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"""
extract alignments voices.
"""
import argparse
import multiprocessing
from pathlib import Path
import numpy
from become_yukarin.dataset.dataset import AcousticFeatureProcess
from become_yukarin.dataset.dataset import WaveFileLoadProcess
from become_yukarin.dataset.utility import MFCCAligner
from become_yukarin.param import AcousticFeatureParam
from become_yukarin.param import VoiceParam
base_voice_param = VoiceParam()
base_acoustic_feature_param = AcousticFeatureParam()
parser = argparse.ArgumentParser()
parser.add_argument('--input1_directory', '-i1', type=Path)
parser.add_argument('--input2_directory', '-i2', type=Path)
parser.add_argument('--output1_directory', '-o1', type=Path)
parser.add_argument('--output2_directory', '-o2', type=Path)
parser.add_argument('--sample_rate', type=int, default=base_voice_param.sample_rate)
parser.add_argument('--top_db', type=float, default=base_voice_param.top_db)
parser.add_argument('--frame_period', type=int, default=base_acoustic_feature_param.frame_period)
parser.add_argument('--order', type=int, default=base_acoustic_feature_param.order)
parser.add_argument('--alpha', type=float, default=base_acoustic_feature_param.alpha)
arguments = parser.parse_args()
def make_feature(
path,
sample_rate,
top_db,
frame_period,
order,
alpha,
):
wave = WaveFileLoadProcess(sample_rate=sample_rate, top_db=top_db)(path, test=True)
feature = AcousticFeatureProcess(frame_period=frame_period, order=order, alpha=alpha)(wave, test=True)
return feature
def generate_feature(path1, path2):
# load wave and padding
wave_file_load_process = WaveFileLoadProcess(
sample_rate=arguments.sample_rate,
top_db=arguments.top_db,
)
wave1 = wave_file_load_process(path1, test=True)
wave2 = wave_file_load_process(path2, test=True)
# m = max(len(wave1.wave), len(wave2.wave))
# wave1 = Wave(wave=numpy.pad(wave1.wave, (0, m - len(wave1.wave)), mode='mean'), sampling_rate=wave1.sampling_rate)
# wave2 = Wave(wave=numpy.pad(wave2.wave, (0, m - len(wave2.wave)), mode='mean'), sampling_rate=wave2.sampling_rate)
# make acoustic feature
acoustic_feature_process = AcousticFeatureProcess(
frame_period=arguments.frame_period,
order=arguments.order,
alpha=arguments.alpha,
)
f1 = acoustic_feature_process(wave1, test=True)
f2 = acoustic_feature_process(wave2, test=True)
# alignment
aligner = MFCCAligner(f1.mfcc, f2.mfcc)
f0_1, f0_2 = aligner.align(f1.f0, f2.f0)
spectrogram_1, spectrogram_2 = aligner.align(f1.spectrogram, f2.spectrogram)
aperiodicity_1, aperiodicity_2 = aligner.align(f1.aperiodicity, f2.aperiodicity)
mfcc_1, mfcc_2 = aligner.align(f1.mfcc, f2.mfcc)
# convert type
f0_1 = f0_1.astype(numpy.float32)
f0_2 = f0_2.astype(numpy.float32)
spectrogram_1 = spectrogram_1.astype(numpy.float32)
spectrogram_2 = spectrogram_2.astype(numpy.float32)
aperiodicity_1 = aperiodicity_1.astype(numpy.float32)
aperiodicity_2 = aperiodicity_2.astype(numpy.float32)
mfcc_1 = mfcc_1.astype(numpy.float32)
mfcc_2 = mfcc_2.astype(numpy.float32)
# save
path = Path(arguments.output1_directory, path1.stem + '.npy')
numpy.save(path.absolute(), dict(f0=f0_1, spectrogram=spectrogram_1, aperiodicity=aperiodicity_1, mfcc=mfcc_1))
print('saved!', path)
path = Path(arguments.output2_directory, path2.stem + '.npy')
numpy.save(path.absolute(), dict(f0=f0_2, spectrogram=spectrogram_2, aperiodicity=aperiodicity_2, mfcc=mfcc_2))
print('saved!', path)
def generate_mean_var(path_directory: Path):
path_mean = Path(path_directory, 'mean.npy')
var_mean = Path(path_directory, 'var.npy')
if path_mean.exists():
path_mean.unlink()
if var_mean.exists():
var_mean.unlink()
f0_list = []
spectrogram_list = []
aperiodicity_list = []
mfcc_list = []
for path in path_directory.glob('*'):
d = numpy.load(path).item() # type: dict
f0_list.append(d['f0'].ravel())
spectrogram_list.append(d['spectrogram'].ravel())
aperiodicity_list.append(d['aperiodicity'].ravel())
mfcc_list.append(d['mfcc'].ravel())
f0_list = numpy.concatenate(f0_list)
spectrogram_list = numpy.concatenate(spectrogram_list)
aperiodicity_list = numpy.concatenate(aperiodicity_list)
mfcc_list = numpy.concatenate(mfcc_list)
mean = dict(
f0=numpy.mean(f0_list),
spectrogram=numpy.mean(spectrogram_list),
aperiodicity=numpy.mean(aperiodicity_list),
mfcc=numpy.mean(mfcc_list),
)
var = dict(
f0=numpy.var(f0_list),
spectrogram=numpy.var(spectrogram_list),
aperiodicity=numpy.var(aperiodicity_list),
mfcc=numpy.var(mfcc_list),
)
numpy.save(path_mean.absolute(), mean)
numpy.save(var_mean.absolute(), var)
def main():
paths1 = list(sorted(arguments.input1_directory.glob('*')))
paths2 = list(sorted(arguments.input2_directory.glob('*')))
assert len(paths1) == len(paths2)
arguments.output1_directory.mkdir(exist_ok=True)
arguments.output2_directory.mkdir(exist_ok=True)
pool = multiprocessing.Pool()
pool.starmap(generate_feature, zip(paths1, paths2))
generate_mean_var(arguments.output1_directory)
generate_mean_var(arguments.output2_directory)
if __name__ == '__main__':
main()
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