| | |
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| | | self.report_wer = report_wer |
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| | | |
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| | | def __call__( |
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| | | self, encoder_out: torch.Tensor, target: torch.Tensor |
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| | | self, encoder_out: torch.Tensor, target: torch.Tensor, encoder_out_lens: torch.Tensor, |
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| | | ) -> Tuple[Optional[float], Optional[float]]: |
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| | | """Calculate sentence-level WER or/and CER score for Transducer model. |
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| | | Args: |
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| | | encoder_out: Encoder output sequences. (B, T, D_enc) |
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| | | target: Target label ID sequences. (B, L) |
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| | | encoder_out_lens: Encoder output sequences length. (B,) |
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| | | Returns: |
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| | | : Sentence-level CER score. |
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| | | : Sentence-level WER score. |
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| | |
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| | | |
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| | | encoder_out = encoder_out.to(next(self.decoder.parameters()).device) |
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| | | |
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| | | batch_nbest = [self.beam_search(encoder_out[b]) for b in range(batchsize)] |
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| | | batch_nbest = [ |
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| | | self.beam_search(encoder_out[b][: encoder_out_lens[b]]) |
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| | | for b in range(batchsize) |
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| | | ] |
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| | | pred = [nbest_hyp[0].yseq[1:] for nbest_hyp in batch_nbest] |
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| | | |
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| | | char_pred, char_target = self.convert_to_char(pred, target) |
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