Rnnt mmi #2
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Rnnt mmi #2
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pkufool
commented
Aug 22, 2022
| params.simple_loss_scale * predictor_simple_loss | ||
| + pruned_loss_scale * predictor_pruned_loss | ||
| + params.simple_loss_scale * hybrid_simple_loss | ||
| + mmi_loss | ||
| ) |
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I gradully added predictor_simple_loss, hybrid_simple_loss, predictor_pruned_loss, num_loss (i.e. hybrid_pruned_loss) , den_loss one by one.
pkufool
commented
Aug 22, 2022
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| probs = torch.softmax(predictor_joiner_output, -1) | ||
| # sampler: https://pytorch.org/docs/stable/distributions.html#categorical | ||
| sampler = Categorical(probs=probs) |
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I used torch.distributions.categorical.Categorical to sample symbols according to the predictor_joiner_output.
pkufool
commented
Aug 22, 2022
| lm_scores.masked_fill_(mask=t_mask.unsqueeze(2), value=0.0) | ||
| # detach lm_scoers, we only train external_lm module with NUM loss | ||
| path_scores = hybrid_scores + lm_scores.detach() | ||
| path_scores_list.append(path_scores) |
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Nenural lm scores were added to lattice here.
pkufool
commented
Aug 22, 2022
| ranges=ranges, | ||
| termination_symbol=blank_id, | ||
| boundary=boundary, | ||
| external_lm=external_lm_out_proj, |
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The neural lm scores were added to num_loss here, it will add to px matrix after a gather operation.
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