# 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, BorderType Type.
Possible enumeration values are: BorderType.CONSTANT, BorderType.EDGE, BorderType.REFLECT, BorderType.SYMMETRIC.
- BorderType.CONSTANT: means it fills the border with constant values.
- BorderType.EDGE: means it pads with the last value on the edge.
- BorderType.REFLECT: means it reflects the values on the edge omitting the last value of edge.
- BorderType.SYMMETRIC: means it reflects the values on the edge repeating the last value of edge.
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 Functions.
Possible enumeration values are: DensityFunction.TPDF, DensityFunction.RPDF,
DensityFunction.GPDF.
- DensityFunction.TPDF: means triangular probability density function.
- DensityFunction.RPDF: means rectangular probability density function.
- DensityFunction.GPDF: means 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 Types.
Possible enumeration values are: MelType.HTK, MelType.SLANEY.
- MelType.NONE: scale the input data with htk.
- MelType.ORTHO: scale the input data with slaney.
"""
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):
"""
Norm Types.
Possible enumeration values are: NormMode.ORTHO, NormMode.NONE.
- NormMode.ORTHO: means the mode of input audio is ortho.
- NormMode.NONE: means the mode of input audio is none.
"""
ORTHO: str = "ortho"
NONE: str = "none"
[docs]class NormType(str, Enum):
"""
Norm Types.
Possible enumeration values are: NormType.SLANEY, NormType.NONE.
- NormType.SLANEY: norm the input data with slaney.
- NormType.NONE: norm the input data with none.
"""
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: means it resample the tensor with sinc interpolation.
- ResampleMethod.KAISER_WINDOW: means it resample the tensor with kaiser window.
"""
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 types,
Possible enumeration values are: WindowType.BARTLETT, WindowType.BLACKMAN, WindowType.HAMMING, WindowType.HANN,
WindowType.KAISER.
- WindowType.BARTLETT: means the type of window function is Bartlett.
- WindowType.BLACKMAN: means the type of window function is Blackman.
- WindowType.HAMMING: means the type of window function is Hamming.
- WindowType.HANN: means the type of window function is Hann.
- WindowType.KAISER: means the type of window function is Kaiser, currently 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).
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}
def linear_fbanks(n_freqs, f_min, f_max, n_filter, sample_rate):
"""
Creates a linear triangular filterbank.
Args:
n_freqs (int): Number of frequency.
f_min (float): Minimum of frequency in Hz.
f_max (float): Maximum of frequency in Hz.
n_filter (int): Number of (linear) triangular filter.
sample_rate (int): Sample rate.
Returns:
numpy.ndarray, the linear triangular filterbank.
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 with shape (n_freqs, n_mels).
Args:
n_freqs (int): Number of frequency.
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.
norm (NormType, optional): Norm to use, 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: NormType.SLANEY).
Returns:
numpy.ndarray, the frequency transformation matrix.
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()