| import collections | |
| from pathlib import Path | |
| from typing import Union | |
| import numpy as np | |
| from typeguard import check_argument_types | |
| from espnet2.fileio.read_text import load_num_sequence_text | |
| class FloatRandomGenerateDataset(collections.abc.Mapping): | |
| """Generate float array from shape.txt. | |
| Examples: | |
| shape.txt | |
| uttA 123,83 | |
| uttB 34,83 | |
| >>> dataset = FloatRandomGenerateDataset("shape.txt") | |
| >>> array = dataset["uttA"] | |
| >>> assert array.shape == (123, 83) | |
| >>> array = dataset["uttB"] | |
| >>> assert array.shape == (34, 83) | |
| """ | |
| def __init__( | |
| self, | |
| shape_file: Union[Path, str], | |
| dtype: Union[str, np.dtype] = "float32", | |
| loader_type: str = "csv_int", | |
| ): | |
| assert check_argument_types() | |
| shape_file = Path(shape_file) | |
| self.utt2shape = load_num_sequence_text(shape_file, loader_type) | |
| self.dtype = np.dtype(dtype) | |
| def __iter__(self): | |
| return iter(self.utt2shape) | |
| def __len__(self): | |
| return len(self.utt2shape) | |
| def __getitem__(self, item) -> np.ndarray: | |
| shape = self.utt2shape[item] | |
| return np.random.randn(*shape).astype(self.dtype) | |
| class IntRandomGenerateDataset(collections.abc.Mapping): | |
| """Generate float array from shape.txt | |
| Examples: | |
| shape.txt | |
| uttA 123,83 | |
| uttB 34,83 | |
| >>> dataset = IntRandomGenerateDataset("shape.txt", low=0, high=10) | |
| >>> array = dataset["uttA"] | |
| >>> assert array.shape == (123, 83) | |
| >>> array = dataset["uttB"] | |
| >>> assert array.shape == (34, 83) | |
| """ | |
| def __init__( | |
| self, | |
| shape_file: Union[Path, str], | |
| low: int, | |
| high: int = None, | |
| dtype: Union[str, np.dtype] = "int64", | |
| loader_type: str = "csv_int", | |
| ): | |
| assert check_argument_types() | |
| shape_file = Path(shape_file) | |
| self.utt2shape = load_num_sequence_text(shape_file, loader_type) | |
| self.dtype = np.dtype(dtype) | |
| self.low = low | |
| self.high = high | |
| def __iter__(self): | |
| return iter(self.utt2shape) | |
| def __len__(self): | |
| return len(self.utt2shape) | |
| def __getitem__(self, item) -> np.ndarray: | |
| shape = self.utt2shape[item] | |
| return np.random.randint(self.low, self.high, size=shape, dtype=self.dtype) | |