239 lines
13 KiB
Python
239 lines
13 KiB
Python
# !/usr/bin/env python3
|
||
# -*- coding: utf-8 -*-
|
||
#
|
||
# Copyright 2016-2099 Ailemon.net
|
||
#
|
||
# This file is part of ASRT Speech Recognition Tool.
|
||
#
|
||
# ASRT is free software: you can redistribute it and/or modify
|
||
# it under the terms of the GNU General Public License as published by
|
||
# the Free Software Foundation, either version 3 of the License, or
|
||
# (at your option) any later version.
|
||
# ASRT is distributed in the hope that it will be useful,
|
||
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||
# GNU General Public License for more details.
|
||
#
|
||
# You should have received a copy of the GNU General Public License
|
||
# along with ASRT. If not, see <https://www.gnu.org/licenses/>.
|
||
# ============================================================================
|
||
|
||
# calculate filterbank features. Provides e.g. fbank and mfcc features for use in ASR applications
|
||
# Author: James Lyons 2012
|
||
|
||
"""
|
||
@author: nl8590687
|
||
ASRT语音识别声学特征基础库模块，一些基础函数实现
|
||
"""
|
||
|
||
from __future__ import division
|
||
import numpy
|
||
from scipy.fftpack import dct
|
||
|
||
from .sigproc import preemphasis, framesig, powspec
|
||
|
||
|
||
def calculate_nfft(samplerate, winlen):
|
||
"""Calculates the FFT size as a power of two greater than or equal to
|
||
the number of samples in a single window length.
|
||
|
||
Having an FFT less than the window length loses precision by dropping
|
||
many of the samples; a longer FFT than the window allows zero-padding
|
||
of the FFT buffer which is neutral in terms of frequency domain conversion.
|
||
:param samplerate: The sample rate of the signal we are working with, in Hz.
|
||
:param winlen: The length of the analysis window in seconds.
|
||
"""
|
||
window_length_samples = winlen * samplerate
|
||
nfft = 1
|
||
while nfft < window_length_samples:
|
||
nfft *= 2
|
||
return nfft
|
||
|
||
|
||
def mfcc(signal, samplerate=16000, winlen=0.025, winstep=0.01, numcep=13,
|
||
nfilt=26, nfft=None, lowfreq=0, highfreq=None, preemph=0.97, ceplifter=22, appendEnergy=True,
|
||
winfunc=lambda x: numpy.ones((x,))):
|
||
"""Compute MFCC features from an audio signal.
|
||
:param signal: the audio signal from which to compute features. Should be an N*1 array
|
||
:param samplerate: the sample rate of the signal we are working with, in Hz.
|
||
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
|
||
:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
|
||
:param numcep: the number of cepstrum to return, default 13
|
||
:param nfilt: the number of filters in the filterbank, default 26.
|
||
:param nfft: the FFT size. Default is None, which uses the calculate_nfft function to choose the smallest size that does not drop sample data.
|
||
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
|
||
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
|
||
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
|
||
:param ceplifter: apply a lifter to final cepstral coefficients. 0 is no lifter. Default is 22.
|
||
:param appendEnergy: if this is true, the zeroth cepstral coefficient is replaced with the log of the total frame energy.
|
||
:param winfunc: the analysis window to apply to each frame. By default no window is applied. You can use numpy window functions here e.g. winfunc=numpy.hamming
|
||
:returns: A numpy array of size (NUMFRAMES by numcep) containing features. Each row holds 1 feature vector.
|
||
"""
|
||
nfft = nfft or calculate_nfft(samplerate, winlen)
|
||
feat, energy = fbank(signal, samplerate, winlen, winstep, nfilt, nfft, lowfreq, highfreq, preemph, winfunc)
|
||
feat = numpy.log(feat)
|
||
feat = dct(feat, type=2, axis=1, norm='ortho')[:, :numcep]
|
||
feat = lifter(feat, ceplifter)
|
||
if appendEnergy: feat[:, 0] = numpy.log(energy) # replace first cepstral coefficient with log of frame energy
|
||
return feat
|
||
|
||
|
||
def fbank(signal, samplerate=16000, winlen=0.025, winstep=0.01,
|
||
nfilt=26, nfft=512, lowfreq=0, highfreq=None, preemph=0.97,
|
||
winfunc=lambda x: numpy.ones((x,))):
|
||
"""Compute Mel-filterbank energy features from an audio signal.
|
||
:param signal: the audio signal from which to compute features. Should be an N*1 array
|
||
:param samplerate: the sample rate of the signal we are working with, in Hz.
|
||
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
|
||
:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
|
||
:param nfilt: the number of filters in the filterbank, default 26.
|
||
:param nfft: the FFT size. Default is 512.
|
||
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
|
||
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
|
||
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
|
||
:param winfunc: the analysis window to apply to each frame. By default no window is applied. You can use numpy window functions here e.g. winfunc=numpy.hamming
|
||
:returns: 2 values. The first is a numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector. The
|
||
second return value is the energy in each frame (total energy, unwindowed)
|
||
"""
|
||
highfreq = highfreq or samplerate / 2
|
||
signal = preemphasis(signal, preemph)
|
||
frames = framesig(signal, winlen * samplerate, winstep * samplerate, winfunc)
|
||
pspec = powspec(frames, nfft)
|
||
energy = numpy.sum(pspec, 1) # this stores the total energy in each frame
|
||
energy = numpy.where(energy == 0, numpy.finfo(float).eps, energy) # if energy is zero, we get problems with log
|
||
|
||
fb = get_filterbanks(nfilt, nfft, samplerate, lowfreq, highfreq)
|
||
feat = numpy.dot(pspec, fb.T) # compute the filterbank energies
|
||
feat = numpy.where(feat == 0, numpy.finfo(float).eps, feat) # if feat is zero, we get problems with log
|
||
|
||
return feat, energy
|
||
|
||
|
||
def logfbank(signal, samplerate=16000, winlen=0.025, winstep=0.01,
|
||
nfilt=26, nfft=512, lowfreq=0, highfreq=None, preemph=0.97,
|
||
winfunc=lambda x: numpy.ones((x,))):
|
||
"""Compute log Mel-filterbank energy features from an audio signal.
|
||
:param signal: the audio signal from which to compute features. Should be an N*1 array
|
||
:param samplerate: the sample rate of the signal we are working with, in Hz.
|
||
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
|
||
:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
|
||
:param nfilt: the number of filters in the filterbank, default 26.
|
||
:param nfft: the FFT size. Default is 512.
|
||
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
|
||
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
|
||
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
|
||
:param winfunc: the analysis window to apply to each frame. By default no window is applied. You can use numpy window functions here e.g. winfunc=numpy.hamming
|
||
:returns: A numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector.
|
||
"""
|
||
feat, energy = fbank(signal, samplerate, winlen, winstep, nfilt, nfft, lowfreq, highfreq, preemph, winfunc)
|
||
return numpy.log(feat)
|
||
|
||
|
||
def ssc(signal, samplerate=16000, winlen=0.025, winstep=0.01,
|
||
nfilt=26, nfft=512, lowfreq=0, highfreq=None, preemph=0.97,
|
||
winfunc=lambda x: numpy.ones((x,))):
|
||
"""Compute Spectral Subband Centroid features from an audio signal.
|
||
:param signal: the audio signal from which to compute features. Should be an N*1 array
|
||
:param samplerate: the sample rate of the signal we are working with, in Hz.
|
||
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
|
||
:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
|
||
:param nfilt: the number of filters in the filterbank, default 26.
|
||
:param nfft: the FFT size. Default is 512.
|
||
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
|
||
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
|
||
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
|
||
:param winfunc: the analysis window to apply to each frame. By default no window is applied. You can use numpy window functions here e.g. winfunc=numpy.hamming
|
||
:returns: A numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector.
|
||
"""
|
||
highfreq = highfreq or samplerate / 2
|
||
signal = preemphasis(signal, preemph)
|
||
frames = framesig(signal, winlen * samplerate, winstep * samplerate, winfunc)
|
||
pspec = powspec(frames, nfft)
|
||
pspec = numpy.where(pspec == 0, numpy.finfo(float).eps, pspec) # if things are all zeros we get problems
|
||
|
||
fb = get_filterbanks(nfilt, nfft, samplerate, lowfreq, highfreq)
|
||
feat = numpy.dot(pspec, fb.T) # compute the filterbank energies
|
||
R = numpy.tile(numpy.linspace(1, samplerate / 2, numpy.size(pspec, 1)), (numpy.size(pspec, 0), 1))
|
||
|
||
return numpy.dot(pspec * R, fb.T) / feat
|
||
|
||
|
||
def hz2mel(hz):
|
||
"""Convert a value in Hertz to Mels
|
||
:param hz: a value in Hz. This can also be a numpy array, conversion proceeds element-wise.
|
||
:returns: a value in Mels. If an array was passed in, an identical sized array is returned.
|
||
"""
|
||
return 2595 * numpy.log10(1 + hz / 700.)
|
||
|
||
|
||
def mel2hz(mel):
|
||
"""Convert a value in Mels to Hertz
|
||
:param mel: a value in Mels. This can also be a numpy array, conversion proceeds element-wise.
|
||
:returns: a value in Hertz. If an array was passed in, an identical sized array is returned.
|
||
"""
|
||
return 700 * (10 ** (mel / 2595.0) - 1)
|
||
|
||
|
||
def get_filterbanks(nfilt=20, nfft=512, samplerate=16000, lowfreq=0, highfreq=None):
|
||
"""Compute a Mel-filterbank. The filters are stored in the rows, the columns correspond
|
||
to fft bins. The filters are returned as an array of size nfilt * (nfft/2 + 1)
|
||
:param nfilt: the number of filters in the filterbank, default 20.
|
||
:param nfft: the FFT size. Default is 512.
|
||
:param samplerate: the sample rate of the signal we are working with, in Hz. Affects mel spacing.
|
||
:param lowfreq: lowest band edge of mel filters, default 0 Hz
|
||
:param highfreq: highest band edge of mel filters, default samplerate/2
|
||
:returns: A numpy array of size nfilt * (nfft/2 + 1) containing filterbank. Each row holds 1 filter.
|
||
"""
|
||
highfreq = highfreq or samplerate / 2
|
||
assert highfreq <= samplerate / 2, "highfreq is greater than samplerate/2"
|
||
|
||
# compute points evenly spaced in mels
|
||
lowmel = hz2mel(lowfreq)
|
||
highmel = hz2mel(highfreq)
|
||
melpoints = numpy.linspace(lowmel, highmel, nfilt + 2)
|
||
# our points are in Hz, but we use fft bins, so we have to convert
|
||
# from Hz to fft bin number
|
||
bin = numpy.floor((nfft + 1) * mel2hz(melpoints) / samplerate)
|
||
|
||
fbank = numpy.zeros([nfilt, nfft // 2 + 1])
|
||
for j in range(0, nfilt):
|
||
for i in range(int(bin[j]), int(bin[j + 1])):
|
||
fbank[j, i] = (i - bin[j]) / (bin[j + 1] - bin[j])
|
||
for i in range(int(bin[j + 1]), int(bin[j + 2])):
|
||
fbank[j, i] = (bin[j + 2] - i) / (bin[j + 2] - bin[j + 1])
|
||
return fbank
|
||
|
||
|
||
def lifter(cepstra, L=22):
|
||
"""Apply a cepstral lifter the the matrix of cepstra. This has the effect of increasing the
|
||
magnitude of the high frequency DCT coeffs.
|
||
:param cepstra: the matrix of mel-cepstra, will be numframes * numcep in size.
|
||
:param L: the liftering coefficient to use. Default is 22. L <= 0 disables lifter.
|
||
"""
|
||
if L > 0:
|
||
nframes, ncoeff = numpy.shape(cepstra)
|
||
n = numpy.arange(ncoeff)
|
||
lift = 1 + (L / 2.) * numpy.sin(numpy.pi * n / L)
|
||
return lift * cepstra
|
||
else:
|
||
# values of L <= 0, do nothing
|
||
return cepstra
|
||
|
||
|
||
def delta(feat, N):
|
||
"""Compute delta features from a feature vector sequence.
|
||
:param feat: A numpy array of size (NUMFRAMES by number of features) containing features. Each row holds 1 feature vector.
|
||
:param N: For each frame, calculate delta features based on preceding and following N frames
|
||
:returns: A numpy array of size (NUMFRAMES by number of features) containing delta features. Each row holds 1 delta feature vector.
|
||
"""
|
||
if N < 1:
|
||
raise ValueError('N must be an integer >= 1')
|
||
NUMFRAMES = len(feat)
|
||
denominator = 2 * sum([i ** 2 for i in range(1, N + 1)])
|
||
delta_feat = numpy.empty_like(feat)
|
||
padded = numpy.pad(feat, ((N, N), (0, 0)), mode='edge') # padded version of feat
|
||
for t in range(NUMFRAMES):
|
||
delta_feat[t] = numpy.dot(numpy.arange(-N, N + 1),
|
||
padded[t: t + 2 * N + 1]) / denominator # [t : t+2*N+1] == [(N+t)-N : (N+t)+N+1]
|
||
return delta_feat
|