mindspore.dataset.audio.MuLawDecoding

class mindspore.dataset.audio.MuLawDecoding(quantization_channels=256)

Decode mu-law encoded signal, refer to mu-law algorithm .

Parameters

quantization_channels (int, optional) – Number of channels, which must be positive. Default: 256.

Raises

• TypeError – If quantization_channels is not of type int.

• ValueError – If quantization_channels is not a positive number.

• RuntimeError – If input tensor is not in shape of <…, time>.

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, 3, 4])  # 5 samples
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"])
>>> transforms = [audio.MuLawDecoding()]
>>> 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
(3, 4) float64
>>>
>>> # Use the transform in eager mode
>>> waveform = np.random.random([3, 4])  # 1 sample
>>> output = audio.MuLawDecoding()(waveform)
>>> print(output.shape, output.dtype)
(3, 4) float64

Tutorial Examples:

• Illustration of audio transforms