def _pml_to_wav(pml_features,
cfg,
shift=0.005,
dftlen=4096,
nm_cont=False,
verbose_level=0,
mean_norm=None,
std_norm=None,
spec_type='mcep',
pp_mcep=False,
find_endpoint=False,
threshold_db=0):
# get the mean and variance, and denormalise
if mean_norm is not None and std_norm is not None:
std_tiled = np.tile(std_norm, (pml_features.shape[0], 1))
mean_tiled = np.tile(mean_norm, (pml_features.shape[0], 1))
pml_features = pml_features * std_tiled + mean_tiled
# f0s is from flf0
f0 = pml_features[:, cfg.acoustic_start_index['lf0']:cfg.
acoustic_start_index['lf0'] +
cfg.acoustic_in_dimension_dict['lf0']]
f0 = np.squeeze(f0) # remove the extra 1 dimension here
f0[f0 > 0] = np.exp(f0[f0 > 0])
ts = shift * np.arange(len(f0))
f0s = np.vstack((ts, f0)).T
# spec comes from fmcep or something else fwbnd
if spec_type == 'mcep':
mcep = pml_features[:, cfg.acoustic_start_index['mgc']:cfg.
acoustic_start_index['mgc'] +
cfg.acoustic_in_dimension_dict['mgc']]
if pp_mcep:
from lib.merlin import generate_pp
mcep = generate_pp.mcep_postproc_sptk(mcep,
cfg.wav_sr,
dftlen=dftlen)
spec = sp.mcep2spec(mcep, sp.bark_alpha(cfg.wav_sr), dftlen)
elif spec_type == 'fwbnd':
compspec = pml_features[:, cfg.acoustic_start_index['mgc']:cfg.
acoustic_start_index['mgc'] +
cfg.acoustic_in_dimension_dict['mgc']]
spec = np.exp(sp.fwbnd2linbnd(compspec, cfg.wav_sr, dftlen))
if pp_mcep:
from lib.merlin import generate_pp
mcep = sp.spec2mcep(spec * cfg.wav_sr, sp.bark_alpha(cfg.wav_sr),
256)
mcep_pp = generate_pp.mcep_postproc_sptk(mcep,
cfg.wav_sr,
dftlen=dftlen)
spec = sp.mcep2spec(
mcep_pp, sp.bark_alpha(cfg.wav_sr), dftlen=dftlen) / cfg.wav_sr
# NM comes from bap
fwnm = pml_features[:, cfg.acoustic_start_index['bap']:cfg.
acoustic_start_index['bap'] +
cfg.acoustic_in_dimension_dict['bap']]
nm = sp.fwbnd2linbnd(fwnm, cfg.wav_sr, dftlen)
# use standard PML vocoder
wav = synthesize(cfg.wav_sr,
f0s,
spec,
NM=nm,
nm_cont=nm_cont,
verbose=verbose_level)
# clip the wav to the endpoint if required
if find_endpoint:
wav = wav[:audio.find_endpoint(wav, threshold_db=threshold_db)]
# return the raw wav data
return wav
def synthesizef(fs,
shift=0.005,
dftlen=4096,
ff0=None,
flf0=None,
fspec=None,
flspec=None,
ffwlspec=None,
ffwcep=None,
fmcep=None,
fpdd=None,
fmpdd=None,
fnm=None,
ffwnm=None,
nm_cont=False,
fsyn=None,
verbose=1):
'''
Call the synthesis from python using file inputs and outputs
'''
if ff0:
f0 = np.fromfile(ff0, dtype=np.float32)
if flf0:
f0 = np.fromfile(flf0, dtype=np.float32)
f0[f0 > 0] = np.exp(f0[f0 > 0])
ts = (shift) * np.arange(len(f0))
f0s = np.vstack((ts, f0)).T
if fspec:
SPEC = np.fromfile(fspec, dtype=np.float32)
SPEC = SPEC.reshape((len(f0), -1))
if flspec:
SPEC = np.fromfile(flspec, dtype=np.float32)
SPEC = np.exp(SPEC.reshape((len(f0), -1)))
if ffwlspec:
FWLSPEC = np.fromfile(ffwlspec, dtype=np.float32)
FWLSPEC = FWLSPEC.reshape((len(f0), -1))
SPEC = np.exp(sp.fwbnd2linbnd(FWLSPEC, fs, dftlen, smooth=True))
if ffwcep:
FWCEP = np.fromfile(ffwcep, dtype=np.float32)
FWCEP = FWCEP.reshape((len(f0), -1))
SPEC = np.exp(sp.fwcep2loghspec(FWCEP, fs, dftlen))
if fmcep: # pragma: no cover
# Cannot test this because it needs SPTK
MCEP = np.fromfile(fmcep, dtype=np.float32)
MCEP = MCEP.reshape((len(f0), -1))
SPEC = sp.mcep2spec(MCEP, sp.bark_alpha(fs), dftlen)
NM = None
pdd_thresh = 0.75 # For this value, see:
# G. Degottex and D. Erro, "A uniform phase representation for the harmonic model in speech synthesis applications," EURASIP, Journal on Audio, Speech, and Music Processing - Special Issue: Models of Speech - In Search of Better Representations, vol. 2014, iss. 1, p. 38, 2014.
if fpdd:
PDD = np.fromfile(fpdd, dtype=np.float32)
PDD = PDD.reshape((len(f0), -1))
NM = PDD.copy()
NM[PDD < pdd_thresh] = 0.0
NM[PDD > pdd_thresh] = 1.0
if fmpdd: # pragma: no cover
# Cannot test this because it needs SPTK
MPDD = np.fromfile(fmpdd, dtype=np.float32)
MPDD = MPDD.reshape((len(f0), -1))
PDD = sp.mcep2spec(MPDD, sp.bark_alpha(fs), dftlen)
NM = PDD.copy()
NM[PDD < pdd_thresh] = 0.0
NM[PDD > pdd_thresh] = 1.0
if fnm:
NM = np.fromfile(fnm, dtype=np.float32)
NM = NM.reshape((len(f0), -1))
if ffwnm:
FWNM = np.fromfile(ffwnm, dtype=np.float32)
FWNM = FWNM.reshape((len(f0), -1))
NM = sp.fwbnd2linbnd(FWNM, fs, dftlen)
syn = synthesize(fs, f0s, SPEC, NM=NM, nm_cont=nm_cont, verbose=verbose)
if fsyn:
sp.wavwrite(fsyn, syn, fs, norm_max_ifneeded=True, verbose=verbose)
return syn
sys.path.append('/home/degottex/Research/CUED/Code')
from lib import sigproc as sp
if __name__ == "__main__":
argpar = argparse.ArgumentParser()
argpar.add_argument("bndspecfile",
default=None,
help="Input spectrum file")
argpar.add_argument(
"--nbbands",
type=int,
help="Number of bands in the warped spectral representation")
argpar.add_argument("--dftlen",
default=4096,
type=int,
help="DFT size for the output spectrum")
argpar.add_argument("--fs",
default=16000,
type=int,
help="Sampling frequency[Hz]")
argpar.add_argument("specfile",
default=None,
help="Output warped spectrum file")
args, unknown = argpar.parse_known_args()
BNDSPEC = np.fromfile(args.bndspecfile, dtype=np.float32)
BNDSPEC = BNDSPEC.reshape((-1, args.nbbands))
SPEC = np.exp(sp.fwbnd2linbnd(BNDSPEC, args.fs, args.dftlen))
SPEC.astype('float32').tofile(args.specfile)
sys.path.append('/home/degottex/Research/CUED/Code')
from lib import sigproc as sp
if __name__ == "__main__" :
argpar = argparse.ArgumentParser()
argpar.add_argument("specfile", default=None, help="Input spectrum file")
argpar.add_argument("--dftlen", default=4096, type=int, help="DFT size for the input spectrum")
argpar.add_argument("--fs", default=16000, type=int, help="Sampling frequency[Hz]")
argpar.add_argument("--nbbands", type=int, help="Number of bands in the warped spectral representation")
argpar.add_argument("bndfwspecfile", default=None, help="Output frequency warped spectrum file")
args, unknown = argpar.parse_known_args()
SPEC = np.fromfile(args.specfile, dtype=np.float32)
SPEC = SPEC.reshape((-1, int(args.dftlen / 2)+1))
FWSPEC = sp.linbnd2fwbnd(np.log(SPEC), args.fs, args.dftlen, args.nbbands)
FWSPEC.astype('float32').tofile(args.bndfwspecfile)
if 0:
shift = 0.005
SPECR = np.exp(sp.fwbnd2linbnd(FWSPEC, args.fs, args.dftlen))
import matplotlib.pyplot as plt
plt.ion()
ts = shift*np.arange(SPEC.shape[0])
plt.subplot(211)
plt.imshow(sp.mag2db(SPEC).T, origin='lower', aspect='auto', interpolation='none', cmap='jet', extent=[0.0, ts[-1], 0.0, args.fs/2])
plt.subplot(212)
plt.imshow(sp.mag2db(SPECR).T, origin='lower', aspect='auto', interpolation='none', cmap='jet', extent=[0.0, ts[-1], 0.0, args.fs/2])
from IPython.core.debugger import Pdb; Pdb().set_trace()