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data_processing.py
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data_processing.py
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import os
import pandas as pd
import librosa
def data_preprocessing():
# Converts .WAV files into data and returns dataframe to main
# Get directories to two music folders
path_not = os.getcwd() + '/Audio/Not VN'
path_vn = os.getcwd() + '/Audio/VN'
# Find the total number of files in each
total_not = len(os.listdir(path_not))
total_vn = len(os.listdir(path_vn))
# Convert .WAV files to dataframes
dataframe = pd.DataFrame(columns=['Classifier', 'Audio'])
songs = []
for file in os.listdir(path_not):
data, sampling_rate = librosa.load(path_not + '/' + file)
songs.append(data)
for file in os.listdir(path_vn):
data, sampling_rate = librosa.load(path_vn + '/' + file)
songs.append(data)
classification = [0] * total_not + [1] * total_vn
dataframe.Classifier = classification
dataframe.Audio = songs
return dataframe
dataset = data_preprocessing()
dataset.to_csv(os.getcwd() + '/AudioData.csv', index=False)