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import librosa |
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import numpy as np |
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import joblib |
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import soundfile as sf |
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scaler = joblib.load("./models/std_scaler(1).pkl") |
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def load_audio_from_uploaded_file(uploaded_file): |
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audio_data, sample_rate = sf.read(uploaded_file) |
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return audio_data, sample_rate |
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Fields = ['name', 'length', 'chroma_stft_mean', 'chroma_stft_var', 'rms_mean', 'rms_var', |
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'spectral_centroid_mean', 'spectral_centroid_var', 'spectral_bandwidth_mean', 'spectral_bandwidth_var', |
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'rolloff_mean', 'rolloff_var', 'zero_crossing_rate_mean', 'zero_crossing_rate_var', |
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'harmony_mean', 'harmony_var', 'percussive_mean', 'percussive_var', 'tempo', |
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'mfcc1_mean', 'mfcc1_var', 'mfcc2_mean', 'mfcc2_var', 'mfcc3_mean', 'mfcc3_var', 'mfcc4_mean', 'mfcc4_var', |
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'mfcc5_mean', 'mfcc5_var', 'mfcc6_mean', 'mfcc6_var', 'mfcc7_mean', 'mfcc7_var', 'mfcc8_mean', 'mfcc8_var', |
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'mfcc9_mean', 'mfcc9_var', 'mfcc10_mean', 'mfcc10_var', 'mfcc11_mean', 'mfcc11_var', 'mfcc12_mean', |
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'mfcc12_var', |
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'mfcc13_mean', 'mfcc13_var', 'mfcc14_mean', 'mfcc14_var', 'mfcc15_mean', 'mfcc15_var', 'mfcc16_mean', |
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'mfcc16_var', |
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'mfcc17_mean', 'mfcc17_var', 'mfcc18_mean', 'mfcc18_var', 'mfcc19_mean', 'mfcc19_var', 'mfcc20_mean', |
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'mfcc20_var'] |
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short_field = Fields[2:] |
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def all_feature_extraction(audio_path, sample_rate=22050): |
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data_list = [] |
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val_field = [] |
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audio_df, sr = librosa.load(audio_path, sr=22050) |
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data_list.append(audio_path) |
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data_list.append(len(audio_df)) |
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chroma_stft = librosa.feature.chroma_stft(y=audio_df, hop_length=512) |
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chroma_stft_mean = np.mean(chroma_stft) |
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chroma_stft_var = np.var(chroma_stft) |
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val_field.append(chroma_stft) |
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data_list.append(chroma_stft_mean) |
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data_list.append(chroma_stft_var) |
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rms = librosa.feature.rms(y=audio_df) |
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rms_mean = np.mean(rms) |
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rms_var = np.var(rms) |
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data_list.append(rms_mean) |
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data_list.append(rms_var) |
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spectral_centroid = librosa.feature.spectral_centroid(y=audio_df) |
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spectral_centroid_mean = np.mean(spectral_centroid) |
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spectral_centroid_var = np.var(spectral_centroid) |
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data_list.append(spectral_centroid_mean) |
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data_list.append(spectral_centroid_var) |
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spectral_bandwidth = librosa.feature.spectral_bandwidth(y=audio_df) |
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spectral_bandwidth_mean = np.mean(spectral_bandwidth) |
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spectral_bandwidth_var = np.var(spectral_bandwidth) |
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data_list.append(spectral_bandwidth_mean) |
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data_list.append(spectral_bandwidth_var) |
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spectral_rolloff = librosa.feature.spectral_rolloff(y=audio_df) |
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spectral_rolloff_mean = np.mean(spectral_rolloff) |
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spectral_rolloff_var = np.var(spectral_rolloff) |
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data_list.append(spectral_rolloff_mean) |
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data_list.append(spectral_rolloff_var) |
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zcr = librosa.feature.zero_crossing_rate(y=audio_df) |
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zcr_mean = np.mean(zcr) |
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zcr_var = np.var(zcr) |
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data_list.append(zcr_mean) |
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data_list.append(zcr_var) |
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harmonic, percussive = librosa.effects.hpss(y=audio_df) |
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harmonic_mean = np.mean(harmonic) |
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harmonic_var = np.var(harmonic) |
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percussive_mean = np.mean(percussive) |
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percussive_var = np.var(percussive) |
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data_list.append(harmonic_mean) |
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data_list.append(harmonic_var) |
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data_list.append(percussive_mean) |
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data_list.append(percussive_var) |
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tempo = librosa.feature.tempo(y=audio_df) |
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tempo = np.mean(tempo) |
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data_list.append(tempo) |
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mfccs = librosa.feature.mfcc(y=audio_df, sr=sr) |
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row_means = np.mean(mfccs, axis=1) |
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row_vars = np.var(mfccs, axis=1) |
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mfcc_means = {} |
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mfcc_vars = {} |
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for i in range(1, 21): |
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variable_name = f'mfcc{i}' |
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mfcc_means[variable_name] = row_means[i - 1] |
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mfcc_vars[variable_name] = row_vars[i - 1] |
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mfcc_list = [value for value in zip(mfcc_means.values(), mfcc_vars.values())] |
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for mean, var in mfcc_list: |
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data_list.append(mean) |
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data_list.append(var) |
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return [data_list,val_field] |
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def scale(initial_features): |
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final_features = initial_features[2:] |
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final_features = np.array(final_features) |
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scaled_data_point = scaler.transform([final_features]) |
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return scaled_data_point |
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