from malaya_speech.model.frame import Frame
from malaya_speech.utils.group import (
combine_frames,
group_frames,
group_frames_threshold,
)
import numpy as np
[docs]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
[docs]def split_vad_duration(
frames,
max_duration: float = 5.0,
negative_threshold: float = 0.1,
):
"""
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.
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 = a.duration
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
