def test_background_noise(self):
# noise > audio
aug = naa.NoiseAug(noises=[self.noise])
augmented_audio = aug.augment(self.audio)
self.assertTrue(augmented_audio is not None)
# audio > noise
aug = naa.NoiseAug(noises=[self.audio])
augmented_audio = aug.augment(self.noise)
self.assertTrue(augmented_audio is not None)
def test_color_noise(self):
colors = naa.NoiseAug().model.COLOR_NOISES
for color in colors:
aug = naa.NoiseAug(color=color)
augmented_audio = aug.augment(self.audio)
self.assertFalse(np.array_equal(self.audio, augmented_audio))
self.assertTrue(len(self.audio), len(augmented_audio))
self.assertTrue(self.sampling_rate > 0)
def test_substitute(self):
aug = naa.NoiseAug()
augmented_audio = aug.augment(self.audio)
self.assertFalse(np.array_equal(self.audio, augmented_audio))
self.assertTrue(len(self.audio), len(augmented_audio))
self.assertTrue(self.sampling_rate > 0)
def augment_audio(data, sampling_rate, loudness_factor, speed_range, pitch_range, shift_max, noise_factor):
"""
OPTIONS:
LOUDNESS AUGMENTER: naa.LoudnessAug()
MASK AUGMENTER: naa.MaskAug()
SPEED AUGMENTER: naa.SpeedAug()
SHIFT AUGMENTER: naa.ShiftAug()
NOISE AUGMENTER: naa.NoiseAug()
CROP AUGMENTER: naa.CropAug()
PITCH AUGMENTER_ naa.PitchAug()
"""
flow = naf.Sequential([naa.LoudnessAug(loudness_factor),
naa.SpeedAug(speed_range),
naa.PitchAug(sampling_rate = sampling_rate, pitch_range = pitch_range),
naa.ShiftAug(sampling_rate = sampling_rate, shift_max = shift_max),
naa.NoiseAug(noise_factor),
])
augmented_audio = flow.augment(data)
return augmented_audio
def testSubsitute(self):
audio, sampling_rate = librosa.load(self.sample_wav_file)
aug = naa.NoiseAug()
augmented_audio = aug.substitute(audio)
self.assertFalse(np.array_equal(audio, augmented_audio))
self.assertTrue(len(audio), len(augmented_audio))
def load(audio_path):
y, sr = librosa.load(audio_path)
aug_noise = naa.NoiseAug(noise_factor=sr / 1000)
augmented_noise = aug_noise.substitute(y)
y_trim = librosa.effects.remix(augmented_noise,
intervals=librosa.effects.split(y))
mfcc = librosa.feature.mfcc(y=y_trim, sr=sr)
return mfcc.T
def test_zone(self):
zone = (0, 1)
coverage = 1.
expected_aug_data_size = int(len(self.audio) * (zone[1] - zone[0]) * coverage)
# background noise
aug = naa.NoiseAug(zone=zone, noises=[self.noise], coverage=coverage)
aug.model.stateless = False
aug.augment(self.audio)
self.assertTrue(-1 <= len(aug.model.aug_data) - expected_aug_data_size <= 1)
# colored noise
aug = naa.NoiseAug(zone=zone, color='pink', coverage=coverage)
aug.model.stateless = False
aug.augment(self.audio)
self.assertTrue(-1 <= len(aug.model.aug_data) - expected_aug_data_size <= 1)
def noise_injection(self):
"""
define a noise augmentation instance using nlpaug
:return: noise augmentation instance
"""
# pick randomly k samples from aug_noises
sampling = random.choices(self.aug_noises, k=3)
# define instance for noise augmentation
noise_injection = naa.NoiseAug(zone=(0.0, 1.0), noises=sampling)
return noise_injection
def gen_augmented(original, sample_rate):
# list of augmentors available from the nlpaug library
augment_list = [
#naa.CropAug(sampling_rate=sample_rate)
naa.NoiseAug(),
naa.SpeedAug(),
naa.LoudnessAug(factor=(0.5, 2)),
naa.VtlpAug(sampling_rate=sample_rate, zone=(0.0, 1.0)),
naa.PitchAug(sampling_rate=sample_rate, factor=(-1, 3))
]
# sample augmentation randomly
aug_idx = random.randint(0, len(augment_list) - 1)
augmented_data = augment_list[aug_idx].augment(original)
return augmented_data
def test_audio(self):
audio, sampling_rate = AudioLoader.load_audio(self.sample_wav_file)
flow = naf.Sequential([
naa.NoiseAug(),
naa.PitchAug(sampling_rate=sampling_rate, factor=(0.2, 1.5)),
naa.ShiftAug(sampling_rate=sampling_rate, duration=2),
naa.SpeedAug(factor=(1.5, 3))
])
augmented_audio = flow.augment(audio)
self.assertFalse(np.array_equal(audio, augmented_audio))
self.assertTrue(len(audio), len(augmented_audio))
def process_audio_files_with_aug(filename, dirpath):
sr = 16000
audio_array, sample_rate = librosa.load(os.path.join(dirpath, 'flac', filename), sr=sr)
aug_crop = naa.CropAug(sampling_rate=sr)
audio_array_crop = aug_crop.augment(audio_array)
aug_loud = naa.LoudnessAug(loudness_factor=(2, 5))
audio_array_loud = aug_loud.augment(audio_array)
aug_noise = naa.NoiseAug(noise_factor=0.03)
audio_array_noise = aug_noise.augment(audio_array)
audio_array_list= [audio_array,audio_array_crop,audio_array_loud,
audio_array_noise ]
out_list = convert_audio_to_processed_list(audio_array_list, filename, dirpath)
return out_list
def test_audio(self):
# https://freewavesamples.com/yamaha-v50-rock-beat-120-bpm
sample_wav_file = os.path.abspath(os.path.join(
os.path.dirname(__file__), '..', '..', 'data', 'Yamaha-V50-Rock-Beat-120bpm.wav'))
audio, sampling_rate = librosa.load(sample_wav_file)
flow = naf.Sequential([
naa.NoiseAug(),
naa.PitchAug(sampling_rate=sampling_rate, factor=(0.2, 1.5)),
naa.ShiftAug(sampling_rate=sampling_rate, duration=2),
naa.SpeedAug(factor=(1.5, 3))
])
augmented_audio = flow.augment(audio)
self.assertFalse(np.array_equal(audio, augmented_audio))
self.assertTrue(len(audio), len(augmented_audio))
def test_audio(self):
# https://freewavesamples.com/yamaha-v50-rock-beat-120-bpm
sample_wav_file = os.path.join(os.environ.get("TEST_DIR"), 'res',
'audio',
'Yamaha-V50-Rock-Beat-120bpm.wav')
audio, sampling_rate = AudioLoader.load_audio(sample_wav_file)
flow = naf.Sequential([
naa.NoiseAug(),
naa.PitchAug(sampling_rate=sampling_rate, factor=(0.2, 1.5)),
naa.ShiftAug(sampling_rate=sampling_rate, duration=2),
naa.SpeedAug(factor=(1.5, 3))
])
augmented_audio = flow.augment(audio)
self.assertFalse(np.array_equal(audio, augmented_audio))
self.assertTrue(len(audio), len(augmented_audio))
def process_audio_files_with_aug(self, audio_path):
sr = 16000
audio_array, sample_rate = librosa.load(audio_path, sr=sr)
aug_crop = naa.CropAug(sampling_rate=sr)
audio_array_crop = aug_crop.augment(audio_array)
aug_loud = naa.LoudnessAug(loudness_factor=(2, 5))
audio_array_loud = aug_loud.augment(audio_array)
aug_noise = naa.NoiseAug(noise_factor=0.03)
audio_array_noise = aug_noise.augment(audio_array)
mel_spec_array_load = melspectrogram(audio_array_loud, hparams=hparams)
mel_spec_array_noise = melspectrogram(audio_array_noise,
hparams=hparams)
audio_array_list = [mel_spec_array_load, mel_spec_array_noise]
# audio_array_list= [audio_array_crop,audio_array_loud,
# audio_array_noise ]
return audio_array_list
def augmentation():
doss = 0
for dossier in os.listdir(
'.'): # on parcourt tous les dossiers du répertoire courant
if os.path.isdir(dossier): # si c'est bien un dossier
cpt = 0
doss += 1
print("Dossier " + str(doss) + " sur 28")
if doss >= 14:
for file in os.listdir(
'./' + dossier + '/'
): # on parcours tous les fichiers .wav de ce repertoire
if not os.path.isdir(
file): # si c'est bien un fichier .wav
path = './' + dossier + '/' + file
audio, sampling_rate = librosa.load(path)
freq = sampling_rate
"""
Noise injection
It simply add some random value into data.
"""
liste_factor = [0.03, 0.02, 0.01]
for fact in liste_factor:
cpt += 1
aug = naa.NoiseAug(nosie_factor=fact)
augmented_audio = aug.substitute(audio)
path = './' + dossier + '/' + str(cpt) + '_' + file
out_f = path
wavf.write(out_f, freq, augmented_audio)
liste_factor = [0.5, 2, 5]
for fact in liste_factor:
cpt += 1
aug = naa.PitchAug(sampling_rate=sampling_rate,
pitch_factor=fact)
augmented_audio = aug.substitute(audio)
path = './' + dossier + '/' + str(cpt) + '_' + file
out_f = path
wavf.write(out_f, freq, augmented_audio)
def test_coverage_and_zone(self):
params = [
((0.3, 0.7), 1),
((0, 1), 1)
]
for zone, coverage in params:
augs = [
naa.LoudnessAug(zone=zone, coverage=coverage, stateless=False),
naa.MaskAug(zone=zone, coverage=coverage, stateless=False),
naa.NoiseAug(zone=zone, coverage=coverage, stateless=False),
naa.PitchAug(zone=zone, coverage=coverage, stateless=False, sampling_rate=self.sampling_rate),
naa.SpeedAug(zone=zone, coverage=coverage, stateless=False),
naa.VtlpAug(zone=zone, coverage=coverage, stateless=False, sampling_rate=self.sampling_rate),
naa.NormalizeAug(zone=zone, coverage=coverage, stateless=False),
naa.PolarityInverseAug(zone=zone, coverage=coverage, stateless=False)
]
for aug in augs:
aug_data = aug.augment(self.audio)
self.assertTrue(len(aug_data[aug.start_pos:aug.end_pos]), int(len(self.audio) * (zone[1] - zone[0]) * coverage))
augment data for one audio recording
"""
import librosa
from nlpaug.util.visual.wave import VisualWave
import nlpaug.augmenter.audio as naa
import nlpaug.flow as naf
path = '/home/akomaty/Documents/Projets/Co-encadrement_Emna_Rejaibi/Audio_files/300_P/split/Participant/300_AUDIO_fixed_5.wav'
audio, sampling_rate = librosa.load(path)
VisualWave.visual('Original', audio, sampling_rate)
"""
Noise injection
It simply add some random value into data.
"""
aug = naa.NoiseAug(nosie_factor=0.05)
augmented_audio = aug.substitute(audio)
VisualWave.visual('Noise', augmented_audio, sampling_rate)
"""
Pitch Augmenter
This augmentation is a wrapper of librosa function. It change pitch randomly
"""
aug = naa.PitchAug(sampling_rate=sampling_rate, pitch_factor=0.5)
augmented_audio = aug.substitute(audio)
VisualWave.visual('Pitch', augmented_audio, sampling_rate)
"""
Shift Augmenter
The idea of shifting time is very simple.
It just shift audio to left/right with a random second.
def aug_noise(data, noise_factor=0.03): aug = naa.NoiseAug(noise_factor=noise_factor) augmented_data = aug.augment(data) return augmented_data
def test_empty_input(self):
audio = np.array([])
aug = naa.NoiseAug()
augmented_audio = aug.augment(audio)
self.assertTrue(np.array_equal(audio, augmented_audio))
csv_file_path = 'D:/Emna/DAIC WOZ # EMNA/DAIC WOZ # EMNA/' + participant_ID + '_P/split/Participant/'
path = 'C:/Users/yobitrust/Desktop/DAIC_augmented/' + participant_ID + '_P/split/Participant/'
try:
os.makedirs(path)
except OSError:
print("Creation of the directory %s failed" % path)
else:
print("Creation of the directory %s success" % path)
if os.path.exists(csv_file_path):
input_directory = Path(csv_file_path)
for my_filename in input_directory.glob("*_AUDIO_*.wav"):
audio, sampling_rate = librosa.load(my_filename)
VisualWave.visual('Original', audio, sampling_rate)
flow = naf.Sequential([
naa.NoiseAug(),
naa.PitchAug(sampling_rate=sampling_rate,
pitch_factor=1.5),
naa.ShiftAug(sampling_rate=sampling_rate, shift_max=2),
naa.SpeedAug(speed_factor=1.5)
])
augmented_audio = flow.augment(audio)
VisualWave.visual('augment', augmented_audio,
sampling_rate)
my_filename = my_filename.stem
librosa.output.write_wav(path + my_filename + '.wav',
augmented_audio,
sampling_rate,
norm=False)
except ValueError:
print("Skipping the following line: ", row[0])
def add_noise(data, sr):
aug = naa.NoiseAug(noise_factor=0.03)
return aug.augment(data)
