm_paragraph:
|
|
full_text = structure_paragraph_with_llm(full_text, args)
|
|
|
|
payload = {"original": full_text, "ts": item.get("ts", time.time())}
|
|
if args.en: payload["en"] = full_text
|
|
|
|
for lang_key, (model, tokenizer) in translation_engines.items():
|
|
chunks = split_text_for_translation(full_text, args.mt_max_chars)
|
|
translated_parts = []
|
|
for chunk in chunks:
|
|
inputs = tokenizer(chunk, return_tensors="pt", padding=True).to(device)
|
|
with torch.no_grad():
|
|
translated_tokens = model.generate(**inputs, max_new_tokens=args.mt_max_new_tokens, num_beams=args.mt_num_beams)
|
|
translated_parts.append(tokenizer.decode(translated_tokens[0], skip_special_tokens=True).strip())
|
|
payload[lang_key] = " ".join(translated_parts)
|
|
|
|
out_queue.put(payload)
|
|
paragraph_buffer = []
|
|
|
|
except Exception as e: print(f"[Translate] Error: {e}")
|
|
|
|
if __name__ == "__main__":
|
|
import multiprocessing
|
|
run_translation(multiprocessing.Queue(), multiprocessing.Queue(), argparse.Namespace())
|