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don't touch original lint

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Yi-Hua Chiu 2019-12-03 16:32:21 +08:00
parent 450483c30b
commit 9b3e4aa9d3
1 changed files with 18 additions and 34 deletions
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52
util/spectrogram_augmentations.py
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@ -11,53 +11,41 @@ def augment_freq_time_mask(mel_spectrogram,
time_max = tf.shape(mel_spectrogram)[2]
# Frequency masking
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)
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
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
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)
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
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_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])
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]),
@ -68,17 +56,13 @@ def augment_pitch_and_tempo(spectrogram,
def augment_speed_up(spectrogram,
speed_std=0.1):
original_shape = tf.shape(spectrogram)
# abs makes sure the augmention will only speed up
choosen_speed = tf.math.abs(tf.random.normal(shape=(), stddev=speed_std))
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])
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)