Update README.md

README.md

@@ -113,6 +113,7 @@ def predict(audio):
 audio_data = (sample_rate, waveform)  # Replace with your actual audio data
 emotion = predict(audio_data)
 print(f"Predicted Emotion: {emotion}")
+```
 
 ## Training Procedure
 
@@ -120,10 +121,10 @@ Preprocessing: Resampled all audio to 16kHz.
 Training: Fine-tuned facebook/wav2vec2-base with emotion labels.
 Hyperparameters: Batch size: 16, Learning rate: 5e-5, Epochs: 5
 
-##Evaluation
+## Evaluation
 Testing Data
 Evaluation was performed on a held-out test set from the CREMA-D and RAVDESS datasets.
 
-##Metrics
+## Metrics
 Accuracy: 85%
 F1-score: 82% (weighted average across all classes)