def subset(self,
first: Optional[int] = None,
last: Optional[int] = None) -> "FeatureSet":
"""
Return a new ``FeatureSet`` according to the selected subset criterion.
Only a single argument to ``subset`` is supported at this time.
:param first: int, the number of first supervisions to keep.
:param last: int, the number of last supervisions to keep.
:return: a new ``FeatureSet`` with the subset results.
"""
assert exactly_one_not_null(
first, last), "subset() can handle only one non-None arg."
if first is not None:
assert first > 0
out = FeatureSet.from_items(islice(self, first))
if len(out) < first:
logging.warning(
f"FeatureSet has only {len(out)} items but first {first} were requested."
)
return out
if last is not None:
assert last > 0
if last > len(self):
logging.warning(
f"FeatureSet has only {len(self)} items but last {last} required; "
f"not doing anything.")
return self
return FeatureSet.from_features(self.features[-last:])
def subset(self,
first: Optional[int] = None,
last: Optional[int] = None) -> 'SupervisionSet':
"""
Return a new ``SupervisionSet`` according to the selected subset criterion.
Only a single argument to ``subset`` is supported at this time.
:param first: int, the number of first supervisions to keep.
:param last: int, the number of last supervisions to keep.
:return: a new ``SupervisionSet`` with the subset results.
"""
assert exactly_one_not_null(
first, last), "subset() can handle only one non-None arg."
if first is not None:
assert first > 0
if first > len(self):
logging.warning(
f'SupervisionSet has only {len(self)} items but first {first} required; '
f'not doing anything.')
return self
return SupervisionSet.from_segments(islice(self, first))
if last is not None:
assert last > 0
if last > len(self):
logging.warning(
f'SupervisionSet has only {len(self)} items but last {last} required; '
f'not doing anything.')
return self
return SupervisionSet.from_segments(
islice(self,
len(self) - last, len(self)))
def __init__(self,
extra_frames: Optional[int] = None,
extra_samples: Optional[int] = None,
extra_seconds: Optional[Seconds] = None,
pad_feat_value: float = LOG_EPSILON,
randomized: bool = False) -> None:
"""
ExtraPadding's constructor.
:param extra_frames: The total number of frames to add to each cut.
We will add half that number on each side of the cut ("both" directions padding).
:param extra_samples: The total number of samples to add to each cut.
We will add half that number on each side of the cut ("both" directions padding).
:param extra_seconds: The total duration in seconds to add to each cut.
We will add half that number on each side of the cut ("both" directions padding).
:param pad_feat_value: When padding a cut with precomputed features, what
value should be used for padding (the default is a very low log-energy).
:param randomized: When ``True``, we will sample a value from a uniform distribution of
``[0, extra_X]`` for each cut (for samples/frames -- sample an int,
for duration -- sample a float).
"""
assert exactly_one_not_null(extra_frames, extra_samples, extra_seconds), \
"For ExtraPadding, you have to specify exactly one of: frames, samples, or duration."
self.extra_frames = extra_frames
self.extra_samples = extra_samples
self.extra_seconds = extra_seconds
self.pad_feat_value = pad_feat_value
self.randomized = randomized
def __post_init__(self) -> None:
assert exactly_one_not_null(*self._constraints) or all(
x is None for x in self._constraints)
for c in self._constraints:
assert is_none_or_gt(c, 0)
