mindspore.dataset.audio.MelScale

class mindspore.dataset.audio.MelScale(n_mels=128, sample_rate=16000, f_min=0.0, f_max=None, n_stft=201, norm=NormType.NONE, mel_type=MelType.HTK)[source]

Convert normal STFT to STFT at the Mel scale.

Parameters

n_mels (int, optional) – Number of mel filterbanks. Default: 128.
sample_rate (int, optional) – Sample rate of audio signal. Default: 16000.
f_min (float, optional) – Minimum frequency. Default: 0.0.
f_max (float, optional) – Maximum frequency. Default: None, will be set to sample_rate // 2 .
n_stft (int, optional) – Number of bins in STFT. Default: 201.
norm (NormType, optional) – Type of norm, value should be NormType.SLANEY or NormType.NONE. If norm is NormType.SLANEY, divide the triangular mel weight by the width of the mel band. Default: NormType.NONE, no narmalization.
mel_type (MelType, optional) – Type to use, value should be MelType.SLANEY or MelType.HTK. Default: MelType.HTK.

Raises

TypeError – If n_mels is not of type int.
ValueError – If n_mels is not positive.
TypeError – If sample_rate is not of type int.
ValueError – If sample_rate is not positive.
TypeError – If f_min is not of type float.
ValueError – If f_min is greater than or equal to f_max .
TypeError – If f_max is not of type float.
ValueError – If f_max is a negative number.
TypeError – If n_stft is not of type int.
ValueError – If n_stft is not positive.
TypeError – If norm is not of type mindspore.dataset.audio.NormType .
TypeError – If mel_type is not of type mindspore.dataset.audio.MelType .

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, 201, 3])  # 5 samples
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"])
>>> transforms = [audio.MelScale(200, 1500, 0.7)]
>>> 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
(200, 3) float64
>>>
>>> # Use the transform in eager mode
>>> waveform = np.random.random([201, 3])  # 1 sample
>>> output = audio.MelScale(200, 1500, 0.7)(waveform)
>>> print(output.shape, output.dtype)
(200, 3) float64

Tutorial Examples:

Illustration of audio transforms