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from typing import NamedTuple, Dict, List
import numpy
import pyworld
_min_mc = -18.3
class Wave(NamedTuple):
wave: numpy.ndarray
sampling_rate: int
class AcousticFeature(NamedTuple):
f0: numpy.ndarray = numpy.nan
spectrogram: numpy.ndarray = numpy.nan
aperiodicity: numpy.ndarray = numpy.nan
mfcc: numpy.ndarray = numpy.nan
voiced: numpy.ndarray = numpy.nan
@staticmethod
def dtypes():
return dict(
f0=numpy.float32,
spectrogram=numpy.float32,
aperiodicity=numpy.float32,
mfcc=numpy.float32,
voiced=numpy.bool,
)
def astype(self, dtype):
return AcousticFeature(
f0=self.f0.astype(dtype),
spectrogram=self.spectrogram.astype(dtype),
aperiodicity=self.aperiodicity.astype(dtype),
mfcc=self.mfcc.astype(dtype),
voiced=self.voiced.astype(dtype),
)
def astype_only_float(self, dtype):
return AcousticFeature(
f0=self.f0.astype(dtype),
spectrogram=self.spectrogram.astype(dtype),
aperiodicity=self.aperiodicity.astype(dtype),
mfcc=self.mfcc.astype(dtype),
voiced=self.voiced,
)
def validate(self):
assert self.f0.ndim == 2
assert self.spectrogram.ndim == 2
assert self.aperiodicity.ndim == 2
assert self.mfcc.ndim == 2
assert self.voiced.ndim == 2
len_time = len(self.f0)
assert len(self.spectrogram) == len_time
assert len(self.aperiodicity) == len_time
assert len(self.mfcc) == len_time
assert len(self.voiced) == len_time
assert self.voiced.dtype == numpy.bool
@staticmethod
def silent(length: int, sizes: Dict[str, int], keys: List[str]):
d = {}
if 'f0' in keys:
d['f0'] = numpy.zeros((length, sizes['f0']), dtype=AcousticFeature.dtypes()['f0'])
if 'spectrogram' in keys:
d['spectrogram'] = numpy.zeros((length, sizes['spectrogram']),
dtype=AcousticFeature.dtypes()['spectrogram'])
if 'aperiodicity' in keys:
d['aperiodicity'] = numpy.zeros((length, sizes['aperiodicity']),
dtype=AcousticFeature.dtypes()['aperiodicity'])
if 'mfcc' in keys:
d['mfcc'] = numpy.hstack((
numpy.ones((length, 1), dtype=AcousticFeature.dtypes()['mfcc']) * _min_mc,
numpy.zeros((length, sizes['mfcc'] - 1), dtype=AcousticFeature.dtypes()['mfcc'])
))
if 'voiced' in keys:
d['voiced'] = numpy.zeros((length, sizes['voiced']), dtype=AcousticFeature.dtypes()['voiced'])
feature = AcousticFeature(**d)
return feature
@staticmethod
def concatenate(fs: List['AcousticFeature'], keys: List[str]):
is_target = lambda a: not numpy.any(numpy.isnan(a))
return AcousticFeature(**{
key: numpy.concatenate([getattr(f, key) for f in fs]) if is_target(getattr(fs[0], key)) else numpy.nan
for key in keys
})
def pick(self, first: int, last: int):
is_target = lambda a: not numpy.any(numpy.isnan(a))
return AcousticFeature(
f0=self.f0[first:last] if is_target(self.f0) else numpy.nan,
spectrogram=self.spectrogram[first:last] if is_target(self.spectrogram) else numpy.nan,
aperiodicity=self.aperiodicity[first:last] if is_target(self.aperiodicity) else numpy.nan,
mfcc=self.mfcc[first:last] if is_target(self.mfcc) else numpy.nan,
voiced=self.voiced[first:last] if is_target(self.voiced) else numpy.nan,
)
@staticmethod
def get_sizes(sampling_rate: int, order: int):
fft_size = pyworld.get_cheaptrick_fft_size(fs=sampling_rate)
return dict(
f0=1,
spectrogram=fft_size // 2 + 1,
aperiodicity=fft_size // 2 + 1,
mfcc=order + 1,
voiced=1,
)
class LowHighSpectrogramFeature(NamedTuple):
low: numpy.ndarray
high: numpy.ndarray
def validate(self):
assert self.low.ndim == 2
assert self.high.ndim == 2
assert self.low.shape == self.high.shape
|
