def main():
parser = argparse.ArgumentParser(
description='estimate the DTW function and the joint features')
parser.add_argument('--melspec', help='Melspec data directory')
parser.add_argument('--npow', help='Npow speaker data directory')
parser.add_argument('--out_dir', help='Output directory')
parser.add_argument('--s_npow_th',
default=-20.,
help='Source speaker npow threshold')
parser.add_argument('--t_npow_th',
default=-15.,
help='Target speaker npow threshold')
parser.add_argument('--n_mix',
default=32,
help='Number of mixture components')
parser.add_argument('--n_iter',
default=100,
help='Number of iterations to get GMM model')
parser.add_argument('--covtype',
default='full',
help='GMM covariance type')
parser.add_argument('--cvtype', default='mlpg', help='conversion mode')
parser.add_argument('--jnt_n_iter',
default=3,
help='Number of iterations to get joint features')
conf = parser.parse_args()
melspec_dict = pickle_read(conf.melspec)
npow_dict = pickle_read(conf.npow)
print('## Alignment mlespec ##')
jmelspc_lst, twfs = align_feature_vectors(odata=melspec_dict['src'],
onpows=npow_dict['src'],
tdata=melspec_dict['tgt'],
tnpows=npow_dict['tgt'],
pconf=conf,
opow=conf.s_npow_th,
tpow=conf.t_npow_th,
itnum=conf.jnt_n_iter,
sd=0)
jnt_melspec = transform_jnt(jmelspc_lst)
if not os.path.isdir(conf.out_dir):
print('Output directory {} does not exist, create one'.format(
conf.out_dir))
print('Save jnt_melspec and twfs to {}'.format(conf.out_dir))
pickle_save(jnt_melspec, os.path.join(conf.out_dir, 'jnt_melspc.pkl'))
pickle_save(twfs, os.path.join(conf.out_dir, 'twf.pkl'))
print('Done!')
return
def align_feature_vectors(odata, onpows, tdata, tnpows, pconf,
opow=-100, tpow=-100, itnum=3, sd=0,
given_twfs=None, otflag=None):
"""Get alignment to create joint feature vector
Paramters
---------
odata : list, (`num_files`)
List of original feature vectors
onpows : list , (`num_files`)
List of original npows
tdata : list, (`num_files`)
List of target feature vectors
tnpows : list , (`num_files`)
List of target npows
opow : float, optional,
Power threshold of original
Default set to -100
tpow : float, optional,
Power threshold of target
Default set to -100
itnum : int , optional,
The number of iteration
Default set to 3
sd : int , optional,
Start dimension of feature vector to be used for alignment
Default set to 0
given_twf : array, shape (`T_new` `dim * 2`)
Use given alignment while 1st iteration
Default set to None
otflag : str, optional
Alignment into the length of specification
'org' : alignment into original length
'tar' : alignment into target length
Default set to None
Returns
-------
jfvs : list,
List of joint feature vectors
twfs : list,
List of time warping functions
"""
num_files = len(odata)
cvgmm, cvdata = None, None
for it in range(1, itnum + 1):
print('{}-th joint feature extraction starts.'.format(it))
twfs, jfvs = [], []
for i in range(num_files):
if it == 1 and given_twfs is not None:
gtwf = given_twfs[i]
else:
gtwf = None
if it > 1:
cvdata = cvgmm.convert(static_delta(odata[i][:, sd:]),
cvtype=pconf.GMM_mcep_cvtype)
jdata, twf, mcd = get_alignment(odata[i],
onpows[i],
tdata[i],
tnpows[i],
opow=opow,
tpow=tpow,
sd=sd,
cvdata=cvdata,
given_twf=gtwf,
otflag=otflag)
twfs.append(twf)
jfvs.append(jdata)
print('distortion [dB] for {}-th file: {}'.format(i + 1, mcd))
jnt_data = transform_jnt(jfvs)
if it != itnum:
# train GMM, if not final iteration
datagmm = GMMTrainer(n_mix=pconf.GMM_mcep_n_mix,
n_iter=pconf.GMM_mcep_n_iter,
covtype=pconf.GMM_mcep_covtype)
datagmm.train(jnt_data)
cvgmm = GMMConvertor(n_mix=pconf.GMM_mcep_n_mix,
covtype=pconf.GMM_mcep_covtype)
cvgmm.open_from_param(datagmm.param)
it += 1
return jfvs, twfs
def main(*argv):
argv = argv if argv else sys.argv[1:]
# Options for python
description = 'estimate joint feature of source and target speakers'
parser = argparse.ArgumentParser(description=description)
parser.add_argument('org_yml', type=str,
help='Yml file of the original speaker')
parser.add_argument('tar_yml', type=str,
help='Yml file of the target speaker')
parser.add_argument('pair_yml', type=str,
help='Yml file of the speaker pair')
parser.add_argument('org_list_file', type=str,
help='List file of original speaker')
parser.add_argument('tar_list_file', type=str,
help='List file of target speaker')
parser.add_argument('pair_dir', type=str,
help='Directory path of h5 files')
args = parser.parse_args(argv)
# read speaker-dependent yml files
oconf = SpeakerYML(args.org_yml)
tconf = SpeakerYML(args.tar_yml)
# read pair-dependent yml file
pconf = PairYML(args.pair_yml)
# read source and target features from HDF file
h5_dir = os.path.join(args.pair_dir, 'h5')
org_mceps = read_feats(args.org_list_file, h5_dir, ext='mcep')
org_npows = read_feats(args.org_list_file, h5_dir, ext='npow')
tar_mceps = read_feats(args.tar_list_file, h5_dir, ext='mcep')
tar_npows = read_feats(args.tar_list_file, h5_dir, ext='npow')
assert len(org_mceps) == len(tar_mceps)
assert len(org_npows) == len(tar_npows)
assert len(org_mceps) == len(org_npows)
# dtw between original and target w/o 0th and silence
print('## Alignment mcep w/o 0-th and silence ##')
jmceps, twfs = align_feature_vectors(org_mceps,
org_npows,
tar_mceps,
tar_npows,
pconf,
opow=oconf.power_threshold,
tpow=tconf.power_threshold,
itnum=pconf.jnt_n_iter,
sd=1,
)
jnt_mcep = transform_jnt(jmceps)
# create joint feature for codeap using given twfs
print('## Alignment codeap using given twf ##')
org_codeaps = read_feats(args.org_list_file, h5_dir, ext='codeap')
tar_codeaps = read_feats(args.tar_list_file, h5_dir, ext='codeap')
jcodeaps = []
for i in range(len(org_codeaps)):
# extract codeap joint feature vector
jcodeap, _, _ = get_alignment(org_codeaps[i],
org_npows[i],
tar_codeaps[i],
tar_npows[i],
opow=oconf.power_threshold,
tpow=tconf.power_threshold,
given_twf=twfs[i])
jcodeaps.append(jcodeap)
jnt_codeap = transform_jnt(jcodeaps)
# save joint feature vectors
jnt_dir = os.path.join(args.pair_dir, 'jnt')
os.makedirs(jnt_dir, exist_ok=True)
jntpath = os.path.join(jnt_dir, 'it' + str(pconf.jnt_n_iter) + '_jnt.h5')
jnth5 = HDF5(jntpath, mode='a')
jnth5.save(jnt_mcep, ext='mcep')
jnth5.save(jnt_codeap, ext='codeap')
jnth5.close()
# save twfs
twf_dir = os.path.join(args.pair_dir, 'twf')
os.makedirs(twf_dir, exist_ok=True)
with open(args.org_list_file, 'r') as fp:
for line, twf in zip(fp, twfs):
f = os.path.basename(line.rstrip())
twfpath = os.path.join(
twf_dir, 'it' + str(pconf.jnt_n_iter) + '_' + f + '.h5')
twfh5 = HDF5(twfpath, mode='a')
twfh5.save(twf, ext='twf')
twfh5.close()
def align_ppg_feature_vectors(odata,
onpows,
tdata,
tnpows,
pconf,
s_list_file,
tar_list_file,
opow=-100,
tpow=-100,
itnum=3,
sd=0,
given_twfs=None,
otflag=None):
s_list_file = np.loadtxt(s_list_file, dtype='str')
tar_list_file = np.loadtxt(tar_list_file, dtype='str')
num_files = len(odata)
cvgmm, cvdata = None, None
jfvs = []
for it in range(1, itnum + 1):
print('{}-th joint feature extraction starts.'.format(it))
twfs = []
for i in range(num_files):
if it == 1 and given_twfs is not None:
gtwf = given_twfs[i]
else:
gtwf = None
if it > 1:
cvdata = cvgmm.convert(static_delta(odata[i][:, sd:]),
cvtype=pconf.GMM_mcep_cvtype)
if it == 1:
jdata = get_ppg_alignment(odata[i],
onpows[i],
tdata[i],
tnpows[i],
s_list_file[i],
tar_list_file[i],
opow=opow,
tpow=tpow,
sd=sd,
cvdata=cvdata,
given_twf=gtwf,
otflag=otflag)
print(s_list_file[i])
jfvs.append(jdata)
jnt_data = transform_jnt(jfvs)
_, twf, _ = get_alignment(odata[i],
onpows[i],
tdata[i],
tnpows[i],
opow=opow,
tpow=tpow,
sd=sd,
cvdata=cvdata,
given_twf=gtwf,
otflag=otflag)
twfs.append(twf)
if it != itnum:
# train GMM, if not final iteration
print("training GMM")
datagmm = GMMTrainer(n_mix=pconf.GMM_mcep_n_mix,
n_iter=pconf.GMM_mcep_n_iter,
covtype=pconf.GMM_mcep_covtype)
datagmm.train(jnt_data)
cvgmm = GMMConvertor(n_mix=pconf.GMM_mcep_n_mix,
covtype=pconf.GMM_mcep_covtype)
cvgmm.open_from_param(datagmm.param)
it += 1
return jfvs, twfs
def align_feature_vectors(odata, onpows, tdata, tnpows, pconf,
opow=-100, tpow=-100, itnum=3, sd=0,
given_twfs=None, otflag=None):
"""Get alignment to create joint feature vector
Paramters
---------
odata : list, (`num_files`)
List of original feature vectors
onpows : list , (`num_files`)
List of original npows
tdata : list, (`num_files`)
List of target feature vectors
tnpows : list , (`num_files`)
List of target npows
opow : float, optional,
Power threshold of original
Default set to -100
tpow : float, optional,
Power threshold of target
Default set to -100
itnum : int , optional,
The number of iteration
Default set to 3
sd : int , optional,
Start dimension of feature vector to be used for alignment
Default set to 0
given_twf : array, shape (`T_new` `dim * 2`)
Use given alignment while 1st iteration
Default set to None
otflag : str, optional
Alignment into the length of specification
'org' : alignment into original length
'tar' : alignment into target length
Default set to None
Returns
-------
jdata : array, shape (`T_new` `dim * 2`)
Joint static and delta feature vector
twf : array, shape (`T_new` `dim * 2`)
Time warping function
mcd : float ,
Mel-cepstrum distortion between arrays
"""
it = 1
num_files = len(odata)
cvgmm, cvdata = None, None
for it in range(1, itnum + 1):
print('{}-th joint feature extraction starts.'.format(it))
# alignment
twfs, jfvs = [], []
for i in range(num_files):
if it == 1 and given_twfs is not None:
gtwf = given_twfs[i]
else:
gtwf = None
if it > 1:
cvdata = cvgmm.convert(static_delta(odata[i][:, sd:]),
cvtype=pconf.GMM_mcep_cvtype)
jdata, twf, mcd = get_alignment(odata[i],
onpows[i],
tdata[i],
tnpows[i],
opow=opow,
tpow=tpow,
sd=sd,
cvdata=cvdata,
given_twf=gtwf,
otflag=otflag)
twfs.append(twf)
jfvs.append(jdata)
print('distortion [dB] for {}-th file: {}'.format(i + 1, mcd))
jnt_data = transform_jnt(jfvs)
if it != itnum:
# train GMM, if not final iteration
datagmm = GMMTrainer(n_mix=pconf.GMM_mcep_n_mix,
n_iter=pconf.GMM_mcep_n_iter,
covtype=pconf.GMM_mcep_covtype)
datagmm.train(jnt_data)
cvgmm = GMMConvertor(n_mix=pconf.GMM_mcep_n_mix,
covtype=pconf.GMM_mcep_covtype)
cvgmm.open_from_param(datagmm.param)
it += 1
return jfvs, twfs
def main(*argv):
argv = argv if argv else sys.argv[1:]
# Options for python
description = 'estimate joint feature of source and target speakers'
parser = argparse.ArgumentParser(description=description)
parser.add_argument('org_yml', type=str,
help='Yml file of the original speaker')
parser.add_argument('tar_yml', type=str,
help='Yml file of the target speaker')
parser.add_argument('pair_yml', type=str,
help='Yml file of the speaker pair')
parser.add_argument('org_list_file', type=str,
help='List file of original speaker')
parser.add_argument('tar_list_file', type=str,
help='List file of target speaker')
parser.add_argument('pair_dir', type=str,
help='Directory path of h5 files')
args = parser.parse_args(argv)
# read speaker-dependent yml files
oconf = SpeakerYML(args.org_yml)
tconf = SpeakerYML(args.tar_yml)
# read pair-dependent yml file
pconf = PairYML(args.pair_yml)
# read source and target features from HDF file
h5_dir = os.path.join(args.pair_dir, 'h5')
org_mceps = read_feats(args.org_list_file, h5_dir, ext='mcep')
org_npows = read_feats(args.org_list_file, h5_dir, ext='npow')
tar_mceps = read_feats(args.tar_list_file, h5_dir, ext='mcep')
tar_npows = read_feats(args.tar_list_file, h5_dir, ext='npow')
assert len(org_mceps) == len(tar_mceps)
assert len(org_npows) == len(tar_npows)
assert len(org_mceps) == len(org_npows)
# dtw between original and target w/o 0th and silence
print('## Alignment mcep w/o 0-th and silence ##')
jmceps, twfs = align_feature_vectors(org_mceps,
org_npows,
tar_mceps,
tar_npows,
pconf,
opow=oconf.power_threshold,
tpow=tconf.power_threshold,
itnum=pconf.jnt_n_iter,
sd=1,
)
jnt_mcep = transform_jnt(jmceps)
# create joint feature for codeap using given twfs
print('## Alignment codeap into target length using given twf ##')
org_codeaps = read_feats(args.org_list_file, h5_dir, ext='codeap')
tar_codeaps = read_feats(args.tar_list_file, h5_dir, ext='codeap')
jcodeaps = []
for i in range(len(org_codeaps)):
# extract codeap joint feature vector
jcodeap, _, _ = get_alignment(org_codeaps[i],
org_npows[i],
tar_codeaps[i],
tar_npows[i],
opow=oconf.power_threshold,
tpow=tconf.power_threshold,
given_twf=twfs[i])
jcodeaps.append(jcodeap)
jnt_codeap = transform_jnt(jcodeaps)
# save joint feature vectors
jnt_dir = os.path.join(args.pair_dir, 'jnt')
os.makedirs(jnt_dir, exist_ok=True)
jntpath = os.path.join(jnt_dir, 'it' + str(pconf.jnt_n_iter) + '_jnt.h5')
jnth5 = HDF5(jntpath, mode='w')
jnth5.save(jnt_mcep, ext='mcep')
jnth5.save(jnt_codeap, ext='codeap')
jnth5.close()
# save twfs
twf_dir = os.path.join(args.pair_dir, 'twf')
os.makedirs(twf_dir, exist_ok=True)
with open(args.org_list_file, 'r') as fp:
for line, twf in zip(fp, twfs):
f = os.path.basename(line.rstrip())
twfpath = os.path.join(
twf_dir, 'it' + str(pconf.jnt_n_iter) + '_' + f + '.h5')
twfh5 = HDF5(twfpath, mode='w')
twfh5.save(twf, ext='twf')
twfh5.close()