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import logging
from collections import OrderedDict
from copy import deepcopy
from functools import partial
from typing import Any, List, Union
import numpy as np
import torch
from speechbrain.dataio.dataset import DynamicItemDataset
from speechbrain.utils.data_pipeline import DynamicItem
from torch.nn.utils.rnn import pad_sequence
logger = logging.getLogger(__name__)
class AugmentedDynamicItemDataset(DynamicItemDataset):
def __init__(
self,
data,
dynamic_items=[],
output_keys=[],
tools: dict = {},
):
super().__init__(data, dynamic_items, output_keys)
assert isinstance(data, OrderedDict)
self._tools = {}
for name, item in tools.items():
self.add_tool(name, item)
def _dynamic_tools(self, id, name):
return self._tools[name]
def add_tool(self, name: str, item: Any) -> None:
"""
Store the :code:`item` in this dataset with the name :code:`name` so it can be used in
:code:`__getitem__`. That is, you can retrieve the :code:`item` with the :code:`takes` argument
of :obj:`add_dynamic_item`.
.. code-block:: python
def tokenize_func(tokenizer, text):
return tokenizer(text)
self.add_tool("tokenizer", tokenizer)
self.add_dynamic_item(tokenize_func, takes=["tokenizer", "text"], provides="tokenized_ids")
You can also later retreive this tool by :obj:`get_tool` or :obj:`all_tools`
"""
self._tools[name] = item
self.add_dynamic_item(
partial(self._dynamic_tools, name=name), takes="id", provides=name
)
def add_tools(self, tools: dict) -> None:
"""
Store each key-value pair in :code:`tools` as a tool. See :obj:`add_tool` for more information
"""
for key, value in tools.items():
self.add_tool(key, value)
def get_tool(self, key) -> Any:
"""
See :obj:`add_tool` for more information
"""
return self._tools[key]
def has_tool(self, key) -> bool:
"""
Checks whether has a tool named :code:`key`.
"""
return key in self._tools
def all_tools(self, copy=True) -> dict:
"""
Return:
dict
Containing all the tools in :code:`name: value` pairs.
See :obj:`add_tool` for more information
"""
return deepcopy(self._tools) if copy else self._tools
def update_output_keys(self, keys: dict) -> None:
"""
Compared to :obj:`set_output_keys`, this method update the output keys mapping
instead of replace it with a new dictionary. This can be useful when you only
want to replace a few mapping and leave others unchanged.
"""
mapping = self.pipeline.output_mapping.copy()
mapping.update(keys)
self.set_output_keys(mapping)
def keys(self) -> List[str]:
"""
List all the :code:`static_item` and :code:`dynamic_item` in the dataset.
:code:`static_item` resides directly in the memory and are given by the dataset
initialization dictionary. :code:`dynamic_item` are content computed
on-the-fly basing on :code:`static_item`.
"""
available_keys: List[str] = list(self.pipeline.key_to_node.keys())
for dynamic_item in self.pipeline.dynamic_items:
provides = dynamic_item.provides
assert isinstance(provides, (list, tuple))
available_keys += provides
available_keys = [
key
for key in available_keys
if not key.startswith("_") and key not in self._tools
]
return available_keys
def set_info(self, info):
self._info = info
def get_info(self, index):
with self.output_keys_as(self._info):
return self.__getitem__(index)
def __getitem__(self, index):
"""
This remain all the usage of the original SpeechBrain DynamicItemDataset.__getitem__,
except that by default it uses :obj:`keys` as the default :code:`output_keys`
"""
if len(self.pipeline.output_mapping) == 0:
with self.output_keys_as(self.keys()):
return super().__getitem__(index)
else:
return super().__getitem__(index)
class DataPipe:
def __call__(
self, dataset: Union[dict, AugmentedDynamicItemDataset], tools: dict = None
) -> Any:
if isinstance(dataset, dict):
dataset = AugmentedDynamicItemDataset(dataset)
if tools is not None:
dataset.add_tools(tools)
return self.forward(dataset)
def forward(
self, dataset: AugmentedDynamicItemDataset
) -> AugmentedDynamicItemDataset:
raise NotImplementedError
def __getattribute__(self, name):
value = super().__getattribute__(name)
if isinstance(value, DynamicItem):
value.func = value.func.__get__(self)
return value
class SequentialDataPipe(DataPipe):
def __init__(self, *pipes: List[DataPipe]) -> None:
self._pipes = pipes
def forward(
self, dataset: AugmentedDynamicItemDataset
) -> AugmentedDynamicItemDataset:
for pipe in self._pipes:
dataset = pipe(dataset)
return dataset
def default_collate_fn(samples, padding_value: int = 0):
"""
Each item in **DynamicItemDataset** is a dict
This function pad (or transform into numpy list) a batch of dict
Args:
samples (List[dict]): Suppose each Container is in
.. code-block:: yaml
wav: a single waveform
label: a single string
Return:
dict
.. code-block:: yaml
wav: padded waveforms
label: np.array([a list of string labels])
"""
assert isinstance(samples[0], dict)
keys = samples[0].keys()
padded_samples = dict()
for key in keys:
values = [sample[key] for sample in samples]
if isinstance(values[0], int):
values = torch.LongTensor(values)
elif isinstance(values[0], float):
values = torch.FloatTensor(values)
elif isinstance(values[0], np.ndarray):
values = [torch.from_numpy(value).float() for value in values]
values = pad_sequence(values, batch_first=True, padding_value=padding_value)
elif isinstance(values[0], torch.Tensor):
values = pad_sequence(values, batch_first=True, padding_value=padding_value)
else:
values = np.array(values, dtype="object")
padded_samples[key] = values
return padded_samples
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