def split_vad(frames, n: int = 3, negative_threshold: float = 0.1):
"""
Split a sample into multiple samples based `n` size of negative VAD.
Parameters
----------
frames: List[Tuple[Frame, label]]
n: int, optional (default=3)
`n` size of negative VAD to assume in one subsample.
negative_threshold: float, optional (default = 0.1)
If `negative_threshold` is 0.1, means that, length negative samples must at least 0.1 second.
Returns
-------
result : List[Frame]
"""
grouped = group_frames(frames)
grouped = group_frames_threshold(grouped,
threshold_to_stop=negative_threshold)
results, temp, not_activities = [], [], 0
for no, g in enumerate(grouped):
a = g[0]
if not g[1]:
not_activities += 1
temp.append(a)
if not_activities >= n:
results.append(combine_frames(temp))
temp = [g[0]]
not_activities = 0
if len(temp):
results.append(combine_frames(temp))
return results
def split_vad_duration(
frames,
max_duration: float = 5.0,
negative_threshold: float = 0.1,
silent_trail=500,
sample_rate: int = 16000,
use_negative_as_silent: bool = False,
):
"""
Split a sample into multiple samples based maximum duration of voice activities.
Parameters
----------
frames: List[Tuple[Frame, label]]
max_duration: float, optional (default = 5.0)
Maximum duration to assume one sample combined from voice activities.
negative_threshold: float, optional (default = 0.1)
If `negative_threshold` is 0.1, means that, length negative samples must at least 0.1 second.
silent_trail: int, optional (default = 500)
If an element is not a voice activity, append with `silent_trail` frame size.
sample_rate: int, optional (default = 16000)
sample rate for frames.
use_negative_as_silent: bool, optional (default = False)
If True, will use negative VAD as silent, else, use zeros array size of `silent_trail`.
Returns
-------
result : List[Frame]
"""
grouped = group_frames(frames)
grouped = group_frames_threshold(grouped,
threshold_to_stop=negative_threshold)
results, temp, lengths, last_silent = [], [], 0, None
for no, g in enumerate(grouped):
if g[1]:
a = g[0]
else:
last_silent = g[0]
if use_negative_as_silent:
a = np.concatenate(
[g[0].array[:silent_trail], g[0].array[-silent_trail:]])
else:
a = np.zeros(shape=(silent_trail))
a = Frame(
array=a,
timestamp=g[0].timestamp,
duration=len(a) / sample_rate,
)
l = len(a.array) / sample_rate
lengths += l
temp.append(a)
if lengths >= max_duration:
results.append(combine_frames(temp))
temp = [last_silent] if last_silent else []
lengths = 0
if len(temp):
results.append(combine_frames(temp))
return results
def split_vad(
frames,
n: int = 3,
negative_threshold: float = 0.1,
silent_trail: int = 500,
sample_rate: int = 16000,
use_negative_as_silent: bool = False,
):
"""
Split a sample into multiple samples based `n` size of negative VAD.
Parameters
----------
frames: List[Tuple[Frame, label]]
n: int, optional (default=3)
`n` size of negative VAD to assume in one subsample.
negative_threshold: float, optional (default = 0.1)
If `negative_threshold` is 0.1, means that, length negative samples must at least 0.1 second.
silent_trail: int, optional (default = 500)
If an element is not a voice activity, append with `silent_trail` frame size.
sample_rate: int, optional (default = 16000)
sample rate for frames.
use_negative_as_silent: bool, optional (default = False)
If True, will use negative VAD as silent, else, use zeros array size of `silent_trail`.
Returns
-------
result : List[Frame]
"""
grouped = group_frames(frames)
grouped = group_frames_threshold(grouped,
threshold_to_stop=negative_threshold)
results, temp, not_activities = [], [], 0
for no, g in enumerate(grouped):
if g[1]:
a = g[0]
else:
not_activities += 1
if use_negative_as_silent:
a = np.concatenate(
[g[0].array[:silent_trail], g[0].array[-silent_trail:]])
else:
a = np.zeros(shape=(silent_trail))
a = Frame(
array=a,
timestamp=g[0].timestamp,
duration=len(a) / sample_rate,
)
temp.append(a)
if not_activities >= n:
results.append(combine_frames(temp))
temp = [g[0]]
not_activities = 0
if len(temp):
results.append(combine_frames(temp))
return results
def split_vad_duration(
frames,
max_duration: float = 5.0,
negative_threshold: float = 0.1,
sample_rate: int = 16000,
):
"""
Split a sample into multiple samples based maximum duration of voice activities.
Parameters
----------
frames: List[Tuple[Frame, label]]
max_duration: float, optional (default = 5.0)
Maximum duration to assume one sample combined from voice activities.
negative_threshold: float, optional (default = 0.1)
If `negative_threshold` is 0.1, means that, length negative samples must at least 0.1 second.
sample_rate: int, optional (default = 16000)
sample rate for frames.
Returns
-------
result : List[Frame]
"""
grouped = group_frames(frames)
grouped = group_frames_threshold(grouped,
threshold_to_stop=negative_threshold)
results, temp, lengths = [], [], 0
for no, g in enumerate(grouped):
a = g[0]
l = len(a.array) / sample_rate
lengths += l
temp.append(a)
if lengths >= max_duration:
results.append(combine_frames(temp))
temp = []
lengths = 0
if len(temp):
results.append(combine_frames(temp))
return results
