# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains the volume perturb augmentation model."""
import random
import numpy as np
from PIL import Image
from PIL.Image import BICUBIC
from paddlespeech.s2t.frontend.augmentor.base import AugmentorBase
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
class SpecAugmentor(AugmentorBase):
"""Augmentation model for Time warping, Frequency masking, Time masking.
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://arxiv.org/abs/1904.08779
SpecAugment on Large Scale Datasets
https://arxiv.org/abs/1912.05533
"""
def __init__(self,
rng,
F,
T,
n_freq_masks,
n_time_masks,
p=1.0,
W=40,
adaptive_number_ratio=0,
adaptive_size_ratio=0,
max_n_time_masks=20,
replace_with_zero=True,
warp_mode='PIL'):
"""SpecAugment class.
Args:
rng (random.Random): random generator object.
F (int): parameter for frequency masking
T (int): parameter for time masking
n_freq_masks (int): number of frequency masks
n_time_masks (int): number of time masks
p (float): parameter for upperbound of the time mask
W (int): parameter for time warping
adaptive_number_ratio (float): adaptive multiplicity ratio for time masking
adaptive_size_ratio (float): adaptive size ratio for time masking
max_n_time_masks (int): maximum number of time masking
replace_with_zero (bool): pad zero on mask if true else use mean
warp_mode (str): "PIL" (default, fast, not differentiable)
or "sparse_image_warp" (slow, differentiable)
"""
super().__init__()
self._rng = rng
self.inplace = True
self.replace_with_zero = replace_with_zero
self.mode = warp_mode
self.W = W
self.F = F
self.T = T
self.n_freq_masks = n_freq_masks
self.n_time_masks = n_time_masks
self.p = p
# adaptive SpecAugment
self.adaptive_number_ratio = adaptive_number_ratio
self.adaptive_size_ratio = adaptive_size_ratio
self.max_n_time_masks = max_n_time_masks
if adaptive_number_ratio > 0:
self.n_time_masks = 0
logger.info('n_time_masks is set ot zero for adaptive SpecAugment.')
if adaptive_size_ratio > 0:
self.T = 0
logger.info('T is set to zero for adaptive SpecAugment.')
self._freq_mask = None
self._time_mask = None
def librispeech_basic(self):
self.W = 80
self.F = 27
self.T = 100
self.n_freq_masks = 1
self.n_time_masks = 1
self.p = 1.0
def librispeech_double(self):
self.W = 80
self.F = 27
self.T = 100
self.n_freq_masks = 2
self.n_time_masks = 2
self.p = 1.0
def switchboard_mild(self):
self.W = 40
self.F = 15
self.T = 70
self.n_freq_masks = 2
self.n_time_masks = 2
self.p = 0.2
def switchboard_strong(self):
self.W = 40
self.F = 27
self.T = 70
self.n_freq_masks = 2
self.n_time_masks = 2
self.p = 0.2
@property
def freq_mask(self):
return self._freq_mask
@property
def time_mask(self):
return self._time_mask
def __repr__(self):
return f"specaug: F-{self.F}, T-{self.T}, F-n-{self.n_freq_masks}, T-n-{self.n_time_masks}"
def time_warp(self, x, mode='PIL'):
"""time warp for spec augment
move random center frame by the random width ~ uniform(-window, window)
Args:
x (np.ndarray): spectrogram (time, freq)
mode (str): PIL or sparse_image_warp
Raises:
NotImplementedError: [description]
NotImplementedError: [description]
Returns:
np.ndarray: time warped spectrogram (time, freq)
"""
window = max_time_warp = self.W
if window == 0:
return x
if mode == "PIL":
t = x.shape[0]
if t - window <= window:
return x
# NOTE: randrange(a, b) emits a, a + 1, ..., b - 1
center = random.randrange(window, t - window)
warped = random.randrange(center - window, center +
window) + 1 # 1 ... t - 1
left = Image.fromarray(x[:center]).resize((x.shape[1], warped),
BICUBIC)
right = Image.fromarray(x[center:]).resize((x.shape[1], t - warped),
BICUBIC)
if self.inplace:
x[:warped] = left
x[warped:] = right
return x
return np.concatenate((left, right), 0)
elif mode == "sparse_image_warp":
raise NotImplementedError('sparse_image_warp')
else:
raise NotImplementedError(
"unknown resize mode: " + mode +
", choose one from (PIL, sparse_image_warp).")
def mask_freq(self, x, replace_with_zero=False):
"""freq mask
Args:
x (np.ndarray): spectrogram (time, freq)
replace_with_zero (bool, optional): Defaults to False.
Returns:
np.ndarray: freq mask spectrogram (time, freq)
"""
n_bins = x.shape[1]
for i in range(0, self.n_freq_masks):
f = int(self._rng.uniform(low=0, high=self.F))
f_0 = int(self._rng.uniform(low=0, high=n_bins - f))
assert f_0 <= f_0 + f
if replace_with_zero:
x[:, f_0:f_0 + f] = 0
else:
x[:, f_0:f_0 + f] = x.mean()
self._freq_mask = (f_0, f_0 + f)
return x
def mask_time(self, x, replace_with_zero=False):
"""time mask
Args:
x (np.ndarray): spectrogram (time, freq)
replace_with_zero (bool, optional): Defaults to False.
Returns:
np.ndarray: time mask spectrogram (time, freq)
"""
n_frames = x.shape[0]
if self.adaptive_number_ratio > 0:
n_masks = int(n_frames * self.adaptive_number_ratio)
n_masks = min(n_masks, self.max_n_time_masks)
else:
n_masks = self.n_time_masks
if self.adaptive_size_ratio > 0:
T = self.adaptive_size_ratio * n_frames
else:
T = self.T
for i in range(n_masks):
t = int(self._rng.uniform(low=0, high=T))
t = min(t, int(n_frames * self.p))
t_0 = int(self._rng.uniform(low=0, high=n_frames - t))
assert t_0 <= t_0 + t
if replace_with_zero:
x[t_0:t_0 + t, :] = 0
else:
x[t_0:t_0 + t, :] = x.mean()
self._time_mask = (t_0, t_0 + t)
return x
def __call__(self, x, train=True):
if not train:
return x
return self.transform_feature(x)
def transform_feature(self, x: np.ndarray):
"""
Args:
x (np.ndarray): `[T, F]`
Returns:
x (np.ndarray): `[T, F]`
"""
assert isinstance(x, np.ndarray)
assert x.ndim == 2
x = self.time_warp(x, self.mode)
x = self.mask_freq(x, self.replace_with_zero)
x = self.mask_time(x, self.replace_with_zero)
return x