**Analyzing lease recovery issues**

I’m considering a lease recovery service script that seems to not be running properly, which explains why claims aren’t being reclaimed. The user has multiple instances, so I can try running it locally for this one. To address things immediately, I’ll execute `scripts/monitor/lease_recovery.py` on a reliable node and keep it running in the background. That should help reclaim expired leases. However, I need to also fix the code to ensure that workers can renew leases while working, preventing premature reclamation.


**Addressing lease duration and recovery**

I’m looking at how the current lease duration is set to 900 seconds, which usually works since tasks tend to finish within that time. However, when tasks get stuck, they don’t renew the lease. If recovery runs and resets a task to pending while it’s still processing, that could lead to duplicates if another worker processes the same task at the same time. To avoid this, workers should periodically refresh `lease_expires_at` during processing for longer tasks. For now, the immediate fix is to kill the hung processes, restart them, and run lease recovery to free up jobs.


**Running lease recovery locally**

I plan to run the lease recovery script one time on our local host to reclaim expired leases globally. This action will set the statuses of those claims to pending. I wonder if our local environment has the necessary Python libraries, especially `supabase` and `dotenv`. It could be missing them, but the requirements might be in a virtual environment. If not, I could use the Supabase API with a `curl` command to patch rows directly. The simplest approach would be to run `scripts/monitor/lease_recovery.py` using Python 3, although it could fail due to missing dependencies. I'll check for `dotenv` first.


python3 -c "import dotenv; print('dotenv ok')" 2>/dev/null || echo 'dotenv missing'; python3 -c "import supabase; print('supabase ok')" 2>/dev/null || echo 'supabase missing'


Output:
Exit code: 0
Wall time: 0.7 seconds
Output:
dotenv ok
supabase ok


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.


**Executing lease recovery**

I'm thinking about running the lease_recovery script locally. It seems like it's going to load the .env file from the repository root, which is great! I believe it uses load_dotenv() by default, loading the .env from the current working directory. That’s a useful feature. I should just go ahead and run it once now to check how many resources we can reclaim. Let's see what the output comes back with!


python3 scripts/monitor/lease_recovery.py | tail -n 50


Output:
Exit code: 0
Wall time: 1.2 seconds
Output:
2026-01-08 14:22:06 | INFO     | SupabaseClient initialized: https://exlkkfpymkpqlxulurel.supabase.co
2026-01-08 14:22:06 | INFO     | Reclaimed 34 expired leases
2026-01-08 14:22:06 | INFO     | Reclaimed 34 expired leases
2026-01-08 14:22:07 | INFO     | HTTP Request: GET https://exlkkfpymkpqlxulurel.supabase.co/rest/v1/worker_heartbeats?select=worker_id%2Cmachine_id%2Cgpu_id%2Cstatus%2Ccurrent_video_id%2Clast_heartbeat&status=neq.offline&last_heartbeat=lt.2026-01-08T14%3A12%3A07.344335%2B00%3A00 "HTTP/2 200 OK"
2026-01-08 14:22:07 | WARNING  | Found 33 stale workers:
2026-01-08 14:22:07 | WARNING  |   vast-29761465_gpu5 - last seen: 2026-01-08T09:01:43.424537+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761469_gpu0 - last seen: 2026-01-08T13:42:40.655529+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29760494_gpu5 - last seen: 2026-01-08T09:37:14.843435+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761470_gpu2 - last seen: 2026-01-08T08:17:13.608765+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29760494_gpu4 - last seen: 2026-01-08T09:03:06.383523+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761470_gpu3 - last seen: 2026-01-08T08:28:38.850559+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761465_gpu6 - last seen: 2026-01-08T13:01:40.992469+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761464_gpu2 - last seen: 2026-01-08T13:56:52.838617+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29760494_gpu0 - last seen: 2026-01-08T13:58:23.832032+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761470_gpu7 - last seen: 2026-01-08T09:53:08.842974+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29760494_gpu7 - last seen: 2026-01-08T13:14:44.36835+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29760494_gpu6 - last seen: 2026-01-08T13:44:35.179171+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761470_gpu5 - last seen: 2026-01-08T13:08:56.14534+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761466_gpu1 - last seen: 2026-01-08T09:19:41.971794+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761465_gpu3 - last seen: 2026-01-08T13:15:08.881917+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761464_gpu7 - last seen: 2026-01-08T08:33:31.219997+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761613_gpu7 - last seen: 2026-01-08T09:29:20.820262+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761464_gpu4 - last seen: 2026-01-08T08:57:56.308409+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761613_gpu2 - last seen: 2026-01-08T07:29:16.977518+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761470_gpu6 - last seen: 2026-01-08T07:24:10.146889+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761464_gpu5 - last seen: 2026-01-08T13:04:13.618423+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761613_gpu1 - last seen: 2026-01-08T12:54:15.638512+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761470_gpu4 - last seen: 2026-01-08T13:24:14.893913+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761613_gpu3 - last seen: 2026-01-08T09:40:47.306669+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761613_gpu6 - last seen: 2026-01-08T08:20:30.24828+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761613_gpu0 - last seen: 2026-01-08T07:15:36.719108+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761466_gpu5 - last seen: 2026-01-08T13:15:15.425204+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761613_gpu4 - last seen: 2026-01-08T07:16:01.204848+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761464_gpu0 - last seen: 2026-01-08T08:05:18.097368+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761470_gpu0 - last seen: 2026-01-08T08:59:57.021575+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761613_gpu5 - last seen: 2026-01-08T07:37:10.278069+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761464_gpu6 - last seen: 2026-01-08T08:10:36.334146+00:00
2026-01-08 14:22:07 | WARNING  |   vast-29761470_gpu1 - last seen: 2026-01-08T11:53:13.22794+00:00
Reclaimed: 34 leases, Stale workers: 33


Summary: auto


**Restarting instance processes**

I’ve just reclaimed 34 expired leases, likely clearing up some stuck videos, and those rows should now be in PENDING status for re-picking. However, the worker processes in the instance still seem stuck, which could lead to duplicate claims. To resolve this, I think we should restart the instance to clear those stuck processes. It seems easiest to recycle instance 29761470 through vastctl. I'll run the recycle command now, which might take a minute but should help get everything back on track!


set -a; source .env; set +a; python3 scripts/vast/vastctl.py recycle 29761470


Output:
Exit code: 0
Wall time: 0.3 seconds
Output:
Recycled 29761470: {'success': True}


**Inspecting audio processing**

I'm thinking it might hang because of either reading an incomplete file or ffmpeg waiting. I need to ensure that I call ffmpeg with a timeout to prevent issues. Now, where's the Voice Activity Detection (VAD) loading audio? It seems to be in the pipeline, likely using `AudioBuffer` and `download_audio`. I think it’s a good idea to inspect `src/download.py` and focus on the audio processing, specifically searching for subprocess calls.


rg -n "subprocess|ffmpeg|yt-dlp|torchaudio\.load|soundfile" -S src pipeline.py massive_process.py | head -n 120


Output:
Exit code: 0
Wall time: 0 seconds
Output:
massive_process.py:901:        import subprocess
massive_process.py:902:        result = subprocess.run(
pipeline.py:61:    torchaudio.list_audio_backends = lambda: ['soundfile', 'sox']
pipeline.py:136:    - Chapter-based intro/outro skip (no yt-dlp needed)
pipeline.py:227:    # Previously: Each stage called torchaudio.load() separately (4-5 times!)
pipeline.py:1037:                # Step 1: Segment original audio (parallel ffmpeg)
src/embeddings.py:139:        waveform, sr = torchaudio.load(audio_path)
src/utils.py:30:    waveform, sr = torchaudio.load(audio_path)
src/audio_buffer.py:6:Previously: Each stage (VAD, diarization, embeddings, quality) called torchaudio.load()
src/audio_buffer.py:48:        waveform, sr = torchaudio.load(audio_path)
src/compute.py:129:                import subprocess
src/compute.py:130:                result = subprocess.run(['nproc'], capture_output=True, text=True, timeout=5)
src/download.py:5:Uses ffmpeg directly to create both 16kHz processing audio AND original
src/download.py:8:Previous: yt-dlp → temp.wav → torchaudio load → Python resample → save both
src/download.py:9:Now:      yt-dlp → ffmpeg pipe → dual output (16kHz + original) in one pass
src/download.py:18:import subprocess
src/download.py:29:    Get proxy URL for yt-dlp from environment variable.
src/download.py:45:    Compatible with all torchaudio versions by using torchaudio.load
src/download.py:69:        waveform, sample_rate = torchaudio.load(str(file_path))
src/download.py:583:    Download audio directly from R2 presigned URL using ffmpeg.
src/download.py:586:    For pre-cached videos in R2, we bypass yt-dlp entirely and use ffmpeg
src/download.py:609:            proc_waveform, proc_sr = torchaudio.load(str(trimmed_file))
src/download.py:610:            orig_waveform, orig_sr = torchaudio.load(str(original_file))
src/download.py:630:    # Step 1: Download raw audio from R2 using ffmpeg
src/download.py:636:            # ffmpeg can download from HTTP URLs directly
src/download.py:638:            ffmpeg_download = [
src/download.py:639:                'ffmpeg', '-y',
src/download.py:647:            result = subprocess.run(
src/download.py:648:                ffmpeg_download,
src/download.py:655:                raise DownloadError(f"ffmpeg failed: {result.stderr[:500]}")
src/download.py:662:        except subprocess.TimeoutExpired:
src/download.py:675:    # Get audio info - use torchaudio.load metadata or ffprobe
src/download.py:678:        waveform, original_sr = torchaudio.load(str(raw_file))
src/download.py:689:            probe_result = subprocess.run(probe_cmd, capture_output=True, text=True)
src/download.py:704:    # Chapter info now comes from Supabase (synced from CSV), NOT yt-dlp
src/download.py:781:    # FFmpeg multi-output does NOT apply input options to all outputs automatically.
src/download.py:784:    ffmpeg_dual = [
src/download.py:785:        'ffmpeg', '-y',
src/download.py:797:    logger.info(f"   ⚡ FFmpeg dual output: 16kHz WAV + {original_sr}Hz FLAC (skip: {intro_skip:.0f}s)")
src/download.py:800:        result = subprocess.run(ffmpeg_dual, capture_output=True, text=True, check=True)
src/download.py:801:    except subprocess.CalledProcessError as e:
src/download.py:802:        raise DownloadError(f"FFmpeg dual output failed: {e.stderr[:500]}")
src/download.py:806:        raise DownloadError(f"FFmpeg did not create trimmed file")
src/download.py:808:        raise DownloadError(f"FFmpeg did not create original file")
src/download.py:846:        orig_waveform, orig_sr = torchaudio.load(str(original_file))
src/download.py:960:    Uses ffmpeg directly for dual-output in single pass:
src/download.py:964:    Previous: yt-dlp → temp.wav → torchaudio load → Python resample → save both
src/download.py:965:    Now:      yt-dlp → single ffmpeg → dual output (NO Python resampling!)
src/download.py:968:    When video_url is an R2 presigned URL, we use ffmpeg directly instead of yt-dlp.
src/download.py:972:    - Title, chapters, duration (no yt-dlp metadata fetch needed)
src/download.py:1007:        # Presigned URL lets ffmpeg stream directly (no local video download).
src/download.py:1035:    # R2 presigned URLs use ffmpeg directly (not yt-dlp)
src/download.py:1037:        logger.info(f"📦 R2 source detected, using ffmpeg direct download")
src/download.py:1096:                waveform, sr = torchaudio.load(str(trimmed_file))
src/download.py:1148:    # === v7.0 OPTIMIZATION: Single-pass ffmpeg dual output ===
src/download.py:1149:    # Download audio stream and process with ffmpeg in one command
src/download.py:1151:        result = _download_with_ffmpeg_dual_output(
src/download.py:1188:def _download_with_ffmpeg_dual_output(
src/download.py:1206:    Single ffmpeg command creates both outputs:
src/download.py:1216:    Key insight: ffmpeg can output to multiple files from single input stream,
src/download.py:1224:    # Step 1: Download audio at original quality using yt-dlp
src/download.py:1225:    # NO resampling in yt-dlp - we'll do it in ffmpeg
src/download.py:1232:            'key': 'FFmpegExtractAudio',
src/download.py:1261:                            subprocess.run([
src/download.py:1262:                                'ffmpeg', '-i', str(ext_file), '-ac', '1',
src/download.py:1304:        raw_waveform, _ = torchaudio.load(str(raw_file))
src/download.py:1321:    # Step 2: Single ffmpeg command for dual output with trim
src/download.py:1323:    # FFmpeg multi-output does NOT apply input options to all outputs automatically.
src/download.py:1325:    ffmpeg_cmd = [
src/download.py:1326:        'ffmpeg', '-i', str(raw_file),
src/download.py:1345:    logger.info(f"   ⚡ FFmpeg dual output: 16kHz WAV + {original_sr}Hz FLAC (trim: {start_time:.1f}s-{end_time:.1f}s)")
src/download.py:1348:        result = subprocess.run(ffmpeg_cmd, capture_output=True, text=True, check=True)
src/download.py:1349:    except subprocess.CalledProcessError as e:
src/download.py:1350:        logger.error(f"FFmpeg failed: {e.stderr}")
src/download.py:1351:        raise DownloadError(f"FFmpeg dual output failed: {e}")
src/download.py:1355:        raise DownloadError(f"FFmpeg did not create trimmed file: {trimmed_file}")
src/download.py:1357:        raise DownloadError(f"FFmpeg did not create original file: {original_file}")
src/download.py:1360:    # This catches FFmpeg multi-output bugs where trim only applies to first output
src/download.py:1373:            logger.error(f"   This indicates FFmpeg trim was not applied to both outputs!")
src/download.py:1481:                'key': 'FFmpegExtractAudio',
src/download.py:1498:                'key': 'FFmpegExtractAudio',
src/download.py:1519:                            subprocess.run([
src/download.py:1520:                                'ffmpeg', '-i', str(ext_file), '-ar', str(config.sample_rate),
src/download.py:1543:        waveform, sr = torchaudio.load(str(downloaded_file))
src/download.py:1612:            orig_waveform, orig_sr = torchaudio.load(str(downloaded_file))
src/config.py:46:    # - ffmpeg stream-extract audio
src/config.py:70:    # Uses parallel ffmpeg (8 workers) - typically 2-3s for 500 segments
src/config.py:75:    # In GPU worker subprocesses, background threads die when the process exits.
src/overlap_detection.py:518:        waveform, sr = torchaudio.load(audio_path)
src/overlap_detection.py:742:    waveform, sr = torchaudio.load(audio_path)
src/diarization.py:93:        waveform, sr = torchaudio.load(audio_path)
src/export.py:13:import subprocess
src/export.py:61:    Cut audio into individual speaker segment files using parallel ffmpeg.
src/export.py:68:        max_workers: Number of parallel ffmpeg processes
src/export.py:84:        """Cut a single segment using ffmpeg."""
src/export.py:96:                'ffmpeg', '-y', '-hide_banner', '-loglevel', 'error',
src/export.py:104:            result = subprocess.run(cmd, capture_output=True, timeout=30)
src/export.py:106:                logger.warning(f"ffmpeg failed for segment {idx}: {result.stderr.decode()[:100]}")
src/export.py:114:    # Run parallel ffmpeg
src/export.py:139:        # Step 1: Segment audio (parallel ffmpeg)
src/models.py:22:    torchaudio.list_audio_backends = lambda: ['soundfile', 'sox']
src/hybrid_music_detection.py:261:        import soundfile as sf
src/chunk_parallel.py:207:        waveform, sr = torchaudio.load(audio_path)
src/vad.py:79:    # Must be set in each subprocess before torch operations
src/vad.py:324:    waveform, sr = torchaudio.load(audio_path)


**Exploring potential hangs in file processes**

I'm considering that VAD probably uses `src/vad.py` and that it might hang if the sound file is corrupted. But then I remember that it uses soundfile, which relies on libsndfile — could that cause a hang? Not sure! However, I saw that download.py uses subprocess.run for ffmpeg with a timeout, specifically a 300-second timeout for R2 downloads. I think I need to check around line 630 to understand this better.


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:


**Examining VAD issues**

I see that the ffmpeg download has a 300-second timeout, so that's likely not the issue. The VAD step happens after the download, and I wonder if `src/vad.py` is using torchaudio.load, which might be getting stuck when trying to read local files.

But if multiple processes are attempting to access libsndfile simultaneously, could a global deadlock be involved? It's possible that there’s a threading issue or even that VAD spawns new processes for each segment, which can create blockages. Let's check the VAD implementation carefully.


**Investigating resource issues**

I'm considering that VAD might be spawning too many processes, which could exhaust system resources and lead to deadlock. I'm curious about whether VAD is implemented using SpeechBrain. It makes sense to check `src/vad.py` to understand its implementation better. If it's using SpeechBrain, there might be specific behavior regarding how processes are managed that could be causing these issues. Let’s take a closer look at the code to figure this out!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Voice Activity Detection with TRUE parallelism + Compute Monitoring.

=== OPTIMIZATION (v6.2) ===
Key improvements:
- Persistent workers: Load Silero model ONCE per worker via initializer
- Memory-efficient: Accept numpy array directly (no file re-read)
- Adaptive worker count based on detected CPU cores
- Optimized for 0.4s event detection (min_speech_ms = 200)
"""

import time
import logging
import warnings
import os
import sys
import contextlib
from typing import List, Dict, Tuple, Optional, Union
from concurrent.futures import ProcessPoolExecutor, as_completed
import numpy as np

# === SUPPRESS NNPACK WARNINGS (v6.9) ===
# NNPACK is for mobile CPU optimization, not needed on GPU machines
# These warnings spam logs and slow down processing
os.environ['NNPACK_DISABLE'] = '1'
warnings.filterwarnings('ignore', message='.*NNPACK.*')
warnings.filterwarnings('ignore', category=UserWarning, module='torch')

import torch
# Suppress PyTorch C++ warnings
torch.set_warn_always(False)

@contextlib.contextmanager
def suppress_stderr():
    """
    Context manager to suppress stderr (C++ warnings like NNPACK).

    === WHY THIS IS NEEDED (v6.9) ===
    PyTorch's NNPACK warnings come from C++ code and can't be suppressed
    via Python's warnings module. Redirecting stderr is the only way.
    """
    # Save original stderr
    old_stderr = sys.stderr
    # Redirect to /dev/null
    sys.stderr = open(os.devnull, 'w')
    try:
        yield
    finally:
        sys.stderr.close()
        sys.stderr = old_stderr

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

# === GLOBAL VAD MODEL (loaded once per worker via initializer) ===
_vad_model = None
_vad_utils = None

def _init_vad_worker():
    """
    Worker initializer - loads Silero VAD model ONCE per worker process.

    === OPTIMIZATION (v6.2) ===
    Previously: Each worker loaded model for EVERY chunk (~200MB x 32 workers = 6.4GB!)
    Now: Model loaded ONCE per worker at startup, reused for all chunks

    === EDGE CASE HANDLING (v6.7) ===
    - Retry logic for network errors when multiple workers start
    - Graceful fallback to cached model
    - Exponential backoff on failures

    Benefit: ~50% VAD speedup, 80% less memory
    """
    global _vad_model, _vad_utils

    # === SUPPRESS NNPACK IN WORKER (v6.9) ===
    # Must be set in each subprocess before torch operations
    import os
    import warnings
    os.environ['NNPACK_DISABLE'] = '1'
    warnings.filterwarnings('ignore', message='.*NNPACK.*')
    warnings.filterwarnings('ignore', category=UserWarning)
    torch.set_warn_always(False)

    max_retries = 5
    for attempt in range(max_retries):
        try:
            _vad_model, _vad_utils = torch.hub.load(
                repo_or_dir='snakers4/silero-vad',
                model='silero_vad',
                force_reload=False,
                onnx=False,
                trust_repo=True,  # Avoid validation check
                verbose=False  # Suppress "Using cache found" messages
            )
            return  # Success!

        except Exception as e:
            if attempt < max_retries - 1:
                # Exponential backoff: 0.5s, 1s, 2s, 4s
                wait_time = 0.5 * (2 ** attempt)
                import time
                import random
                # Add jitter to avoid thundering herd
                jitter = random.uniform(0, 0.5)
                time.sleep(wait_time + jitter)
                continue
            else:
                # Last attempt failed - try to load from cache directory directly
                import os
                cache_dir = os.path.expanduser('~/.cache/torch/hub/snakers4_silero-vad_master')
                if os.path.exists(cache_dir):
                    try:
                        _vad_model, _vad_utils = torch.hub.load(
                            repo_or_dir=cache_dir,
                            model='silero_vad',
                            source='local',
                            force_reload=False,
                            onnx=False,
                            verbose=False
                        )
                        return
                    except:
                        pass

                # Final fallback failed
                raise RuntimeError(
                    f"Failed to load VAD model after {max_retries} attempts. "
                    f"Last error: {e}. Try running the main script first to download the model."
                )

def _vad_worker(args: Tuple[np.ndarray, int, dict, float]) -> List[Dict]:
    """
    Worker function for parallel VAD processing.

    Uses globally initialized model (loaded via _init_vad_worker).
    Accepts tuple for map() compatibility.
    """
    global _vad_model, _vad_utils
    audio_chunk, sample_rate, config_dict, chunk_start = args

    # Use pre-loaded model (from initializer)
    model = _vad_model
    (get_speech_timestamps, _, _, _, _) = _vad_utils

    # Convert to torch tensor
    wav_tensor = torch.from_numpy(audio_chunk).float()

    # Run VAD with config params
    # Wrap in suppress_stderr to silence C++ NNPACK warnings
    with suppress_stderr():
        speech_timestamps = get_speech_timestamps(
            wav_tensor,
            model,
            threshold=config_dict.get('vad_threshold', 0.5),
            min_speech_duration_ms=config_dict.get('vad_min_speech_ms', 200),  # For 0.4s events
            min_silence_duration_ms=config_dict.get('vad_min_silence_ms', 200),
            window_size_samples=config_dict.get('vad_window_size_samples', 512),
            speech_pad_ms=config_dict.get('vad_speech_pad_ms', 30),
            return_seconds=True,
            sampling_rate=sample_rate
        )

    # Adjust timestamps to global time
    segments = []
    for ts in speech_timestamps:
        global_start = chunk_start + ts['start']
        global_end = chunk_start + ts['end']
        segments.append({
            'start': float(global_start),
            'end': float(global_end),
            'duration': float(global_end - global_start)
        })

    return segments

def run_vad_from_buffer(
    waveform_np: np.ndarray,
    sample_rate: int,
    config,
    pool=None
) -> List[Dict[str, float]]:
    """
    Run Silero VAD on pre-loaded audio buffer with persistent workers.

    === OPTIMIZATION (v6.2) ===
    - Accepts numpy array directly (no file re-read)
    - Uses initializer to load model ONCE per worker

    === OPTIMIZATION (v7.9 - Stage 1) ===
    - Accepts optional GlobalVADPool for cross-video persistence
    - When pool provided: reuses workers across videos (eliminates 2-3s overhead)
    - When pool is None: creates local pool (backward compatible)

    Args:
        waveform_np: Audio samples as numpy array (mono)
        sample_rate: Sample rate
        config: Pipeline configuration
        pool: Optional GlobalVADPool for persistent workers

    Returns:
        List of speech segments with start/end/duration
    """
    total_duration = len(waveform_np) / sample_rate
    pool_mode = "global" if pool is not None else "local"
    logger.info(f"🎤 Running Silero VAD ({pool_mode} pool, {config.vad_workers} workers)...")
    start = time.time()

    # Create chunks for parallel processing
    chunk_size_samples = int(config.vad_chunk_size * sample_rate)
    chunk_args: List[Tuple[np.ndarray, int, dict, float]] = []

    # Convert config to dict for pickling
    config_dict = {
        'vad_threshold': config.vad_threshold,
        'vad_min_speech_ms': config.vad_min_speech_ms,
        'vad_min_silence_ms': config.vad_min_silence_ms,
        'vad_window_size_samples': config.vad_window_size_samples,
        'vad_speech_pad_ms': config.vad_speech_pad_ms,
    }

    for i in range(0, len(waveform_np), chunk_size_samples):
        chunk_data = waveform_np[i:i + chunk_size_samples]
        chunk_start_time = i / sample_rate
        chunk_args.append((chunk_data, sample_rate, config_dict, chunk_start_time))

    logger.info(f"   Processing {len(chunk_args)} chunks ({config.vad_chunk_size}s each)...")

    all_segments = []
    completed = 0

    # === USE GLOBAL POOL IF PROVIDED (Stage 1 optimization) ===
    if pool is not None:
        def progress_cb(done, total):
            nonlocal completed
            completed = done


**Considering process risks**

I'm looking at how ProcessPoolExecutor is used for VAD and thinking about the potential for worker hangs due to process pool initialization deadlock. This seems especially risky on multi-GPU systems with many workers. Each worker might be creating its own ProcessPoolExecutor, leading to a huge number of processes that could exhaust resources and cause freezing. Plus, potential deadlocks may happen when trying to load VAD models from cache concurrently. I'm noticing logs show workers stuck after downloading but before completing VAD.


**Analyzing VAD operations**

I'm exploring how VAD starts and how it creates a pool. The pool initializer might hang during the torch.hub loading, possibly due to a repository lock. Many workers seem to get stuck since the logs show downloads complete but lack further activity. It's interesting that VAD logs are from "FastPipelineV6.VAD" and could be suppressed. The pipeline does print after VAD finishes, but I need to verify if earlier logs were missed. I suspect the hang might relate to the VAD ProcessPoolExecutor, though.


**Evaluating VAD process pools**

I'm considering that if VAD creates a local pool for each video, it ends up spawning several processes for each call, which can be heavy and may lead to hangs if the 'fork' start method is used. When using the 'spawn' method, it should work but can be costly. The lengthy hang indicates a potential deadlock. It seems Stage 1 optimization might not be in use, causing zombie processes. To address this, I think creating a global VAD pool per GPU worker that persists across videos would be the best solution.


**Searching for GlobalVADPool**

I’m considering whether there's a class named GlobalVADPool in another module that I need to check out. It could be relevant to optimizing the VAD process! I guess I'll need to search for it to see if it exists and what its purpose might be. Figuring this out could help in streamlining how we handle VAD across videos, so I’m curious to learn more about it. Let’s find out!


rg -n "class GlobalVADPool|GlobalVADPool|vad_pool" -S src pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/worker_pools.py:8:- GlobalVADPool: Persistent VAD workers with pre-loaded Silero models
src/worker_pools.py:12:    from src.worker_pools import GlobalVADPool, DownloadPool
src/worker_pools.py:15:    vad_pool = GlobalVADPool.get_instance(num_workers=32)
src/worker_pools.py:18:    result = vad_pool.process_chunks(chunk_args)
src/worker_pools.py:21:    vad_pool.shutdown()
src/worker_pools.py:40:class GlobalVADPool:
src/worker_pools.py:50:    _instance: Optional['GlobalVADPool'] = None
src/worker_pools.py:66:    def get_instance(cls, num_workers: int = 32) -> 'GlobalVADPool':
src/worker_pools.py:84:        logger.info(f"Initializing GlobalVADPool with {self.num_workers} workers...")
src/worker_pools.py:125:        logger.info(f"GlobalVADPool ready in {elapsed:.1f}s ({self.num_workers} workers)")
src/worker_pools.py:147:            raise RuntimeError("GlobalVADPool not initialized. Call get_instance() first.")
src/worker_pools.py:150:            raise RuntimeError("GlobalVADPool has been shut down.")
src/worker_pools.py:189:            logger.info("Shutting down GlobalVADPool...")
src/vad.py:195:    - Accepts optional GlobalVADPool for cross-video persistence
src/vad.py:203:        pool: Optional GlobalVADPool for persistent workers
src/vad.py:306:    - Accepts optional GlobalVADPool for cross-video persistence
src/vad.py:313:        pool: Optional GlobalVADPool for persistent workers


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Persistent Worker Pools for High-Throughput Pipeline

Stage 1 optimization: Eliminate per-video worker pool creation overhead.

Contains:
- GlobalVADPool: Persistent VAD workers with pre-loaded Silero models
- DownloadPool: Aggressive prefetcher for hiding download latency

Usage:
    from src.worker_pools import GlobalVADPool, DownloadPool

    # Initialize once at startup
    vad_pool = GlobalVADPool.get_instance(num_workers=32)

    # Use for all videos
    result = vad_pool.process_chunks(chunk_args)

    # Shutdown on exit
    vad_pool.shutdown()
"""

import os
import time
import logging
import threading
import hashlib
from queue import Queue, Empty
from typing import List, Dict, Tuple, Optional, Any, Callable
from concurrent.futures import ProcessPoolExecutor, Future, as_completed
from dataclasses import dataclass
from pathlib import Path

import numpy as np

logger = logging.getLogger("WorkerPools")

class GlobalVADPool:
    """
    Singleton persistent VAD worker pool.

    Workers are initialized once with Silero model loaded, then reused
    across all videos. Eliminates ~2-3s model loading overhead per video.

    Thread-safe singleton pattern ensures only one pool exists.
    """

    _instance: Optional['GlobalVADPool'] = None
    _lock = threading.Lock()

    def __init__(self, num_workers: int = 32):
        """
        Initialize the VAD pool. Use get_instance() instead.

        Args:
            num_workers: Number of parallel VAD workers
        """
        self.num_workers = num_workers
        self._executor: Optional[ProcessPoolExecutor] = None
        self._ready = False
        self._shutdown = False

    @classmethod
    def get_instance(cls, num_workers: int = 32) -> 'GlobalVADPool':
        """
        Get or create the singleton VAD pool instance.

        Thread-safe lazy initialization.
        """
        if cls._instance is None:
            with cls._lock:
                if cls._instance is None:
                    cls._instance = cls(num_workers)
                    cls._instance._initialize()
        return cls._instance

    def _initialize(self):
        """Initialize the worker pool with VAD models."""
        if self._ready:
            return

        logger.info(f"Initializing GlobalVADPool with {self.num_workers} workers...")
        start = time.time()

        # Pre-download model to avoid worker race condition
        try:
            import torch
            torch.hub.load(
                repo_or_dir='snakers4/silero-vad',
                model='silero_vad',
                force_reload=False,
                onnx=False,
                trust_repo=True,
                verbose=False
            )
        except Exception as e:
            logger.warning(f"Pre-download attempt failed (will retry in workers): {e}")

        # Import the initializer from vad module
        from src.vad import _init_vad_worker

        # Create persistent executor
        self._executor = ProcessPoolExecutor(
            max_workers=self.num_workers,
            initializer=_init_vad_worker
        )

        # Warm up workers by submitting dummy tasks
        warmup_futures = []
        for _ in range(min(self.num_workers, 4)):
            future = self._executor.submit(lambda: True)
            warmup_futures.append(future)

        # Wait for warmup
        for f in warmup_futures:
            try:
                f.result(timeout=60)
            except Exception as e:
                logger.warning(f"Warmup task failed: {e}")

        elapsed = time.time() - start
        self._ready = True
        logger.info(f"GlobalVADPool ready in {elapsed:.1f}s ({self.num_workers} workers)")

    def process_chunks(
        self,
        chunk_args: List[Tuple[np.ndarray, int, dict, float]],
        worker_fn: Callable,
        timeout_per_chunk: int = 120,
        progress_callback: Optional[Callable[[int, int], None]] = None
    ) -> List[Any]:
        """
        Process VAD chunks using the persistent pool.

        Args:
            chunk_args: List of (audio_chunk, sample_rate, config_dict, chunk_start)
            worker_fn: Worker function to execute
            timeout_per_chunk: Timeout in seconds per chunk
            progress_callback: Optional callback(completed, total)

        Returns:
            List of results from all chunks (unordered)
        """
        if not self._ready or self._executor is None:
            raise RuntimeError("GlobalVADPool not initialized. Call get_instance() first.")

        if self._shutdown:
            raise RuntimeError("GlobalVADPool has been shut down.")

        results = []
        completed = 0
        total = len(chunk_args)

        # Submit all chunks
        futures: Dict[Future, int] = {}
        for i, args in enumerate(chunk_args):
            future = self._executor.submit(worker_fn, args)
            futures[future] = i

        # Collect results
        for future in as_completed(futures):
            try:
                result = future.result(timeout=timeout_per_chunk)
                results.extend(result)
                completed += 1

                if progress_callback:
                    progress_callback(completed, total)

            except Exception as e:
                logger.error(f"VAD chunk failed: {e}")

        return results

    def is_ready(self) -> bool:
        """Check if pool is ready to accept work."""
        return self._ready and not self._shutdown

    def shutdown(self, wait: bool = True):
        """
        Shutdown the worker pool.

        Args:
            wait: Wait for pending tasks to complete
        """
        if self._executor is not None:
            logger.info("Shutting down GlobalVADPool...")
            self._executor.shutdown(wait=wait)
            self._shutdown = True
            self._ready = False

    @classmethod
    def reset(cls):
        """Reset the singleton (mainly for testing)."""
        with cls._lock:
            if cls._instance is not None:
                cls._instance.shutdown(wait=False)
            cls._instance = None

@dataclass
class PrefetchedVideo:
    """Container for prefetched video data."""
    url: str
    audio_path: str
    metadata: Dict[str, Any]
    timestamp: float

class DownloadPool:
    """
    Aggressive download prefetcher.

    Maintains a queue of pre-downloaded videos so processing never waits
    for downloads. Downloads run in background threads.

    Usage:
        pool = DownloadPool(prefetch_depth=50)
        pool.start(video_urls)

        # Processing loop
        for _ in range(len(urls)):
            video = pool.get_next()  # Returns immediately (already downloaded)
            process(video)
            pool.mark_complete(video.url)

        pool.stop()
    """

    def __init__(
        self,
        prefetch_depth: int = 50,
        download_workers: int = 4,
        storage_path: str = "/dev/shm",
        max_storage_gb: float = 50.0
    ):
        """
        Initialize download pool.


**Identifying multiprocessing issues**

It seems that GlobalVADPool exists but might not be utilized effectively. The VAD module has a `run_vad_from_buffer` function that uses a local pool, which can cause heavy load and potential hanging. I’m thinking a fix would involve using GlobalVADPool within each `gpu_worker` process. There's also a known issue with creating a `ProcessPoolExecutor` inside a multiprocessing process after importing torch, as this can lead to deadlocks. The default start method is ‘fork’, which seems to be a common problem here.


**Optimizing multiprocessing for VAD**

I can set the multiprocessing context for `ProcessPoolExecutor` by passing `mp_context`. In Python 3.7+, I could create it with `mp_context=mp.get_context("spawn")`. However, since they may be using the default `fork` method, especially on Linux, this poses a risk of deadlocks, particularly with torch. It seems best to avoid using `ProcessPoolExecutor` within workers and instead run VAD sequentially on the CPU or use threads to ensure stability, despite potential performance impacts. Alternatively, using a single global pool before importing torch could help balance speed and stability.


**Addressing threading and multiprocessing issues**

Threads don’t actually fork, but they might use interop threads, which could lead to oversubscription. I could set `torch.set_num_threads(1)` to manage it. Alternatively, I can use `GlobalVADPool` from `worker_pools`, but it still relies on `ProcessPoolExecutor`, which defaults to fork. The key adjustment would be to use `mp_context="spawn"` to avoid deadlock. I’d implement this in `src/worker_pools.py` and `src/vad.py` to create the `ProcessPoolExecutor` with the spawn context. This way, I can manage overhead while reducing potential issues.


**Managing VAD pool configurations**

I should make sure to prevent the creation of too many VAD pools by ensuring that the pipeline uses either a global pool or a local one per process, rather than creating one for each video. At the moment, it seems like a local pool is created with every call, passing None. I wonder if `vad_pool` could be set as a global variable in the configuration? It might be worth checking if the config has a `use_global_vad_pool` option to explore.


rg -n "GlobalVADPool|vad_pool|use_global|vad_workers" -S src/config.py pipeline.py src/vad.py | head -n 200


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/vad.py:195:    - Accepts optional GlobalVADPool for cross-video persistence
src/vad.py:203:        pool: Optional GlobalVADPool for persistent workers
src/vad.py:210:    logger.info(f"🎤 Running Silero VAD ({pool_mode} pool, {config.vad_workers} workers)...")
src/vad.py:266:            max_workers=config.vad_workers,
src/vad.py:306:    - Accepts optional GlobalVADPool for cross-video persistence
src/vad.py:313:        pool: Optional GlobalVADPool for persistent workers
pipeline.py:925:            'vad_workers': config.vad_workers,
pipeline.py:1189:    logger.info(f"   VAD Workers: {config.vad_workers}")
src/config.py:107:    vad_workers: int = 32          # Will be auto-tuned


Summary: auto


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    # Uses parallel ffmpeg (8 workers) - typically 2-3s for 500 segments
    segment_audio: bool = True

    # === BACKGROUND EXPORT ===
    # NOTE: Background export is experimental and not recommended for multiprocessing.
    # In GPU worker subprocesses, background threads die when the process exits.
    # The sync export (5-6s for 60min video) runs AFTER GPU work completes,
    # and overlaps with the NEXT video's download prefetch, so overhead is minimal.
    background_export: bool = False  # Keep False for stability
    background_export_workers: int = 4  # Number of parallel export workers (unused currently)

    # === AUDIO PROCESSING ===
    sample_rate: int = 16000
    intro_skip_seconds: float = 0.0
    outro_skip_seconds: float = 0.0

    # === INTRO SKIP (v6.8 - Dynamic) ===
    # auto_intro_skip: Automatically skip intro based on video duration
    # When True and no chapter-based intro detected:
    #   - Video ≤ 30 min: skip 180s (3 min)
    #   - Video > 30 min: skip 300s (5 min)
    # Set to False to disable automatic intro skipping
    # Manual intro_skip_seconds > 0 always takes priority
    auto_intro_skip: bool = True

    # === HIGH QUALITY AUDIO PRESERVATION (v6.8+) ===
    # preserve_original_audio: Download at highest quality, process at 16kHz
    # When True:
    #   - Downloads original audio format (bestaudio - typically 48kHz)
    #   - Creates 16kHz copy for processing (VAD, diarization, embeddings)
    #   - After processing, deletes 16kHz and keeps only original
    # Final export cuts from original high-quality audio using timestamps
    preserve_original_audio: bool = True
    original_audio_sample_rate: int = 0  # 0 = keep original (highest), or specify (e.g. 48000)

    # === PARALLELISM (Auto-detected, these are fallbacks) ===
    # Actual values computed by COMPUTE.get_optimal_config()
    vad_workers: int = 32          # Will be auto-tuned
    max_workers: int = 32          # Will be auto-tuned
    download_workers: int = 4      # Parallel downloads for batch
    chunk_workers: int = 4         # GPU diarization (VRAM limited)

    # === CHUNKING (for diarization input) ===
    chunk_duration: float = 300.0  # 5 min max chunks
    min_chunk_duration: float = 30.0

    # === VAD (Silero) - Tuned for 0.4s detection ===
    # Per instructions.md: min_duration_on = 0.2s to catch short events
    vad_threshold: float = 0.5
    vad_min_speech_ms: int = 200   # CHANGED: 250 -> 200 for 0.4s events
    vad_min_silence_ms: int = 200  # CHANGED: 100 -> 200 per instructions
    vad_window_size_samples: int = 512
    vad_speech_pad_ms: int = 30
    vad_chunk_size: float = 60.0   # Parallel VAD chunk size

    # === MODEL SELECTION ===
    # Per user request: community-1 first, 3.1 fallback
    use_community_model: bool = True  # Try community-1 first

    # === SEGMENTATION (Frame-level for micro-changes) ===
    # Per instructions: 17ms frame resolution, NOT 1.5s chunks
    segmentation_step: float = 2.5     # Sliding window step (seconds)
    segmentation_duration: float = 5.0  # Window duration (seconds)
    min_segment_duration: float = 0.2   # Min segment (catch 0.4s events)

    # === OVERLAP DETECTION ===
    detect_overlap: bool = True
    overlap_threshold: float = 0.35     # Probability for active speaker
    overlap_min_duration: float = 0.1  # Min overlap to mark
    overlap_padding_ms: int = 100      # Pad overlap boundaries (±100ms)

    # === OVERLAP DENSITY DETECTION (v6.9) ===
    # Mark short segments sandwiched between overlaps as unusable
    # These are "islands" of supposedly clean audio in high-overlap regions
    overlap_density_filter: bool = True
    overlap_density_max_gap: float = 1.0      # Max gap to adjacent overlap (seconds)
    overlap_density_max_duration: float = 3.0  # Max segment duration to mark as unusable

    # === QUALITY FILTERING (v6.1) ===
    filter_by_quality: bool = True     # Enable SNR/quality filtering
    min_snr_db: float = 15.0           # Minimum SNR for TTS usability
    min_quality_score: float = 0.3     # Minimum quality score (0-1)

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

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

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

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


**Inspecting pipeline's VAD usage**

I’m considering that there isn’t a global VAD pool setting, which means the pipeline likely isn't utilizing it. Alright, I should focus on inspecting the pipeline’s VAD call next. I’ll need to search for how `run_vad` is being used to see if it provides any insights or potential adjustments. Let’s analyze that closely to ensure everything is functioning as intended!


rg -n "run_vad|run_vad_from_buffer|vad_pool" pipeline.py src -S | head -n 80


Output:
Exit code: 0
Wall time: 0 seconds
Output:
pipeline.py:80:from src.vad import run_vad_parallel, find_optimal_cut_point
pipeline.py:242:        vad_segments = run_vad_parallel(audio_path, config, audio_buffer=audio_buffer)
src/audio_buffer.py:36:        vad_result = run_vad(buffer)
src/vad.py:181:def run_vad_from_buffer(
src/vad.py:297:def run_vad_parallel(audio_path: str, config, audio_buffer=None, pool=None) -> List[Dict[str, float]]:
src/vad.py:320:        return run_vad_from_buffer(audio_buffer.waveform_np, audio_buffer.sample_rate, config, pool=pool)
src/vad.py:326:    return run_vad_from_buffer(waveform_np, sr, config, pool=pool)
src/worker_pools.py:15:    vad_pool = GlobalVADPool.get_instance(num_workers=32)
src/worker_pools.py:18:    result = vad_pool.process_chunks(chunk_args)
src/worker_pools.py:21:    vad_pool.shutdown()


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    # ========================================
    # STAGE 1: Download (or use prefetched)
    # ========================================
    log_stage(1, "Download", "START")
    if prefetched_audio is not None and prefetched_metadata is not None:
        # === v7.0: Use prefetched download (batch optimization) ===
        logger.info("⚡ Using prefetched download (batch optimization)")
        audio_path = prefetched_audio
        metadata = prefetched_metadata
        # Prefetched in background thread/process - still record download wall time
        # so end-to-end benchmark metrics include source I/O even when overlapped.
        try:
            timings['download'] = float(metadata.get('download_wall_time_sec', 0.0))
        except Exception:
            timings['download'] = 0.0
        compute_stats['download'] = {'cpu': 0, 'gpu': 0}
    else:
        # Standard download (with optional Supabase metadata for chapters)
        with COMPUTE.monitor_stage("Download") as metrics:
            audio_path, metadata = download_audio(video_url, config, supabase_metadata=supabase_metadata)
        timings['download'] = metrics.duration
        compute_stats['download'] = {'cpu': metrics.cpu_percent_avg, 'gpu': metrics.gpu_percent_avg}
    log_stage(1, "Download", "DONE", f"{timings['download']:.1f}s")

    # ========================================
    # === OPTIMIZATION (v6.2): SHARED AUDIO BUFFER ===
    # Load audio ONCE here, pass buffer to all stages
    # Previously: Each stage called torchaudio.load() separately (4-5 times!)
    # Now: Single load, reuse across VAD, chunking, quality, embeddings
    # ========================================
    with COMPUTE.monitor_stage("LoadBuffer") as metrics:
        audio_buffer = AudioBuffer.from_file(audio_path)
    total_duration = audio_buffer.duration
    timings['load_buffer'] = metrics.duration

    # ========================================
    # STAGE 2: VAD (Parallel CPU with persistent workers)
    # === OPTIMIZATION (v6.2): Uses audio_buffer (no file re-read) ===
    # === OPTIMIZATION (v6.2): Model loaded ONCE per worker via initializer ===
    # ========================================
    log_stage(2, "VAD", "START", f"duration={total_duration:.0f}s")
    with COMPUTE.monitor_stage("VAD", items=int(total_duration / config.vad_chunk_size)) as metrics:
        vad_segments = run_vad_parallel(audio_path, config, audio_buffer=audio_buffer)
    timings['vad'] = metrics.duration
    compute_stats['vad'] = {'cpu': metrics.cpu_percent_avg, 'gpu': metrics.gpu_percent_avg}
    log_stage(2, "VAD", "DONE", f"{timings['vad']:.1f}s | {len(vad_segments)} segments")

    # ========================================
    # STAGE 3: Chunking (VAD-aware, IN-MEMORY)
    # === OPTIMIZATION (v6.2): No disk writes for chunks ===
    # Pass waveform tensors directly to diarization
    # ========================================
    log_stage(3, "Chunking", "START")
    with COMPUTE.monitor_stage("Chunking") as metrics:
        chunks = create_chunks(audio_path, vad_segments, config,
                              audio_buffer=audio_buffer, in_memory=True)
    timings['chunking'] = metrics.duration
    compute_stats['chunking'] = {'cpu': metrics.cpu_percent_avg, 'gpu': metrics.gpu_percent_avg}
    log_stage(3, "Chunking", "DONE", f"{timings['chunking']:.1f}s | {len(chunks)} chunks")

    # ========================================
    # STAGE 4: UNIFIED Diarization + OSD (GPU) - SINGLE PASS!
    # === OPTIMIZATION (v6.1) ===
    # Previously: OSD ran diarization, then Diarization stage ran it again
    # Now: Single diarization pass extracts BOTH segments AND overlaps
    # Saves ~50% GPU compute time!
    # ========================================
    log_stage(4, "Diarization+OSD", "START", f"{len(chunks)} chunks")
    overlap_segments = []
    nonspeech_segments = []

    with COMPUTE.monitor_stage("Diarization+OSD", items=len(chunks)) as metrics:
        # Run unified diarization + overlap extraction
        segments, overlap_segments = run_diarization(
            chunks, config,
            extract_overlaps=config.detect_overlap
        )

        # Detect non-speech from VAD gaps
        nonspeech_segments = detect_nonspeech_regions(vad_segments, total_duration)

    timings['diarization'] = metrics.duration
    compute_stats['diarization'] = {'cpu': metrics.cpu_percent_avg, 'gpu': metrics.gpu_percent_avg}
    log_stage(4, "Diarization+OSD", "DONE", f"{timings['diarization']:.1f}s | {len(segments)} segments")

    # ========================================
    # STAGE 4b: Split segments on overlaps (salvage clean portions)
    # === OPTIMIZATION (v6.1) ===
    # Previously: remove_overlap_regions() dropped entire segments if >50% overlap
    # Now: split_on_overlaps() salvages clean portions around overlaps
    # ========================================
    if overlap_segments and config.detect_overlap:
        original_count = len(segments)
        segments = split_on_overlaps(segments, overlap_segments, min_segment=config.min_segment_duration)
        logger.info(f"   Split overlaps: {original_count} → {len(segments)} segments (salvaged clean portions)")

    # ========================================
    # STAGE 4c: Quality filtering (SNR, clipping, etc.)
    # === OPTIMIZATION (v6.2): Uses audio_buffer (no file re-read) ===
    # Mark low-quality segments as unusable to protect TTS training data
    # ========================================
    low_quality_segments = []
    if config.filter_by_quality:
        with COMPUTE.monitor_stage("QualityFilter") as metrics:
            segments, low_quality_segments = filter_by_quality(
                segments, audio_path,
                min_snr_db=config.min_snr_db,
                min_quality_score=config.min_quality_score,
                audio_buffer=audio_buffer  # v6.2: Use buffer
            )
        timings['quality_filter'] = metrics.duration
        compute_stats['quality_filter'] = {'cpu': metrics.cpu_percent_avg, 'gpu': metrics.gpu_percent_avg}

    # NOTE: Duration filtering moved to AFTER clustering (Stage 7b) per user request
    # This ensures we don't waste compute on segments that will be filtered anyway
    # AND allows us to sort by duration (keep ones closest to 1s threshold)

    # ========================================
    # STAGE 5: FUSED EMBEDDINGS + MUSIC DETECTION (v6.9 - Phase 1 Optimization)
    # === PARALLEL CPU PREP: Music cache prep runs during embedding GPU inference ===
    # BEFORE: Embeddings(8s) → Music(24s) = 32s total


**Analyzing VAD process limits**

I'm thinking about ensuring the pool shuts down when the program exits, maybe using atexit. But creating 32 workers per GPU worker could lead to 256 processes with 8 GPU workers, which seems heavy, even on 128 cores. I realize this might cause freezing due to the number of processes. For a 4090 host with 128 cores, the default config spawns too many VAD processes, possibly hitting limits and causing issues. Silero model loading seems manageable, but at that scale, it might be excessive.


**Optimizing VAD workers**

I’m considering that I should set the number of vad_workers per GPU to a smaller figure, like 8, and use a global pool instead of having it per GPU. However, sharing across processes can be tricky. I think maybe it’s redundant to run VAD in parallel across GPU workers; instead, doing it sequentially within each worker while still using 8 GPUs might be sufficient. For stability, it makes sense to use threads in each worker or run VAD in a single process to avoid complexity and reduce deadlocks.


**Implementing VAD improvements**

I’m thinking about adding a configuration option called `vad_parallel_mode`, defaulting to either "thread" or "processpool_spawn." For minimal disruption, I could modify `src/vad.py` to use `GlobalVADPool` by default, while limiting the worker count to a smaller number, like 8 per GPU, in supabase mode. However, this still doesn't address oversubscription across 8 GPUs. I could scale based on CPU cores and the number of GPUs, possibly computing effective vad_workers using environment variable `GPU_COUNT`. I should check `src/compute.py` for auto-tuning options in `compute.get_optimal_config`.


rg -n "vad_workers" src/compute.py src/config.py src/compute.py src/compute.py -S && sed -n '1,220p' src/compute.py


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 239
Output:
src/compute.py:50:    optimal_vad_workers: int = 8
src/compute.py:186:        logger.info(f"   VAD workers: {self.resources.optimal_vad_workers}")
src/compute.py:211:        r.optimal_vad_workers = max(8, min(base_workers, 64))
src/compute.py:215:        r.optimal_vad_workers = min(r.optimal_vad_workers, max_by_ram)
src/compute.py:216:        r.optimal_vad_workers = max(4, r.optimal_vad_workers)  # Minimum 4
src/compute.py:278:            'vad_workers': r.optimal_vad_workers,
src/compute.py:50:    optimal_vad_workers: int = 8
src/compute.py:186:        logger.info(f"   VAD workers: {self.resources.optimal_vad_workers}")
src/compute.py:211:        r.optimal_vad_workers = max(8, min(base_workers, 64))
src/compute.py:215:        r.optimal_vad_workers = min(r.optimal_vad_workers, max_by_ram)
src/compute.py:216:        r.optimal_vad_workers = max(4, r.optimal_vad_workers)  # Minimum 4
src/compute.py:278:            'vad_workers': r.optimal_vad_workers,
src/compute.py:50:    optimal_vad_workers: int = 8
src/compute.py:186:        logger.info(f"   VAD workers: {self.resources.optimal_vad_workers}")
src/compute.py:211:        r.optimal_vad_workers = max(8, min(base_workers, 64))
src/compute.py:215:        r.optimal_vad_workers = min(r.optimal_vad_workers, max_by_ram)
src/compute.py:216:        r.optimal_vad_workers = max(4, r.optimal_vad_workers)  # Minimum 4
src/compute.py:278:            'vad_workers': r.optimal_vad_workers,
src/config.py:107:    vad_workers: int = 32          # Will be auto-tuned
#!/usr/bin/env python3
"""
Compute Resource Detection & Monitoring - Adaptive Pipeline Configuration

Auto-detects: nproc, vCPUs, GPU vRAM, CPU cores
Monitors: CPU/GPU utilization at each stage
Adapts: Worker counts, batch sizes, parallelism based on available resources

Key principle: Use ALL available compute at every stage.
- CPU-bound: Maximize workers up to core count
- GPU-bound: Keep GPU saturated, prepare CPU work in parallel
- Memory-bound: Adapt batch sizes to available RAM/VRAM
"""

import os
import time
import logging
import threading
from dataclasses import dataclass, field
from typing import Dict, Optional, Callable, Any
from contextlib import contextmanager

logger = logging.getLogger("FastPipelineV5.Compute")

@dataclass
class ComputeResources:
    """Detected compute resources for the current system."""
    # CPU
    cpu_count: int = 1           # Physical cores
    cpu_count_logical: int = 1    # Logical cores (with hyperthreading)
    nproc: int = 1               # nproc output (usable processors)

    # Memory
    ram_total_gb: float = 0.0
    ram_available_gb: float = 0.0

    # GPU
    gpu_available: bool = False
    gpu_name: str = ""
    gpu_vram_total_gb: float = 0.0
    gpu_vram_free_gb: float = 0.0
    gpu_compute_capability: str = ""

    # === UTILIZATION CAP (v6.9) ===
    # Target max utilization - leave headroom for spikes/system stability
    max_utilization: float = 0.80  # 80% cap

    # Derived optimal settings (auto-computed based on hardware)
    optimal_vad_workers: int = 8
    optimal_embedding_batch: int = 8
    optimal_music_batch: int = 64
    optimal_download_workers: int = 4
    optimal_cpu_workers: int = 8
    optimal_chunk_workers: int = 1  # GPU diarization parallelism
    optimal_chunk_duration: float = 300.0  # seconds; smaller on low-VRAM GPUs to avoid OOM

@dataclass
class StageMetrics:
    """Metrics for a single pipeline stage."""
    stage_name: str
    start_time: float = 0.0
    end_time: float = 0.0
    duration: float = 0.0
    cpu_percent_avg: float = 0.0
    cpu_percent_max: float = 0.0
    gpu_percent_avg: float = 0.0
    gpu_percent_max: float = 0.0
    gpu_memory_used_gb: float = 0.0
    items_processed: int = 0
    throughput: float = 0.0  # items/sec

class ComputeMonitor:
    """
    Singleton compute resource monitor.

    Features:
    1. Auto-detect system resources on init
    2. Compute optimal worker counts
    3. Track utilization during stages
    4. Suggest optimizations
    """
    _instance = None

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

    def __init__(self):
        if self._initialized:
            return
        self._initialized = True

        self.resources = ComputeResources()
        self.stage_metrics: Dict[str, StageMetrics] = {}
        self._monitoring = False
        self._monitor_thread: Optional[threading.Thread] = None
        self._current_stage: Optional[str] = None
        self._cpu_samples: list = []
        self._gpu_samples: list = []

        # Auto-detect resources
        self._detect_resources()

    def _detect_resources(self):
        """Detect all available compute resources."""
        logger.info("=" * 70)
        logger.info("🖥  DETECTING COMPUTE RESOURCES (Adaptive Pipeline)")
        logger.info("=" * 70)
       …202 chars truncated…l cores (more accurate for worker planning)
            try:
                import psutil
                self.resources.cpu_count = psutil.cpu_count(logical=False) or self.resources.cpu_count_logical
            except ImportError:
                self.resources.cpu_count = self.resources.cpu_count_logical

            # Get nproc (what's actually usable)
            try:
                import subprocess
                result = subprocess.run(['nproc'], capture_output=True, text=True, timeout=5)
                self.resources.nproc = int(result.stdout.strip())
            except Exception:
                self.resources.nproc = self.resources.cpu_count_logical

        except Exception as e:
            logger.warning(f"CPU detection failed: {e}, using defaults")
            self.resources.cpu_count = 4
            self.resources.cpu_count_logical = 4
            self.resources.nproc = 4

        logger.info(f"   CPU: {self.resources.cpu_count} physical / {self.resources.cpu_count_logical} logical cores")
        logger.info(f"   nproc: {self.resources.nproc} usable processors")

        # === Memory Detection ===
        try:
            import psutil
            mem = psutil.virtual_memory()
            self.resources.ram_total_gb = mem.total / (1024**3)
            self.resources.ram_available_gb = mem.available / (1024**3)
            logger.info(f"   RAM: {self.resources.ram_available_gb:.1f} / {self.resources.ram_total_gb:.1f} GB available")
        except ImportError:
            logger.warning("psutil not available, memory detection skipped")

        # === GPU Detection ===
        try:
            import torch
            if torch.cuda.is_available():
                self.resources.gpu_available = True
                self.resources.gpu_name = torch.cuda.get_device_name(0)

                # Get VRAM info
                total = torch.cuda.get_device_properties(0).total_memory / (1024**3)
                allocated = torch.cuda.memory_allocated(0) / (1024**3)
                reserved = torch.cuda.memory_reserved(0) / (1024**3)

                self.resources.gpu_vram_total_gb = total
                self.resources.gpu_vram_free_gb = total - reserved

                # Compute capability
                props = torch.cuda.get_device_properties(0)
                self.resources.gpu_compute_capability = f"{props.major}.{props.minor}"

                logger.info(f"   GPU: {self.resources.gpu_name}")
                logger.info(f"   VRAM: {self.resources.gpu_vram_free_gb:.1f} / {self.resources.gpu_vram_total_gb:.1f} GB free")
                logger.info(f"   Compute: SM {self.resources.gpu_compute_capability}")
            else:
                logger.info("   GPU: None available (CPU-only mode)")
        except Exception as e:
            logger.warning(f"GPU detection failed: {e}")

        # === Compute Optimal Settings ===
        self._compute_optimal_settings()

        logger.info("-" * 70)
        logger.info(f"🎯 OPTIMAL SETTINGS (auto-computed @ {self.resources.max_utilization*100:.0f}% cap):")
        logger.info(f"   VAD workers: {self.resources.optimal_vad_workers}")
        logger.info(f"   Embedding batch: {self.resources.optimal_embedding_batch}")
        logger.info(f"   Music batch: {self.resources.optimal_music_batch}")
        logger.info(f"   Download workers: {self.resources.optimal_download_workers}")
        logger.info(f"   CPU workers: {self.resources.optimal_cpu_workers}")
        logger.info(f"   Diarization workers: {self.resources.optimal_chunk_workers}")
        logger.info(f"   Chunk duration: {self.resources.optimal_chunk_duration:.0f}s")
        logger.info("=" * 70)

    def _compute_optimal_settings(self):
        """
        Compute optimal worker counts based on detected resources.

        All settings respect max_utilization cap (default 80%) to leave headroom
        for system stability and unexpected spikes.
        """
        r = self.resources
        cap = r.max_utilization  # 0.80 by default

        # === VAD Workers (CPU-bound, I/O-light) ===
        # VAD is embarrassingly parallel - use most CPU cores
        # Each worker loads ~200MB model, so balance with RAM

        # Use available processors with utilization cap
        base_workers = int(max(r.cpu_count, r.nproc) * cap)
        r.optimal_vad_workers = max(8, min(base_workers, 64))

        # Adjust based on RAM (each VAD worker ~300MB with buffer)
        max_by_ram = int(r.ram_available_gb * cap * 1024 / 300)
        r.optimal_vad_workers = min(r.optimal_vad_workers, max_by_ram)
        r.optimal_vad_workers = max(4, r.optimal_vad_workers)  # Minimum 4

        # === Embedding Batch Size (GPU-bound, VRAM-sensitive) ===
        # ECAPA-TDNN uses ~0.3GB per batch of 64
        # Scale with available VRAM, respecting cap


rg -n "get_optimal_config\(" -S pipeline.py massive_process.py src/compute.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/compute.py:274:    def get_optimal_config(self) -> Dict[str, Any]:
src/compute.py:309:        optimal = self.get_optimal_config()


Output:
Exit code: 0
Wall time: 0 seconds
Output:
        r.optimal_download_workers = max(2, min(8, int(r.nproc * cap / 8)))

        # === General CPU Workers ===
        r.optimal_cpu_workers = max(4, int(r.nproc * cap / 4))

        # === Diarization Chunk Workers (GPU VRAM-bound) ===
        # Pyannote diarization uses ~6-8GB VRAM per stream
        # Most systems can only run 1-2 concurrent diarizations
        if r.gpu_available:
            total_vram = r.gpu_vram_total_gb
            usable_vram = total_vram * cap
            vram_per_diarization = 8.0  # GB per concurrent stream (heuristic)
            r.optimal_chunk_workers = max(1, int(usable_vram / vram_per_diarization))

            # On 16GB GPUs we force single-stream settings and smaller chunks for stability.
            if total_vram <= 16.5:
                r.optimal_chunk_workers = 1
                r.optimal_chunk_duration = 180.0
            else:
                r.optimal_chunk_duration = 300.0
        else:
            r.optimal_chunk_workers = 1
            r.optimal_chunk_duration = 300.0

    def get_optimal_config(self) -> Dict[str, Any]:
        """Return optimal configuration dict for Config class."""
        r = self.resources
        return {
            'vad_workers': r.optimal_vad_workers,
            'max_workers': r.optimal_cpu_workers,
            'download_workers': r.optimal_download_workers,
            'chunk_workers': r.optimal_chunk_workers,
            'chunk_duration': r.optimal_chunk_duration,
            'embedding_batch_size': r.optimal_embedding_batch,
            'music_batch_size': r.optimal_music_batch,
        }

    def set_max_utilization(self, cap: float):
        """
        Set max utilization cap and recompute optimal settings.

        Args:
            cap: Utilization cap between 0.1 and 1.0 (default 0.8 = 80%)
        """
        cap = max(0.1, min(1.0, cap))
        self.resources.max_utilization = cap
        self._compute_optimal_settings()
        logger.info(f"🎚  Utilization cap updated to {cap*100:.0f}%, settings recomputed")

    def apply_to_config(self, config) -> None:
        """
        Apply optimal settings to a Config object.

        This updates the config's parallelism and batch size settings
        based on auto-detected system resources.

        Args:
            config: Config dataclass instance to update
        """
        optimal = self.get_optimal_config()
        for key, value in optimal.items():
            if hasattr(config, key):
                setattr(config, key, value)

    @contextmanager
    def monitor_stage(self, stage_name: str, items: int = 0):
        """
        Context manager to monitor a pipeline stage.

        Usage:
            with COMPUTE.monitor_stage("VAD", items=100) as metrics:
                # do work
            print(metrics.duration, metrics.cpu_percent_avg)
        """
        metrics = StageMetrics(stage_name=stage_name, items_processed=items)
        metrics.start_time = time.time()

        self._current_stage = stage_name
        self._cpu_samples = []
        self._gpu_samples = []

        # Start background monitoring
        self._start_monitoring()

        try:
            yield metrics
        finally:
            # Stop monitoring
            self._stop_monitoring()

            metrics.end_time = time.time()


**Investigating configuration settings**

I’m thinking about how to compute the optimal configuration without multi-process considerations. In the massive_process context, I wonder if they use compute to override the configuration. I should look into the config_overrides that get passed into gpu_worker_supabase. It seems like there’s a parameter called config_overrides. Maybe in run_supabase_queue, I could set it to COMPUTE.get_optimal_config? I’ll need to inspect the run_supabase_queue section around line 740 for more details.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                    video_url=video_url,
                    video_id=video_id,
                    success=False,
                    error=error_msg,
                    process_time_sec=process_time,
                    gpu_id=gpu_id,
                    worker_id=worker_id
                )

            result_queue.put(video_result)
            videos_processed += 1

            # Clear GPU cache between videos
            MODELS.clear_cache(aggressive=True)

        # Drain background exports (if enabled in this worker)
        if getattr(config, "background_export", False):
            try:
                from src.export import shutdown_export_manager
                worker_logger.info("Waiting for background exports to complete...")
                export_results = shutdown_export_manager(wait=True, timeout=600)
                failed = [r for r in export_results.values() if not getattr(r, "success", False)]
                if failed:
                    worker_logger.warning(f"Background exports failed: {len(failed)}/{len(export_results)}")
                    for r in failed[:5]:
                        worker_logger.warning(f"  {r.video_id}: {r.error}")
            except Exception:
                pass

        # Graceful shutdown - flush remaining analytics events
        analytics.shutdown()
        worker_logger.info(f"Shutting down. Processed {videos_processed} videos.")

    except Exception as e:
        worker_logger.error(f"Worker failed: {e}")
        traceback.print_exc()
        # Try to shutdown analytics even on error (may not be initialized)
        try:
            if analytics is not None:
                analytics.shutdown()
        except Exception:
            pass

def run_supabase_queue(
    gpu_ids: List[int],
    output_dir: str,
    config_overrides: Optional[Dict] = None,
    max_videos_per_worker: int = 0,
    lease_duration_sec: int = 900,
    poll_interval_sec: int = 10
) -> BenchmarkStats:
    """
    Run distributed processing with Supabase queue.

    Workers claim videos from Supabase, process them, and update status.
    Continues until no more PENDING videos or stop signal.
    """
    config_overrides = config_overrides or {}

    workers_per_gpu = config_overrides.get('workers_per_gpu', 1)
    total_workers = len(gpu_ids) * workers_per_gpu

    # Get initial queue stats
    supabase = SupabaseClient()
    initial_stats = supabase.get_stats()
    pending_count = initial_stats.get('PENDING', 0)

    logger.info("=" * 70)
    logger.info("SUPABASE QUEUE - Distributed Processing")
    logger.info("=" * 70)
    logger.info(f"Pending videos: {pending_count:,}")
    logger.info(f"GPUs: {len(gpu_ids)} ({gpu_ids})")
    logger.info(f"Workers/GPU: {workers_per_gpu} (total: {total_workers})")
    logger.info(f"Output: {output_dir}")
    logger.info(f"Lease duration: {lease_duration_sec}s")
    if max_videos_per_worker > 0:
        logger.info(f"Max videos/worker: {max_videos_per_worker}")
    logger.info("=" * 70)

    # Create output directory
    Path(output_dir).mkdir(parents=True, exist_ok=True)

    # Setup multiprocessing
    manager = Manager()
    result_queue = manager.Queue()
    stop_signal = manager.Value('b', False)

    # Setup graceful shutdown handlers
    setup_signal_handlers(stop_signal)

    # Start workers
    workers = []
    start_time = time.time()

    worker_id = 0
    for gpu_id in gpu_ids:
        for w in range(workers_per_gpu):
            p = Process(
                target=gpu_worker_supabase,
                args=(
                    worker_id, gpu_id, result_queue, stop_signal,
                    output_dir, config_overrides, lease_duration_sec,
                    poll_interval_sec, max_videos_per_worker
                ),
                name=f"GPU{gpu_id}-Supabase-{w}"
            )
            p.start()
            workers.append(p)
            logger.info(f"Started Supabase worker {worker_id} on GPU {gpu_id}")
            worker_id += 1

    # Collect results
    results: List[VideoResult] = []
    active_workers = len(workers)

    try:
        while active_workers > 0:
            # Check for results
            try:
                result = result_queue.get(timeout=30)
                results.append(result)

                status = "OK" if result.success else "FAIL"
                logger.info(
                    f"[{len(results)}] {status}: {result.video_id or result.video_url[:50]} "
                    f"({result.process_time_sec:.1f}s on GPU {result.gpu_id})"
                )

            except Exception:
                pass  # Timeout, check worker status

            # Check if workers are still alive
            active_workers = sum(1 for p in workers if p.is_alive())

    except KeyboardInterrupt:
        logger.warning("Interrupted! Stopping workers...")
        stop_signal.value = True

    # Wait for workers to finish
    for p in workers:
        p.join(timeout=60)
        if p.is_alive():
            logger.warning(f"Worker {p.name} did not stop, terminating...")
            p.terminate()

    end_time = time.time()
    wall_time = end_time - start_time

    # Compute statistics
    successful = [r for r in results if r.success]
    failed = [r for r in results if not r.success]

    total_audio_hours = sum(r.duration_sec for r in successful) / 3600
    total_process_time = sum(r.process_time_sec for r in successful)

    stats = BenchmarkStats(
        total_videos=len(results),
        successful=len(successful),
        failed=len(failed),
        wall_time_sec=wall_time,
        total_audio_hours=total_audio_hours,
        total_process_time_sec=total_process_time,
        avg_process_time_sec=(total_process_time / len(successful)) if successful else 0,
        avg_rtf=sum(r.rtf for r in successful) / len(successful) if successful else 0,
        throughput_hours_audio_per_hour=(total_audio_hours / (wall_time / 3600)) if wall_time > 0 else 0,
        parallel_efficiency=(total_process_time / (wall_time * total_workers)) if wall_time > 0 else 0,
        gpu_count=len(gpu_ids),
        workers_per_gpu=workers_per_gpu,
        start_timestamp=datetime.fromtimestamp(start_time).isoformat(),
        end_timestamp=datetime.fromtimestamp(end_time).isoformat(),
    )

    # Get final queue stats
    final_stats = supabase.get_stats()

    logger.info("=" * 70)
    logger.info("SUPABASE QUEUE COMPLETE")
    logger.info("=" * 70)
    logger.info(f"Processed: {stats.total_videos} ({stats.successful} OK, {stats.failed} failed)")
    logger.info(f"Wall time: {wall_time/60:.1f} min")
    logger.info(f"Audio hours: {total_audio_hours:.1f}h")
    logger.info("-" * 70)
    logger.info("Queue Status:")
    for status, count in sorted(final_stats.items()):
        logger.info(f"   {status}: {count:,}")
    logger.info("=" * 70)

    if failed:
        logger.warning(f"\nFailed videos:")
        for r in failed[:10]:
            logger.warning(f"   {r.video_id or r.video_url}: {r.error[:80]}")
        if len(failed) > 10:
            logger.warning(f"   ... and {len(failed) - 10} more")

    return stats

def get_available_gpus() -> List[int]:
    """Detect available NVIDIA GPUs."""
    try:
        import subprocess
        result = subprocess.run(
            ['nvidia-smi', '--query-gpu=index', '--format=csv,noheader'],
            capture_output=True, text=True, timeout=10
        )
        gpu_ids = [int(x.strip()) for x in result.stdout.strip().split('\n') if x.strip()]
        return gpu_ids
    except Exception as e:
        logger.error(f"Failed to detect GPUs: {e}")
        return [0]

def load_video_urls(input_path: str) -> List[str]:
    """Load video URLs from file (one per line)."""
    urls = []
    with open(input_path, 'r') as f:
        for line in f:
            line = line.strip()
            if line and not line.startswith('#'):
                urls.append(line)


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/compute.py:299:    def apply_to_config(self, config) -> None:
src/config.py:302:                COMPUTE.apply_to_config(self)


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    # We intentionally allow short natural pauses (breathing/thinking) so a
    # continuous single-speaker monologue doesn't get fragmented into many clips.
    #
    # Keep this <~1s so we don't pull long silences (or missed micro-interjections)
    # into a "speech" segment.
    max_silence_gap: float = 0.5

    # === VAD-AWARE BOUNDARY REFINEMENT (v7.9) ===
    # Prevents mid-speech cuts at segment boundaries by trimming to last VAD gap.
    # Performance: ~0.5ms for 1-hour podcast (pure CPU, no model inference)
    # False positive risk: Near zero (only trims to VAD-detected silence gaps)
    enable_boundary_refinement: bool = True
    boundary_refinement_tolerance_ms: float = 100.0  # How close to VAD end = "mid-speech"
    boundary_refinement_min_gap_ms: float = 300.0    # Minimum silence gap to cut at
    boundary_refinement_min_remaining: float = 1.0   # Don't trim if result < 1s

    # === GPU OPTIMIZATION ===
    clear_cache_every_n_chunks: int = 3

    # === COMPUTE MONITORING ===
    monitor_compute: bool = True       # Track CPU/GPU utilization
    log_compute_stats: bool = True     # Log stats after each stage

    # === RESOURCE MANAGEMENT (v6.9) ===
    # Auto-apply optimal settings from ComputeMonitor
    auto_tune_resources: bool = True  # Auto-tune on init based on system
    max_utilization: float = 0.80     # Target 80% max utilization

    def apply_adaptive_settings(self, compute_config: dict):
        """
        Apply auto-detected optimal settings from ComputeMonitor.

        Called after COMPUTE.get_optimal_config()
        """
        for key, value in compute_config.items():
            if hasattr(self, key):
                setattr(self, key, value)

    def __post_init__(self):
        """Validate configuration and auto-tune resources."""
        # Ensure output dir exists
        os.makedirs(self.output_dir, exist_ok=True)

        # === AUTO-TUNE RESOURCES (v6.9) ===
        # Apply optimal settings based on detected system resources
        if self.auto_tune_resources:
            try:
                from src.compute import COMPUTE
                # Set utilization cap first
                if self.max_utilization != COMPUTE.resources.max_utilization:
                    COMPUTE.set_max_utilization(self.max_utilization)
                # Apply optimal settings
                COMPUTE.apply_to_config(self)
            except ImportError:
                pass  # COMPUTE not available, use defaults

        # Validate thresholds
        assert 0 < self.cluster_merge_threshold <= 1.0, "merge threshold must be (0, 1]"
        assert 0 < self.vad_threshold <= 1.0, "VAD threshold must be (0, 1]"
        assert self.min_segment_duration >= 0.1, "min_segment too small"


**Evaluating GPU resource settings**

I realize that Config auto-tunes settings, which can lead to issues if multiple GPU worker processes are involved, as each process may set vad_workers too high. This can overload the system. To fix this, I need to disable auto-tuning or perhaps adjust the maximum utilization in the massive_process.gpu_worker_supabase. I might also want to consider the GPU_COUNT environment variable and ensure that settings reflect the total number of workers to optimize performance efficiently.


**Optimizing resource settings**

I'm realizing that the easiest way to address the issue is to set `config.auto_tune_resources = False` in massive_process.gpu_worker_supabase right after constructing Config. The auto-tune option can be initially set in the dataclass, and we can also use `config_overrides` for adjustments. Since this pipeline might be called from various scripts, I can make these changes work for distributed mode specifically to fix stalls. To resolve the worker deadlock due to VAD oversubscription, I’ll compute `vad_workers` to fit the number of workers and ensure it’s effectively managed.


**Improving VAD resource management**

When running on a 128-core setup with eight total workers, each VAD worker handles 16, which reduces oversubscription but still creates a new ProcessPool for each video—this is quite costly. Right now, it spawns 16 processes per video, which could lead to hangs. I could either make `run_vad_parallel` use a global pool or switch to single-threaded VAD operations. Given the pipeline's CPU reliance and the eight GPUs available, running VAD sequentially with one or two threads seems like the stable choice for now. To implement this, I could modify `run_vad_from_buffer` to handle in-process operations directly when `vad_workers` is one or less.


**Refining VAD process execution**

I see that the current implementation always uses a pool or creates a local ProcessPoolExecutor. To improve this, I could adjust the approach so that if `config.vad_workers` is less than or equal to one, I would run `_vad_worker` directly on the full waveform. However, since `_vad_worker` expects chunks, it might be better to call silero VAD directly in the same process without using multiprocessing. I need to check for any serial path in `vad.py`, especially around the `run_vad_parallel` section starting around line 240. Let's explore that further!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
            completed = done
            if done % max(1, total // 10) == 0:
                logger.info(f"   VAD progress: {done}/{total} chunks")

        all_segments = pool.process_chunks(
            chunk_args,
            worker_fn=_vad_worker,
            timeout_per_chunk=120,
            progress_callback=progress_cb
        )
    else:
        # === LEGACY: Create local pool (backward compatible) ===
        # Pre-download model to avoid worker initialization race
        try:
            torch.hub.load(
                repo_or_dir='snakers4/silero-vad',
                model='silero_vad',
                force_reload=False,
                onnx=False,
                trust_repo=True,
                verbose=False
            )
        except Exception as e:
            logger.warning(f"   Pre-download attempt failed (will retry in workers): {e}")

        with ProcessPoolExecutor(
            max_workers=config.vad_workers,
            initializer=_init_vad_worker
        ) as executor:
            futures = {executor.submit(_vad_worker, args): i for i, args in enumerate(chunk_args)}

            for future in as_completed(futures):
                try:
                    segments = future.result(timeout=120)
                    all_segments.extend(segments)
                    completed += 1

                    if completed % max(1, len(chunk_args) // 10) == 0:
                        logger.info(f"   VAD progress: {completed}/{len(chunk_args)} chunks")
                except Exception as e:
                    logger.error(f"VAD chunk failed: {e}")

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

    # Merge adjacent segments (fix boundary splits from chunking)
    merged = _merge_adjacent_vad_segments(all_segments, merge_gap=0.1)

    total_speech = sum(s['duration'] for s in merged)
    elapsed = time.time() - start

    logger.info(f"✅ VAD: {elapsed:.1f}s | {len(merged)} segments | {total_speech:.1f}s speech")
    logger.info(f"   Speedup: {total_duration/elapsed:.1f}x realtime | Mode: {pool_mode}")

    return merged

def run_vad_parallel(audio_path: str, config, audio_buffer=None, pool=None) -> List[Dict[str, float]]:
    """
    Run Silero VAD with TRUE parallelism and persistent workers.

    === OPTIMIZATION (v6.2) ===
    - If audio_buffer provided, uses pre-loaded numpy array (no file re-read)
    - Uses initializer to load Silero model ONCE per worker (not per chunk!)

    === OPTIMIZATION (v7.9 - Stage 1) ===
    - Accepts optional GlobalVADPool for cross-video persistence
    - Eliminates ~2-3s pool creation overhead per video

    Args:
        audio_path: Path to audio file (used if audio_buffer is None)
        config: Pipeline configuration
        audio_buffer: Optional AudioBuffer with pre-loaded waveform
        pool: Optional GlobalVADPool for persistent workers

    Returns:
        List of speech segments with start/end/duration
    """
    # Use buffer if provided, otherwise load from file
    if audio_buffer is not None:
        return run_vad_from_buffer(audio_buffer.waveform_np, audio_buffer.sample_rate, config, pool=pool)

    # Legacy path: load from file (still uses persistent workers)
    import torchaudio
    waveform, sr = torchaudio.load(audio_path)
    waveform_np = waveform.squeeze(0).numpy()
    return run_vad_from_buffer(waveform_np, sr, config, pool=pool)

def _merge_adjacent_vad_segments(segments: List[Dict], merge_gap: float = 0.1) -> List[Dict]:
    """
    Merge VAD segments that were split at chunk boundaries.

    Args:
        segments: Sorted list of VAD segments
        merge_gap: Maximum gap (seconds) to merge across
    """
    if not segments:
        return []

    merged = []
    current = segments[0].copy()

    for seg in segments[1:]:
        gap = seg['start'] - current['end']

        # Merge if very close (likely chunk boundary artifact)
        if gap < merge_gap:
            current['end'] = seg['end']
            current['duration'] = current['end'] - current['start']
        else:
            merged.append(current)
            current = seg.copy()

    merged.append(current)
    return merged

def find_silence_boundaries(vad_segments: List[Dict],
                           total_duration: float,
                           min_silence: float = 0.3) -> List[Dict]:
    """
    Find silence regions (gaps between VAD segments).

    Useful for:
    - Finding optimal chunk boundaries
    - Marking non-speech regions

    Args:
        vad_segments: List of speech segments from VAD
        total_duration: Total audio duration
        min_silence: Minimum silence duration to report

    Returns:
        List of silence segments with start/end/duration
    """
    silences = []

    # Check start of audio
    if vad_segments and vad_segments[0]['start'] >= min_silence:
        silences.append({
            'start': 0.0,
            'end': vad_segments[0]['start'],
            'duration': vad_segments[0]['start']
        })

    # Gaps between segments
    for i in range(len(vad_segments) - 1):
        gap_start = vad_segments[i]['end']
        gap_end = vad_segments[i + 1]['start']
        gap_duration = gap_end - gap_start

        if gap_duration >= min_silence:
            silences.append({
                'start': gap_start,
                'end': gap_end,
                'duration': gap_duration
            })

    # Check end of audio
    if vad_segments and total_duration - vad_segments[-1]['end'] >= min_silence:
        silences.append({
            'start': vad_segments[-1]['end'],
            'end': total_duration,
            'duration': total_duration - vad_segments[-1]['end']
        })

    return silences

def find_optimal_cut_point(vad_segments: List[Dict],
                          target_time: float,
                          window: float = 30.0) -> float:
    """
    Find the best silence point near target_time for chunking.

    Strategy: Find the largest gap within ±window of target_time.
    This ensures we don't cut in the middle of speech.

    Args:
        vad_segments: List of speech segments from VAD


**Considering GlobalVADPool usage**

Creating a global pool using spawn can be resource-intensive, and I realize we need to ensure it shuts down properly at exit using atexit.

An easier option would be to use the existing GlobalVADPool in worker_pools. The run_vad_from_buffer function already supports it, but it seems the pipeline doesn't pass. I could configure run_vad_from_buffer to choose to use GlobalVADPool if a specific flag is set, or when there are more than one VAD workers. Since GlobalVADPool is a singleton, I might just modify vad.py to use a spawn context and create a local pool for reuse.