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preprocess_data.py
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preprocess_data.py
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# -*- coding: utf-8 -*-
import numpy as np
import glob
import os
import sys
import scipy.io.wavfile as wavf
import scipy.signal
import h5py
import json
import librosa
import multiprocessing
import argparse
def preprocess_data(src, dst, src_meta, n_processes=15):
"""
Calls for distibuted preprocessing of the data.
Parameters:
-----------
src: string
Path to data directory.
dst: string
Path to directory where preprocessed data shall be stored.
stc_meta: string
Path to meta_information file.
n_processes: int
number of simultaneous processes to use for data preprocessing.
"""
folders = []
for folder in os.listdir(src):
# only process folders
if not os.path.isdir(os.path.join(src, folder)):
continue
folders.append(folder)
pool=multiprocessing.Pool(processes=n_processes)
_=pool.map(_preprocess_data, [(os.path.join(src, folder),
os.path.join(dst, folder),
src_meta) for folder in sorted(folders)])
def _preprocess_data(src_dst_meta):
"""
Preprocessing for all data files in given directory.
Preprocessing includes:
AlexNet: resampling to 8000 Hz,
embedding in zero vector,
transformation to amplitute spectrogram representation in dB.
AudioNet: resampling to 8000 Hz,
embedding in zero vector,
normalization at 95th percentile.
Preprocessed data will be stored in hdf5 files with one datum per file.
In terms of I/O, this is not very efficient but it allows to easily change
training, validation, and test sets without re-preprocessing or redundant
storage of preprocessed files.
Parameters:
-----------
src_dst_meta: tuple of 3 strings
Tuple (path to data directory, path to destination directory, path
to meta file)
"""
src, dst, src_meta = src_dst_meta
print("processing {}".format(src))
metaData = json.load(open(src_meta))
# create folder for hdf5 files
if not os.path.exists(dst):
os.makedirs(dst)
# loop over recordings
for filepath in sorted(glob.glob(os.path.join(src, "*.wav"))):
# infer sample info from name
dig, vp, rep = filepath.rstrip(".wav").split("/")[-1].split("_")
# read data
fs, data = wavf.read(filepath)
# resample
data = librosa.core.resample(y=data.astype(np.float32), orig_sr=fs, target_sr=8000, res_type="scipy")
# zero padding
if len(data) > 8000:
raise ValueError("data length cannot exceed padding length.")
elif len(data) < 8000:
embedded_data = np.zeros(8000)
offset = np.random.randint(low = 0, high = 8000 - len(data))
embedded_data[offset:offset+len(data)] = data
elif len(data) == 8000:
# nothing to do here
embedded_data = data
pass
##### AlexNet #####
# stft, with seleced parameters, spectrogram will have shape (228,230)
f, t, Zxx = scipy.signal.stft(embedded_data, 8000, nperseg = 455, noverlap = 420, window='hann')
# get amplitude
Zxx = np.abs(Zxx[0:227, 2:-1])
Zxx = np.atleast_3d(Zxx).transpose(2,0,1)
# convert to decibel
Zxx = librosa.amplitude_to_db(Zxx, ref = np.max)
# save as hdf5 file
with h5py.File(os.path.join(dst, "AlexNet_{}_{}_{}.hdf5".format(dig, vp, rep)), "w") as f:
tmp_X = np.zeros([1, 1, 227, 227])
tmp_X[0, 0] = Zxx
f['data'] = tmp_X
f['label'] = np.array([[int(dig), 0 if metaData[vp]["gender"] == "male" else 1]])
##### AudioNet #####
embedded_data /= (np.percentile(embedded_data, 95) + 0.001)
with h5py.File(os.path.join(dst, "AudioNet_{}_{}_{}.hdf5".format(dig, vp, rep)), "w") as f:
tmp_X = np.zeros([1, 1, 1, 8000])
tmp_X[0, 0, 0] = embedded_data
f['data'] = tmp_X
f['label'] = np.array([[int(dig), 0 if metaData[vp]["gender"] == "male" else 1]])
return
def create_splits(src, dst):
"""
Creation of text files specifying which files training, validation and test
set consist of for each cross-validation split.
Parameters:
-----------
src: string
Path to directory containing the directories for each subject that
hold the preprocessed data in hdf5 format.
dst: string
Destination where to store the text files specifying training,
validation and test splits.
"""
print("creating splits")
splits={"digit":{ "train":[ set([28, 56, 7, 19, 35, 1, 6, 16, 23, 34, 46, 53, 36, 57, 9, 24, 37, 2, \
8, 17, 29, 39, 48, 54, 43, 58, 14, 25, 38, 3, 10, 20, 30, 40, 49, 55]),
set([36, 57, 9, 24, 37, 2, 8, 17, 29, 39, 48, 54, 43, 58, 14, 25, 38, 3, \
10, 20, 30, 40, 49, 55, 12, 47, 59, 15, 27, 41, 4, 11, 21, 31, 44, 50]),
set([43, 58, 14, 25, 38, 3, 10, 20, 30, 40, 49, 55, 12, 47, 59, 15, 27, 41, \
4, 11, 21, 31, 44, 50, 26, 52, 60, 18, 32, 42, 5, 13, 22, 33, 45, 51]),
set([12, 47, 59, 15, 27, 41, 4, 11, 21, 31, 44, 50, 26, 52, 60, 18, 32, 42, \
5, 13, 22, 33, 45, 51, 28, 56, 7, 19, 35, 1, 6, 16, 23, 34, 46, 53]),
set([26, 52, 60, 18, 32, 42, 5, 13, 22, 33, 45, 51, 28, 56, 7, 19, 35, 1, \
6, 16, 23, 34, 46, 53, 36, 57, 9, 24, 37, 2, 8, 17, 29, 39, 48, 54])],
"validate":[set([12, 47, 59, 15, 27, 41, 4, 11, 21, 31, 44, 50]),
set([26, 52, 60, 18, 32, 42, 5, 13, 22, 33, 45, 51]),
set([28, 56, 7, 19, 35, 1, 6, 16, 23, 34, 46, 53]),
set([36, 57, 9, 24, 37, 2, 8, 17, 29, 39, 48, 54]),
set([43, 58, 14, 25, 38, 3, 10, 20, 30, 40, 49, 55])],
"test":[ set([26, 52, 60, 18, 32, 42, 5, 13, 22, 33, 45, 51]),
set([28, 56, 7, 19, 35, 1, 6, 16, 23, 34, 46, 53]),
set([36, 57, 9, 24, 37, 2, 8, 17, 29, 39, 48, 54]),
set([43, 58, 14, 25, 38, 3, 10, 20, 30, 40, 49, 55]),
set([12, 47, 59, 15, 27, 41, 4, 11, 21, 31, 44, 50])]},
"gender":{ "train":[ set([36, 47, 56, 26, 12, 57, 2, 44, 50, 25, 37, 45]),
set([26, 12, 57, 43, 28, 52, 25, 37, 45, 48, 53, 41]),
set([43, 28, 52, 58, 59, 60, 48, 53, 41, 7, 23, 38]),
set([58, 59, 60, 36, 47, 56, 7, 23, 38, 2, 44, 50])],
"validate":[set([43, 28, 52, 48, 53, 41]),
set([58, 59, 60, 7, 23, 38]),
set([36, 47, 56, 2, 44, 50]),
set([26, 12, 57, 25, 37, 45])],
"test":[ set([58, 59, 60, 7, 23, 38]),
set([36, 47, 56, 2, 44, 50]),
set([26, 12, 57, 25, 37, 45]),
set([43, 28, 52, 48, 53, 41])]}}
for split in range(5):
for modus in ["train", "validate", "test"]:
for task in ["digit", "gender"]:
if task == "gender" and split > 3:
continue
with open(os.path.join(dst, "AlexNet_{}_{}_{}.txt".format(task, split, modus)), mode = "w") as txt_file:
for vp in splits[task][modus][split]:
for filepath in glob.glob(os.path.join(src, "{:02d}".format(vp), "AlexNet*.hdf5")):
txt_file.write(filepath+"\n")
with open(os.path.join(dst, "AudioNet_{}_{}_{}.txt".format(task, split, modus)), mode = "w") as txt_file:
for vp in splits[task][modus][split]:
for filepath in glob.glob(os.path.join(src, "{:02d}".format(vp), "AudioNet*.hdf5")):
txt_file.write(filepath+"\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--source', '-src', default=os.path.join(os.getcwd(), "reduced_data"), help="Path to folder containing each participant's data directory.")
parser.add_argument('--destination', '-dst', default=os.path.join(os.getcwd(), "preprocessed_data"), help="Destination where preprocessed data shall be stored.")
parser.add_argument('--meta', '-m', default=os.path.join(os.getcwd(), "reduced_data", "audioMNIST_meta.txt"), help="Path to meta_information json file.")
args = parser.parse_args()
# preprocessing
preprocess_data(src=args.source, dst=args.destination, src_meta=args.meta)
# create training, validation and test sets
create_splits(src=args.destination, dst=args.destination)