vak.datasets.frame_classification.frames_dataset.FramesDataset

class vak.datasets.frame_classification.frames_dataset.FramesDataset(dataset_path: str | Path, dataset_df: DataFrame, input_type: str, split: str, sample_ids: ndarray[Any, dtype[_ScalarType_co]], inds_in_sample: ndarray[Any, dtype[_ScalarType_co]], frame_dur: float, subset: str | None = None, item_transform: Callable | None = None)

Bases: object

A dataset class used for neural network models with the frame classification task, where the source data consists of audio signals or spectrograms of varying lengths.

dataset_path

Path to directory that represents a frame classification dataset, as created by vak.prep.prep_frame_classification_dataset().

Type:

pathlib.Path

split

The name of a split from the dataset, one of {‘train’, ‘val’, ‘test’}.

Type:

str

subset

Name of subset to use. If specified, this takes precedence over split. Subsets are typically taken from the training data for use when generating a learning curve.

Type:

str, optional

dataset_df

A frame classification dataset, represented as a pandas.DataFrame. This will be only the rows that correspond to either subset or split from the dataset_df that was passed in when instantiating the class.

Type:

pandas.DataFrame

frames_paths

Paths to npy files containing frames, either spectrograms or audio signals that are input to the model.

Type:

numpy.ndarray

frame_labels_paths

Paths to npy files containing vectors with a label for each frame. The targets for the outputs of the model.

Type:

numpy.ndarray

input_type

The type of input to the neural network model. One of {‘audio’, ‘spect’}.

Type:

str

sample_ids

Indexing vector representing which sample from the dataset every frame belongs to.

Type:

numpy.ndarray

inds_in_sample

Indexing vector representing which index within each sample from the dataset that every frame belongs to.

Type:

numpy.ndarray

frame_dur

Duration of a frame, i.e., a single sample in audio or a single timebin in a spectrogram.

Type:

float

item_transform

Transform applied to each item \((x, y)\) returned by FramesDataset.__getitem__().

Type:

callable, optional

__init__(dataset_path: str | Path, dataset_df: DataFrame, input_type: str, split: str, sample_ids: ndarray[Any, dtype[_ScalarType_co]], inds_in_sample: ndarray[Any, dtype[_ScalarType_co]], frame_dur: float, subset: str | None = None, item_transform: Callable | None = None)

Initialize a new instance of a FramesDataset.

Parameters:

• dataset_path (pathlib.Path) – Path to directory that represents a frame classification dataset, as created by vak.prep.prep_frame_classification_dataset().

• dataset_df (pandas.DataFrame) – A frame classification dataset, represented as a pandas.DataFrame.

• input_type (str) – The type of input to the neural network model. One of {‘audio’, ‘spect’}.

• split (str) – The name of a split from the dataset, one of {‘train’, ‘val’, ‘test’}.

• sample_ids (numpy.ndarray) – Indexing vector representing which sample from the dataset every frame belongs to.

• inds_in_sample (numpy.ndarray) – Indexing vector representing which index within each sample from the dataset that every frame belongs to.

• frame_dur (float) – Duration of a frame, i.e., a single sample in audio or a single timebin in a spectrogram.

• subset (str, optional) – Name of subset to use. If specified, this takes precedence over split. Subsets are typically taken from the training data for use when generating a learning curve.

• item_transform (callable, optional) – Transform applied to each item \((x, y)\) returned by FramesDataset.__getitem__().

Methods

__init__(dataset_path, dataset_df, ...[, ...])	Initialize a new instance of a FramesDataset.
from_dataset_path(dataset_path[, split, ...])	Make a FramesDataset instance, given the path to a frame classification dataset.

Attributes

duration
shape

classmethod from_dataset_path(dataset_path: str | Path, split: str = 'val', subset: str | None = None, item_transform: Callable | None = None)

Make a FramesDataset instance, given the path to a frame classification dataset.

Parameters:

• dataset_path (pathlib.Path) – Path to directory that represents a frame classification dataset, as created by vak.prep.prep_frame_classification_dataset().

• split (str) – The name of a split from the dataset, one of {‘train’, ‘val’, ‘test’}.

• subset (str, optional) – Name of subset to use. If specified, this takes precedence over split. Subsets are typically taken from the training data for use when generating a learning curve.

• item_transform (callable, optional) – Transform applied to each item \((x, y)\) returned by FramesDataset.__getitem__().

Returns:

frames_dataset

Return type:

FramesDataset