diff --git a/.gitignore b/.gitignore index f8b2103..672587c 100644 --- a/.gitignore +++ b/.gitignore @@ -24,4 +24,8 @@ hs_err_pid* replay_pid* # pycache -*.pyc \ No newline at end of file +*.pyc + +# +/.conda +/.venv \ No newline at end of file diff --git a/Audio_Classifier.py b/Audio_Classifier.py index de24404..682e026 100644 --- a/Audio_Classifier.py +++ b/Audio_Classifier.py @@ -7,6 +7,7 @@ # Option 1 # Create a virtual environment +# $ python -m venv .venv # Activate the virtual environment # Run "python install_packages.py" to install the necessary packages @@ -32,6 +33,7 @@ from feature_extraction.Feature_Extractor import generate_csv from classifier.classifier import train_model import joblib +from sklearn.metrics import classification_report class AudioClassifierGUI: def __init__(self, root): @@ -107,11 +109,15 @@ def load_folder(self): def extract_features_and_train(self, folder_path): generate_csv(folder_path) x_train, x_test, y_train, y_test, self.model, self.train_files, self.test_files, self.test_labels = train_model() + # Save the model for later use joblib.dump(self.model, 'audio_classifier_model.pkl') - self.update_file_lists() + # Evaluate after training + self.evaluate_model_on_test_set() + + def update_file_lists(self): # Clear existing lists self.train_listbox.delete(0, tk.END) @@ -187,6 +193,42 @@ def play_audio(self): # Play the audio file using pygame pygame.mixer.music.load(file_path) pygame.mixer.music.play() + + + def evaluate_model_on_test_set(self): + if not self.model: + self.model = joblib.load('audio_classifier_model.pkl') + scaler = joblib.load('scaler.pkl') + + # Load all feature data + features_data = pd.read_csv('feature_extraction/features.csv') + + y_true = [] + y_pred = [] + + for file in self.test_files: + row = features_data[features_data['fileName'] == file] + if not row.empty and file in self.test_labels: + features = row.drop(["Label", "fileName"], axis=1) + normalized_features = scaler.transform(features) + prediction = self.model.predict(normalized_features)[0] + + # Store ground truth and prediction + y_pred.append(prediction) + y_true.append(1 if self.test_labels[file] == "yes" else 0) + else: + print(f"Skipping {file} — missing features or label.") + + # Create and print/save the report + report = classification_report(y_true, y_pred, target_names=["Speech", "Music"]) + + print("\n=== Evaluation Metrics ===") + print(report) + + # Save to file + with open("evaluation_metrics.txt", "w") as f: + f.write("=== Evaluation Metrics ===\n") + f.write(report) def print_summary(self): print("File, Model Output, Ground Truth Label") diff --git a/SVM_model.pkl b/SVM_model.pkl index 0064a76..f09dc8b 100644 Binary files a/SVM_model.pkl and b/SVM_model.pkl differ diff --git a/audio_classifier_model.pkl b/audio_classifier_model.pkl index 3a79aeb..89cf31c 100644 Binary files a/audio_classifier_model.pkl and b/audio_classifier_model.pkl differ diff --git a/evaluation_metrics.txt b/evaluation_metrics.txt new file 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print("Checking and installing necessary Python packages for the Audio Classifier project...") - packages = ["pygame", "librosa", "scikit-learn"] + packages = ["pygame", "librosa", "scikit-learn", "pandas"] for package in packages: if is_package_installed(package): diff --git a/scaler.pkl b/scaler.pkl index e6ccd79..6ec589c 100644 Binary files a/scaler.pkl and b/scaler.pkl differ