Output:
Exit code: 1
Wall time: 1.8 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]
Traceback (most recent call last):
  File "<stdin>", line 7, in <module>
  File "/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/__init__.py", line 6, in <module>
    from .core import Brain, Stage, create_experiment_directory, parse_arguments
  File "/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/core.py", line 39, in <module>
    from speechbrain.dataio.dataloader import LoopedLoader, SaveableDataLoader
  File "/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/dataio/dataloader.py", line 46, in <module>
    from speechbrain.dataio.dataset import DynamicItemDataset
  File "/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/dataio/dataset.py", line 15, in <module>
    from speechbrain.dataio.dataio import load_data_csv, load_data_json
  File "/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/dataio/dataio.py", line 36, in <module>
    check_torchaudio_backend()
  File "/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/utils/torch_audio_backend.py", line 57, in check_torchaudio_backend
    available_backends = torchaudio.list_audio_backends()
AttributeError: module 'torchaudio' has no attribute 'list_audio_backends'


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.


python3 - <<'PY'
import torch
import torchaudio
if not hasattr(torchaudio, 'list_audio_backends'):
    torchaudio.list_audio_backends = lambda: ['soundfile','sox']

import math
from pathlib import Path
from collections import defaultdict

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')):
    parts = f.name.split('_')
    speaker = '_'.join(parts[:2])  # 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('centroid_counts', {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_spk=None
for t0,t1,spk,score,sims in frames:
    if prev_spk is None or spk!=prev_spk:
        runner_up = sorted(sims.items(), key=lambda x:x[1], reverse=True)[1]
        print(f'{t0:5.2f}-{t1:5.2f} best={spk} sim={score:.3f} runner_up={runner_up[0]}({runner_up[1]:.3f})')
    prev_spk=spk
PY


Output:
Exit code: 0
Wall time: 9.9 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]
/home/ubuntu/.local/lib/python3.10/site-packages/torch/backends/__init__.py:46: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.)
  self.setter(val)
/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/utils/autocast.py:188: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
  wrapped_fwd = torch.cuda.amp.custom_fwd(fwd, cast_inputs=cast_inputs)
centroid_counts {'SPEAKER_00': 16, 'SPEAKER_01': 60, 'SPEAKER_02': 3}
target_dur_sec 5.332 windows 18
 0.00- 1.00 best=SPEAKER_01 sim=0.614 runner_up=SPEAKER_02(0.428)
 1.25- 2.25 best=SPEAKER_00 sim=0.420 runner_up=SPEAKER_01(0.222)


Summary: auto


**Evaluating split issue**

I’m trying to figure out why the detection didn’t work. It could be that the `min_split_portion` is set to 1.0, and early portions of only 1 second might be borderline. The persistence gate may need 1 or 2 consecutive low-sim transitions. In our segment, there’s just one boundary between speakers, which may not meet the persistence gate requirement. Also, using 1.5-second chunks with short interruptions could lead to mixed embeddings. The sliding windows show change, but the chunk duration may not capture those interruptions effectively.


**Analyzing chunk duration**

I'm considering that if the pre-portion is shorter than the chunk duration, it could lead to only one chunk mixing both speakers, causing the post to still match the original. To fix this, maybe I should use a shorter chunk duration or more overlap for shorter segments or interruptions near boundaries. I could also recompute centroids in the segment exporter. However, I’ll replicate the issue with the `UnifiedChunkEmbeddingCache` to see if it detects speaker changes, noting the min 3 chunk requirement. A quick debug could involve setting the chunk duration to 0.75s or 0.5s.


**Examining chunk reassignment**

I’m thinking about the chunk reassignment, which uses 1.5s embeddings and requires both persistence and centroid gates. The contamination seems to arise from around 1 second at the start, which is too small for effective chunking and sitting at the boundary, hence the detection didn’t identify a stable outlier. There’s also a possibility that the segment originally belonged to SPEAKER_01 but was relabeled or merged incorrectly. The diarization step could have inaccurately assigned this segment to the wrong speaker. If the boundary refinement extended unexpectedly, it might have caused the contamination by overlapping with the previous speaker's region.


**Investigating segment boundaries**

I’m trying to figure out why this segment starts at 199 when the preceding segment ends at 197.7 with speaker01. There’s a gap of 1.3 seconds that might include an utterance from speaker01, but our analysis suggests otherwise. It seems the segment's start is too early and should actually be around 200.25. It’s possible that boundary refinement moved the start earlier to capture more speech, but might have incorrectly included the other speaker. I’ll need to check if the pipeline includes a boundary refinement stage and how it might be adjusting based on non-speech boundaries. Let's inspect the `boundary_refinement` logic closely.


rg -n "boundary_refine|refine_all_segments|Boundary" pipeline.py src/boundary_refinement.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/boundary_refinement.py:3:VAD-Aware Boundary Refinement for TTS Audio Segments.
src/boundary_refinement.py:19:logger = logging.getLogger("FastPipelineV6.BoundaryRefinement")
src/boundary_refinement.py:23:class BoundaryRefinementConfig:
src/boundary_refinement.py:134:    config: BoundaryRefinementConfig
src/boundary_refinement.py:148:        config: BoundaryRefinementConfig with thresholds
src/boundary_refinement.py:187:            segment['boundary_refinement'] = {
src/boundary_refinement.py:198:            segment['boundary_refinement'] = {
src/boundary_refinement.py:208:        segment['boundary_refinement'] = {
src/boundary_refinement.py:219:def refine_all_segments(
src/boundary_refinement.py:222:    config: Optional[BoundaryRefinementConfig] = None
src/boundary_refinement.py:239:        config = BoundaryRefinementConfig()
src/boundary_refinement.py:267:        refinement = segment.get('boundary_refinement', {})
src/boundary_refinement.py:284:        logger.info(f"   Boundary refinement: {stats['segments_trimmed']} trimmed "
pipeline.py:773:        boundary_refinement_stats = None
pipeline.py:774:        if config.enable_boundary_refinement:
pipeline.py:775:            from src.boundary_refinement import (
pipeline.py:776:                refine_all_segments,
pipeline.py:777:                BoundaryRefinementConfig
pipeline.py:779:            refinement_config = BoundaryRefinementConfig(
pipeline.py:780:                end_tolerance_ms=config.boundary_refinement_tolerance_ms,
pipeline.py:781:                min_gap_ms=config.boundary_refinement_min_gap_ms,
pipeline.py:782:                min_remaining_duration=config.boundary_refinement_min_remaining,
pipeline.py:785:            segments, boundary_refinement_stats = refine_all_segments(
pipeline.py:912:            'boundary_refinement': boundary_refinement_stats if boundary_refinement_stats else {},
pipeline.py:944:            'boundary_refinement_enabled': config.enable_boundary_refinement,


Output:
Exit code: 0
Wall time: 0 seconds
Output:
        speaker_segs = [s for s in segments if s.get('speaker') not in ['OVERLAP', 'NON_SPEECH', None]]
        overlap_segs = [s for s in segments if s.get('speaker') == 'OVERLAP']

        internal_filtered_count = 0
        internal_filtered_duration = 0.0

        for seg in nonspeech_segments:
            ns_start, ns_end = seg['start'], seg['end']

            # Check if this NON_SPEECH falls entirely within any single speaker segment
            is_internal = False
            for sp in speaker_segs:
                if sp['start'] <= ns_start and ns_end <= sp['end']:
                    has_overlap_nearby = any(
                        ov['start'] < ns_end and ov['end'] > ns_start  # overlap intersects
                        for ov in overlap_segs
                    )
                    if not has_overlap_nearby:
                        is_internal = True
                        break

            if is_internal:
                internal_filtered_count += 1
                internal_filtered_duration += seg['duration']
            else:
                segments.append({
                    'start': seg['start'],
                    'end': seg['end'],
                    'duration': seg['duration'],
                    'speaker': 'NON_SPEECH',
                    'status': 'unusable',
                    'unusable_reason': 'non_speech'
                })

        if internal_filtered_count > 0:
            logger.info(
                f"   Filtered {internal_filtered_count} internal pauses "
                f"({internal_filtered_duration:.1f}s) within speaker segments"
            )

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

        # === OVERLAP DENSITY FILTER (v6.9) ===
        # Mark short segments sandwiched between overlaps as unusable
        # This catches "islands" of supposedly clean audio in high-overlap regions
        from src.overlap_detection import filter_overlap_sandwich_segments
        segments = filter_overlap_sandwich_segments(segments, config)

        # === VAD-AWARE BOUNDARY REFINEMENT (v7.9) ===
        # Prevent mid-speech cuts by trimming segment ends to VAD silence gaps.
        # Uses already-computed VAD segments from Stage 2 (no additional inference).
        # Performance: ~0.5ms for 1-hour podcast (pure CPU list operations).
        boundary_refinement_stats = None
        if config.enable_boundary_refinement:
            from src.boundary_refinement import (
                refine_all_segments,
                BoundaryRefinementConfig
            )
            refinement_config = BoundaryRefinementConfig(
                end_tolerance_ms=config.boundary_refinement_tolerance_ms,
                min_gap_ms=config.boundary_refinement_min_gap_ms,
                min_remaining_duration=config.boundary_refinement_min_remaining,
                enabled=True
            )
            segments, boundary_refinement_stats = refine_all_segments(
                segments, vad_segments, refinement_config
            )

    timings['finalization'] = metrics.duration
    log_stage(8, "Finalize", "DONE", f"{timings['finalization']:.1f}s")

    # Cleanup temp files (failure-safe via TEMP_MANAGER)
    cleanup_chunks(audio_path)
    TEMP_MANAGER.cleanup_for_audio(audio_path)  # v6.2: Also cleanup via manager

    # ========================================
    # STAGE 9: Sample Generation (optional)
    # ========================================
    log_stage(9, "Output", "START")
    output_dir = Path(metadata['output_dir'])
    sample_clips = {}

    if config.generate_sample_clips:
        with COMPUTE.monitor_stage("Samples") as metrics:
            sample_clips = generate_samples(audio_path, segments, config, output_dir)
        timings['samples'] = metrics.duration

    total_time = time.time() - total_start
    log_stage(9, "Output", "DONE", f"total={total_time:.1f}s")

    # ========================================
    # Compute Statistics
    # ========================================
    all_speakers = list(set(s['speaker'] for s in segments if s['speaker'] not in ['OVERLAP', 'NON_SPEECH']))
    usable_segs = [s for s in segments if s.get('status') == 'usable']
    overlap_segs = [s for s in segments if s['speaker'] == 'OVERLAP']
    nonspeech_segs = [s for s in segments if s['speaker'] == 'NON_SPEECH']
    low_quality_segs = [s for s in segments if s.get('unusable_reason') == 'low_quality']
    overlap_proximity_segs = [s for s in segments if s.get('unusable_reason') == 'overlap_proximity']  # v6.9


**Inspecting boundary refinement**

I’m looking at the boundary refinement process used in the finalization stage after applying the overlap density filter. It’s meant to trim segment ends to VAD silence gaps, but it could possibly also trim starts? I need to check the boundary_refinement configuration. It specifically mentions trimming ends to VAD silence, but I’m wondering if the contamination I'm seeing at the start is because the start isn’t being trimmed. I’ll need to quickly inspect the code in boundary_refinement.py to verify what’s happening with segment adjustments.


sed -n '1,260p' src/boundary_refinement.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
VAD-Aware Boundary Refinement for TTS Audio Segments.

=== v7.9 FEATURE: Clean Sentence Boundaries ===
Prevents mid-speech cuts at segment boundaries by:
1. Detecting when segment END falls within active speech (VAD)
2. Trimming back to the last silence gap (≥300ms)
3. Flagging segments as "truncated" if no suitable gap exists

Performance: ~0.5ms for 1-hour podcast (pure CPU, no model inference)
False positive risk: Near zero (only trims to VAD-detected silence)
"""

import logging
from typing import List, Dict, Tuple, Optional
from dataclasses import dataclass

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

@dataclass
class BoundaryRefinementConfig:
    """Configuration for VAD-aware boundary refinement."""

    # Trigger: how close to VAD speech end counts as "mid-speech"
    end_tolerance_ms: float = 100.0  # 100ms tolerance

    # Minimum silence gap to consider for trimming
    min_gap_ms: float = 300.0  # 300ms minimum gap

    # Don't trim if remaining segment would be shorter than this
    min_remaining_duration: float = 1.0  # 1 second minimum

    # Enable/disable the feature
    enabled: bool = True

def is_end_mid_speech(
    segment_end: float,
    vad_segments: List[Dict],
    tolerance_sec: float = 0.1
) -> Tuple[bool, Optional[Dict]]:
    """
    Check if a segment end falls within active speech.

    Args:
        segment_end: End timestamp of the segment
        vad_segments: List of VAD speech regions {'start', 'end', 'duration'}
        tolerance_sec: Tolerance in seconds (100ms default)

    Returns:
        (is_mid_speech, active_vad_segment or None)
    """
    for vad_seg in vad_segments:
        # Check if segment_end falls within this VAD speech region
        # We add tolerance to vad_seg['end'] to account for VAD slight inaccuracies
        if vad_seg['start'] < segment_end < vad_seg['end'] + tolerance_sec:
            return True, vad_seg

    return False, None

def find_gaps_within_segment(
    segment_start: float,
    segment_end: float,
    vad_segments: List[Dict],
    min_gap_sec: float = 0.3
) -> List[Dict]:
    """
    Find all silence gaps (≥min_gap) within the segment boundaries.

    A gap is the space between consecutive VAD speech regions.

    Args:
        segment_start: Start of the segment
        segment_end: End of the segment
        vad_segments: List of VAD speech regions (sorted by start time)
        min_gap_sec: Minimum gap duration to consider (300ms default)

    Returns:
        List of gaps {'start', 'end', 'duration'} sorted by start time
    """
    gaps = []

    # Filter VAD segments that overlap with our segment
    relevant_vad = [
        vad for vad in vad_segments
        if vad['end'] > segment_start and vad['start'] < segment_end
    ]

    if not relevant_vad:
        # No VAD in this segment = entire segment is silence (unusual)
        return []

    # Sort by start time (should already be sorted, but ensure)
    relevant_vad = sorted(relevant_vad, key=lambda x: x['start'])

    # Check gap at the start of segment (before first VAD)
    first_vad_start = max(relevant_vad[0]['start'], segment_start)
    if first_vad_start > segment_start:
        gap_duration = first_vad_start - segment_start
        if gap_duration >= min_gap_sec:
            gaps.append({
                'start': segment_start,
                'end': first_vad_start,
                'duration': gap_duration
            })

    # Check gaps between consecutive VAD segments
    prev_end = max(relevant_vad[0]['end'], segment_start)

    for vad in relevant_vad[1:]:
        vad_start = vad['start']

        # Gap between previous VAD end and this VAD start
        if vad_start > prev_end:
            gap_duration = vad_start - prev_end
            if gap_duration >= min_gap_sec:
                gaps.append({
                    'start': prev_end,
                    'end': vad_start,
                    'duration': gap_duration
                })

        prev_end = max(vad['end'], prev_end)

    return gaps

def refine_segment_end(
    segment: Dict,
    vad_segments: List[Dict],
    config: BoundaryRefinementConfig
) -> Dict:
    """
    Refine a single segment's END boundary to avoid mid-speech cuts.

    Algorithm:
    1. Check if segment.end falls within VAD-detected speech
    2. If yes, find the last silence gap (≥300ms) within the segment
    3. Trim segment.end to the start of that gap (end of previous speech)
    4. If no suitable gap exists, mark as "truncated" for QA review

    Args:
        segment: Dict with 'start', 'end', 'duration', 'speaker', 'status'
        vad_segments: List of VAD speech regions from Stage 2
        config: BoundaryRefinementConfig with thresholds

    Returns:
        Refined segment (modified in place and returned)
    """
    if not config.enabled:
        return segment

    # Skip non-usable segments
    if segment.get('status') != 'usable':
        return segment

    segment_start = segment['start']
    segment_end = segment['end']

    # Step 1: Check if end is mid-speech
    tolerance_sec = config.end_tolerance_ms / 1000.0
    is_mid, active_vad = is_end_mid_speech(segment_end, vad_segments, tolerance_sec)

    if not is_mid:
        # End is clean (in silence), no refinement needed
        return segment

    # Step 2: Find gaps within the segment
    min_gap_sec = config.min_gap_ms / 1000.0
    gaps = find_gaps_within_segment(segment_start, segment_end, vad_segments, min_gap_sec)

    # Step 3: Apply refinement
    if gaps:
        # Pick the LAST gap (closest to original end = minimize content loss)
        last_gap = gaps[-1]
        new_end = last_gap['start']  # Trim to start of gap (end of speech before gap)

        # Safety: Don't make segment too short
        new_duration = new_end - segment_start
        if new_duration >= config.min_remaining_duration:
            old_end = segment['end']
            segment['end'] = new_end
            segment['duration'] = new_duration
            segment['boundary_refinement'] = {
                'action': 'trimmed',
                'original_end': old_end,
                'new_end': new_end,
                'trimmed_ms': (old_end - new_end) * 1000,
                'gap_used': last_gap
            }
            logger.debug(f"Refined segment {segment.get('speaker', 'UNKNOWN')}: "
                        f"{old_end:.3f}s → {new_end:.3f}s (trimmed {(old_end-new_end)*1000:.0f}ms)")
        else:
            # Trimming would make it too short - flag instead
            segment['boundary_refinement'] = {
                'action': 'flagged',
                'reason': 'trim_too_short',
                'would_be_duration': new_duration
            }
            # Don't change status to preserve usability, just flag for info
            logger.debug(f"Segment {segment.get('speaker', 'UNKNOWN')}: "
                        f"mid-speech but trim would leave only {new_duration:.2f}s")
    else:
        # No suitable gap found - flag for QA review
        segment['boundary_refinement'] = {
            'action': 'flagged',
            'reason': 'no_gap_found',
            'active_speech': {'start': active_vad['start'], 'end': active_vad['end']} if active_vad else None
        }
        logger.debug(f"Segment {segment.get('speaker', 'UNKNOWN')}: "
                    f"mid-speech at {segment_end:.3f}s, no gap ≥{min_gap_sec*1000:.0f}ms found")

    return segment

def refine_all_segments(
    segments: List[Dict],
    vad_segments: List[Dict],
    config: Optional[BoundaryRefinementConfig] = None
) -> Tuple[List[Dict], Dict]:
    """
    Refine END boundaries for all segments to avoid mid-speech cuts.

    This is the main entry point for the boundary refinement feature.
    Should be called in Stage 8 (Finalization) before export.

    Args:
        segments: List of diarization segments
        vad_segments: List of VAD speech regions from Stage 2
        config: Optional config (uses defaults if None)

    Returns:
        (refined_segments, stats_dict)
    """
    if config is None:
        config = BoundaryRefinementConfig()

    if not config.enabled:
        return segments, {'enabled': False, 'segments_processed': 0}

    stats = {
        'enabled': True,
        'segments_processed': 0,
        'segments_trimmed': 0,
        'segments_flagged_no_gap': 0,
        'segments_flagged_too_short': 0,
        'segments_clean': 0,
        'total_trimmed_ms': 0.0,
    }

    for segment in segments:
        # Only process usable speaker segments
        if segment.get('status') != 'usable':
            continue
        if segment.get('speaker') in ['OVERLAP', 'NON_SPEECH']:
            continue


**Understanding audio seeking accuracy**

I’m thinking about how using `-ss` before `-i` can lead to fast seeking but less accuracy, potentially including audio from a second earlier than intended. This might explain why the segment start looks right but contains extra audio due to this seeking inaccuracy. For precise cuts, I realize that placing `-ss` after `-i` or using it before and after is important. It seems that export settings with `audio_path` as flac could affect overall timing, but I'm trying to clarify how significant this timing issue really is.


**Analyzing ffmpeg audio seeking accuracy**

I’m considering how ffmpeg's input seeking with `-ss` before `-i` might work, especially for formats like FLAC, which could be accurate down to the nearest frame or keyframe. Although small audio frames can give accurate results, there might still be some inaccuracies. The mention of "full precision timestamps" in the pipeline suggests they're aware of rounding errors. I want to test this with audio chunks, but I don’t have the original file. However, I observe that if the audio starts earlier by a second, that could significantly affect speaker identification.


**Investigating speaker overlap and accuracy**

I'm considering whether the backchannel by speaker01 is part of the segment itself or a result of a time shift. To clarify, I can analyze other segments. If ffmpeg consistently starts earlier, I'd expect to see contamination in those too. For example, if `SPEAKER_00_0004_47.03-49.17.flac` starts at 47.03, the previous segment could affect it. I want to quickly check the first second of that file to see if it matches speaker01. I could script this analysis for each SPEAKER_00 segment and compare who speaks in the first second. If there are mismatches, it might indicate cutting inaccuracies or boundary issues. Reusing my model for this will be smart, despite being expensive!


**Creating a speaker comparison script**

I'm planning to write a Python script to analyze all the FLAC files. For each file, I'll compute the embedding for the first second and compare it to the centroid labels. If the best-matching speaker differs from the label by a certain margin, that could indicate a systematic issue. Since I've already loaded the SpeechBrain model previously, I can reuse those cached model weights to save time. In the new script, I’ll also establish a threshold where the best score exceeds the second-best by 0.05 and doesn't match the label. Let’s get started!


python3 - <<'PY'
import torch
import torchaudio
if not hasattr(torchaudio, 'list_audio_backends'):
    torchaudio.list_audio_backends = lambda: ['soundfile','sox']

from pathlib import Path
from collections import defaultdict
from speechbrain.inference.speaker import EncoderClassifier

SEG_DIR = Path('tmp/r2debug/extracted/--jp1imJD9w/segments')
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(wav):
    with torch.no_grad():
        return model.encode_batch(wav.to(device)).detach().cpu().squeeze()

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

# centroids from whole clips (middle 8s)
embs=defaultdict(list)
for f in SEG_DIR.glob('*.flac'):
    speaker='_'.join(f.name.split('_')[:2])
    wav,sr=load_resample(f)
    max_len=min(wav.shape[1], int(sr*8.0))
    start=(wav.shape[1]-max_len)//2
    e=embed(wav[:,start:start+max_len])
    embs[speaker].append(e)
centroids={spk: torch.stack(v).mean(dim=0) for spk,v in embs.items()}

# check first second of each clip
win=int(16000*1.0)
flags=[]
for f in sorted(SEG_DIR.glob('*.flac')):
    label='_'.join(f.name.split('_')[:2])
    wav,sr=load_resample(f)
    if wav.shape[1] < win:
        continue
    e=embed(wav[:,:win])
    sims=sorted(((spk, cos(e,c)) for spk,c in centroids.items()), key=lambda x:x[1], reverse=True)
    best, second = sims[0], sims[1]
    # flag if best is different and confidently so
    if best[0]!=label and (best[1]-second[1])>=0.05:
        flags.append((f.name,label,best,second))

print('total_files', len(list(SEG_DIR.glob('*.flac'))))
print('flagged', len(flags))
for name,label,best,second in flags[:30]:
    print(f'{name} label={label} best={best[0]}({best[1]:.3f}) second={second[0]}({second[1]:.3f})')
PY


Output:
Exit code: 124
Wall time: 10.2 seconds
Output:
command timed out after 10157 milliseconds
/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]
/home/ubuntu/.local/lib/python3.10/site-packages/torch/backends/__init__.py:46: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.)
  self.setter(val)
/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/utils/autocast.py:188: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
  wrapped_fwd = torch.cuda.amp.custom_fwd(fwd, cast_inputs=cast_inputs)


It looks like I ran into a timeout issue with the command because the default limit is set to 10 seconds. I think I can address this by increasing the timeout parameter to 60 seconds. That should give the tool enough time to run without hitting the limit. I'll rerun the command with this new timeout setting and see if that resolves the issue! Let's give it another shot.


python3 - <<'PY'
import torch
import torchaudio
if not hasattr(torchaudio, 'list_audio_backends'):
    torchaudio.list_audio_backends = lambda: ['soundfile','sox']

from pathlib import Path
from collections import defaultdict
from speechbrain.inference.speaker import EncoderClassifier

SEG_DIR = Path('tmp/r2debug/extracted/--jp1imJD9w/segments')
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(wav):
    with torch.no_grad():
        return model.encode_batch(wav.to(device)).detach().cpu().squeeze()

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

files = sorted(SEG_DIR.glob('*.flac'))

# centroids from whole clips (middle 8s)
embs=defaultdict(list)
for f in files:
    speaker='_'.join(f.name.split('_')[:2])
    wav,sr=load_resample(f)
    max_len=min(wav.shape[1], int(sr*8.0))
    start=(wav.shape[1]-max_len)//2
    e=embed(wav[:,start:start+max_len])
    embs[speaker].append(e)
centroids={spk: torch.stack(v).mean(dim=0) for spk,v in embs.items()}

# check first second of each clip
win=int(16000*1.0)
flags=[]
for f in files:
    label='_'.join(f.name.split('_')[:2])
    wav,sr=load_resample(f)
    if wav.shape[1] < win:
        continue
    e=embed(wav[:,:win])
    sims=sorted(((spk, cos(e,c)) for spk,c in centroids.items()), key=lambda x:x[1], reverse=True)
    best, second = sims[0], sims[1]
    # flag if best is different and confidently so
    if best[0]!=label and (best[1]-second[1])>=0.05:
        flags.append((f.name,label,best,second))

print('total_files', len(files))
print('flagged', len(flags))
for name,label,best,second in flags[:50]:
    print(f'{name} label={label} best={best[0]}({best[1]:.3f}) second={second[0]}({second[1]:.3f})')
PY


Output:
Exit code: 0
Wall time: 12.6 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]
/home/ubuntu/.local/lib/python3.10/site-packages/torch/backends/__init__.py:46: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.)
  self.setter(val)
/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/utils/autocast.py:188: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
  wrapped_fwd = torch.cuda.amp.custom_fwd(fwd, cast_inputs=cast_inputs)
total_files 79
flagged 9
SPEAKER_00_0021_199.00-204.34.flac label=SPEAKER_00 best=SPEAKER_01(0.614) second=SPEAKER_02(0.428)
SPEAKER_00_0046_429.89-430.99.flac label=SPEAKER_00 best=SPEAKER_01(0.583) second=SPEAKER_00(0.333)
SPEAKER_01_0022_215.71-225.73.flac label=SPEAKER_01 best=SPEAKER_02(0.250) second=SPEAKER_01(0.176)
SPEAKER_01_0026_254.73-256.28.flac label=SPEAKER_01 best=SPEAKER_00(0.572) second=SPEAKER_01(0.083)
SPEAKER_01_0033_287.26-308.44.flac label=SPEAKER_01 best=SPEAKER_02(0.224) second=SPEAKER_00(0.151)
SPEAKER_01_0053_459.89-467.90.flac label=SPEAKER_01 best=SPEAKER_02(0.476) second=SPEAKER_01(0.422)
SPEAKER_01_0060_522.55-527.12.flac label=SPEAKER_01 best=SPEAKER_00(0.633) second=SPEAKER_01(0.362)
SPEAKER_01_0063_535.48-542.48.flac label=SPEAKER_01 best=SPEAKER_00(0.610) second=SPEAKER_01(0.249)
SPEAKER_01_0073_613.32-618.00.flac label=SPEAKER_01 best=SPEAKER_02(0.266) second=SPEAKER_01(0.188)


**Inspecting diarization issues**

I think the issue might stem from the start segment at 199.00 being incorrect, possibly due to a diarization error that mislabeled this portion as speaker00. I should check the metadata segments around 199 because there's no segment covering 198-199. So, speaker01 might be the actual speaker around 199, indicating a diarization error. I want to verify it with pyannote diarization on the raw 199-204 window, but I don't have the original raw audio. Maybe I can find the original audio path by looking at the metadata's source URL.


python3 - <<'PY'
import json
from pathlib import Path
meta=json.loads(Path('tmp/r2debug/extracted/--jp1imJD9w/metadata.json').read_text())
for k in ['video_id','source_url','original_audio_path','processed_audio_path','output_dir','timing_total','r2_upload_uri','export']:
    if k in meta:
        v=meta[k]
        if isinstance(v, dict):
            print(k, {kk: v.get(kk) for kk in list(v.keys())[:10]})
        else:
            print(k, v)
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
video_id --jp1imJD9w
source_url test/podcasts/--jp1imJD9w.webm
original_audio_path output/--jp1imJD9w/--jp1imJD9w_original.flac
timing_total 22.91


Summary: auto


mkdir -p tmp/r2debug/source && set -a; source .env; set +a; python3 - <<'PY'
from pathlib import Path
from src.r2_client import R2Client, R2Config

bucket_type='source'  # bucket name 'test'
remote_key='podcasts/--jp1imJD9w.webm'
local=Path('tmp/r2debug/source/--jp1imJD9w.webm')

base=R2Client(bucket_type='production')
# Use same endpoint/creds, but bucket 'test'
cfg=R2Config(endpoint_url=base.config.endpoint_url, access_key_id=base.config.access_key_id, secret_access_key=base.config.secret_access_key, bucket='test', prefix='')
r2=R2Client(config=cfg, bucket_type='custom')

print(f'Downloading s3://{r2.bucket}/{remote_key} -> {local}')
ok=r2.download_file(remote_key, str(local))
print('OK' if ok else 'FAIL')
PY


Output:
Exit code: 0
Wall time: 1.7 seconds
Output:
Downloading s3://test/podcasts/--jp1imJD9w.webm -> tmp/r2debug/source/--jp1imJD9w.webm
OK


Output:
Exit code: 0
Wall time: 0 seconds
Output:
-rw-rw-r-- 1 ubuntu ubuntu 11M Jan  8 20:06 tmp/r2debug/source/--jp1imJD9w.webm


**Extracting audio segments**

I’m excited to work on extracting the region 199-204.34 from the original audio using a precise method, allowing for a comparison to the segment file. However, I need to remember that the segment file might originate from an earlier FLAC file created from a WEBM after decoding. I can utilize FFmpeg to extract from the WEBM. The goal is to ensure that the cuts align by also checking cross-correlation for accuracy. If the segment file has extra audio due to inaccurate seeking, that will stand out!


**Generating audio files**

I’ll start by producing `accurate.flac` using FFmpeg with the command `ffmpeg -i webm -ss 199 -t 5.34 -c:a flac accurate.flac`, making sure I place `-ss` after `-i`. For `fast.flac`, I’ll put `-ss` before `-i`, similar to an export. Then, I’ll create `cut_after.flac` and `cut_before.flac` from the source WEBM at a sample rate of 48k mono. I'll use specific FFmpeg commands for this, ensuring everything aligns correctly for comparison with the segment file using methods like embedding similarity and calculating mean squared error.


**Considering FFmpeg seeking**

I need to keep in mind that using `-ss` before the input file might result in faster but approximate seeking, especially with WEBM files, which can be keyframe-based and may start earlier than expected. There's a chance that this keyframe issue affects FLAC exports as well. If seeking with FFmpeg is inaccurate, I can check if the segment audio matches `cut_after` or `cut_before`. To verify this, I plan to run FFmpeg to create those audio files for comparison.


mkdir -p tmp/r2debug/cuts && \
DUR=5.3325 && \
SRC=tmp/r2debug/source/--jp1imJD9w.webm && \
ffmpeg -y -hide_banner -loglevel error -ss 199 -t $DUR -i $SRC -ac 1 -ar 48000 -c:a flac tmp/r2debug/cuts/cut_ss_before.flac && \
ffmpeg -y -hide_banner -loglevel error -i $SRC -ss 199 -t $DUR -ac 1 -ar 48000 -c:a flac tmp/r2debug/cuts/cut_ss_after.flac && \
ffprobe -hide_banner -loglevel error -show_format tmp/r2debug/cuts/cut_ss_before.flac | rg "duration=" && \
ffprobe -hide_banner -loglevel error -show_format tmp/r2debug/cuts/cut_ss_after.flac | rg "duration="


Output:
Exit code: 0
Wall time: 0.7 seconds
Output:
duration=5.332500
duration=5.332500


**Comparing audio segments**

I need to compare our exported segment file using cut_after and cut_before. My plan is to compute waveform correlation for alignment checks. I’ll use Python to load the audio and ensure both files have the same sample rate. Then, I’ll compute normalized cross-correlation at small lags to find the best offset. I’ll also compute the RMS difference for the best alignment within about +/- 1 second. That should help us figure out how well they match!


python3 - <<'PY'
from pathlib import Path
import torch
import torchaudio

if not hasattr(torchaudio, 'list_audio_backends'):
    torchaudio.list_audio_backends = lambda: ['soundfile','sox']

seg_path = Path('tmp/r2debug/extracted/--jp1imJD9w/segments/SPEAKER_00_0021_199.00-204.34.flac')
cut_before = Path('tmp/r2debug/cuts/cut_ss_before.flac')
cut_after = Path('tmp/r2debug/cuts/cut_ss_after.flac')

def load_mono(path):
    wav, sr = torchaudio.load(str(path))
    if wav.shape[0] > 1:
        wav = wav.mean(dim=0, keepdim=True)
    return wav.squeeze(0), sr

def best_corr(a, b, max_lag_samples):
    # returns (best_lag, best_corr)
    a = a - a.mean()
    b = b - b.mean()
    a = a / (a.norm() + 1e-9)
    b = b / (b.norm() + 1e-9)
    best = (-999, -1.0)
    for lag in range(-max_lag_samples, max_lag_samples+1, max(1, max_lag_samples//200)):
        if lag < 0:
            x = a[-lag:]
            y = b[:len(x)]
        else:
            x = a[:len(a)-lag]
            y = b[lag:lag+len(x)]
        if len(x) < 1000:
            continue
        corr = float((x*y).sum())
        if corr > best[1]:
            best = (lag, corr)
    return best

seg, sr1 = load_mono(seg_path)
bef, sr2 = load_mono(cut_before)
aft, sr3 = load_mono(cut_after)
assert sr1==sr2==sr3==48000, (sr1,sr2,sr3)

max_lag = int(0.8*sr1)

for name, ref in [('cut_ss_before', bef), ('cut_ss_after', aft)]:
    lag, corr = best_corr(seg, ref, max_lag)
    print(name, 'best_lag_s', lag/sr1, 'corr', corr)
PY


Output:
Exit code: 0
Wall time: 12.7 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]
cut_ss_before best_lag_s -0.724 corr 0.031121231615543365
cut_ss_after best_lag_s -0.712 corr 0.03444407135248184


python3 - <<'PY'
import json
from pathlib import Path
meta=json.loads(Path('tmp/r2debug/extracted/--jp1imJD9w/metadata.json').read_text())
for k in ['intro_skipped','processed_duration','duration','download','timing','trimmed_audio_path','processed_audio_path']:
    if k in meta:
        print(k, meta[k])
print('download keys', list((meta.get('download') or {}).keys())[:20])
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
intro_skipped 180.0
processed_duration 662.78
timing {'download': 6.12, 'load_buffer': 0.1, 'vad': 3.51, 'chunking': 0.0, 'diarization': 7.6, 'quality_filter': 1.0, 'unified_embeddings': 0.5, 'segment_embeddings': 0.01, 'clustering': 0.01, 'chunk_reassignment': 0.52, 'micro_contamination': 0.52, 'music_detection': 3.01, 'finalization': 0.0}
download keys []


**Clarifying audio segments**

I need to keep in mind that the timestamps I'm working with are relative to the audio after skipping the intro. This means I have to add 180 seconds to get the actual starting point. So when I extracted data at 199 seconds, I should have really looked at 379 seconds instead. That’s a great catch! Now, I’ll make sure to extract the region correctly at that new timestamp and aim for a high cross-correlation. Let's do it again.


DUR=5.3325 && OFFSET=180 && START=199 && SRC=tmp/r2debug/source/--jp1imJD9w.webm && \
REAL=$(python3 - <<'PY'
START=199.00
OFFSET=180.0
print(f"{START+OFFSET:.4f}")
PY) && \
echo real_start=$REAL && \
ffmpeg -y -hide_banner -loglevel error -ss $REAL -t $DUR -i $SRC -ac 1 -ar 48000 -c:a flac tmp/r2debug/cuts/cut_real_ss_before.flac && \
ffmpeg -y -hide_banner -loglevel error -i $SRC -ss $REAL -t $DUR -ac 1 -ar 48000 -c:a flac tmp/r2debug/cuts/cut_real_ss_after.flac && \
ffprobe -hide_banner -loglevel error -show_format tmp/r2debug/cuts/cut_real_ss_before.flac | rg "duration=" && \
ffprobe -hide_banner -loglevel error -show_format tmp/r2debug/cuts/cut_real_ss_after.flac | rg "duration="


Output:
Exit code: 0
Wall time: 1.1 seconds
Output:
/bin/bash: line 11: warning: here-document at line 7 delimited by end-of-file (wanted `PY')
real_start=379.0000
duration=5.332500
duration=5.332500


python3 - <<'PY'
from pathlib import Path
import torch
import torchaudio
if not hasattr(torchaudio, 'list_audio_backends'):
    torchaudio.list_audio_backends = lambda: ['soundfile','sox']

seg_path = Path('tmp/r2debug/extracted/--jp1imJD9w/segments/SPEAKER_00_0021_199.00-204.34.flac')
cut_before = Path('tmp/r2debug/cuts/cut_real_ss_before.flac')
cut_after = Path('tmp/r2debug/cuts/cut_real_ss_after.flac')

def load_mono(path):
    wav, sr = torchaudio.load(str(path))
    if wav.shape[0] > 1:
        wav = wav.mean(dim=0, keepdim=True)
    return wav.squeeze(0), sr

def best_corr(a, b, max_lag_samples, step):
    a = a - a.mean(); b = b - b.mean()
    a = a / (a.norm() + 1e-9); b = b / (b.norm() + 1e-9)
    best_lag = 0
    best_corr = -1e9
    for lag in range(-max_lag_samples, max_lag_samples+1, step):
        if lag < 0:
            x = a[-lag:]
            y = b[:len(x)]
        else:
            x = a[:len(a)-lag]
            y = b[lag:lag+len(x)]
        if len(x) < 1000:
            continue
        corr = float((x*y).sum())
        if corr > best_corr:
            best_corr = corr
            best_lag = lag
    return best_lag, best_corr

seg, sr1 = load_mono(seg_path)
bef, sr2 = load_mono(cut_before)
aft, sr3 = load_mono(cut_after)
assert sr1==sr2==sr3==48000, (sr1,sr2,sr3)

max_lag = int(0.6*sr1)
step = int(0.005*sr1)  # 5ms

for name, ref in [('cut_real_ss_before', bef), ('cut_real_ss_after', aft)]:
    lag, corr = best_corr(seg, ref, max_lag, step)
    print(name, 'best_lag_ms', lag/sr1*1000, 'corr', corr)
PY


Output:
Exit code: 0
Wall time: 7.9 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]
cut_real_ss_before best_lag_ms 15.0 corr 0.17639826238155365
cut_real_ss_after best_lag_ms 25.0 corr 0.1573745757341385


**Analyzing audio correlation**

I noticed that the correlation improved to around 0.17, but that's still low. Maybe it's due to how the segment file is made from processed audio, like demucs and normalization. The original file path `--jp1imJD9w_original.flac` is located in a specific output. I wonder if audio differences from the pipeline's download step are affecting this. It seems like the trimmed audio may not match because of adjustments like loudness normalization or resampling. I'll need to check the download pipeline details for a clearer picture!


**Exploring audio processing issues**

I’m considering that our cross-correlation might not be good because the audio is speech with variable elements. However, if they were identical, I’d expect a strong alignment. Maybe there's a miscode happening somewhere. It's also possible that the segment file was processed through demucs, but since the segments seem clean, demucs may not have been applied. Still, there could be "trim" or "resample" effects. I’ll need to check the metadata for any indication of whether demucs was applied.


python3 - <<'PY'
import json
from pathlib import Path
meta=json.loads(Path('tmp/r2debug/extracted/--jp1imJD9w/metadata.json').read_text())
print('demucs', meta.get('demucs'))
print('music', meta.get('quality_stats', {}).get('music_detection', {}))
print('micro_contamination', meta.get('quality_stats', {}).get('micro_contamination', {}))
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
demucs None
music {'segments_clean': 139, 'segments_needs_demucs': 7, 'segments_heavy_contamination': 9, 'duration_clean': 499.81787499999956, 'duration_needs_demucs': 13.516875000000006, 'duration_heavy_contamination': 54.16874999999999, 'pct_clean': 88.1, 'pct_needs_demucs': 2.4, 'pct_heavy_contamination': 9.5}
micro_contamination {'segments_checked': 62, 'segments_suspicious': 8, 'segments_contaminated': 2, 'contamination_regions_found': 2, 'segments_split_by_contamination': 2, 'portions_burned': 4, 'clean_portions_kept': 2, 'total_duration_burned': 7.813406249999986, 'stage1_time': 0.017653703689575195, 'stage2_time': 0.29567837715148926, 'total_time': 0.31}


**Searching for metadata segments**

I’m noticing that Demucs hasn’t been applied globally, and instead, there's a process of using a micro contamination stage to split and burn contaminated regions. It might have applied, but since we're before the overlap region, it’s uncertain. In the metadata, it indicates that two segments are contaminated and split, but which ones? I wonder if we can identify these segments in the metadata using `unusable_reason`, possibly looking for `micro_contamination`? Let's search for that.


python3 - <<'PY'
import json
from pathlib import Path
meta=json.loads(Path('tmp/r2debug/extracted/--jp1imJD9w/metadata.json').read_text())
segs=meta['segments']
reasons={}
for s in segs:
    r=s.get('unusable_reason')
    if r:
        reasons[r]=reasons.get(r,0)+1
print('unusable_reasons', reasons)

# any segments flagged by micro contamination?
hits=[s for s in segs if s.get('unusable_reason','').startswith('micro') or 'contamin' in (s.get('unusable_reason') or '')]
print('contamination_reason_hits', len(hits))
for s in hits[:20]:
    print(s['speaker'], s['start'], s['end'], s.get('unusable_reason'))
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
unusable_reasons {'overlap': 39, 'non_speech': 33, 'micro_contamination': 3, 'overlap_proximity': 3, 'heavy_contamination': 9}
contamination_reason_hits 12
SPEAKER_01 77.30159375000001 81.67 micro_contamination
SPEAKER_01 81.67 83.855 micro_contamination
SPEAKER_00 151.02846875 152.1134375 micro_contamination
SPEAKER_02 370.11971875 372.75221875 heavy_contamination
SPEAKER_02 375.80659375 381.08846875 heavy_contamination
SPEAKER_02 381.54409375 383.88971875 heavy_contamination
SPEAKER_02 386.80909375 392.74909375 heavy_contamination
SPEAKER_02 393.64346875 401.86159375 heavy_contamination
SPEAKER_02 402.09784375000004 404.84846875 heavy_contamination
SPEAKER_02 405.10159375 421.35221875 heavy_contamination
SPEAKER_02 424.32221875 429.63784375 heavy_contamination
SPEAKER_01 570.12221875 575.55596875 heavy_contamination


Summary: auto


**Exploring audio processing steps**

I'm looking into ways to produce trimmed audio by removing the introduction and converting to FLAC at 48k. It seems like the process might involve cutting a segment from a trimmed file to see how the parameters affect the output. Maybe the `download.py` script is responsible for creating the original FLAC file from a source like a WebM or YouTube file, and then we can analyze how to replicate this processing. I'm curious about ensuring everything matches accurately, but I'm not entirely sure about the steps yet.


**Investigating audio file origins**

I'm exploring the segment file which seems to originate from `output/..._original.flac`. I'm starting to wonder if that might actually be the trimmed version instead. The metadata shows an `original_audio_path`, indicating it could be the file with the intro cut off. It's tricky, though, because they store the `intro_skipped` file separately. I should investigate the pipeline, especially around the download stage, to understand how `original_audio_path` is set and what file it really refers to.


rg -n "original_audio_path" -n pipeline.py src/download.py src/audio_buffer.py | head -120


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/download.py:622:                'original_audio_path': str(original_file),
src/download.py:894:        'original_audio_path': str(original_file),  # Local path to FLAC (for cutting)
src/download.py:1125:                        'original_audio_path': str(actual_original) if actual_original else None,
src/download.py:1431:        'original_audio_path': str(original_file),  # Local path to FLAC
src/download.py:1557:    original_audio_path = None
src/download.py:1560:        original_audio_path = str(original_file)
src/download.py:1652:        'original_audio_path': original_audio_saved,  # Path to original quality audio (if preserved)
pipeline.py:872:        'original_audio_path': metadata.get('original_audio_path'),  # Local path to FLAC
pipeline.py:969:        original_file = Path(metadata.get('original_audio_path', ''))
pipeline.py:989:        original_audio_path = Path(metadata.get('original_audio_path', ''))
pipeline.py:1003:                    original_audio_path=original_audio_path,
pipeline.py:1039:                if getattr(config, 'segment_audio', True) and original_audio_path.exists():
pipeline.py:1042:                        audio_path=original_audio_path,


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    Example:
        Input: https://xxx.r2.cloudflarestorage.com/test/podcasts/VIDEO.webm?X-Amz-...
        Output: test/podcasts/VIDEO.webm
    """
    try:
        from urllib.parse import urlparse
        parsed = urlparse(presigned_url)
        # Path is like /bucket/key - remove leading slash
        path = parsed.path.lstrip('/')
        return path
    except:
        return presigned_url  # Fallback to full URL

def _download_from_r2(
    r2_url: str,
    video_id: str,
    output_dir: Path,
    config,
    max_retries: int = 3,
    supabase_metadata: Dict[str, Any] = None,
) -> Tuple[str, Dict[str, Any]]:
    """
    Download audio directly from R2 presigned URL using ffmpeg.

    === v2: R2 DIRECT DOWNLOAD ===
    For pre-cached videos in R2, we bypass yt-dlp entirely and use ffmpeg
    to download and convert in a single pass.

    Creates:
    - {video_id}_trimmed.wav (16kHz mono for processing)
    - {video_id}_original.flac (original quality mono for export)

    Returns:
        (audio_path, metadata): Path to processed audio and basic metadata
    """
    logger.info(f"⚡ R2 direct download: {video_id}")
    start = time.time()

    # Output file paths
    trimmed_file = output_dir / f"{video_id}_trimmed.wav"
    original_file = output_dir / f"{video_id}_original.flac"
    raw_file = output_dir / f"{video_id}_raw.wav"

    # Check cache
    if trimmed_file.exists() and original_file.exists():
        logger.info(f"✅ Using cached audio: {trimmed_file}")
        try:
            # Load a bit to get metadata (torchaudio.info deprecated in newer versions)
            proc_waveform, proc_sr = torchaudio.load(str(trimmed_file))
            orig_waveform, orig_sr = torchaudio.load(str(original_file))
            return str(trimmed_file), {
                'video_id': video_id,
                'youtube_url': f'https://www.youtube.com/watch?v={video_id}',
                'title': f'R2 Source: {video_id}',
                'original_duration': orig_waveform.shape[1] / orig_sr,
                'intro_skipped': 0.0,
                'processed_duration': proc_waveform.shape[1] / proc_sr,
                'output_dir': str(output_dir),
                'chapters': [],
                'sample_rate': proc_sr,
                'original_sample_rate': orig_sr,
                'original_audio_path': str(original_file),
                'original_audio_preserved': True,
                'audio_format': 'flac',
                'source_type': 'r2',
            }
        except Exception as e:
            logger.warning(f"Cache validation failed: {e}, re-downloading")

    # Step 1: Download raw audio from R2 using ffmpeg
    last_error = None
    for attempt in range(max_retries):
        try:
            logger.info(f"   R2 download attempt {attempt + 1}/{max_retries}")

            # ffmpeg can download from HTTP URLs directly
            # Convert to mono WAV, preserve original sample rate
            ffmpeg_download = [
                'ffmpeg', '-y',
                '-i', r2_url,
                '-vn',  # No video
                '-ac', '1',  # Mono
                '-f', 'wav',
                str(raw_file)
            ]

            result = subprocess.run(
                ffmpeg_download,
                capture_output=True,
                text=True,
                timeout=300  # 5 min timeout
            )

            if result.returncode != 0:
                raise DownloadError(f"ffmpeg failed: {result.stderr[:500]}")

            if raw_file.exists() and raw_file.stat().st_size > 10000:
                break
            else:
                raise DownloadError("Downloaded file too small or missing")

        except subprocess.TimeoutExpired:
            last_error = DownloadError("Download timed out")
            logger.warning(f"   R2 download timeout, attempt {attempt + 1}")
        except Exception as e:
            last_error = e
            logger.warning(f"   R2 download attempt {attempt + 1} failed: {e}")
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)
            continue

    if not raw_file.exists():
        raise DownloadError(f"R2 download failed after {max_retries} attempts: {last_error}")

    # Get audio info - use torchaudio.load metadata or ffprobe
    try:
        # Load a small portion to get metadata
        waveform, original_sr = torchaudio.load(str(raw_file))
        actual_duration = waveform.shape[1] / original_sr
        del waveform  # Free memory
    except Exception as e:
        logger.warning(f"Could not get audio info via torchaudio: {e}, trying ffprobe")
        try:
            # Fallback to ffprobe
            probe_cmd = [
                'ffprobe', '-v', 'error', '-show_entries',
                'format=duration:stream=sample_rate', '-of', 'json', str(raw_file)
            ]
            probe_result = subprocess.run(probe_cmd, capture_output=True, text=True)
            import json
            probe_data = json.loads(probe_result.stdout)
            actual_duration = float(probe_data.get('format', {}).get('duration', 0))
            original_sr = int(probe_data.get('streams', [{}])[0].get('sample_rate', 48000))
        except Exception as e2:
            logger.warning(f"ffprobe also failed: {e2}")
            original_sr = 48000
            actual_duration = 0

    logger.info(f"   📊 Raw audio: {original_sr}Hz, {actual_duration:.1f}s")

    # NOTE: Spectral analysis moved to AFTER intro skip (analyzes actual speech content)

    # === v3: Use Supabase metadata for chapter-based intro skip ===
    # Chapter info now comes from Supabase (synced from CSV), NOT yt-dlp
    intro_skip = 0.0
    outro_skip = 0.0
    skip_reason = "none"
    matched_title = None
    video_title = f'R2 Source: {video_id}'

    # Use Supabase metadata if provided
    if supabase_metadata:
        video_title = supabase_metadata.get('title') or video_title
        chapters = supabase_metadata.get('chapters') or []
        has_chapters = supabase_metadata.get('has_chapters', False)
        source_duration = supabase_metadata.get('source_duration_min', 0) * 60 if supabase_metadata.get('source_duration_min') else actual_duration

        if chapters and has_chapters:
            logger.info(f"   📖 Using Supabase chapters ({len(chapters)} chapters)")
            # Convert chapter format if needed (Supabase stores as JSON with 'seconds' key)
            normalized_chapters = []
            for i, ch in enumerate(chapters):
                start_sec = ch.get('seconds', ch.get('start_time', 0))
                # Calculate end_time from next chapter or total duration
                if i + 1 < len(chapters):
                    end_sec = chapters[i + 1].get('seconds', chapters[i + 1].get('start_time', source_duration))
                else:
                    end_sec = source_duration
                normalized_chapters.append({
                    'title': ch.get('title', ''),
                    'start_time': start_sec,
                    'end_time': end_sec,
                })

            # Use chapter-based intro detection
            intro_skip, outro_skip, skip_reason, matched_title = detect_intro_outro_from_chapters(
                normalized_chapters, source_duration, config
            )
            logger.info(f"   📖 Chapter-based skip: intro={intro_skip:.1f}s, reason={skip_reason}")
        else:
            # No chapters in Supabase, use dynamic skip
            logger.info(f"   📊 No chapters in Supabase, using dynamic skip")
            if actual_duration > 0:
                # FIX: Removed 10% cap that was overriding 3min/5min logic
                # The get_dynamic_intro_skip function already implements proper duration-based skip
                intro_skip = get_dynamic_intro_skip(actual_duration)
                skip_reason = "dynamic:no_supabase_chapters"
                logger.info(f"   ⏱ Dynamic skip: {intro_skip:.0f}s")
    else:
        # No Supabase metadata provided, use dynamic skip
        logger.info(f"   ⚠ No Supabase metadata, using dynamic skip")
        if actual_duration > 0 and getattr(config, 'auto_intro_skip', True) and getattr(config, 'intro_skip_seconds', 0.0) == 0.0:
            # NOTE: get_dynamic_intro_skip already implements the intended policy:
            # - ≤30min: 180s (3min)
            # - >30min: 300s (5min)
            # A previous 10% cap here caused regressions (e.g., ~52s for an 8.6min file).
            intro_skip = get_dynamic_intro_skip(actual_duration)
            skip_reason = "dynamic:no_metadata"
            logger.info(f"   ⏱ Dynamic skip: {intro_skip:.0f}s")


rg -n "intro_skip" -n src/download.py | head -40


Output:
Exit code: 0
Wall time: 0 seconds
Output:
218:def get_dynamic_intro_skip(total_duration: float) -> float:
307:    intro_skip = 0.0
319:        intro_skip = first_chapter_end
339:                intro_skip = second_chapter_end
359:        if getattr(config, 'auto_intro_skip', True) and config.intro_skip_seconds == 0.0:
360:            intro_skip = get_dynamic_intro_skip(total_duration)
364:            logger.info(f"   ⏱ Dynamic intro skip (no chapters): {intro_skip:.0f}s (video {duration_threshold})")
366:    return intro_skip, outro_skip, skip_reason, matched_title
616:                'intro_skipped': 0.0,
705:    intro_skip = 0.0
736:            intro_skip, outro_skip, skip_reason, matched_title = detect_intro_outro_from_chapters(
739:            logger.info(f"   📖 Chapter-based skip: intro={intro_skip:.1f}s, reason={skip_reason}")
745:                # The get_dynamic_intro_skip function already implements proper duration-based skip
746:                intro_skip = get_dynamic_intro_skip(actual_duration)
748:                logger.info(f"   ⏱ Dynamic skip: {intro_skip:.0f}s")
752:        if actual_duration > 0 and getattr(config, 'auto_intro_skip', True) and getattr(config, 'intro_skip_seconds', 0.0) == 0.0:
753:            # NOTE: get_dynamic_intro_skip already implements the intended policy:
757:            intro_skip = get_dynamic_intro_skip(actual_duration)
759:            logger.info(f"   ⏱ Dynamic skip: {intro_skip:.0f}s")
762:    if getattr(config, 'intro_skip_seconds', 0.0) > 0 and intro_skip != config.intro_skip_seconds:
763:        logger.info(f"⚙ Manual intro skip override: {config.intro_skip_seconds:.1f}s (was: {intro_skip:.1f}s)")
764:        intro_skip = config.intro_skip_seconds
768:    start_time = intro_skip
776:        intro_skip = 0
797:    logger.info(f"   ⚡ FFmpeg dual output: 16kHz WAV + {original_sr}Hz FLAC (skip: {intro_skip:.0f}s)")
886:        'intro_skipped': intro_skip,
887:        'intro_skip_reason': skip_reason,
888:        'intro_skip_matched_title': matched_title,
1119:                        'intro_skipped': 0.0,
1141:    intro_skip, outro_skip, skip_reason, matched_title = detect_intro_outro_from_chapters(chapters, original_duration, config)
1144:    if config.intro_skip_seconds > 0 and intro_skip != config.intro_skip_seconds:
1145:        logger.info(f"⚙ Manual intro skip override: {config.intro_skip_seconds:.1f}s (was: {intro_skip:.1f}s)")
1146:        intro_skip = config.intro_skip_seconds
1158:            intro_skip=intro_skip,
1174:            intro_skip=intro_skip,
1195:    intro_skip: float,
1317:    start_time = intro_skip if intro_skip > 0 else 0
1425:        'intro_skipped': intro_skip,
1444:        'intro_skip_reason': skip_reason,
1445:        'intro_skip_matched_title': matched_title,


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

    # === MANUAL OVERRIDE (Priority 3) ===
    if getattr(config, 'intro_skip_seconds', 0.0) > 0 and intro_skip != config.intro_skip_seconds:
        logger.info(f"⚙ Manual intro skip override: {config.intro_skip_seconds:.1f}s (was: {intro_skip:.1f}s)")
        intro_skip = config.intro_skip_seconds
        skip_reason = "manual_override"

    # Calculate trim with safety check
    start_time = intro_skip
    end_time = max(actual_duration - outro_skip, start_time + 10)  # At least 10s of audio
    trim_duration = end_time - start_time

    # If duration unknown or very short, don't trim
    if actual_duration < 60 or trim_duration < 30:
        start_time = 0
        trim_duration = actual_duration if actual_duration > 0 else 999999  # No trim
        intro_skip = 0
        skip_reason = "none:too_short_or_unknown"

    # Step 2: Create dual output (16kHz processing + original FLAC)
    # CRITICAL FIX (v7.6): -ss and -t must be specified BEFORE EACH output!
    # FFmpeg multi-output does NOT apply input options to all outputs automatically.
    target_sr = config.sample_rate

    ffmpeg_dual = [
        'ffmpeg', '-y',
        '-i', str(raw_file),
        # Output 1: 16kHz mono WAV (with trim)
        '-ss', str(start_time),
        '-t', str(trim_duration),
        '-map', '0:a', '-ar', str(target_sr), '-ac', '1', str(trimmed_file),
        # Output 2: Original quality FLAC (SAME trim - repeat -ss/-t!)
        '-ss', str(start_time),
        '-t', str(trim_duration),
        '-map', '0:a', '-ac', '1', '-c:a', 'flac', '-compression_level', '5', str(original_file),
    ]

    logger.info(f"   ⚡ FFmpeg dual output: 16kHz WAV + {original_sr}Hz FLAC (skip: {intro_skip:.0f}s)")

    try:
        result = subprocess.run(ffmpeg_dual, capture_output=True, text=True, check=True)
    except subprocess.CalledProcessError as e:
        raise DownloadError(f"FFmpeg dual output failed: {e.stderr[:500]}")

    # Verify outputs
    if not trimmed_file.exists():
        raise DownloadError(f"FFmpeg did not create trimmed file")
    if not original_file.exists():
        raise DownloadError(f"FFmpeg did not create original file")

    # === v7.6: CRITICAL VERIFICATION - Both files MUST have same duration ===
    try:
        trimmed_info = get_audio_info(str(trimmed_file))
        original_info = get_audio_info(str(original_file))

        trimmed_duration = trimmed_info['duration']
        original_duration_check = original_info['duration']

        duration_diff = abs(trimmed_duration - original_duration_check)

        if duration_diff > 0.1:  # Allow 0.1s tolerance
            logger.error(f"   ❌ DURATION MISMATCH! trimmed={trimmed_duration:.2f}s, original={original_duration_check:.2f}s")
            raise DownloadError(f"Duration mismatch between outputs: {duration_diff:.2f}s difference")

        processed_duration = trimmed_duration
        logger.info(f"   ✅ Duration verified: {trimmed_duration:.2f}s (diff={duration_diff:.3f}s)")

    except DownloadError:
        raise
    except Exception as e:
        logger.warning(f"   ⚠ Could not verify durations: {e}")
        processed_duration = trim_duration

    # Cleanup raw file
    try:
        raw_file.unlink()
    except:
        pass

    # === Spectral quality analysis (on TRIMMED content, not intro) ===
    # This ensures we analyze actual speech, not intro music/silence
    spectral_quality = None
    spectral_info = {}
    try:
        from src.spectral_analysis import analyze_spectral_quality
        # Load original FLAC (intro-skipped) for analysis - better quality than 16kHz WAV
        orig_waveform, orig_sr = torchaudio.load(str(original_file))
        spectral_quality = analyze_spectral_quality(
            orig_waveform.squeeze(0).numpy(), orig_sr
        )
        logger.info(f"   🔬 Spectral: {spectral_quality.original_format_guess} "
                   f"(rolloff={spectral_quality.rolloff_frequency:.0f}Hz)")
        spectral_info = {
            # Container vs actual quality
            'claimed_sample_rate': orig_sr,  # What the container says
            'effective_sample_rate': spectral_quality.effective_sample_rate,  # Estimated true quality
            'detected_nyquist': round(spectral_quality.detected_nyquist, 0),  # Where content ends
            'rolloff_frequency': round(spectral_quality.rolloff_frequency, 0),  # Energy rolloff point
            # Quality assessment
            'is_upsampled': spectral_quality.is_upsampled,
            'original_format': spectral_quality.original_format_guess,


**Creating trimmed audio files**

I’m thinking about how to create trimmed original FLAC files from downloaded WEBM files. It seems easiest to convert WEBM to WAV first, but I can also go straight to using `ffmpeg` for the conversion, specifying parameters to achieve the desired output. I know I need to skip the intro, which is 180 seconds, and the final length should be 662.78 seconds. This should match the exported segment, so I’m focusing on getting this right!


**Recreating original FLAC files**

I’m planning to create a new original FLAC file at `tmp/r2debug/recreated_original.flac` using specific parameters for accuracy, like `-ss` and `-t`. Next, I'll cut a segment from 199 to 204.34 seconds from this recreated file and compare it to the existing segment file to see if there was any cutoff during export. I also want to ensure the sample rate is correct, probably matching the original at 48,000 Hz, before proceeding with the cutting. Let's get to it!


mkdir -p tmp/r2debug/recreated && \
SRC=tmp/r2debug/source/--jp1imJD9w.webm && \
INTRO=180.0 && DUR=662.78 && \
ffmpeg -y -hide_banner -loglevel error -i $SRC -vn -ac 1 -ss $INTRO -t $DUR -c:a flac -compression_level 5 tmp/r2debug/recreated/trimmed_original.flac && \
ffprobe -hide_banner -loglevel error -show_streams -show_format tmp/r2debug/recreated/trimmed_original.flac | rg -n "duration=|sample_rate=|channels=" | head


Output:
Exit code: 0
Wall time: 2.8 seconds
Output:
10:sample_rate=48000
11:channels=1
21:duration=662.780000
48:duration=662.780000


**Processing audio cuts**

I need to cut segments at 199.0 from my trimmed_original. I’ll generate `recreated_cut_before.flac` using -ss before -i, which is the export style. Then, I’ll create `recreated_cut_after.flac` with -ss after -i for accuracy. The next step is to compare these files to verify the segments properly align with my expectations. It seems straightforward, but I want to ensure I'm precise with the timing and format during this process.


ORIG=tmp/r2debug/recreated/trimmed_original.flac && START=199.00 && DUR=5.3325 && \
ffmpeg -y -hide_banner -loglevel error -ss $START -t $DUR -i $ORIG -c:a flac tmp/r2debug/recreated/cut_export_style.flac && \
ffmpeg -y -hide_banner -loglevel error -i $ORIG -ss $START -t $DUR -c:a flac tmp/r2debug/recreated/cut_accurate_style.flac && \
ffprobe -hide_banner -loglevel error -show_format tmp/r2debug/recreated/cut_export_style.flac | rg "duration=" && \
ffprobe -hide_banner -loglevel error -show_format tmp/r2debug/recreated/cut_accurate_style.flac | rg "duration="


Output:
Exit code: 0
Wall time: 0.3 seconds
Output:
duration=5.332500
duration=5.332500


python3 - <<'PY'
from pathlib import Path
import torch
import torchaudio
if not hasattr(torchaudio, 'list_audio_backends'):
    torchaudio.list_audio_backends = lambda: ['soundfile','sox']

seg_path = Path('tmp/r2debug/extracted/--jp1imJD9w/segments/SPEAKER_00_0021_199.00-204.34.flac')
cut_export = Path('tmp/r2debug/recreated/cut_export_style.flac')
cut_accurate = Path('tmp/r2debug/recreated/cut_accurate_style.flac')

def load_mono(path):
    wav, sr = torchaudio.load(str(path))
    if wav.shape[0] > 1:
        wav = wav.mean(dim=0, keepdim=True)
    return wav.squeeze(0), sr

def best_corr(a, b, max_lag_samples, step):
    a = a - a.mean(); b = b - b.mean()
    a = a / (a.norm() + 1e-9); b = b / (b.norm() + 1e-9)
    best_lag = 0
    best_corr = -1e9
    for lag in range(-max_lag_samples, max_lag_samples+1, step):
        if lag < 0:
            x = a[-lag:]
            y = b[:len(x)]
        else:
            x = a[:len(a)-lag]
            y = b[lag:lag+len(x)]
        if len(x) < 1000:
            continue
        corr = float((x*y).sum())
        if corr > best_corr:
            best_corr = corr
            best_lag = lag
    return best_lag, best_corr

seg, sr1 = load_mono(seg_path)
exp, sr2 = load_mono(cut_export)
acc, sr3 = load_mono(cut_accurate)
assert sr1==sr2==sr3==48000

max_lag = int(0.6*sr1)
step = int(0.002*sr1)  # 2ms

for name, ref in [('export_style', exp), ('accurate_style', acc)]:
    lag, corr = best_corr(seg, ref, max_lag, step)
    print(name, 'best_lag_ms', lag/sr1*1000, 'corr', corr)
PY


Output:
Exit code: 0
Wall time: 15.4 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]
export_style best_lag_ms 8.0 corr 0.2418232262134552
accurate_style best_lag_ms 8.0 corr 0.2418232262134552


**Verifying audio cuts**

The correlation improved to 0.24, which isn't very high, but the cuts seem to match equally. It looks like the export style and accurate style from the original files produce the same audio. This could be because FLAC supports accurate seeking even when using the `-ss` option before `-i`. Since both cuts are identical, it means FFmpeg’s start is accurate. To confirm this, I plan to compare the MD5 hashes of the two cuts.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
6c83110bfcec857031c106cd05a07be9  tmp/r2debug/recreated/cut_export_style.flac
6c83110bfcec857031c106cd05a07be9  tmp/r2debug/recreated/cut_accurate_style.flac


rg -n "class UnifiedChunkEmbeddingCache" -n src/unified_embeddings.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
96:class UnifiedChunkEmbeddingCache:


python3 - <<'PY'
from pathlib import Path
lines=Path('src/unified_embeddings.py').read_text().splitlines()
for i in range(80, 200):
    print(f"{i+1:04d} {lines[i]}")
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
0081     severe_threshold: float = 0.25  # Circuit-breaker (from Maya)
0082     normal_threshold: float = 0.40  # Look-ahead confirmed (from Stabilized)
0083
0084
0085 @dataclass
0086 class CacheBuildStats:
0087     """Statistics from cache building."""
0088     total_chunks: int = 0
0089     eligible_chunks: int = 0
0090     embeddings_extracted: int = 0
0091     build_time_sec: float = 0.0
0092     gpu_batch_count: int = 0
0093     avg_batch_size: float = 0.0
0094
0095
0096 class UnifiedChunkEmbeddingCache:
0097     """
0098     Single source of truth for ALL embeddings in the pipeline.
0099
0100     Computes 1.5s chunk embeddings ONCE, provides instant access for:
0101     - Segment embeddings (clustering)
0102     - Speaker change detection (chunk reassignment)
0103     - Portion embeddings (reassignment confidence)
0104
0105     Key insight: All downstream operations become CPU-only (cache lookups + numpy ops)
0106     """
0107
0108     def __init__(
0109         self,
0110         audio_buffer,  # AudioBuffer instance
0111         vad_segments: List[Dict],  # VAD speech segments
0112         embedding_model,  # SpeechBrain ECAPA-TDNN
0113         vad_model=None,  # Silero VAD for speech ratio (optional)
0114         vad_utils=None,  # Silero utils
0115         config: Optional[UnifiedEmbeddingConfig] = None,
0116         device: Optional[torch.device] = None
0117     ):
0118         """
0119         Initialize the cache.
0120
0121         Args:
0122             audio_buffer: AudioBuffer with waveform_np and sample_rate
0123             vad_segments: List of {'start': float, 'end': float} from VAD
0124             embedding_model: ECAPA-TDNN model
0125             vad_model: Optional Silero VAD for speech ratio computation
0126             vad_utils: Silero utilities (get_speech_timestamps, etc.)
0127             config: UnifiedEmbeddingConfig
0128             device: torch device (auto-detected if None)
0129         """
0130         self.audio_buffer = audio_buffer
0131         self.vad_segments = vad_segments
0132         self.embedding_model = embedding_model
0133         self.vad_model = vad_model
0134         self.vad_utils = vad_utils
0135         self.config = config or UnifiedEmbeddingConfig()
0136         self.device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu")
0137
0138         # Cache storage
0139         self.chunks: Dict[int, ChunkInfo] = {}  # chunk_idx -> ChunkInfo
0140         self._time_index: Dict[Tuple[float, float], int] = {}  # (start, end) -> chunk_idx
0141         self._built = False
0142         self._stats = CacheBuildStats()
0143
0144     def build(self) -> int:
0145         """
0146         Build the cache by extracting ALL chunk embeddings in ONE batch.
0147
0148         This is the ONLY GPU operation - everything else is cache lookups.
0149
0150         Returns:
0151             Number of chunks with embeddings
0152         """
0153         if self._built:
0154             logger.info(f"   Cache already built ({len(self.chunks)} chunks)")
0155             return len(self.chunks)
0156
0157         start_time = time.time()
0158
0159         logger.info("=" * 70)
0160         logger.info("⚡ UNIFIED CHUNK EMBEDDING CACHE (Compute Once, Use Everywhere)")
0161         logger.info(f"   Chunk duration: {self.config.chunk_duration}s")
0162         logger.info(f"   Min speech ratio: {self.config.min_speech_ratio}")
0163         logger.info("=" * 70)
0164
0165         # Step 1: Create ALL chunks from VAD segments
0166         t0 = time.time()
0167         chunk_list = self._create_all_chunks()
0168         self._stats.total_chunks = len(chunk_list)
0169         logger.info(f"   [TIMING] Chunk creation: {(time.time()-t0)*1000:.0f}ms")
0170
0171         if not chunk_list:
0172             logger.warning("   No chunks to process")
0173             self._built = True
0174             return 0
0175
0176         # Step 2: Filter by speech ratio (VAD eligibility)
0177         t0 = time.time()
0178         eligible_chunks = [c for c in chunk_list if c.speech_ratio >= self.config.min_speech_ratio]
0179         self._stats.eligible_chunks = len(eligible_chunks)
0180         logger.info(f"   [TIMING] Filtering: {(time.time()-t0)*1000:.0f}ms")
0181
0182         logger.info(f"   Created {len(chunk_list)} chunks, {len(eligible_chunks)} eligible (≥{self.config.min_speech_ratio} speech)")
0183
0184         if not eligible_chunks:
0185             logger.warning("   No eligible chunks after speech ratio filter")
0186             self._built = True
0187             return 0
0188
0189         # Step 3: MEGA-BATCH GPU inference (THE OPTIMIZATION)
0190         t0 = time.time()
0191         embeddings = self._batch_extract_embeddings(eligible_chunks)
0192         self._stats.embeddings_extracted = len(embeddings)
0193         logger.info(f"   [TIMING] Embedding extraction: {(time.time()-t0)*1000:.0f}ms")
0194
0195         # Step 4: Store in cache
0196         t0 = time.time()
0197         for chunk, emb in zip(eligible_chunks, embeddings):
0198             chunk.embedding = emb
0199             self.chunks[chunk.idx] = chunk
0200             # Index by time for fast segment->chunk mapping


**Running Python script**

I’m planning to run a Python script to patch the torchaudio list of audio backends. I need to import `AudioBuffer` from `src/audio_buffer.py`. Next, I'll load the audio buffer from a segment file, making sure it’s possibly converted to 16k. I wonder if the audio buffer might resample to the configured sample rate, so I'll need to inspect the audio_buffer quickly. Let’s hope everything goes smoothly!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Shared Audio Buffer - Load Once, Use Everywhere

=== OPTIMIZATION (v6.2) ===
Previously: Each stage (VAD, diarization, embeddings, quality) called torchaudio.load()
Now: Load audio ONCE, pass numpy array/slices to all stages

Benefits:
- Eliminates 4-5 redundant file reads
- Reduces disk I/O by ~80%
- Faster processing (no decode overhead)
- Memory-efficient (single copy)
"""

import logging
import numpy as np
import torch
import torchaudio
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, Tuple
import atexit
import shutil

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

@dataclass
class AudioBuffer:
    """
    Holds loaded audio waveform and provides slicing utilities.

    Usage:
        buffer = AudioBuffer.from_file("audio.wav")
        vad_result = run_vad(buffer)
        embeddings = extract_embeddings(buffer, segments)
    """
    waveform_np: np.ndarray  # Shape: (samples,) - mono
    sample_rate: int
    audio_path: str  # Original path for reference
    duration: float

    @classmethod
    def from_file(cls, audio_path: str) -> 'AudioBuffer':
        """Load audio file into buffer."""
        logger.info(f"📥 Loading audio into shared buffer: {Path(audio_path).name}")
        waveform, sr = torchaudio.load(audio_path)
        waveform_np = waveform.squeeze(0).numpy()  # Convert to mono numpy
        duration = len(waveform_np) / sr
        logger.info(f"   Loaded: {duration:.1f}s @ {sr}Hz ({len(waveform_np):,} samples)")
        return cls(
            waveform_np=waveform_np,
            sample_rate=sr,
            audio_path=audio_path,
            duration=duration
        )

    def get_slice(self, start_sec: float, end_sec: float) -> np.ndarray:
        """Get audio slice by time range (seconds)."""
        start_sample = int(start_sec * self.sample_rate)
        end_sample = int(end_sec * self.sample_rate)
        return self.waveform_np[start_sample:end_sample]

    def get_slice_samples(self, start_sample: int, end_sample: int) -> np.ndarray:
        """Get audio slice by sample indices."""
        return self.waveform_np[start_sample:end_sample]

    def get_torch_tensor(self, start_sec: float = 0, end_sec: float = None) -> torch.Tensor:
        """Get slice as torch tensor for GPU processing."""
        if end_sec is None:
            end_sec = self.duration
        chunk = self.get_slice(start_sec, end_sec)
        return torch.from_numpy(chunk).float()

    def get_full_waveform_2d(self) -> Tuple[torch.Tensor, int]:
        """Get full waveform as 2D tensor (1, samples) for torchaudio compatibility."""
        return torch.from_numpy(self.waveform_np).unsqueeze(0), self.sample_rate

class TempDirManager:
    """
    Failure-safe temporary directory manager.

    === OPTIMIZATION (v6.2) ===
    Registers atexit handler to clean up temp files even on crash.
    Prevents disk quota exhaustion from orphaned chunk files.
    """
    _instance = None
    _temp_dirs: list = []

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
            cls._instance._temp_dirs = []
            # Register cleanup on exit (normal or exception)
            atexit.register(cls._instance.cleanup_all)
        return cls._instance

    def register(self, path: Path):
        """Register a temp directory for cleanup on exit."""
        self._temp_dirs.append(path)

    def unregister(self, path: Path):
        """Unregister a temp directory (when successfully cleaned)."""
        if path in self._temp_dirs:
            self._temp_dirs.remove(path)

    def cleanup_all(self):
        """Clean up all registered temp directories."""
        for path in self._temp_dirs[:]:  # Copy list to avoid modification during iteration
            try:
                if path.exists():
                    shutil.rmtree(path, ignore_errors=True)
                    logger.debug(f"   Cleaned up temp dir: {path}")
                self._temp_dirs.remove(path)
            except Exception as e:
                logger.warning(f"   Failed to cleanup {path}: {e}")

    def cleanup_for_audio(self, audio_path: str):
        """Clean up temp dirs associated with an audio file."""
        chunk_dir = Path(audio_path).parent / "chunks"
        if chunk_dir.exists():
            try:
                shutil.rmtree(chunk_dir)
                self.unregister(chunk_dir)
                logger.debug(f"   Cleaned up chunks: {chunk_dir}")
            except Exception as e:
                logger.warning(f"   Failed to cleanup chunks: {e}")

# Global instance
TEMP_MANAGER = TempDirManager()

def get_vram_info() -> Tuple[float, float, float]:
    """
    Get GPU memory info for memory-aware batching.

    === OPTIMIZATION (v6.2) ===
    Use torch.cuda.mem_get_info() to query actual VRAM availability.
    Scale batch sizes dynamically based on available memory.

    Returns:
        (free_gb, total_gb, utilization_pct)
    """
    if not torch.cuda.is_available():
        return (0.0, 0.0, 0.0)

    try:
        free, total = torch.cuda.mem_get_info()
        free_gb = free / (1024**3)
        total_gb = total / (1024**3)
        utilization = (1 - free / total) * 100
        return (free_gb, total_gb, utilization)
    except Exception:
        # Fallback for older PyTorch
        total = torch.cuda.get_device_properties(0).total_memory
        reserved = torch.cuda.memory_reserved(0)
        free = total - reserved
        return (free / (1024**3), total / (1024**3), (reserved / total) * 100)

def compute_optimal_batch_size(
    num_items: int,
    avg_item_samples: int,
    base_batch: int = 8,
    min_batch: int = 2,
    max_batch: int = 64,
    vram_headroom_gb: float = 2.0
) -> int:
    """
    Compute optimal batch size based on available VRAM.

    === OPTIMIZATION (v6.2) ===
    Instead of fixed batch sizes, dynamically scale based on:
    - Available VRAM (torch.cuda.mem_get_info)
    - Average item size (samples)
    - Safety headroom for GPU operations

    Args:
        num_items: Total items to process
        avg_item_samples: Average samples per item
        base_batch: Default batch size
        min_batch: Minimum batch size
        max_batch: Maximum batch size
        vram_headroom_gb: Reserved VRAM headroom

    Returns:
        Optimal batch size
    """
    free_gb, total_gb, _ = get_vram_info()

    if free_gb == 0:  # CPU-only
        return min(base_batch, num_items)

    # Available VRAM for batching (minus headroom)
    available_gb = max(0.5, free_gb - vram_headroom_gb)

    # Estimate memory per batch item (float32 audio + embedding overhead)


python3 - <<'PY'
import numpy as np
import torch
import torchaudio
if not hasattr(torchaudio, 'list_audio_backends'):
    torchaudio.list_audio_backends = lambda: ['soundfile','sox']

from pathlib import Path
from speechbrain.inference.speaker import EncoderClassifier

from src.audio_buffer import AudioBuffer
from src.unified_embeddings import UnifiedChunkEmbeddingCache, UnifiedEmbeddingConfig

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

# Load and resample to 16k (matches pipeline embedding assumptions)
wav, sr = torchaudio.load(str(TARGET))
if wav.shape[0] > 1:
    wav = wav.mean(dim=0, keepdim=True)
if sr != 16000:
    wav = torchaudio.functional.resample(wav, sr, 16000)
wav_np = wav.squeeze(0).numpy()
dur = len(wav_np)/16000

buf = AudioBuffer(waveform_np=wav_np, sample_rate=16000, audio_path=str(TARGET), duration=dur)

# Pretend all is speech for chunk creation
vad_segments=[{'start':0.0,'end':dur,'duration':dur}]

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

cfg = UnifiedEmbeddingConfig(chunk_duration=1.5, chunk_overlap=0.75, min_speech_ratio=0.0, severe_threshold=0.15, normal_threshold=0.30)
cache = UnifiedChunkEmbeddingCache(audio_buffer=buf, vad_segments=vad_segments, embedding_model=model, config=cfg, device=device)
cache.build()

print('dur', dur, 'chunks', len(cache.chunks))
print('chunk_times', [(c.start_time, c.end_time) for c in list(cache.chunks.values())[:5]])

sims = cache.get_chunk_similarities(0.0, dur)
print('adjacent_similarities', sims)

# Try detection with persistence 1 and 2
for persistence in [1,2]:
    splits, reasons, severe, lookahead = cache.detect_speaker_changes(0.0, dur, require_persistence=True, persistence_count=persistence, use_relative_valley=True)
    print('persistence', persistence, 'splits', splits, 'reasons', reasons, 'severe', severe, 'lookahead', lookahead)

# Try no persistence (lookahead/severe)
splits, reasons, severe, lookahead = cache.detect_speaker_changes(0.0, dur, require_persistence=False, use_relative_valley=False, severe_threshold=0.15, normal_threshold=0.30)
print('no_persistence_fixed_th splits', splits, 'reasons', reasons)
PY


Output:
Exit code: 1
Wall time: 5.2 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]
/home/ubuntu/.local/lib/python3.10/site-packages/torch/backends/__init__.py:46: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.)
  self.setter(val)
/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/utils/autocast.py:188: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
  wrapped_fwd = torch.cuda.amp.custom_fwd(fwd, cast_inputs=cast_inputs)
Traceback (most recent call last):
  File "<stdin>", line 38, in <module>
TypeError: UnifiedEmbeddingConfig.__init__() got an unexpected keyword argument 'chunk_overlap'


**Inspecting unified embedding config**

I’m thinking that UnifiedEmbeddingConfig probably uses `chunk_hop` or `chunk_stride` instead of overlap. It seems like it’s important to verify how that’s actually set up. So, I need to check the dataclass definition from earlier. Hmm, let me look at the top of the unified_embeddings to get more clarity. It's a bit of digging, but I want to ensure I’m on the right path!


rg -n "class UnifiedEmbeddingConfig" -n src/unified_embeddings.py && sed -n '40,90p' src/unified_embeddings.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
71:class UnifiedEmbeddingConfig:
    # For speaker change detection (replaces Stage 6b chunk processing)
    split_points = cache.detect_speaker_changes(seg['start'], seg['end'])

    # For reassignment (replaces portion embedding extraction)
    portion_emb = cache.get_segment_embedding(portion_start, portion_end)
"""

import time
import logging
from dataclasses import dataclass, field
from typing import Dict, List, Tuple, Optional, Any
import numpy as np
import torch
from sklearn.metrics.pairwise import cosine_similarity

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

@dataclass
class ChunkInfo:
    """Information about a single 1.5s chunk."""
    idx: int
    start_time: float
    end_time: float
    start_sample: int
    end_sample: int
    speech_ratio: float
    embedding: Optional[np.ndarray] = None

@dataclass
class UnifiedEmbeddingConfig:
    """Configuration for unified embedding extraction."""
    chunk_duration: float = 1.5  # 1.5s chunks (Kimi-Audio standard)
    min_speech_ratio: float = 0.6  # From ChatGPT VAD eligibility
    batch_size: int = 128  # Aggressive batching for GPU saturation
    vram_headroom_gb: float = 1.0  # Less conservative headroom
    max_batch_size: int = 256  # Upper limit
    min_batch_size: int = 32  # Lower limit

    # Speaker change detection thresholds (from chunk_reassignment)
    severe_threshold: float = 0.25  # Circuit-breaker (from Maya)
    normal_threshold: float = 0.40  # Look-ahead confirmed (from Stabilized)

@dataclass
class CacheBuildStats:
    """Statistics from cache building."""
    total_chunks: int = 0
    eligible_chunks: int = 0
    embeddings_extracted: int = 0


Summary: auto


rg -n "_create_all_chunks\(|chunk_duration\s*\*|0\.75" src/unified_embeddings.py | head -80


Output:
Exit code: 0
Wall time: 0 seconds
Output:
167:        chunk_list = self._create_all_chunks()
214:    def _create_all_chunks(self) -> List[ChunkInfo]:
224:        chunk_samples = int(self.config.chunk_duration * self.audio_buffer.sample_rate)
280:        chunk_samples = int(self.config.chunk_duration * self.audio_buffer.sample_rate)


sed -n '200,340p' src/unified_embeddings.py