# Differences with torchaudio.transforms.Resample [](https://gitee.com/mindspore/docs/blob/r2.3.q1/docs/mindspore/source_en/note/api_mapping/pytorch_diff/Resample.md) ## torchaudio.transforms.Resample ```python class torchaudio.transforms.Resample(orig_freq: int = 16000, new_freq: int = 16000, resampling_method: str = 'sinc_interpolation') ``` For more information, see [torchaudio.transforms.Resample](https://pytorch.org/audio/0.8.0/transforms.html#torchaudio.transforms.Resample.html). ## mindspore.dataset.audio.Resample ```python class mindspore.dataset.audio.Resample(orig_freq=16000, new_freq=16000, resample_method=ResampleMethod.SINC_INTERPOLATION, lowpass_filter_width=6, rolloff=0.99, beta=None) ``` For more information, see [mindspore.dataset.audio.Resample](https://mindspore.cn/docs/en/r2.3.0rc1/api_python/dataset_audio/mindspore.dataset.audio.Resample.html#mindspore.dataset.audio.Resample). ## Differences PyTorch: Resample a signal from one frequency to another. MindSpore: Resample a signal from one frequency to another. Extra filter option is supported. | Categories | Subcategories |PyTorch | MindSpore | Difference | | --- | --- | --- | --- |--- | |Parameter | Parameter1 | orig_freq | orig_freq | - | | | Parameter2 | new_freq | new_freq | - | | | Parameter3 | resampling_method | resample_method | - | | | Parameter4 | - | lowpass_filter_width | Sharpness of the filter | | | Parameter5 | - | rolloff | The roll-off frequency of the filter | | | Parameter6 | - | beta | The shape parameter used for kaiser window | ## Code Example ```python import numpy as np fake_input = np.array([[[[-0.2197528, 0.3821656]]], [[[0.57418776, 0.46741104]]], [[[0.76986176, -0.5793846]]]]).astype(np.float32) # PyTorch import torch import torchaudio.transforms as T transformer = T.Resample(orig_freq=16000, new_freq=24000) torch_result = transformer(torch.from_numpy(fake_input)) print(torch_result) # Out: tensor([[[[-0.2140, 0.2226, 0.3510]]], # [[[ 0.5728, 0.6145, 0.2789]]], # [[[ 0.7568, -0.1601, -0.6101]]]]) # MindSpore import mindspore.dataset.audio as audio transformer = audio.Resample(orig_freq=16000, new_freq=24000) ms_result = transformer(fake_input) print(ms_result) # Out: [[[[-0.21398525 0.22255361 0.35099414]]] # [[[ 0.5728122 0.614469 0.2788692 ]]] # [[[ 0.75675076 -0.16008556 -0.61005235]]]] ```