feat: add split mode to AutoLoopCalculatorTTS

This commit introduces a new dropdown to the `AutoLoopCalculatorTTS` node inside `video.py`, allowing text to be split by either "Párrafos (Saltos de línea)" or "Frases (Puntos)". When splitting by sentences, it cleanly preserves the periods to maintain proper intonation in TTS systems. Output pins are renamed (`current_text`, `total_chunks`) for better semantics.

video.py

@@ -582,16 +582,17 @@ class AutoLoopCalculatorTTS:
         return {
             "required": {
                 "text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
+                "split_mode": (["Párrafos (Saltos de línea)", "Frases (Puntos)"], {"default": "Párrafos (Saltos de línea)"}),
                 "current_loop_index": ("INT", {"forceInput": True}),
             }
         }
 
     RETURN_TYPES = ("STRING", "INT", "INT", "STRING")
-    RETURN_NAMES = ("current_paragraph", "current_index", "total_paragraphs", "log")
+    RETURN_NAMES = ("current_text", "current_index", "total_chunks", "log")
     FUNCTION = "calculate"
     CATEGORY = "🔁 Sequential Batcher/Text"
 
-    def calculate(self, text, current_loop_index):
+    def calculate(self, text, split_mode, current_loop_index):
         log_output = []
         def _log(msg):
             print(msg)
@@ -599,32 +600,40 @@ class AutoLoopCalculatorTTS:
 
         from . import loop
 
-        # 1. Limpiar y separar párrafos ignorando saltos de línea vacíos
-        raw_paragraphs = [p.strip() for p in text.split('\n') if p.strip()]
-        total_paragraphs = len(raw_paragraphs)
+        # 1. Separar el texto según el modo elegido
+        if split_mode == "Frases (Puntos)":
+            # Dividimos por punto, eliminamos vacíos y restauramos el punto final
+            raw_chunks = [p.strip() + "." for p in text.split('.') if p.strip()]
+            chunk_type_name = "Frases"
+        else:
+            # Comportamiento original: Dividir por párrafos (saltos de línea)
+            raw_chunks = [p.strip() for p in text.split('\n') if p.strip()]
+            chunk_type_name = "Párrafos"
 
-        if total_paragraphs == 0:
-            raw_paragraphs = [""]
-            total_paragraphs = 1
+        total_chunks = len(raw_chunks)
+
+        if total_chunks == 0:
+            raw_chunks = [""]
+            total_chunks = 1
 
         # 2. Seguridad de índice
-        safe_index = min(current_loop_index, total_paragraphs - 1)
-        current_paragraph = raw_paragraphs[safe_index]
+        safe_index = min(current_loop_index, total_chunks - 1)
+        current_text = raw_chunks[safe_index]
 
-        # 3. 🧠 HACK CORE: Inyectamos los párrafos como si fueran frames
-        # para que el SequentialLoopTrigger entienda el progreso.
-        loop.global_source_frame_count = total_paragraphs
+        # 3. 🧠 HACK CORE: Inyectamos los bloques como si fueran frames
+        loop.global_source_frame_count = total_chunks
         loop.global_accumulated_frames = safe_index + 1
-        loop.global_is_final_chunk = (safe_index + 1) >= total_paragraphs
+        loop.global_is_final_chunk = (safe_index + 1) >= total_chunks
 
         _log(f"\n{'='*50}")
         _log(f"🗣️ [Secuencial Batcher] NODO: Auto Loop Calculator (TTS)")
-        _log(f"   -> Párrafos detectados: {total_paragraphs}")
-        _log(f"   -> Timeline: Párrafo {safe_index + 1} de {total_paragraphs}")
-        _log(f"   -> 📜 Texto a procesar: {current_paragraph[:75]}...")
+        _log(f"   -> Modo de división: {split_mode}")
+        _log(f"   -> {chunk_type_name} detectados: {total_chunks}")
+        _log(f"   -> Timeline: Bloque {safe_index + 1} de {total_chunks}")
+        _log(f"   -> 📜 Texto a procesar: {current_text[:75]}...")
         _log(f"{'='*50}\n")
 
-        return (current_paragraph, safe_index, total_paragraphs, "\n".join(log_output))
+        return (current_text, safe_index, total_chunks, "\n".join(log_output))
 
 @register_node
 class IncrementalVideoStitcher: