def get_ceps_true_speaker(speaker_samples_ceps='combined'):
nfft = 512
t_frame = 20 * 10**(-3) # Duration in seconds of each frame
if speaker_samples_ceps == 'combined':
[fs, TS_audiofiles] = get_np_audiofiles(UBM=False,
TS=True,
normalize=True)
nwin = t_frame * fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
ceps, mspec, spec = mfcc(TS_audiofiles, nwin, nfft, fs, nceps)
return ceps
else:
files_in_folder = os.listdir(
os.getcwd()) # List of files in current directory
true_speaker_files = [
x for x in files_in_folder
if ('.wav' in x) and ('true_speaker' in x)
]
[fs, audio_01] = read_audiofile(true_speaker_files[0], normalize=True)
nwin = t_frame * fs
ceps_01, mspec, spec = mfcc(audio_01, nwin, nfft, fs, nceps)
[fs, audio_02] = read_audiofile(true_speaker_files[1], normalize=True)
nwin = t_frame * fs
ceps_02, mspec, spec = mfcc(audio_02, nwin, nfft, fs, nceps)
[fs, audio_03] = read_audiofile(true_speaker_files[2], normalize=True)
nwin = t_frame * fs
ceps_03, mspec, spec = mfcc(audio_03, nwin, nfft, fs, nceps)
return ceps_01, ceps_02, ceps_03
def get_ceps_UBM(str_gender='all', exclude_speaker=None): # ------------------- # Getting audio files in Numpy array format # ------------------- [fs, np_audio_male, np_audio_female, np_audio_all] = get_np_audiofiles(UBM=True, exclude_speaker=None) # ---------------------------------------------------- # Computing MFCC # ---------------------------------------------------- t_frame = 20*10**(-3) # Duration in seconds of each frame nwin = t_frame*fs # nwin is the number of samples per frame. # Para t_frame=20ms e fs=16kHz, nwin=320 nfft = 512 if str_gender == 'male': ceps, mspec, spec = mfcc(np_audio_male, nwin, nfft, fs, nceps) if str_gender == 'female': ceps, mspec, spec = mfcc(np_audio_female, nwin, nfft, fs, nceps) else: ceps, mspec, spec = mfcc(np_audio_all, nwin, nfft, fs, nceps) return ceps
def get_ceps_UBM(str_gender='all', exclude_speaker=None):
# -------------------
# Getting audio files in Numpy array format
# -------------------
[fs, np_audio_male, np_audio_female,
np_audio_all] = get_np_audiofiles(UBM=True, exclude_speaker=None)
# ----------------------------------------------------
# Computing MFCC
# ----------------------------------------------------
t_frame = 20 * 10**(-3) # Duration in seconds of each frame
nwin = t_frame * fs
# nwin is the number of samples per frame.
# Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
if str_gender == 'male':
ceps, mspec, spec = mfcc(np_audio_male, nwin, nfft, fs, nceps)
if str_gender == 'female':
ceps, mspec, spec = mfcc(np_audio_female, nwin, nfft, fs, nceps)
else:
ceps, mspec, spec = mfcc(np_audio_all, nwin, nfft, fs, nceps)
return ceps
def get_ceps_true_speaker(speaker_samples_ceps='combined'):
nfft = 512
t_frame = 20*10**(-3) # Duration in seconds of each frame
if speaker_samples_ceps=='combined':
[fs, TS_audiofiles] = get_np_audiofiles(UBM=False, TS=True, normalize=True)
nwin = t_frame*fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
ceps, mspec, spec = mfcc(TS_audiofiles, nwin, nfft, fs, nceps)
return ceps
else:
files_in_folder = os.listdir(os.getcwd()) # List of files in current directory
true_speaker_files = [x for x in files_in_folder if ('.wav' in x) and ('true_speaker' in x)]
[fs,audio_01] = read_audiofile(true_speaker_files[0],normalize=True)
nwin = t_frame*fs
ceps_01, mspec, spec = mfcc(audio_01, nwin, nfft, fs, nceps)
[fs,audio_02] = read_audiofile(true_speaker_files[1],normalize=True)
nwin = t_frame*fs
ceps_02, mspec, spec = mfcc(audio_02, nwin, nfft, fs, nceps)
[fs,audio_03] = read_audiofile(true_speaker_files[2],normalize=True)
nwin = t_frame*fs
ceps_03, mspec, spec = mfcc(audio_03, nwin, nfft, fs, nceps)
return ceps_01, ceps_02, ceps_03
def audio2ceps(filename, flag_normalize=True):
'''
Reads an audio file and creates a text file with its MFCC.
'''
file = filename.split('.')[0] # Separates file name and extension
# --------------------------------------------
# File reading into numpy array
[fs, np_audio] = read_audiofile(filename, normalize=flag_normalize)
# --------------------------------------------
# MFCC calculation
t_frame = 20 * 10**(-3) # Duration in seconds of each frame
nwin = t_frame * fs
# nwin is the number of samples per frame.
# Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps, mspec, spec = mfcc(np_audio, nwin, nfft, fs, nceps)
[nframes, ncolumns] = ceps.shape
# --------------------------------------------
# Text file creation
np.savetxt(file + '_ceps.txt',
ceps,
header='MFCC from file ' + filename +
'.\nInfo:\n\tSample rate: ' + str(RATE) +
'\n\tNumber of MFCC per frame: ' + str(nceps) +
'\n\tNumber of frames (samples): ' + str(nframes) + '\n\n')
def audio2ceps(filename,flag_normalize=True):
'''
Reads an audio file and creates a text file with its MFCC.
'''
file = filename.split('.')[0] # Separates file name and extension
# --------------------------------------------
# File reading into numpy array
[fs, np_audio] = read_audiofile(filename,normalize = flag_normalize)
# --------------------------------------------
# MFCC calculation
t_frame = 20*10**(-3) # Duration in seconds of each frame
nwin = t_frame*fs
# nwin is the number of samples per frame.
# Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps, mspec, spec = mfcc(np_audio, nwin, nfft, fs, nceps)
[nframes, ncolumns] = ceps.shape
# --------------------------------------------
# Text file creation
np.savetxt(file + '_ceps.txt',ceps,header='MFCC from file ' + filename + '.\nInfo:\n\tSample rate: ' + str(RATE) + '\n\tNumber of MFCC per frame: ' + str(nceps) + '\n\tNumber of frames (samples): ' + str(nframes) + '\n\n')
def audio2ceps(filename,flag_normalize=True):
'''
Reads an audio file and creates a text file with its MFCC.
Problem to fix: in the Raspberry, the function np.savetxt somehow doesn't accept the keyword 'header'.
'''
file = filename.split('.')[0] # Separates file name and extension
# --------------------------------------------
# File reading into numpy array
[fs, np_audio] = read_audiofile(filename,normalize = flag_normalize)
# --------------------------------------------
# MFCC calculation
t_frame = 20*10**(-3) # Duration in seconds of each frame
nwin = t_frame*fs
# nwin is the number of samples per frame.
# Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps, mspec, spec = mfcc(np_audio, nwin, nfft, fs, nceps)
[nframes, ncolumns] = ceps.shape
# --------------------------------------------
# Text file creation
str_header = 'MFCC from file ' + filename + '.\nInfo:\n\tSample rate: ' + str(RATE) + '\n\tNumber of MFCC per frame: ' + str(nceps) + '\n\tNumber of frames (samples): ' + str(nframes) + '\n\n'
if sys.platform=='linux2': # in Raspbian
np.savetxt(file + '_ceps.txt',ceps)
else:
np.savetxt(file + '_ceps.txt',ceps,header=str_header)
def audio2ceps(filename, flag_normalize=True):
'''
Reads an audio file and creates a text file with its MFCC.
Problem to fix: in the Raspberry, the function np.savetxt somehow doesn't accept the keyword 'header'.
'''
file = filename.split('.')[0] # Separates file name and extension
# --------------------------------------------
# File reading into numpy array
[fs, np_audio] = read_audiofile(filename, normalize=flag_normalize)
# --------------------------------------------
# MFCC calculation
t_frame = 20 * 10**(-3) # Duration in seconds of each frame
nwin = t_frame * fs
# nwin is the number of samples per frame.
# Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps, mspec, spec = mfcc(np_audio, nwin, nfft, fs, nceps)
[nframes, ncolumns] = ceps.shape
# --------------------------------------------
# Text file creation
str_header = 'MFCC from file ' + filename + '.\nInfo:\n\tSample rate: ' + str(
RATE) + '\n\tNumber of MFCC per frame: ' + str(
nceps) + '\n\tNumber of frames (samples): ' + str(nframes) + '\n\n'
if sys.platform == 'linux2': # in Raspbian
np.savetxt(file + '_ceps.txt', ceps)
else:
np.savetxt(file + '_ceps.txt', ceps, header=str_header)
def get_ceps_threshold_files():
files_in_folder = os.listdir(os.getcwd()) # List of files in current directory
threshold_files = [x for x in files_in_folder if ('threshold_audio.wav' in x)]
t_frame = 20*10**(-3) # Duration in seconds of each frame
nfft = 512
[fs,np_threshold] = read_audiofile(threshold_files[0],normalize=True)
nwin = t_frame*fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
ceps_01, mspec, spec = mfcc(np_threshold, nwin, nfft, fs, nceps)
[fs,np_threshold] = read_audiofile(threshold_files[1],normalize=True)
nwin = t_frame*fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
ceps_02, mspec, spec = mfcc(np_threshold, nwin, nfft, fs, nceps)
[fs,np_threshold] = read_audiofile(threshold_files[2],normalize=True)
nwin = t_frame*fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
ceps_03, mspec, spec = mfcc(np_threshold, nwin, nfft, fs, nceps)
return ceps_01, ceps_02, ceps_03
def get_ceps_test_speaker():
files_in_folder = os.listdir(os.getcwd()) # List of files in current directory
test_file = [x for x in files_in_folder if ('test_speaker.wav' in x)]
[fs,np_test] = read_audiofile(test_file[0],normalize=True)
t_frame = 20*10**(-3) # Duration in seconds of each frame
nwin = t_frame*fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps, mspec, spec = mfcc(np_test, nwin, nfft, fs, nceps)
return ceps
def get_ceps_threshold_files():
files_in_folder = os.listdir(
os.getcwd()) # List of files in current directory
threshold_files = [
x for x in files_in_folder if ('threshold_audio.wav' in x)
]
t_frame = 20 * 10**(-3) # Duration in seconds of each frame
nfft = 512
[fs, np_threshold] = read_audiofile(threshold_files[0], normalize=True)
nwin = t_frame * fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
ceps_01, mspec, spec = mfcc(np_threshold, nwin, nfft, fs, nceps)
[fs, np_threshold] = read_audiofile(threshold_files[1], normalize=True)
nwin = t_frame * fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
ceps_02, mspec, spec = mfcc(np_threshold, nwin, nfft, fs, nceps)
[fs, np_threshold] = read_audiofile(threshold_files[2], normalize=True)
nwin = t_frame * fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
ceps_03, mspec, spec = mfcc(np_threshold, nwin, nfft, fs, nceps)
return ceps_01, ceps_02, ceps_03
def get_ceps_test_speaker():
files_in_folder = os.listdir(
os.getcwd()) # List of files in current directory
test_file = [x for x in files_in_folder if ('test_speaker.wav' in x)]
[fs, np_test] = read_audiofile(test_file[0], normalize=True)
t_frame = 20 * 10**(-3) # Duration in seconds of each frame
nwin = t_frame * fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps, mspec, spec = mfcc(np_test, nwin, nfft, fs, nceps)
return ceps
def evaluate_all_vs_single_true(flag_speaker_samples='combined'):
# ----------------------------------------
# Model of True Speaker
if flag_speaker_samples=='combined':
model_true = get_GMM_true_speaker(speaker_samples=flag_speaker_samples)
else: # Neste caso flag_speaker_samples=='separated'
[model_true_01, model_true_02, model_true_03] = get_GMM_true_speaker(speaker_samples=flag_speaker_samples)
# ----------------------------------------
# Setting up text file
files_in_folder = os.listdir(os.getcwd()) # List of files in current directory
text_output_files = [x for x in files_in_folder if ('Verification_Test_04_true' in x) and (speaker_samples in x)]
number = len(text_output_files) + 1
f = open('Verification_Test_04_true_' + speaker_samples + '_0' + str(number) + '.txt','w')
f.write('# ' + text_dependency + ' ASV\n# Header: a single ASV test is carried out for each test speaker, with the true speaker fixed, but having three elocutions.\n# GENDER\tINDEX\tAGE\tSCORE\tDECISION\n')
n_accepted = 0
n_false_acceptance = 0
n_rejected = 0
n_false_rejection = 0
'''
----------------------------------------------------
Training UBM for all UBM-speakers except the True one
----------------------------------------------------
'''
cov_type = 'full'
UBM_all = get_UBM_all(cov_type='full')
'''
----------------------------------------------------
Getting threshold
----------------------------------------------------
'''
threshold = get_score_threshold(speaker_samples_threshold = speaker_samples)
files_in_folder = os.listdir(os.getcwd()) # List of files in current directory
audiofiles_male = [x for x in files_in_folder if ('.wav' in x) and ('M' in x) and ('UBM' not in x) and ('_test' not in x) and ('_true' not in x)]
audiofiles_female = [x for x in files_in_folder if ('.wav' in x) and ('F' in x) and ('UBM' not in x) and ('_test' not in x) and ('_true' not in x)]
audiofiles_S = [x for x in files_in_folder if ('.wav' in x) and ('S' in x) and ('UBM' not in x) and ('_test' not in x) and ('_true' not in x)]
# ------------------------------------------------------------------------
# Running through the lists of utterances from non-True speakers
for gender_file_list in [audiofiles_male, audiofiles_female]:
for current_test_speaker in gender_file_list:
print '\nTEST 04'
print 'For file ' + current_test_speaker + ':'
[gender,index,aux] = current_test_speaker.split('_') # "aux" is just a disposable variable
[age,aux] = aux.split('.')
'''
----------------------------------------------------
Training test speaker
----------------------------------------------------
'''
[fs, audio_test] = read_audiofile(current_test_speaker,normalize=True)
t_frame = 20*10**(-3) # Duration in seconds of each frame
nwin = t_frame*fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps_test, mspec, spec = mfcc(audio_test, nwin, nfft, fs, nceps)
'''
----------------------------------------------------
Scoring
----------------------------------------------------
'''
if speaker_samples=='combined':
score_UBM = UBM_all.score(ceps_test)
score_true = model_true.score(ceps_test)
score = np.sum(score_true - score_UBM)
print '\nScore: ', score
else: # Neste caso speaker_samples=='separated'
score_UBM = UBM_all.score(ceps_test)
score01 = model_true_01.score(ceps_test)
score02 = model_true_02.score(ceps_test)
score03 = model_true_03.score(ceps_test)
score_true = np.max(np.array([score01, score02, score03]))
score = np.sum(score_true - score_UBM)
print '\nScore: ', score
print "Difference between sum(log) probabilites (True VS. UBM_all): ", score
# ----------------------------------
# DECISION
# ----------------------------------
if score >= threshold:
decision = 'ACCEPT'
n_accepted += 1
n_false_acceptance += 1
else:
decision = 'Reject'
n_rejected += 1
f.write(gender + '\t' + index + '\t' + age + '\t' + str(score) + '\t' + decision + '\n')
# ------------------------------------------------------------------------
# Running through the utterances of true speaker (prefix = S)
for current_test_speaker in audiofiles_S:
print '\nTEST 04'
print 'For file ' + current_test_speaker + ':'
[gender,index,aux] = current_test_speaker.split('_') # "aux" is just a disposable variable
[age,aux] = aux.split('.')
'''
----------------------------------------------------
Training test speaker
----------------------------------------------------
'''
[fs, audio_test] = read_audiofile(current_test_speaker,normalize=True)
t_frame = 20*10**(-3) # Duration in seconds of each frame
nwin = t_frame*fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps_test, mspec, spec = mfcc(audio_test, nwin, nfft, fs, nceps)
'''
----------------------------------------------------
Scoring
----------------------------------------------------
'''
if speaker_samples=='combined':
score_UBM = UBM_all.score(ceps_test)
score_true = model_true.score(ceps_test)
score = np.sum(score_true - score_UBM)
print '\nScore: ', score
else: # Neste caso speaker_samples=='separated'
score_UBM = UBM_all.score(ceps_test)
score01 = model_true_01.score(ceps_test)
score02 = model_true_02.score(ceps_test)
score03 = model_true_03.score(ceps_test)
score_true = np.max(np.array([score01, score02, score03]))
score = np.sum(score_true - score_UBM)
print '\nScore: ', score
print "Difference between sum(log) probabilites (True VS. UBM_all): ", score
# ----------------------------------
# DECISION
# ----------------------------------
if score >= threshold:
decision = 'ACCEPT'
n_accepted += 1
else:
decision = 'Reject'
n_rejected += 1
n_false_rejection += 1
f.write(gender + '\t' + index + '\t' + age + '\t' + str(score) + '\t' + decision + '\n')
f.write('# Number of accepted: ' + str(n_accepted) + '\n# Number of rejected: ' + str(n_rejected) + '\n# Number of false-acceptance: ' + str(n_false_acceptance) + '\n# Number of false-rejection: ' + str(n_false_rejection) + '\n# Threshold: ' + str(threshold))
f.close()
def evaluate_all_vs_true():
"""
# ======================================================================
# TESTING WITH ALL SPEAKERS AGAINST A SINGLE TRUE SPEAKER
"""
files_in_folder = os.listdir(os.getcwd()) # List of files in current directory
audiofiles_male = [
x
for x in files_in_folder
if (".wav" in x) and ("M" in x) and ("UBM" not in x) and ("_true" not in x) and ("_test" not in x)
]
audiofiles_female = [
x
for x in files_in_folder
if (".wav" in x) and ("F" in x) and ("UBM" not in x) and ("_true" not in x) and ("_test" not in x)
]
print "len(audiofiles_male): ", len(audiofiles_male)
print "len(audiofiles_female): ", len(audiofiles_female)
true_speakers_list = (
audiofiles_male + audiofiles_female + ["01_true_speaker.wav", "02_true_speaker.wav", "03_true_speaker.wav"]
) # Concatenation of both lists
for true_speaker in true_speakers_list:
"""
----------------------------------------------------
Training UBM for all training speakers (male & female) except the True one
----------------------------------------------------
"""
cov_type = "full"
UBM_all = get_UBM_all(cov_type="full", exclude_speaker=true_speaker)
"""
----------------------------------------------------
Setting up .txt files and lists
----------------------------------------------------
"""
f = open("Verification_Test_02_" + true_speaker.split(".")[0] + ".txt", "w")
f.write(
"# Header: a single ASV test is carried out for each speaker S, with the speaker "
+ true_speaker.split(".")[0]
+ " being the True Speaker in all tests.\n# GENDER\tINDEX\tAGE\tSCORE\n"
)
for gender_file_list in [audiofiles_male, audiofiles_female]:
for current_test_speaker in gender_file_list:
print "\nTEST 02 (all against one)"
print "For file " + current_test_speaker + ":"
[gender, index, aux] = current_test_speaker.split("_") # "aux" is just a disposable variable
[age, aux] = aux.split(".")
"""
----------------------------------------------------
Training true speaker
----------------------------------------------------
"""
fs, audio_true = spwave.read(true_speaker)
t_frame = 20 * 10 ** (-3) # Duration in seconds of each frame
nwin = t_frame * fs
# nwin is the number of samples per frame.
# Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps_true, mspec, spec = mfcc(audio_true, nwin, nfft, fs, nceps)
ngaussians = 10
cov_type = "full"
gmm = GMM(n_components=ngaussians, covariance_type=cov_type)
model_true = gmm.fit(ceps_true)
print "model_true converged? ", model_true.converged_
"""
----------------------------------------------------
Training test speaker
----------------------------------------------------
"""
fs, audio_test = spwave.read(current_test_speaker)
nwin = t_frame * fs
# nwin is the number of samples per frame.
# Para t_frame=20ms e fs=16kHz, nwin=320
ceps_test, mspec, spec = mfcc(audio_test, nwin, nfft, fs, nceps)
"""
----------------------------------------------------
Scoring
----------------------------------------------------
"""
log_prob_true = model_true.score(ceps_test)
log_prob_UBM = UBM_all.score(ceps_test)
print np.sum(log_prob_true)
print np.sum(log_prob_UBM)
score = np.sum(log_prob_true) - np.sum(log_prob_UBM)
print "Difference between sum(log) probabilites (True VS. UBM_all): ", score
f.write(gender + "\t" + index + "\t" + age + "\t" + str(score) + "\n")
f.close()
def evaluate_all_vs_single_true(speaker_samples="combined"):
current_folder = os.getcwd()
text_dependency = current_folder.split("/")[
-1
] # Takes the name of the enclosing folder, if it is TD (text dependent) or TI (text independent)
files_in_folder = os.listdir(os.getcwd()) # List of files in current directory
text_output_files = [x for x in files_in_folder if ("Verification_Test_04_true" in x) and (speaker_samples in x)]
number = len(text_output_files) + 1
f = open("Verification_Test_04_true_" + speaker_samples + "_0" + str(number) + ".txt", "w")
f.write(
"# "
+ text_dependency
+ " ASV\n# Header: a single ASV test is carried out for each test speaker, with the true speaker fixed, but having three elocutions.\n# GENDER\tINDEX\tAGE\tSCORE\tDECISION\n"
)
n_accepted = 0
n_false_acceptance = 0
n_rejected = 0
n_false_rejection = 0
"""
----------------------------------------------------
Training UBM for all UBM-speakers except the True one
----------------------------------------------------
"""
cov_type = "full"
UBM_all = get_UBM_all(cov_type="full")
"""
----------------------------------------------------
Getting threshold
----------------------------------------------------
"""
threshold = get_score_threshold(speaker_samples_threshold=speaker_samples)
files_in_folder = os.listdir(os.getcwd()) # List of files in current directory
audiofiles_male = [
x
for x in files_in_folder
if (".wav" in x) and ("M" in x) and ("UBM" not in x) and ("_test" not in x) and ("_true" not in x)
]
audiofiles_female = [
x
for x in files_in_folder
if (".wav" in x) and ("F" in x) and ("UBM" not in x) and ("_test" not in x) and ("_true" not in x)
]
audiofiles_S = [
x
for x in files_in_folder
if (".wav" in x) and ("S" in x) and ("UBM" not in x) and ("_test" not in x) and ("_true" not in x)
]
# Running through the lists of utterances from non-True speakers
for gender_file_list in [audiofiles_male, audiofiles_female]:
for current_test_speaker in gender_file_list:
print "\nTEST 04"
print "For file " + current_test_speaker + ":"
[gender, index, aux] = current_test_speaker.split("_") # "aux" is just a disposable variable
[age, aux] = aux.split(".")
"""
----------------------------------------------------
Training test speaker
----------------------------------------------------
"""
[fs, audio_test] = read_audiofile(current_test_speaker, normalize=True)
t_frame = 20 * 10 ** (-3) # Duration in seconds of each frame
nwin = t_frame * fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps_test, mspec, spec = mfcc(audio_test, nwin, nfft, fs, nceps)
"""
----------------------------------------------------
Scoring
----------------------------------------------------
"""
if speaker_samples == "combined":
model_true = get_GMM_true_speaker(speaker_samples="combined")
score_UBM = UBM_all.score(ceps_test)
score_true = model_true.score(ceps_test)
score = np.sum(score_true - score_UBM)
print "\nScore: ", score
else: # Neste caso speaker_samples=='separated'
[model_true_01, model_true_02, model_true_03] = get_GMM_true_speaker(speaker_samples="separated")
score_UBM = UBM_all.score(ceps_test)
score01 = model_true_01.score(ceps_test)
score02 = model_true_02.score(ceps_test)
score03 = model_true_03.score(ceps_test)
score_true = np.max(np.array([score01, score02, score03]))
score = np.sum(score_true - score_UBM)
print "\nScore: ", score
print "Difference between sum(log) probabilites (True VS. UBM_all): ", score
# ----------------------------------
# DECISION
# ----------------------------------
if score >= threshold:
decision = "ACCEPT"
n_accepted += 1
n_false_acceptance += 1
else:
decision = "Reject"
n_rejected += 1
f.write(gender + "\t" + index + "\t" + age + "\t" + str(score) + "\t" + decision + "\n")
# Running through the utterances of true speaker (prefix = S)
for current_test_speaker in audiofiles_S:
print "\nTEST 04"
print "For file " + current_test_speaker + ":"
[gender, index, aux] = current_test_speaker.split("_") # "aux" is just a disposable variable
[age, aux] = aux.split(".")
"""
----------------------------------------------------
Training test speaker
----------------------------------------------------
"""
[fs, audio_test] = read_audiofile(current_test_speaker, normalize=True)
t_frame = 20 * 10 ** (-3) # Duration in seconds of each frame
nwin = t_frame * fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps_test, mspec, spec = mfcc(audio_test, nwin, nfft, fs, nceps)
"""
----------------------------------------------------
Scoring
----------------------------------------------------
"""
if speaker_samples == "combined":
model_true = get_GMM_true_speaker(speaker_samples="combined")
score_UBM = UBM_all.score(ceps_test)
score_true = model_true.score(ceps_test)
score = np.sum(score_true - score_UBM)
print "\nScore: ", score
else: # Neste caso speaker_samples=='separated'
[model_true_01, model_true_02, model_true_03] = get_GMM_true_speaker(speaker_samples="separated")
score_UBM = UBM_all.score(ceps_test)
score01 = model_true_01.score(ceps_test)
score02 = model_true_02.score(ceps_test)
score03 = model_true_03.score(ceps_test)
score_true = np.max(np.array([score01, score02, score03]))
score = np.sum(score_true - score_UBM)
print "\nScore: ", score
print "Difference between sum(log) probabilites (True VS. UBM_all): ", score
# ----------------------------------
# DECISION
# ----------------------------------
if score >= threshold:
decision = "ACCEPT"
n_accepted += 1
else:
decision = "Reject"
n_rejected += 1
n_false_rejection += 1
f.write(gender + "\t" + index + "\t" + age + "\t" + str(score) + "\t" + decision + "\n")
f.write(
"# Number of accepted: "
+ str(n_accepted)
+ "\n# Number of rejected: "
+ str(n_rejected)
+ "\n# Number of false-acceptance: "
+ str(n_false_acceptance)
+ "\n# Number of false-rejection: "
+ str(n_false_rejection)
+ "\n# Threshold: "
+ str(threshold)
)
f.close()
def evaluate_all_vs_true():
'''
# ======================================================================
# TESTING WITH ALL SPEAKERS AGAINST A SINGLE TRUE SPEAKER
'''
files_in_folder = os.listdir(
os.getcwd()) # List of files in current directory
audiofiles_male = [
x for x in files_in_folder if ('.wav' in x) and ('M' in x) and (
'UBM' not in x) and ('_true' not in x) and ('_test' not in x)
]
audiofiles_female = [
x for x in files_in_folder if ('.wav' in x) and ('F' in x) and (
'UBM' not in x) and ('_true' not in x) and ('_test' not in x)
]
print 'len(audiofiles_male): ', len(audiofiles_male)
print 'len(audiofiles_female): ', len(audiofiles_female)
true_speakers_list = audiofiles_male + audiofiles_female + [
'01_true_speaker.wav', '02_true_speaker.wav', '03_true_speaker.wav'
] # Concatenation of both lists
for true_speaker in true_speakers_list:
'''
----------------------------------------------------
Training UBM for all training speakers (male & female) except the True one
----------------------------------------------------
'''
cov_type = 'full'
UBM_all = get_UBM_all(cov_type='full', exclude_speaker=true_speaker)
'''
----------------------------------------------------
Setting up .txt files and lists
----------------------------------------------------
'''
f = open('Verification_Test_02_' + true_speaker.split('.')[0] + '.txt',
'w')
f.write(
'# Header: a single ASV test is carried out for each speaker S, with the speaker '
+ true_speaker.split('.')[0] +
' being the True Speaker in all tests.\n# GENDER\tINDEX\tAGE\tSCORE\n'
)
for gender_file_list in [audiofiles_male, audiofiles_female]:
for current_test_speaker in gender_file_list:
print '\nTEST 02 (all against one)'
print 'For file ' + current_test_speaker + ':'
[gender, index, aux] = current_test_speaker.split(
'_') # "aux" is just a disposable variable
[age, aux] = aux.split('.')
'''
----------------------------------------------------
Training true speaker
----------------------------------------------------
'''
fs, audio_true = spwave.read(true_speaker)
t_frame = 20 * 10**(-3) # Duration in seconds of each frame
nwin = t_frame * fs
# nwin is the number of samples per frame.
# Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps_true, mspec, spec = mfcc(audio_true, nwin, nfft, fs,
nceps)
ngaussians = 10
cov_type = 'full'
gmm = GMM(n_components=ngaussians, covariance_type=cov_type)
model_true = gmm.fit(ceps_true)
print "model_true converged? ", model_true.converged_
'''
----------------------------------------------------
Training test speaker
----------------------------------------------------
'''
fs, audio_test = spwave.read(current_test_speaker)
nwin = t_frame * fs
# nwin is the number of samples per frame.
# Para t_frame=20ms e fs=16kHz, nwin=320
ceps_test, mspec, spec = mfcc(audio_test, nwin, nfft, fs,
nceps)
'''
----------------------------------------------------
Scoring
----------------------------------------------------
'''
log_prob_true = model_true.score(ceps_test)
log_prob_UBM = UBM_all.score(ceps_test)
print np.sum(log_prob_true)
print np.sum(log_prob_UBM)
score = np.sum(log_prob_true) - np.sum(log_prob_UBM)
print "Difference between sum(log) probabilites (True VS. UBM_all): ", score
f.write(gender + '\t' + index + '\t' + age + '\t' +
str(score) + '\n')
f.close()
def evaluate_all_vs_single_true(flag_speaker_samples='combined'):
# ----------------------------------------
# Model of True Speaker
if flag_speaker_samples == 'combined':
model_true = get_GMM_true_speaker(speaker_samples=flag_speaker_samples)
else: # Neste caso flag_speaker_samples=='separated'
[model_true_01, model_true_02, model_true_03
] = get_GMM_true_speaker(speaker_samples=flag_speaker_samples)
# ----------------------------------------
# Setting up text file
files_in_folder = os.listdir(
os.getcwd()) # List of files in current directory
text_output_files = [
x for x in files_in_folder
if ('Verification_Test_04_true' in x) and (speaker_samples in x)
]
number = len(text_output_files) + 1
f = open(
'Verification_Test_04_true_' + speaker_samples + '_0' + str(number) +
'.txt', 'w')
f.write(
'# ' + text_dependency +
' ASV\n# Header: a single ASV test is carried out for each test speaker, with the true speaker fixed, but having three elocutions.\n# GENDER\tINDEX\tAGE\tSCORE\tDECISION\n'
)
n_accepted = 0
n_false_acceptance = 0
n_rejected = 0
n_false_rejection = 0
'''
----------------------------------------------------
Training UBM for all UBM-speakers except the True one
----------------------------------------------------
'''
cov_type = 'full'
UBM_all = get_UBM_all(cov_type='full')
'''
----------------------------------------------------
Getting threshold
----------------------------------------------------
'''
threshold = get_score_threshold(speaker_samples_threshold=speaker_samples)
files_in_folder = os.listdir(
os.getcwd()) # List of files in current directory
audiofiles_male = [
x for x in files_in_folder if ('.wav' in x) and ('M' in x) and (
'UBM' not in x) and ('_test' not in x) and ('_true' not in x)
]
audiofiles_female = [
x for x in files_in_folder if ('.wav' in x) and ('F' in x) and (
'UBM' not in x) and ('_test' not in x) and ('_true' not in x)
]
audiofiles_S = [
x for x in files_in_folder if ('.wav' in x) and ('S' in x) and (
'UBM' not in x) and ('_test' not in x) and ('_true' not in x)
]
# ------------------------------------------------------------------------
# Running through the lists of utterances from non-True speakers
for gender_file_list in [audiofiles_male, audiofiles_female]:
for current_test_speaker in gender_file_list:
print '\nTEST 04'
print 'For file ' + current_test_speaker + ':'
[gender, index, aux] = current_test_speaker.split(
'_') # "aux" is just a disposable variable
[age, aux] = aux.split('.')
'''
----------------------------------------------------
Training test speaker
----------------------------------------------------
'''
[fs, audio_test] = read_audiofile(current_test_speaker,
normalize=True)
t_frame = 20 * 10**(-3) # Duration in seconds of each frame
nwin = t_frame * fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps_test, mspec, spec = mfcc(audio_test, nwin, nfft, fs, nceps)
'''
----------------------------------------------------
Scoring
----------------------------------------------------
'''
if speaker_samples == 'combined':
score_UBM = UBM_all.score(ceps_test)
score_true = model_true.score(ceps_test)
score = np.sum(score_true - score_UBM)
print '\nScore: ', score
else: # Neste caso speaker_samples=='separated'
score_UBM = UBM_all.score(ceps_test)
score01 = model_true_01.score(ceps_test)
score02 = model_true_02.score(ceps_test)
score03 = model_true_03.score(ceps_test)
score_true = np.max(np.array([score01, score02, score03]))
score = np.sum(score_true - score_UBM)
print '\nScore: ', score
print "Difference between sum(log) probabilites (True VS. UBM_all): ", score
# ----------------------------------
# DECISION
# ----------------------------------
if score >= threshold:
decision = 'ACCEPT'
n_accepted += 1
n_false_acceptance += 1
else:
decision = 'Reject'
n_rejected += 1
f.write(gender + '\t' + index + '\t' + age + '\t' + str(score) +
'\t' + decision + '\n')
# ------------------------------------------------------------------------
# Running through the utterances of true speaker (prefix = S)
for current_test_speaker in audiofiles_S:
print '\nTEST 04'
print 'For file ' + current_test_speaker + ':'
[gender, index, aux] = current_test_speaker.split(
'_') # "aux" is just a disposable variable
[age, aux] = aux.split('.')
'''
----------------------------------------------------
Training test speaker
----------------------------------------------------
'''
[fs, audio_test] = read_audiofile(current_test_speaker, normalize=True)
t_frame = 20 * 10**(-3) # Duration in seconds of each frame
nwin = t_frame * fs # nwin is the number of samples per frame. Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps_test, mspec, spec = mfcc(audio_test, nwin, nfft, fs, nceps)
'''
----------------------------------------------------
Scoring
----------------------------------------------------
'''
if speaker_samples == 'combined':
score_UBM = UBM_all.score(ceps_test)
score_true = model_true.score(ceps_test)
score = np.sum(score_true - score_UBM)
print '\nScore: ', score
else: # Neste caso speaker_samples=='separated'
score_UBM = UBM_all.score(ceps_test)
score01 = model_true_01.score(ceps_test)
score02 = model_true_02.score(ceps_test)
score03 = model_true_03.score(ceps_test)
score_true = np.max(np.array([score01, score02, score03]))
score = np.sum(score_true - score_UBM)
print '\nScore: ', score
print "Difference between sum(log) probabilites (True VS. UBM_all): ", score
# ----------------------------------
# DECISION
# ----------------------------------
if score >= threshold:
decision = 'ACCEPT'
n_accepted += 1
else:
decision = 'Reject'
n_rejected += 1
n_false_rejection += 1
f.write(gender + '\t' + index + '\t' + age + '\t' + str(score) + '\t' +
decision + '\n')
f.write('# Number of accepted: ' + str(n_accepted) +
'\n# Number of rejected: ' + str(n_rejected) +
'\n# Number of false-acceptance: ' + str(n_false_acceptance) +
'\n# Number of false-rejection: ' + str(n_false_rejection) +
'\n# Threshold: ' + str(threshold))
f.close()
def evaluate_all_vs_true():
'''
# ======================================================================
# TESTING WITH ALL SPEAKERS AGAINST A SINGLE TRUE SPEAKER
'''
files_in_folder = os.listdir(os.getcwd()) # List of files in current directory
audiofiles_male = [x for x in files_in_folder if ('.wav' in x) and ('M' in x) and ('UBM' not in x) and ('_true' not in x) and ('_test' not in x)]
audiofiles_female = [x for x in files_in_folder if ('.wav' in x) and ('F' in x) and ('UBM' not in x) and ('_true' not in x) and ('_test' not in x)]
print 'len(audiofiles_male): ', len(audiofiles_male)
print 'len(audiofiles_female): ', len(audiofiles_female)
true_speakers_list = audiofiles_male + audiofiles_female + ['01_true_speaker.wav','02_true_speaker.wav','03_true_speaker.wav'] # Concatenation of both lists
for true_speaker in true_speakers_list:
'''
----------------------------------------------------
Training UBM for all training speakers (male & female) except the True one
----------------------------------------------------
'''
cov_type = 'full'
UBM_all = get_UBM_all(cov_type='full',exclude_speaker = true_speaker)
'''
----------------------------------------------------
Setting up .txt files and lists
----------------------------------------------------
'''
f = open('Verification_Test_02_' + true_speaker.split('.')[0] + '.txt','w')
f.write('# Header: a single ASV test is carried out for each speaker S, with the speaker ' + true_speaker.split('.')[0] + ' being the True Speaker in all tests.\n# GENDER\tINDEX\tAGE\tSCORE\n')
for gender_file_list in [audiofiles_male, audiofiles_female]:
for current_test_speaker in gender_file_list:
print '\nTEST 02 (all against one)'
print 'For file ' + current_test_speaker + ':'
[gender,index,aux] = current_test_speaker.split('_') # "aux" is just a disposable variable
[age,aux] = aux.split('.')
'''
----------------------------------------------------
Training true speaker
----------------------------------------------------
'''
fs, audio_true = spwave.read(true_speaker)
t_frame = 20*10**(-3) # Duration in seconds of each frame
nwin = t_frame*fs
# nwin is the number of samples per frame.
# Para t_frame=20ms e fs=16kHz, nwin=320
nfft = 512
ceps_true, mspec, spec = mfcc(audio_true, nwin, nfft, fs, nceps)
ngaussians = 10
cov_type = 'full'
gmm = GMM(n_components = ngaussians, covariance_type = cov_type)
model_true = gmm.fit(ceps_true)
print "model_true converged? ",model_true.converged_
'''
----------------------------------------------------
Training test speaker
----------------------------------------------------
'''
fs, audio_test = spwave.read(current_test_speaker)
nwin = t_frame*fs
# nwin is the number of samples per frame.
# Para t_frame=20ms e fs=16kHz, nwin=320
ceps_test, mspec, spec = mfcc(audio_test, nwin, nfft, fs, nceps)
'''
----------------------------------------------------
Scoring
----------------------------------------------------
'''
log_prob_true = model_true.score(ceps_test)
log_prob_UBM = UBM_all.score(ceps_test)
print np.sum(log_prob_true)
print np.sum(log_prob_UBM)
score = np.sum(log_prob_true) - np.sum(log_prob_UBM)
print "Difference between sum(log) probabilites (True VS. UBM_all): ", score
f.write(gender + '\t' + index + '\t' + age + '\t' + str(score) + '\n')
f.close()