mindspore.dataset.audio.Fade

class mindspore.dataset.audio.Fade(fade_in_len=0, fade_out_len=0, fade_shape=FadeShape.LINEAR)[source]

Add a fade in and/or fade out to an waveform.

Parameters

  • fade_in_len (int, optional) – Length of fade-in (time frames), which must be non-negative. Default: 0.

  • fade_out_len (int, optional) – Length of fade-out (time frames), which must be non-negative. Default: 0.

  • fade_shape (FadeShape, optional) –

    Shape of fade, five different types can be chosen as defined in FadeShape. Default: FadeShape.LINEAR.

    • FadeShape.QUARTER_SINE, means it tend to 0 in an quarter sin function.

    • FadeShape.HALF_SINE, means it tend to 0 in an half sin function.

    • FadeShape.LINEAR, means it linear to 0.

    • FadeShape.LOGARITHMIC, means it tend to 0 in an logrithmic function.

    • FadeShape.EXPONENTIAL, means it tend to 0 in an exponential function.

Raises
Supported Platforms:

CPU

Examples

>>> import numpy as np
>>> import mindspore.dataset as ds
>>> import mindspore.dataset.audio as audio
>>>
>>> # Use the transform in dataset pipeline mode
>>> waveform = np.random.random([5, 16])  # 5 samples
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"])
>>> transforms = [audio.Fade(fade_in_len=3, fade_out_len=2, fade_shape=audio.FadeShape.LINEAR)]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms, input_columns=["audio"])
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["audio"].shape, item["audio"].dtype)
...     break
(16,) float64
>>>
>>> # Use the transform in eager mode
>>> waveform = np.random.random([16])  # 1 sample
>>> output = audio.Fade(fade_in_len=3, fade_out_len=2, fade_shape=audio.FadeShape.LINEAR)(waveform)
>>> print(output.shape, output.dtype)
(16,) float64
Tutorial Examples: