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67 lines
3.8 KiB
Python
67 lines
3.8 KiB
Python
import tensorflow as tf
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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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