forked from Open-CT/opendata
52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
import librosa
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import numpy as np
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import os
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import csv
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def extract_audio_feature(path):
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y, sr = librosa.load(path, mono=True, sr=None)
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chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
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rmse = librosa.feature.rms(y=y)
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spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr)
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spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr)
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rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
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zcr = librosa.feature.zero_crossing_rate(y)
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mfcc = librosa.feature.mfcc(y=y, sr=sr)
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features = f'{np.mean(chroma_stft)} {np.mean(rmse)} {np.mean(spec_cent)} {np.mean(spec_bw)} {np.mean(rolloff)} {np.mean(zcr)}'
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for e in mfcc:
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features += f' {np.mean(e)}'
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return features
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def preprocess_ravdess():
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header = 'filename chroma_stft rmse spectral_centroid spectral_bandwidth rolloff zero_crossing_rate'
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for i in range(1, 21):
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header += f' mfcc{i}'
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header += ' label'
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header = header.split()
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file = open('../Output/only_english.csv', 'w', newline='')
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with file:
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writer = csv.writer(file)
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writer.writerow(header)
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emotions = 'neutral calm happy sad angry fear disgust surprise'.split()
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channels = 'Song Speech'.split()
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for channel in channels:
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for actor in os.listdir(f'../AudioData/Audio_{channel}_Actors_01-24'):
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for file in os.listdir(f'../AudioData/Audio_{channel}_Actors_01-24/{actor}'):
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audio_path = f'../AudioData/Audio_{channel}_Actors_01-24/{actor}/{file}'
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to_append = f'{file}'
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features = extract_audio_feature(audio_path)
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to_append += f' {features}'
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if int(file.split('-')[2]) - 1 not in [1,6]:
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emotion = emotions[int(file.split('-')[2]) - 1]
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to_append += f' {emotion}'
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file = open('../Output/only_english.csv', 'a', newline='')
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with file:
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writer = csv.writer(file)
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writer.writerow(to_append.split())
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if __name__ == '__main__':
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preprocess_ravdess() |