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augmentation.py
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augmentation.py
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import numpy as np
import librosa
import os
import scipy
import json
def change_pitch_speech(samples):
y_pitch_speed = samples.copy()
length_change = np.random.uniform(low = 0.8, high = 1)
speed_fac = 1.0 / length_change
tmp = np.interp(
np.arange(0, len(y_pitch_speed), speed_fac),
np.arange(0, len(y_pitch_speed)),
y_pitch_speed,
)
minlen = min(y_pitch_speed.shape[0], tmp.shape[0])
y_pitch_speed *= 0
y_pitch_speed[0:minlen] = tmp[0:minlen]
return y_pitch_speed
def change_amplitude(samples):
y_aug = samples.copy()
dyn_change = np.random.uniform(low = 1.5, high = 3)
return y_aug * dyn_change
def add_noise(samples):
y_noise = samples.copy()
noise_amp = 0.01 * np.random.uniform() * np.amax(y_noise)
return y_noise.astype('float64') + noise_amp * np.random.normal(
size = y_noise.shape[0]
)
def add_hpss(samples):
y_hpss = librosa.effects.hpss(samples.astype('float64'))
return y_hpss[1]
def strech(samples):
input_length = len(samples)
streching = samples.copy()
random_strech = np.random.uniform(low = 0.5, high = 1.3)
print('random_strech = ', random_strech)
streching = librosa.effects.time_stretch(
streching.astype('float'), random_strech
)
return streching
def random_augmentation(samples):
cp = samples.copy()
if np.random.randint(0, 2):
length_change = np.random.uniform(low = 0.8, high = 1)
speed_fac = 1.0 / length_change
print('resample length_change = ', length_change)
tmp = np.interp(
np.arange(0, len(cp), speed_fac), np.arange(0, len(cp)), cp
)
minlen = min(cp.shape[0], tmp.shape[0])
cp *= 0
cp[0:minlen] = tmp[0:minlen]
if np.random.randint(0, 2):
dyn_change = np.random.uniform(low = 1.5, high = 3)
print('dyn_change = ', dyn_change)
cp = cp * dyn_change
if np.random.randint(0, 2):
noise_amp = 0.005 * np.random.uniform() * np.amax(cp)
cp = cp.astype('float64') + noise_amp * np.random.normal(
size = cp.shape[0]
)
if np.random.randint(0, 2):
timeshift_fac = 0.2 * 2 * (np.random.uniform() - 0.5)
print('timeshift_fac = ', timeshift_fac)
start = int(cp.shape[0] * timeshift_fac)
if start > 0:
cp = np.pad(cp, (start, 0), mode = 'constant')[0 : cp.shape[0]]
else:
cp = np.pad(cp, (0, -start), mode = 'constant')[0 : cp.shape[0]]
return cp
with open('train-test.json') as fopen:
wavs = json.load(fopen)['train']
if not os.path.exists('augment'):
os.makedirs('augment')
for no, wav in enumerate(wavs):
try:
root, ext = os.path.splitext(wav)
if (no + 1) % 100 == 0:
print(no + 1, root, ext)
root = root.replace('/', '<>')
root = '%s/%s'%('augment', root)
sample_rate, samples = scipy.io.wavfile.read(wav)
aug = change_pitch_speech(samples)
librosa.output.write_wav(
'%s-1%s' % (root, ext),
aug.astype('float32'),
sample_rate,
norm = True,
)
aug = change_amplitude(samples)
librosa.output.write_wav(
'%s-2%s' % (root, ext),
aug.astype('float32'),
sample_rate,
norm = True,
)
aug = add_noise(samples)
librosa.output.write_wav(
'%s-3%s' % (root, ext),
aug.astype('float32'),
sample_rate,
norm = True,
)
aug = add_hpss(samples)
librosa.output.write_wav(
'%s-4%s' % (root, ext),
aug.astype('float32'),
sample_rate,
norm = True,
)
aug = strech(samples)
librosa.output.write_wav(
'%s-5%s' % (root, ext),
aug.astype('float32'),
sample_rate,
norm = True,
)
aug = random_augmentation(samples)
librosa.output.write_wav(
'%s-6%s' % (root, ext),
aug.astype('float32'),
sample_rate,
norm = True,
)
except Exception as e:
print(e)
pass