# Differences with torchaudio.transforms.SpectralCentroid

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## torchaudio.transforms.SpectralCentroid

```python
class torchaudio.transforms.SpectralCentroid(sample_rate: int, 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>,
                                             wkwargs: Optional[dict] = None)
```

For more information, see [torchaudio.transforms.SpectralCentroid](https://pytorch.org/audio/0.8.0/transforms.html#torchaudio.transforms.SpectralCentroid.html).

## mindspore.dataset.audio.SpectralCentroid

```python
class mindspore.dataset.audio.SpectralCentroid(sample_rate, n_fft=400, win_length=None, hop_length=None,
                                               pad=0, window=WindowType.HANN)
```

For more information, see [mindspore.dataset.audio.SpectralCentroid](https://mindspore.cn/docs/en/r2.3.0rc1/api_python/dataset_audio/mindspore.dataset.audio.SpectralCentroid.html#mindspore.dataset.audio.SpectralCentroid).

## Differences

PyTorch：Compute the spectral centroid for each channel along the time axis. Customized window function and different parameter configs for window function are both supported.

MindSpore：Compute the spectral centroid for each channel along the time axis.

| Categories | Subcategories |PyTorch | MindSpore | Difference |
| --- | ---   | ---   | ---        |---  |
|Parameter | Parameter1 | sample_rate      | sample_rate      | - |
|     | Parameter2 | n_fft    |  n_fft  | - |
|     | Parameter3 | win_length  | win_length    | - |
|     | Parameter4 | hop_length  | hop_length    | - |
|     | Parameter5 | pad    | pad   |  |
|     | Parameter6 | window_fn   | window     | MindSpore only supports 5 window functions |
|     | Parameter7 | wkwargs  | -    | Arguments for window function, not supported by MindSpore |

## Code Example

```python
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.SpectralCentroid(sample_rate=44100, n_fft=8, window_fn=torch.hann_window)
torch_result = transformer(torch.from_numpy(fake_input))
print(torch_result)
# Out: tensor([[[4436.1182, 3768.7986]]])

# MindSpore
import mindspore.dataset.audio as audio

transformer = audio.SpectralCentroid(sample_rate=44100, n_fft=8, window=audio.WindowType.HANN)
ms_result = transformer(fake_input)
print(ms_result)
# Out: [[[[4436.117  3768.7979]]]]
```