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from distutils.version import LooseVersion
from typing import Sequence
from typing import Union
import torch
from espnet2.asr.specaug.abs_specaug import AbsSpecAug
from espnet2.layers.mask_along_axis import MaskAlongAxis
from espnet2.layers.time_warp import TimeWarp
if LooseVersion(torch.__version__) >= LooseVersion("1.1"):
DEFAULT_TIME_WARP_MODE = "bicubic"
else:
# pytorch1.0 doesn't implement bicubic
DEFAULT_TIME_WARP_MODE = "bilinear"
class SpecAug(AbsSpecAug):
"""Implementation of SpecAug.
Reference:
Daniel S. Park et al.
"SpecAugment: A Simple Data
Augmentation Method for Automatic Speech Recognition"
.. warning::
When using cuda mode, time_warp doesn't have reproducibility
due to `torch.nn.functional.interpolate`.
"""
def __init__(
self,
apply_time_warp: bool = True,
time_warp_window: int = 5,
time_warp_mode: str = DEFAULT_TIME_WARP_MODE,
apply_freq_mask: bool = True,
freq_mask_width_range: Union[int, Sequence[int]] = (0, 20),
num_freq_mask: int = 2,
apply_time_mask: bool = True,
time_mask_width_range: Union[int, Sequence[int]] = (0, 100),
num_time_mask: int = 2,
):
if not apply_time_warp and not apply_time_mask and not apply_freq_mask:
raise ValueError(
"Either one of time_warp, time_mask, or freq_mask should be applied",
)
super().__init__()
self.apply_time_warp = apply_time_warp
self.apply_freq_mask = apply_freq_mask
self.apply_time_mask = apply_time_mask
if apply_time_warp:
self.time_warp = TimeWarp(window=time_warp_window, mode=time_warp_mode)
else:
self.time_warp = None
if apply_freq_mask:
self.freq_mask = MaskAlongAxis(
dim="freq",
mask_width_range=freq_mask_width_range,
num_mask=num_freq_mask,
)
else:
self.freq_mask = None
if apply_time_mask:
self.time_mask = MaskAlongAxis(
dim="time",
mask_width_range=time_mask_width_range,
num_mask=num_time_mask,
)
else:
self.time_mask = None
def forward(self, x, x_lengths=None):
if self.time_warp is not None:
x, x_lengths = self.time_warp(x, x_lengths)
if self.freq_mask is not None:
x, x_lengths = self.freq_mask(x, x_lengths)
if self.time_mask is not None:
x, x_lengths = self.time_mask(x, x_lengths)
return x, x_lengths
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