**Investigating split rejection**

I'm thinking that to confirm whether our segment has rejected a split, I'd need to re-run the chunk reassignment with debugging or check the logs. Maybe the pipeline generated a log file? It might be in the tar file where I could find `logs.txt`. I should list all file names using `tar -tf` to see what else is included, like `transcript.json`, and review that list.


tar -tf tmp/r2debug/--jp1imJD9w.tar | rg -v '/segments/' | head -200


Output:
Exit code: 0
Wall time: 0 seconds
Output:
--jp1imJD9w/
--jp1imJD9w/metadata.json


Summary: auto

## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.


**Analyzing voice segments**

I need to ensure we have the right token, but I won’t print it. I could use `ffmpeg` to compute a spectrogram to analyze two voice prints. However, I can't listen to it directly. It seems like I can derive some details from the metadata too. There’s a gap from 197.70 to 199.00 seconds, possibly indicating a switch from one speaker to another. I should note that the diarization might be a bit coarse due to chunking.


**Examining speaker segmentation issues**

I wonder if the issue arises because the segment is within a "VAD segment" that includes two speakers. The diarization model might assign just one speaker label since it can't create new speaker labels within a chunk due to the "max_speakers=2" constraint. However, overall, there are three speakers. There might be cross-talk leading to misassignment, especially since overlaps were detected around specific times, though not in the segment of interest. The user mentioned that multi-speaker doesn’t always imply overlap.


**Exploring audio processing options**

I’m looking into opening the FLAC file and running minimal audio processing to detect any voice changes. I could compute embeddings on sliding windows using models like `resemblyzer` or `unified_embeddings`. However, that might be resource-intensive. I should inspect the `src/chunk_reassignment.py` file to better understand the algorithm and how the "centroid gate" functions. I’ll focus on the relevant sections around centroid gating and split point validation by using search tools like `sed`.


rg -n "centroid gate|rejected_by_centroid_gate|centroid_gate" -n src/chunk_reassignment.py src/unified_embeddings.py src/clustering.py


sed -n '560,760p' src/chunk_reassignment.py


sed -n '760,980p' src/chunk_reassignment.py


sed -n '980,1220p' src/chunk_reassignment.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/unified_embeddings.py:696:    1. Same-speaker centroid gate: Reject split if both sides still match original speaker
src/unified_embeddings.py:734:        'rejected_by_centroid_gate': 0,  # NEW
src/unified_embeddings.py:799:                    stats['rejected_by_centroid_gate'] += 1
src/unified_embeddings.py:807:                    stats['rejected_by_centroid_gate'] += 1
src/unified_embeddings.py:820:                    stats['rejected_by_centroid_gate'] += 1
src/unified_embeddings.py:827:                    stats['rejected_by_centroid_gate'] += 1


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                    'end': end_time,
                    'duration': duration,
                    'speaker': original_segment.get('speaker', 'UNKNOWN'),
                    'status': 'unusable',
                    'unusable_reason': 'too_short_after_split',
                    'was_split': True,
                    'original_segment_start': original_segment['start']
                })
                continue

            # Extract audio for this portion
            start_sample = int(start_time * sample_rate)
            end_sample = int(end_time * sample_rate)
            portion_audio = audio_buffer[start_sample:end_sample]

            # Extract embedding for portion
            try:
                padded = torch.tensor(portion_audio).unsqueeze(0).to(self.device)
                wav_lens = torch.tensor([1.0]).to(self.device)

                with torch.no_grad():
                    portion_embedding = self.embedding_model.encode_batch(padded, wav_lens).cpu().numpy()
                    if portion_embedding.ndim == 3:
                        portion_embedding = portion_embedding.squeeze()

                del padded, wav_lens

            except Exception as e:
                logger.error(f"Portion embedding extraction failed: {e}")
                portions.append({
                    'start': start_time,
                    'end': end_time,
                    'duration': duration,
                    'speaker': original_segment.get('speaker', 'UNKNOWN'),
                    'status': 'unusable',
                    'unusable_reason': 'embedding_failed',
                    'was_split': True
                })
                continue

            # === MARGIN-BASED REASSIGNMENT (from ChatGPT) ===
            assigned_speaker, confidence, reason = self.reassign_with_margin(
                portion_embedding, speaker_centroids
            )

            if assigned_speaker is None:
                portions.append({
                    'start': start_time,
                    'end': end_time,
                    'duration': duration,
                    'speaker': original_segment.get('speaker', 'UNKNOWN'),
                    'status': 'unusable',
                    'unusable_reason': reason,
                    'was_split': True,
                    'reassignment_confidence': confidence
                })
            else:
                portions.append({
                    'start': start_time,
                    'end': end_time,
                    'duration': duration,
                    'speaker': assigned_speaker,
                    'status': 'usable',
                    'was_split': True,
                    'reassignment_confidence': confidence,
                    'reassignment_reason': reason,
                    'original_speaker': original_segment.get('speaker', 'UNKNOWN')
                })

                # Track for quality metrics
                self._all_embeddings.append(portion_embedding)
                self._speaker_assignments.append((portion_embedding, assigned_speaker))

        return portions

    def process_segment(
        self,
        segment: Dict,
        audio_buffer: np.ndarray,
        sample_rate: int,
        speaker_centroids: Dict[str, np.ndarray]
    ) -> Tuple[List[Dict], ChunkAnalysisResult]:
        """
        Process a single segment through the full algorithm.

        Full pipeline:
        1. Extract chunks with eligibility
        2. Batch extract embeddings
        3. Detect speaker changes
        4. Split and reassign

        Args:
            segment: Segment dict with start/end/duration
            audio_buffer: Full audio waveform
            sample_rate: Sample rate
            speaker_centroids: Dict mapping speaker_id to centroid

        Returns:
            (resulting_segments, analysis_result)
        """
        start_time = time.time()

        # Phase 1: Extract chunks with eligibility
        chunks = self.extract_chunks_with_eligibility(segment, audio_buffer, sample_rate)

        if len(chunks) < 2:
            # Too few chunks to analyze
            return [segment], ChunkAnalysisResult(
                segment_idx=-1,
                segment_duration=segment['duration'],
                num_chunks=len(chunks),
                num_eligible_chunks=sum(1 for c in chunks if c.eligible),
                has_speaker_change=False,
                change_points=[],
                chunk_similarities=[],
                detection_reasons=[],
                time_taken_ms=(time.time() - start_time) * 1000
            )

        # Phase 2: Batch extract embeddings
        embeddings = self.batch_extract_embeddings(chunks, audio_buffer, sample_rate)

        num_eligible = sum(1 for c in chunks if c.eligible)

        if len(embeddings) < 2:
            # Too few eligible chunks
            return [segment], ChunkAnalysisResult(
                segment_idx=-1,
                segment_duration=segment['duration'],
                num_chunks=len(chunks),
                num_eligible_chunks=num_eligible,
                has_speaker_change=False,
                change_points=[],
                chunk_similarities=[],
                detection_reasons=[],
                time_taken_ms=(time.time() - start_time) * 1000
            )

        # Phase 3: Detect speaker changes
        split_points, detection_reasons, all_similarities = self.detect_speaker_changes(
            chunks, embeddings
        )

        analysis_result = ChunkAnalysisResult(
            segment_idx=-1,
            segment_duration=segment['duration'],
            num_chunks=len(chunks),
            num_eligible_chunks=num_eligible,
            has_speaker_change=len(split_points) > 0,
            change_points=split_points,
            chunk_similarities=all_similarities,
            detection_reasons=detection_reasons,
            time_taken_ms=0  # Will be set at end
        )

        if not split_points:
            # No changes detected
            analysis_result.time_taken_ms = (time.time() - start_time) * 1000
            return [segment], analysis_result

        # Phase 4: Split and reassign
        result_segments = self.split_and_reassign(
            segment, split_points, audio_buffer, sample_rate, speaker_centroids
        )

        analysis_result.time_taken_ms = (time.time() - start_time) * 1000

        return result_segments, analysis_result

    def compute_quality_metrics(
        self,
        speaker_centroids: Dict[str, np.ndarray]
    ) -> QualityMetrics:
        """
        Compute quality metrics after processing (from ChatGPT).

        These metrics help verify that chunk reassignment is improving
        speaker cluster purity:

        - within_speaker_variance: Should DECREASE (embeddings more consistent)
        - cross_speaker_separation: Should INCREASE (speakers more distinct)
        - ambiguous_fraction: Lower is better

        Returns:
            QualityMetrics dataclass
        """
        if not self._speaker_assignments:
            return self.quality_metrics

        # Group embeddings by speaker
        speaker_embeddings = {}
        for emb, speaker in self._speaker_assignments:
            if speaker not in speaker_embeddings:
                speaker_embeddings[speaker] = []
            speaker_embeddings[speaker].append(emb)

        # Compute within-speaker variance
        variances = []
        for speaker, embs in speaker_embeddings.items():
            if len(embs) > 1:
                embs_array = np.array(embs)


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                embs_array = np.array(embs)
                variance = np.mean(np.var(embs_array, axis=0))
                variances.append(variance)

        if variances:
            self.quality_metrics.within_speaker_variance = float(np.mean(variances))

        # Compute cross-speaker separation (if we have centroids)
        if speaker_centroids and len(speaker_centroids) > 1:
            centroid_list = list(speaker_centroids.values())
            separations = []
            for i in range(len(centroid_list)):
                for j in range(i + 1, len(centroid_list)):
                    sep = 1 - float(cosine_similarity([centroid_list[i]], [centroid_list[j]])[0, 0])
                    separations.append(sep)

            if separations:
                self.quality_metrics.cross_speaker_separation = float(np.mean(separations))

        return self.quality_metrics

    def get_quality_metrics(self) -> Dict:
        """Return quality metrics as dict for logging."""
        return {
            'within_speaker_variance': self.quality_metrics.within_speaker_variance,
            'cross_speaker_separation': self.quality_metrics.cross_speaker_separation,
            'ambiguous_fraction': self.quality_metrics.ambiguous_fraction,
            'severe_changes_count': self.quality_metrics.severe_changes_count,
            'lookahead_changes_count': self.quality_metrics.lookahead_changes_count
        }

# === CONVENIENCE FUNCTIONS (backward compatible with existing code) ===

def extract_chunk_embeddings_batched(
    audio_chunks: List[np.ndarray],
    sample_rate: int,
    embedding_model,
    device: torch.device,
    batch_size: int = 32
) -> np.ndarray:
    """
    Extract embeddings for all chunks in batched GPU inference.

    Backward-compatible wrapper for existing code.
    """
    if not audio_chunks:
        return np.array([])

    all_embeddings = []

    for batch_start in range(0, len(audio_chunks), batch_size):
        batch_end = min(batch_start + batch_size, len(audio_chunks))
        batch_chunks = audio_chunks[batch_start:batch_end]

        max_len = max(len(c) for c in batch_chunks)

        padded = torch.zeros(len(batch_chunks), max_len)
        wav_lens = torch.zeros(len(batch_chunks))

        for i, chunk in enumerate(batch_chunks):
            padded[i, :len(chunk)] = torch.from_numpy(chunk).float()
            wav_lens[i] = len(chunk) / max_len

        padded = padded.to(device)
        wav_lens = wav_lens.to(device)

        with torch.no_grad():
            embs = embedding_model.encode_batch(padded, wav_lens).cpu().numpy()
            if embs.ndim == 3 and embs.shape[1] == 1:
                embs = embs.squeeze(1)
            elif embs.ndim == 3:
                embs = embs.reshape(embs.shape[0], -1)

        all_embeddings.append(embs)
        del padded, wav_lens

    return np.vstack(all_embeddings) if all_embeddings else np.array([])

def validate_reassignment_impact(
    segments: List[Dict],
    audio_buffer,
    embedding_model,
    device: torch.device,
    config: Optional[ChunkReassignmentConfig] = None,
    vad_model=None,
    vad_utils=None
) -> Tuple[ReassignmentStats, List[ChunkAnalysisResult]]:
    """
    VALIDATION MODE: Analyze segments without making changes.

    Uses the Precision Surgical Splitter to detect speaker changes
    and report statistics on how many segments would be affected.

    Args:
        segments: List of segment dicts from diarization
        audio_buffer: Pre-loaded AudioBuffer
        embedding_model: ECAPA-TDNN model
        device: torch device
        config: Optional ChunkReassignmentConfig (uses defaults if None)
        vad_model: Optional Silero VAD model for speech ratio
        vad_utils: Optional Silero VAD utilities

    Returns:
        (stats, detailed_results): Overall stats and per-segment analysis
    """
    config = config or ChunkReassignmentConfig()

    logger.info("=" * 70)
    logger.info("🔍 PRECISION SURGICAL SPLITTER - VALIDATION MODE")
    logger.info(f"   Hybrid approach: ChatGPT + Maya + Stabilized")
    logger.info(f"   Normal threshold: {config.normal_threshold} (look-ahead)")
    logger.info(f"   Severe threshold: {config.severe_threshold} (circuit-breaker)")
    logger.info(f"   Min speech ratio: {config.min_speech_ratio} (VAD eligibility)")
    logger.info(f"   Min split portion: {config.min_split_portion_seconds}s")
    logger.info("=" * 70)

    start_time = time.time()

    # Initialize splitter
    splitter = PrecisionSurgicalSplitter(
        config=config,
        embedding_model=embedding_model,
        vad_model=vad_model,
        vad_utils=vad_utils,
        device=device
    )

    # Get audio data
    waveform_np = audio_buffer.waveform_np
    sr = audio_buffer.sample_rate

    # Filter to analyzable segments
    analyzable = [
        (i, seg) for i, seg in enumerate(segments)
        if seg.get('speaker') not in ['OVERLAP', 'NON_SPEECH']
        and seg['duration'] >= config.min_segment_for_chunking
    ]

    logger.info(f"   Segments to analyze: {len(analyzable)}/{len(segments)} (duration >= {config.min_segment_for_chunking}s)")

    results = []
    total_chunks = 0
    total_eligible = 0
    segments_with_changes = 0
    total_split_points = 0

    for idx, (seg_idx, segment) in enumerate(analyzable):
        # Use empty centroids for validation (we just want to detect changes)
        _, result = splitter.process_segment(
            segment, waveform_np, sr, speaker_centroids={}
        )
        result.segment_idx = seg_idx
        results.append(result)

        total_chunks += result.num_chunks
        total_eligible += result.num_eligible_chunks

        if result.has_speaker_change:
            segments_with_changes += 1
            total_split_points += len(result.change_points)

        # Progress logging
        if (idx + 1) % 50 == 0 or idx == len(analyzable) - 1:
            logger.info(f"   Progress: {idx + 1}/{len(analyzable)} segments analyzed")

    total_time = time.time() - start_time
    avg_time_per_seg = (total_time * 1000 / len(analyzable)) if analyzable else 0
    affected_pct = (segments_with_changes / len(analyzable) * 100) if analyzable else 0

    stats = ReassignmentStats(
        total_segments=len(segments),
        segments_analyzed=len(analyzable),
        segments_with_changes=segments_with_changes,
        total_chunks_processed=total_chunks,
        total_eligible_chunks=total_eligible,
        total_split_points=total_split_points,
        new_speakers_created=0,  # Not applicable in validation mode
        portions_marked_unusable=0,
        total_time_sec=total_time,
        avg_time_per_segment_ms=avg_time_per_seg,
        segments_affected_pct=affected_pct
    )

    # Print summary
    logger.info("=" * 70)
    logger.info("📊 VALIDATION RESULTS (Precision Surgical Splitter)")
    logger.info("=" * 70)
    logger.info(f"   Total segments: {stats.total_segments}")
    logger.info(f"   Segments analyzed (>{config.min_segment_for_chunking}s): {stats.segments_analyzed}")
    logger.info(f"   Total chunks: {stats.total_chunks_processed}")
    logger.info(f"   Eligible chunks (≥{config.min_speech_ratio} speech): {stats.total_eligible_chunks}")
    logger.info(f"   Segments with detected changes: {stats.segments_with_changes} ({stats.segments_affected_pct:.1f}%)")
    logger.info(f"   Total split points: {stats.total_split_points}")
    logger.info(f"   Severe (circuit-breaker): {splitter.quality_metrics.severe_changes_count}")
    logger.info(f"   Look-ahead confirmed: {splitter.quality_metrics.lookahead_changes_count}")
    logger.info(f"   Total time: {stats.total_time_sec:.2f}s")
    logger.info(f"   Avg time per segment: {stats.avg_time_per_segment_ms:.1f}ms")
    logger.info("=" * 70)

    # Detailed breakdown of affected segments
    if segments_with_changes > 0 and segments_with_changes <= 20:
        logger.info("\n🔴 SEGMENTS WITH DETECTED SPEAKER CHANGES:")
        for result in results:
            if result.has_speaker_change:
                seg = segments[result.segment_idx]
                logger.info(f"   Segment {result.segment_idx}: {seg['start']:.1f}-{seg['end']:.1f}s "
                           f"({result.segment_duration:.1f}s) speaker={seg.get('speaker', '?')}")
                logger.info(f"      Chunks: {result.num_chunks} (eligible: {result.num_eligible_chunks})")
                logger.info(f"      Change points: {result.change_points}")
                logger.info(f"      Reasons: {result.detection_reasons}")
                sims = [f"{s[2]:.2f}" for s in result.chunk_similarities[:5]]
                logger.info(f"      Similarities (first 5): {sims}")

    return stats, results

def execute_chunk_reassignment(
    segments: List[Dict],
    audio_buffer,


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    audio_buffer,
    embedding_model,
    speaker_centroids: Dict[str, np.ndarray],
    device: torch.device,
    config: Optional[ChunkReassignmentConfig] = None,
    vad_model=None,
    vad_utils=None
) -> Tuple[List[Dict], ReassignmentStats]:
    """
    EXECUTE MODE: Actually perform chunk-based reassignment.

    Uses the Precision Surgical Splitter to:
    1. Detect speaker changes within segments
    2. Split segments at change points
    3. Reassign each resulting portion to best matching speaker

    Args:
        segments: Original segment list
        audio_buffer: Pre-loaded AudioBuffer
        embedding_model: ECAPA-TDNN model
        speaker_centroids: Dict mapping speaker_id -> centroid embedding
        device: torch device
        config: Optional ChunkReassignmentConfig
        vad_model: Optional Silero VAD model
        vad_utils: Optional Silero VAD utilities

    Returns:
        (refined_segments, stats): New segment list with splits applied, and stats
    """
    config = config or ChunkReassignmentConfig()

    logger.info("=" * 70)
    logger.info("⚡ PRECISION SURGICAL SPLITTER - EXECUTE MODE")
    logger.info("=" * 70)

    start_time = time.time()

    # Initialize splitter
    splitter = PrecisionSurgicalSplitter(
        config=config,
        embedding_model=embedding_model,
        vad_model=vad_model,
        vad_utils=vad_utils,
        device=device
    )

    # Get audio data
    waveform_np = audio_buffer.waveform_np
    sr = audio_buffer.sample_rate

    refined_segments = []
    segments_analyzed = 0
    segments_with_changes = 0
    total_chunks = 0
    total_eligible = 0
    total_splits = 0
    new_speakers = 0
    unusable_portions = 0

    for seg_idx, segment in enumerate(segments):
        # Pass through segments that shouldn't be analyzed
        if segment.get('speaker') in ['OVERLAP', 'NON_SPEECH']:
            refined_segments.append(segment)
            continue

        if segment['duration'] < config.min_segment_for_chunking:
            refined_segments.append(segment)
            continue

        segments_analyzed += 1

        # Process segment
        result_segs, analysis = splitter.process_segment(
            segment, waveform_np, sr, speaker_centroids
        )

        total_chunks += analysis.num_chunks
        total_eligible += analysis.num_eligible_chunks

        if analysis.has_speaker_change:
            segments_with_changes += 1
            total_splits += len(analysis.change_points)

        # Count new speakers and unusable portions
        for seg in result_segs:
            if seg.get('speaker', '').startswith('SPEAKER_NEW_'):
                new_speakers += 1
            if seg.get('status') == 'unusable':
                unusable_portions += 1

        refined_segments.extend(result_segs)

    # Sort by start time
    refined_segments.sort(key=lambda x: x['start'])

    # Compute quality metrics
    quality_metrics = splitter.compute_quality_metrics(speaker_centroids)

    total_time = time.time() - start_time
    avg_time = (total_time * 1000 / segments_analyzed) if segments_analyzed > 0 else 0
    affected_pct = (segments_with_changes / segments_analyzed * 100) if segments_analyzed > 0 else 0

    stats = ReassignmentStats(
        total_segments=len(segments),
        segments_analyzed=segments_analyzed,
        segments_with_changes=segments_with_changes,
        total_chunks_processed=total_chunks,
        total_eligible_chunks=total_eligible,
        total_split_points=total_splits,
        new_speakers_created=new_speakers,
        portions_marked_unusable=unusable_portions,
        total_time_sec=total_time,
        avg_time_per_segment_ms=avg_time,
        segments_affected_pct=affected_pct
    )

    logger.info(f"✅ Reassignment complete:")
    logger.info(f"   Original segments: {len(segments)}")
    logger.info(f"   Refined segments: {len(refined_segments)}")
    logger.info(f"   Segments split: {segments_with_changes}")
    logger.info(f"   New speakers created: {new_speakers}")
    logger.info(f"   Portions marked unusable: {unusable_portions}")
    logger.info(f"   Quality metrics: {splitter.get_quality_metrics()}")
    logger.info(f"   Total time: {total_time:.2f}s")

    return refined_segments, stats

# === STANDALONE VALIDATION SCRIPT ===
if __name__ == "__main__":
    """
    Run standalone validation on existing processed data.

    Usage:
        python -m src.chunk_reassignment VIDEO_ID [--threshold 0.40]
    """
    import sys
    import json
    import argparse
    from pathlib import Path

    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s | %(levelname)-8s | %(message)s',
        datefmt='%H:%M:%S'
    )

    parser = argparse.ArgumentParser(description="Validate chunk reassignment impact")
    parser.add_argument("video_id", help="Video ID to analyze")
    parser.add_argument("--threshold", type=float, default=0.40,
                       help="Normal threshold (default: 0.40)")
    parser.add_argument("--severe", type=float, default=0.25,
                       help="Severe threshold (default: 0.25)")
    parser.add_argument("--min-speech", type=float, default=0.6,
                       help="Minimum speech ratio (default: 0.6)")
    args = parser.parse_args()

    video_id = args.video_id
    data_dir = Path("data/fast_output_v6") / video_id

    if not data_dir.exists():
        print(f"Error: Data directory not found: {data_dir}")
        sys.exit(1)

    # Load metadata
    metadata_file = data_dir / "metadata.json"
    if not metadata_file.exists():
        print(f"Error: metadata.json not found in {data_dir}")
        sys.exit(1)

    with open(metadata_file) as f:
        metadata = json.load(f)

    segments = metadata['segments']

    # Find audio file
    audio_file = data_dir / f"{video_id}_trimmed.wav"
    if not audio_file.exists():
        audio_file = data_dir / f"{video_id}.wav"

    if not audio_file.exists():
        print(f"Error: Audio file not found in {data_dir}")
        sys.exit(1)

    print(f"\n📁 Loaded: {video_id}")
    print(f"   Segments: {len(segments)}")
    print(f"   Audio: {audio_file}")

    # Load models and audio buffer
    from src.audio_buffer import AudioBuffer
    from src.models import MODELS
    from src.config import Config

    config_obj = Config()
    MODELS.load_all(config_obj)

    audio_buffer = AudioBuffer.from_file(str(audio_file))
    device = MODELS.get_device()

    # Configure splitter
    splitter_config = ChunkReassignmentConfig(
        normal_threshold=args.threshold,
        severe_threshold=args.severe,
        min_speech_ratio=args.min_speech
    )

    # Run validation
    stats, results = validate_reassignment_impact(
        segments=segments,
        audio_buffer=audio_buffer,
        embedding_model=MODELS.embedding_model,
        device=device,
        config=splitter_config,
        vad_model=MODELS.silero_vad,
        vad_utils=MODELS.silero_utils
    )

    print(f"\n📊 Summary:")
    print(f"   Would affect: {stats.segments_with_changes}/{stats.segments_analyzed} segments ({stats.segments_affected_pct:.1f}%)")
    print(f"   Time cost: {stats.total_time_sec:.2f}s for this video")


Summary: auto


rg -n "execute_chunk_reassignment|validate_reassignment_impact|ChunkReassignment" pipeline.py src/unified_embeddings.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/unified_embeddings.py:10:- Stage 6b (ChunkReassignment): Load SAME model → extract embeddings for 1.5s chunks → extract AGAIN for split portions
src/unified_embeddings.py:674:def execute_chunk_reassignment_cached(
pipeline.py:26:- Eliminates "Double Compute" bottleneck (was: Embeddings + ChunkReassignment both used GPU)
pipeline.py:91:    ChunkReassignmentConfig,
pipeline.py:92:    execute_chunk_reassignment
pipeline.py:100:    execute_chunk_reassignment_cached
pipeline.py:451:        with COMPUTE.monitor_stage("ChunkReassignment") as metrics:
pipeline.py:470:            segments, reassignment_stats = execute_chunk_reassignment_cached(


sed -n '620,920p' src/unified_embeddings.py


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 301
Output:
            'gpu_batch_count': self._stats.gpu_batch_count,
            'avg_batch_size': round(self._stats.avg_batch_size, 1),
        }

    @staticmethod
    def _cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
        """Compute cosine similarity between two vectors."""
        norm_a = np.linalg.norm(a)
        norm_b = np.linalg.norm(b)
        if norm_a == 0 or norm_b == 0:
            return 0.0
        return float(np.dot(a, b) / (norm_a * norm_b))

# =============================================================================
# INTEGRATION HELPERS
# =============================================================================

def build_segment_embeddings_from_cache(
    segments: List[Dict],
    cache: UnifiedChunkEmbeddingCache
) -> Dict[int, np.ndarray]:
    """
    Build segment embeddings dict from cache (drop-in replacement for extract_embeddings_batched).

    This is INSTANT (CPU only) - no GPU call!

    Args:
        segments: List of segment dicts with 'start' and 'end'
        cache: Built UnifiedChunkEmbeddingCache

    Returns:
        Dict mapping segment index to embedding (same format as extract_embeddings_batched)
    """
    embeddings = {}
    skipped = 0

    for i, seg in enumerate(segments):
        # Skip overlap/non-speech
        if seg.get('speaker') in ['OVERLAP', 'NON_SPEECH']:
            continue

        emb = cache.get_segment_embedding(seg['start'], seg['end'])
        if emb is not None:
            embeddings[i] = emb
        else:
            skipped += 1

    if skipped > 0:
        logger.debug(f"   Skipped {skipped} segments (no chunks in range)")

    return embeddings

def execute_chunk_reassignment_cached(
    segments: List[Dict],
    cache: UnifiedChunkEmbeddingCache,
    speaker_centroids: Dict[str, np.ndarray],
    min_segment_for_analysis: float = 3.0,
    min_split_portion: float = 1.5,
    assign_min_similarity: float = 0.55,
    margin_min: float = 0.10,
    create_new_on_ambiguous: bool = False,  # CHANGED: Default False to prevent speaker explosion
    # === v7.8: Tunable gate parameters for catching small interruptions ===
    persistence_threshold: int = 2,  # Min consecutive low-sim chunks (1 = more aggressive, 2 = conservative)
    centroid_match_threshold: float = 0.60,  # Both portions must be < this to split (lower = more aggressive)
) -> Tuple[List[Dict], Dict[str, Any]]:
    """
    Execute chunk reassignment with VALIDATION GATES using the unified cache.

    === v7.7: VALIDATION GATES TO PREVENT FALSE POSITIVE SPLITS ===

    The old implementation caused 66x speaker fragmentation (6 speakers → 400+)
    because it created new speakers on ANY ambiguous assignment.

    NEW VALIDATION GATES:
    1. Same-speaker centroid gate: Reject split if both sides still match original speaker
    2. No-new-speaker-unless-proven: Keep original speaker on ambiguous (default)
    3. Minimum portion persistence: Don't create 1.5s fragments
    4. Energy boundary gate: Prefer splits near low-energy boundaries

    === v7.8: Tunable gate parameters ===
    - persistence_threshold: Min consecutive low-sim chunks needed (1=aggressive, 2=conservative)
    - centroid_match_threshold: Max similarity to original speaker for both portions (lower=aggressive)

    Args:
        segments: Original segments from diarization
        cache: Built UnifiedChunkEmbeddingCache
        speaker_centroids: Speaker ID -> centroid embedding
        min_segment_for_analysis: Only analyze segments >= this duration
        min_split_portion: Minimum portion duration after split
        assign_min_similarity: Minimum similarity for assignment
        margin_min: Required margin between best and second-best speaker
        create_new_on_ambiguous: Create new speaker ID on ambiguous (DEFAULT: FALSE!)
        persistence_threshold: Min consecutive low-sim chunks (v7.8: configurable)
        centroid_match_threshold: Centroid gate threshold (v7.8: configurable)

    Returns:
        (refined_segments, stats_dict)
    """
    start_time = time.time()
    logger.debug(f"Chunk Reassignment v7.8 (persist={persistence_threshold}, centroid={centroid_match_threshold})")

    # Count eligible segments upfront
    eligible_count = sum(1 for seg in segments
                        if seg.get('speaker') not in ['OVERLAP', 'NON_SPEECH']
                        and seg['duration'] >= min_segment_for_analysis)

    refined_segments = []
    stats = {
        'segments_analyzed': 0,
        'segments_with_changes': 0,
        'total_split_points': 0,
        'validated_split_points': 0,  # NEW: After validation gates
        'rejected_by_centroid_gate': 0,  # NEW
        'new_speakers_created': 0,
        'portions_unusable': 0,
        'severe_triggers': 0,
        'lookahead_triggers': 0,
        'kept_original_speaker': 0,  # NEW: Ambiguous assignments kept as original
    }

    new_speaker_counter = len(speaker_centroids)

    for…3787 chars truncated…est_sim, assign_reason = _reassign_with_margin(
                    post_emb, speaker_centroids, assign_min_similarity, margin_min
                )

                if post_assigned is None:
                    # Can't confidently assign to any speaker - likely not a real change
                    stats['rejected_by_centroid_gate'] += 1
                    logger.debug(f"   Centroid gate B3 rejected at {split_time:.2f}s: "
                               f"post-portion has no confident assignment ({assign_reason})")
                    continue

                if post_assigned == original_speaker:
                    # Post-portion still best matches original speaker
                    stats['rejected_by_centroid_gate'] += 1
                    logger.debug(f"   Centroid gate B3 rejected at {split_time:.2f}s: "
                               f"post still matches original (sim={post_best_sim:.3f})")
                    continue

                # Valid split: post clearly belongs to a different speaker
                logger.debug(f"   Split VALIDATED at {split_time:.2f}s: "
                           f"pre→{original_speaker}({pre_sim_orig:.2f}), "
                           f"post→{post_assigned}({post_best_sim:.2f})")

            validated_splits.append(split_time)

        if not validated_splits:
            refined_segments.append(seg)
            continue

        # Segment has VALIDATED speaker changes - split it
        stats['segments_with_changes'] += 1
        stats['validated_split_points'] += len(validated_splits)

        # === v7.7: ENERGY BOUNDARY SNAPPING ===
        # Snap each split point to local energy minimum to avoid mid-word cuts
        snapped_splits = []
        for split_time in sorted(validated_splits):
            snapped_time = _snap_to_energy_minimum(
                cache.audio_buffer, split_time,
                search_window_ms=200.0,  # ±200ms search
                frame_size_ms=10.0
            )
            # Ensure snapped time stays within segment bounds (with small margin)
            snapped_time = max(seg['start'] + 0.1, min(snapped_time, seg['end'] - 0.1))
            snapped_splits.append(snapped_time)

        # Create portions from snapped split points
        all_times = [seg['start']] + sorted(snapped_splits) + [seg['end']]

        for i in range(len(all_times) - 1):
            portion_start = all_times[i]
            portion_end = all_times[i + 1]
            duration = portion_end - portion_start

            # === MINIMUM PORTION GATE: Filter out too-short portions ===
            if duration < min_split_portion:
                # CHANGED: Mark as unusable instead of creating tiny fragments
                refined_segments.append({
                    'start': portion_start,
                    'end': portion_end,
                    'duration': duration,
                    'speaker': original_speaker,  # Keep original, don't create new
                    'status': 'unusable',
                    'unusable_reason': 'too_short_after_split',
                    'was_split': True,
                })
                stats['portions_unusable'] += 1
                continue

            # Get portion embedding from cache (CPU only!)
            portion_emb = cache.get_segment_embedding(portion_start, portion_end)

            if portion_emb is None:
                # No cached chunks for this portion - keep original speaker
                refined_segments.append({
                    'start': portion_start,
                    'end': portion_end,
                    'duration': duration,
                    'speaker': original_speaker,
                    'status': 'usable',
                    'was_split': True,
                    'note': 'no_cached_embedding'
                })
                continue

            # Reassign with margin-based confidence
            assigned, confidence, reason = _reassign_with_margin(
                portion_emb,
                speaker_centroids,
                assign_min_similarity,
                margin_min
            )

            if assigned is None:
                # === NO-NEW-SPEAKER-UNLESS-PROVEN RULE ===
                # This is the KEY fix that prevents speaker explosion
                if create_new_on_ambiguous:
                    assigned = f"SPEAKER_NEW_{new_speaker_counter:02d}"
                    new_speaker_counter += 1
                    stats['new_speakers_created'] += 1
                else:
                    # CHANGED DEFAULT: Keep original speaker on ambiguous assignment
                    # This prevents 6 speakers → 400+ fragmentation
                    refined_segments.append({
                        'start': portion_start,
                        'end': portion_end,
                        'duration': duration,


I need to check the `execute_chunk_reassignment_cached` output because it seems truncated. I’ll focus on the earlier part, especially around the split detection and centroid gate. Opening the file from around 920 onward should help me gather the context I need. I wonder if this will make the issue clearer or if there's more to uncover. Let’s see what insights I can find by reviewing that content!


python3 - <<'PY'
from pathlib import Path
import itertools

p=Path('src/unified_embeddings.py').read_text().splitlines()
start=660
end=880
for i,line in enumerate(p[start-1:end], start):
    print(f"{i:04d} {line}")
PY


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 221
Output:
0660             continue
0661
0662         emb = cache.get_segment_embedding(seg['start'], seg['end'])
0663         if emb is not None:
0664             embeddings[i] = emb
0665         else:
0666             skipped += 1
0667
0668     if skipped > 0:
0669         logger.debug(f"   Skipped {skipped} segments (no chunks in range)")
0670
0671     return embeddings
0672
0673
0674 def execute_chunk_reassignment_cached(
0675     segments: List[Dict],
0676     cache: UnifiedChunkEmbeddingCache,
0677     speaker_centroids: Dict[str, np.ndarray],
0678     min_segment_for_analysis: float = 3.0,
0679     min_split_portion: float = 1.5,
0680     assign_min_similarity: float = 0.55,
0681     margin_min: float = 0.10,
0682     create_new_on_ambiguous: bool = False,  # CHANGED: Default False to prevent speaker explosion
0683     # === v7.8: Tunable gate parameters for catching small interruptions ===
0684     persistence_threshold: int = 2,  # Min consecutive low-sim chunks (1 = more aggressive, 2 = conservative)
0685     centroid_match_threshold: float = 0.60,  # Both portions must be < this to split (lower = more aggressive)
0686 ) -> Tuple[List[Dict], Dict[str, Any]]:
0687     """
0688     Execute chunk reassignment with VALIDATION GATES using the unified cache.
0689
0690     === v7.7: VALIDATION GATES TO PREVENT FALSE POSITIVE SPLITS ===
0691
0692     The old implementation caused 66x speaker fragmentation (6 speakers → 400+)
0693     because it created new speakers on ANY ambiguous assignment.
0694
0695     NEW VALIDATION GATES:
0696     1. Same-speaker centroid gate: Reject split if both sides still match original speaker
0697     2. No-new-speaker-unless-proven: Keep original speaker on ambiguous (default)
0698     3. Minimum portion persistence: Don't create 1.5s fragments
0699     4. Energy boundary gate: Prefer splits near low-energy boundaries
0700
0701     === v7.8: Tunable gate parameters ===
0702     - persistence_threshold: Min consecutive low-sim chunks needed (1=aggressive, 2=conservative)
0703     - centroid_match_threshold: Max similarity to original speaker for both portions (lower=aggressive)
0704
0705     Args:
0706         segments: Original segments from diarization
0707         cache: Built UnifiedChunkEmbeddingCache
0708         speaker_centroids: Speaker ID -> centroid embedding
0709         min_segment_for_analysis: Only analyze segments >= this duration
0710         min_split_portion: Minimum portion duration after split
0711         assign_min_similarity: Minimum similarity for assignment
0712         margin_min: Required margin between best and second-best speaker
0713         create_new_on_ambiguous: Create new speaker ID on ambiguous (DEFAULT: FALSE!)
0714         persistence_threshold: Min consecutive low-sim chunks (v7.8: configurable)
0715         centroid_match_threshold: Centroid gate threshold (v7.8: configurable)
0716
0717     Returns:
0718         (refined_segments, stats_dict)
0719     """
0720     start_time = time.time()
0721     logger.debug(f"Chunk Reassignment v7.8 (persist={persistence_threshold}, centroid={centroid_match_threshold})")
0722
0723     # Count eligible segments upfront
0724     eligible_count = sum(1 for seg in segments
0725                         if seg.get('speaker') not in ['OVERLAP', 'NON_SPEECH']
0726                         and seg['duration'] >= min_segment_for_analysis)
0727
0728     refined_segments = []
0729     stats = {
0730         'segments_analyzed': 0,
0731         'segments_with_changes': 0,
0732         'total_split_points': 0,
0733         'validated_split_points': 0,  # NEW: After validation gates
0734         'rejected_by_centroid_gate': 0,  # NEW
0735         'new_speakers_created': 0,
0736         'portions_unusable': 0,
0737         'severe_triggers': 0,
0738         'lookahead_triggers': 0,
0739         'kept_original_speaker': 0,  # NEW: Ambiguous assignments kept as original
0740     }
0741
0742     new_speaker_counter = len(speaker_centroids)
0743
0744     for seg in segments:
0745         # Pass through non-analyzable segments
0746         if seg.get('speaker') in ['OVERLAP', 'NON_SPEECH']:
0747             refined_segments.append(seg)
0748             continue
0749
0750         if seg['duration'] < min_segment_for_analysis:
0751             refined_segments.append(seg)
0752             continue
0753
0754         stats['segments_analyzed'] += 1
0755         original_speaker = seg.get('speaker', 'UNKNOWN')
0756         original_centroid = speaker_centroids.get(original_speaker)
0757
0758         # Detect speaker changes using CACHED embeddings (CPU only!)
0759         # Now with persistence and relative valley gates
0760         # v7.8: Pass configurable persistence threshold
0761         split_points, reasons, severe, lookahead = cache.detect_speaker_changes(
0762             seg['start'], seg['end'],
0763             require_persistence=persistence_threshold…1800 chars truncated…real speaker change (natural embedding variation)
0797                 # v7.8: Use configurable threshold (0.55=aggressive, 0.60=conservative)
0798                 if pre_sim_orig > centroid_match_threshold and post_sim_orig > centroid_match_threshold:
0799                     stats['rejected_by_centroid_gate'] += 1
0800                     logger.debug(f"   Centroid gate B1 rejected at {split_time:.2f}s: "
0801                                f"both match original (pre={pre_sim_orig:.3f}, post={post_sim_orig:.3f})")
0802                     continue
0803
0804                 # GATE B2: Post-portion must be LESS similar to original (directional)
0805                 # Pre-portion should stay close to original
0806                 if post_sim_orig >= pre_sim_orig - 0.05:  # Post not clearly different
0807                     stats['rejected_by_centroid_gate'] += 1
0808                     logger.debug(f"   Centroid gate B2 rejected at {split_time:.2f}s: "
0809                                f"post not clearly different (pre={pre_sim_orig:.3f}, post={post_sim_orig:.3f})")
0810                     continue
0811
0812                 # GATE B3: Post-portion must match a SPECIFIC DIFFERENT speaker with margin
0813                 # Not just "doesn't match original" - must clearly belong somewhere else
0814                 post_assigned, post_best_sim, assign_reason = _reassign_with_margin(
0815                     post_emb, speaker_centroids, assign_min_similarity, margin_min
0816                 )
0817
0818                 if post_assigned is None:
0819                     # Can't confidently assign to any speaker - likely not a real change
0820                     stats['rejected_by_centroid_gate'] += 1
0821                     logger.debug(f"   Centroid gate B3 rejected at {split_time:.2f}s: "
0822                                f"post-portion has no confident assignment ({assign_reason})")
0823                     continue
0824
0825                 if post_assigned == original_speaker:
0826                     # Post-portion still best matches original speaker
0827                     stats['rejected_by_centroid_gate'] += 1
0828                     logger.debug(f"   Centroid gate B3 rejected at {split_time:.2f}s: "
0829                                f"post still matches original (sim={post_best_sim:.3f})")
0830                     continue
0831
0832                 # Valid split: post clearly belongs to a different speaker
0833                 logger.debug(f"   Split VALIDATED at {split_time:.2f}s: "
0834                            f"pre→{original_speaker}({pre_sim_orig:.2f}), "
0835                            f"post→{post_assigned}({post_best_sim:.2f})")
0836
0837             validated_splits.append(split_time)
0838
0839         if not validated_splits:
0840             refined_segments.append(seg)
0841             continue
0842
0843         # Segment has VALIDATED speaker changes - split it
0844         stats['segments_with_changes'] += 1
0845         stats['validated_split_points'] += len(validated_splits)
0846
0847         # === v7.7: ENERGY BOUNDARY SNAPPING ===
0848         # Snap each split point to local energy minimum to avoid mid-word cuts
0849         snapped_splits = []
0850         for split_time in sorted(validated_splits):
0851             snapped_time = _snap_to_energy_minimum(
0852                 cache.audio_buffer, split_time,
0853                 search_window_ms=200.0,  # ±200ms search
0854                 frame_size_ms=10.0
0855             )
0856             # Ensure snapped time stays within segment bounds (with small margin)
0857             snapped_time = max(seg['start'] + 0.1, min(snapped_time, seg['end'] - 0.1))
0858             snapped_splits.append(snapped_time)
0859
0860         # Create portions from snapped split points
0861         all_times = [seg['start']] + sorted(snapped_splits) + [seg['end']]
0862
0863         for i in range(len(all_times) - 1):
0864             portion_start = all_times[i]
0865             portion_end = all_times[i + 1]
0866             duration = portion_end - portion_start
0867
0868             # === MINIMUM PORTION GATE: Filter out too-short portions ===
0869             if duration < min_split_portion:
0870                 # CHANGED: Mark as unusable instead of creating tiny fragments
0871                 refined_segments.append({
0872                     'start': portion_start,
0873                     'end': portion_end,
0874                     'duration': duration,
0875                     'speaker': original_speaker,  # Keep original, don't create new
0876                     'status': 'unusable',
0877                     'unusable_reason': 'too_short_after_split',
0878                     'was_split': True,
0879                 })
0880                 stats['portions_unusable'] += 1


python3 - <<'PY'
from pathlib import Path
lines=Path('src/unified_embeddings.py').read_text().splitlines()
for i in range(754, 842):
    print(f"{i+1:04d} {lines[i]}")
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
0755         original_speaker = seg.get('speaker', 'UNKNOWN')
0756         original_centroid = speaker_centroids.get(original_speaker)
0757
0758         # Detect speaker changes using CACHED embeddings (CPU only!)
0759         # Now with persistence and relative valley gates
0760         # v7.8: Pass configurable persistence threshold
0761         split_points, reasons, severe, lookahead = cache.detect_speaker_changes(
0762             seg['start'], seg['end'],
0763             require_persistence=persistence_threshold > 0,  # GATE: Enable persistence check
0764             use_relative_valley=True,  # GATE: Use per-segment adaptive threshold
0765             persistence_count=persistence_threshold,  # v7.8: Configurable (1=aggressive, 2=conservative)
0766         )
0767
0768         stats['severe_triggers'] += severe
0769         stats['lookahead_triggers'] += lookahead
0770         stats['total_split_points'] += len(split_points)
0771
0772         if not split_points:
0773             refined_segments.append(seg)
0774             continue
0775
0776         # === SAME-SPEAKER CENTROID GATE ===
0777         # Validate each split point: reject if BOTH portions still match original speaker
0778         validated_splits = []
0779         for split_time in split_points:
0780             pre_emb = cache.get_segment_embedding(seg['start'], split_time)
0781             post_emb = cache.get_segment_embedding(split_time, seg['end'])
0782
0783             if pre_emb is None or post_emb is None:
0784                 continue  # Can't validate, skip this split
0785
0786             # === GATE B: DIRECTIONAL CENTROID SEPARATION ===
0787             # A valid split requires:
0788             #   - Pre-portion looks like original speaker
0789             #   - Post-portion does NOT look like original speaker
0790             #   - Post-portion looks like SOME SPECIFIC OTHER speaker with margin
0791             if original_centroid is not None:
0792                 pre_sim_orig = _cosine_sim(pre_emb, original_centroid)
0793                 post_sim_orig = _cosine_sim(post_emb, original_centroid)
0794
0795                 # GATE B1: If BOTH portions still match original speaker strongly,
0796                 # this is likely NOT a real speaker change (natural embedding variation)
0797                 # v7.8: Use configurable threshold (0.55=aggressive, 0.60=conservative)
0798                 if pre_sim_orig > centroid_match_threshold and post_sim_orig > centroid_match_threshold:
0799                     stats['rejected_by_centroid_gate'] += 1
0800                     logger.debug(f"   Centroid gate B1 rejected at {split_time:.2f}s: "
0801                                f"both match original (pre={pre_sim_orig:.3f}, post={post_sim_orig:.3f})")
0802                     continue
0803
0804                 # GATE B2: Post-portion must be LESS similar to original (directional)
0805                 # Pre-portion should stay close to original
0806                 if post_sim_orig >= pre_sim_orig - 0.05:  # Post not clearly different
0807                     stats['rejected_by_centroid_gate'] += 1
0808                     logger.debug(f"   Centroid gate B2 rejected at {split_time:.2f}s: "
0809                                f"post not clearly different (pre={pre_sim_orig:.3f}, post={post_sim_orig:.3f})")
0810                     continue
0811
0812                 # GATE B3: Post-portion must match a SPECIFIC DIFFERENT speaker with margin
0813                 # Not just "doesn't match original" - must clearly belong somewhere else
0814                 post_assigned, post_best_sim, assign_reason = _reassign_with_margin(
0815                     post_emb, speaker_centroids, assign_min_similarity, margin_min
0816                 )
0817
0818                 if post_assigned is None:
0819                     # Can't confidently assign to any speaker - likely not a real change
0820                     stats['rejected_by_centroid_gate'] += 1
0821                     logger.debug(f"   Centroid gate B3 rejected at {split_time:.2f}s: "
0822                                f"post-portion has no confident assignment ({assign_reason})")
0823                     continue
0824
0825                 if post_assigned == original_speaker:
0826                     # Post-portion still best matches original speaker
0827                     stats['rejected_by_centroid_gate'] += 1
0828                     logger.debug(f"   Centroid gate B3 rejected at {split_time:.2f}s: "
0829                                f"post still matches original (sim={post_best_sim:.3f})")
0830                     continue
0831
0832                 # Valid split: post clearly belongs to a different speaker
0833                 logger.debug(f"   Split VALIDATED at {split_time:.2f}s: "
0834                            f"pre→{original_speaker}({pre_sim_orig:.2f}), "
0835                            f"post→{post_assigned}({post_best_sim:.2f})")
0836
0837             validated_splits.append(split_time)
0838
0839         if not validated_splits:
0840             refined_segments.append(seg)
0841             continue
0842


**Exploring audio analysis options**

I’m considering whether we can approximate using speaker centroids from metadata, but it looks like that might not be present. I could compute centroids by analyzing the audio segments and creating embeddings, but I need to check if the embedding model (ECAPA) is loaded. I’ll take a look at `src/models.py` and see if it uses SpeechBrain. Running a small script might help, possibly involving speaker embedding and VAD, which may require Torch. Let's check that!


python3 - <<'PY'
import torch
print('torch', torch.__version__, 'cuda', torch.cuda.is_available())
PY


Output:
Exit code: 0
Wall time: 1.7 seconds
Output:
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
torch 2.9.0+cu128 cuda True


**Checking audio properties**

I'm not entirely sure about the specifics yet, but I think we can check if the audio file has two speakers by using diarization tools like `pyannote.audio` or `speechbrain`. It might also be helpful to run `ffprobe` to check the audio file's properties such as duration. I guess we could use `sox` for further processing too. But I'll start by checking the audio file properties with `ffprobe` first.


ffprobe -hide_banner -loglevel error -show_streams -show_format tmp/r2debug/extracted/--jp1imJD9w/segments/SPEAKER_00_0021_199.00-204.34.flac | rg -n "duration=|sample_rate=|channels=|codec_name="


Output:
Exit code: 0
Wall time: 0 seconds
Output:
3:codec_name=flac
10:sample_rate=48000
11:channels=1
21:duration=5.332500
48:duration=5.332500


**Considering diarization methods**

I'm looking at a 48kHz mono audio file that's 5.3325 seconds long. I want to compute indicators to check for speaker changes, energy shifts, and spectral characteristics. I could run "local diarization" using the `AudioBuffer` class, but pyannote may need a token. An alternative is using SpeechBrain's `speaker-diarization`. I'll also consider creating a `UnifiedChunkEmbeddingCache` from the audio file to help detect speaker changes efficiently while keeping in mind the need for speaker centroids.


**Computing speaker centroids**

I can ignore the gate for now and focus on showing `split_points` and similarities with other computed centroids for each speaker, like SPEAKER_00 and SPEAKER_01. I'll calculate centroids based on segments in the tar files that include both speakers. This involves computing embeddings from individual flac segments and averaging them for each speaker. I know the pipeline's centroids were originally from segment embeddings built from the full audio, but segment-level embeddings from these flac files should be similar enough. I'll need additional flac segments to extract for accurate centroid computation.


**Extracting usable segments**

The tar file contains many flac segments, and since it's only 16 MB, I think extracting all of them is feasible. I'll need to remember that some segments might be unusable due to overlap or proximity issues. The export step should only include segments marked as usable, which means using the `segment_audio_parallel` function that filters for those. It's important to note that while the metadata might list some unusable overlap segments, those are not in the segments folder. I need to ensure that I'm filtering correctly during export.


**Focusing on usable segments**

Only segments marked as usable will be exported, so segments 44 and 46, which are unusable due to overlap, won't be included. I checked the segments folder for SPEAKER_00 between 208.18-210.97, but it looks like that's not in the tar list. The initial tar listing confirms only usable segments are included. We need to detect multi-speaker segments, especially since chunk reassignment didn't split anything. I'll try to replicate chunk reassignment for this specific segment using the embedding cache and centroids from other speakers. I just need to ensure I have the absolute timing for the original audio.


**Analyzing speaker change detection**

The process uses a cache built from the full audio and time segments. I can map the time relative to the segment audio file by shifting it to 0, but I need to consider if the centroids from other segments are also relative. Their embeddings don't rely on absolute time, though. I’ll create a fake segment dictionary for 0-5.33 seconds. The detection will use cached chunk embeddings with 1.5-second windows, and I expect around four chunks for the 5.33 seconds. However, the failure to split may stem from conservative gate settings, so I'll explore testing different parameters. I also want to check the implementation of `detect_speaker_changes` in the `UnifiedChunkEmbeddingCache`.


rg -n "def detect_speaker_changes\(" -n src/unified_embeddings.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
445:    def detect_speaker_changes(


python3 - <<'PY'
from pathlib import Path
lines=Path('src/unified_embeddings.py').read_text().splitlines()
for i in range(430, 620):
    print(f"{i+1:04d} {lines[i]}")
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
0431         Used by: Clustering stage (replaces extract_embeddings_batched)
0432
0433         This is INSTANT (CPU only) - no GPU call!
0434         """
0435         chunks = self.get_chunks_in_range(start, end)
0436         if not chunks:
0437             return None
0438
0439         embeddings = [c.embedding for c in chunks if c.embedding is not None]
0440         if not embeddings:
0441             return None
0442
0443         return np.mean(embeddings, axis=0)
0444
0445     def detect_speaker_changes(
0446         self,
0447         seg_start: float,
0448         seg_end: float,
0449         severe_threshold: Optional[float] = None,
0450         normal_threshold: Optional[float] = None,
0451         require_persistence: bool = True,
0452         use_relative_valley: bool = True,
0453         persistence_count: int = 2,  # v7.8: Configurable (1=aggressive, 2=conservative)
0454     ) -> Tuple[List[float], List[str], int, int]:
0455         """
0456         Detect speaker changes within a segment with VALIDATION GATES.
0457
0458         === v7.7: VALIDATION GATES TO PREVENT FALSE POSITIVE SPLITS ===
0459
0460         Gates (all must pass for a split to be proposed):
0461         1. Persistence gate: Require low-sim evidence for ≥N consecutive chunks
0462         2. Relative valley gate: Only split if dip is statistical outlier for this segment
0463         3. Chunk boundary snapping: Ensure split aligns with actual low-energy points
0464
0465         The old approach was too aggressive with fixed thresholds (0.40/0.25) that
0466         triggered on natural intra-speaker embedding variation.
0467
0468         === v7.8: Configurable persistence_count ===
0469         - persistence_count=1: More aggressive, catches "yeah" and "ok" interruptions
0470         - persistence_count=2: Conservative, requires ~3s of evidence (original behavior)
0471
0472         Args:
0473             seg_start: Segment start time
0474             seg_end: Segment end time
0475             severe_threshold: Override config severe threshold
0476             normal_threshold: Override config normal threshold
0477             require_persistence: Enable persistence check (default: True)
0478             use_relative_valley: Use per-segment adaptive threshold (default: True)
0479             persistence_count: Min consecutive low-sim chunks needed (v7.8: 1=aggressive, 2=conservative)
0480
0481         Returns:
0482             (split_points, reasons, severe_count, lookahead_count)
0483         """
0484         severe_th = severe_threshold or self.config.severe_threshold
0485         normal_th = normal_threshold or self.config.normal_threshold
0486
0487         chunks = self.get_chunks_in_range(seg_start, seg_end)
0488
0489         if len(chunks) < 3:  # GATE: Need at least 3 chunks for meaningful analysis
0490             return [], [], 0, 0
0491
0492         # Compute ALL adjacent similarities for this segment (for relative valley gate)
0493         adjacent_sims = []
0494         for i in range(len(chunks) - 1):
0495             emb_i = chunks[i].embedding
0496             emb_next = chunks[i + 1].embedding
0497             if emb_i is not None and emb_next is not None:
0498                 sim = self._cosine_similarity(emb_i, emb_next)
0499                 adjacent_sims.append((i, sim))
0500
0501         if len(adjacent_sims) < 2:
0502             return [], [], 0, 0
0503
0504         # === RELATIVE VALLEY GATE: Compute segment-specific adaptive threshold ===
0505         # Only split if similarity is a clear outlier for THIS segment
0506         sims_values = [s[1] for s in adjacent_sims]
0507         median_sim = np.median(sims_values)
0508         mad = np.median(np.abs(np.array(sims_values) - median_sim))  # Median Absolute Deviation
0509
0510         # Robust threshold: similarity must be below median - 2.5*MAD to be an outlier
0511         # This prevents splitting in segments where all similarities are a bit lower
0512         adaptive_threshold = max(severe_th, median_sim - 2.5 * max(mad, 0.05))
0513
0514         if use_relative_valley:
0515             logger.debug(f"   Segment {seg_start:.1f}-{seg_end:.1f}: "
0516                         f"median_sim={median_sim:.3f}, MAD={mad:.3f}, "
0517                         f"adaptive_th={adaptive_threshold:.3f}")
0518
0519         split_points = []
0520         reasons = []
0521         severe_count = 0
0522         lookahead_count = 0
0523
0524         # Track consecutive low-sim chunks for persistence gate
0525         consecutive_low = 0
0526         pending_split_idx = None
0527
0528         for idx, (i, sim_next) in enumerate(adjacent_sims):
0529             emb_i = chunks[i].embedding
0530
0531             # Determine if this is a potential split point
0532             is_severe = sim_next < severe_th
0533
0534             # Use adaptive threshold if enabled, else fixed normal threshold
0535             effective_normal_th = adaptive_threshold if use_relative_valley else normal_th
0536             is_low = sim_next < effective_normal_th
0537
0538             # === PERSISTENCE GATE: Track consecutive low-sim chunks ===
0539             if require_persistence:
0540                 if is_low or is_severe:
0541                     consecutive_low += 1
0542                     if pending_split_idx is None:
0543                         pending_split_idx = i  # Mark first low-sim position
0544                 else:
0545                     # Check if we had enough consecutive evidence before resetting
0546                     # v7.8: Use configurable persistence_count (1=aggressive, 2=conservative)
0547                     if consecutive_low >= persistence_count and pending_split_idx is not None:
0548                         # Confirmed split - use the original pending position
0549                         split_time = chunks[pending_split_idx].end_time
0550                         split_points.append(split_time)
0551                         reasons.append(
0552                             f"PERSISTENT: {consecutive_low} consecutive low-sim chunks, "
0553                             f"start_sim={adjacent_sims[pending_split_idx - adjacent_sims[0][0]][1]:.3f}"
0554                         )
0555                         lookahead_count += 1
0556                     # Reset tracking
0557                     consecutive_low = 0
0558                     pending_split_idx = None
0559                 continue  # Don't process individual triggers in persistence mode
0560
0561             # === CIRCUIT-BREAKER (from Maya): Severe drop triggers immediately ===
0562             # Only if NOT in persistence mode (persistence mode is stricter)
0563             if is_severe:
0564                 split_points.append(chunks[i].end_time)
0565                 reasons.append(f"SEVERE: sim={sim_next:.3f} < {severe_th}")
0566                 severe_count += 1
0567                 continue
0568
0569             # === LOOK-AHEAD PATTERN (from Stabilized): Confirm with chunk[i+2] ===
0570             if i + 2 < len(chunks):
0571                 emb_after = chunks[i + 2].embedding
0572                 if emb_after is not None:
0573                     sim_after = self._cosine_similarity(emb_i, emb_after)
0574
0575                     if sim_next < effective_normal_th and sim_after < effective_normal_th:
0576                         split_points.append(chunks[i].end_time)
0577                         reasons.append(
0578                             f"LOOKAHEAD: sim_next={sim_next:.3f}, sim_after={sim_after:.3f} < {effective_normal_th:.3f}"
0579                         )
0580                         lookahead_count += 1
0581
0582         # Handle any remaining persistent evidence at end of segment
0583         # v7.8: Use configurable persistence_count
0584         if require_persistence and consecutive_low >= persistence_count and pending_split_idx is not None:
0585             split_time = chunks[pending_split_idx].end_time
0586             split_points.append(split_time)
0587             reasons.append(
0588                 f"PERSISTENT_END: {consecutive_low} consecutive low-sim chunks"
0589             )
0590             lookahead_count += 1
0591
0592         return split_points, reasons, severe_count, lookahead_count
0593
0594     def get_chunk_similarities(self, seg_start: float, seg_end: float) -> List[Tuple[int, int, float]]:
0595         """
0596         Get similarity scores between adjacent chunks in a segment.
0597
0598         Useful for debugging/visualization.
0599         """
0600         chunks = self.get_chunks_in_range(seg_start, seg_end)
0601         similarities = []
0602
0603         for i in range(len(chunks) - 1):
0604             emb_i = chunks[i].embedding
0605             emb_next = chunks[i + 1].embedding
0606
0607             if emb_i is not None and emb_next is not None:
0608                 sim = self._cosine_similarity(emb_i, emb_next)
0609                 similarities.append((chunks[i].idx, chunks[i + 1].idx, sim))
0610
0611         return similarities
0612
0613     def get_stats(self) -> Dict[str, Any]:
0614         """Get cache build statistics."""
0615         return {
0616             'total_chunks': self._stats.total_chunks,
0617             'eligible_chunks': self._stats.eligible_chunks,
0618             'embeddings_extracted': self._stats.embeddings_extracted,
0619             'build_time_sec': round(self._stats.build_time_sec, 2),
0620             'gpu_batch_count': self._stats.gpu_batch_count,


Summary: auto


**Investigating configuration settings**

I'm thinking about the new v7.8 tunable that catches "yeah/ok" interruptions. It looks like the persistence threshold needs to be set to 1, with a lower centroid match threshold. I wonder if the pipeline defaults to persistence=2 and centroid=0.6. For this specific case, we definitely need 1. I think the configuration might be in `src/config.py`, so I'll search for `persistence_threshold` there to confirm.


rg -n "persistence_threshold|centroid_match_threshold|min_segment_for_analysis|assign_min_similarity|margin_min" -n src/config.py pipeline.py src/unified_embeddings.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/unified_embeddings.py:678:    min_segment_for_analysis: float = 3.0,
src/unified_embeddings.py:680:    assign_min_similarity: float = 0.55,
src/unified_embeddings.py:681:    margin_min: float = 0.10,
src/unified_embeddings.py:684:    persistence_threshold: int = 2,  # Min consecutive low-sim chunks (1 = more aggressive, 2 = conservative)
src/unified_embeddings.py:685:    centroid_match_threshold: float = 0.60,  # Both portions must be < this to split (lower = more aggressive)
src/unified_embeddings.py:702:    - persistence_threshold: Min consecutive low-sim chunks needed (1=aggressive, 2=conservative)
src/unified_embeddings.py:703:    - centroid_match_threshold: Max similarity to original speaker for both portions (lower=aggressive)
src/unified_embeddings.py:709:        min_segment_for_analysis: Only analyze segments >= this duration
src/unified_embeddings.py:711:        assign_min_similarity: Minimum similarity for assignment
src/unified_embeddings.py:712:        margin_min: Required margin between best and second-best speaker
src/unified_embeddings.py:714:        persistence_threshold: Min consecutive low-sim chunks (v7.8: configurable)
src/unified_embeddings.py:715:        centroid_match_threshold: Centroid gate threshold (v7.8: configurable)
src/unified_embeddings.py:721:    logger.debug(f"Chunk Reassignment v7.8 (persist={persistence_threshold}, centroid={centroid_match_threshold})")
src/unified_embeddings.py:726:                        and seg['duration'] >= min_segment_for_analysis)
src/unified_embeddings.py:750:        if seg['duration'] < min_segment_for_analysis:
src/unified_embeddings.py:763:            require_persistence=persistence_threshold > 0,  # GATE: Enable persistence check
src/unified_embeddings.py:765:            persistence_count=persistence_threshold,  # v7.8: Configurable (1=aggressive, 2=conservative)
src/unified_embeddings.py:798:                if pre_sim_orig > centroid_match_threshold and post_sim_orig > centroid_match_threshold:
src/unified_embeddings.py:815:                    post_emb, speaker_centroids, assign_min_similarity, margin_min
src/unified_embeddings.py:903:                assign_min_similarity,
src/unified_embeddings.py:904:                margin_min
src/unified_embeddings.py:1027:    margin_min: float
src/unified_embeddings.py:1057:        if margin < margin_min:
pipeline.py:474:                min_segment_for_analysis=config.chunk_reassignment_min_analysis,  # 2.0s (was 4.0)
pipeline.py:476:                assign_min_similarity=0.55,
pipeline.py:477:                margin_min=0.10,
pipeline.py:480:                persistence_threshold=config.chunk_reassignment_persistence,  # 1 (was hardcoded 2)
pipeline.py:481:                centroid_match_threshold=config.chunk_reassignment_centroid_threshold,  # 0.55 (was 0.60)


**Reviewing configuration settings**

I’m looking into the pipeline configuration to see the values for persistence and centroid thresholds. I wonder if it would be more effective to set those thresholds more aggressively. My next step is to check the default config settings and open up the relevant configuration details to see what adjustments might be necessary. It's all about optimizing performance, so I'll ensure everything aligns with the desired outcomes!


rg -n "chunk_reassignment_" src/config.py | head -80


Output:
Exit code: 0
Wall time: 0 seconds
Output:
187:    chunk_reassignment_threshold: float = 0.30  # Normal threshold (per-segment adaptive overrides)
188:    chunk_reassignment_severe: float = 0.15  # Severe circuit-breaker (only extreme changes)
189:    chunk_reassignment_min_speech: float = 0.6  # Min speech ratio for eligibility
190:    chunk_reassignment_min_portion: float = 1.0  # LOWERED: 1.0s to catch "yeah/ok" (was 2.0s)
194:    chunk_reassignment_min_analysis: float = 2.0  # Min segment duration to analyze (was hardcoded 3.0)
195:    chunk_reassignment_persistence: int = 1  # Min consecutive low-sim chunks (was hardcoded 2)
196:    chunk_reassignment_centroid_threshold: float = 0.55  # Match threshold for centroid gate (was hardcoded 0.60)


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    min_snr_db: float = 15.0           # Minimum SNR for TTS usability
    min_quality_score: float = 0.3     # Minimum quality score (0-1)

    # === SEGMENT DURATION FILTERING (v6.1) ===
    # Drop speaker segments shorter than this from the FINAL output.
    #
    # IMPORTANT: we do adjacent same-speaker merging before applying this filter,
    # so short fragments (e.g. 0.4-0.9s) can be absorbed into longer continuous
    # turns when safe to do so.
    min_tts_duration: float = 1.0      # Minimum segment duration for TTS

    # === DIARIZATION ===
    min_speakers: int = 1
    max_speakers: int = 10

    # === EMBEDDINGS (OOM-protected) ===
    embedding_batch_size: int = 8      # Will be auto-tuned
    max_embedding_length: int = 16000 * 10  # Max 10s per segment

    # === CLUSTERING (Conservative - no poison merges) ===
    # Per instructions: 0.75-0.80 to prevent merging different speakers
    cluster_merge_threshold: float = 0.80
    min_segments_for_merge: int = 2

    # === CHUNK REASSIGNMENT (v7.7 - With Validation Gates) ===
    # Detects within-segment speaker changes and surgically splits/reassigns
    #
    # === v7.7 FIX: VALIDATION GATES prevent false positive splits ===
    # Previous issue: 66x speaker fragmentation (6 speakers → 400+)
    # Root cause: Fixed thresholds + create_new_on_ambiguous triggered on natural variation
    #
    # NEW GATES (see unified_embeddings.py):
    # 1. Persistence gate: Require ≥N consecutive low-sim chunks
    # 2. Relative valley gate: Per-segment adaptive threshold (outlier detection)
    # 3. Same-speaker centroid gate: Reject if both sides match original speaker
    # 4. No-new-speaker-unless-proven: Keep original speaker on ambiguous (key fix!)
    enable_chunk_reassignment: bool = True  # RE-ENABLED with validation gates
    chunk_reassignment_threshold: float = 0.30  # Normal threshold (per-segment adaptive overrides)
    chunk_reassignment_severe: float = 0.15  # Severe circuit-breaker (only extreme changes)
    chunk_reassignment_min_speech: float = 0.6  # Min speech ratio for eligibility
    chunk_reassignment_min_portion: float = 1.0  # LOWERED: 1.0s to catch "yeah/ok" (was 2.0s)

    # === v7.8: TUNABLE GATE PARAMETERS for catching small interruptions ===
    # Lower values = more aggressive splitting (catches more but risks false positives)
    chunk_reassignment_min_analysis: float = 2.0  # Min segment duration to analyze (was hardcoded 3.0)
    chunk_reassignment_persistence: int = 1  # Min consecutive low-sim chunks (was hardcoded 2)
    chunk_reassignment_centroid_threshold: float = 0.55  # Match threshold for centroid gate (was hardcoded 0.60)

    # === MUSIC DETECTION (v7.4 - Hybrid INA+PANNs) ===
    # Two-stage detection: Fast INA screening + PANNs verification
    # Stage 1: inaSpeechSegmenter (fast CNN, ~10x realtime)
    # Stage 2: PANNs CNN14 (only on suspicious segments)
    enable_music_detection: bool = True  # Enable music detection
    use_hybrid_detection: bool = False  # DISABLED: Run PANNs on EVERYTHING for strict music detection
    music_chunk_duration: float = 1.5  # Same as embedding chunks
    music_batch_size: int = 128  # Batch size for PANNs inference (v7.5: optimized for A100)

    # === STRICT DETECTION THRESHOLDS FOR TTS QUALITY (v7.1) ===
    # Zero tolerance: ANY contamination → needs_demucs or unusable

    # Per-chunk thresholds (RESTORED to working values)
    music_prob_threshold: float = 0.20  # Chunk "has music" if prob > 20%
    noise_prob_threshold: float = 0.25  # Chunk "has noise" if prob > 25%

    # Per-segment decision thresholds (STRICT but realistic)
    # Based on empirical testing: baseline neural net output is ~0.04-0.06
    music_ratio_clean: float = 0.0      # ANY chunk with music detection → needs_demucs
    music_ratio_demucs: float = 0.15    # 0-15% → demucs, >15% → unusable
    music_mean_clean: float = 0.10      # ADJUSTED: 0.10 accounts for neural net baseline (was 0.05)
    music_mean_demucs: float = 0.25     # Threshold for heavy_contamination

    # Noise thresholds (NEW)
    noise_ratio_clean: float = 0.0      # ANY chunk with noise detection → needs processing
    noise_ratio_demucs: float = 0.20    # 0-20% → demucs, >20% → unusable
    noise_mean_clean: float = 0.10      # ADJUSTED: 0.10 accounts for neural net baseline (was 0.05)
    noise_mean_demucs: float = 0.30

    # Strict TTS mode: Zero tolerance for contamination
    strict_tts_mode: bool = True  # When True: ANY detection → needs_demucs

    # === v7.0: Detection Early-Exit ===


**Analyzing speaker merging issues**

I'm examining if two speakers were merged into the same cluster, labeled SPEAKER_00, which could impact how we identify them in the metadata. It looks like there are supposed to be three distinct speakers, but I wonder if they were merged by mistake. If there's quick interjection from speaker 01, that part should match them, but brief overlaps might lead to mixed embeddings, causing issues. The user mentions a multi-speaker problem with contamination, so proper segmentation is crucial for training!


**Proposing adjustments for speaker detection**

I'm thinking about adjusting the centroid gate and splitting based on voice activity detection (VAD) boundaries or energy minima along with some additional checks. Next, I’ll analyze the audio chunk by running `cache.detect_speaker_changes` on it and computing similarity values. I also plan to calculate speaker embeddings for the chunk halves and compare them to centroids from other segments in the video. I need to quickly load the embedding model used by the pipeline for this! I'll investigate if there's a simpler way to load just that model.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Model management - Hot inference with adaptive loading.

Key improvements:
- community-1 FIRST (better per pyannote docs), 3.1 as fallback
- Lazy loading with compute-aware batching
- Better GPU memory management

=== v7.0 OPTIMIZATION: cuDNN Benchmark + TF32 ===
Enables CUDA optimizations for faster inference:
- cudnn.benchmark=True: Auto-tune convolution algorithms
- TF32 precision: 3x faster matmul on Ampere+ GPUs
Expected gain: 5-10% on diarization (conv-heavy model)
"""

# === COMPATIBILITY FIX: torchaudio 2.9+ removed list_audio_backends() ===
# speechbrain calls this old API; we monkey-patch before any imports
import os
import torchaudio
if not hasattr(torchaudio, 'list_audio_backends'):
    torchaudio.list_audio_backends = lambda: ['soundfile', 'sox']
# === END COMPATIBILITY FIX ===

import time
import logging
import gc
import torch

# === COMPATIBILITY FIX: speechbrain expects hf_hub_download(use_auth_token=...) ===
# Newer huggingface_hub versions renamed this to `token`. Rather than pinning a
# fragile dependency set, we shim the old kwarg to the new one.
try:
    import inspect
    import huggingface_hub  # type: ignore

    if hasattr(huggingface_hub, "hf_hub_download"):
        _orig_hf_hub_download = huggingface_hub.hf_hub_download
        _hf_sig = inspect.signature(_orig_hf_hub_download)
        if "use_auth_token" not in _hf_sig.parameters:
            def _patched_hf_hub_download(*args, use_auth_token=None, token=None, **kwargs):
                if token is None and use_auth_token is not None:
                    token = use_auth_token
                return _orig_hf_hub_download(*args, token=token, **kwargs)

            huggingface_hub.hf_hub_download = _patched_hf_hub_download  # type: ignore
except Exception:
    # Best-effort shim; if anything goes wrong we fall back to stock behavior.
    pass

# === v7.0 OPTIMIZATION: Enable CUDA optimizations ===
# These flags enable hardware-specific optimizations:
# - cudnn.benchmark: Auto-select fastest conv algorithm for input sizes
# - TF32: Use TensorFloat-32 for faster matrix ops (Ampere+ GPUs)
# Must be set BEFORE model loading for full effect
if torch.cuda.is_available():
    torch.backends.cudnn.benchmark = True  # Auto-tune convolutions
    torch.backends.cuda.matmul.allow_tf32 = True  # Fast matmul
    torch.backends.cudnn.allow_tf32 = True  # Fast convolutions

# === CRITICAL: Patch torch.load BEFORE any other imports ===
_original_torch_load = torch.load
def _patched_torch_load(*args, **kwargs):
    kwargs['weights_only'] = False
    return _original_torch_load(*args, **kwargs)
torch.load = _patched_torch_load

# Patch PyTorch Lightning too
try:
    import pytorch_lightning.core.saving as pl_saving
    _orig_pl_load = pl_saving.pl_load
    def _patched_pl_load(path_or_url, map_location=None, **kwargs):
        return _original_torch_load(path_or_url, map_location=map_location, weights_only=False)
    pl_saving.pl_load = _patched_pl_load
except ImportError:
    pass

try:
    import lightning_fabric.utilities.cloud_io as cloud_io
    _orig_fabric_load = cloud_io._load
    def _patched_fabric_load(path_or_url, map_location=None):
        return _original_torch_load(path_or_url, map_location=map_location, weights_only=False)
    cloud_io._load = _patched_fabric_load
except (ImportError, AttributeError):
    pass

logger = logging.getLogger("FastPipelineV6.Models")

class ModelManager:
    """
    Singleton model manager - loads all models once, keeps them hot.

    Model priority (per user request):
    1. community-1 (better performance per pyannote docs)
    2. speaker-diarization-3.1 (fallback)

    Key optimization: Load once, inference many times.
    """
    _instance = None

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
            cls._instance._initialized = False
        return cls._instance

    def __init__(self):
        if self._initialized:
            return
        self._initialized = True
        self.device = None
        self.silero_vad = None
        self.silero_utils = None
        self.diarization_pipeline = None
        self.segmentation_model = None
        self.embedding_model = None
        self.panns_model = None  # v6.7: PANNs CNN14 for music detection
        self._loaded = False
        self._panns_loaded = False  # Separate flag for optional PANNs
        self._model_name = None  # Track which model loaded

    def get_device(self) -> torch.device:
        """Get CUDA device or fallback to CPU."""
        if self.device is None:
            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        return self.device

    def load_all(self, config):
        """
        Load all models once into GPU memory.

        Order: VAD -> Diarization (community-1 first) -> Segmentation -> Embedding
        """
        if self._loaded:
            logger.info(f"✅ Models already loaded: {self._model_name} (hot inference)")
            return

        # Try to get token from config, fallback to environment variable
        hf_token = getattr(config, "hf_token", None) or os.environ.get('HF_TOKEN', '')
        if not hf_token:
            raise RuntimeError(
                "HF_TOKEN is required to load pyannote models.\n"
                "- Set env var HF_TOKEN (do not hard-code it)\n"
                "- Ensure you've accepted the model terms on Hugging Face:\n"
                "  - pyannote/speaker-diarization-community-1\n"
                "  - pyannote/segmentation-3.0 (if needed)\n"
            )
        # Update config with token for later use
        config.hf_token = hf_token

        logger.info("=" * 70)
        logger.info("🚀 LOADING ALL MODELS INTO GPU")
        logger.info("=" * 70)
        start = time.time()

        device = self.get_device()
        logger.info(f"Device: {device}")

        # Log GPU memory before
        if torch.cuda.is_available():
            free = torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_reserved(0)
            logger.info(f"GPU Memory before: {free / 1e9:.2f}GB free")

        # 1. Silero VAD (CPU - fast)
        logger.debug("Loading Silero VAD...")
        model, utils = torch.hub.load(
            repo_or_dir='snakers4/silero-vad',
            model='silero_vad',
            force_reload=False,
            onnx=False,
            verbose=False  # Suppress "Using cache found" messages
        )
        self.silero_vad = model
        self.silero_utils = utils
        logger.debug("Silero VAD loaded")

        # 2. PyAnnote Diarization - COMMUNITY-1 FIRST (per user request)
        from pyannote.audio import Pipeline
        from pyannote.audio.pipelines import SpeakerDiarization
        import inspect

        logger.info("Loading PyAnnote Diarization...")

        # Patch SpeakerDiarization to ignore unsupported params (like 'plda')
        _original_sd_init = SpeakerDiarization.__init__
        _valid_params = set(inspect.signature(_original_sd_init).parameters.keys())

        def _patched_sd_init(self, *args, **kwargs):
            filtered_kwargs = {k: v for k, v in kwargs.items() if k in _valid_params}
            removed = set(kwargs.keys()) - set(filtered_kwargs.keys())
            if removed:
                logger.debug(f"Filtered out unsupported params: {removed}")
            return _original_sd_init(self, *args, **filtered_kwargs)

        SpeakerDiarization.__init__ = _patched_sd_init

        # === PRIORITY: community-1 FIRST (better performance per pyannote docs) ===
        # Repo: https://huggingface.co/pyannote/speaker-diarization-community-1
        # Fallback: speaker-diarization-3.1 if community-1 fails
        models_to_try = [
            ("pyannote/speaker-diarization-community-1", "community-1"),  # FIRST CHOICE
            ("pyannote/speaker-diarization-3.1", "speaker-diarization-3.1"),  # FALLBACK
        ]

        for model_name, short_name in models_to_try:
            try:
                logger.info(f"Trying {short_name}...")
                # Try both 'token' (pyannote 3.3+) and 'use_auth_token' (older versions)
                try:
                    self.diarization_pipeline = Pipeline.from_pretrained(
                        model_name,
                        token=config.hf_token
                    )
                except TypeError:
                    # Fallback for older pyannote.audio versions
                    self.diarization_pipeline = Pipeline.from_pretrained(
                        model_name,
                        use_auth_token=config.hf_token
                    )


**Considering model loading**

I'm loading pyannote and silero, but I'm thinking we might not need the full diarization pipeline—just the embedding model could suffice. Using `load_all` could be heavy, but I can manage it. Maybe I've already downloaded the pyannote model since I have network access. Still, I wonder if there's a way to load just the embedding model. I’ll search the ModelManager for `load_embedding` and look further into `embedding_model`.


rg -n "embedding_model" src/models.py | head -80


Output:
Exit code: 0
Wall time: 0 seconds
Output:
117:        self.embedding_model = None
243:        self.embedding_model = EncoderClassifier.from_hparams(


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                    )
                self._model_name = short_name
                logger.info(f"✅ Loaded {short_name} (preferred model)")
                break
            except Exception as e:
                logger.warning(f"Failed to load {short_name}: {e}")
                continue

        if self.diarization_pipeline is None:
            raise RuntimeError("Failed to load any diarization pipeline")

        self.diarization_pipeline.to(device)
        logger.info("✅ PyAnnote Diarization loaded on GPU")

        # 3. Segmentation model - DISABLED (not used in current pipeline)
        # NOTE: segmentation-3.0 was for frame-level OSD but we use community-1 diarization instead
        # The community-1 model handles both diarization AND overlap detection in one pass
        # Keeping this disabled to avoid noisy "gated model" warnings
        self.segmentation_model = None

        # 4. ECAPA-TDNN for embeddings
        logger.info("Loading ECAPA-TDNN...")
        from speechbrain.inference.speaker import EncoderClassifier
        self.embedding_model = EncoderClassifier.from_hparams(
            source="speechbrain/spkrec-ecapa-voxceleb",
            savedir="/ephemeral/models/ecapa",
            run_opts={"device": str(device)}
        )
        logger.info("✅ ECAPA-TDNN loaded")

        self._loaded = True
        elapsed = time.time() - start

        # Log GPU memory after
        if torch.cuda.is_available():
            allocated = torch.cuda.memory_allocated() / 1e9
            reserved = torch.cuda.memory_reserved() / 1e9
            logger.info(f"GPU Memory: {allocated:.2f}GB allocated, {reserved:.2f}GB reserved")

        logger.info(f"✅ All models loaded in {elapsed:.1f}s (STAYING HOT)")
        logger.info("=" * 70)

    def clear_cache(self, aggressive=False):
        """
        Clear GPU cache between operations.

        Args:
            aggressive: If True, also empty reserved memory and run gc
        """
        if torch.cuda.is_available():
            if aggressive:
                gc.collect()
                torch.cuda.empty_cache()
                torch.cuda.reset_peak_memory_stats()
            else:
                torch.cuda.empty_cache()

    def get_vram_free_gb(self) -> float:
        """Get current free VRAM in GB."""
        if torch.cuda.is_available():
            total = torch.cuda.get_device_properties(0).total_memory
            reserved = torch.cuda.memory_reserved(0)
            return (total - reserved) / (1024**3)
        return 0.0

    def load_panns(self, device: str = 'cuda'):
        """
        Load PANNs CNN14 model for music detection (lazy loading).

        === v7.4: Pre-baked model support ===
        If PANNS_MODEL_PATH env var is set, loads from that path (Docker pre-baked).
        Otherwise downloads from default location.

        Args:
            device: 'cuda' or 'cpu'

        Returns:
            Loaded AudioTagging model
        """
        if self._panns_loaded and self.panns_model is not None:
            logger.info("✅ PANNs CNN14 already loaded (hot inference)")
            return self.panns_model

        logger.info("Loading PANNs CNN14 for music detection...")
        start = time.time()

        def _load_once() -> "AudioTagging":
            from panns_inference import AudioTagging

            # Determine device
            resolved_device = device


**Crafting a debug script**

I’m planning to write a debug script with specific tasks. First, I’ll load the speechbrain encoder classifier and the audio chunk. Then, I’ll compute embeddings using sliding windows—like 1-second windows with 0.2-second hops—to check for shifts.

Next, I’ll extract representative segments for each speaker and compute centroids to compare embeddings. By assessing each sliding window’s similarity to these centroids, I can determine if we have multiple speakers, even if I can't identify their voices right away. It sounds manageable!


**Extracting speaker segments**

I’ve realized that our tar only contains some segments for speaker00, like 0004, 0005, and 0021, which is our chunk. For speaker01, there are more segments available. To compute centroids reliably, it makes sense to extract all segments. Since the tar is relatively small at 16MB with about 16 segments, I’ll go ahead and extract the entire segments directory. I can do this with the command `tar -xf ... -C extracted -- --jp1imJD9w/segments`. Let's get started!


tar -xf tmp/r2debug/--jp1imJD9w.tar -C tmp/r2debug/extracted -- --jp1imJD9w/segments


Output:
Exit code: 0
Wall time: 0 seconds
Output:


**Processing audio segments**

I have all the FLAC audio segments ready. Now, I’ll run a Python script to compute embeddings for each file, averaging them per speaker. For audio reading, I might use torchaudio or soundfile, and I know Speechbrain's EncoderClassifier expects both the waveform and sample rate. It looks like I can resample to 16,000 Hz since the model expects it. I'll need to make sure the segments are resampled correctly before computing the embeddings and analyze the segments with some windowing.


**Preparing the embedding script**

Let's continue by loading the embedding model from Speechbrain, likely using GPU for speed. I think it’s best to use CUDA, even though it might strain memory a bit.

Next, I'll iterate over the audio segment files. For each one, I’ll use torchaudio to load the audio, convert it to mono, and resample it to 16,000 Hz. I’ll compute embeddings for the first 10 seconds, even if some segments are longer, to fit the pipeline’s limitations. To unify the approach, I'll take an 8-second clip from the middle of each file.


**Computing speaker centroids**

Next, I'll compute the speaker centroids by taking the mean of embeddings across all files for each speaker.

For the target chunk file, I’ll process embeddings using sliding windows to compute similarity to the centroids. I'll assign the best speaker label accordingly and create a timeline showing which speaker is assigned when.

I aim to keep the output concise, perhaps summarizing any switches, like from SPEAKER_00 to SPEAKER_01 at a specific time. If there aren’t any switches, I might need to verify the user’s information about having two speakers. Let’s run the script!


python3 - <<'PY'
import math
from pathlib import Path
from collections import defaultdict

import torch
import torchaudio
from speechbrain.inference.speaker import EncoderClassifier

SEG_DIR = Path('tmp/r2debug/extracted/--jp1imJD9w/segments')
TARGET = SEG_DIR / 'SPEAKER_00_0021_199.00-204.34.flac'

device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = EncoderClassifier.from_hparams(
    source='speechbrain/spkrec-ecapa-voxceleb',
    savedir='tmp/r2debug/models/ecapa',
    run_opts={'device': device},
)

def load_resample(path: Path, target_sr: int = 16000):
    wav, sr = torchaudio.load(str(path))
    if wav.ndim == 2 and wav.shape[0] > 1:
        wav = wav.mean(dim=0, keepdim=True)
    if sr != target_sr:
        wav = torchaudio.functional.resample(wav, sr, target_sr)
    return wav, target_sr

def embed_clip(wav, sr, max_sec=8.0):
    max_len = int(sr * max_sec)
    if wav.shape[1] > max_len:
        start = (wav.shape[1] - max_len) // 2
        wav = wav[:, start:start+max_len]
    with torch.no_grad():
        emb = model.encode_batch(wav.to(device)).detach().cpu().squeeze(0).squeeze(0)
    return emb

def cos(a, b):
    return float(torch.nn.functional.cosine_similarity(a, b, dim=0))

# Build per-speaker embeddings from all segment files
speaker_embs = defaultdict(list)
for f in sorted(SEG_DIR.glob('*.flac')):
    speaker = f.name.split('_')[0] + '_' + f.name.split('_')[1]  # SPEAKER_00
    wav, sr = load_resample(f)
    speaker_embs[speaker].append(embed_clip(wav, sr))

centroids = {spk: torch.stack(embs).mean(dim=0) for spk, embs in speaker_embs.items() if embs}
print('centroids', {k: len(v) for k,v in speaker_embs.items()})

# Analyze target with sliding windows
wav, sr = load_resample(TARGET)
dur = wav.shape[1] / sr
win_sec = 1.0
hop_sec = 0.25
win = int(sr * win_sec)
hop = int(sr * hop_sec)

frames = []
for start in range(0, max(1, wav.shape[1] - win + 1), hop):
    chunk = wav[:, start:start+win]
    emb = embed_clip(chunk, sr, max_sec=win_sec)
    sims = {spk: cos(emb, c) for spk, c in centroids.items()}
    best = max(sims.items(), key=lambda x: x[1])
    frames.append((start/sr, min(dur, (start+win)/sr), best[0], best[1], sims))

# Print compact timeline
print(f'target_dur_sec {dur:.3f} windows {len(frames)}')
prev=None
for t0,t1,spk,score,sims in frames:
    if prev is None or spk!=prev[2]:
        print(f'{t0:5.2f}-{t1:5.2f} best={spk} sim={score:.3f} runner_up={sorted(sims.items(), key=lambda x:x[1], reverse=True)[1][0]}')
    prev=(t0,t1,spk,score)
PY