# Copyright 2021-2022 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""Enum for audio ops."""
from __future__ import absolute_import
from enum import Enum
import mindspore._c_dataengine as cde
from mindspore.dataset.core.validator_helpers import check_non_negative_float32, check_non_negative_int32, \
check_pos_float32, check_pos_int32, type_check
[docs]class BorderType(str, Enum):
"""
Padding mode.
Possible enumeration values are: ``BorderType.CONSTANT``, ``BorderType.EDGE``, ``BorderType.REFLECT``,
``BorderType.SYMMETRIC``.
- BorderType.CONSTANT: Pad with a constant value.
- BorderType.EDGE: Pad with the last value on the edge.
- BorderType.REFLECT: Reflect the value on the edge while omitting the last one.
For example, pad [1, 2, 3, 4] with 2 elements on both sides will result in [3, 2, 1, 2, 3, 4, 3, 2].
- BorderType.SYMMETRIC: Reflect the value on the edge while repeating the last one.
For example, pad [1, 2, 3, 4] with 2 elements on both sides will result in [2, 1, 1, 2, 3, 4, 4, 3].
Note:
This class derived from class str to support json serializable.
"""
CONSTANT: str = "constant"
EDGE: str = "edge"
REFLECT: str = "reflect"
SYMMETRIC: str = "symmetric"
[docs]class DensityFunction(str, Enum):
"""
Density function type.
Possible enumeration values are: ``DensityFunction.TPDF``, ``DensityFunction.RPDF``,
``DensityFunction.GPDF``.
- DensityFunction.TPDF: Triangular Probability Density Function.
- DensityFunction.RPDF: Rectangular Probability Density Function.
- DensityFunction.GPDF: Gaussian Probability Density Function.
"""
TPDF: str = "TPDF"
RPDF: str = "RPDF"
GPDF: str = "GPDF"
[docs]class FadeShape(str, Enum):
"""
Fade Shapes.
Possible enumeration values are: ``FadeShape.QUARTER_SINE``, ``FadeShape.HALF_SINE``, ``FadeShape.LINEAR``,
``FadeShape.LOGARITHMIC``, ``FadeShape.EXPONENTIAL``.
- FadeShape.QUARTER_SINE: means the fade shape is quarter_sine mode.
- FadeShape.HALF_SINE: means the fade shape is half_sine mode.
- FadeShape.LINEAR: means the fade shape is linear mode.
- FadeShape.LOGARITHMIC: means the fade shape is logarithmic mode.
- FadeShape.EXPONENTIAL: means the fade shape is exponential mode.
"""
QUARTER_SINE: str = "quarter_sine"
HALF_SINE: str = "half_sine"
LINEAR: str = "linear"
LOGARITHMIC: str = "logarithmic"
EXPONENTIAL: str = "exponential"
[docs]class GainType(str, Enum):
"""
Gain Types.
Possible enumeration values are: ``GainType.AMPLITUDE``, ``GainType.POWER``, ``GainType.DB``.
- GainType.AMPLITUDE: means input gain type is amplitude.
- GainType.POWER: means input gain type is power.
- GainType.DB: means input gain type is decibel.
"""
AMPLITUDE: str = "amplitude"
POWER: str = "power"
DB: str = "db"
[docs]class Interpolation(str, Enum):
"""
Interpolation Type.
Possible enumeration values are: ``Interpolation.LINEAR``, ``Interpolation.QUADRATIC``.
- Interpolation.LINEAR: means input interpolation type is linear.
- Interpolation.QUADRATIC: means input interpolation type is quadratic.
"""
LINEAR: str = "linear"
QUADRATIC: str = "quadratic"
[docs]class MelType(str, Enum):
"""
Mel scale implementation type.
Possible enumeration values are: ``MelType.HTK``, ``MelType.SLANEY``.
- MelType.HTK: The Hidden Markov Toolkit (HTK) implementation, refer to `HTK <https://htk.eng.cam.ac.uk/>`_ .
- MelType.SLANEY: The MATLAB Auditory Toolbox of Slaney implementation,
refer to `Auditory Toolbox <https://engineering.purdue.edu/~malcolm/interval/1998-010/>`_ .
"""
HTK: str = "htk"
SLANEY: str = "slaney"
[docs]class Modulation(str, Enum):
"""
Modulation Type.
Possible enumeration values are: Modulation.SINUSOIDAL, Modulation.TRIANGULAR.
- Modulation.SINUSOIDAL: means input modulation type is sinusoidal.
- Modulation.TRIANGULAR: means input modulation type is triangular.
"""
SINUSOIDAL: str = "sinusoidal"
TRIANGULAR: str = "triangular"
[docs]class NormMode(str, Enum):
"""
Normalization mode.
Possible enumeration values are: ``NormMode.ORTHO``, ``NormMode.NONE``.
- NormMode.ORTHO: Use an ortho-normal DCT basis.
- NormMode.NONE: No normalization.
"""
ORTHO: str = "ortho"
NONE: str = "none"
[docs]class NormType(str, Enum):
"""
Normalization type.
Possible enumeration values are: ``NormType.SLANEY``, ``NormType.NONE``.
- NormType.SLANEY: Use an area normalization.
- NormType.NONE: No narmalization.
"""
SLANEY: str = "slaney"
NONE: str = "none"
[docs]class ResampleMethod(str, Enum):
"""
Resample method.
Possible enumeration values are: ``ResampleMethod.SINC_INTERPOLATION``, ``ResampleMethod.KAISER_WINDOW``.
- ResampleMethod.SINC_INTERPOLATION: The Whittaker-Shannon interpolation or sinc interpolation formula.
- ResampleMethod.KAISER_WINDOW: The Kaiser window interpolation.
"""
SINC_INTERPOLATION: str = "sinc_interpolation"
KAISER_WINDOW: str = "kaiser_window"
[docs]class ScaleType(str, Enum):
"""
Scale Types.
Possible enumeration values are: ``ScaleType.POWER``, ``ScaleType.MAGNITUDE``.
- ScaleType.POWER: means the scale of input audio is power.
- ScaleType.MAGNITUDE: means the scale of input audio is magnitude.
"""
POWER: str = "power"
MAGNITUDE: str = "magnitude"
[docs]class WindowType(str, Enum):
"""
Window function type.
Possible enumeration values are: ``WindowType.BARTLETT``, ``WindowType.BLACKMAN``,
``WindowType.HAMMING``, ``WindowType.HANN``, ``WindowType.KAISER``.
- WindowType.BARTLETT: Bartlett window function.
- WindowType.BLACKMAN: Blackman window function.
- WindowType.HAMMING: Hamming window function.
- WindowType.HANN: Hann window function.
- WindowType.KAISER: Kaiser window function. Currently, it is not supported on macOS.
"""
BARTLETT: str = "bartlett"
BLACKMAN: str = "blackman"
HAMMING: str = "hamming"
HANN: str = "hann"
KAISER: str = "kaiser"
DE_C_NORM_MODE = {NormMode.ORTHO: cde.NormMode.DE_NORM_MODE_ORTHO,
NormMode.NONE: cde.NormMode.DE_NORM_MODE_NONE}
[docs]def create_dct(n_mfcc, n_mels, norm=NormMode.NONE):
"""
Create a DCT transformation matrix with shape (n_mels, n_mfcc), normalized depending on norm.
Args:
n_mfcc (int): Number of mfc coefficients to retain, the value must be greater than 0.
n_mels (int): Number of mel filterbanks, the value must be greater than 0.
norm (NormMode, optional): Normalization mode, can be ``NormMode.NONE`` or ``NormMode.ORTHO``.
Default: ``NormMode.NONE``.
Returns:
numpy.ndarray, the transformation matrix, to be right-multiplied to row-wise data of size (n_mels, n_mfcc).
Raises:
TypeError: If `n_mfcc` is not of type int.
ValueError: If `n_mfcc` is not positive.
TypeError: If `n_mels` is not of type int.
ValueError: If `n_mels` is not positive.
TypeError: If `n_mels` is not of type :class:`mindspore.dataset.audio.NormMode` .
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.audio import create_dct, NormMode
>>>
>>> dct = create_dct(100, 200, NormMode.NONE)
"""
if not isinstance(n_mfcc, int):
raise TypeError("n_mfcc with value {0} is not of type {1}, but got {2}.".format(
n_mfcc, int, type(n_mfcc)))
if not isinstance(n_mels, int):
raise TypeError("n_mels with value {0} is not of type {1}, but got {2}.".format(
n_mels, int, type(n_mels)))
if not isinstance(norm, NormMode):
raise TypeError("norm with value {0} is not of type {1}, but got {2}.".format(
norm, NormMode, type(norm)))
if n_mfcc <= 0:
raise ValueError("n_mfcc must be greater than 0, but got {0}.".format(n_mfcc))
if n_mels <= 0:
raise ValueError("n_mels must be greater than 0, but got {0}.".format(n_mels))
return cde.create_dct(n_mfcc, n_mels, DE_C_NORM_MODE[norm]).as_array()
DE_C_MEL_TYPE = {MelType.HTK: cde.MelType.DE_MEL_TYPE_HTK,
MelType.SLANEY: cde.MelType.DE_MEL_TYPE_SLANEY}
DE_C_NORM_TYPE = {NormType.SLANEY: cde.NormType.DE_NORM_TYPE_SLANEY,
NormType.NONE: cde.NormType.DE_NORM_TYPE_NONE}
[docs]def linear_fbanks(n_freqs, f_min, f_max, n_filter, sample_rate):
"""
Creates a linear triangular filterbank.
Args:
n_freqs (int): Number of frequencies to highlight/apply.
f_min (float): Minimum frequency in Hz.
f_max (float): Maximum frequency in Hz.
n_filter (int): Number of (linear) triangular filter.
sample_rate (int): Sample rate of the waveform.
Returns:
numpy.ndarray, the linear triangular filterbank.
Raises:
TypeError: If `n_freqs` is not of type int.
ValueError: If `n_freqs` is negative.
TypeError: If `f_min` is not of type float.
ValueError: If `f_min` is negative.
TypeError: If `f_max` is not of type float.
ValueError: If `f_max` is negative.
ValueError: If `f_min` is larger than `f_max`.
TypeError: If `n_filter` is not of type int.
ValueError: If `n_filter` is not positive.
TypeError: If `sample_rate` is not of type int.
ValueError: If `sample_rate` is not positive.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.audio import linear_fbanks
>>>
>>> fbanks = linear_fbanks(n_freqs=4096, f_min=0, f_max=8000, n_filter=40, sample_rate=16000)
"""
type_check(n_freqs, (int,), "n_freqs")
check_non_negative_int32(n_freqs, "n_freqs")
type_check(f_min, (int, float,), "f_min")
check_non_negative_float32(f_min, "f_min")
type_check(f_max, (int, float,), "f_max")
check_pos_float32(f_max, "f_max")
if f_min > f_max:
raise ValueError(
"Input f_min should be no more than f_max, but got f_min: {0} and f_max: {1}.".format(f_min, f_max))
type_check(n_filter, (int,), "n_filter")
check_pos_int32(n_filter, "n_filter")
type_check(sample_rate, (int,), "sample_rate")
check_pos_int32(sample_rate, "sample_rate")
return cde.linear_fbanks(n_freqs, f_min, f_max, n_filter, sample_rate).as_array()
[docs]def melscale_fbanks(n_freqs, f_min, f_max, n_mels, sample_rate, norm=NormType.NONE, mel_type=MelType.HTK):
"""
Create a frequency transformation matrix.
Args:
n_freqs (int): Number of frequencies to highlight/apply.
f_min (float): Minimum of frequency in Hz.
f_max (float): Maximum of frequency in Hz.
n_mels (int): Number of mel filterbanks.
sample_rate (int): Sample rate of the audio waveform.
norm (NormType, optional): Normalization method, can be ``NormType.NONE`` or ``NormType.SLANEY``.
Default: ``NormType.NONE``.
mel_type (MelType, optional): Scale to use, can be ``MelType.HTK`` or ``MelType.SLANEY``.
Default: ``MelType.HTK``.
Returns:
numpy.ndarray, the frequency transformation matrix with shape ( `n_freqs` , `n_mels` ).
Raises:
TypeError: If `n_freqs` is not of type int.
ValueError: If `n_freqs` is a negative number.
TypeError: If `f_min` is not of type float.
ValueError: If `f_min` is greater than `f_max` .
TypeError: If `f_max` is not of type float.
ValueError: If `f_max` is a negative number.
TypeError: If `n_mels` is not of type int.
ValueError: If `n_mels` is not positive.
TypeError: If `sample_rate` is not of type int.
ValueError: If `sample_rate` is not positive.
TypeError: If `norm` is not of type :class:`mindspore.dataset.audio.NormType` .
TypeError: If `mel_type` is not of type :class:`mindspore.dataset.audio.MelType` .
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.audio import melscale_fbanks
>>>
>>> fbanks = melscale_fbanks(n_freqs=4096, f_min=0, f_max=8000, n_mels=40, sample_rate=16000)
"""
type_check(n_freqs, (int,), "n_freqs")
check_non_negative_int32(n_freqs, "n_freqs")
type_check(f_min, (int, float,), "f_min")
check_non_negative_float32(f_min, "f_min")
type_check(f_max, (int, float,), "f_max")
check_pos_float32(f_max, "f_max")
if f_min > f_max:
raise ValueError(
"Input f_min should be no more than f_max, but got f_min: {0} and f_max: {1}.".format(f_min, f_max))
type_check(n_mels, (int,), "n_mels")
check_pos_int32(n_mels, "n_mels")
type_check(sample_rate, (int,), "sample_rate")
check_pos_int32(sample_rate, "sample_rate")
type_check(norm, (NormType,), "norm")
type_check(mel_type, (MelType,), "mel_type")
return cde.melscale_fbanks(n_freqs, f_min, f_max, n_mels, sample_rate, DE_C_NORM_TYPE[norm],
DE_C_MEL_TYPE[mel_type]).as_array()