Commit

main.py

@@ -0,0 +1,90 @@
+import numpy as np
+import librosa
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score
+from sklearn.preprocessing import LabelEncoder
+import os
+import tkinter as tk
+from tkinter import filedialog, messagebox
+
+
+# Function to extract features from an audio file
+def extract_features(audio_path):
+    y, sr = librosa.load(audio_path, sr=None)
+
+    # Extract features
+    mfccs = np.mean(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13).T, axis=0)
+    chroma = np.mean(librosa.feature.chroma_stft(y=y, sr=sr).T, axis=0)
+    mel = np.mean(librosa.feature.melspectrogram(y=y, sr=sr).T, axis=0)
+    contrast = np.mean(librosa.feature.spectral_contrast(y=y, sr=sr).T, axis=0)
+
+    return np.hstack([mfccs, chroma, mel, contrast])
+
+
+# Example dataset
+audio_files = ['neutral.wav', 'happy.wav', 'sad.wav', 'mad.wav']
+audio_labels = ['neutral', 'happy', 'sad', 'mad']
+
+# Extract features and labels
+data = []
+labels = []
+
+for audio, label in zip(audio_files, audio_labels):
+    if os.path.exists(audio):
+        features = extract_features(audio)
+        data.append(features)
+        labels.append(label)
+    else:
+        print(f"File {audio} not found!")
+
+# Convert to numpy arrays
+X = np.array(data)
+y = np.array(labels)
+
+# Encode labels
+le = LabelEncoder()
+y_encoded = le.fit_transform(y)
+
+# Split the dataset
+X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42)
+
+# Train a Support Vector Classifier
+model = SVC(kernel='linear')
+model.fit(X_train, y_train)
+
+# Tkinter GUI setup
+root = tk.Tk()
+root.title("Voice Tone Detector")
+
+
+# Function to predict the tone of a new audio file
+def predict_tone(audio_path):
+    if os.path.exists(audio_path):
+        features = extract_features(audio_path)
+        features = features.reshape(1, -1)  # Reshape for a single prediction
+        prediction = model.predict(features)
+        return le.inverse_transform(prediction)[0]
+    else:
+        return "File not found!"
+
+
+# Function to open file dialog and display the tone
+def open_file():
+    file_path = filedialog.askopenfilename(filetypes=[("Audio Files", "*.wav")])
+    if file_path:
+        tone = predict_tone(file_path)
+        result_label.config(text=f"Predicted Tone: {tone}")
+    else:
+        messagebox.showwarning("Warning", "No file selected")
+
+
+# Tkinter widgets
+open_button = tk.Button(root, text="Open Audio File", command=open_file)
+open_button.pack(pady=20)
+
+result_label = tk.Label(root, text="Predicted Tone: None")
+result_label.pack(pady=20)
+
+# Start the Tkinter main loop
+root.mainloop()