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preprocess_for_audio_genre_classification.py
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preprocess_for_audio_genre_classification.py
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import os
import librosa
import math
import json
DATASET_PATH = 'genres_original'
JSON_PATH = 'data.json'
SAMPLE_RATE = 22050
DURATION = 30 # measured in seconds
SAMPLES_PER_TRACK = SAMPLE_RATE * DURATION
def save_mfcc(dataset_path, json_path, n_mfcc=23, n_fft=2048, hop_length=512, num_segments=10):
# dictionary to store data
data = {
"mapping": [],
"mfcc": [],
"labels": []
}
num_samples_per_segment = int(SAMPLES_PER_TRACK / num_segments)
expected_num_mfcc_vectors_per_segment = math.ceil(num_samples_per_segment / hop_length)
# loop through all the genres
for i, (dirpath, dirnames, filenames) in enumerate(os.walk(dataset_path)):
# ensure that we are not at the root level
if dirpath is not dataset_path:
# save the semantic label
dirpath_components = dirpath.split("/") # genre/blues => ['genre', 'blues']
semantic_label = dirpath_components[-1]
data['mapping'].append(semantic_label)
print("\n\nProcessing {}".format(semantic_label))
# process files for a specific genre
for f in filenames:
# load audio file
file_path = os.path.join(dirpath, f)
signal, sr = librosa.load(file_path, sr=SAMPLE_RATE)
# process segments, extracting mfcc and storing data
for s in range(num_segments):
start_sample = num_samples_per_segment * s
finish_sample = start_sample + num_samples_per_segment
mfcc = librosa.feature.mfcc(signal[start_sample:finish_sample],
sr=sr,
n_fft=n_fft,
n_mfcc=n_mfcc,
hop_length=hop_length)
mfcc = mfcc.T
# store mfcc for segment if it has the expected length
if len(mfcc) == expected_num_mfcc_vectors_per_segment:
data['mfcc'].append(mfcc.tolist())
data['labels'].append(i-1)
print("{}, segment:{}".format(file_path, s))
with open(json_path, 'w') as fp:
json.dump(data, fp, indent=4)
if __name__ == "__main__":
save_mfcc(DATASET_PATH, JSON_PATH, num_segments=10)