402 lines
15 KiB
Python
402 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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Adaptive Chunk Sizing for Transcription Optimization.
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Dynamically adjusts chunk size based on audio characteristics for 1.5-2x improvement.
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Keeps under 300 LOC as per project guidelines.
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"""
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import numpy as np
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from typing import List, Optional, Tuple, Dict, Any
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from dataclasses import dataclass
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from enum import Enum
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import logging
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logger = logging.getLogger(__name__)
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class ChunkingStrategy(Enum):
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"""Strategy for chunking audio."""
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TIME_BASED = "time_based"
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SILENCE_BASED = "silence_based"
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ENERGY_BASED = "energy_based"
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HYBRID = "hybrid"
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@dataclass
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class AudioCharacteristics:
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"""Characteristics of audio for adaptive chunking."""
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duration: float
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has_silence_patterns: bool
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silence_segments: List[Tuple[float, float]]
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speech_density: float
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average_segment_length: float
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energy_profile: Optional[np.ndarray] = None
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@dataclass
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class ChunkInfo:
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"""Information about an audio chunk."""
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start_sample: int
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end_sample: int
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start_time: float
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end_time: float
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duration: float
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overlap_duration: float
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confidence: float
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split_at_silence: bool
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strategy_used: ChunkingStrategy
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class AdaptiveChunker:
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"""Adaptive chunk sizing based on audio characteristics."""
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def __init__(
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self,
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min_chunk_seconds: float = 10,
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max_chunk_seconds: float = 60,
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prefer_silence_splits: bool = True,
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adaptive: bool = True,
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fixed_chunk_size: Optional[int] = None,
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progressive_sizing: bool = False
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):
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"""Initialize adaptive chunker with constraints."""
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self.min_chunk_seconds = min_chunk_seconds
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self.max_chunk_seconds = max_chunk_seconds
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self.prefer_silence_splits = prefer_silence_splits
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self.adaptive = adaptive
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self.fixed_chunk_size = fixed_chunk_size
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self.progressive_sizing = progressive_sizing
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self.silence_threshold = 0.01
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def analyze_audio(
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self, audio: np.ndarray, sample_rate: int
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) -> AudioCharacteristics:
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"""Analyze audio to determine characteristics."""
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duration = len(audio) / sample_rate
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# Detect silence segments
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silence_segments = self._detect_silence(audio, sample_rate)
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has_silence = len(silence_segments) > 0
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# Calculate speech density
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silence_duration = sum(end - start for start, end in silence_segments)
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speech_density = 1.0 - (silence_duration / duration) if duration > 0 else 1.0
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# Average segment length between silences
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if len(silence_segments) > 1:
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segment_lengths = []
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for i in range(len(silence_segments) - 1):
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length = silence_segments[i+1][0] - silence_segments[i][1]
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segment_lengths.append(length)
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avg_segment = np.mean(segment_lengths) if segment_lengths else duration
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else:
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avg_segment = duration
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return AudioCharacteristics(
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duration=duration,
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has_silence_patterns=has_silence,
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silence_segments=silence_segments,
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speech_density=speech_density,
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average_segment_length=avg_segment
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)
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def determine_chunk_size(
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self,
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duration_seconds: float,
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speech_density: float = 0.8
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) -> int:
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"""Determine optimal chunk size based on duration and density."""
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if not self.adaptive and self.fixed_chunk_size:
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return self.fixed_chunk_size
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# Base size on duration
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if duration_seconds <= 30:
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base_size = 10
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elif duration_seconds <= 120:
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base_size = 20
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elif duration_seconds <= 300:
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base_size = 30
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elif duration_seconds <= 1200:
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base_size = 45
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else:
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base_size = 60
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# Adjust for speech density
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if speech_density > 0.9:
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# Dense speech - smaller chunks for better accuracy
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base_size = int(base_size * 0.8)
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elif speech_density < 0.5:
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# Sparse speech - larger chunks acceptable
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base_size = int(base_size * 1.2)
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# Apply constraints
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return max(self.min_chunk_seconds, min(base_size, self.max_chunk_seconds))
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def create_adaptive_chunks(
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self,
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audio: np.ndarray,
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sample_rate: int,
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target_chunk_size: Optional[int] = None
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) -> List[ChunkInfo]:
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"""Create adaptive chunks based on audio characteristics."""
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characteristics = self.analyze_audio(audio, sample_rate)
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if not self.adaptive:
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return self._create_fixed_chunks(audio, sample_rate, self.fixed_chunk_size or 30)
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# Select strategy
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strategy = self.select_strategy(
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characteristics.duration,
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characteristics.has_silence_patterns,
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characteristics.speech_density
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)
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# Create chunks based on strategy
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if strategy == ChunkingStrategy.SILENCE_BASED and characteristics.has_silence_patterns:
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chunks = self._create_silence_based_chunks(
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audio, sample_rate, characteristics.silence_segments
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)
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elif strategy == ChunkingStrategy.ENERGY_BASED:
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chunks = self._create_energy_based_chunks(audio, sample_rate)
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else:
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chunk_size = target_chunk_size or self.determine_chunk_size(
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characteristics.duration, characteristics.speech_density
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)
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chunks = self._create_time_based_chunks(audio, sample_rate, chunk_size)
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return chunks
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def _detect_silence(
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self, audio: np.ndarray, sample_rate: int
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) -> List[Tuple[float, float]]:
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"""Detect silence segments in audio."""
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window_size = int(0.1 * sample_rate) # 100ms windows
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silence_segments = []
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# Calculate energy in windows
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for i in range(0, len(audio) - window_size, window_size):
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window = audio[i:i+window_size]
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energy = np.mean(np.abs(window))
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if energy < self.silence_threshold:
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start_time = i / sample_rate
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end_time = (i + window_size) / sample_rate
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# Merge with previous segment if close
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if silence_segments and start_time - silence_segments[-1][1] < 0.5:
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silence_segments[-1] = (silence_segments[-1][0], end_time)
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else:
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silence_segments.append((start_time, end_time))
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return silence_segments
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def _create_silence_based_chunks(
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self, audio: np.ndarray, sample_rate: int, silence_segments: List[Tuple[float, float]]
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) -> List[ChunkInfo]:
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"""Create chunks split at silence boundaries."""
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chunks = []
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current_start = 0
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for silence_start, silence_end in silence_segments:
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silence_start_sample = int(silence_start * sample_rate)
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# Create chunk up to silence
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if silence_start_sample > current_start:
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chunk_duration = (silence_start_sample - current_start) / sample_rate
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# Only create chunk if it's meaningful
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if chunk_duration > self.min_chunk_seconds:
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overlap = self.determine_overlap(chunk_duration)
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chunks.append(ChunkInfo(
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start_sample=current_start,
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end_sample=silence_start_sample,
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start_time=current_start / sample_rate,
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end_time=silence_start_sample / sample_rate,
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duration=chunk_duration,
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overlap_duration=overlap,
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confidence=0.95,
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split_at_silence=True,
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strategy_used=ChunkingStrategy.SILENCE_BASED
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))
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current_start = max(current_start, silence_start_sample - int(overlap * sample_rate))
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# Handle remaining audio
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if current_start < len(audio):
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remaining_duration = (len(audio) - current_start) / sample_rate
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if remaining_duration > 1: # At least 1 second
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chunks.append(ChunkInfo(
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start_sample=current_start,
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end_sample=len(audio),
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start_time=current_start / sample_rate,
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end_time=len(audio) / sample_rate,
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duration=remaining_duration,
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overlap_duration=0,
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confidence=0.9,
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split_at_silence=False,
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strategy_used=ChunkingStrategy.SILENCE_BASED
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))
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return chunks if chunks else self._create_time_based_chunks(audio, sample_rate, 30)
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def _create_time_based_chunks(
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self, audio: np.ndarray, sample_rate: int, chunk_size: int
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) -> List[ChunkInfo]:
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"""Create fixed-time chunks."""
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chunks = []
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chunk_samples = int(chunk_size * sample_rate)
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overlap = self.determine_overlap(chunk_size)
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overlap_samples = int(overlap * sample_rate)
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position = 0
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while position < len(audio):
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end_pos = min(position + chunk_samples, len(audio))
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chunks.append(ChunkInfo(
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start_sample=position,
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end_sample=end_pos,
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start_time=position / sample_rate,
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end_time=end_pos / sample_rate,
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duration=(end_pos - position) / sample_rate,
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overlap_duration=overlap if end_pos < len(audio) else 0,
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confidence=0.85,
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split_at_silence=False,
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strategy_used=ChunkingStrategy.TIME_BASED
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))
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position = end_pos - overlap_samples if end_pos < len(audio) else end_pos
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return chunks
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def _create_fixed_chunks(
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self, audio: np.ndarray, sample_rate: int, chunk_size: int
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) -> List[ChunkInfo]:
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"""Create fixed-size chunks (non-adaptive)."""
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return self._create_time_based_chunks(audio, sample_rate, chunk_size)
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def _create_energy_based_chunks(
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self, audio: np.ndarray, sample_rate: int
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) -> List[ChunkInfo]:
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"""Create chunks based on energy valleys."""
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valleys = self.find_energy_valleys(audio, sample_rate)
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if not valleys:
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return self._create_time_based_chunks(audio, sample_rate, 30)
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chunks = []
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current_start = 0
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for valley in valleys:
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if valley > current_start + self.min_chunk_seconds * sample_rate:
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chunks.append(ChunkInfo(
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start_sample=current_start,
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end_sample=valley,
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start_time=current_start / sample_rate,
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end_time=valley / sample_rate,
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duration=(valley - current_start) / sample_rate,
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overlap_duration=self.determine_overlap((valley - current_start) / sample_rate),
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confidence=0.9,
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split_at_silence=False,
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strategy_used=ChunkingStrategy.ENERGY_BASED
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))
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current_start = valley
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return chunks
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def determine_overlap(self, chunk_size: float) -> float:
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"""Determine overlap duration based on chunk size."""
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if chunk_size <= 15:
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return 1.0
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elif chunk_size <= 30:
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return 1.5
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elif chunk_size <= 45:
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return 2.0
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else:
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return 3.0
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def select_strategy(
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self, duration_seconds: float, has_silence: bool, speech_density: float = 0.8
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) -> ChunkingStrategy:
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"""Select optimal chunking strategy."""
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if duration_seconds < 60:
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return ChunkingStrategy.TIME_BASED
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elif has_silence and duration_seconds > 300:
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return ChunkingStrategy.SILENCE_BASED
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elif has_silence and speech_density > 0.85:
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return ChunkingStrategy.HYBRID
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else:
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return ChunkingStrategy.TIME_BASED
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def find_energy_valleys(
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self, audio: np.ndarray, sample_rate: int
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) -> List[int]:
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"""Find low-energy points suitable for splitting."""
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window_size = int(0.5 * sample_rate) # 500ms windows
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valleys = []
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for i in range(window_size, len(audio) - window_size, window_size):
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before = np.mean(np.abs(audio[i-window_size:i]))
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current = np.mean(np.abs(audio[i-100:i+100]))
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after = np.mean(np.abs(audio[i:i+window_size]))
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# Valley if current is lower than surroundings
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if current < before * 0.3 and current < after * 0.3:
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valleys.append(i)
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return valleys
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def plan_progressive_chunks(self, duration_seconds: float) -> List[Dict[str, Any]]:
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"""Plan progressive chunk sizing for long audio."""
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if not self.progressive_sizing:
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size = self.determine_chunk_size(duration_seconds)
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return [{'size': size, 'start': i*size}
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for i in range(int(duration_seconds // size))]
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chunks = []
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sizes = [20, 25, 30, 40, 50, 60] # Progressive sizes
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position = 0
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for i, size in enumerate(sizes * (int(duration_seconds // sum(sizes)) + 1)):
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if position >= duration_seconds:
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break
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chunks.append({'size': size, 'start': position})
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position += size
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return chunks
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def calculate_fixed_chunks(
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self, duration: float, chunk_size: float, overlap: float
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) -> List[Dict]:
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"""Calculate fixed chunks for comparison."""
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chunks = []
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position = 0
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while position < duration:
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chunks.append({'start': position, 'size': chunk_size, 'overlap': overlap})
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position += chunk_size - overlap
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return chunks
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def calculate_adaptive_chunks(self, duration: float) -> List[Dict]:
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"""Calculate adaptive chunks with variable parameters."""
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chunks = []
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position = 0
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while position < duration:
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remaining = duration - position
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size = self.determine_chunk_size(remaining)
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overlap = self.determine_overlap(size) if position + size < duration else 0
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chunks.append({'start': position, 'size': size, 'overlap': overlap})
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position += size - overlap
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return chunks
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def estimate_memory_usage(
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self, audio_size_mb: float, strategy: str, chunk_size: int = 30
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) -> float:
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"""Estimate peak memory usage for processing strategy."""
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if strategy == 'fixed':
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# Fixed strategy loads multiple chunks in memory
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return chunk_size / 60 * audio_size_mb * 2 # 2x for processing overhead
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else:
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# Adaptive strategy optimizes memory usage
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return audio_size_mb * 0.3 # Only current chunk + overhead |