Quantifiers¶
Quantifiers are used to specify rules for how a sequence of transformations or quantifiers should be applied.
Each quantifier class is a subclass of the more generic Quantifier
base class,
which provides a basic interface that can also be used to write custom quantifiers,
though there is rarely a need for this.
As with transformations, Sigment offers a familiar interface for quantifiers, taking inspiration from popular augmentation libraries such as imgaug and nlpaug.
Section contents
Available quantifiers¶
In the below quantifiers, the steps argument is a list of Transform
and/or Quantifier
objects,
specifying a sequence of transformations or quantifiers to be applied.
Some Of¶

class
sigment.quantifiers.
SomeOf
(steps, n, random_order=False, random_state=None)[source]¶ Randomly applies a number of the provided transformation or quantifier steps.
 Parameters
 n: int [n > 0] or (int, int)
The number of transformation or quantifier steps to apply.
Notes
The chosen steps will still be applied in the same order they were defined by default.
Using quantifiers¶
Each quantifier class comes with a number of methods that can be used to apply the transformations to either a numpy.ndarray
or WAV file.
All quantifiers accept the random_order and random_state parameters, inherited from the Quantifier
base class described below.

class
sigment.quantifiers.
Quantifier
(steps, random_order=False, random_state=None)[source]¶ Specifies how to execute transformation or quantifier steps.
Note
As this is a base class, it should not be directly instantiated.
 Parameters
 steps: List[Transform, Quantifier]
A collection of transformation or quantifier steps to apply.
 random_order: bool
Whether or not to randomize the order of execution of steps.
 random_state: numpy.RandomState, int, optional
A random state object or seed for reproducible randomness.

__call__
(self, X, sr=None)[source]¶ Runs the transformations or quantifiers on a provided input signal.
 Parameters
 X: numpy.ndarray [shape (T,) or (1xT) for mono, (2xT) for stereo]
The input signal to transform.
 sr: int [sr > 0], optional
The sample rate for the input signal.
Note
Not required if using transformations that do not require a sample rate.
 Returns
 transformed: numpy.ndarray [shape (T,) for mono, (2xT) for stereo]
The transformed signal, clipped so that it fits into the \([1,1]\) range required for 32bit floating point WAVs.
Note
If a mono signal X of shape (1xT) was used, the output is reshaped to (T,).
Examples
>>> import numpy as np >>> from sigment.quantifiers import SomeOf >>> from sigment.transforms import GaussianWhiteNoise, PitchShift, EdgeCrop >>> # Create an example stereo signal. >>> X = np.array([ >>> [0.325, 0.53 , 0.393, 0.211], >>> [0.21 , 0.834, 0.022, 0.38 ] >>> ]) >>> # Use the SomeOf quantifier to run only 1 to 2 of the transformations. >>> transform = SomeOf([ >>> GaussianWhiteNoise(scale=(0.05, 0.15)), >>> PitchShift(n_steps=(1., 1.)), >>> EdgeCrop(side='start', crop_size=(0.02, 0.05)) >>> ], n=(1, 2)) >>> # Run the __call__ method on the quantifier object to transform X. >>> # NOTE: Pitch shifting requires a sample rate when called, therefore >>> # we must call the quantifier with a specified sample rate parameter. >>> X_transform = transform(X, sr=10)

generate
(self, X, n, sr=None)[source]¶ Runs the transformations or quantifiers on a provided input signal, producing multiple augmented copies of the input signal.
 Parameters
 X: numpy.ndarray [shape (T,) or (1xT) for mono, (2xT) for stereo]
The input signal to transform.
 n: int [n > 0]
Number of augmented copies of X to generate.
 sr: int [sr > 0], optional
The sample rate for the input signal.
Note
Not required if not using transformations that require a sample rate.
 Returns
 augmented: List[numpy.ndarray] or numpy.ndarray
The augmented copies (or copy if n=1) of the signal X, clipped so that they fit into the \([1,1]\) range required for 32bit floating point WAVs.
Note
If a mono signal X of shape (1xT) was used, the output is reshaped to (T,).
Examples
>>> import numpy as np >>> from sigment.quantifiers import Sometimes, OneOf >>> from sigment.transforms import LinearFade, GaussianWhiteNoise >>> # Create an example stereo signal. >>> X = np.array([ >>> [0.325, 0.53 , 0.393, 0.211], >>> [0.21 , 0.834, 0.022, 0.38 ] >>> ]) >>> # Use the Sometimes and OneOf quantifiers to sometimes (with probability 0.65) >>> # apply a Gaussian white noise transformation and either a fadein or fadeout. >>> transform = Sometimes([ >>> GaussianWhiteNoise(scale=(0.05, 0.15)), >>> OneOf([ >>> LinearFade(direction='in', fade_size=(0.05, 0.1)), >>> LinearFade(direction='out', fade_size=(0.05, 0.1)) >>> ]) >>> ], p=0.65) >>> # Generate 5 augmented versions of X, using the quantifier object. >>> Xs_transform = transform.generate(X, n=5)

apply_to_wav
(self, source, out=None)¶ Applies the augmentation to the provided input WAV file and writes the resulting signal back to a WAV file.
Note
The resulting signal is always clipped so that it fits into the \([1,1]\) range required for 32bit floating point WAVs.
 Parameters
 source: str, Path or pathlike
Path to the input WAV file.
 out: str, Path or pathlike
Output WAV path for the augmented signal.
Warning
If out is set to
None
(which is the default) or the same as source, the input WAV file will be overwritten!
Examples
>>> from sigment import * >>> # Create a transformation or quantifier object. >>> transform = ... >>> # Apply the transformation to the input WAV file and write it to the output file >>> transform.apply_to_wav('in.wav', 'out.wav')

generate_from_wav
(self, source, n=1)¶ Applies the augmentation to the provided input WAV file and returns a
numpy.ndarray
. Parameters
 source: str, Path or pathlike
Path to the input WAV file.
 n: int [n > 0]
Number of augmented versions of the source signal to generate.
 Returns
 augmented: List[numpy.ndarray] or numpy.ndarray
The augmented versions (or version if n=1) of the source signal, clipped so that they fit into the \([1,1]\) range required for 32bit floating point WAVs.
Examples
>>> from sigment import * >>> # Create a transformation or quantifier object. >>> transform = ... >>> # Generate 5 augmented versions of the signal data from 'signal.wav' as numpy.ndarrays. >>> transformed = transform.generate_from_wav('signal.wav', n=5)