def generate_feature(path: Path):
out = Path(arguments.output, path.stem + '.npy')
if out.exists() and not arguments.enable_overwrite:
return
# load wave and padding
wave = Wave.load(path=path, sampling_rate=arguments.sampling_rate)
wave = wave.pad(pre_second=arguments.pad_second,
post_second=arguments.pad_second)
# make acoustic feature
feature = AcousticFeature.extract(
wave=wave,
frame_period=arguments.frame_period,
f0_floor=arguments.f0_floor,
f0_ceil=arguments.f0_ceil,
fft_length=arguments.fft_length,
order=arguments.order,
alpha=arguments.alpha,
dtype=arguments.dtype,
)
if arguments.threshold_db is not None:
if arguments.sampling_rate_for_thresholding is not None:
wave_ref = Wave.load(
path=path,
sampling_rate=arguments.sampling_rate_for_thresholding)
wave_ref = wave_ref.pad(pre_second=arguments.pad_second,
post_second=arguments.pad_second)
else:
wave_ref = wave
effective = wave_ref.get_effective_frame(
threshold_db=arguments.threshold_db,
fft_length=arguments.fft_length,
frame_period=arguments.frame_period,
)
# there is possibility mismatch of length
# https://github.com/mmorise/World/blob/c41e580c24c8d360f322ba6e2092ad4785d2d5b9/src/harvest.cpp#L1220
len_wave = wave.get_hop_and_length(arguments.frame_period)[1]
len_wave_ref = wave_ref.get_hop_and_length(arguments.frame_period)[1]
if len_wave == len_wave_ref - 1:
effective = effective[:-1]
feature = feature.indexing(effective)
# save
feature.save(path=out, ignores=arguments.ignore_feature)
def generate_feature(path: Path):
out = Path(arguments.output, path.stem + '.npy')
if out.exists() and not arguments.enable_overwrite:
return
# load wave and padding
wave = Wave.load(path=path, sampling_rate=arguments.sampling_rate)
wave = wave.pad(pre_second=arguments.pad_second,
post_second=arguments.pad_second)
# make acoustic feature
feature = AcousticFeature.extract(
wave=wave,
frame_period=arguments.frame_period,
f0_floor=arguments.f0_floor,
f0_ceil=arguments.f0_ceil,
fft_length=arguments.fft_length,
order=arguments.order,
alpha=arguments.alpha,
dtype=arguments.dtype,
)
if arguments.threshold_db is not None:
index = wave.get_effective_frame(
threshold_db=arguments.threshold_db,
fft_length=arguments.fft_length,
frame_period=arguments.frame_period,
)
feature = feature.indexing(index)
# save
feature.save(path=out, validate=True, ignores=arguments.ignore_feature)
def load_wave(self, path: Path):
return Wave.load(path, sampling_rate=self._param.sampling_rate)