Checkpoint current transcription pipeline state

config.json

@@ -19,6 +19,7 @@
     "post_correct_llm": false,
     "post_correct_model": "qwen:2b",
     "llm_paragraph": false,
+    "only_translate_llm": false,
     "temperature_fallback": [0.0, 0.2, 0.4, 0.6, 0.8, 1.0],
     "logprob_threshold": -0.8,
     "compression_threshold": 2.2,

engine_distribute.py

@@ -57,18 +57,36 @@ def run_distribution(in_queue, args):
                     log_debug(lang_msg)
 
             if args.ingest:
-                delay, max_delay, success = 1, 15, False
+                # Flat JSON Schema: Keep only what's needed for the server
-                while not success:
+                ingest_payload = {
-                    try:
+                    "original": payload.get("original"),
-                        response = requests.post(INGEST_URL, json=payload, timeout=5)
+                    "speaker": payload.get("speaker"),
-                        if response.status_code == 200: success = True
+                    "ts": payload.get("ts")
-                        else: print(f"[Distribute Error] {response.status_code}. Retrying...")
+                }
-                    except Exception as e:
+                has_any_translation = False
-                        print(f"[Distribute Error] {e}. Retrying...")
+                for lang in ["es", "fr", "ar", "en"]:
-                    
+                    if payload.get(lang):
-                    if not success:
+                        ingest_payload[lang] = payload[lang]
-                        time.sleep(delay)
+                        has_any_translation = True
-                        delay = min(delay * 2, max_delay)
+                
+                # If only-translate-llm is on, ONLY send when we have a translation (paragraph-level)
+                should_send = True
+                if getattr(args, "only_translate_llm", False) and not has_any_translation:
+                    should_send = False
+                
+                if should_send:
+                    delay, max_delay, success = 1, 15, False
+                    while not success:
+                        try:
+                            response = requests.post(INGEST_URL, json=ingest_payload, timeout=5)
+                            if response.status_code == 200: success = True
+                            else: print(f"[Distribute Error] {response.status_code}. Retrying...")
+                        except Exception as e:
+                            print(f"[Distribute Error] {e}. Retrying...")
+                        
+                        if not success:
+                            time.sleep(delay)
+                            delay = min(delay * 2, max_delay)
                 
         except Exception as e:
             print(f"[Distribute] Error: {e}")

engine_translate.py

@@ -77,23 +77,29 @@ def run_translation(in_queue, out_queue, args):
                 "speaker": item.get("speaker"),
                 "ts": item.get("ts", time.time())
             }
-            if args.en: payload["en"] = corrected_text or raw_text
+            
+            if args.only_translate_llm:
+                text_to_translate = paragraph_text
+                if args.en and paragraph_text:
+                    payload["en"] = paragraph_text
+            else:
+                text_to_translate = paragraph_text or corrected_text or raw_text
+                if args.en:
+                    payload["en"] = corrected_text or raw_text
 
-            text_to_translate = paragraph_text or corrected_text or raw_text
+            if text_to_translate and translation_engines:
-            if text_to_translate:
+                for lang_key, (model, tokenizer) in translation_engines.items():
-                if translation_engines:
+                    chunks = split_text_for_translation(text_to_translate, args.mt_max_chars)
-                    for lang_key, (model, tokenizer) in translation_engines.items():
+                    translated_parts = []
-                        chunks = split_text_for_translation(text_to_translate, args.mt_max_chars)
+                    for chunk in chunks:
-                        translated_parts = []
+                        inputs = tokenizer(chunk, return_tensors="pt", padding=True).to(device)
-                        for chunk in chunks:
+                        with torch.no_grad():
-                            inputs = tokenizer(chunk, return_tensors="pt", padding=True).to(device)
+                            translated_tokens = model.generate(**inputs, max_new_tokens=args.mt_max_new_tokens, num_beams=args.mt_num_beams)
-                            with torch.no_grad():
+                        translated_parts.append(tokenizer.decode(translated_tokens[0], skip_special_tokens=True).strip())
-                                translated_tokens = model.generate(**inputs, max_new_tokens=args.mt_max_new_tokens, num_beams=args.mt_num_beams)
+                    payload[lang_key] = " ".join(translated_parts)
-                            translated_parts.append(tokenizer.decode(translated_tokens[0], skip_special_tokens=True).strip())
+            
-                        payload[lang_key] = " ".join(translated_parts)
+            # ALWAYS put in queue, even if no translations were done
-                
+            out_queue.put(payload)
-                # ALWAYS put in queue, even if no translations were done
-                out_queue.put(payload)
         except Exception as e: print(f"[Translate] Error: {e}")
 
 if __name__ == "__main__":

main_v2.py

@@ -57,6 +57,7 @@ def main():
     parser.add_argument("-fr", action="store_true", default=defaults.get("fr", False))
     parser.add_argument("-ar", action="store_true", default=defaults.get("ar", False))
     parser.add_argument("-en", action="store_true", default=defaults.get("en", False))
+    parser.add_argument("--only-translate-llm", action="store_true", default=defaults.get("only_translate_llm", False), help="Only translate when the LLM produces a refined paragraph.")
     parser.add_argument("--mt-max-chars", type=int, default=defaults.get("mt_max_chars", 250))
     parser.add_argument("--mt-max-new-tokens", type=int, default=defaults.get("mt_max_new_tokens", 150))
     parser.add_argument("--mt-num-beams", type=int, default=defaults.get("mt_num_beams", 4))
@@ -66,6 +67,13 @@ def main():
     
     args = parser.parse_args()
 
+    # Auto-enable required flags
+    if args.post_correct_llm and not args.post_correct:
+        args.post_correct = True
+    if args.only_translate_llm and not args.llm_paragraph:
+        print("[Main] Enabling --llm-paragraph because --only-translate-llm was requested.")
+        args.llm_paragraph = True
+
     # Handle device listing
     if args.list_devices:
         list_audio_devices()

transcribe.py

@@ -610,6 +610,7 @@ def main():
     parser.add_argument("--mt-max-chars", type=int, default=250, help="Max chars per translation chunk before sentence-aware splitting (default: 250)")
     parser.add_argument("--mt-max-new-tokens", type=int, default=150, help="Max new tokens per translation chunk (default: 150)")
     parser.add_argument("--mt-num-beams", type=int, default=4, help="Beam size for Marian translation generation (default: 4)")
+    parser.add_argument("--only-translate-llm", action="store_true", help="Only translate into target languages when a refined LLM paragraph is available.")
     parser.add_argument("--mt-no-repeat-ngram-size", type=int, default=3, help="No-repeat n-gram size for Marian generation (default: 3)")
     parser.add_argument("--mt-length-penalty", type=float, default=1.0, help="Length penalty for Marian generation (default: 1.0)")
     parser.add_argument("--mt-repetition-penalty", type=float, default=1.05, help="Repetition penalty for Marian generation (default: 1.05)")
@@ -648,6 +649,10 @@ def main():
         args.post_correct = True
         print("[SYSTEM]: Enabling --post-correct because --post-correct-llm was requested.")
         log_session("[SYSTEM]: Enabling --post-correct because --post-correct-llm was requested.")
+    if args.only_translate_llm and not args.llm_paragraph:
+        args.llm_paragraph = True
+        print("[SYSTEM]: Enabling --llm-paragraph because --only-translate-llm was requested.")
+        log_session("[SYSTEM]: Enabling --llm-paragraph because --only-translate-llm was requested.")
     file_glossary_pairs = load_glossary_file(args.glossary_file)
     merged_glossary_pairs = file_glossary_pairs + args.glossary_pair
     if file_glossary_pairs:
@@ -1064,9 +1069,8 @@ def main():
                                     rolling_context = (rolling_context + " " + original_text)[-200:].strip()
 
                                 # 3. Translate from English to other languages
-                                if not args.llm_paragraph and english_for_translation and translation_engines:
+                                if not args.llm_paragraph and not args.only_translate_llm and english_for_translation and translation_engines:
-                                    english_chunks = split_text_for_translation(english_for_translation, max_chars=args.mt_max_chars)
+                                    english_chunks = split_text_for_translation(english_for_translation, max_chars=args.mt_max_chars)                                    
-                                    
                                     for lang_key, (model, tokenizer) in translation_engines.items():
                                         # Skip if we already filled this (e.g. detected lang was 'es')
                                         if lang_key in payload: