| | |
| | | |
| | | # data: (Nsamples,) -> (1, Nsamples) |
| | | # lengths: (1,) |
| | | if len(speech.size()) < 3: |
| | | speech = speech.unsqueeze(0).to(getattr(torch, self.dtype)) |
| | | speech_lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1)) |
| | | # if len(speech.size()) < 3: |
| | | # speech = speech.unsqueeze(0).to(getattr(torch, self.dtype)) |
| | | # speech_lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1)) |
| | | lfr_factor = max(1, (speech.size()[-1]//80)-1) |
| | | |
| | | batch = {"speech": speech, "speech_lengths": speech_lengths} |