# Source: https://huggingface.co/docs/transformers/v5.0.0rc1/main_classes/feature_extractor.md

# Feature Extractor

A feature extractor is in charge of preparing input features for audio models. This includes feature extraction from sequences, e.g., pre-processing audio files to generate Log-Mel Spectrogram features, and conversion to NumPy and PyTorch tensors.

## FeatureExtractionMixin[[transformers.FeatureExtractionMixin]]

#### transformers.FeatureExtractionMixin[[transformers.FeatureExtractionMixin]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/feature_extraction_utils.py#L220)

This is a feature extraction mixin used to provide saving/loading functionality for sequential and audio feature
extractors.

from_pretrainedtransformers.FeatureExtractionMixin.from_pretrainedhttps://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/feature_extraction_utils.py#L244[{"name": "pretrained_model_name_or_path", "val": ": typing.Union[str, os.PathLike]"}, {"name": "cache_dir", "val": ": typing.Union[str, os.PathLike, NoneType] = None"}, {"name": "force_download", "val": ": bool = False"}, {"name": "local_files_only", "val": ": bool = False"}, {"name": "token", "val": ": typing.Union[str, bool, NoneType] = None"}, {"name": "revision", "val": ": str = 'main'"}, {"name": "**kwargs", "val": ""}]- **pretrained_model_name_or_path** (`str` or `os.PathLike`) --
  This can be either:

  - a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on
    huggingface.co.
  - a path to a *directory* containing a feature extractor file saved using the
    [save_pretrained()](/docs/transformers/v5.0.0rc1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained) method, e.g.,
    `./my_model_directory/`.
  - a path or url to a saved feature extractor JSON *file*, e.g.,
    `./my_model_directory/preprocessor_config.json`.
- **cache_dir** (`str` or `os.PathLike`, *optional*) --
  Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
  standard cache should not be used.
- **force_download** (`bool`, *optional*, defaults to `False`) --
  Whether or not to force to (re-)download the feature extractor files and override the cached versions
  if they exist.
- **proxies** (`dict[str, str]`, *optional*) --
  A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
  'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
- **token** (`str` or `bool`, *optional*) --
  The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
  the token generated when running `hf auth login` (stored in `~/.huggingface`).
- **revision** (`str`, *optional*, defaults to `"main"`) --
  The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
  git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
  identifier allowed by git.

  

  To test a pull request you made on the Hub, you can pass `revision="refs/pr/"`.

  

- **return_unused_kwargs** (`bool`, *optional*, defaults to `False`) --
  If `False`, then this function returns just the final feature extractor object. If `True`, then this
  functions returns a `Tuple(feature_extractor, unused_kwargs)` where *unused_kwargs* is a dictionary
  consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
  `kwargs` which has not been used to update `feature_extractor` and is otherwise ignored.
- **kwargs** (`dict[str, Any]`, *optional*) --
  The values in kwargs of any keys which are feature extractor attributes will be used to override the
  loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is
  controlled by the `return_unused_kwargs` keyword parameter.0A feature extractor of type [FeatureExtractionMixin](/docs/transformers/v5.0.0rc1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin).

Instantiate a type of [FeatureExtractionMixin](/docs/transformers/v5.0.0rc1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin) from a feature extractor, *e.g.* a
derived class of [SequenceFeatureExtractor](/docs/transformers/v5.0.0rc1/en/main_classes/feature_extractor#transformers.SequenceFeatureExtractor).

Examples:

```python
# We can't instantiate directly the base class *FeatureExtractionMixin* nor *SequenceFeatureExtractor* so let's show the examples on a
# derived class: *Wav2Vec2FeatureExtractor*
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
    "facebook/wav2vec2-base-960h"
)  # Download feature_extraction_config from huggingface.co and cache.
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
    "./test/saved_model/"
)  # E.g. feature_extractor (or model) was saved using *save_pretrained('./test/saved_model/')*
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("./test/saved_model/preprocessor_config.json")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
    "facebook/wav2vec2-base-960h", return_attention_mask=False, foo=False
)
assert feature_extractor.return_attention_mask is False
feature_extractor, unused_kwargs = Wav2Vec2FeatureExtractor.from_pretrained(
    "facebook/wav2vec2-base-960h", return_attention_mask=False, foo=False, return_unused_kwargs=True
)
assert feature_extractor.return_attention_mask is False
assert unused_kwargs == {"foo": False}
```

**Parameters:**

pretrained_model_name_or_path (`str` or `os.PathLike`) : This can be either:  - a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on huggingface.co. - a path to a *directory* containing a feature extractor file saved using the [save_pretrained()](/docs/transformers/v5.0.0rc1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained) method, e.g., `./my_model_directory/`. - a path or url to a saved feature extractor JSON *file*, e.g., `./my_model_directory/preprocessor_config.json`.

cache_dir (`str` or `os.PathLike`, *optional*) : Path to a directory in which a downloaded pretrained model feature extractor should be cached if the standard cache should not be used.

force_download (`bool`, *optional*, defaults to `False`) : Whether or not to force to (re-)download the feature extractor files and override the cached versions if they exist.

proxies (`dict[str, str]`, *optional*) : A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.

token (`str` or `bool`, *optional*) : The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use the token generated when running `hf auth login` (stored in `~/.huggingface`).

revision (`str`, *optional*, defaults to `"main"`) : The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.     To test a pull request you made on the Hub, you can pass `revision="refs/pr/"`.   

return_unused_kwargs (`bool`, *optional*, defaults to `False`) : If `False`, then this function returns just the final feature extractor object. If `True`, then this functions returns a `Tuple(feature_extractor, unused_kwargs)` where *unused_kwargs* is a dictionary consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of `kwargs` which has not been used to update `feature_extractor` and is otherwise ignored.

kwargs (`dict[str, Any]`, *optional*) : The values in kwargs of any keys which are feature extractor attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is controlled by the `return_unused_kwargs` keyword parameter.

**Returns:**

A feature extractor of type [FeatureExtractionMixin](/docs/transformers/v5.0.0rc1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin).
#### save_pretrained[[transformers.FeatureExtractionMixin.save_pretrained]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/feature_extraction_utils.py#L341)

Save a feature_extractor object to the directory `save_directory`, so that it can be re-loaded using the
[from_pretrained()](/docs/transformers/v5.0.0rc1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained) class method.

**Parameters:**

save_directory (`str` or `os.PathLike`) : Directory where the feature extractor JSON file will be saved (will be created if it does not exist).

push_to_hub (`bool`, *optional*, defaults to `False`) : Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace).

kwargs (`dict[str, Any]`, *optional*) : Additional key word arguments passed along to the [push_to_hub()](/docs/transformers/v5.0.0rc1/en/main_classes/model#transformers.utils.PushToHubMixin.push_to_hub) method.

## SequenceFeatureExtractor[[transformers.SequenceFeatureExtractor]]

#### transformers.SequenceFeatureExtractor[[transformers.SequenceFeatureExtractor]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/feature_extraction_sequence_utils.py#L30)

This is a general feature extraction class for speech recognition.

padtransformers.SequenceFeatureExtractor.padhttps://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/feature_extraction_sequence_utils.py#L53[{"name": "processed_features", "val": ": typing.Union[transformers.feature_extraction_utils.BatchFeature, list[transformers.feature_extraction_utils.BatchFeature], dict[str, transformers.feature_extraction_utils.BatchFeature], dict[str, list[transformers.feature_extraction_utils.BatchFeature]], list[dict[str, transformers.feature_extraction_utils.BatchFeature]]]"}, {"name": "padding", "val": ": typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = True"}, {"name": "max_length", "val": ": typing.Optional[int] = None"}, {"name": "truncation", "val": ": bool = False"}, {"name": "pad_to_multiple_of", "val": ": typing.Optional[int] = None"}, {"name": "return_attention_mask", "val": ": typing.Optional[bool] = None"}, {"name": "return_tensors", "val": ": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"}]- **processed_features** ([BatchFeature](/docs/transformers/v5.0.0rc1/en/main_classes/image_processor#transformers.BatchFeature), list of [BatchFeature](/docs/transformers/v5.0.0rc1/en/main_classes/image_processor#transformers.BatchFeature), `dict[str, list[float]]`, `dict[str, list[list[float]]` or `list[dict[str, list[float]]]`) --
  Processed inputs. Can represent one input ([BatchFeature](/docs/transformers/v5.0.0rc1/en/main_classes/image_processor#transformers.BatchFeature) or `dict[str, list[float]]`) or a batch of
  input values / vectors (list of [BatchFeature](/docs/transformers/v5.0.0rc1/en/main_classes/image_processor#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 `list[float]` you can have tensors (numpy arrays or PyTorch tensors),
  see the note above for the return type.
- **padding** (`bool`, `str` or [PaddingStrategy](/docs/transformers/v5.0.0rc1/en/internal/file_utils#transformers.utils.PaddingStrategy), *optional*, defaults to `True`) --
  Select a strategy to pad the returned sequences (according to the model's padding side and padding
  index) among:

  - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
    sequence if provided).
  - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
    acceptable input length for the model if that argument is not provided.
  - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
    lengths).
- **max_length** (`int`, *optional*) --
  Maximum length of the returned list and optionally padding length (see above).
- **truncation** (`bool`) --
  Activates truncation to cut input sequences longer than `max_length` to `max_length`.
- **pad_to_multiple_of** (`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** (`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#attention-mask)
- **return_tensors** (`str` or [TensorType](/docs/transformers/v5.0.0rc1/en/internal/file_utils#transformers.TensorType), *optional*) --
  If set, will return tensors instead of list of python integers. Acceptable values are:

  - `'pt'`: Return PyTorch `torch.Tensor` objects.
  - `'np'`: Return Numpy `np.ndarray` objects.0

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`)

If the `processed_features` passed are dictionary of numpy arrays or PyTorch 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.

**Parameters:**

feature_size (`int`) : The feature dimension of the extracted features.

sampling_rate (`int`) : The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).

padding_value (`float`) : The value that is used to fill the padding values / vectors.

## BatchFeature[[transformers.BatchFeature]]

#### transformers.BatchFeature[[transformers.BatchFeature]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/feature_extraction_utils.py#L57)

Holds the output of the [pad()](/docs/transformers/v5.0.0rc1/en/main_classes/feature_extractor#transformers.SequenceFeatureExtractor.pad) and feature extractor specific `__call__` methods.

This class is derived from a python dictionary and can be used as a dictionary.

convert_to_tensorstransformers.BatchFeature.convert_to_tensorshttps://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/feature_extraction_utils.py#L141[{"name": "tensor_type", "val": ": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"}]- **tensor_type** (`str` or [TensorType](/docs/transformers/v5.0.0rc1/en/internal/file_utils#transformers.TensorType), *optional*) --
  The type of tensors to use. If `str`, should be one of the values of the enum [TensorType](/docs/transformers/v5.0.0rc1/en/internal/file_utils#transformers.TensorType). If
  `None`, no modification is done.0

Convert the inner content to tensors.

**Parameters:**

data (`dict`, *optional*) : Dictionary of lists/arrays/tensors returned by the __call__/pad methods ('input_values', 'attention_mask', etc.).

tensor_type (`Union[None, str, TensorType]`, *optional*) : You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.
#### to[[transformers.BatchFeature.to]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/feature_extraction_utils.py#L172)

Send all values to device by calling `v.to(*args, **kwargs)` (PyTorch only). This should support casting in
different `dtypes` and sending the `BatchFeature` to a different `device`.

**Parameters:**

args (`Tuple`) : Will be passed to the `to(...)` function of the tensors.

kwargs (`Dict`, *optional*) : Will be passed to the `to(...)` function of the tensors. To enable asynchronous data transfer, set the `non_blocking` flag in `kwargs` (defaults to `False`).

**Returns:**

`[BatchFeature](/docs/transformers/v5.0.0rc1/en/main_classes/image_processor#transformers.BatchFeature)`

The same instance after modification.

## ImageFeatureExtractionMixin[[transformers.ImageFeatureExtractionMixin]]

#### transformers.ImageFeatureExtractionMixin[[transformers.ImageFeatureExtractionMixin]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/image_utils.py#L559)

Mixin that contain utilities for preparing image features.

center_croptransformers.ImageFeatureExtractionMixin.center_crophttps://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/image_utils.py#L793[{"name": "image", "val": ""}, {"name": "size", "val": ""}]- **image** (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape (n_channels, height, width) or (height, width, n_channels)) --
  The image to resize.
- **size** (`int` or `tuple[int, int]`) --
  The size to which crop the image.0new_imageA center cropped `PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape: (n_channels,
height, width).

Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the
size given, it will be padded (so the returned result has the size asked).

**Parameters:**

image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape (n_channels, height, width) or (height, width, n_channels)) : The image to resize.

size (`int` or `tuple[int, int]`) : The size to which crop the image.

**Returns:**

`new_image`

A center cropped `PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape: (n_channels,
height, width).
#### convert_rgb[[transformers.ImageFeatureExtractionMixin.convert_rgb]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/image_utils.py#L601)

Converts `PIL.Image.Image` to RGB format.

**Parameters:**

image (`PIL.Image.Image`) : The image to convert.
#### expand_dims[[transformers.ImageFeatureExtractionMixin.expand_dims]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/image_utils.py#L654)

Expands 2-dimensional `image` to 3 dimensions.

**Parameters:**

image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`) : The image to expand.
#### flip_channel_order[[transformers.ImageFeatureExtractionMixin.flip_channel_order]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/image_utils.py#L868)

Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of
`image` to a NumPy array if it's a PIL Image.

**Parameters:**

image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`) : The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should be first.
#### normalize[[transformers.ImageFeatureExtractionMixin.normalize]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/image_utils.py#L674)

Normalizes `image` with `mean` and `std`. Note that this will trigger a conversion of `image` to a NumPy array
if it's a PIL Image.

**Parameters:**

image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`) : The image to normalize.

mean (`list[float]` or `np.ndarray` or `torch.Tensor`) : The mean (per channel) to use for normalization.

std (`list[float]` or `np.ndarray` or `torch.Tensor`) : The standard deviation (per channel) to use for normalization.

rescale (`bool`, *optional*, defaults to `False`) : Whether or not to rescale the image to be between 0 and 1. If a PIL image is provided, scaling will happen automatically.
#### rescale[[transformers.ImageFeatureExtractionMixin.rescale]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/image_utils.py#L615)

Rescale a numpy image by scale amount
#### resize[[transformers.ImageFeatureExtractionMixin.resize]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/image_utils.py#L726)

Resizes `image`. Enforces conversion of input to PIL.Image.

**Parameters:**

image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`) : The image to resize.

size (`int` or `tuple[int, int]`) : The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be matched to this.  If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If `size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size).

resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`) : The filter to user for resampling.

default_to_square (`bool`, *optional*, defaults to `True`) : How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a square (`size`,`size`). If set to `False`, will replicate [`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize) with support for resizing only the smallest edge and providing an optional `max_size`.

max_size (`int`, *optional*, defaults to `None`) : The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater than `max_size` after being resized according to `size`, then the image is resized again so that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller edge may be shorter than `size`. Only used if `default_to_square` is `False`.

**Returns:**

`image`

A resized `PIL.Image.Image`.
#### rotate[[transformers.ImageFeatureExtractionMixin.rotate]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/image_utils.py#L885)

Returns a rotated copy of `image`. This method returns a copy of `image`, rotated the given number of degrees
counter clockwise around its centre.

**Parameters:**

image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`) : The image to rotate. If `np.ndarray` or `torch.Tensor`, will be converted to `PIL.Image.Image` before rotating.

**Returns:**

`image`

A rotated `PIL.Image.Image`.
#### to_numpy_array[[transformers.ImageFeatureExtractionMixin.to_numpy_array]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/image_utils.py#L622)

Converts `image` to a numpy array. Optionally rescales it and puts the channel dimension as the first
dimension.

**Parameters:**

image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`) : The image to convert to a NumPy array.

rescale (`bool`, *optional*) : Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Will default to `True` if the image is a PIL Image or an array/tensor of integers, `False` otherwise.

channel_first (`bool`, *optional*, defaults to `True`) : Whether or not to permute the dimensions of the image to put the channel dimension first.
#### to_pil_image[[transformers.ImageFeatureExtractionMixin.to_pil_image]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0rc1/src/transformers/image_utils.py#L571)

Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if
needed.

**Parameters:**

image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`) : The image to convert to the PIL Image format.

rescale (`bool`, *optional*) : Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default to `True` if the image type is a floating type, `False` otherwise.