Differences with torchaudio.transforms.MelScale
torchaudio.transforms.MelScale
class torchaudio.transforms.MelScale(n_mels: int = 128, sample_rate: int = 16000, f_min: float = 0.0, f_max: Optional[float] = None,
n_stft: Optional[int] = None, norm: Optional[str] = None)
For more information, see torchaudio.transforms.MelScale.
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)
For more information, see mindspore.dataset.audio.MelScale.
Differences
PyTorch: Convert normal STFT to STFT at the Mel scale.
MindSpore: Convert normal STFT to STFT at the Mel scale.. Mel scale can be specified.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameter |
Parameter1 |
n_mels |
n_mels |
- |
Parameter2 |
sample_rate |
sample_rate |
- |
|
Parameter4 |
f_min |
f_min |
- |
|
Parameter5 |
f_max |
f_max |
- |
|
Parameter6 |
n_stft |
n_stft |
- |
|
Parameter10 |
norm |
norm |
- |
|
Parameter11 |
- |
mel_type |
Mel scale to use |
Code Example
import numpy as np
fake_input = np.array([[1., 1.],
[0., 0.],
[1., 1.],
[1., 1.]]).astype(np.float32)
# PyTorch
import torch
import torchaudio.transforms as T
transformer = T.MelScale(n_stft=4, n_mels=2)
torch_result = transformer(torch.from_numpy(fake_input))
print(torch_result)
# Out: tensor([[0.0000, 0.0000],
# [0.5394, 0.5394]])
# MindSpore
import mindspore.dataset.audio as audio
transformer = audio.MelScale(n_stft=4, n_mels=2)
ms_result = transformer(fake_input)
print(ms_result)
# Out: [[0. 0. ]
# [0.53936154 0.53936154]]