mindspore.dataset.audio.PhaseVocoder
- class mindspore.dataset.audio.PhaseVocoder(rate, phase_advance)[源代码]
对给定的STFT频谱,在不改变音高的情况下以一定比率进行加速。
- 参数:
rate (float) - 加速比率。
phase_advance (numpy.ndarray) - 每个频段的预期相位提前量,shape为<freq, 1>。
- 异常:
TypeError - 当 rate 的类型不为float。
ValueError - 当 rate 不为正数。
TypeError - 当 phase_advance 的类型不为
numpy.ndarray
。RuntimeError - 当输入音频的shape不为<…, freq, num_frame, complex=2>。
- 支持平台:
CPU
样例:
>>> import numpy as np >>> import mindspore.dataset as ds >>> import mindspore.dataset.audio as audio >>> >>> # Use the transform in dataset pipeline mode >>> waveform = np.random.random([5, 44, 10, 2]) # 5 samples >>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"]) >>> transforms = [audio.PhaseVocoder(rate=2, phase_advance=np.random.random([44, 1]))] >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms, input_columns=["audio"]) >>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True): ... print(item["audio"].shape, item["audio"].dtype) ... break (44, 5, 2) float64 >>> >>> # Use the transform in eager mode >>> waveform = np.random.random([44, 10, 2]) # 1 sample >>> output = audio.PhaseVocoder(rate=2, phase_advance=np.random.random([44, 1]))(waveform) >>> print(output.shape, output.dtype) (44, 5, 2) float64
- 教程样例: