Update README.md

README.md

@@ -20,21 +20,22 @@ This model classifies music genres based on audio signals. It was fine-tuned on
 - **Validation Accuracy**: 75%
 - **F1 Score**: 74%
 - **Validation Loss**: 0.77
-
-## Usage
+  
+## Example Usage
 ```python
 from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
 import torch
 
-# Load the model and feature extractor
-model = Wav2Vec2ForSequenceClassification.from_pretrained("username/repo-name")
-feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("username/repo-name")
+# Load model and feature extractor
+model = Wav2Vec2ForSequenceClassification.from_pretrained("username/repo_name")
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("username/repo_name")
 
-# Prepare input
-audio = ... # Your audio array
-inputs = feature_extractor(audio, sampling_rate=16000, return_tensors="pt")
+# Process audio file
+audio_path = "path/to/audio.wav"
+audio_input = feature_extractor(audio_array, sampling_rate=16000, return_tensors="pt", padding=True)
 
-# Make predictions
-logits = model(**inputs).logits
-predicted_class = torch.argmax(logits, dim=-1).item()
-print(predicted_class)
+# Predict
+with torch.no_grad():
+    logits = model(audio_input["input_values"])
+    predicted_class = torch.argmax(logits.logits, dim=-1)
+print(predicted_class)