def matching_filenames(list1, list_of_lists):
list1_files = []
if isinstance(list1[0], tuple):
for item in list1:
if len(item) != 2:
# ensures expected length of 2: (encoded_label, pathway)
raise ValueError('Expected a list of tuple pairs: encoded '+\
'label and associated pathway. Received tuple of length ', len(item))
# checks to ensure encoded label comes first
if isinstance(
item[0],
int) or isinstance(item[0], str) and item[0].isdigit():
list1_files.append(item[1])
elif isinstance(list1[0], str) or isinstance(
list1[0], pathlib.PosixPath) or isinstance(list1[0],
pathlib.PurePath):
list1_files = list1
other_lists_files = []
for l in list_of_lists:
if isinstance(l[0], tuple):
for item in l:
if len(item) != 2:
# ensures expected length of 2: (encoded_label, pathway)
raise ValueError('Expected a list of tuple pairs: encoded '+\
'label and associated pathway. Received tuple of length ', len(item))
# checks to ensure encoded label comes first
if isinstance(
item[0],
int) or isinstance(item[0], str) and item[0].isdigit():
# ensure pathway is string, not pathlib (for iteration purposes)
other_lists_files.append(str(item[1]))
elif isinstance(l[0], str) or isinstance(
l[0], pathlib.PosixPath) or isinstance(l[0], pathlib.PurePath):
other_lists_files.append(l)
# ensure list of lists is flat list
flatten = lambda l: [item for sublist in l for item in sublist]
other_lists_files = flatten(other_lists_files)
contanimated_files = []
for item in list1_files:
if isinstance(item, str):
item = sp.string2pathlib(item)
fname = item.stem
fname_parts = fname.split('-')
fname_head = fname_parts[:-1]
fname_head = '-'.join(fname_head)
for i in other_lists_files:
if fname_head in i:
contanimated_files.append(item)
break
return contanimated_files
pass
except SyntaxError:
pass
#########################################################
# For the purposes of plotting, let's use some of the settings defined:
feature_type = feat_settings['feature_type']
sr = feat_settings['sr']
######################################################
# Provide new audio for the denoiser to denoise!
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#########################################################
# We'll use sample speech from the soundpy repo:
speech = sp.string2pathlib('{}audiodata/python.wav'.format(sp_dir))
s, sr = sp.loadsound(speech, sr=sr)
#########################################################
# Let's add some white noise (10 SNR)
s_n = sp.augment.add_white_noise(s, sr=sr, snr=10)
##############################################################
# What does the noisy audio sound like?
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
ipd.Audio(s_n, rate=sr)
##############################################################
# What does the noisy audio look like?
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
sp.plotsound(s_n, sr=sr, feature_type='signal', subprocess=True)
# Let's import soundpy, and ipd for playing audio data
import soundpy as sp
import IPython.display as ipd
######################################################
# Define the noisy and clean speech audio files.
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Note: these files are available in the soundpy repo.
# Designate path relevant for accessing audiodata
sp_dir = '../../../'
##########################################################
# Noise sample:
noise = '{}audiodata/background_samples/traffic.wav'.format(sp_dir)
noise = sp.string2pathlib(noise)
speech = '{}audiodata/python.wav'.format(sp_dir)
speech = sp.utils.string2pathlib(speech)
##########################################################
# For filtering, we will set the sample rate to be quite high:
sr = 48000
##########################################################
# Create noisy speech signal as SNR 10
noisy, snr_measured = sp.dsp.add_backgroundsound(speech,
noise,
sr=sr,
snr=10,
total_len_sec=2,
pad_mainsound_sec=0.5)