| | |
| | | array = torch.from_numpy(array) |
| | | array = torchaudio.transforms.Resample(orig_freq=audio_fs, |
| | | new_freq=model_fs)(array) |
| | | array = array.numpy() |
| | | if self.mc: |
| | | data[name] = array.transpose(0, 1).numpy() |
| | | data[name] = array.transpose(0, 1) |
| | | else: |
| | | data[name] = array[0].numpy() |
| | | data[name] = array[0] |
| | | |
| | | if self.preprocess is not None: |
| | | data = self.preprocess(uid, data) |
| | |
| | | array = torch.from_numpy(array) |
| | | array = torchaudio.transforms.Resample(orig_freq=audio_fs, |
| | | new_freq=model_fs)(array) |
| | | array = array.numpy() |
| | | if _type == "sound": |
| | | if self.mc: |
| | | data[name] = array.transpose(0, 1).numpy() |
| | | data[name] = array.transpose(0, 1) |
| | | else: |
| | | data[name] = array[0].numpy() |
| | | data[name] = array[0] |
| | | else: |
| | | data[name] = array |
| | | if self.non_iterable_dataset is not None: |