# for preparing the segan training and test data
import tensorflow as tf
from data_ops import *
import scipy.io.wavfile as wavfile
import numpy as np
import os
import hdf5storage
def slice_1dsignal(signal, window_size, minlength, stride=0.5):
"""
Return windows of the given signal by sweeping in stride fractions
of window
Slices that are less than minlength are omitted
"""
n_samples = signal.shape[0]
offset = int(window_size * stride)
num_slices = (n_samples)
slices = np.array([]).reshape(0, window_size) # initialize empty array
for beg_i in range(0, n_samples, offset):
end_i = beg_i + window_size
if n_samples - beg_i < minlength :
break
if end_i <= n_samples :
slice_ = np.array([signal[beg_i:end_i]])
else :
slice_ = np.concatenate((np.array([signal[beg_i:]]), np.zeros((1, end_i - n_samples))), axis=1)
slices = np.concatenate((slices, slice_), axis=0)
return slices.astype('float32')
def read_and_slice1d(wavfilename, window_size, minlength, stride=0.5):
"""
Reads and slices the wavfile into windowed chunks
"""
fs, signal = wavfile.read(wavfilename)
if fs != 16000:
raise ValueError('Sampling rate is expected to be 16kHz!')
sliced = slice_1dsignal(signal, window_size, minlength, stride=stride)
return sliced
def prepare_sliced_data1d(opts):
wavfolder = opts['wavfolder']
window_size = opts['window_size']
stride = opts['stride']
minlength = opts['minlength']
filenames = opts['filenames']
full_sliced = [] # initialize empty list
dfi = []
dfi_begin = 0
with open(filenames) as f:
wav_files = f.read().splitlines() # to get rid of the \n while using readlines()
print ("**** Reading from " + wavfolder)
print ("**** The folder has " + str(len(wav_files)) + " files.")
for ind, wav_file in enumerate(wav_files):
if ind % 10 == 0 :
print("Processing " + str(ind) + " of " + str(len(wav_files)) + " files.")
wavfilename = os.path.join(wavfolder, wav_file)
sliced = read_and_slice1d(wavfilename, window_size, minlength, stride=stride)
full_sliced.append(sliced)
dfi.append(np.array([[dfi_begin, dfi_begin + sliced.shape[0]]]))
dfi_begin += sliced.shape[0]
full_slicedstack = np.vstack(full_sliced)
dfistack = np.vstack(dfi)
return full_slicedstack, dfistack.astype('int')
if __name__ == '__main__':
opts = {}
opts ['datafolder'] = "data"
opts ['window_size'] = 2**14
opts['stride']= 0.5
opts['minlength']= 0.5 * (2 ** 14)
testfilenames = os.path.join(opts['datafolder'], "test_wav.txt")
trainfilenames = os.path.join(opts['datafolder'], "train_wav.txt")
# for test set
opts['filenames'] = testfilenames
# for clean set
opts['wavfolder'] = os.path.join(opts['datafolder'], "clean_testset_wav_16kHz")
clean_test_sliced, dfi = prepare_sliced_data1d(opts)
# for noisy set
opts['wavfolder'] = os.path.join(opts['datafolder'], "noisy_testset_wav_16kHz")
noisy_test_sliced, dfi = prepare_sliced_data1d(opts)
if clean_test_sliced.shape[0] != noisy_test_sliced.shape[0] :
raise ValueError('Clean sliced and noisy sliced are not of the same size!')
if clean_test_sliced.shape[0] != dfi[-1,1] :
raise ValueError('Sliced matrices have a different size than mentioned in dfi !')
matcontent={}
matcontent[u'feat_data'] = clean_test_sliced
matcontent[u'dfi'] = dfi
destinationfilenameclean = "./data/clean_test_segan1d.mat"
hdf5storage.savemat(destinationfilenameclean, matcontent)
matcontent={}
matcontent[u'feat_data'] = noisy_test_sliced
matcontent[u'dfi'] = dfi
destinationfilenamenoisy = "./data/noisy_test_segan1d.mat"
hdf5storage.savemat(destinationfilenamenoisy, matcontent)
# for train set
opts['filenames'] = trainfilenames
# for clean set
opts['wavfolder'] = os.path.join(opts['datafolder'], "clean_trainset_56spk_wav_16kHz")
clean_train_sliced, dfi = prepare_sliced_data1d(opts)
# for noisy set
opts['wavfolder'] = os.path.join(opts['datafolder'], "noisy_trainset_56spk_wav_16kHz")
noisy_train_sliced, dfi = prepare_sliced_data1d(opts)
if clean_train_sliced.shape[0] != noisy_train_sliced.shape[0] :
raise ValueError('Clean sliced and noisy sliced are not of the same size!')
if clean_train_sliced.shape[0] != dfi[-1,1] :
raise ValueError('Sliced matrices have a different size than mentioned in dfi !')
matcontent={}
matcontent[u'feat_data'] = clean_train_sliced
matcontent[u'dfi'] = dfi
destinationfilenameclean = "./data/clean_train_segan1d.mat"
hdf5storage.savemat(destinationfilenameclean, matcontent)
matcontent={}
matcontent[u'feat_data'] = noisy_train_sliced
matcontent[u'dfi'] = dfi
destinationfilenamenoisy = "./data/noisy_train_segan1d.mat"
hdf5storage.savemat(destinationfilenamenoisy, matcontent)
