Create label_encoder.joblib

label_encoder.joblib

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+import joblib
+from sklearn.preprocessing import LabelEncoder
+
+# Assuming you have the dataset path (same as in your training code)
+#dataset_path = "path/to/your/dataset"  # Update this
+
+# Initialize label encoder (same as in VoiceDataset)
+label_encoder = LabelEncoder()
+
+# Extract labels from dataset folders
+labels = []
+for user_folder in os.listdir(dataset_path):
+    if os.path.isdir(os.path.join(dataset_path, user_folder)):
+        labels.append(user_folder)
+
+# Fit the label encoder
+label_encoder.fit(labels)
+
+# Save to file
+joblib.dump(label_encoder, "label_encoder.joblib")
+print(f"Label encoder saved with classes: {label_encoder.classes_}")