Generates a tf.data.Dataset
from audio files in a directory.
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
)
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.
Args |
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). Default: False.
|
shuffle
|
Whether to shuffle the data. Default: 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.