mindspore.dataset.audio.TimeStretch

class mindspore.dataset.audio.TimeStretch(hop_length=None, n_freq=201, fixed_rate=None)[source]

Stretch Short Time Fourier Transform (STFT) in time without modifying pitch for a given rate.

Note

The shape of the audio waveform to be processed needs to be <…, freq, time, complex=2>. The first dimension represents the real part while the second represents the imaginary.

Parameters

hop_length (int, optional) – Length of hop between STFT windows, i.e. the number of samples between consecutive frames. Default: None, will use n_freq - 1 .
n_freq (int, optional) – Number of filter banks from STFT. Default: 201.
fixed_rate (float, optional) – Rate to speed up or slow down by. Default: None, will keep the original rate.

Raises

TypeError – If hop_length is not of type int.
ValueError – If hop_length is not a positive number.
TypeError – If n_freq is not of type int.
ValueError – If n_freq is not a positive number.
TypeError – If fixed_rate is not of type float.
ValueError – If fixed_rate is not a positive number.
RuntimeError – If input tensor is not in shape of <…, freq, num_frame, complex=2>.

Supported Platforms:: CPU

Examples

>>> import numpy as np
>>> import mindspore.dataset as ds
>>> import mindspore.dataset.audio as audio
>>>
>>> waveform = np.random.random([44, 10, 2])
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"])
>>> transforms = [audio.TimeStretch()]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms, input_columns=["audio"])

Tutorial Examples:

Illustration of audio transforms