比较与torchaudio.transforms.GriffinLim的差异
torchaudio.transforms.GriffinLim
class torchaudio.transforms.GriffinLim(n_fft: int = 400, n_iter: int = 32, win_length: Optional[int] = None, hop_length: Optional[int] = None,
window_fn: Callable[[...], torch.Tensor] = <built-in method hann_window of type object>, power: float = 2.0,
normalized: bool = False, wkwargs: Optional[dict] = None, momentum: float = 0.99,
length: Optional[int] = None, rand_init: bool = True)
mindspore.dataset.audio.GriffinLim
class mindspore.dataset.audio.GriffinLim(n_fft=400, n_iter=32, win_length=None, hop_length=None,
window_type=WindowType.HANN, power=2.0,
momentum=0.99, length=None, rand_init=True)
差异对比
PyTorch:使用Griffin-Lim算法从线性幅度频谱图中计算信号波形。支持自定义窗函数或对窗函数传入不同的配置参数。支持对STFT结果进行幅值规范化。
MindSpore:使用Griffin-Lim算法从线性幅度频谱图中计算信号波形。
分类 |
子类 |
PyTorch |
MindSpore |
差异 |
---|---|---|---|---|
参数 |
参数1 |
n_fft |
n_fft |
- |
参数2 |
n_iter |
n_iter |
- |
|
参数3 |
win_length |
win_length |
- |
|
参数4 |
hop_length |
hop_length |
- |
|
参数5 |
window_fn |
window_type |
MindSpore仅支持5种窗函数 |
|
参数6 |
power |
power |
- |
|
参数7 |
normalized |
- |
STFT后幅值规范化,MindSpore不支持 |
|
参数8 |
wkwargs |
- |
自定义窗函数的入参,MindSpore不支持 |
|
参数9 |
momentum |
momentum |
- |
|
参数10 |
length |
length |
- |
|
参数11 |
rand_init |
rand_init |
- |
代码示例
import numpy as np
fake_input = np.ones((151, 36)).astype(np.float32)
# PyTorch
import torch
import torchaudio.transforms as T
torch.manual_seed(1)
transformer = T.GriffinLim(n_fft=300, n_iter=10, win_length=None, hop_length=None, window_fn=torch.hann_window, power=2, momentum=0.5)
torch_result = transformer(torch.from_numpy(fake_input))
print(torch_result)
# Out: tensor([-0.0800, 0.1134, -0.0888, ..., -0.0610, -0.0206, -0.1800])
# MindSpore
import mindspore as ms
import mindspore.dataset.audio as audio
ms.dataset.config.set_seed(3)
transformer = audio.GriffinLim(n_fft=300, n_iter=10, win_length=None, hop_length=None, window_type=audio.WindowType.HANN, power=2, momentum=0.5)
ms_result = transformer(fake_input)
print(ms_result)
# Out: [-0.08666667 0.06763329 -0.03155987 ... -0.07218403 -0.01178891 -0.00664348]