mindspore.dataset.audio.Magphase
- class mindspore.dataset.audio.Magphase(power=1.0)[source]
Separate a complex-valued spectrogram with shape \((..., 2)\) into its magnitude and phase.
- Parameters
power (float) – Power of the norm, which must be non-negative. Default:
1.0
.- Raises
RuntimeError – If the shape of input audio waveform does not match (…, 2).
- 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, 2]) # 5 samples >>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"]) >>> transforms = [audio.Magphase()] >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms, input_columns=["audio"], ... output_columns=["spect", "phase"]) >>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True): ... print(item["spect"].shape, item["spect"].dtype) ... break (16,) float64 >>> >>> # Use the transform in eager mode >>> waveform = np.random.random([16, 2]) # 1 sample >>> output = audio.Magphase()(waveform) >>> print(output[0].shape, output[0].dtype) (16,) float64
- Tutorial Examples: