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# This code is supporting material for the book

# Building Machine Learning Systems with Python

# by Willi Richert and Luis Pedro Coelho

# published by PACKT Publishing

#

# It is made available under the MIT License

import os

import glob

import sys

import numpy as np

import scipy

import scipy.io.wavfile

from scikits.talkbox.features import mfcc

from utils import GENRE_DIR

def write_ceps(ceps, fn):

"""

Write the MFCC to separate files to speed up processing.

"""

base_fn, ext = os.path.splitext(fn)

data_fn = base_fn + ".ceps"

np.save(data_fn, ceps)

print("Written %s"%data_fn)

def create_ceps(fn):

sample_rate, X = scipy.io.wavfile.read(fn)

ceps, mspec, spec = mfcc(X)

write_ceps(ceps, fn)

def read_ceps(genre_list, base_dir=GENRE_DIR):

X = []

y = []

for label, genre in enumerate(genre_list):

for fn in glob.glob(os.path.join(base_dir, genre, "*.ceps.npy")):

ceps = np.load(fn)

num_ceps = len(ceps)

X.append(

np.mean(ceps[int(num_ceps / 10):int(num_ceps * 9 / 10)], axis=0))

y.append(label)

return np.array(X), np.array(y)

if __name__ == "__main__":

os.chdir(GENRE_DIR)

glob_wav = os.path.join(sys.argv[1], "*.wav")

print(glob_wav)

for fn in glob.glob(glob_wav):

create_ceps(fn)