First Commit

nodes/indextts2_node_emotext.py

@@ -0,0 +1,71 @@
+import os
+from typing import Tuple
+
+
+_QWEN_CACHE = None
+
+
+def _get_qwen(model_dir: str):
+    global _QWEN_CACHE
+    if _QWEN_CACHE is not None:
+        return _QWEN_CACHE
+
+    try:
+        from modelscope import AutoModelForCausalLM  # noqa: F401
+    except Exception:
+        raise ImportError("modelscope is required for Emotion From Text. Install with: pip install modelscope")
+
+    import sys
+    base_dir = os.path.dirname(os.path.abspath(__file__))
+    ext_root = os.path.dirname(base_dir)
+    if ext_root not in sys.path:
+        sys.path.insert(0, ext_root)
+    from indextts.infer_v2 import QwenEmotion
+
+    qwen = QwenEmotion(model_dir)
+    _QWEN_CACHE = qwen
+    return qwen
+
+class IndexTTS2EmotionFromText:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": ("STRING", {"multiline": True}),
+            },
+        }
+
+    RETURN_TYPES = ("EMOTION_VECTOR", "STRING")
+    FUNCTION = "build"
+    CATEGORY = "Audio/IndexTTS"
+
+    def build(self, text: str) -> Tuple[list, str]:
+        if not isinstance(text, str) or len(text.strip()) == 0:
+            raise ValueError("Text is empty. Please provide descriptive emotion text.")
+
+        base_dir = os.path.dirname(os.path.abspath(__file__))
+        qwen_dir = os.path.join(os.path.dirname(base_dir), "checkpoints", "qwen0.6bemo4-merge")
+        if not os.path.isdir(qwen_dir):
+            raise FileNotFoundError(f"QwenEmotion model not found at: {qwen_dir}")
+
+        qwen = _get_qwen(qwen_dir)
+        emo_dict = qwen.inference(text)
+
+        order = ["happy", "angry", "sad", "afraid", "disgusted", "melancholic", "surprised", "calm"]
+        vec = [float(max(0.0, emo_dict.get(k, 0.0))) for k in order]
+
+        #Clamp individual max to 1.4 (UI/Gradio baseline)
+        vec = [min(1.4, v) for v in vec]
+
+        cap = 1.5
+        total = sum(vec)
+        if total > cap:
+            raise ValueError(
+                f"Emotion vector sum {total:.3f} exceeds maximum {cap}. Reduce intensities or adjust with the Emotion Vector node."
+            )
+
+        info = (
+            f"Detected emotion vector (sum={sum(vec):.2f}):\n"
+            + ", ".join(f"{k}={v:.2f}" for k, v in zip(order, vec))
+        )
+        return vec, info