| | |
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| | | |
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| | | # ASF Core |
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| | | if nfilter > 0 and nfilter < num_hot_word: |
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| | | for dec in self.seaco_decoder.decoders: |
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| | | dec.reserve_attn = True |
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| | | # cif_attended, _ = self.decoder2(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens) |
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| | | dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens) |
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| | | # cif_filter = torch.topk(self.decoder2.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1], min(nfilter, num_hot_word-1))[1].tolist() |
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| | | hotword_scores = self.seaco_decoder.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1] |
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| | | hotword_scores = self.seaco_decoder.forward_asf6(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens) |
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| | | hotword_scores = hotword_scores[0].sum(0).sum(0) |
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| | | # hotword_scores /= torch.sqrt(torch.tensor(hw_lengths)[:-1].float()).to(hotword_scores.device) |
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| | | dec_filter = torch.topk(hotword_scores, min(nfilter, num_hot_word-1))[1].tolist() |
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| | | add_filter = dec_filter |
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| | |
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| | | contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device) |
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| | | num_hot_word = contextual_info.shape[1] |
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| | | _contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device) |
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| | | for dec in self.seaco_decoder.decoders: |
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| | | dec.attn_mat = [] |
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| | | dec.reserve_attn = False |
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| | | |
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| | | # SeACo Core |
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| | | cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens) |
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