mindspore.dataset.audio.Spectrogram

class mindspore.dataset.audio.Spectrogram(n_fft=400, win_length=None, hop_length=None, pad=0, window=WindowType.HANN, power=2.0, normalized=False, center=True, pad_mode=BorderType.REFLECT, onesided=True)[源代码]

Create a spectrogram from an audio signal.

Parameters

  • n_fft (int, optional) – Size of FFT, creates n_fft // 2 + 1 bins (default=400).

  • win_length (int, optional) – Window size (default=None, will use n_fft).

  • hop_length (int, optional) – Length of hop between STFT windows (default=None, will use win_length // 2).

  • pad (int) – Two sided padding of signal (default=0).

  • window (WindowType, optional) – Window function that is applied/multiplied to each frame/window, which can be WindowType.BARTLETT, WindowType.BLACKMAN, WindowType.HAMMING, WindowType.HANN or WindowType.KAISER (default=WindowType.HANN). Currently kaiser window is not supported on macOS.

  • power (float, optional) – Exponent for the magnitude spectrogram, which must be greater than or equal to 0, e.g., 1 for energy, 2 for power, etc. (default=2.0).

  • normalized (bool, optional) – Whether to normalize by magnitude after stft (default=False).

  • center (bool, optional) – Whether to pad waveform on both sides (default=True).

  • pad_mode (BorderType, optional) – Controls the padding method used when center is True, which can be BorderType.REFLECT, BorderType.CONSTANT, BorderType.EDGE, BorderType.SYMMETRIC (default=BorderType.REFLECT).

  • onesided (bool, optional) – Controls whether to return half of results to avoid redundancy (default=True).

Examples

>>> import numpy as np
>>>
>>> waveform = np.random.random([5, 10, 20])
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"])
>>> transforms = [audio.Spectrogram()]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms, input_columns=["audio"])