mindspore.dataset.audio.InverseMelScale

class mindspore.dataset.audio.InverseMelScale(n_stft, n_mels=128, sample_rate=16000, f_min=0.0, f_max=None, max_iter=100000, tolerance_loss=1e-05, tolerance_change=1e-08, sgdargs=None, norm=NormType.NONE, mel_type=MelType.HTK)[source]

Solve for a normal STFT from a mel frequency STFT, using a conversion matrix.

Parameters

  • n_stft (int) – Number of bins in STFT.

  • 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 .

  • max_iter (int, optional) – Maximum number of optimization iterations. Default: 100000.

  • tolerance_loss (float, optional) – Value of loss to stop optimization at. Default: 1e-5.

  • tolerance_change (float, optional) – Difference in losses to stop optimization at. Default: 1e-8.

  • sgdargs (dict, optional) – Arguments for the SGD optimizer. Default: None, will be set to {‘sgd_lr’: 0.1, ‘sgd_momentum’: 0.9}.

  • norm (NormType, optional) – Normalization method, can be NormType.SLANEY or NormType.NONE. Default: NormType.NONE.

  • mel_type (MelType, optional) – Mel scale to use, can be MelType.SLANEY or MelType.HTK. Default: MelType.HTK.

Examples

>>> import numpy as np
>>>
>>> waveform = np.random.randn(2, 2, 3, 2)
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"])
>>> transforms = [audio.InverseMelScale(20, 3, 16000, 0, 8000, 10)]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms, input_columns=["audio"])