mindspore.dataset.audio.SlidingWindowCmn
- class mindspore.dataset.audio.SlidingWindowCmn(cmn_window=600, min_cmn_window=100, center=False, norm_vars=False)[source]
Apply sliding-window cepstral mean (and optionally variance) normalization per utterance.
- Parameters
cmn_window (int, optional) – Window in frames for running average CMN computation. Default:
600
.min_cmn_window (int, optional) – Minimum CMN window used at start of decoding (adds latency only at start). Only applicable if center is
False
, ignored if center isTrue
. Default:100
.center (bool, optional) – If
True
, use a window centered on the current frame. IfFalse
, window is to the left. Default:False
.norm_vars (bool, optional) – If
True
, normalize variance to one. Default:False
.
- Raises
TypeError – If cmn_window is not of type int.
ValueError – If cmn_window is a negative number.
TypeError – If min_cmn_window is not of type int.
ValueError – If min_cmn_window is a negative number.
TypeError – If center is not of type bool.
TypeError – If norm_vars is not of type bool.
- Supported Platforms:
CPU
Examples
>>> import numpy as np >>> import mindspore.dataset as ds >>> import mindspore.dataset.audio as audio >>> >>> waveform = np.array([[[1, 2, 3], [4, 5, 6]]], dtype=np.float64) >>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"]) >>> transforms = [audio.SlidingWindowCmn()] >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms, input_columns=["audio"])
- Tutorial Examples: