def decode_waves()

in docker_images/k2/app/common.py [0:0]

• 10 lines of code
• 6 McCabe index (conditional complexity)

    def decode_waves(self, waves: List[torch.Tensor]) -> List[List[str]]:
        """
        Args:
          waves:
            A list of 1-D torch.float32 tensors containing audio samples.
            wavs[i] contains audio samples for the i-th utterance.

            Note:
              Whether it should be in the range [-32768, 32767] or be normalized
              to [-1, 1] depends on which range you used for your training data.
              For instance, if your training data used [-32768, 32767],
              then the given waves have to contain samples in this range.

              All models trained in icefall use the normalized range [-1, 1].
        Returns:
          Return a list of decoded results. `ans[i]` contains the decoded
          results for `wavs[i]`.
        """
        waves = [w.to(self.device) for w in waves]
        features = self.feature_extractor(waves)

        tokens = self.nn_and_decoding_func(self.model, features)

        if hasattr(self, "sp"):
            results = self.sp.decode(tokens)
        else:
            results = [[self.token_table[i] for i in hyp] for hyp in tokens]
            results = ["".join(r) for r in results]

        return results