    def __call__(self, batch):
        """
        SVCOnlineDataset.__getitem__:
            wav: (T,)
            wav_len: int
            target_len: int
            mask: (n_frames, 1)
            spk_id: (1)

            wav_{sr}: (T,)
            wav_{sr}_len: int

        Returns:
            wav: (B, T), torch.float32
            wav_len: (B), torch.long
            target_len: (B), torch.long
            mask: (B, n_frames, 1), torch.long
            spk_id: (B, 1), torch.int32

            wav_{sr}: (B, T)
            wav_{sr}_len: (B), torch.long
        """
        packed_batch_features = dict()

        for key in batch[0].keys():
            if "_len" in key:
                packed_batch_features[key] = torch.LongTensor([b[key] for b in batch])
            else:
                packed_batch_features[key] = pad_sequence(
                    [b[key] for b in batch], batch_first=True, padding_value=0
                )
        return packed_batch_features
SVCOnlineDataset.__getitem__:
wav: (T,)
wav_len: int
target_len: int
mask: (n_frames, 1)
spk_id: (1)

wav_{sr}: (T,)
wav_{sr}_len: int

Returns:
wav: (B, T), torch.float32
wav_len: (B), torch.long
target_len: (B), torch.long
mask: (B, n_frames, 1), torch.long
spk_id: (B, 1), torch.int32

wav_{sr}: (B, T)
wav_{sr}_len: (B), torch.long
models/svc/base/svc_dataset.py