Differences with torchaudio.transforms.Spectrogram
torchaudio.transforms.Spectrogram
class torchaudio.transforms.Spectrogram(n_fft: int = 400, win_length: Optional[int] = None, hop_length: Optional[int] = None,
pad: int = 0, window_fn: Callable[[...], torch.Tensor] = <built-in method hann_window of type object>,
power: Optional[float] = 2.0, normalized: bool = False, wkwargs: Optional[dict] = None,
center: bool = True, pad_mode: str = 'reflect', onesided: bool = True)
For more information, see torchaudio.transforms.Spectrogram.
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)
For more information, see mindspore.dataset.audio.Spectrogram.
Differences
PyTorch:Compute waveform from a linear scale magnitude spectrogram. Customized window function and different parameter configs for window function are both supported.
MindSpore:Compute waveform from a linear scale magnitude spectrogram.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameter |
Parameter1 |
n_fft |
n_fft |
- |
Parameter2 |
win_length |
win_length |
- |
|
Parameter3 |
hop_length |
hop_length |
- |
|
Parameter4 |
pad |
pad |
- |
|
Parameter5 |
window_fn |
window |
MindSpore only supports 5 window functions |
|
Parameter6 |
power |
power |
- |
|
Parameter7 |
normalized |
normalized |
- |
|
Parameter8 |
wkwargs |
- |
Arguments for window function, not supported by MindSpore |
|
Parameter9 |
center |
center |
- |
|
Parameter10 |
pad_mode |
pad_mode |
- |
|
Parameter11 |
onesided |
onesided |
- |
Code Example
import numpy as np
fake_input = np.array([[[1, 1, 2, 2, 3, 3, 4]]]).astype(np.float32)
# PyTorch
import torch
import torchaudio.transforms as T
transformer = T.Spectrogram(n_fft=8, window_fn=torch.hamming_window)
torch_result = transformer(torch.from_numpy(fake_input))
print(torch_result)
# Out: tensor([[[[3.5874e+01, 1.3237e+02],
# [1.8943e+00, 3.2839e+01],
# [8.4640e-01, 2.1553e-01],
# [2.0643e-02, 2.4623e-01],
# [6.5697e-01, 1.2876e+00]]]])
# MindSpore
import mindspore.dataset.audio as audio
transformer = audio.Spectrogram(n_fft=8, window=audio.WindowType.HAMMING)
ms_result = transformer(fake_input)
print(ms_result)
# Out: [[[[3.5873653e+01 1.3237122e+02]
# [1.8942689e+00 3.2838711e+01]
# [8.4640014e-01 2.1552797e-01]
# [2.0642618e-02 2.4623220e-01]
# [6.5697211e-01 1.2876146e+00]]]]