Audio data loading

[source]

audio_dataset_from_directory function

tf_keras.utils.audio_dataset_from_directory(
    directory,
    labels="inferred",
    label_mode="int",
    class_names=None,
    batch_size=32,
    sampling_rate=None,
    output_sequence_length=None,
    ragged=False,
    shuffle=True,
    seed=None,
    validation_split=None,
    subset=None,
    follow_links=False,
)

Generates a tf.data.Dataset from audio files in a directory.

If your directory structure is:

main_directory/
...class_a/
......a_audio_1.wav
......a_audio_2.wav
...class_b/
......b_audio_1.wav
......b_audio_2.wav

Then calling audio_dataset_from_directory(main_directory, labels='inferred') will return a tf.data.Dataset that yields batches of audio files from the subdirectories class_a and class_b, together with labels 0 and 1 (0 corresponding to class_a and 1 corresponding to class_b).

Only .wav files are supported at this time.

Arguments

• directory: Directory where the data is located. If labels is "inferred", it should contain subdirectories, each containing audio files for a class. Otherwise, the directory structure is ignored.
• labels: Either "inferred" (labels are generated from the directory structure), None (no labels), or a list/tuple of integer labels of the same size as the number of audio files found in the directory. Labels should be sorted according to the alphanumeric order of the audio file paths (obtained via os.walk(directory) in Python).
• label_mode: String describing the encoding of labels. Options are:
  • "int": means that the labels are encoded as integers (e.g. for sparse_categorical_crossentropy loss).
  • "categorical" means that the labels are encoded as a categorical vector (e.g. for categorical_crossentropy loss)
  • "binary" means that the labels (there can be only 2) are encoded as float32 scalars with values 0 or 1 (e.g. for binary_crossentropy).
  • None (no labels).
• class_names: Only valid if "labels" is "inferred". This is the explicit list of class names (must match names of subdirectories). Used to control the order of the classes (otherwise alphanumerical order is used).
• batch_size: Size of the batches of data. Default: 32. If None, the data will not be batched (the dataset will yield individual samples).
• sampling_rate: Audio sampling rate (in samples per second).
• output_sequence_length: Maximum length of an audio sequence. Audio files longer than this will be truncated to output_sequence_length. If set to None, then all sequences in the same batch will be padded to the length of the longest sequence in the batch.
• ragged: Whether to return a Ragged dataset (where each sequence has its own length). Defaults to False.
• shuffle: Whether to shuffle the data. Defaults to True. If set to False, sorts the data in alphanumeric order.
• seed: Optional random seed for shuffling and transformations.
• validation_split: Optional float between 0 and 1, fraction of data to reserve for validation.
• subset: Subset of the data to return. One of "training", "validation" or "both". Only used if validation_split is set.
• follow_links: Whether to visits subdirectories pointed to by symlinks. Defaults to False.

Returns

A tf.data.Dataset object.

• If label_mode is None, it yields string tensors of shape (batch_size,), containing the contents of a batch of audio files.
• Otherwise, it yields a tuple (audio, labels), where audio has shape (batch_size, sequence_length, num_channels) and labels follows the format described below.

Rules regarding labels format:

• if label_mode is int, the labels are an int32 tensor of shape (batch_size,).
• if label_mode is binary, the labels are a float32 tensor of 1s and 0s of shape (batch_size, 1).
• if label_mode is categorical, the labels are a float32 tensor of shape (batch_size, num_classes), representing a one-hot encoding of the class index.