import collections
from pathlib import Path
from typing import Union
import numpy as np
from typeguard import typechecked
from espnet2.fileio.read_text import load_num_sequence_text
[docs]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)
"""
@typechecked
def __init__(
self,
shape_file: Union[Path, str],
dtype: Union[str, np.dtype] = "float32",
loader_type: str = "csv_int",
):
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)
[docs]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)
"""
@typechecked
def __init__(
self,
shape_file: Union[Path, str],
low: int,
high: int = None,
dtype: Union[str, np.dtype] = "int64",
loader_type: str = "csv_int",
):
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)