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e42b364 385b8b6 e42b364 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | import librosa
import joblib
import numpy as np
def features_extractor(file):
audio,sample_rate = librosa.load(file,duration=2)
mfccs_features = librosa.feature.mfcc(y=audio,sr=sample_rate,n_mfcc=40)
mfccs_scaled_features = np.mean(mfccs_features.T,axis=0)
return mfccs_scaled_features
def extract_audio_features(y, sr):
features = {}
# RMS Energy
rms = librosa.feature.rms(y=y)
features['rms_mean'] = np.mean(rms)
features['rms_var'] = np.var(rms)
# Spectral Rolloff
rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
features['rolloff_mean'] = np.mean(rolloff)
features['rolloff_var'] = np.var(rolloff)
# Spectral Centroid
spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)
features['spectral_centroid_mean'] = np.mean(spectral_centroid)
features['spectral_centroid_var'] = np.var(spectral_centroid)
# Zero Crossing Rate
zcr = librosa.feature.zero_crossing_rate(y)
features['zero_crossing_rate_mean'] = np.mean(zcr)
features['zero_crossing_rate_var'] = np.var(zcr)
# Harmony
harmony = librosa.effects.harmonic(y)
features['harmony_mean'] = np.mean(harmony)
features['harmony_var'] = np.var(harmony)
# Tempo
onset_env = librosa.onset.onset_strength(y=y, sr=sr)
tempo, _ = librosa.beat.beat_track(onset_envelope=onset_env, sr=sr)
features['tempo'] = tempo
return features
fit = joblib.load('models/preprocessing/scaler.pkl')
def predict_audio_class(model, audio_path):
"""
Predicts the genre for a wav audio file
"""
# Load the audio
y, sr = librosa.load(audio_path, sr=None)
# Extract features
features = extract_audio_features(y, sr)
# Convert features to a numpy array (in correct order)
feature_array = np.array([
features['rms_mean'],
features['rms_var'],
features['rolloff_mean'],
features['spectral_centroid_mean'],
features['spectral_centroid_var'],
features['rolloff_var'],
features['zero_crossing_rate_mean'],
features['zero_crossing_rate_var'],
features['harmony_mean'],
features['harmony_var'],
features['tempo'][0]
]).reshape(1, -1) # Reshape to 2D array for model input
# Predict with the model
z = fit.transform(feature_array)
print(z)
prediction = model.predict(z)
return prediction
from pydub import AudioSegment
def convert_mp3_to_wav(mp3_file, wav_file):
# Load the MP3 file
audio = AudioSegment.from_mp3(mp3_file)
# Export as WAV
audio.export(wav_file, format='wav')
####LSTM Model Functions
genres = {
0: 'blues', 1: 'classical', 2: 'country', 3: 'disco', 4: 'hiphop',
5: 'metal', 6: 'pop', 7: 'reggae', 8: 'rock', 9: 'jazz'
}
def predict_genre_LSTM(mp3_file,model):
# Convert mp3 to wav
wav_file = "temp_converted_audio.wav"
convert_mp3_to_wav(mp3_file, wav_file)
# X = np.array(combined_df['feature'].tolist())
# y = np.array(combined_df['class'].tolist())
# labelencoder = LabelEncoder()
# y = to_categorical(labelencoder.fit_transform(y))
# X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)
# X_train = np.expand_dims(X_train, axis=-1) # One timestep per sample
# X_test = np.expand_dims(X_test, axis=-1)
# Extract features and make prediction
features = features_extractor(wav_file)
features = np.expand_dims(features, axis=0)
features = np.expand_dims(features, axis=-1)
prediction = model.predict(features)
return prediction |