75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
import numpy as np
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import librosa
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import os, copy
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from scipy import signal
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import paddle.fluid.layers as layers
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def get_positional_table(d_pos_vec, n_position=1024):
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position_enc = np.array([
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[pos / np.power(10000, 2*i/d_pos_vec) for i in range(d_pos_vec)]
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if pos != 0 else np.zeros(d_pos_vec) for pos in range(n_position)])
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position_enc[1:, 0::2] = np.sin(position_enc[1:, 0::2]) # dim 2i
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position_enc[1:, 1::2] = np.cos(position_enc[1:, 1::2]) # dim 2i+1
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return position_enc
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def get_sinusoid_encoding_table(n_position, d_hid, padding_idx=None):
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''' Sinusoid position encoding table '''
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def cal_angle(position, hid_idx):
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return position / np.power(10000, 2 * (hid_idx // 2) / d_hid)
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def get_posi_angle_vec(position):
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return [cal_angle(position, hid_j) for hid_j in range(d_hid)]
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sinusoid_table = np.array([get_posi_angle_vec(pos_i) for pos_i in range(n_position)])
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sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
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sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
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if padding_idx is not None:
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# zero vector for padding dimension
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sinusoid_table[padding_idx] = 0.
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return sinusoid_table
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def get_non_pad_mask(seq):
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return layers.unsqueeze((seq != 0).astype(np.float32),[-1])
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def get_attn_key_pad_mask(seq_k, seq_q):
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''' For masking out the padding part of key sequence. '''
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# Expand to fit the shape of key query attention matrix.
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len_q = seq_q.shape[1]
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padding_mask = (seq_k != 0).astype(np.float32)
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padding_mask = layers.expand(layers.unsqueeze(padding_mask,[1]), [1, len_q, 1])
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return padding_mask
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def get_triu_tensor(seq_k, seq_q):
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''' For make a triu tensor '''
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len_k = seq_k.shape[1]
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len_q = seq_q.shape[1]
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batch_size = seq_k.shape[0]
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triu_tensor = np.triu(np.ones([len_k, len_q]), 1)
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triu_tensor = np.repeat(np.expand_dims(triu_tensor, axis=0) ,batch_size, axis=0)
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return triu_tensor
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def guided_attention(N, T, g=0.2):
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'''Guided attention. Refer to page 3 on the paper.'''
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W = np.zeros((N, T), dtype=np.float32)
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for n_pos in range(W.shape[0]):
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for t_pos in range(W.shape[1]):
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W[n_pos, t_pos] = 1 - np.exp(-(t_pos / float(T) - n_pos / float(N)) ** 2 / (2 * g * g))
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return W
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def cross_entropy(input, label, position_weight=1.0, epsilon=1e-30):
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output = -1 * label * layers.log(input + epsilon) - (1-label) * layers.log(1 - input + epsilon)
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output = output * (label * (position_weight - 1) + 1)
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return layers.reduce_sum(output, dim=[0, 1])
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