Delete label_encoder.joblib

label_encoder.joblib

@@ -1,44 +0,0 @@
-# save_assets.py
-import joblib
-from sklearn.preprocessing import LabelEncoder
-import os
-
-# 1. Create and save label encoder
-def create_label_encoder(dataset_path):
-    label_encoder = LabelEncoder()
-    
-    # Get user folders (classes)
-    user_folders = [d for d in os.listdir(dataset_path) 
-                   if os.path.isdir(os.path.join(dataset_path, d))]
-    
-    # Fit the encoder
-    label_encoder.fit(user_folders)
-    
-    # Save it
-    joblib.dump(label_encoder, "label_encoder.joblib")
-    print(f"Label encoder saved with classes: {label_encoder.classes_}")
-
-# 2. Create and save feature parameters
-def create_feature_params():
-    feature_params = {
-        'max_pad_len': 174,  # From your extract_features()
-        'sample_rate': 22050,
-        'n_mfcc': 40,  # From your MFCC extraction
-        'augmentation_config': {
-            'add_noise': True,
-            'time_shift': True,
-            'pitch_shift': True,
-            'time_stretch': True,
-            'volume_change': True,
-            'background_noise': True,
-            'freq_mask': True,
-            'time_mask': True
-        }
-    }
-    joblib.dump(feature_params, "feature_params.joblib")
-    print("Feature parameters saved.")
-
-if __name__ == "__main__":
-    dataset_path = "path/to/your/dataset"  # Update this
-    create_label_encoder(dataset_path)
-    create_feature_params()