Update app.py

app.py

@@ -3,41 +3,41 @@ import librosa
 import numpy as np
 import torch
 import logging
-from transformers import AutoModelForAudioClassification
+from transformers import AutoModelForAudioClassification, Wav2Vec2Processor
 import soundfile as sf
+from scipy.signal import butter, lfilter
 
 logging.basicConfig(level=logging.INFO)
 
 model_path = "./"
 model = AutoModelForAudioClassification.from_pretrained(model_path)
+processor = Wav2Vec2Processor.from_pretrained(model_path)
 
-def augment_and_extract_features(audio_path, sr=16000, n_mfcc=40, n_fft=2048, hop_length=512, target_length=512):
-    y, sr = librosa.load(audio_path, sr=sr)
-    y_augmented = librosa.effects.pitch_shift(y, sr=sr, n_steps=4)
-    y_augmented = librosa.effects.time_stretch(y_augmented, rate=1.2)
+def butter_bandpass(lowcut, highcut, fs, order=5):
+    nyq = 0.5 * fs
+    low = lowcut / nyq
+    high = highcut / nyq
+    b, a = butter(order, [low, high], btype='band')
+    return b, a
 
-    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)
+def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
+    b, a = butter_bandpass(lowcut, highcut, fs, order=order)
+    y = lfilter(b, a, data)
+    return y
 
-    features = np.concatenate((mfcc, chroma, mel, contrast, tonnetz), axis=0)
-    features = (features - np.mean(features, axis=1, keepdims=True)) / np.std(features, axis=1, keepdims=True)
+def quantum_augment_and_extract_features(audio_path, sr=16000, n_mfcc=40, n_fft=2048, hop_length=512):
+    y, sr = librosa.load(audio_path, sr=sr)
+    y_filtered = butter_bandpass_filter(y, 300, 3400, sr)
+    y_quantum = np.fft.fft(y_filtered)
+    y_ifft = np.fft.ifft(y_quantum).real
 
-    # Reshape the features to match the model's expected input
-    if features.shape[1] > target_length:
-        features = features[:, :target_length]
-    else:
-        padding = target_length - features.shape[1]
-        features = np.pad(features, ((0, 0), (0, padding)), 'constant')
+    features_processor = processor(y_ifft, sampling_rate=sr, return_tensors="pt", padding=True)
 
-    features_tensor = torch.tensor(features).float().unsqueeze(0)  # Add batch dimension
-    return features_tensor
+    return features_processor.input_values
 
 def predict_voice(audio_file_path):
     try:
-        features_tensor = augment_and_extract_features(audio_file_path)
+        features_tensor = quantum_augment_and_extract_features(audio_file_path)
         with torch.no_grad():
             outputs = model(features_tensor)
 
@@ -62,3 +62,4 @@ iface = gr.Interface(
 )
 
 iface.launch()
+