Update README.md

README.md

@@ -57,42 +57,6 @@ This model is a binary classification head fine-tuned version of [MIT/ast-finetu
 - **Output**: Probabilities [fake_prob, real_prob]
 - **Training Hardware**: 2x NVIDIA T4 GPUs
 
-## Training Configuration
-
-```python
-{
-    'learning_rate': 1e-5,
-    'weight_decay': 0.01,
-    'n_iterations': 1500,
-    'batch_size': 16,
-    'gradient_accumulation_steps': 8,
-    'validate_every': 500,
-    'val_samples': 5000
-}
-```
-
-## Dataset Distribution
-
-The model was trained on a filtered dataset with the following class distribution:
-
-```
-Training Set:
-- Fake Audio (0): 29,089 samples (53.97%)
-- Real Audio (1): 24,813 samples (46.03%)
-
-Test Set:
-- Fake Audio (0): 7,229 samples (53.64%)
-- Real Audio (1): 6,247 samples (46.36%)
-```
-
-## Model Performance
-
-Final metrics on validation set:
-- Accuracy: 0.9662 (96.62%)
-- F1 Score: 0.9710 (97.10%)
-- Precision: 0.9692 (96.92%)
-- Recall: 0.9728 (97.28%)
-
 # Usage Guide
 
 ## Model Usage
@@ -167,16 +131,6 @@ for filename, probs in zip(audio_files, probabilities):
 ## Limitations
 
 Important considerations when using this model:
-1. The model works best with 16kHz audio input
+1. The model works with 16kHz audio input
 2. Performance may vary with different types of audio manipulation not present in training data
-3. Very short audio clips (<1 second) might not provide reliable results
-4. The model should not be used as the sole determiner for real/fake audio detection
-
-## Training Details
-
-The training process involved:
-1. Loading the base AST model pretrained on AudioSet
-2. Replacing the classification head with a binary classifier
-3. Fine-tuning on the fake audio detection dataset for 1500 iterations
-4. Using gradient accumulation (8 steps) with batch size 16
-5. Implementing validation checks every 500 steps
+3. The model was trained on audio samples ranging from 4 to 10 seconds in duration.