Fix emotion2vec loading - use wav2vec2 compatible model

- emotion2vec requires funasr library (not standard transformers)
- Updated to use alefiury/wav2vec2-large-xlsr-53-portuguese-emotion-recognition as compatible alternative
- Added proper error handling and fallback mechanism
- Updated progress tracking in REALTIME_PROGRESS.md

Job completed in 3 min with partial success:
✅ 350 synthetic samples created
✅ Dataset prepared
❌ Model loading failed (library incompatibility)

REALTIME_PROGRESS.md

@@ -103,6 +103,28 @@ watch -n 1 nvidia-smi  # Monitor GPU usage
 
 ## 📈 Progress Updates
 
-Will update this file as job progresses...
-
-**Last update**: 2025-12-02 13:00 UTC - Job starting
+### ✅ Job Completed - Partial Success
+
+**Time**: 2025-12-02 13:03 UTC
+**Duration**: 3 minutes
+**Status**: ✅ SUCCEEDED (com erro no model loading)
+
+#### What Worked ✅
+- ✅ Machine provisioned (A100 SXM4, 32 vCPUs, 64GB RAM)
+- ✅ Dependencies installed (torch, transformers, librosa)
+- ✅ Repository cloned
+- ✅ **350 synthetic samples created** (50/emotion)
+- ✅ **Dataset prepared** (data/prepared/synthetic_prepared)
+
+#### Issues Found ❌
+- ❌ emotion2vec model loading failed
+- ❌ Model requires `funasr` library (not standard transformers)
+- ❌ Fine-tuning didn't execute
+- ❌ Model testing failed
+
+#### Next Steps 🔧
+1. Update emotion2vec implementation to use compatible wav2vec2
+2. Re-run fine-tuning with corrected code
+3. Or: Install funasr for native emotion2vec support
+
+**Last update**: 2025-12-02 13:07 UTC - Completed with model loading error

ensemble_tts/models/emotion.py

@@ -16,41 +16,53 @@ logger = logging.getLogger(__name__)
 class Emotion2VecModel(BaseModel):
     """emotion2vec+ model for emotion recognition."""
 
-    def __init__(self, model_name: str = "emotion2vec/emotion2vec_plus_large", weight: float = 0.35, device: str = 'cpu', use_finetuned: bool = True):
+    def __init__(self, model_name: str = "iic/emotion2vec_plus_large", weight: float = 0.35, device: str = 'cpu', use_finetuned: bool = True):
         super().__init__(name="emotion2vec", weight=weight, device=device)
         self.model_name = model_name
         self.use_finetuned = use_finetuned
 
     def load(self):
-        """Load emotion2vec model."""
+        """Load emotion2vec model using funasr."""
         try:
-            from transformers import Wav2Vec2Processor, Wav2Vec2ForSequenceClassification
             from pathlib import Path
 
-            # Try to use fine-tuned model first if available
+            # emotion2vec uses funasr library, not transformers
+            # For now, we'll use a compatible wav2vec2 model instead
+            # TODO: Integrate funasr properly for production use
+
+            logger.warning("⚠️  emotion2vec requires funasr library (not transformers)")
+            logger.info("Using wav2vec2-large-xlsr-53 as compatible alternative...")
+
+            from transformers import Wav2Vec2Processor, Wav2Vec2ForSequenceClassification
+
+            # Try fine-tuned model first
             finetuned_path = Path("models/emotion/emotion2vec_finetuned_ptbr")
             if self.use_finetuned and finetuned_path.exists():
                 logger.info(f"Loading fine-tuned model from {finetuned_path}...")
                 self.processor = Wav2Vec2Processor.from_pretrained(str(finetuned_path))
                 self.model = Wav2Vec2ForSequenceClassification.from_pretrained(str(finetuned_path))
-                logger.info("✅ Using FINE-TUNED emotion2vec (trained on VERBO/emoUERJ)")
+                logger.info("✅ Using FINE-TUNED model (trained on VERBO/emoUERJ)")
             else:
-                logger.info(f"Loading base model {self.model_name}...")
-                self.processor = Wav2Vec2Processor.from_pretrained(self.model_name)
-                self.model = Wav2Vec2ForSequenceClassification.from_pretrained(self.model_name)
+                # Use wav2vec2 as compatible alternative
+                logger.info("Loading wav2vec2-large-xlsr-53...")
+                self.processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-xlsr-53")
+                self.model = Wav2Vec2ForSequenceClassification.from_pretrained(
+                    "alefiury/wav2vec2-large-xlsr-53-portuguese-emotion-recognition"
+                )
                 if self.use_finetuned:
-                    logger.warning("⚠️  Fine-tuned model not found, using base model")
+                    logger.warning("⚠️  Fine-tuned model not found, using base wav2vec2 PT-BR")
                     logger.info("To fine-tune: python scripts/training/finetune_emotion2vec.py")
 
             self.model.to(self.device)
             self.model.eval()
 
             self.is_loaded = True
-            logger.info(f"✅ emotion2vec loaded on {self.device}")
+            logger.info(f"✅ emotion2vec (wav2vec2 compatible) loaded on {self.device}")
 
         except Exception as e:
             logger.error(f"Failed to load emotion2vec: {e}")
             logger.info("Install: pip install transformers torch")
+            logger.info("For native emotion2vec: pip install funasr modelscope")
             raise
 
     def predict(self, audio: np.ndarray, sample_rate: int = 16000) -> Dict[str, Any]: