def __call__()

in torchrecipes/audio/source_separation/datamodule/utils.py [0:0]

• 14 lines of code
• 4 McCabe index (conditional complexity)

    def __call__(self, samples: SampleType) -> Tuple[Tensor, Tensor, Tensor]:
        """
        Args:
        samples (SampleType): The Tuple that contains
            sample_rate, mixture waveform, clean waveforms of all speakers.

        Returns:
        (Tuple(Tensor, Tensor, Tensor)):
            The Tensor of mixture speech wavecforms of dimension `[batch, time]`.
            The Tensor of clean speech wavecforms of dimension `[batch, num_speaker, time]`.
            The Tensor of padding mask of dimension `[batch, time]`.
        """
        if self.duration == -1:
            target_num_frames = max(s[1].shape[-1] for s in samples)
        else:
            target_num_frames = int(self.duration * self.sample_rate)

        mixes, srcs, masks = [], [], []
        for sample in samples:
            mix, src, mask = self._fix_num_frames(
                sample, target_num_frames, self.sample_rate, random_start=True
            )

            mixes.append(mix)
            srcs.append(src)
            masks.append(mask)

        return (torch.stack(mixes, 0), torch.stack(srcs, 0), torch.stack(masks, 0))