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@ -688,62 +688,6 @@ def compute_loss(
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use_double_scores=params.use_double_scores,
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use_double_scores=params.use_double_scores,
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)
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)
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'''
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dense_fsa_vec = k2.DenseFsaVec(
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nnet_output[0],
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supervision_segments,
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allow_truncate=params.subsampling_factor - 1,
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)
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dense_fsa_vec_inter = [
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#k2.DenseFsaVec(
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# nnet_output[1][2],
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# supervision_segments,
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# allow_truncate=params.subsampling_factor - 1,
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#),
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#k2.DenseFsaVec(
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# nnet_output[1][5],
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# supervision_segments,
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# allow_truncate=params.subsampling_factor - 1,
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#),
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k2.DenseFsaVec(
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nnet_output[1][8],
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supervision_segments,
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allow_truncate=params.subsampling_factor - 1,
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),
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#k2.DenseFsaVec(
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# nnet_output[1][11],
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# supervision_segments,
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# allow_truncate=params.subsampling_factor - 1,
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#),
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#k2.DenseFsaVec(
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# nnet_output[1][14],
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# supervision_segments,
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# allow_truncate=params.subsampling_factor - 1,
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#)
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]
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ctc_loss = k2.ctc_loss(
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decoding_graph=decoding_graph,
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dense_fsa_vec=dense_fsa_vec,
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output_beam=params.beam_size,
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reduction=params.reduction,
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use_double_scores=params.use_double_scores,
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)
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inter_ctc_loss = 0
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for fsa in dense_fsa_vec_inter:
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inter_ctc_loss += k2.ctc_loss(
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decoding_graph=decoding_graph,
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dense_fsa_vec=fsa,
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output_beam=params.beam_size,
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reduction=params.reduction,
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use_double_scores=params.use_double_scores,
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)
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ctc_loss = (1-params.interctc_weight) * ctc_loss + params.interctc_weight * inter_ctc_loss
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'''
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if params.att_rate > 0.0:
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if params.att_rate > 0.0:
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with torch.set_grad_enabled(is_training):
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with torch.set_grad_enabled(is_training):
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mmodel = model.module if hasattr(model, "module") else model
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mmodel = model.module if hasattr(model, "module") else model
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