tfds.features.Audio

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tfds.features.FeatureConnector for audio.

Inherits From: Tensor, FeatureConnector

tfds.features.Audio(
    *,
    file_format: Optional[str] = None,
    shape: utils.Shape = (None,),
    dtype: type_utils.TfdsDType = np.int64,
    sample_rate: Optional[int] = None,
    encoding: Union[str, Encoding] = tfds.features.Encoding.NONE,
    doc: feature_lib.DocArg = None,
    lazy_decode: bool = False
)

In _generate_examples, Audio accept:

• A np.ndarray of shape (length,) or (length, channels)
• A path to a .mp3, .wav,... file.
• A file-object (e.g. with path.open('rb') as fobj:)

By default, Audio features are decoded as the raw integer wave form tf.Tensor(shape=(None,), dtype=tf.int64).

When encoding an audio with a different number of channels than expected by the feature, TFDS automatically tries to correct the number of channels.

Args
file_format	str, the audio file format. Can be any format ffmpeg understands. If None, will attempt to infer from the file extension.
shape	tuple, shape of the data.
dtype	The dtype of the data.
sample_rate	int, additional metadata exposed to the user through info.features['audio'].sample_rate. This value isn't used neither in encoding nor decoding.
encoding	Internal encoding. See tfds.features.Encoding for available values.
doc	Documentation of this feature (e.g. description).
lazy_decode	bool, if set True then stores audio as is and decodes it to numpy array when loaded. Otherwise saves decoded audio.

Attributes
doc
dtype	Return the dtype (or dict of dtype) of this FeatureConnector.
np_dtype
numpy_dtype
sample_rate	Returns the sample_rate metadata associated with the dataset.
shape	Return the shape (or dict of shape) of this FeatureConnector.
tf_dtype

Methods

catalog_documentation

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catalog_documentation() -> List[CatalogFeatureDocumentation]

Returns the feature documentation to be shown in the catalog.

cls_from_name

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@classmethod
cls_from_name(
    python_class_name: str
) -> Type['FeatureConnector']

Returns the feature class for the given Python class.

decode_batch_example

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decode_batch_example(
    example_data
)

See base class for details.

decode_example

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decode_example(
    tfexample_data
)

See base class for details.

decode_example_np

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decode_example_np(
    example_data: type_utils.NpArrayOrScalar
) -> type_utils.NpArrayOrScalar

Encode the feature dict into NumPy-compatible input.

Args
example_data	Value to convert to NumPy.

Returns
np_data	Data as NumPy-compatible type: either a Python primitive (bytes, int, etc) or a NumPy array.

decode_ragged_example

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decode_ragged_example(
    example_data
)

See base class for details.

encode_example

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encode_example(
    audio_or_path_or_fobj
)

Convert the given audio into a dict convertible to tf example.

from_config

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@classmethod
from_config(
    root_dir: str
) -> FeatureConnector

Reconstructs the FeatureConnector from the config file.

Usage:

features = FeatureConnector.from_config('path/to/dir')

Args
root_dir	Directory containing the features.json file.

Returns
The reconstructed feature instance.

from_json

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@classmethod
from_json(
    value: Json
) -> FeatureConnector

FeatureConnector factory.

This function should be called from the tfds.features.FeatureConnector base class. Subclass should implement the from_json_content.

Example:

feature = tfds.features.FeatureConnector.from_json(
    {'type': 'Image', 'content': {'shape': [32, 32, 3], 'dtype': 'uint8'} }
)
assert isinstance(feature, tfds.features.Image)

Args
value	dict(type=, content=) containing the feature to restore. Match dict returned by to_json.

Returns
The reconstructed FeatureConnector.

from_json_content

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@classmethod
from_json_content(
    value: Union[Json, feature_pb2.AudioFeature]
) -> 'Audio'

FeatureConnector factory (to overwrite).

Subclasses should overwrite this method. This method is used when importing the feature connector from the config.

This function should not be called directly. FeatureConnector.from_json should be called instead.

See existing FeatureConnectors for implementation examples.

Args
value	FeatureConnector information represented as either Json or a Feature proto. The content must match what is returned by to_json_content.
doc	Documentation of this feature (e.g. description).

Returns
The reconstructed FeatureConnector.

from_proto

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@classmethod
from_proto(
    feature_proto: feature_pb2.Feature
) -> T

Instantiates a feature from its proto representation.

get_serialized_info

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get_serialized_info()

See base class for details.

get_tensor_info

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get_tensor_info() -> feature_lib.TensorInfo

See base class for details.

get_tensor_spec

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get_tensor_spec() -> TreeDict[tf.TensorSpec]

Returns the tf.TensorSpec of this feature (not the element spec!).

Note that the output of this method may not correspond to the element spec of the dataset. For example, currently this method does not support RaggedTensorSpec.

load_metadata

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load_metadata(
    data_dir: epath.PathLike, feature_name: Optional[str]
)

Restore the feature metadata from disk.

If a dataset is re-loaded and generated files exists on disk, this function will restore the feature metadata from the saved file.

Args
data_dir	path to the dataset folder to which save the info (ex: ~/datasets/cifar10/1.2.0/)
feature_name	the name of the feature (from the FeaturesDict key)

repr_html

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repr_html(
    ex: np.ndarray
) -> str

Audio are displayed in the player.

repr_html_batch

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repr_html_batch(
    ex: np.ndarray
) -> str

Returns the HTML str representation of the object (Sequence).

repr_html_ragged

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repr_html_ragged(
    ex: np.ndarray
) -> str

Returns the HTML str representation of the object (Nested sequence).

save_config

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save_config(
    root_dir: str
) -> None

Exports the FeatureConnector to a file.

Args
root_dir	path/to/dir containing the features.json

save_metadata

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save_metadata(
    data_dir: epath.PathLike, feature_name: Optional[str]
) -> None

Save the feature metadata on disk.

This function is called after the data has been generated (by _download_and_prepare) to save the feature connector info with the generated dataset.

Some dataset/features dynamically compute info during _download_and_prepare. For instance:

• Labels are loaded from the downloaded data
• Vocabulary is created from the downloaded data
• ImageLabelFolder compute the image dtypes/shape from the manual_dir

After the info have been added to the feature, this function allow to save those additional info to be restored the next time the data is loaded.

By default, this function do not save anything, but sub-classes can overwrite the function.

Args
data_dir	path to the dataset folder to which save the info (ex: ~/datasets/cifar10/1.2.0/)
feature_name	the name of the feature (from the FeaturesDict key)

to_json

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to_json() -> Json

Exports the FeatureConnector to Json.

Each feature is serialized as a dict(type=..., content=...).

• type: The cannonical name of the feature (module.FeatureName).
• content: is specific to each feature connector and defined in to_json_content. Can contain nested sub-features (like for tfds.features.FeaturesDict and tfds.features.Sequence).

For example:

tfds.features.FeaturesDict({
    'input': tfds.features.Image(),
    'target': tfds.features.ClassLabel(num_classes=10),
})

Is serialized as:

{
    "type": "tensorflow_datasets.core.features.features_dict.FeaturesDict",
    "content": {
        "input": {
            "type": "tensorflow_datasets.core.features.image_feature.Image",
            "content": {
                "shape": [null, null, 3],
                "dtype": "uint8",
                "encoding_format": "png"
            }
        },
        "target": {
            "type":
            "tensorflow_datasets.core.features.class_label_feature.ClassLabel",
            "content": {
              "num_classes": 10
            }
        }
    }
}

Returns
A dict(type=, content=). Will be forwarded to from_json when reconstructing the feature.

to_json_content

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to_json_content() -> feature_pb2.AudioFeature

FeatureConnector factory (to overwrite).

This function should be overwritten by the subclass to allow re-importing the feature connector from the config. See existing FeatureConnector for example of implementation.

Returns
The FeatureConnector metadata in either a dict, or a Feature proto. This output is used in from_json_content when reconstructing the feature.

to_proto

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to_proto() -> feature_pb2.Feature

Exports the FeatureConnector to the Feature proto.

For features that have a specific schema defined in a proto, this function needs to be overriden. If there's no specific proto schema, then the feature will be represented using JSON.

Returns
The feature proto describing this feature.

Class Variables
ALIASES	['tensorflow_datasets.core.features.feature.Tensor']