From 0cc5ff230f572040c993dd8d061addb8f0df42a6 Mon Sep 17 00:00:00 2001 From: Bernardo Henz Date: Thu, 1 Aug 2019 21:26:45 -0300 Subject: [PATCH] -spectrogram augmentations --- util/feeding.py | 27 +++++ util/flags.py | 19 ++++ util/sparse_image_warp.py | 177 ++++++++++++++++++++++++++++++ util/spectrogram_augmentations.py | 97 ++++++++++++++++ 4 files changed, 320 insertions(+) create mode 100644 util/sparse_image_warp.py create mode 100644 util/spectrogram_augmentations.py diff --git a/util/feeding.py b/util/feeding.py index fd8c400d..c65590ee 100644 --- a/util/feeding.py +++ b/util/feeding.py @@ -16,6 +16,7 @@ from util.config import Config from util.logging import log_error from util.text import text_to_char_array from util.flags import FLAGS +from util.spectrogram_augmentations import augment_sparse_deform, augment_freq_time_mask, augment_dropout, augment_pitch_and_tempo, augment_speed_up def read_csvs(csv_files): source_data = None @@ -36,6 +37,32 @@ def samples_to_mfccs(samples, sample_rate): window_size=Config.audio_window_samples, stride=Config.audio_step_samples, magnitude_squared=True) + + if FLAGS.augmention_sparse_deform: + spectrogram = augment_sparse_deform(spectrogram, + time_warping_para=FLAGS.augmentation_time_warp_max_warping, + normal_around_warping_std=FLAGS.augmentation_sparse_deform_std_warp) + + if FLAGS.augmentation_spec_dropout_keeprate < 1: + spectrogram = augment_dropout(spectrogram, + keep_prob=FLAGS.augmentation_spec_dropout_keeprate) + + if FLAGS.augmentation_freq_and_time_masking: + spectrogram = augment_freq_time_mask(spectrogram, + frequency_masking_para=FLAGS.augmentation_freq_and_time_masking_freq_mask_range, + time_masking_para=FLAGS.augmentation_freq_and_time_masking_time_mask_range, + frequency_mask_num=FLAGS.augmentation_freq_and_time_masking_number_freq_masks, + time_mask_num=FLAGS.augmentation_freq_and_time_masking_number_time_masks) + + if FLAGS.augmentation_pitch_and_tempo_scaling: + spectrogram = augment_pitch_and_tempo(spectrogram, + max_tempo=FLAGS.augmentation_pitch_and_tempo_scaling_max_tempo, + max_pitch=FLAGS.augmentation_pitch_and_tempo_scaling_max_pitch, + min_pitch=FLAGS.augmentation_pitch_and_tempo_scaling_min_pitch) + + if FLAGS.augmentation_speed_up_std > 0: + spectrogram = augment_speed_up(spectrogram, speed_std=FLAGS.augmentation_speed_up_std) + mfccs = contrib_audio.mfcc(spectrogram, sample_rate, dct_coefficient_count=Config.n_input) mfccs = tf.reshape(mfccs, [-1, Config.n_input]) diff --git a/util/flags.py b/util/flags.py index 3dc58fdd..7119e4a1 100644 --- a/util/flags.py +++ b/util/flags.py @@ -27,6 +27,25 @@ def create_flags(): f.DEFINE_float('data_aug_features_additive', 0, 'std of the Gaussian additive noise') f.DEFINE_float('data_aug_features_multiplicative', 0, 'std of normal distribution around 1 for multiplicative noise') + f.DEFINE_integer('augmention_sparse_deform', 0, 'whether to use time-warping augmentation') + f.DEFINE_integer('augmentation_time_warp_max_warping', 12, 'max value for warping') + f.DEFINE_float('augmentation_sparse_deform_std_warp', 0.5, 'std for warping different values to different frequencies') + + f.DEFINE_float('augmentation_spec_dropout_keeprate', 1, 'keep rate of dropout augmentation on spectrogram (if 1, no dropout will be performed on spectrogram)') + + f.DEFINE_integer('augmentation_freq_and_time_masking', 0, 'whether to use frequency and time masking augmentation') + f.DEFINE_integer('augmentation_freq_and_time_masking_freq_mask_range', 5, 'max range of masks in the frequency domain when performing freqtime-mask augmentation') + f.DEFINE_integer('augmentation_freq_and_time_masking_number_freq_masks', 3, 'number of masks in the frequency domain when performing freqtime-mask augmentation') + f.DEFINE_integer('augmentation_freq_and_time_masking_time_mask_range', 2, 'max range of masks in the time domain when performing freqtime-mask augmentation') + f.DEFINE_integer('augmentation_freq_and_time_masking_number_time_masks', 3, 'number of masks in the time domain when performing freqtime-mask augmentation') + + f.DEFINE_float('augmentation_speed_up_std', 0.5, 'std for speeding-up tempo. If std is 0, this augmentation is not performed') + + f.DEFINE_integer('augmentation_pitch_and_tempo_scaling', 0, 'whether to use spectrogram speed and tempo scaling') + f.DEFINE_float('augmentation_pitch_and_tempo_scaling_min_pitch', 0.95, 'min value of pitch scaling') + f.DEFINE_float('augmentation_pitch_and_tempo_scaling_max_pitch', 1.2, 'max value of pitch scaling') + f.DEFINE_float('augmentation_pitch_and_tempo_scaling_max_tempo', 1.2, 'max vlaue of tempo scaling') + # Global Constants # ================ diff --git a/util/sparse_image_warp.py b/util/sparse_image_warp.py new file mode 100644 index 00000000..2bd69d45 --- /dev/null +++ b/util/sparse_image_warp.py @@ -0,0 +1,177 @@ +## Implementation of sparse_image_warp that handles dynamic shapes +from tensorflow.contrib.image.python.ops import dense_image_warp +from tensorflow.contrib.image.python.ops import interpolate_spline + +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops + + +def _get_grid_locations(image_height, image_width): + """Wrapper for array_ops.meshgrid.""" + + y_range = math_ops.linspace(0.0, math_ops.to_float(image_height) - 1, + image_height) + x_range = math_ops.linspace(0.0, math_ops.to_float(image_width) - 1, + image_width) + y_grid, x_grid = array_ops.meshgrid(y_range, x_range, indexing='ij') + return array_ops.stack((y_grid, x_grid), -1) + + +def _expand_to_minibatch(array, batch_size): + """Tile arbitrarily-sized array to include new batch dimension.""" + batch_size = array_ops.expand_dims(batch_size, 0) + array_ones = array_ops.ones((array_ops.rank(array)), dtype=dtypes.int32) + tiles = array_ops.concat([batch_size, array_ones], axis=0) + return array_ops.tile(array_ops.expand_dims(array, 0), tiles) + + +def _get_boundary_locations(image_height, image_width, num_points_per_edge): + """Compute evenly-spaced indices along edge of image.""" + image_height = math_ops.to_float(image_height) + image_width = math_ops.to_float(image_width) + y_range = math_ops.linspace(0.0, image_height - 1, num_points_per_edge + 2) + x_range = math_ops.linspace(0.0, image_width - 1, num_points_per_edge + 2) + ys, xs = array_ops.meshgrid(y_range, x_range, indexing='ij') + is_boundary = math_ops.logical_or( + math_ops.logical_or(math_ops.equal(xs, 0), # pylint: disable=bad-continuation + math_ops.equal(xs, image_width - 1)), + math_ops.logical_or(math_ops.equal(ys, 0), # pylint: disable=bad-continuation + math_ops.equal(ys, image_height - 1))) + return array_ops.stack([array_ops.boolean_mask(ys, is_boundary), + array_ops.boolean_mask(xs, is_boundary)], axis=-1) + + +def _add_zero_flow_controls_at_boundary(control_point_locations, + control_point_flows, image_height, + image_width, boundary_points_per_edge): + """Add control points for zero-flow boundary conditions. + Augment the set of control points with extra points on the + boundary of the image that have zero flow. + Args: + control_point_locations: input control points + control_point_flows: their flows + image_height: image height + image_width: image width + boundary_points_per_edge: number of points to add in the middle of each + edge (not including the corners). + The total number of points added is + 4 + 4*(boundary_points_per_edge). + Returns: + merged_control_point_locations: augmented set of control point locations + merged_control_point_flows: augmented set of control point flows + """ + + batch_size = tensor_shape.dimension_value(control_point_locations.shape[0]) + + boundary_point_locations = _get_boundary_locations(image_height, image_width, + boundary_points_per_edge) + + boundary_point_flows = array_ops.zeros([array_ops.shape(boundary_point_locations)[0], 2]) + + boundary_point_locations = _expand_to_minibatch(boundary_point_locations, + batch_size) + + boundary_point_flows = _expand_to_minibatch(boundary_point_flows, batch_size) + + merged_control_point_locations = array_ops.concat([control_point_locations, boundary_point_locations], 1) + + merged_control_point_flows = array_ops.concat([control_point_flows, boundary_point_flows], 1) + + return merged_control_point_locations, merged_control_point_flows + + +def sparse_image_warp(image, + source_control_point_locations, + dest_control_point_locations, + interpolation_order=2, + regularization_weight=0.0, + num_boundary_points=0, + name='sparse_image_warp'): + """Image warping using correspondences between sparse control points. + Apply a non-linear warp to the image, where the warp is specified by + the source and destination locations of a (potentially small) number of + control points. First, we use a polyharmonic spline + (`tf.contrib.image.interpolate_spline`) to interpolate the displacements + between the corresponding control points to a dense flow field. + Then, we warp the image using this dense flow field + (`tf.contrib.image.dense_image_warp`). + Let t index our control points. For regularization_weight=0, we have: + warped_image[b, dest_control_point_locations[b, t, 0], + dest_control_point_locations[b, t, 1], :] = + image[b, source_control_point_locations[b, t, 0], + source_control_point_locations[b, t, 1], :]. + For regularization_weight > 0, this condition is met approximately, since + regularized interpolation trades off smoothness of the interpolant vs. + reconstruction of the interpolant at the control points. + See `tf.contrib.image.interpolate_spline` for further documentation of the + interpolation_order and regularization_weight arguments. + Args: + image: `[batch, height, width, channels]` float `Tensor` + source_control_point_locations: `[batch, num_control_points, 2]` float + `Tensor` + dest_control_point_locations: `[batch, num_control_points, 2]` float + `Tensor` + interpolation_order: polynomial order used by the spline interpolation + regularization_weight: weight on smoothness regularizer in interpolation + num_boundary_points: How many zero-flow boundary points to include at + each image edge.Usage: + num_boundary_points=0: don't add zero-flow points + num_boundary_points=1: 4 corners of the image + num_boundary_points=2: 4 corners and one in the middle of each edge + (8 points total) + num_boundary_points=n: 4 corners and n-1 along each edge + name: A name for the operation (optional). + Note that image and offsets can be of type tf.half, tf.float32, or + tf.float64, and do not necessarily have to be the same type. + Returns: + warped_image: `[batch, height, width, channels]` float `Tensor` with same + type as input image. + flow_field: `[batch, height, width, 2]` float `Tensor` containing the dense + flow field produced by the interpolation. + """ + + image = ops.convert_to_tensor(image) + source_control_point_locations = ops.convert_to_tensor( + source_control_point_locations) + dest_control_point_locations = ops.convert_to_tensor( + dest_control_point_locations) + + control_point_flows = ( + dest_control_point_locations - source_control_point_locations) + + clamp_boundaries = num_boundary_points > 0 + boundary_points_per_edge = num_boundary_points - 1 + + with ops.name_scope(name): + batch_size, image_height, image_width = (array_ops.shape(image)[0], + array_ops.shape(image)[1], + array_ops.shape(image)[2]) + # This generates the dense locations where the interpolant + # will be evaluated. + grid_locations = _get_grid_locations(image_height, image_width) + + flattened_grid_locations = array_ops.reshape(grid_locations, + [image_height*image_width, 2]) + + flattened_grid_locations = _expand_to_minibatch(flattened_grid_locations, + batch_size) + + if clamp_boundaries: + (dest_control_point_locations, + control_point_flows) = _add_zero_flow_controls_at_boundary(dest_control_point_locations, + control_point_flows, image_height, + image_width, boundary_points_per_edge) + + flattened_flows = interpolate_spline.interpolate_spline(dest_control_point_locations, control_point_flows, + flattened_grid_locations, interpolation_order, + regularization_weight) + + dense_flows = array_ops.reshape(flattened_flows, + [batch_size, image_height, image_width, 2]) + + warped_image = dense_image_warp.dense_image_warp(image, dense_flows) + + return warped_image, dense_flows \ No newline at end of file diff --git a/util/spectrogram_augmentations.py b/util/spectrogram_augmentations.py new file mode 100644 index 00000000..012c1bd2 --- /dev/null +++ b/util/spectrogram_augmentations.py @@ -0,0 +1,97 @@ +import tensorflow as tf +from util.sparse_image_warp import sparse_image_warp + +def augment_sparse_deform(mel_spectrogram, + time_warping_para=12, + normal_around_warping_std=0.5): + mel_spectrogram = tf.expand_dims(mel_spectrogram, -1) + freq_max = tf.shape(mel_spectrogram)[1] + time_max = tf.shape(mel_spectrogram)[2] + center_freq = tf.cast(freq_max, tf.float32)/2.0 + random_time_point = tf.random.uniform(shape=(), minval=time_warping_para, maxval=tf.cast(time_max, tf.float32) - time_warping_para) + chosen_warping = tf.random.uniform(shape=(), minval=0, maxval=time_warping_para) + #add different warping values to different frequencies + normal_around_warping = tf.random.normal(mean=chosen_warping, stddev=normal_around_warping_std, shape=(3,)) + + control_point_freqs = tf.stack([0.0, center_freq, tf.cast(freq_max, tf.float32)], axis=0) + control_point_times_src = tf.stack([random_time_point, random_time_point, random_time_point], axis=0) + control_point_times_dst = control_point_times_src+normal_around_warping + + control_src = tf.expand_dims(tf.stack([control_point_freqs, control_point_times_src], axis=-1), 0) + control_dst = tf.expand_dims(tf.stack([control_point_freqs, control_point_times_dst], axis=1), 0) + warped_mel_spectrogram, _ = sparse_image_warp(mel_spectrogram, + source_control_point_locations=control_src, + dest_control_point_locations=control_dst, + interpolation_order=2, + regularization_weight=0, + num_boundary_points=1 + ) + warped_mel_spectrogram = warped_mel_spectrogram[:, :, :, 0] + return warped_mel_spectrogram + +def augment_freq_time_mask(mel_spectrogram, + frequency_masking_para=30, + time_masking_para=10, + frequency_mask_num=3, + time_mask_num=3): + freq_max = tf.shape(mel_spectrogram)[1] + time_max = tf.shape(mel_spectrogram)[2] + # Frequency masking + # Testing without loop + for _ in range(frequency_mask_num): + f = tf.random.uniform(shape=(), minval=0, maxval=frequency_masking_para, dtype=tf.dtypes.int32) + f0 = tf.random.uniform(shape=(), minval=0, maxval=freq_max - f, dtype=tf.dtypes.int32) + value_ones_freq_prev = tf.ones(shape=[1, f0, time_max]) + value_zeros_freq = tf.zeros(shape=[1, f, time_max]) + value_ones_freq_next = tf.ones(shape=[1, freq_max-(f0+f), time_max]) + freq_mask = tf.concat([value_ones_freq_prev, value_zeros_freq, value_ones_freq_next], axis=1) + #mel_spectrogram[:, f0:f0 + f, :] = 0 #can't assign to tensor + #mel_spectrogram[:, f0:f0 + f, :] = value_zeros_freq #can't assign to tensor + mel_spectrogram = mel_spectrogram*freq_mask + + # Time masking + # Testing without loop + for _ in range(time_mask_num): + t = tf.random.uniform(shape=(), minval=0, maxval=time_masking_para, dtype=tf.dtypes.int32) + t0 = tf.random.uniform(shape=(), minval=0, maxval=time_max - t, dtype=tf.dtypes.int32) + value_zeros_time_prev = tf.ones(shape=[1, freq_max, t0]) + value_zeros_time = tf.zeros(shape=[1, freq_max, t]) + value_zeros_time_next = tf.ones(shape=[1, freq_max, time_max-(t0+t)]) + time_mask = tf.concat([value_zeros_time_prev, value_zeros_time, value_zeros_time_next], axis=2) + #mel_spectrogram[:, :, t0:t0 + t] = 0 #can't assign to tensor + #mel_spectrogram[:, :, t0:t0 + t] = value_zeros_time #can't assign to tensor + mel_spectrogram = mel_spectrogram*time_mask + + return mel_spectrogram + +def augment_pitch_and_tempo(spectrogram, + max_tempo=1.2, + max_pitch=1.1, + min_pitch=0.95): + original_shape = tf.shape(spectrogram) + choosen_pitch = tf.random.uniform(shape=(), minval=min_pitch, maxval=max_pitch) + choosen_tempo = tf.random.uniform(shape=(), minval=1, maxval=max_tempo) + new_height = tf.cast(tf.cast(original_shape[1], tf.float32)*choosen_pitch, tf.int32) + new_width = tf.cast(tf.cast(original_shape[2], tf.float32)/(choosen_tempo), tf.int32) + spectrogram_aug = tf.image.resize_bilinear(tf.expand_dims(spectrogram, -1), [new_height, new_width]) + spectrogram_aug = tf.image.crop_to_bounding_box(spectrogram_aug, offset_height=0, offset_width=0, target_height=tf.minimum(original_shape[1],new_height), target_width=tf.shape(spectrogram_aug)[2]) + spectrogram_aug = tf.cond(choosen_pitch < 1, + lambda: tf.image.pad_to_bounding_box(spectrogram_aug, offset_height=0, offset_width=0, + target_height=original_shape[1], target_width=tf.shape(spectrogram_aug)[2]), + lambda: spectrogram_aug) + return spectrogram_aug[:, :, :, 0] + + +def augment_speed_up(spectrogram, + speed_std=0.1): + original_shape = tf.shape(spectrogram) + choosen_speed = tf.math.abs(tf.random.normal(shape=(), stddev=speed_std)) # abs makes sure the augmention will only speed up + choosen_speed = 1 + choosen_speed + new_height = tf.cast(tf.cast(original_shape[1], tf.float32), tf.int32) + new_width = tf.cast(tf.cast(original_shape[2], tf.float32)/(choosen_speed), tf.int32) + spectrogram_aug = tf.image.resize_bilinear(tf.expand_dims(spectrogram, -1), [new_height, new_width]) + return spectrogram_aug[:, :, :, 0] + +def augment_dropout(spectrogram, + keep_prob=0.95): + return tf.nn.dropout(spectrogram, rate=1-keep_prob)