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import numpy as np
import torch
import random
def frame_shift(mels, labels, net_pooling=4):
bsz, n_bands, frames = mels.shape
shifted = []
new_labels = []
for bindx in range(bsz):
shift = int(random.gauss(0, 90))
shifted.append(torch.roll(mels[bindx], shift, dims=-1))
shift = -abs(shift) // net_pooling if shift < 0 else shift // net_pooling
new_labels.append(torch.roll(labels[bindx], shift, dims=-1))
return torch.stack(shifted), torch.stack(new_labels)
def mixup(data, target=None, alpha=0.2, beta=0.2, mixup_label_type="soft"):
"""Mixup data augmentation by permuting the data
Args:
data: input tensor, must be a batch so data can be permuted and mixed.
target: tensor of the target to be mixed, if None, do not return targets.
alpha: float, the parameter to the np.random.beta distribution
beta: float, the parameter to the np.random.beta distribution
mixup_label_type: str, the type of mixup to be used choice between {'soft', 'hard'}.
Returns:
torch.Tensor of mixed data and labels if given
"""
with torch.no_grad():
batch_size = data.size(0)
c = np.random.beta(alpha, beta)
perm = torch.randperm(batch_size)
mixed_data = c * data + (1 - c) * data[perm, :]
if target is not None:
if mixup_label_type == "soft":
mixed_target = torch.clamp(
c * target + (1 - c) * target[perm, :], min=0, max=1
)
elif mixup_label_type == "hard":
mixed_target = torch.clamp(target + target[perm, :], min=0, max=1)
else:
raise NotImplementedError(
f"mixup_label_type: {mixup_label_type} not implemented. choice in "
f"{'soft', 'hard'}"
)
return mixed_data, mixed_target
else:
return mixed_data
def add_noise(mels, snrs=(6, 30), dims=(1, 2)):
""" Add white noise to mels spectrograms
Args:
mels: torch.tensor, mels spectrograms to apply the white noise to.
snrs: int or tuple, the range of snrs to choose from if tuple (uniform)
dims: tuple, the dimensions for which to compute the standard deviation (default to (1,2) because assume
an input of a batch of mel spectrograms.
Returns:
torch.Tensor of mels with noise applied
"""
if isinstance(snrs, (list, tuple)):
snr = (snrs[0] - snrs[1]) * torch.rand(
(mels.shape[0],), device=mels.device
).reshape(-1, 1, 1) + snrs[1]
else:
snr = snrs
snr = 10 ** (snr / 20) # linear domain
sigma = torch.std(mels, dim=dims, keepdim=True) / snr
mels = mels + torch.randn(mels.shape, device=mels.device) * sigma
return mels
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