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[O] Reformat

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Azalea (on HyDEV-Daisy)
2022-04-07 15:53:14 -04:00
parent 689e7d5e83
commit d322444f03
1 changed files with 47 additions and 61 deletions
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extract_features.py
+47 -61
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@@ -7,10 +7,11 @@ import os
import math
from inaSpeechSegmenter.features import to_wav
from numba import float32
from scipy.fftpack.realtransforms import dct
from scipy.signal import lfilter, hamming
from scipy.fftpack import fft, ifft
#from scikits.talkbox.linpred import lpc # obsolete
# from scikits.talkbox.linpred import lpc # obsolete
from helpers.conch_lpc import lpc
from helpers.utilities import *
@@ -18,18 +19,18 @@ epsilon = 0.0000000001
prefac = .97
def build_data(wav, begin=None,end=None):
def build_data(wav, begin=None, end=None):
wav_in_file = wave.Wave_read(str(wav))
wav_in_num_samples = wav_in_file.getnframes()
N = wav_in_file.getnframes()
dstr = wav_in_file.readframes(N)
data = np.fromstring(dstr, np.int16)
if begin is not None and end is not None:
#return data[begin*16000:end*16000] #numpy 1.11.0
return data[np.int(begin*16000):np.int(end*16000)] #numpy 1.14.0
# return data[begin*16000:end*16000] #numpy 1.11.0
return data[np.int(begin * 16000):np.int(end * 16000)] # numpy 1.14.0
X = []
l = len(data)
for i in range(0, l-100, 160):
for i in range(0, l - 100, 160):
X.append(data[i:i + 480])
return X
@@ -183,13 +184,13 @@ def atal(x, order, num_coefs):
a, e, kk = lpc(x, order)
c = np.zeros(num_coefs)
c[0] = a[0]
for m in range(1, order+1):
for m in range(1, order + 1):
c[m] = - a[m]
for k in range(1, m):
c[m] += (float(k)/float(m)-1)*a[k]*c[m-k]
for m in range(order+1, num_coefs):
for k in range(1, order+1):
c[m] += (float(k)/float(m)-1)*a[k]*c[m-k]
c[m] += (float(k) / float(m) - 1) * a[k] * c[m - k]
for m in range(order + 1, num_coefs):
for k in range(1, order + 1):
c[m] += (float(k) / float(m) - 1) * a[k] * c[m - k]
return c
@@ -198,7 +199,7 @@ def preemp(input, p):
return lfilter([1., -p], 1, input)
def arspecs(input_wav,order,Atal=False):
def arspecs(input_wav, order, Atal=False):
data = input_wav
if Atal:
ar = atal(data, order, 30)
@@ -207,8 +208,8 @@ def arspecs(input_wav,order,Atal=False):
ar = []
ars = arspec(data, order, 4096)
for k, l in zip(ars[0], ars[1]):
ar.append(math.log(math.sqrt((k**2)+(l**2))))
for val in range(0,len(ar)):
ar.append(math.log(math.sqrt((k ** 2) + (l ** 2))))
for val in range(0, len(ar)):
if ar[val] < 0.0:
ar[val] = np.nan
elif ar[val] == 0.0:
@@ -219,41 +220,43 @@ def arspecs(input_wav,order,Atal=False):
return ar[:30]
def specPS(input_wav,pitch):
N = len(input_wav)
samps = N // pitch
if samps == 0:
samps = 1
frames = N // samps
data = input_wav[0:frames]
specs = periodogram(data,nfft=4096)
for i in range(1,int(samps)):
data = input_wav[frames*i:frames*(i+1)]
peri = periodogram(data,nfft=4096)
for sp in range(len(peri[0])):
specs[0][sp] += peri[0][sp]
for s in range(len(specs[0])):
specs[0][s] /= float(samps)
peri = []
for k, l in zip(specs[0], specs[1]):
m = math.sqrt((k ** 2) + (l ** 2))
if m > 0: m = math.log(m)
if m == 0: m = epsilon
elif m < 0: m = np.nan
peri.append(m)
# Filter the spectrum through the triangle filterbank
mspec = np.log10(peri)
# Use the DCT to 'compress' the coefficients (spectrum -> cepstrum domain)
ceps = dct(mspec, type=2, norm='ortho', axis=-1)
return ceps[:50]
def specPS(input_wav, pitch):
N = len(input_wav)
samps = N // pitch
if samps == 0:
samps = 1
frames = N // samps
data = input_wav[0:frames]
specs = periodogram(data, nfft=4096)
for i in range(1, int(samps)):
data = input_wav[frames * i:frames * (i + 1)]
peri = periodogram(data, nfft=4096)
for sp in range(len(peri[0])):
specs[0][sp] += peri[0][sp]
for s in range(len(specs[0])):
specs[0][s] /= float(samps)
peri = []
for k, l in zip(specs[0], specs[1]):
m = math.sqrt((k ** 2) + (l ** 2))
if m > 0: m = math.log(m)
if m == 0:
m = epsilon
elif m < 0:
m = np.nan
peri.append(m)
# Filter the spectrum through the triangle filterbank
mspec = np.log10(peri)
# Use the DCT to 'compress' the coefficients (spectrum -> cepstrum domain)
ceps = dct(mspec, type=2, norm='ortho', axis=-1)
return ceps[:50]
def build_single_feature_row(data, Atal):
lpcs = [8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
def build_single_feature_row(data: float32[:], Atal):
lpc_orders = np.array([8, 9, 10, 11, 12, 13, 14, 15, 16, 17])
arr = []
periodo = specPS(data, 50)
arr.extend(periodo)
for j in lpcs:
for j in lpc_orders:
if Atal:
ars = arspecs(data, j, Atal=True)
else:
@@ -280,24 +283,7 @@ def create_features(input_wav_filename, feature_filename, begin=None, end=None,
arr.extend(build_single_feature_row(X[i], Atal))
arcep_mat.append(arr)
np.savetxt(feature_filename, np.asarray(arcep_mat), delimiter=",", fmt="%s")
os.remove(wav)
return arcep_mat
if __name__ == "__main__":
# parse arguments
parser = argparse.ArgumentParser(description='Extract features for formants estimation.')
parser.add_argument('wav_file', default='', help="WAV audio filename (single vowel or an whole utternace)")
parser.add_argument('feature_file', default='', help="output feature text file")
parser.add_argument('--begin', help="beginning time in the WAV file", default=0.0, type=float)
parser.add_argument('--end', help="end time in the WAV file", default=-1.0, type=float)
args = parser.parse_args()
if args.begin > 0.0 or args.end > 0.0:
create_features(args.wav_file, args.feature_file, args.begin, args.end)
else:
create_features(args.wav_file, args.feature_file)