mindspore.dataset.audio.MFCC
- class mindspore.dataset.audio.MFCC(sample_rate=16000, n_mfcc=40, dct_type=2, norm=NormMode.ORTHO, log_mels=False, melkwargs=None)[source]
Create MFCC for a raw audio signal.
- Parameters
sample_rate (int, optional) – Sampling rate of audio signal (in Hz), can't be less than 0. Default:
16000
.n_mfcc (int, optional) – Number of mfc coefficients to retain, can't be less than 0. Default:
40
.dct_type (int, optional) – Type of DCT (discrete cosine transform) to use, can only be
2
. Default:2
.norm (NormMode, optional) – Norm to use. Default:
NormMode.ORTHO
.log_mels (bool, optional) – Whether to use log-mel spectrograms instead of db-scaled. Default:
False
.melkwargs (dict, optional) –
Arguments for
mindspore.dataset.audio.MelSpectrogram
. Default:None
, the default setting is a dict including'n_fft': 400
'win_length': n_fft
'hop_length': win_length // 2
'f_min': 0.0
'f_max': sample_rate // 2
'pad': 0
'window': WindowType.HANN
'power': 2.0
'normalized': False
'center': True
'pad_mode': BorderType.REFLECT
'onesided': True
'norm': NormType.NONE
'mel_scale': MelType.HTK
- Raises
TypeError – If sample_rate is not of type int.
TypeError – If log_mels is not of type bool.
TypeError – If norm is not of type
mindspore.dataset.audio.NormMode
.TypeError – If n_mfcc is not of type int.
TypeError – If melkwargs is not of type dict.
ValueError – If sample_rate is a negative number.
ValueError – If n_mfcc is a negative number.
ValueError – If dct_type is not
2
.
- Supported Platforms:
CPU
Examples
>>> 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, 500]) # 5 samples >>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"]) >>> transforms = [audio.MFCC(4000, 128, 2)] >>> 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 (128, 3) float32 >>> >>> # Use the transform in eager mode >>> waveform = np.random.random([500]) # 1 sample >>> output = audio.MFCC(4000, 128, 2)(waveform) >>> print(output.shape, output.dtype) (128, 3) float32
- Tutorial Examples: