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update forward attention
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@@ -208,7 +208,7 @@ class Attention(nn.Module):
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_, n = prev_alpha.max(1)
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val, n2 = alpha.max(1)
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for b in range(alignment.shape[0]):
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alpha[b, n[b] + 2:] = 0
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alpha[b, n[b] + 3:] = 0
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alpha[b, :(n[b] - 1)] = 0 # ignore all previous states to prevent repetition.
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alpha[b, (n[b] - 2)] = 0.01 * val[b] # smoothing factor for the prev step
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# compute attention weights
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