Source code for transformers.feature_extraction_sequence_utils
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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"""
Sequence feature extraction class for common feature extractors to preprocess sequences.
"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .file_utils import (
PaddingStrategy,
TensorType,
_is_tensorflow,
_is_torch,
is_tf_available,
is_torch_available,
to_numpy,
)
from .utils import logging
logger = logging.get_logger(__name__)
[docs]class SequenceFeatureExtractor(FeatureExtractionMixin):
"""
This is a general feature extraction class for speech recognition.
Args:
feature_size (:obj:`int`):
The feature dimension of the extracted features.
sampling_rate (:obj:`int`):
The sampling rate at which the audio files should be digitalized expressed in Hertz per second (Hz).
padding_value (:obj:`float`):
The value that is used to fill the padding values / vectors.
"""
def __init__(self, feature_size: int, sampling_rate: int, padding_value: float, **kwargs):
self.feature_size = feature_size
self.sampling_rate = sampling_rate
self.padding_value = padding_value
self.padding_side = kwargs.pop("padding_side", "right")
self.return_attention_mask = kwargs.pop("return_attention_mask", True)
super().__init__(**kwargs)
[docs] def pad(
self,
processed_features: Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
],
padding: Union[bool, str, PaddingStrategy] = True,
max_length: Optional[int] = None,
truncation: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
) -> BatchFeature:
"""
Pad input values / input vectors or a batch of input values / input vectors up to predefined length or to the
max sequence length in the batch.
Padding side (left/right) padding values are defined at the feature extractor level (with
``self.padding_side``, ``self.padding_value``)
.. note::
If the ``processed_features`` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors,
the result will use the same type unless you provide a different tensor type with ``return_tensors``. In
the case of PyTorch tensors, you will lose the specific device of your tensors however.
Args:
processed_features (:class:`~transformers.BatchFeature`, list of :class:`~transformers.BatchFeature`, :obj:`Dict[str, List[float]]`, :obj:`Dict[str, List[List[float]]` or :obj:`List[Dict[str, List[float]]]`):
Processed inputs. Can represent one input (:class:`~transformers.BatchFeature` or :obj:`Dict[str,
List[float]]`) or a batch of input values / vectors (list of :class:`~transformers.BatchFeature`,
`Dict[str, List[List[float]]]` or `List[Dict[str, List[float]]]`) so you can use this method during
preprocessing as well as in a PyTorch Dataloader collate function.
Instead of :obj:`List[float]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow
tensors), see the note above for the return type.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
single sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
max_length (:obj:`int`, `optional`):
Maximum length of the returned list and optionally padding length (see above).
truncation (:obj:`bool`):
Activates truncation to cut input sequences longer than :obj:`max_length` to :obj:`max_length`.
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
>= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
return_attention_mask (:obj:`bool`, `optional`):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific feature_extractor's default.
`What are attention masks? <../glossary.html#attention-mask>`__
return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
If set, will return tensors instead of list of python integers. Acceptable values are:
* :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
* :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
* :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
"""
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(processed_features, (list, tuple)) and isinstance(processed_features[0], (dict, BatchFeature)):
processed_features = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"You should supply an instance of :class:`~transformers.BatchFeature` or list of :class:`~transformers.BatchFeature` to this method"
f"that includes {self.model_input_names[0]}, but you provided {list(processed_features.keys())}"
)
required_input = processed_features[self.model_input_names[0]]
return_attention_mask = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if not required_input:
if return_attention_mask:
processed_features["attention_mask"] = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
first_element = required_input[0]
if isinstance(first_element, (list, tuple)):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
index = 0
while len(required_input[index]) == 0:
index += 1
if index < len(required_input):
first_element = required_input[index][0]
if return_tensors is None:
if is_tf_available() and _is_tensorflow(first_element):
return_tensors = "tf"
elif is_torch_available() and _is_torch(first_element):
return_tensors = "pt"
elif isinstance(first_element, (int, float, list, tuple, np.ndarray)):
return_tensors = "np"
else:
raise ValueError(
f"type of {first_element} unknown: {type(first_element)}. "
f"Should be one of a python, numpy, pytorch or tensorflow object."
)
for key, value in processed_features.items():
if isinstance(value[0], (int, float)):
processed_features[key] = to_numpy(value)
else:
processed_features[key] = [to_numpy(v) for v in value]
# Convert padding_strategy in PaddingStrategy
padding_strategy = self._get_padding_strategies(padding=padding, max_length=max_length)
required_input = processed_features[self.model_input_names[0]]
batch_size = len(required_input)
if not all(len(v) == batch_size for v in processed_features.values()):
raise ValueError("Some items in the output dictionary have a different batch size than others.")
truncated_inputs = []
for i in range(batch_size):
inputs = dict((k, v[i]) for k, v in processed_features.items())
# truncation
inputs_slice = self._truncate(
inputs,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
truncation=truncation,
)
truncated_inputs.append(inputs_slice)
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
max_length = max(len(input_slice[self.model_input_names[0]]) for input_slice in truncated_inputs)
padding_strategy = PaddingStrategy.MAX_LENGTH
batch_outputs = {}
for i in range(batch_size):
# padding
outputs = self._pad(
truncated_inputs[i],
max_length=max_length,
padding_strategy=padding_strategy,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
if value.dtype is np.dtype(np.float64):
value = value.astype(np.float32)
batch_outputs[key].append(value)
return BatchFeature(batch_outputs, tensor_type=return_tensors)
def _pad(
self,
processed_features: Union[Dict[str, np.ndarray], BatchFeature],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Pad inputs (on left/right and up to predefined length or max length in the batch)
Args:
processed_features: Dictionary of input values (`np.ndarray[float]`) / input vectors (`List[np.ndarray[float]]`) or batch of inputs values (`List[np.ndarray[int]]`) / input vectors (`List[np.ndarray[int]]`)
max_length: maximum length of the returned list and optionally padding length (see below)
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The feature_extractor padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
>= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
required_input = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) < max_length
if needs_to_be_padded:
difference = max_length - len(required_input)
if self.padding_side == "right":
if return_attention_mask:
attention_mask = np.zeros(max_length, dtype=np.int32)
attention_mask[: len(required_input)] = 1
processed_features["attention_mask"] = attention_mask
padding_shape = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
processed_features[self.model_input_names[0]] = np.pad(
required_input, padding_shape, "constant", constant_values=self.padding_value
)
elif self.padding_side == "left":
if return_attention_mask:
attention_mask = np.zeros(max_length, dtype=np.int32)
attention_mask[-len(required_input) :] = 1
processed_features["attention_mask"] = attention_mask
padding_shape = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
processed_features[self.model_input_names[0]] = np.pad(
required_input, padding_shape, "constant", constant_values=self.padding_value
)
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side))
elif return_attention_mask and "attention_mask" not in processed_features:
processed_features["attention_mask"] = np.ones(len(required_input), dtype=np.int32)
return processed_features
def _truncate(
self,
processed_features: Union[Dict[str, np.ndarray], BatchFeature],
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
truncation: Optional[bool] = None,
):
"""
Truncate inputs to predefined length or max length in the batch
Args:
processed_features: Dictionary of input values (`np.ndarray[float]`) / input vectors (`List[np.ndarray[float]]`) or batch of inputs values (`List[np.ndarray[int]]`) / input vectors (`List[np.ndarray[int]]`)
max_length: maximum length of the returned list and optionally padding length (see below)
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
>= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
truncation: (optional) Activates truncation to cut input sequences longer than `max_length` to `max_length`.
"""
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined.")
required_input = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_truncated = len(required_input) > max_length
if needs_to_be_truncated:
processed_features[self.model_input_names[0]] = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
processed_features["attention_mask"] = processed_features["attention_mask"][:max_length]
return processed_features
def _get_padding_strategies(self, padding=False, max_length=None):
"""
Find the correct padding strategy
"""
# Get padding strategy
if padding is not False:
if padding is True:
padding_strategy = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(padding, PaddingStrategy):
padding_strategy = PaddingStrategy(padding)
elif isinstance(padding, PaddingStrategy):
padding_strategy = padding
else:
padding_strategy = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that" f" max_length is defined"
)
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"Asking to pad but the feature_extractor does not have a padding value. "
"Please select a value to use as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."
)
return padding_strategy