Function

class auglib.transform.Function(function, function_args=None, *, preserve_level=False, bypass_prob=None)

Apply a custom function to the signal.

The function gets as input a numpy.ndarray of shape (channels, samples) with the content of the audio signal and the sampling rate. Additional arguments can be provided with the function_args dictionary. Observable arguments (e.g. auglib.IntUni) are automatically evaluated. The function must return a numpy.ndarray.

Note that the object is not serializable if the function relies on other locally defined functions. For instance, in this example object f is not serializable:

def _plus_1(x, sr):
    return x + 1


def plus_1(x, sr):
    return _plus_1(x, sr)  # calls local function -> not serializable


f = auglib.transform.Function(plus_1)

Parameters:

• function (collections.abc.Callable[..., numpy.ndarray]) – (lambda) function object

• function_args (dict) – dictionary with additional function arguments

• preserve_level (bool | auglib.core.observe.Base) – if True the root mean square value of the augmented signal will be the same as before augmentation

• bypass_prob (float | auglib.core.observe.Base) – probability to bypass the transformation

Examples

Define a shutter function and set every second sample to 0 in a speech signal.

>>> import audb
>>> import audiofile
>>> import audplot
>>> import auglib
>>> import numpy as np
>>> def shutter(signal, sampling_rate, block=1, non_block=1):
...     n = 0
...     augmented_signal = signal.copy()
...     while n < augmented_signal.shape[1]:
...         augmented_signal[:, n + non_block : n + non_block + block] = 0
...         n += block + non_block
...     return augmented_signal
>>> transform = auglib.transform.Function(shutter)
>>> files = audb.load_media("emodb", "wav/03a01Fa.wav", version="1.4.1")
>>> signal, _ = audiofile.read(files[0])
>>> augmented_signal = transform(signal)
>>> audplot.waveform(augmented_signal)

Repeatedly set 400 samples to zero, and leave 800 samples untouched of a speech signal.

>>> transform = auglib.transform.Function(
...     shutter, {"block": 400, "non_block": 800}
... )
>>> augmented_signal = transform(signal)
>>> audplot.waveform(augmented_signal)

__call__()

Function.__call__(signal, sampling_rate=None)

Apply transform to signal.

Parameters:

• signal (numpy.ndarray) – signal to be transformed

• sampling_rate (int) – sampling rate in Hz

Return type:

numpy.ndarray

Returns:

augmented signal

Raises:

• ValueError – if the signal shape is not support by chosen transform parameters

• ValueError – if sampling_rate is None, but the transform requires a sampling rate

• RuntimeError – if the given sampling rate is incompatible with the transform

arguments

Function.arguments

Returns arguments that are serialized.

Returns:

Dictionary of arguments and their values.

Raises:

RuntimeError – if arguments are found that are not assigned to attributes of the same name

Examples

>>> import audobject.testing
>>> o = audobject.testing.TestObject("test", point=(1, 1))
>>> o.arguments
{'name': 'test', 'point': (1, 1)}

borrowed_arguments

Function.borrowed_arguments

Returns borrowed arguments.

Returns:

Dictionary with borrowed arguments.

from_dict()

static Function.from_dict(d, root=None, **kwargs)

Return type:

audobject.core.object.Object

from_yaml()

static Function.from_yaml(path_or_stream, **kwargs)

Return type:

audobject.core.object.Object

from_yaml_s()

static Function.from_yaml_s(yaml_string, **kwargs)

Return type:

audobject.core.object.Object

hidden_arguments

Function.hidden_arguments

Returns hidden arguments.

Returns:

List with names of hidden arguments.

id

Function.id

Object identifier.

The ID of an object ID is created from its non-hidden arguments.

Returns:

object identifier

Examples

>>> class Foo(Object):
...     def __init__(self, bar: str):
...         self.bar = bar
>>> foo1 = Foo("I am unique!")
>>> foo1.id
'893df240-babe-d796-cdf1-c436171b7a96'
>>> foo2 = Foo("I am different!")
>>> foo2.id
'9303f2a5-bfc9-e5ff-0ffa-a9846e2d2190'
>>> foo3 = Foo("I am unique!")
>>> foo1.id == foo3.id
True

is_loaded_from_dict

Function.is_loaded_from_dict

Check if object was loaded from a dictionary.

Returns True if object was initialized from a dictionary, e.g. after loading it from a YAML file.

Returns:

True if object was loaded from a dictionary,

otherwise False

resolvers

Function.resolvers

Return resolvers.

Returns:

Dictionary with resolvers.

short_id

Function.short_id

Short object identifier.

The short ID consists of eight characters and is created from its non-hidden arguments.

Returns:

short object identifier

Examples

>>> class Foo(Object):
...     def __init__(self, bar: str):
...         self.bar = bar
>>> foo1 = Foo("I am unique!")
>>> foo1.id
'893df240-babe-d796-cdf1-c436171b7a96'
>>> foo1.short_id
'171b7a96'
>>> foo2 = Foo("I am different!")
>>> foo2.short_id
'6e2d2190'
>>> foo3 = Foo("I am unique!")
>>> foo1.short_id == foo3.short_id
True

to_dict()

Function.to_dict(*, include_version=True, flatten=False, root=None)

Converts object to a dictionary.

Includes items from audobject.Object.arguments. If an argument has a resolver, its value is encoded. Usually, the object can be re-instantiated using audobject.Object.from_dict(). However, if flatten=True, this is not possible.

Parameters:

• include_version (bool) – add version to class name

• flatten (bool) – flatten the dictionary

• root (str) – if file is written to disk, set to target directory

Return type:

collections.abc.Mapping[str, typing.Union[bool, datetime.datetime, dict, float, int, list, None, str]]

Returns:

dictionary that represent the object

Examples

>>> import audobject.testing
>>> o = audobject.testing.TestObject("test", point=(1, 1))
>>> o.to_dict(include_version=False)
{'$audobject.core.testing.TestObject': {'name': 'test', 'point': [1, 1]}}
>>> o.to_dict(flatten=True)
{'name': 'test', 'point.0': 1, 'point.1': 1}

to_samples()

Function.to_samples(value, sampling_rate=None, *, length=None, allow_negative=True)

Convert duration value to samples.

Return type:

int

to_yaml()

Function.to_yaml(path_or_stream, *, include_version=True)

Save object to YAML file.

Parameters:

• path_or_stream (str | io.IOBase) – file path or stream

• include_version (bool) – add version to class name

to_yaml_s()

Function.to_yaml_s(*, include_version=True)

Convert object to YAML string.

Parameters:

include_version (bool) – add version to class name

Return type:

str

Returns:

YAML string

Examples

>>> import audobject.testing
>>> o = audobject.testing.TestObject("test", point=(1, 1))
>>> print(o.to_yaml_s(include_version=False))
$audobject.core.testing.TestObject:
  name: test
  point:
  - 1
  - 1