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122 lines
6.3 KiB
Python
122 lines
6.3 KiB
Python
import tensorflow as tf
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import tensorflow.compat.v1 as tfv1
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from util.sparse_image_warp import sparse_image_warp
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def augment_freq_time_mask(mel_spectrogram,
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frequency_masking_para=30,
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time_masking_para=10,
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frequency_mask_num=3,
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time_mask_num=3):
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freq_max = tf.shape(mel_spectrogram)[1]
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time_max = tf.shape(mel_spectrogram)[2]
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# Frequency masking
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for _ in range(frequency_mask_num):
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f = tf.random.uniform(shape=(), minval=0, maxval=frequency_masking_para, dtype=tf.dtypes.int32)
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f0 = tf.random.uniform(shape=(), minval=0, maxval=freq_max - f, dtype=tf.dtypes.int32)
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value_ones_freq_prev = tf.ones(shape=[1, f0, time_max])
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value_zeros_freq = tf.zeros(shape=[1, f, time_max])
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value_ones_freq_next = tf.ones(shape=[1, freq_max-(f0+f), time_max])
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freq_mask = tf.concat([value_ones_freq_prev, value_zeros_freq, value_ones_freq_next], axis=1)
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#mel_spectrogram[:, f0:f0 + f, :] = 0 #can't assign to tensor
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#mel_spectrogram[:, f0:f0 + f, :] = value_zeros_freq #can't assign to tensor
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mel_spectrogram = mel_spectrogram*freq_mask
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# Time masking
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for _ in range(time_mask_num):
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t = tf.random.uniform(shape=(), minval=0, maxval=time_masking_para, dtype=tf.dtypes.int32)
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t0 = tf.random.uniform(shape=(), minval=0, maxval=time_max - t, dtype=tf.dtypes.int32)
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value_zeros_time_prev = tf.ones(shape=[1, freq_max, t0])
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value_zeros_time = tf.zeros(shape=[1, freq_max, t])
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value_zeros_time_next = tf.ones(shape=[1, freq_max, time_max-(t0+t)])
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time_mask = tf.concat([value_zeros_time_prev, value_zeros_time, value_zeros_time_next], axis=2)
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#mel_spectrogram[:, :, t0:t0 + t] = 0 #can't assign to tensor
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#mel_spectrogram[:, :, t0:t0 + t] = value_zeros_time #can't assign to tensor
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mel_spectrogram = mel_spectrogram*time_mask
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return mel_spectrogram
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def augment_pitch_and_tempo(spectrogram,
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max_tempo=1.2,
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max_pitch=1.1,
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min_pitch=0.95):
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original_shape = tf.shape(spectrogram)
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choosen_pitch = tf.random.uniform(shape=(), minval=min_pitch, maxval=max_pitch)
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choosen_tempo = tf.random.uniform(shape=(), minval=1, maxval=max_tempo)
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new_height = tf.cast(tf.cast(original_shape[1], tf.float32)*choosen_pitch, tf.int32)
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new_width = tf.cast(tf.cast(original_shape[2], tf.float32)/(choosen_tempo), tf.int32)
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spectrogram_aug = tf.image.resize_bilinear(tf.expand_dims(spectrogram, -1), [new_height, new_width])
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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])
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spectrogram_aug = tf.cond(choosen_pitch < 1,
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lambda: tf.image.pad_to_bounding_box(spectrogram_aug, offset_height=0, offset_width=0,
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target_height=original_shape[1], target_width=tf.shape(spectrogram_aug)[2]),
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lambda: spectrogram_aug)
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return spectrogram_aug[:, :, :, 0]
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def augment_speed_up(spectrogram,
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speed_std=0.1):
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original_shape = tf.shape(spectrogram)
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choosen_speed = tf.math.abs(tf.random.normal(shape=(), stddev=speed_std)) # abs makes sure the augmention will only speed up
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choosen_speed = 1 + choosen_speed
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new_height = tf.cast(tf.cast(original_shape[1], tf.float32), tf.int32)
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new_width = tf.cast(tf.cast(original_shape[2], tf.float32)/(choosen_speed), tf.int32)
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spectrogram_aug = tf.image.resize_bilinear(tf.expand_dims(spectrogram, -1), [new_height, new_width])
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return spectrogram_aug[:, :, :, 0]
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def augment_dropout(spectrogram,
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keep_prob=0.95):
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return tf.nn.dropout(spectrogram, rate=1-keep_prob)
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def augment_sparse_warp(spectrogram, time_warping_para=80, interpolation_order=2, regularization_weight=0.0, num_boundary_points=1):
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"""Reference: https://arxiv.org/pdf/1904.08779.pdf
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Args:
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spectrogram: `[batch, time, frequency]` float `Tensor`
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time_warping_para: 'W' parameter in paper
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interpolation_order: used to put into `sparse_image_warp`
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regularization_weight: used to put into `sparse_image_warp`
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num_boundary_points: used to put into `sparse_image_warp`,
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default=1 means boundary points on 4 corners of the image
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Returns:
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warped_spectrogram: `[batch, time, frequency]` float `Tensor` with same
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type as input image.
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"""
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# resize to fit `sparse_image_warp`'s input shape
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spectrogram = tf.expand_dims(spectrogram, -1) # (1, time steps, freq, 1), batch_size must be 1
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original_shape = tf.shape(spectrogram)
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tau, freq_size = original_shape[1], original_shape[2]
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# to protect short audio
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time_warping_para = tf.math.minimum(
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time_warping_para, tf.math.floordiv(tau, 2))
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mid_tau = tf.math.floordiv(tau, 2)
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mid_freq = tf.math.floordiv(freq_size, 2)
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left_mid_point = [0, mid_freq]
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right_mid_point = [tau, mid_freq]
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# dest control point must between (W, tau-W), which means the first and last W interval of the spectrogram won't be warped
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random_dest_time_point = tfv1.random_uniform(
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[], time_warping_para, tau - time_warping_para, tf.int32)
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source_control_point_locations = tf.cast([[
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left_mid_point,
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[mid_tau, mid_freq], # source control point must start from the center of the image
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right_mid_point
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]], tf.float32)
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dest_control_point_locations = tf.cast([[
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left_mid_point,
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[random_dest_time_point, mid_freq],
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right_mid_point
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]], tf.float32)
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warped_spectrogram, _ = sparse_image_warp(spectrogram,
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source_control_point_locations=source_control_point_locations,
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dest_control_point_locations=dest_control_point_locations,
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interpolation_order=interpolation_order,
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regularization_weight=regularization_weight,
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num_boundary_points=num_boundary_points)
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return tf.reshape(warped_spectrogram, shape=(1, -1, freq_size))
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