Update utils.py

utils.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 import librosa
 import numpy as np
 from keras import layers, models
@@ -112,126 +111,4 @@ def stretch(data, rate=0.8):
     return librosa.effects.time_stretch(data, rate=rate)
 
 def pitch(data, sample_rate, pitch_factor=0.7):
-    return librosa.effects.pitch_shift(data, sr=sample_rate, n_steps=pitch_factor)
-
-
-
-=======
-import librosa
-import numpy as np
-from keras import layers, models
-
-def create_cnn_model(input_shape):
-    model = models.Sequential()
-
-    # First Convolutional Layer
-    model.add(layers.Conv1D(32, 3, activation='relu', input_shape=input_shape))
-    model.add(layers.MaxPooling1D(pool_size=2))
-
-    # Second Convolutional Layer
-    model.add(layers.Conv1D(64, 3, activation='relu'))
-    model.add(layers.MaxPooling1D(pool_size=2))
-
-    # Flatten layer
-    model.add(layers.Flatten())
-
-    # Dense layers
-    model.add(layers.Dense(128, activation='relu', input_shape=input_shape))
-    model.add(layers.Dense(256, activation='relu', input_shape=input_shape))
-    model.add(layers.Dense(512, activation='relu', input_shape=input_shape))
-    model.add(layers.Dense(512, activation='relu', input_shape=input_shape))
-    model.add(layers.Dense(256, activation='relu', input_shape=input_shape))
-    model.add(layers.Dense(128, activation='relu', input_shape=input_shape))
-
-    # Output layer
-    model.add(layers.Dense(1, activation='sigmoid'))
-
-    return model
-
-
-def get_features(path, duration=6):
-    try:
-        # Load audio file with specific duration and offset to handle silent parts
-        data, sample_rate = librosa.load(path, duration=2.5, offset=0.6)
-    except Exception as e:
-        print(f"Error loading {path}: {e}")
-        return None  # Skip the file if there's an error
-
-    # Without augmentation
-    res1 = extract_features(data, sample_rate)
-    result = np.array(res1)
-
-    # With noise
-    noise_data = noise(data)
-    res2 = extract_features(noise_data, sample_rate)
-    result = np.vstack((result, res2))
-
-    # Stretching and pitching
-    new_data = stretch(data)
-    data_stretch_pitch = pitch(new_data, sample_rate)
-    res3 = extract_features(data_stretch_pitch, sample_rate)
-    result = np.vstack((result, res3))
-
-    return result
-
-
-def extract_features(data, sample_rate, target_shape=40):
-    result = np.array([])
-
-    # ZCR
-    zcr = librosa.feature.zero_crossing_rate(y=data)
-    zcr = np.mean(zcr.T, axis=0)
-    zcr = pad_or_trim(zcr, target_shape)
-    result = np.hstack((result, zcr))
-
-    # Chroma_stft
-    stft = np.abs(librosa.stft(data))
-    chroma_stft = librosa.feature.chroma_stft(S=stft, sr=sample_rate)
-    chroma_stft = np.mean(chroma_stft.T, axis=0)
-    chroma_stft = pad_or_trim(chroma_stft, target_shape)
-    result = np.hstack((result, chroma_stft))
-
-    # MFCC
-    mfcc = librosa.feature.mfcc(y=data, sr=sample_rate, n_mfcc=13)
-    mfcc = np.mean(mfcc.T, axis=0)
-    mfcc = pad_or_trim(mfcc, target_shape)
-    result = np.hstack((result, mfcc))
-
-    # Root Mean Square Value
-    rms = librosa.feature.rms(y=data)
-    rms = np.mean(rms.T, axis=0)
-    rms = pad_or_trim(rms, target_shape)
-    result = np.hstack((result, rms))
-
-    # MelSpectrogram
-    mel = librosa.feature.melspectrogram(y=data, sr=sample_rate)
-    mel = np.mean(mel.T, axis=0)
-    mel = pad_or_trim(mel, target_shape)
-    result = np.hstack((result, mel))
-
-    return result
-
-
-def pad_or_trim(feature, target_shape):
-    """Pad or trim feature array to ensure a consistent shape."""
-    if len(feature) > target_shape:
-        feature = feature[:target_shape]
-    elif len(feature) < target_shape:
-        feature = np.pad(feature, (0, target_shape - len(feature)), mode='constant')
-    return feature
-
-
-def noise(data, noise_factor=0.005):
-    noise_amp = noise_factor * np.random.uniform() * np.amax(data)
-    data = data + noise_amp * np.random.normal(size=data.shape[0])
-    return data
-
-def stretch(data, rate=0.8):
-    return librosa.effects.time_stretch(data, rate=rate)
-
-def pitch(data, sample_rate, pitch_factor=0.7):
-    return librosa.effects.pitch_shift(data, sr=sample_rate, n_steps=pitch_factor)
-
-
-
->>>>>>> f3090616676ed6b7fcf9d16589c788e1843b194c
+    return librosa.effects.pitch_shift(data, sr=sample_rate, n_steps=pitch_factor)