100 lines
3.7 KiB
Python
100 lines
3.7 KiB
Python
import numpy as np
|
|
|
|
|
|
class NoiseInjection(object):
|
|
|
|
def __init__(self, noise_factor: float, sigma=0.5, mu=0.5):
|
|
assert noise_factor >= 0, f'max_shift_ratio has to be greater then 0, but was: {noise_factor}.'
|
|
self.mu = mu
|
|
self.sigma = sigma
|
|
self.noise_factor = noise_factor
|
|
|
|
def __call__(self, x: np.ndarray):
|
|
if self.noise_factor:
|
|
noise = np.random.uniform(0, self.noise_factor, size=x.shape)
|
|
augmented_data = x + x * noise
|
|
# Cast back to same data type
|
|
augmented_data = augmented_data.astype(x.dtype)
|
|
return augmented_data
|
|
else:
|
|
return x
|
|
|
|
|
|
class LoudnessManipulator(object):
|
|
|
|
def __init__(self, max_factor: float):
|
|
assert 1 > max_factor >= 0, f'max_shift_ratio has to be between [0,1], but was: {max_factor}.'
|
|
|
|
self.max_factor = max_factor
|
|
|
|
def __call__(self, x: np.ndarray):
|
|
if self.max_factor:
|
|
augmented_data = x + x * (np.random.random() * self.max_factor)
|
|
# Cast back to same data type
|
|
augmented_data = augmented_data.astype(x.dtype)
|
|
return augmented_data
|
|
else:
|
|
return x
|
|
|
|
|
|
class ShiftTime(object):
|
|
|
|
valid_shifts = ['right', 'left', 'any']
|
|
|
|
def __init__(self, max_shift_ratio: float, shift_direction: str = 'any'):
|
|
assert 1 > max_shift_ratio >= 0, f'max_shift_ratio has to be between [0,1], but was: {max_shift_ratio}.'
|
|
assert shift_direction.lower() in self.valid_shifts, f'shift_direction has to be one of: {self.valid_shifts}'
|
|
self.max_shift_ratio = max_shift_ratio
|
|
self.shift_direction = shift_direction.lower()
|
|
|
|
def __call__(self, x: np.ndarray):
|
|
if self.max_shift_ratio:
|
|
shift = np.random.randint(max(int(self.max_shift_ratio * x.shape[-1]), 1))
|
|
if self.shift_direction == 'right':
|
|
shift = -1 * shift
|
|
elif self.shift_direction == 'any':
|
|
direction = np.random.choice([1, -1], 1)
|
|
shift = direction * shift
|
|
augmented_data = np.roll(x, shift)
|
|
# Set to silence for heading/ tailing
|
|
shift = int(shift)
|
|
if shift > 0:
|
|
augmented_data[:shift, :] = 0
|
|
else:
|
|
augmented_data[shift:, :] = 0
|
|
return augmented_data
|
|
else:
|
|
return x
|
|
|
|
|
|
class MaskAug(object):
|
|
|
|
w_idx = -1
|
|
h_idx = -2
|
|
|
|
def __init__(self, duration_ratio_max=0.3, mask_with_noise=True):
|
|
assertion = f'"duration_ratio" has to be within [0..1], but was: {duration_ratio_max}'
|
|
if isinstance(duration_ratio_max, (tuple, list)):
|
|
assert all([0 < max_val < 1 for max_val in duration_ratio_max]), assertion
|
|
if isinstance(duration_ratio_max, (float, int)):
|
|
assert 0 <= duration_ratio_max < 1, assertion
|
|
super().__init__()
|
|
|
|
self.mask_with_noise = mask_with_noise
|
|
self.duration_ratio_max = duration_ratio_max if isinstance(duration_ratio_max, (tuple, list)) \
|
|
else (duration_ratio_max, duration_ratio_max)
|
|
|
|
def __call__(self, x):
|
|
for dim in (self.w_idx, self.h_idx):
|
|
if self.duration_ratio_max[dim]:
|
|
start = int(np.random.choice(x.shape[dim], 1))
|
|
v_max = x.shape[dim] * self.duration_ratio_max[dim]
|
|
size = int(np.random.randint(0, v_max, 1))
|
|
end = int(min(start + size, x.shape[dim]))
|
|
size = end - start
|
|
if dim == self.w_idx:
|
|
x[:, start:end] = np.random.random((x.shape[self.h_idx], size)) if self.mask_with_noise else 0
|
|
else:
|
|
x[start:end, :] = np.random.random((size, x.shape[self.w_idx])) if self.mask_with_noise else 0
|
|
return x
|