Spaces:
Running
Running
File size: 8,333 Bytes
ebba35f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 | """
Suspicious Pause Detector
Detects abnormally long silences that may indicate the speaker is looking up
answers or receiving help during a test.
"""
import numpy as np
from dataclasses import dataclass, field
from typing import List, Optional
@dataclass
class SuspiciousPause:
"""A detected suspicious pause."""
start: float
end: float
duration: float
context: str = "" # What happened before/after
@dataclass
class PauseResult:
"""Result of suspicious pause detection."""
detected: bool
pauses: List[SuspiciousPause] = field(default_factory=list)
total_suspicious_time: float = 0.0
longest_pause: float = 0.0
@property
def count(self) -> int:
return len(self.pauses)
class SuspiciousPauseDetector:
"""
Detects suspicious long pauses in speech.
In natural conversation, pauses are typically:
- Short (< 2 seconds) for thinking
- Medium (2-4 seconds) for complex thoughts
Suspicious pauses (> 5 seconds) may indicate:
- Looking up answers
- Receiving external help
- Reading from a source
"""
def __init__(self,
min_suspicious_duration: float = 5.0,
warning_duration: float = 3.0,
max_natural_pause: float = 2.0):
"""
Args:
min_suspicious_duration: Minimum pause duration to flag as suspicious
warning_duration: Duration to flag as a warning (not fully suspicious)
max_natural_pause: Maximum duration for a natural pause
"""
self.min_suspicious_duration = min_suspicious_duration
self.warning_duration = warning_duration
self.max_natural_pause = max_natural_pause
def detect(self, speech_segments: List[dict],
total_duration: float,
transcription_segments: List[dict] = None) -> PauseResult:
"""
Detect suspicious pauses between speech segments.
Args:
speech_segments: List of {'start': float, 'end': float} for speech
total_duration: Total audio duration in seconds
transcription_segments: Optional transcription with timestamps for context
Returns:
PauseResult with detected suspicious pauses
"""
if not speech_segments:
return PauseResult(detected=False)
# Sort segments by start time
sorted_segments = sorted(speech_segments, key=lambda s: s.get('start', 0))
suspicious_pauses = []
# Check pause at the beginning
first_start = sorted_segments[0].get('start', 0)
if first_start >= self.min_suspicious_duration:
context = self._get_context(0, first_start, transcription_segments, "start")
suspicious_pauses.append(SuspiciousPause(
start=0,
end=first_start,
duration=round(first_start, 2),
context=context
))
# Check pauses between segments
for i in range(1, len(sorted_segments)):
prev_end = sorted_segments[i-1].get('end', 0)
curr_start = sorted_segments[i].get('start', 0)
gap = curr_start - prev_end
if gap >= self.min_suspicious_duration:
context = self._get_context(prev_end, curr_start, transcription_segments, "middle")
suspicious_pauses.append(SuspiciousPause(
start=round(prev_end, 2),
end=round(curr_start, 2),
duration=round(gap, 2),
context=context
))
# Check pause at the end
last_end = sorted_segments[-1].get('end', 0)
end_gap = total_duration - last_end
if end_gap >= self.min_suspicious_duration:
context = self._get_context(last_end, total_duration, transcription_segments, "end")
suspicious_pauses.append(SuspiciousPause(
start=round(last_end, 2),
end=round(total_duration, 2),
duration=round(end_gap, 2),
context=context
))
# Calculate summary statistics
total_suspicious_time = sum(p.duration for p in suspicious_pauses)
longest_pause = max((p.duration for p in suspicious_pauses), default=0)
return PauseResult(
detected=len(suspicious_pauses) > 0,
pauses=suspicious_pauses,
total_suspicious_time=round(total_suspicious_time, 2),
longest_pause=round(longest_pause, 2)
)
def detect_from_vad(self, vad_result: dict, total_duration: float) -> PauseResult:
"""
Detect suspicious pauses using VAD output.
Args:
vad_result: VAD result with 'segments' list
total_duration: Total audio duration
Returns:
PauseResult with detected suspicious pauses
"""
segments = vad_result.get('segments', [])
return self.detect(segments, total_duration)
def _get_context(self, start: float, end: float,
transcription_segments: List[dict],
position: str) -> str:
"""
Get context about what happened before/after the pause.
"""
if not transcription_segments:
if position == "start":
return "Long silence at audio start"
elif position == "end":
return "Long silence at audio end"
else:
return "Long silence mid-conversation"
# Find text before and after the pause
text_before = ""
text_after = ""
for seg in transcription_segments:
seg_end = seg.get('end', 0)
seg_start = seg.get('start', 0)
seg_text = seg.get('text', '').strip()
# Text ending just before pause
if seg_end <= start + 0.5 and seg_end >= start - 1.0:
text_before = seg_text[-50:] if len(seg_text) > 50 else seg_text
# Text starting just after pause
if seg_start >= end - 0.5 and seg_start <= end + 1.0:
text_after = seg_text[:50] if len(seg_text) > 50 else seg_text
if text_before and text_after:
return f"After: '{text_before}...' | Before: '...{text_after}'"
elif text_before:
return f"After: '{text_before}...'"
elif text_after:
return f"Before: '...{text_after}'"
else:
return f"Silence at {position} of audio"
def analyze_pause_pattern(self, speech_segments: List[dict],
total_duration: float) -> dict:
"""
Analyze the overall pause pattern in the audio.
Returns statistics about pause behavior.
"""
if not speech_segments or len(speech_segments) < 2:
return {
'avg_pause': 0,
'max_pause': 0,
'pause_count': 0,
'speech_ratio': 0
}
sorted_segments = sorted(speech_segments, key=lambda s: s.get('start', 0))
pauses = []
for i in range(1, len(sorted_segments)):
prev_end = sorted_segments[i-1].get('end', 0)
curr_start = sorted_segments[i].get('start', 0)
gap = curr_start - prev_end
if gap > 0.1: # Ignore very small gaps
pauses.append(gap)
if not pauses:
return {
'avg_pause': 0,
'max_pause': 0,
'pause_count': 0,
'speech_ratio': 1.0
}
# Calculate speech time
speech_time = sum(
seg.get('end', 0) - seg.get('start', 0)
for seg in sorted_segments
)
return {
'avg_pause': round(np.mean(pauses), 2),
'max_pause': round(max(pauses), 2),
'pause_count': len(pauses),
'speech_ratio': round(speech_time / total_duration, 2) if total_duration > 0 else 0,
'natural_pauses': sum(1 for p in pauses if p <= self.max_natural_pause),
'warning_pauses': sum(1 for p in pauses if self.max_natural_pause < p < self.min_suspicious_duration),
'suspicious_pauses': sum(1 for p in pauses if p >= self.min_suspicious_duration)
}
|