def main():
if len(sys.argv) != 5:
print(
'<plda-out-domain> <adapt-ivector-rspecifier> <plda-in-domain> <plda-adapt> \n',
)
sys.exit()
plda_out_domain = sys.argv[1]
train_vecs_adapt = sys.argv[2]
plda_in_domain = sys.argv[3]
plda_adapt = sys.argv[4]
coral = CORAL()
coral.plda_read(plda_out_domain)
for _, vec in kaldi_io.read_vec_flt_auto(train_vecs_adapt):
coral.add_stats(1, vec)
coral.update_plda()
cipreg = CIPReg()
cipreg.plda_read(plda_in_domain)
cipreg.interpolation(coral)
plda_new = PLDA()
plda_new.mean = cipreg.mean
plda_new.within_var = cipreg.within_var
plda_new.between_var = cipreg.between_var
plda_new.get_output()
plda_new.plda_trans_write(plda_adapt)
def main():
if len(sys.argv) != 4:
print("Usage: " + sys.argv[0] +
" <spk2utt-rspecifier> <ivector-rspecifier> <plda>\n")
print("e.g.: " + sys.argv[0] + " spk2utt ivectors.ark plda")
sys.exit()
spk2utt = sys.argv[1]
ivectors_reader = sys.argv[2]
plda_out = sys.argv[3]
logger.info('Load vecs and accumulate the stats of vecs.....')
utt2spk_dict = {}
with open(spk2utt, 'r') as f:
for line in f:
temp_list = line.strip().split()
spk = temp_list[0]
del temp_list[0]
for utt in temp_list:
utt2spk_dict[utt] = spk
spk2vectors = {}
for key, vector in kaldi_io.read_vec_flt_auto(ivectors_reader):
dim = vector.shape[0]
spk = utt2spk_dict[key]
try:
tmp_list = spk2vectors[spk]
tmp_list.append(vector)
spk2vectors[spk] = tmp_list
except KeyError:
spk2vectors[spk] = [vector]
plda_stats = PldaStats(dim)
for key in spk2vectors.keys():
vectors = np.array(spk2vectors[key], dtype=float)
weight = 1.0
plda_stats.add_samples(weight, vectors)
logger.info('Estimate the parameters of PLDA by EM algorithm...')
plda_stats.sort()
plda_estimator = PldaEstimation(plda_stats)
plda_estimator.estimate()
logger.info('Save the parameters for the PLDA adaptation...')
plda_estimator.plda_write(plda_out + '.ori')
plda_trans = plda_estimator.get_output()
logger.info(
'Save the parameters for scoring directly, which is the same with the plda in kaldi...'
)
plda_trans.plda_trans_write(plda_out)
def main():
if len(sys.argv) != 4:
print('<plda> <adapt-ivector-rspecifier> <plda-adapt> \n', )
sys.exit()
plda = sys.argv[1]
train_vecs_adapt = sys.argv[2]
plda_adapt = sys.argv[3]
coralplus = CORALPlus()
coralplus.plda_read(plda)
for _, vec in kaldi_io.read_vec_flt_auto(train_vecs_adapt):
coralplus.add_stats(1, vec)
coralplus.update_plda()
plda_new = PLDA()
plda_new.mean = coralplus.mean
plda_new.within_var = coralplus.within_var
plda_new.between_var = coralplus.between_var
plda_new.get_output()
plda_new.plda_trans_write(plda_adapt)