def sp_to_mcep(m_sp, n_coeffs=60, alpha=0.77, in_type=3, fft_len=0):
#Pre:
temp_sp = lu.ins_pid('temp.sp')
temp_mgc = lu.ins_pid('temp.mgc')
# Writing input data:
lu.write_binfile(m_sp, temp_sp)
if fft_len is 0: # case fft automatic
fft_len = 2 * (np.size(m_sp, 1) - 1)
# MCEP:
curr_cmd = _sptk_mcep_bin + " -a %1.2f -m %d -l %d -e 1.0E-8 -j 0 -f 0.0 -q %d %s > %s" % (
alpha, n_coeffs - 1, fft_len, in_type, temp_sp, temp_mgc)
call(curr_cmd, shell=True)
# Read MGC File:
m_mgc = lu.read_binfile(temp_mgc, n_coeffs)
# Deleting temp files:
os.remove(temp_sp)
os.remove(temp_mgc)
#$sptk/mcep -a $alpha -m $mcsize -l $nFFT -e 1.0E-8 -j 0 -f 0.0 -q 3 $sp_dir/$sentence.sp > $mgc_dir/$sentence.mgc
return m_mgc
def sp_to_mcep(m_sp, n_coeffs=60, alpha=0.77, in_type=3, fft_len=0):
#Pre:
temp_sp = lu.ins_pid('temp.sp')
temp_mgc = lu.ins_pid('temp.mgc')
# Writing input data:
lu.write_binfile(m_sp, temp_sp)
if fft_len is 0: # case fft automatic
fft_len = 2*(np.size(m_sp,1) - 1)
# MCEP:
curr_cmd = _sptk_mcep_bin + " -a %1.2f -m %d -l %d -e 1.0E-8 -j 0 -f 0.0 -q %d %s > %s" % (alpha, n_coeffs-1, fft_len, in_type, temp_sp, temp_mgc)
call(curr_cmd, shell=True)
# Read MGC File:
m_mgc = lu.read_binfile(temp_mgc , n_coeffs)
# Deleting temp files:
os.remove(temp_sp)
os.remove(temp_mgc)
#$sptk/mcep -a $alpha -m $mcsize -l $nFFT -e 1.0E-8 -j 0 -f 0.0 -q 3 $sp_dir/$sentence.sp > $mgc_dir/$sentence.mgc
return m_mgc
def wavgen_magphase(gen_dir, file_id_list, cfg, logger):
# Import MagPhase and libraries:
sys.path.append(cfg.magphase_bindir)
import libutils as lu
import libaudio as la
import magphase as mp
nfiles = len(file_id_list)
for nxf in xrange(nfiles):
filename_token = file_id_list[nxf]
logger.info('Creating waveform for %4d of %4d: %s' %
(nxf + 1, nfiles, filename_token))
# Post-Filter:
if cfg.do_post_filtering and not cfg.use_magphase_pf:
mcep_file = os.path.join(gen_dir, filename_token + '.mcep')
mcep_file_pf = os.path.join(gen_dir, filename_token + '_pf.mcep')
mag_file = os.path.join(gen_dir, filename_token + '.mag')
# Mag to Mcep:
m_mag_mel_log = lu.read_binfile(mag_file, dim=cfg.mag_dim)
m_mcep = la.rceps(m_mag_mel_log, in_type='log', out_type='compact')
lu.write_binfile(m_mcep, mcep_file)
# Apply post-filter:
post_filter(mcep_file, mcep_file_pf, cfg.mag_dim, cfg.pf_coef,
cfg.fw_alpha, cfg.co_coef, cfg.fl, gen_dir, cfg)
# Mcep to Mag:
m_mcep_pf = lu.read_binfile(mcep_file_pf, dim=cfg.mag_dim)
m_mag_mel_log_pf = la.mcep_to_sp_cosmat(m_mcep_pf,
cfg.mag_dim,
alpha=0.0,
out_type='log')
# Protection agains possible nans:
m_mag_mel_log_pf[np.isnan(m_mag_mel_log_pf)] = la.MAGIC
# Saving to file:
lu.write_binfile(m_mag_mel_log_pf, mag_file)
# Removing temp files:
os.remove(mcep_file)
os.remove(mcep_file_pf)
# Synthesis:
mp.synthesis_from_acoustic_modelling(
gen_dir,
filename_token,
gen_dir,
cfg.mag_dim,
cfg.real_dim,
cfg.sr,
b_postfilter=(cfg.do_post_filtering and cfg.use_magphase_pf))
return
def feat_extraction(wav_file, out_feats_dir):
# Constants:
fft_len = 4096
mvf = 4500
nbins_mel = 60
nbins_phase = 45
# Parsing path:
file_name_token = os.path.basename(os.path.splitext(wav_file)[0])
# Display:
print("Analysing file: " + file_name_token + '.wav' +
'................................')
# Files setup:
est_file = os.path.join(out_feats_dir, file_name_token + '.est')
# Epochs detection:
la.reaper(wav_file, est_file)
# Feature extraction:
m_mag_mel_log, m_real_mel, m_imag_mel, v_shift, v_lf0, fs = mp.analysis_with_del_comp__ph_enc__f0_norm__from_files2(
wav_file,
est_file,
fft_len,
mvf,
f0_type='lf0',
mag_mel_nbins=nbins_mel,
cmplx_ph_mel_nbins=nbins_phase)
if fs != fs_expected:
print(
"The wavefile's sample rate (%dHz) does not match the expected sample rate (%dHz)."
% (fs, fs_expected))
sys.exit(1)
# Zeros for unvoiced segments in phase features:
v_voi = (np.exp(v_lf0) > 5.0).astype(int) # 5.0: tolerance (just in case)
m_real_mel_zeros = m_real_mel * v_voi[:, None]
m_imag_mel_zeros = m_imag_mel * v_voi[:, None]
# Saving features:
lu.write_binfile(m_mag_mel_log,
out_feats_dir + '/' + file_name_token + '.mag')
lu.write_binfile(m_real_mel_zeros,
out_feats_dir + '/' + file_name_token + '.real')
lu.write_binfile(m_imag_mel_zeros,
out_feats_dir + '/' + file_name_token + '.imag')
lu.write_binfile(v_lf0, out_feats_dir + '/' + file_name_token + '.lf0')
# Saving auxiliary feature shift (hop length). It is useful for posterior modifications of labels in Merlin.
lu.write_binfile(v_shift, out_feats_dir + '/' + file_name_token + '.shift')
return
def feat_extraction(in_wav_dir,
file_name_token,
out_feats_dir,
fft_len,
mvf,
nbins_mel=60,
nbins_phase=45):
# Display:
print("Analysing file: " + file_name_token + '.wav')
# Files setup:
wav_file = in_wav_dir + '/' + file_name_token + '.wav'
est_file = out_feats_dir + '/' + file_name_token + '.est'
# Epochs detection:
la.reaper(wav_file, est_file)
# Feature extraction:
m_mag_mel_log, m_real_mel, m_imag_mel, v_shift, v_lf0, fs = mp.analysis_with_del_comp__ph_enc__f0_norm__from_files2(
wav_file,
est_file,
fft_len,
mvf,
f0_type='lf0',
mag_mel_nbins=nbins_mel,
cmplx_ph_mel_nbins=nbins_phase)
# Zeros for unvoiced segments in phase features:
v_voi = (np.exp(v_lf0) > 5.0).astype(int) # 5.0: tolerance (just in case)
m_real_mel_zeros = m_real_mel * v_voi[:, None]
m_imag_mel_zeros = m_imag_mel * v_voi[:, None]
# Saving features:
lu.write_binfile(m_mag_mel_log,
out_feats_dir + '/' + file_name_token + '.mag')
lu.write_binfile(m_real_mel_zeros,
out_feats_dir + '/' + file_name_token + '.real')
lu.write_binfile(m_imag_mel_zeros,
out_feats_dir + '/' + file_name_token + '.imag')
lu.write_binfile(v_lf0, out_feats_dir + '/' + file_name_token + '.lf0')
# Saving auxiliary feature shift (hop length). It is useful for posterior modifications of labels in Merlin.
lu.write_binfile(v_shift, out_feats_dir + '/' + file_name_token + '.shift')
return
load_config(a.config)
from __init__ import *
with open(a.senlst) as f:
sentences = [l.rstrip() for l in f if l]
hts2 = [path.join(HTS2DIR, s + '.lab') for s in sentences]
lab1 = [path.join(LAB1DIR, s + '.lab') for s in sentences]
lab2 = [path.join(LAB2DIR, s + '.lab') for s in sentences]
lab3 = [path.join(LAB3DIR, s + '.lab') for s in sentences]
binarizer = HTSLabelNormalisation(
question_file_name=path.join(RESDIR, '600.hed'))
binarizer.perform_normalisation(hts2, lab1)
remover = SilenceRemover(n_cmp=binarizer.dimension,
silence_pattern=['*-#+*'])
remover.remove_silence(lab1, hts2, lab2)
normalizer = MinMaxNormalisation(feature_dimension=binarizer.dimension,
min_value=0.01,
max_value=0.99)
normalizer.find_min_max_values(lab2)
print1(normalizer.min_vector)
print1(normalizer.max_vector)
lu.write_binfile(normalizer.min_vector, path.join(LABSDIR, 'min'))
lu.write_binfile(normalizer.max_vector, path.join(LABSDIR, 'max'))
# normalizer.normalise_data(lab2, lab3)
def magphase_analysis(wav_file, outdir='', fft_len=None, nbins_mel=60, nbins_phase=45, pm_dir='', skip_low=False, cepstra=False):
'''
Function to combine Felipe's analysis_lossless and analysis_compressed with
little redundancy, and storing pitchmark files.
'''
try:
outdir_hi = os.path.join(outdir, 'high')
outdir_lo = os.path.join(outdir, 'low')
file_id = os.path.basename(wav_file).split(".")[0]
# Read file:
v_sig, fs = sf.read(wav_file)
if not pm_dir:
# Epoch detection:
est_file = os.path.join(outdir, 'pm', file_id + '.pm')
la.reaper(wav_file, est_file)
else:
est_file = os.path.join(pm_dir, file_id + '.pm')
v_pm_sec, v_voi = la.read_reaper_est_file(est_file, check_len_smpls=len(v_sig), fs=fs)
v_pm_smpls = v_pm_sec * fs
# Spectral analysis:
m_fft, v_shift = mp.analysis_with_del_comp_from_pm(v_sig, fs, v_pm_smpls, fft_len=fft_len)
# Getting high-ress magphase feats:
m_mag, m_real, m_imag, v_f0 = mp.compute_lossless_feats(m_fft, v_shift, v_voi, fs)
### write high-dimensional data:
lu.write_binfile(m_mag, os.path.join(outdir_hi, 'mag', file_id + '.mag'))
lu.write_binfile(m_real, os.path.join(outdir_hi, 'real', file_id + '.real'))
lu.write_binfile(m_imag, os.path.join(outdir_hi, 'imag', file_id + '.imag'))
lu.write_binfile(v_f0, os.path.join(outdir_hi, 'f0', file_id + '.f0'))
lu.write_binfile(v_shift, os.path.join(outdir, 'shift', file_id + '.shift'))
if not skip_low:
# Low dimension (Formatting for Acoustic Modelling):
m_mag_mel_log, m_real_mel, m_imag_mel, v_lf0_smth = mp.format_for_modelling(m_mag, m_real, m_imag, v_f0, fs, mag_dim=nbins_mel, phase_dim=nbins_phase)
# fft_len = 2*(np.size(m_mag,1) - 1)
### write low-dim data:
lu.write_binfile(m_mag_mel_log, os.path.join(outdir_lo, 'mag', file_id + '.mag'))
lu.write_binfile(m_real_mel, os.path.join(outdir_lo, 'real', file_id + '.real'))
lu.write_binfile(m_imag_mel, os.path.join(outdir_lo, 'imag', file_id + '.imag'))
lu.write_binfile(v_lf0_smth, os.path.join(outdir_lo, 'lf0', file_id + '.lf0'))
if cepstra:
alpha = {48000: 0.77, 16000: 58}[fs]
m_mag_mcep = la.sp_to_mcep(m_mag, n_coeffs=nbins_mel, alpha=alpha, in_type=3)
m_real_mcep = la.sp_to_mcep(m_real, n_coeffs=nbins_phase, alpha=alpha, in_type=2)
m_imag_mcep = la.sp_to_mcep(m_imag, n_coeffs=nbins_phase, alpha=alpha, in_type=2)
lu.write_binfile(m_mag_mcep, os.path.join(outdir_lo, 'mag_cc', file_id + '.mag_cc'))
lu.write_binfile(m_real_mcep, os.path.join(outdir_lo, 'real_cc', file_id + '.real_cc'))
lu.write_binfile(m_imag_mcep, os.path.join(outdir_lo, 'imag_cc', file_id + '.imag_cc'))
except KeyboardInterrupt, e:
pass
mag = lu.read_binfile(path.join(ACO2DIR, s + '.mag'),
dim=ax.MAG_DIM)
real = lu.read_binfile(path.join(ACO2DIR, s + '.real'),
dim=ax.REAL_DIM)
imag = lu.read_binfile(path.join(ACO2DIR, s + '.imag'),
dim=ax.IMAG_DIM)
lf0 = lu.read_binfile(path.join(ACO2DIR, s+'.lf0'), dim=ax.LF0_DIM)\
.reshape([-1,ax.LF0_DIM])
x = ax.acoustic(mag=mag, real=real, imag=imag, lf0=lf0)
if ds.EXP_F0:
x[:, ax.LF0] = np.exp(x[:, ax.LF0])
v = ax.voicing(x, 1.0)
else:
v = ax.voicing(x, 0.0)
x = ax.interpolate_f0(x)
if ds.USE_DELTA:
dx = ax.velocity(x)
ddx = ax.acceleration(x)
x = np.concatenate([x, dx, ddx, v], axis=1)
else:
x = np.concatenate([x, v], axis=1)
lu.write_binfile(x, path.join(ACO3DIR, s + '.aco'))
except (KeyboardInterrupt, SystemExit):
raise
except Exception as e:
print2(e)
pass
else:
print1(s)
x[:,ax.LF0][v <= mean[-1]] = 0.0
x[:,ax.LF0] = np.log(x[:,ax.LF0])
else:
x[:,ax.LF0][v <= mean[-1]] = float('-Inf')
# if a.plot_f0:
# t = [n * 0.005 for n in range(x.shape[0])]
# x2 = lu.read_binfile(path.join(ACO3DIR, sentence+'.aco'), dim=ds.AX_DIM)
# x2[:,ax.LF0][x2[:,-1] == 0.0] = 0.0
# pyplot.plot(t, x2[:len(t),1])
# pyplot.plot(t, x[:,1])#np.exp(x[:,ax.LF0]))
# # pyplot.plot(t, [y if y > 0 else 0.0 for y in x2[:len(t),0]])
# pyplot.savefig(path.join(outdir, sentence+'_mag.pdf'))
# pyplot.close()
lu.write_binfile(x[:,ax.MAG], path.join(outdir, sentence+'.mag'))
lu.write_binfile(x[:,ax.REAL], path.join(outdir, sentence+'.real'))
lu.write_binfile(x[:,ax.IMAG], path.join(outdir, sentence+'.imag'))
lu.write_binfile(x[:,ax.LF0], path.join(outdir, sentence+'.lf0'))
try:
call('{script} -s {sentence} -o {outdir} -m {magdim} -p {phadim} -f merlin {const_rate}'\
.format(script=path.join(SRCDIR, 'wavify.py'),
sentence=sentence,
#vocdir=outdir,
outdir=outdir,
magdim=ax.MAG_DIM,
phadim=ax.PHASE_DIM,
const_rate='' if ds.CONST_RATE else '-v').split())
except Exception as e:
print2(e)
from argparse import ArgumentParser
if __name__ == '__main__':
p = ArgumentParser()
p.add_argument('-s', '--senlst', dest='senlst', required=True)
p.add_argument('-c', '--config', dest='config', required=True)
a = p.parse_args()
load_config(a.config)
from __init__ import *
import acoustic as ax
import dataset as ds
with open(a.senlst) as f:
sentences = [l.rstrip() for l in f if l]
mean = np.zeros([1, ds.AX_DIM])
stddev = np.zeros([1, ds.AX_DIM])
n = 0
for s in sentences:
data = lu.read_binfile(path.join(ACO3DIR, s + '.aco'), dim=ds.AX_DIM)
for dt in data:
mean += dt
stddev += np.multiply(dt, dt)
n += 1
mean /= n
stddev = np.sqrt(stddev / n - np.multiply(mean, mean))
lu.write_binfile(mean, path.join(STTDIR, 'mean'))
lu.write_binfile(stddev, path.join(STTDIR, 'stddev'))
