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0456b70 cb27c1b 0456b70 95e1515 0456b70 95e1515 0456b70 95e1515 0456b70 | 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 | """
Audio Segment Model
Represents a contiguous portion of audio with speaker and timing information.
"""
from dataclasses import dataclass
from enum import Enum
from typing import Optional
class SegmentType(Enum):
"""Classification of audio segment types."""
SPEECH = "speech"
NONVERBAL = "nonverbal"
SILENCE = "silence"
OVERLAP = "overlap" # Multiple speakers talking simultaneously
@dataclass
class AudioSegment:
"""
Audio segment with time range and speaker information.
IMPORTANT: This model stores ONLY metadata (timestamps, speaker info, classification).
Audio data is NEVER stored in AudioSegment instances. Audio is extracted on-demand
from source files using the stored timestamps during concatenation or processing.
This metadata-only design enables memory-efficient processing of large audio files
(>1 hour) by avoiding storage of thousands of audio arrays in memory.
Attributes:
start_time: Beginning timestamp in seconds
end_time: Ending timestamp in seconds
speaker_id: Identifier of the speaker in this segment
confidence: Certainty of speaker identification (0.0-1.0)
segment_type: Classification of the segment
audio_file: Path to the source audio file (optional, for reference only)
Usage Pattern:
# Create segment with metadata only
segment = AudioSegment(
start_time=10.5,
end_time=15.3,
speaker_id="speaker_00",
confidence=0.95,
segment_type=SegmentType.SPEECH
)
# Extract audio on-demand when needed
start_sample = int(segment.start_time * sample_rate)
end_sample = int(segment.end_time * sample_rate)
segment_audio = source_audio[start_sample:end_sample]
"""
start_time: float
end_time: float
speaker_id: str
confidence: float = 1.0
segment_type: SegmentType = SegmentType.SPEECH
audio_file: Optional[str] = None
def __post_init__(self):
"""Validate audio segment data."""
if self.start_time < 0:
raise ValueError(f"Start time cannot be negative: {self.start_time}")
if self.end_time <= self.start_time:
raise ValueError(
f"End time ({self.end_time}) must be after start time ({self.start_time})"
)
if not 0.0 <= self.confidence <= 1.0:
raise ValueError(f"Confidence must be between 0.0 and 1.0, got {self.confidence}")
# Ensure no audio data is accidentally stored (metadata-only enforcement)
if hasattr(self, "audio") or "audio" in self.__dict__:
raise ValueError(
"AudioSegment must not contain 'audio' attribute. "
"Audio data should be extracted on-demand using timestamps."
)
@property
def duration(self) -> float:
"""Calculate duration of the segment in seconds."""
return self.end_time - self.start_time
def overlaps_with(self, other: "AudioSegment") -> bool:
"""Check if this segment overlaps with another segment."""
return not (self.end_time <= other.start_time or other.end_time <= self.start_time)
def contains_time(self, time: float) -> bool:
"""Check if a timestamp falls within this segment."""
return self.start_time <= time <= self.end_time
def __repr__(self) -> str:
return (
f"AudioSegment("
f"speaker='{self.speaker_id}', "
f"time={self.start_time:.2f}-{self.end_time:.2f}s, "
f"duration={self.duration:.2f}s, "
f"confidence={self.confidence:.2f}, "
f"type={self.segment_type.value})"
)
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