Added code it use it locally.

README.md

@@ -82,4 +82,47 @@ The following hyperparameters were used during training:
 
 ### Github Repository
 
-[Animal Sound Classification](https://github.com/rawbeen248/audio_classification_finetuning)
+[Animal Sound Classification](https://github.com/rawbeen248/audio_classification_finetuning)
+
+
+### To try it locally
+
+```
+import librosa
+import torch
+from transformers import HubertForSequenceClassification, Wav2Vec2FeatureExtractor
+
+# Load the fine-tuned model and feature extractor
+model_name = "ardneebwar/wav2vec2-animal-sounds-finetuned-hubert-finetuned-animals"
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
+model = HubertForSequenceClassification.from_pretrained(model_name)
+
+# Prepare the device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()  # Set the model to evaluation mode
+
+# Function to predict the class of an audio file
+def predict_audio_class(audio_file, feature_extractor, model, device):
+    # Load and preprocess the audio file
+    speech, sr = librosa.load(audio_file, sr=16000)
+    input_values = feature_extractor(speech, return_tensors="pt", sampling_rate=16000).input_values
+    input_values = input_values.to(device)
+
+    # Predict
+    with torch.no_grad():
+        logits = model(input_values).logits
+
+    # Get the predicted class ID
+    predicted_id = torch.argmax(logits, dim=-1)
+    # Convert the predicted ID to the class name
+    predicted_class = model.config.id2label[predicted_id.item()]
+    
+    return predicted_class
+
+# Replace 'path_to_your_new_audio_file.wav' with the actual path to the new audio file
+audio_file_path = "path_to_audio_file.wav"
+predicted_class = predict_audio_class(audio_file_path, feature_extractor, model, device)
+print(f"Predicted class: {predicted_class}")
+
+```