[FIX] decprecate fixed-frequency-edge, which always have chance to raise tensor invertible error
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@ -94,28 +94,21 @@ def augment_sparse_warp(spectrogram, time_warping_para=80, interpolation_order=2
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time_warping_para = tf.math.minimum(
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time_warping_para, tf.math.subtract(tf.math.floordiv(tau, 2), 1))
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choosen_freqs = tf.random.shuffle(tf.add(tf.range(freq_size), 1))[
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0: num_control_points]
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choosen_freqs = tf.random.shuffle(tf.add(tf.range(freq_size - 3), 1))[0: num_control_points]
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sources = []
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dests = []
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for i in range(num_control_points):
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source_max = tau - time_warping_para - 1
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# to protect short audio
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source_min = tf.math.minimum(source_max - 1, time_warping_para)
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rand_source_time = tfv1.random_uniform( # generate source points `t` of time axis between (W, tau-W)
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[], source_min, source_max, tf.int32)
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rand_dest_time = tfv1.random_uniform( # generate dest points `t'` of time axis between (t-W, t+W), !!! if rand_dest_time == 0, might raise invertible error
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[], tf.math.maximum(tf.math.subtract(rand_source_time, time_warping_para), 1), tf.math.add(rand_source_time, time_warping_para), tf.int32)
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rand_dest_time = tfv1.random_uniform( # generate dest points `t'` of time axis between (t-W, t+W)
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[], tf.math.maximum(tf.math.subtract(rand_source_time, time_warping_para), 0), tf.math.add(rand_source_time, time_warping_para), tf.int32)
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choosen_freq = choosen_freqs[i]
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sources.append([0, choosen_freq])
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sources.append([rand_source_time, choosen_freq])
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sources.append([tau, choosen_freq])
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dests.append([0, choosen_freq])
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dests.append([rand_dest_time, choosen_freq])
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dests.append([tau, choosen_freq])
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source_control_point_locations = tf.cast([sources], tf.float32)
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