| | |
|---|
| | | loss_ideal = None |
|---|
| | | ''' |
|---|
| | | loss_ideal = None |
|---|
| | | |
|---|
| | | |
|---|
| | | if decoder_out_1st is None: |
|---|
| | | decoder_out_1st = decoder_out |
|---|
| | | # 2. Compute attention loss |
|---|
| | |
|---|
| | | hw_embed = torch.nn.utils.rnn.pack_padded_sequence(hw_embed, hw_lengths, batch_first=True, |
|---|
| | | enforce_sorted=False) |
|---|
| | | _, (h_n, _) = self.bias_encoder(hw_embed) |
|---|
| | | # hw_embed, _ = torch.nn.utils.rnn.pad_packed_sequence(hw_embed, batch_first=True) |
|---|
| | | if h_n.shape[1] > 2000: # large hotword list |
|---|
| | | _h_n = self.pick_hwlist_group(h_n.squeeze(0), encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens) |
|---|
| | | if _h_n is not None: |
|---|
| | | h_n = _h_n |
|---|
| | | hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1) |
|---|
| | | |
|---|
| | | decoder_outs = self.decoder( |
|---|
| | |
|---|
| | | decoder_out = decoder_outs[0] |
|---|
| | | decoder_out = torch.log_softmax(decoder_out, dim=-1) |
|---|
| | | return decoder_out, ys_pad_lens |
|---|
| | | |
|---|
| | | def pick_hwlist_group(self, hw_embed, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens): |
|---|
| | | max_attn_score = 0.0 |
|---|
| | | # max_attn_index = 0 |
|---|
| | | argmax_g = None |
|---|
| | | non_blank = hw_embed[-1] |
|---|
| | | hw_embed_groups = hw_embed[:-1].split(2000) |
|---|
| | | for i, g in enumerate(hw_embed_groups): |
|---|
| | | g = torch.cat([g, non_blank.unsqueeze(0)], dim=0) |
|---|
| | | _ = self.decoder( |
|---|
| | | encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=g.unsqueeze(0) |
|---|
| | | ) |
|---|
| | | attn = self.decoder.bias_decoder.src_attn.attn[0] |
|---|
| | | _max_attn_score = attn.max(0)[0][:,:-1].max() |
|---|
| | | if _max_attn_score > max_attn_score: |
|---|
| | | max_attn_score = _max_attn_score |
|---|
| | | # max_attn_index = i |
|---|
| | | argmax_g = g |
|---|
| | | # import pdb; pdb.set_trace() |
|---|
| | | return argmax_g |
|---|
