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prepare_dataset_MFCC.py
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prepare_dataset_MFCC.py
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import librosa
import os
import json
dataset_path = 'dataset'
json_path = 'data.json'
SAMPLES_TO_CONSIDER = 22050 # Samples per second
def preprocess_dataset(dataset_path, json_path, num_mfcc=13, n_fft=2048, hop_length=512):
"""Extracts MFCCs from music dataset and saves them into a json file.
:param dataset_path (str): Path to dataset
:param json_path (str): Path to json file used to save MFCCs
:param num_mfcc (int): Number of coefficients to extract
:param n_fft (int): Interval we consider to apply FFT. Measured in # of samples
:param hop_length (int): Sliding window for FFT. Measured in # of samples
:return:
"""
# dictionary where we'll store mapping, labels, MFCCs and filenames
data = {
"mapping": [],
"labels": [],
"MFCCs": [],
"files": []
}
# loop through all sub-dirs
for i, (dirpath, dirnames, filenames) in enumerate(os.walk(dataset_path)):
# ensure we're at sub-folder level
if dirpath is not dataset_path:
# save label (i.e., sub-folder name) in the mapping
dirpath_components = dirpath.split("/") # Por ejemplo genre/blues => ["genre", "blues"]
label = dirpath_components[-1]
data["mapping"].append(label)
print("\nProcessing {}".format(label))
# process all audio files in sub-dir and store MFCCs
for f in filenames:
file_path = os.path.join(dirpath, f)
# load audio file and slice it to ensure length consistency among different files
signal, sample_rate = librosa.load(file_path)
# drop audio files with less than pre-decided number of samples
if len(signal) >= SAMPLES_TO_CONSIDER:
# ensure consistency of the length of the signal
signal = signal[:SAMPLES_TO_CONSIDER]
# extract MFCCs
MFCCs = librosa.feature.mfcc(signal, sample_rate, n_mfcc=num_mfcc, n_fft=n_fft,
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