比较与torchaudio.transforms.InverseMelScale的差异

torchaudio.transforms.InverseMelScale

class torchaudio.transforms.InverseMelScale(n_stft: int, n_mels: int = 128, sample_rate: int = 16000, f_min: float = 0.0, f_max: Optional[float] = None,
                                            max_iter: int = 100000, tolerance_loss: float = 1e-05, tolerance_change: float = 1e-08, sgdargs: Optional[dict] = None,
                                            norm: Optional[str] = None)

更多内容详见torchaudio.transforms.InverseMelScale。

mindspore.dataset.audio.InverseMelScale

class mindspore.dataset.audio.InverseMelScale(n_stft, n_mels=128, sample_rate=16000, f_min=0.0, f_max=None,
                                              max_iter=100000, tolerance_loss=1e-5, tolerance_change=1e-8, sgdargs=None,
                                              norm=NormType.NONE, mel_type=MelType.HTK)

更多内容详见mindspore.dataset.audio.InverseMelScale。

差异对比

PyTorch：使用转换矩阵从梅尔频率STFT求解普通频率的STFT。

MindSpore：使用转换矩阵从梅尔频率STFT求解普通频率的STFT，支持指定梅尔频谱的尺度。

分类	子类	PyTorch	MindSpore	差异
参数	参数1	n_stft	n_stft	-
	参数2	n_mels	n_mels	-
	参数3	sample_rate	sample_rate	-
	参数4	f_min	f_min	-
	参数5	f_max	f_max	-
	参数6	max_iter	max_iter	-
	参数7	tolerance_loss	tolerance_loss	-
	参数8	tolerance_change	tolerance_change	-
	参数9	sgdargs	sgdargs	-
	参数10	norm	norm	-
	参数11	-	mel_type	要使用的Mel尺度

代码示例

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
torch.manual_seed(1)

transformer = T.InverseMelScale(n_stft=2, n_mels=4)
torch_result = transformer(torch.from_numpy(fake_input))
print(torch_result)
# Out: tensor([[0.7576, 0.4031],
#              [0.2793, 0.7347]])

# MindSpore
import mindspore as ms
import mindspore.dataset.audio as audio
ms.dataset.config.set_seed(3)

transformer = audio.InverseMelScale(n_stft=2, n_mels=4)
ms_result = transformer(fake_input)
print(ms_result)
# Out: [[[0.5507979  0.07072488]
#        [0.7081478  0.8399491 ]]]