File size: 6,730 Bytes
7c268e9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | """Numpy port of NeMo's Sortformer/VAD post-processing (preds -> RTTM).
Mirrors ``predlist_to_timestamps`` / ``binarization_vectorized`` / ``filtering``
from ``nemo/collections/asr/parts/utils/vad_utils.py`` and
``generate_diarization_output_lines`` from ``speaker_utils.py`` (NeMo Speech 3.0).
"""
from dataclasses import dataclass
import numpy as np
FRAME_LENGTH_IN_SEC = 0.08
@dataclass
class PostProcessingParams:
onset: float = 0.5
offset: float = 0.5
pad_onset: float = 0.0
pad_offset: float = 0.0
min_duration_on: float = 0.0
min_duration_off: float = 0.0
filter_speech_first: float = 1.0
def merge_overlap_segment(segments: np.ndarray) -> np.ndarray:
if segments.shape[0] <= 1:
return segments
segments = segments[np.argsort(segments[:, 0])]
merge_boundary = segments[:-1, 1] >= segments[1:, 0]
head_padded = np.concatenate([[False], merge_boundary])
tail_padded = np.concatenate([merge_boundary, [False]])
head = segments[~head_padded, 0]
tail = segments[~tail_padded, 1]
return np.stack([head, tail], axis=1)
def filter_short_segments(segments: np.ndarray, threshold: float) -> np.ndarray:
return segments[segments[:, 1] - segments[:, 0] >= threshold]
def get_gap_segments(segments: np.ndarray) -> np.ndarray:
segments = segments[np.argsort(segments[:, 0])]
return np.column_stack((segments[:-1, 1], segments[1:, 0]))
def remove_segments(original_segments: np.ndarray, to_be_removed: np.ndarray) -> np.ndarray:
keep = np.ones(original_segments.shape[0], dtype=bool)
for segment in to_be_removed:
keep &= ~(original_segments == segment).all(axis=1)
return original_segments[keep]
def filtering(segments: np.ndarray, params: PostProcessingParams) -> np.ndarray:
if segments.shape[0] == 0:
return segments
def filter_speech():
if params.min_duration_on > 0:
return filter_short_segments(segments, params.min_duration_on)
return segments
def restore_short_gaps(current: np.ndarray) -> np.ndarray:
if params.min_duration_off <= 0 or current.shape[0] == 0:
return current
non_speech = get_gap_segments(current)
short_gaps = remove_segments(non_speech, filter_short_segments(non_speech, params.min_duration_off))
if short_gaps.shape[0] == 0:
return current
return merge_overlap_segment(np.concatenate([current, short_gaps], axis=0))
if params.filter_speech_first == 1.0:
segments = filter_speech()
segments = restore_short_gaps(segments)
else:
segments = restore_short_gaps(segments)
segments = filter_speech()
return segments
def binarization_vectorized(sequence: np.ndarray, params: PostProcessingParams) -> np.ndarray:
empty = np.empty((0, 2), dtype=np.float32)
num_frames = sequence.shape[0]
if num_frames == 0:
return empty
positions = np.arange(1, num_frames + 1)
onset, offset = params.onset, params.offset
if onset >= offset:
force_on = sequence > onset
force_off = sequence < offset
has_event = force_on | force_off
event_positions = np.where(has_event, positions, 0)
last_event_positions = np.maximum.accumulate(event_positions)
event_states = np.concatenate([[False], force_on])
above = event_states[last_event_positions]
else:
force_on = sequence >= offset
force_off = sequence <= onset
toggle = (sequence > onset) & (sequence < offset)
has_reset = force_on | force_off
reset_positions = np.where(has_reset, positions, 0)
last_reset_positions = np.maximum.accumulate(reset_positions)
reset_states = np.concatenate([[0], force_on.astype(np.int64)])
base_state = reset_states[last_reset_positions]
toggle_prefix = np.concatenate([[0], np.cumsum(toggle.astype(np.int64))])
toggles_since_reset = toggle_prefix[positions] - toggle_prefix[last_reset_positions]
above = np.logical_xor(base_state.astype(bool), (toggles_since_reset % 2).astype(bool))
padded = np.pad(above.astype(np.float32), (1, 1))
diff = padded[1:] - padded[:-1]
starts = np.where(diff > 0.5)[0]
ends = np.where(diff < -0.5)[0]
if starts.shape[0] == 0:
return empty
start_times = np.clip(starts.astype(np.float32) * FRAME_LENGTH_IN_SEC - params.pad_onset, 0.0, None)
end_times = ends.astype(np.float32) * FRAME_LENGTH_IN_SEC + params.pad_offset
valid = end_times > start_times
if not valid.any():
return empty
segments = np.stack([start_times[valid], end_times[valid]], axis=1)
if params.pad_onset > 0 or params.pad_offset > 0:
segments = merge_overlap_segment(segments)
return segments
def predlist_to_timestamps(
preds: np.ndarray,
offset: float = 0.0,
params: PostProcessingParams = None,
bypass_postprocessing: bool = False,
precision: int = 2,
):
"""Convert (num_frames, num_speakers) probabilities to per-speaker timestamps."""
if params is None:
params = PostProcessingParams()
if bypass_postprocessing:
params = PostProcessingParams(onset=0.5, offset=0.5)
timestamps = []
for spk in range(preds.shape[1]):
segments = binarization_vectorized(preds[:, spk], params)
if not bypass_postprocessing:
segments = filtering(segments, params)
if segments.shape[0] == 0:
timestamps.append([])
continue
segments = segments + offset
timestamps.append([[round(float(start), precision), round(float(end), precision)] for start, end in segments])
return timestamps
def generate_diarization_output_lines(timestamps, model_spk_num: int):
lines = []
for spk_idx in range(model_spk_num):
if not timestamps[spk_idx]:
continue
intervals = np.asarray(timestamps[spk_idx], dtype=np.float32).reshape(-1, 2)
for start, end in merge_overlap_segment(intervals):
lines.append(f"{start:.3f} {end:.3f} speaker_{int(spk_idx)}")
return lines
def timestamps_to_rttm_lines(timestamps, uri: str, model_spk_num: int):
lines = []
for spk_idx in range(model_spk_num):
intervals = timestamps[spk_idx]
if not intervals:
continue
merged = merge_overlap_segment(np.asarray(intervals, dtype=np.float32).reshape(-1, 2))
for start, end in merged:
duration = float(end) - float(start)
if duration > 0:
lines.append(
f"SPEAKER {uri} 1 {float(start):.3f} {duration:.3f} <NA> <NA> speaker_{int(spk_idx)} <NA>"
)
return lines
|