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| | | import numpy as np |
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| | | import torch |
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| | | from typeguard import check_argument_types |
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| | | from typeguard import check_return_type |
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| | | |
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| | | from funasr.modules.nets_utils import pad_list |
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| | | not_sequence: Collection[str] = (), |
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| | | max_sample_size=None |
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| | | ): |
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| | | assert check_argument_types() |
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| | | self.float_pad_value = float_pad_value |
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| | | self.int_pad_value = int_pad_value |
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| | | self.not_sequence = set(not_sequence) |
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| | |
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| | | ) -> Tuple[List[str], Dict[str, torch.Tensor]]: |
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| | | """Concatenate ndarray-list to an array and convert to torch.Tensor. |
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| | | """ |
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| | | assert check_argument_types() |
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| | | uttids = [u for u, _ in data] |
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| | | data = [d for _, d in data] |
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| | | |
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| | |
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| | | output[key + "_lengths"] = lens |
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| | | |
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| | | output = (uttids, output) |
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| | | assert check_return_type(output) |
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| | | return output |
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| | | |
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| | | def crop_to_max_size(feature, target_size): |
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