mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
87 lines
2.3 KiB
Python
87 lines
2.3 KiB
Python
import collections
|
|
from pathlib import Path
|
|
from typing import Union
|
|
|
|
import numpy as np
|
|
from typeguard import check_argument_types
|
|
|
|
from funasr.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)
|