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import random
import torch
from src.simulation.effect import Effect
################################################################################
# Random time-domain offset
################################################################################
class Offset(Effect):
def __init__(self, compute_grad: bool = True, length: any = None):
"""
Shift audio and trim/zero-pad to maintain length
:param compute_grad: if False, use straight-through gradient estimator
:param length: offset length in seconds; sign indicates direction
"""
super().__init__(compute_grad)
self.min_length, self.max_length = self.parse_range(
length,
float,
f'Invalid offset length {length}'
)
self.length = None
self.sample_params()
def forward(self, x: torch.Tensor):
shifted = torch.roll(x, shifts=self.length, dims=-1)
if self.length >= 0:
shifted[..., :self.length] = 0
else:
shifted[..., self.length:] = 0
return shifted
def sample_params(self):
self.length = round(
random.uniform(self.min_length, self.max_length) * self.sample_rate
)
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