# 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