def test_spc_and_npow(self):
path = dirpath + '/data/test16000.wav'
fs, x = wavfile.read(path)
af = FeatureExtractor(analyzer='world', fs=fs, shiftms=5)
_, spc, _ = af.analyze(x)
npow = af.npow()
assert spc.shape[0] == npow.shape[0]
def _analyze_world_features(self, x, f0_only=False):
feat = FeatureExtractor(
analyzer="world",
fs=self.conf["fs"],
fftl=self.conf["fftl"],
shiftms=self.conf["shiftms"],
minf0=self.sconf["minf0"],
maxf0=self.sconf["maxf0"],
)
# analyze world based features
self.feats["f0"], self.feats["spc"], self.feats["ap"] = feat.analyze(x)
self.feats["uv"], self.feats["cf0"] = convert_continuos_f0(
self.feats["f0"])
self.feats["lf0"] = np.log(self.feats["f0"] + EPS)
self.feats["lcf0"] = np.log(self.feats["cf0"])
if f0_only:
return
if self.conf["fftl"] != 256 and self.conf["fs"] > 16000:
# NOTE: 256 fft_size sometimes causes errors
self.feats["mcep"] = feat.mcep(dim=self.conf["mcep_dim"],
alpha=self.conf["mcep_alpha"])
self.feats["npow"] = feat.npow()
self.feats["cap"] = feat.codeap()
cap = self.feats["cap"]
self.feats["ccap"] = np.zeros(cap.shape)
self.feats["cap_uv"] = np.zeros(cap.shape)
for d in range(self.feats["ccap"].shape[-1]):
cap[np.where(cap[:, d] == max(cap[:, d])), d] = 0.0
(
self.feats["cap_uv"][:, d],
self.feats["ccap"][:, d],
) = convert_continuos_f0(cap[:, d])
def test_spc_and_npow(self):
path = dirpath + '/data/test16000.wav'
fs, x = wavfile.read(path)
af = FeatureExtractor(analyzer='world', fs=fs, shiftms=5)
_, spc, _ = af.analyze(x)
npow = af.npow()
assert spc.shape[0] == npow.shape[0]
def main(*argv):
argv = argv if argv else sys.argv[1:]
dcp = 'Create histogram for speaker-dependent configure'
parser = argparse.ArgumentParser(description=dcp)
parser.add_argument('speaker', type=str, help='Input speaker label')
parser.add_argument('list_file',
type=str,
help='List file of the input speaker')
parser.add_argument('wav_dir', type=str, help='Directory of wav file')
parser.add_argument('figure_dir',
type=str,
help='Directory for figure output')
args = parser.parse_args(argv)
# open list file
with open(args.list_file, 'r') as fp:
files = fp.readlines()
f0s = []
npows = []
for f in files:
# open waveform
f = f.rstrip()
wavf = os.path.join(args.wav_dir, f + '.wav')
fs, x = wavfile.read(wavf)
x = np.array(x, dtype=np.float)
print("Extract: " + wavf)
# constract FeatureExtractor class
feat = FeatureExtractor(analyzer='world', fs=fs)
# f0 and npow extraction
f0, _, _ = feat.analyze(x)
npow = feat.npow()
f0s.append(f0)
npows.append(npow)
f0s = np.hstack(f0s).flatten()
npows = np.hstack(npows).flatten()
# create a histogram to visualize F0 range of the speaker
f0histogrampath = os.path.join(args.figure_dir,
args.speaker + '_f0histogram.png')
create_histogram(f0s,
f0histogrampath,
range_min=40,
range_max=700,
step=50,
xlabel='Fundamental frequency [Hz]')
# create a histogram to visualize npow range of the speaker
npowhistogrampath = os.path.join(args.figure_dir,
args.speaker + '_npowhistogram.png')
create_histogram(npows,
npowhistogrampath,
range_min=-70,
range_max=20,
step=10,
xlabel="Frame power [dB]")
def extract_f0_and_npow(wavf):
"""
F0 and npow extraction
Parameters
----------
wavf : str,
File path of waveform file
Returns
-------
dict :
Dictionary consisting of F0 and npow arrays
"""
# open waveform
print("Extract: " + wavf)
fs, x = wavfile.read(wavf)
x = np.array(x, dtype=np.float)
x = low_cut_filter(x, fs, cutoff=70)
# constract FeatureExtractor class
feat = FeatureExtractor(analyzer="world", fs=fs)
# f0 and npow extraction
f0, _, _ = feat.analyze(x)
npow = feat.npow()
return {"f0": f0, "npow": npow}
def _analyze_world_features(self, x):
feat = FeatureExtractor(
analyzer="world",
fs=self.conf["fs"],
fftl=self.conf["fftl"],
shiftms=self.conf["shiftms"],
minf0=self.sconf["minf0"],
maxf0=self.sconf["maxf0"],
)
# analyze world based features
self.feats["f0"], self.feats["spc"], self.feats["ap"] = feat.analyze(x)
self.feats["mcep"] = feat.mcep(dim=self.conf["mcep_dim"],
alpha=self.conf["mcep_alpha"])
self.feats["npow"] = feat.npow()
self.feats["cap"] = feat.codeap()
cap = self.feats["cap"]
self.feats["ccap"] = np.zeros(cap.shape)
self.feats["cap_uv"] = np.zeros(cap.shape)
for d in range(self.feats["ccap"].shape[-1]):
cap[np.where(cap[:, d] == max(cap[:, d])), d] = 0.0
self.feats["cap_uv"][:, d], self.feats[
"ccap"][:, d] = convert_continuos_f0(cap[:, d])
self.feats["uv"], self.feats["cf0"] = convert_continuos_f0(
self.feats["f0"])
self.feats["lf0"] = np.log(self.feats["f0"] + EPS)
self.feats["lcf0"] = np.log(self.feats["cf0"])
def main(*argv):
argv = argv if argv else sys.argv[1:]
dcp = 'Create histogram for speaker-dependent configure'
parser = argparse.ArgumentParser(description=dcp)
parser.add_argument('speaker', type=str,
help='Input speaker label')
parser.add_argument('list_file', type=str,
help='List file of the input speaker')
parser.add_argument('wav_dir', type=str,
help='Directory of wav file')
parser.add_argument('figure_dir', type=str,
help='Directory for figure output')
args = parser.parse_args(argv)
# open list file
with open(args.list_file, 'r') as fp:
files = fp.readlines()
f0s = []
npows = []
for f in files:
# open waveform
f = f.rstrip()
wavf = os.path.join(args.wav_dir, f + '.wav')
fs, x = wavfile.read(wavf)
x = np.array(x, dtype=np.float)
print("Extract: " + wavf)
# constract FeatureExtractor class
feat = FeatureExtractor(analyzer='world', fs=fs)
# f0 and npow extraction
f0, _, _ = feat.analyze(x)
npow = feat.npow()
f0s.append(f0)
npows.append(npow)
f0s = np.hstack(f0s).flatten()
npows = np.hstack(npows).flatten()
# create a histogram to visualize F0 range of the speaker
f0histogrampath = os.path.join(
args.figure_dir, args.speaker + '_f0histogram.png')
create_histogram(f0s, f0histogrampath, range_min=40, range_max=700,
step=50, xlabel='Fundamental frequency [Hz]')
# create a histogram to visualize npow range of the speaker
npowhistogrampath = os.path.join(
args.figure_dir, args.speaker + '_npowhistogram.png')
create_histogram(npows, npowhistogrampath, range_min=-70, range_max=20,
step=10, xlabel="Frame power [dB]")
def main(*argv):
argv = argv if argv else sys.argv
# Options for python
dcp = 'Extract aoucstic features for the speaker'
parser = argparse.ArgumentParser(description=dcp)
parser.add_argument('--overwrite',
default=False,
action='store_true',
help='Overwrite h5 file')
parser.add_argument('speaker', type=str, help='Input speaker label')
parser.add_argument('ymlf', type=str, help='Yml file of the input speaker')
parser.add_argument('list_file',
type=str,
help='List file of the input speaker')
parser.add_argument('wav_dir',
type=str,
help='Wav file directory of the speaker')
parser.add_argument('pair_dir',
type=str,
help='Directory of the speaker pair')
args = parser.parse_args(argv)
# read parameters from speaker yml
sconf = SpeakerYML(args.ymlf)
h5_dir = os.path.join(args.pair_dir, 'h5')
anasyn_dir = os.path.join(args.pair_dir, 'anasyn')
if not os.path.exists(os.path.join(h5_dir, args.speaker)):
os.makedirs(os.path.join(h5_dir, args.speaker))
if not os.path.exists(os.path.join(anasyn_dir, args.speaker)):
os.makedirs(os.path.join(anasyn_dir, args.speaker))
# constract FeatureExtractor class
feat = FeatureExtractor(analyzer=sconf.analyzer,
fs=sconf.wav_fs,
fftl=sconf.wav_fftl,
shiftms=sconf.wav_shiftms,
minf0=sconf.f0_minf0,
maxf0=sconf.f0_maxf0)
# constract Synthesizer class
synthesizer = Synthesizer(fs=sconf.wav_fs,
fftl=sconf.wav_fftl,
shiftms=sconf.wav_shiftms)
# open list file
with open(args.list_file, 'r') as fp:
for line in fp:
f = line.rstrip()
h5f = os.path.join(h5_dir, f + '.h5')
if (not os.path.exists(h5f)) or args.overwrite:
wavf = os.path.join(args.wav_dir, f + '.wav')
fs, x = wavfile.read(wavf)
x = np.array(x, dtype=np.float)
x = low_cut_filter(x, fs, cutoff=70)
assert fs == sconf.wav_fs
print("Extract acoustic features: " + wavf)
# analyze F0, spc, and ap
f0, spc, ap = feat.analyze(x)
mcep = feat.mcep(dim=sconf.mcep_dim, alpha=sconf.mcep_alpha)
npow = feat.npow()
codeap = feat.codeap()
# save features into a hdf5 file
h5 = HDF5(h5f, mode='w')
h5.save(f0, ext='f0')
# h5.save(spc, ext='spc')
# h5.save(ap, ext='ap')
h5.save(mcep, ext='mcep')
h5.save(npow, ext='npow')
h5.save(codeap, ext='codeap')
h5.close()
# analysis/synthesis using F0, mcep, and ap
wav = synthesizer.synthesis(
f0,
mcep,
ap,
alpha=sconf.mcep_alpha,
)
wav = np.clip(wav, -32768, 32767)
anasynf = os.path.join(anasyn_dir, f + '.wav')
wavfile.write(anasynf, fs, np.array(wav, dtype=np.int16))
else:
print("Acoustic features already exist: " + h5f)
def world_feature_extract(queue, wav_list, config):
"""WORLD feature extraction
Args:
queue (multiprocessing.Queue): the queue to store the file name of utterance
wav_list (list): list of the wav files
config (dict): feature extraction config
"""
# define feature extractor
feature_extractor = FeatureExtractor(analyzer="world",
fs=config['sampling_rate'],
shiftms=config['shiftms'],
minf0=config['minf0'],
maxf0=config['maxf0'],
fftl=config['fft_size'])
# extraction
for i, wav_name in enumerate(wav_list):
logging.info("now processing %s (%d/%d)" %
(wav_name, i + 1, len(wav_list)))
# load wavfile and apply low cut filter
fs, x = wavfile.read(wav_name)
x = np.array(x, dtype=np.float32)
if config['highpass_cutoff'] != 0:
x = low_cut_filter(x, fs, cutoff=config['highpass_cutoff'])
# check sampling frequency
if not fs == config['sampling_rate']:
logging.error("sampling frequency of %s is not matched." %
wav_name)
sys.exit(1)
# extract features
f0, spc, ap = feature_extractor.analyze(x)
codeap = feature_extractor.codeap()
mcep = feature_extractor.mcep(dim=config['mcep_dim'],
alpha=config['mcep_alpha'])
npow = feature_extractor.npow()
uv, cont_f0 = convert_continuos_f0(f0)
lpf_fs = int(1.0 / (config['shiftms'] * 0.001))
cont_f0_lpf = low_pass_filter(cont_f0, lpf_fs, cutoff=20)
next_cutoff = 70
while not (cont_f0_lpf >= [0]).all():
cont_f0_lpf = low_pass_filter(cont_f0, lpf_fs, cutoff=next_cutoff)
next_cutoff *= 2
# concatenate
cont_f0_lpf = np.expand_dims(cont_f0_lpf, axis=-1)
uv = np.expand_dims(uv, axis=-1)
feats = np.concatenate([uv, cont_f0_lpf, mcep, codeap], axis=1)
# save feature
feat_name = path_replace(wav_name,
config['indir'],
config['outdir'],
extname=config['feature_format'])
write_hdf5(feat_name, "/%s" % (config["feat_type"]), feats)
if config['save_f0']:
write_hdf5(feat_name, "/f0", f0)
if config['save_ap']:
write_hdf5(feat_name, "/ap", ap)
if config['save_spc']:
write_hdf5(feat_name, "/spc", spc)
if config['save_npow']:
write_hdf5(feat_name, "/npow", npow)
queue.put('Finish')
def main(*argv):
argv = argv if argv else sys.argv
# Options for python
dcp = 'Extract aoucstic features for the speaker'
parser = argparse.ArgumentParser(description=dcp)
parser.add_argument('--overwrite', default=False, action='store_true',
help='Overwrite h5 file')
parser.add_argument('speaker', type=str,
help='Input speaker label')
parser.add_argument('ymlf', type=str,
help='Yml file of the input speaker')
parser.add_argument('list_file', type=str,
help='List file of the input speaker')
parser.add_argument('wav_dir', type=str,
help='Wav file directory of the speaker')
parser.add_argument('pair_dir', type=str,
help='Directory of the speaker pair')
args = parser.parse_args(argv)
# read parameters from speaker yml
sconf = SpeakerYML(args.ymlf)
h5_dir = os.path.join(args.pair_dir, 'h5')
anasyn_dir = os.path.join(args.pair_dir, 'anasyn')
if not os.path.exists(os.path.join(h5_dir, args.speaker)):
os.makedirs(os.path.join(h5_dir, args.speaker))
if not os.path.exists(os.path.join(anasyn_dir, args.speaker)):
os.makedirs(os.path.join(anasyn_dir, args.speaker))
# constract FeatureExtractor class
feat = FeatureExtractor(analyzer=sconf.analyzer,
fs=sconf.wav_fs,
fftl=sconf.wav_fftl,
shiftms=sconf.wav_shiftms,
minf0=sconf.f0_minf0,
maxf0=sconf.f0_maxf0)
# constract Synthesizer class
synthesizer = Synthesizer(fs=sconf.wav_fs,
fftl=sconf.wav_fftl,
shiftms=sconf.wav_shiftms)
# open list file
with open(args.list_file, 'r') as fp:
for line in fp:
f = line.rstrip()
h5f = os.path.join(h5_dir, f + '.h5')
if (not os.path.exists(h5f)) or args.overwrite:
wavf = os.path.join(args.wav_dir, f + '.wav')
fs, x = wavfile.read(wavf)
x = np.array(x, dtype=np.float)
x = low_cut_filter(x, fs, cutoff=70)
assert fs == sconf.wav_fs
print("Extract acoustic features: " + wavf)
# analyze F0, spc, and ap
f0, spc, ap = feat.analyze(x)
mcep = feat.mcep(dim=sconf.mcep_dim, alpha=sconf.mcep_alpha)
npow = feat.npow()
codeap = feat.codeap()
# save features into a hdf5 file
h5 = HDF5(h5f, mode='w')
h5.save(f0, ext='f0')
# h5.save(spc, ext='spc')
# h5.save(ap, ext='ap')
h5.save(mcep, ext='mcep')
h5.save(npow, ext='npow')
h5.save(codeap, ext='codeap')
h5.close()
# analysis/synthesis using F0, mcep, and ap
wav = synthesizer.synthesis(f0,
mcep,
ap,
alpha=sconf.mcep_alpha,
)
wav = np.clip(wav, -32768, 32767)
anasynf = os.path.join(anasyn_dir, f + '.wav')
wavfile.write(anasynf, fs, np.array(wav, dtype=np.int16))
else:
print("Acoustic features already exist: " + h5f)
def main():
if args.file == 'con':
file = 'converted'
elif args.file == 'tar':
file = 'target'
else:
raise ValueError("The file is incorrect")
feat = FeatureExtractor(analyzer='world',
fs=22050,
fftl=1024,
shiftms=5,
minf0=args.minf0,
maxf0=args.maxf0)
# constract Synthesizer class
synthesizer = Synthesizer(fs=22050, fftl=1024, shiftms=5)
# open list file
with open('./list/' + file + '.list', 'r') as fp:
for line in fp:
f = line.rstrip()
h5f = os.path.join('./' + file + '/h5f/', f + '.h5')
if (not os.path.exists(h5f)):
wavf = os.path.join('./' + file + '/wav/', f + '.wav')
fs, x = wavfile.read(wavf)
x = np.array(x, dtype=np.float)
x = low_cut_filter(x, fs, cutoff=70)
print("Extract acoustic features: " + wavf)
# analyze F0, spc, and ap
f0, spc, ap = feat.analyze(x)
mcep = feat.mcep(dim=34, alpha=0.544)
npow = feat.npow()
codeap = feat.codeap()
# save features into a hdf5 file
h5 = HDF5(h5f, mode='w')
h5.save(f0, ext='f0')
# h5.save(spc, ext='spc')
# h5.save(ap, ext='ap')
h5.save(mcep, ext='mcep')
h5.save(npow, ext='npow')
h5.save(codeap, ext='codeap')
h5.close()
# analysis/synthesis using F0, mcep, and ap
wav = synthesizer.synthesis(
f0,
mcep,
ap,
alpha=0.544,
)
wav = np.clip(wav, -32768, 32767)
anasynf = os.path.join('./' + file + '/anasyn/', f + '.wav')
wavfile.write(anasynf, fs, np.array(wav, dtype=np.int16))
else:
print("Acoustic features already exist: " + h5f)
