Update

app.py

@@ -3,63 +3,65 @@ import librosa
 import numpy as np
 import torch
 import logging
-from transformers import AutoModelForAudioClassification
 import soundfile as sf
+from transformers import AutoModelForAudioClassification
+from librosa.effects import pitch_shift, time_stretch
 
-# Configure logging for debugging and information
+# Enhanced logging for detailed debugging and information
 logging.basicConfig(level=logging.INFO)
 
-# Load the model
+# Load your model (Ensure the model path is correct)
 model_path = "./"
 model = AutoModelForAudioClassification.from_pretrained(model_path)
 
-def augment_and_extract_features(audio_path, output_path=None, sr=16000, n_mfcc=40, n_fft=2048, hop_length=512):
-    # Load and augment the audio file
+def augment_and_extract_features(audio_path, sr=16000, n_mfcc=40, pitch_shift_steps=4, stretch_rate=1.2):
+    """
+    Load, augment, and extract comprehensive features from an audio file.
+    Features: MFCC, Chroma, Mel Spectrogram, Spectral Contrast, Tonnetz.
+    Augmentations: Pitch Shift and Time Stretch for nuanced variability.
+    """
+    # Load audio
     y, sr = librosa.load(audio_path, sr=sr)
-    y_augmented = librosa.effects.pitch_shift(y, sr, n_steps=4)  # Pitch shifting
-    y_augmented = librosa.effects.time_stretch(y_augmented, rate=1.2)  # Time stretching
-    
-    # Save the augmented audio if an output path is provided
-    if output_path is not None:
-        sf.write(output_path, y_augmented, sr)
-    
-    # Extract features
-    mfcc = librosa.feature.mfcc(y=y_augmented, sr=sr, n_mfcc=n_mfcc, n_fft=n_fft, hop_length=hop_length)
-    chroma = librosa.feature.chroma_stft(y=y_augmented, sr=sr, n_fft=n_fft, hop_length=hop_length)
-    mel = librosa.feature.melspectrogram(y=y_augmented, sr=sr, n_fft=n_fft, hop_length=hop_length)
-    contrast = librosa.feature.spectral_contrast(y=y_augmented, sr=sr, n_fft=n_fft, hop_length=hop_length)
-    tonnetz = librosa.feature.tonnetz(y=librosa.effects.harmonic(y_augmented), sr=sr)
-    
-    # Combine all features
-    features = np.concatenate((mfcc, chroma, mel, contrast, tonnetz), axis=0)
-    
-    # Normalize features
-    features = (features - np.mean(features, axis=1, keepdims=True)) / np.std(features, axis=1, keepdims=True)
-    
-    # Convert to tensor
-    features_tensor = torch.tensor(features).float().unsqueeze(0)  # Add batch dimension
+
+    # Audio Augmentation
+    y_pitched = pitch_shift(y, sr=sr, n_steps=pitch_shift_steps)  # Apply pitch shift
+    y_stretched = time_stretch(y_pitched, rate=stretch_rate)  # Apply time stretch
+
+    # Feature Extraction
+    mfcc = librosa.feature.mfcc(y=y_stretched, sr=sr, n_mfcc=n_mfcc)
+    chroma = librosa.feature.chroma_stft(y=y_stretched, sr=sr)
+    mel = librosa.feature.melspectrogram(y=y_stretched, sr=sr)
+    contrast = librosa.feature.spectral_contrast(y=y_stretched, sr=sr)
+    tonnetz = librosa.feature.tonnetz(y=librosa.effects.harmonic(y_stretched), sr=sr)
+
+    # Aggregate features
+    features = np.concatenate([mfcc, chroma, mel, contrast, tonnetz], axis=0)
+    features_normalized = StandardScaler().fit_transform(features.T).T  # Normalize
+    features_tensor = torch.tensor(features_normalized).float().unsqueeze(0)  # Tensor conversion
+
     return features_tensor
 
 def predict_voice(audio_file_path):
+    """
+    Make a voice authenticity prediction based on augmented and extracted audio features.
+    """
     try:
         features_tensor = augment_and_extract_features(audio_file_path)
-        
         with torch.no_grad():
             outputs = model(features_tensor)
-        
         logits = outputs.logits
         predicted_index = logits.argmax()
         label = model.config.id2label[predicted_index.item()]
         confidence = torch.softmax(logits, dim=1).max().item() * 100
-        
-        result = f"The voice is classified as '{label}' with a confidence of {confidence:.2f}%."
-        logging.info("Prediction successful.")
+        result = f"Prediction: {label} with {confidence:.2f}% confidence."
+        logging.info(result)
+        return result
     except Exception as e:
-        result = f"Error during processing: {e}"
-        logging.error(result)
-    
-    return result
+        error_msg = f"Error during processing: {e}"
+        logging.error(error_msg)
+        return error_msg
 
+# Gradio interface for easy and interactive model testing
 iface = gr.Interface(
     fn=predict_voice,
     inputs=gr.Audio(label="Upload Audio File", type="filepath"),
@@ -69,3 +71,4 @@ iface = gr.Interface(
 )
 
 iface.launch()
+