Output:
Exit code: 0
Wall time: 107.3 seconds
Total output lines: 7253
Output:
==> Building image: maya3-pipeline:local
#0 building with "default" instance using docker driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 2.44kB done
#1 DONE 0.0s

#2 [internal] load metadata for docker.io/nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
#2 DONE 0.0s

#3 [internal] load .dockerignore
#3 transferring context: 255B done
#3 DONE 0.0s

#4 [1/8] FROM docker.io/nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
#4 DONE 0.0s

#5 [internal] load build context
#5 transferring context: 6.14kB done
#5 DONE 0.0s

#6 [4/8] RUN mkdir -p /root/.cache/torch/hub     && mkdir -p /root/.cache/huggingface     && mkdir -p /ephemeral/models
#6 CACHED

#7 [2/8] RUN apt-get update && apt-get install -y --no-install-recommends     python3     python3-venv     python3-dev     python3-pip     python-is-python3     build-essential     ffmpeg     libsndfile1     libsox-fmt-all     sox     git     curl     wget     && rm -rf /var/lib/apt/lists/*
#7 CACHED

#8 [5/8] COPY requirements.txt .
#8 CACHED

#9 [3/8] WORKDIR /workspace/maya3_data
#9 CACHED

#10 [6/8] RUN python3 -m venv .venv &&     .venv/bin/pip install --upgrade pip &&     .venv/bin/pip install --no-cache-dir torch torchaudio --index-url https://download.pytorch.org/whl/cu121 &&     .venv/bin/pip install --no-cache-dir -r requirements.txt &&     .venv/bin/pip install --no-cache-dir supabase &&     .venv/bin/pip install --no-cache-dir "nvidia-nccl-cu12==2.28.9"
#10 CACHED

#11 [7/8] COPY . .
#11 DONE 0.0s

#12 [8/8] RUN mkdir -p ./output
#12 DONE 0.1s

#13 exporting to image
#13 exporting layers 0.0s done
#13 writing image sha256:9d451bad9153e62049645495cfee84fbb0d5a05044d91b1ab265431cddb69892 done
#13 naming to docker.io/library/maya3-pipeline:local done
#13 DONE 0.0s
==> Basic entrypoint check (help output)
==> GPU + imports smoke test
torch 2.5.1+cu121
cuda_available True
gpu_count 1
==> Supabase connectivity smoke test
{'PENDING': 172598, 'CLAIMED': 0, 'COMPLETED': 3, 'FAILED': 10, 'SKIPPED': 0}
==> R2 connectivity smoke test
source_bucket test
production_bucket 1-cleaned-data
==> E2E test: process VIDEO_ID --E8hBLmJvw on GPU 0 (no Supabase claims)
19:28:23 | MassiveCompute | INFO     | Detected 1 GPUs: [0]
19:28:23 | MassiveCompute | INFO     | HF_TOKEN captured (length=37)
19:28:23 | FastPipelineV6.Export | INFO     | BackgroundExportManager started with 2 workers
19:28:23 | MassiveCompute | INFO     | Background export enabled (2 workers)
19:28:23 | MassiveCompute | INFO     | ======================================================================
19:28:23 | MassiveCompute | INFO     | MASSIVE COMPUTE - Multi-GPU Parallel Processing
19:28:23 | MassiveCompute | INFO     | ======================================================================
19:28:23 | MassiveCompute | INFO     | Videos: 1
19:28:23 | MassiveCompute | INFO     | GPUs: 1 ([0])
19:28:23 | MassiveCompute | INFO     | Workers/GPU: 1 (total: 1)
19:28:23 | MassiveCompute | INFO     | Output: ./output
19:28:23 | MassiveCompute | INFO     | ======================================================================
19:28:23 | MassiveCompute | INFO     | Started worker 0 on GPU 0
19:28:23 | Worker-0     | INFO     | Starting on GPU 0
19:28:25 | Worker-0     | INFO     | GPU: NVIDIA A100-SXM4-80GB (85.1GB)
Downloading: "https://github.com/snakers4/silero-vad/zipball/master" to /root/.cache/torch/hub/master.zip
19:28:34 | Worker-0     | INFO     | Prefetch thread started
19:28:37 | Worker-0     | INFO     | Processing (prefetched): https://www.youtube.com/watch?v=--E8hBLmJvw
[GPU0] PICK   --E8hBLmJvw
[GPU0] 1/10 Download       3.4s
[GPU0] 2/10 VAD            2.5s  | 1 segments
[GPU0] 3/10 Chunking       0.0s  | 1 chunks
[GPU0] 4/10 Diarization+   6.2s  | 1 segments
--2026-01-07 19:28:47--  http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/class_labels_indices.csv
Resolving storage.googleapis.com (storage.googleapis.com)... 64.233.179.207, 74.125.126.207, 108.177.121.207, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|64.233.179.207|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 14675 (14K) [application/octet-stream]
Saving to: ‘/root/panns_data/class_labels_indices.csv’

     0K .......... ....                                       100%  563K=0.03s

2026-01-07 19:28:47 (563 KB/s) - ‘/root/panns_data/class_labels_indices.csv’ saved [14675/14675]

Checkpoint path: /root/panns_data/Cnn14_mAP=0.431.pth
--2026-01-07 19:28:47--  https://zenodo.org/record/3987831/files/Cnn14_mAP%3D0.431.pth?download=1
Resolving zenodo.org (zenodo.org)... 188.185.43.153, 188.185.48.75, 137.138.52.235, ...
Connecting to zenodo.org (zenodo.org)|188.185.43.153|:443... connected.
HTTP request sent, awaiting response... 301 MOVED PERMANENTLY
Location: /records/3987831/files/Cnn14_mAP=0.431.pth [following]
--2026-01-07 19:28:51--  https://zenodo.org/records/3987831/files/Cnn14_mAP=0.431.pth
Reusing existing connection to zenodo.org:443.
HT…542575 chars truncated…0K .......... .......... .......... .......... .......... 10%  600K 10m1s
 33450K .......... .......... .......... .......... .......... 10%  975K 10m1s
 33500K .......... .......... .......... .......... .......... 10%  375K 10m1s
 33550K .......... .......... .......[GPU0] 7/10 MusicDetecti  38.5s
... .......... .......... 10%  313K 10m1s
 33600K .......... .......... .......... .......... .......... 10%  385K 10m1s
 33650K .......... .......... .......... .......... .......... 10%  721K 10m1s
 33700K .......... .......... .......... .......... .......... 10%  308K 10m1s
 33750K .......... .......... .[GPU0] 8/10 Finalize       0.5s
[GPU0] 9/10 Output         0.0s
[GPU0] DONE   --E8hBLmJvw 87.3s | 4.7min | 1 spk | 30%
......... .......... .......... 10%  555K 10m1s
 33800K .......... .......... .......... .......... .......... 10%  844K 10m1s
 33850K .......... .......... .......... .......... .......... 10%  574K 10m0s
 33900K .......... .......... .......... .......... .......... 10%  369K 10m0s
 33950K .......... .......... .......... .......... .......... 10%  623K 10m0s
 34000K .........19:30:05 | Worker-0     | INFO     | Done: --E8hBLmJvw | 87.7s | 1 speakers | 29.7% usable
19:30:05 | MassiveCompute | INFO     | [1/1] OK: --E8hBLmJvw (87.7s on GPU 0)
. .......... .......... .........19:30:05 | Worker-0     | INFO     | Received stop signal
19:30:05 | Worker-0     | INFO     | Shutting down. Processed 1 videos.
19:30:05 | R2Client     | ERROR    | Upload failed: cannot schedule new futures after interpreter shutdown
19:30:05 | FastPipelineV6.Export | ERROR    | [--E8hBLmJvw] Export failed: R2 upload failed: smoke_test/20260107_192823/--E8hBLmJvw.tar
. .......... 10%  109K 10m3s
 34050K .......... .......... .......... .......... .......... 10%  110M 10m2s
 34100K .......... .......... .......... .......... .......... 10%  669K 10m2s
 34150K .......... ..........19:30:05 | MassiveCompute | INFO     |
======================================================================
19:30:05 | MassiveCompute | INFO     | BENCHMARK RESULTS
19:30:05 | MassiveCompute | INFO     | ======================================================================
19:30:05 | MassiveCompute | INFO     | Videos: 1/1 successful
19:30:05 | MassiveCompute | INFO     | Failed: 0
19:30:05 | MassiveCompute | INFO     | Wall time: 102.2s (1.7 min)
19:30:05 | MassiveCompute | INFO     | Total audio: 0.08 hours
19:30:05 | MassiveCompute | INFO     | ----------------------------------------------------------------------
19:30:05 | MassiveCompute | INFO     | THROUGHPUT:
19:30:05 | MassiveCompute | INFO     |    Videos/min: 0.59
19:30:05 | MassiveCompute | INFO     |    Audio hours/wall hour: 2.78x realtime
19:30:05 | MassiveCompute | INFO     |    Parallel efficiency: 85.8%
19:30:05 | MassiveCompute | INFO     | ----------------------------------------------------------------------
19:30:05 | MassiveCompute | INFO     | Avg process time/video: 87.7s
19:30:05 | MassiveCompute | INFO     | ----------------------------------------------------------------------
19:30:05 | MassiveCompute | INFO     | AVG PER-STAGE TIME (successful videos):
19:30:05 | MassiveCompute | INFO     |    music_detection          38.50s  ( 63.4%)
19:30:05 | MassiveCompute | INFO     |    unified_embeddings        6.51s  ( 10.7%)
19:30:05 | MassiveCompute | INFO     |    diarization               6.23s  ( 10.3%)
19:30:05 | MassiveCompute | INFO     |    download                  3.41s  (  5.6%)
19:30:05 | MassiveCompute | INFO     |    vad                       2.51s  (  4.1%)
19:30:05 | MassiveCompute | INFO     |    micro_contamination       1.00s  (  1.6%)
19:30:05 | MassiveCompute | INFO     |    load_buffer               0.54s  (  0.9%)
19:30:05 | MassiveCompute | INFO     |    quality_filter            0.50s  (  0.8%)
19:30:05 | MassiveCompute | INFO     |    segment_embeddings        0.50s  (  0.8%)
19:30:05 | MassiveCompute | INFO     |    chunk_reassignment        0.50s  (  0.8%)
19:30:05 | MassiveCompute | INFO     |    finalization              0.50s  (  0.8%)
19:30:05 | MassiveCompute | INFO     |    chunking                  0.00s  (  0.0%)
19:30:05 | MassiveCompute | INFO     |    clustering                0.00s  (  0.0%)
19:30:05 | MassiveCompute | INFO     | ======================================================================
19:30:05 | MassiveCompute | INFO     | Results saved to: output/benchmark_results.json
 .....19:30:05 | MassiveCompute | INFO     | Waiting for background exports to complete...
19:30:05 | FastPipelineV6.Export | INFO     | Shutting down ExportManager (pending: 0)
19:30:05 | MassiveCompute | INFO     | Background exports: 0/0 successful
19:30:05 | MassiveCompute | INFO     |   Total segments: 0
19:30:05 | MassiveCompute | INFO     |   Export time: 0.0s
19:30:05 | MassiveCompute | INFO     |   Upload time: 0.0s
.==> Verifying TAR does not contain _original.flac: ./output/--E8hBLmJvw.tar
OK: _original.flac not present in TAR
==> Done


Summary: auto


**Analyzing export manager failure**

The E2E test succeeded with processing and tar verification, but the background export manager upload failed with the error "cannot schedule new futures after interpreter shutdown." This suggests that the export manager might have been shut down too late. The E2E run used `--background-export`, so tasks were running in background threads, but the main process could end before those were done. It seems the thread pool might be shutting down too early while R2 upload tasks are still scheduled.


**Investigating export failure**

The background exports show 0 successful, indicating the export task may not be tracked properly. The logs reveal an error related to R2 upload, suggesting the task failed during interpreter shutdown. After the main process prints "Waiting for background exports to complete," it follows with "Shutting down ExportManager," hinting the export task might have been removed due to failure. The tar file exists, but it doesn't contain the original flac, which poses issues for production. I need to address this bug before a wider deployment.


**Clarifying the error source**

I think there might be an issue connected to spurious output from `wget` during the music detection stage, but that's not the main problem. The error "cannot schedule new futures after interpreter shutdown" often occurs when tasks are submitted after the interpreter is shutting down, particularly if a background thread tries to create a new thread pool after the main thread has ended. In this case, the R2Client may create an S3 transfer manager that uses a thread pool. If we start the R2 upload at the end, during finalization, it could fail.


**Exploring upload error**

The log shows that the R2 upload failed with the error "cannot schedule new futures after interpreter shutdown." This suggests the interpreter was shutting down when the upload was attempted, even though logs were still being printed. It seems that after processing in direct URLs mode, the main process collects results and calls the shutdown export manager, which should wait for background exports. If the R2Client uses a `ThreadPoolExecutor`, it might be created lazily, and there's a possibility the background export manager runs in the main process, which could explain the issue.


I'm not sure what the best solution is here, but the error occurs at 19:30:05 when a worker finishes, which might be right before the main process calls shutdown_export_manager. It seems the background export continues running, which could be problematic. The main process should ideally wait for the background tasks to finish before exiting. Since the export manager is initialized in the main but also interacts with workers, there's a chance that when the interpreter shuts down, the thread pools have issues. It looks like we might need to rethink how the background exports are handled in this multiprocessing context. Running them synchronously or avoiding exporting in workers seems necessary to prevent errors.


**Addressing the ThreadPool issue**

I realized that initializing a `ThreadPoolExecutor` in the parent process and then forking child processes can lead to problems. Instead, we should initialize it after forking in each worker or consider using the spawn method. The export manager was created in the main process before spawning workers, which is problematic. To fix this, we shouldn't create the export manager in the main process; each worker should initialize its own after starting. I need to examine the `src/export.py` file for further details.


ls -la src && rg -n "init_export_manager|ExportManager|BackgroundExport" -S src | head -n 80


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 524
drwxrwxr-x 3 ubuntu ubuntu  4096 Jan  7 00:22 .
drwxrwxr-x 7 ubuntu ubuntu  4096 Jan  7 19:14 ..
-rw-rw-r-- 1 ubuntu ubuntu 16362 Jan  7 00:43 analytics.py
-rw-rw-r-- 1 ubuntu ubuntu  7422 Jan  6 22:22 audio_buffer.py
-rw-rw-r-- 1 ubuntu ubuntu 10083 Jan  6 22:23 boundary_refinement.py
-rw-rw-r-- 1 ubuntu ubuntu 22464 Jan  6 22:23 chunk_parallel.py
-rw-rw-r-- 1 ubuntu ubuntu 45803 Jan  6 22:22 chunk_reassignment.py
-rw-rw-r-- 1 ubuntu ubuntu  6822 Jan  6 22:22 clustering.py
-rw-rw-r-- 1 ubuntu ubuntu 20012 Jan  6 22:22 compute.py
-rw-rw-r-- 1 ubuntu ubuntu 15384 Jan  6 22:23 config.py
-rw-rw-r-- 1 ubuntu ubuntu 13684 Jan  6 22:22 diarization.py
-rw-rw-r-- 1 ubuntu ubuntu 66987 Jan  6 22:23 download.py
-rw-rw-r-- 1 ubuntu ubuntu 11756 Jan  6 22:22 embeddings.py
-rw-rw-r-- 1 ubuntu ubuntu 12111 Jan  6 22:23 export.py
-rw-rw-r-- 1 ubuntu ubuntu 22937 Jan  6 22:22 hybrid_music_detection.py
-rw-rw-r-- 1 ubuntu ubuntu  4668 Jan  6 22:23 logger.py
-rw-rw-r-- 1 ubuntu ubuntu 13735 Jan  7 00:21 models.py
-rw-rw-r-- 1 ubuntu ubuntu 25903 Jan  6 22:22 music_detection.py
-rw-rw-r-- 1 ubuntu ubuntu 25355 Jan  6 22:22 overlap_detection.py
drwxrwxr-x 2 ubuntu ubuntu  4096 Jan  7 00:43 __pycache__
-rw-rw-r-- 1 ubuntu ubuntu  9881 Jan  6 22:23 r2_client.py
-rw-rw-r-- 1 ubuntu ubuntu 15304 Jan  6 22:22 spectral_analysis.py
-rw-rw-r-- 1 ubuntu ubuntu 14744 Jan  6 22:23 supabase_client.py
-rw-rw-r-- 1 ubuntu ubuntu 70894 Jan  6 22:23 unified_embeddings.py
-rw-rw-r-- 1 ubuntu ubuntu  2596 Jan  6 22:22 utils.py
-rw-rw-r-- 1 ubuntu ubuntu 15294 Jan  6 22:23 vad.py
-rw-rw-r-- 1 ubuntu ubuntu 12188 Jan  6 22:23 worker_pools.py
src/export.py:198:class BackgroundExportManager:
src/export.py:203:        manager = BackgroundExportManager(max_workers=4)
src/export.py:231:        logger.info(f"BackgroundExportManager started with {self.max_workers} workers")
src/export.py:240:            logger.error("ExportManager not started")
src/export.py:294:        logger.info(f"Shutting down ExportManager (pending: {len(self.pending_futures)})")
src/export.py:322:_EXPORT_MANAGER: Optional[BackgroundExportManager] = None
src/export.py:325:def get_export_manager() -> Optional[BackgroundExportManager]:
src/export.py:330:def init_export_manager(max_workers: int = 4) -> BackgroundExportManager:
src/export.py:334:        _EXPORT_MANAGER = BackgroundExportManager(max_workers=max_workers)


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Background Export Manager - Async audio segmentation and R2 upload.

Runs export jobs in background threads so GPU processing isn't blocked.
Properly handles shutdown and waits for in-flight jobs to complete.
"""

import os
import json
import time
import logging
import subprocess
import tarfile
from pathlib import Path
from typing import Dict, List, Optional, Any
from concurrent.futures import ThreadPoolExecutor, Future, as_completed
from dataclasses import dataclass, field
from threading import Lock
from queue import Queue

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

@dataclass
class ExportJob:
    """A single export job (segmentation + optional R2 upload)."""
    video_id: str
    output_dir: Path
    original_audio_path: Path
    segments: List[Dict]
    intro_offset: float = 0.0
    r2_upload_enabled: bool = False
    r2_upload_key: str = ""
    r2_bucket_type: str = "production"
    include_original_in_tar: bool = False  # Don't include _original.flac in TAR (source is in R2)
    submitted_at: float = field(default_factory=time.time)

@dataclass
class ExportResult:
    """Result of an export job."""
    video_id: str
    success: bool
    segments_exported: int = 0
    export_time_sec: float = 0.0
    r2_upload_time_sec: float = 0.0
    tar_size_bytes: int = 0
    error: Optional[str] = None

def segment_audio_parallel(
    audio_path: Path,
    segments: List[Dict],
    output_dir: Path,
    intro_offset: float = 0.0,
    max_workers: int = 8,
    output_format: str = "flac"
) -> int:
    """
    Cut audio into individual speaker segment files using parallel ffmpeg.

    Args:
        audio_path: Path to original quality audio (FLAC)
        segments: List of segment dicts with 'start', 'end', 'speaker', 'status'
        output_dir: Directory to write segment files
        intro_offset: Offset already applied to audio (for timestamp adjustment)
        max_workers: Number of parallel ffmpeg processes
        output_format: Output format (flac, wav, mp3)

    Returns:
        Number of segments exported
    """
    # Filter usable segments only
    usable = [s for s in segments if s.get('status') == 'usable']
    if not usable:
        logger.warning(f"No usable segments to export")
        return 0

    segments_dir = output_dir / "segments"
    segments_dir.mkdir(exist_ok=True)

    def cut_segment(seg: Dict, idx: int) -> Optional[str]:
        """Cut a single segment using ffmpeg."""
        try:
            speaker = seg.get('speaker', 'UNKNOWN')
            start = seg['start']
            end = seg['end']
            duration = end - start

            # Filename: SPEAKER_IDX_START-END.flac
            filename = f"{speaker}_{idx:04d}_{start:.2f}-{end:.2f}.{output_format}"
            out_path = segments_dir / filename

            cmd = [
                'ffmpeg', '-y', '-hide_banner', '-loglevel', 'error',
                '-ss', str(start),
                '-t', str(duration),
                '-i', str(audio_path),
                '-c:a', 'flac' if output_format == 'flac' else 'copy',
                str(out_path)
            ]

            result = subprocess.run(cmd, capture_output=True, timeout=30)
            if result.returncode != 0:
                logger.warning(f"ffmpeg failed for segment {idx}: {result.stderr.decode()[:100]}")
                return None

            return str(out_path)
        except Exception as e:
            logger.warning(f"Failed to cut segment {idx}: {e}")
            return None

    # Run parallel ffmpeg
    exported = 0
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = {
            executor.submit(cut_segment, seg, i): i
            for i, seg in enumerate(usable)
        }
        for future in as_completed(futures):
            result = future.result()
            if result:
                exported += 1

    return exported

def run_export_job(job: ExportJob) -> ExportResult:
    """
    Execute a complete export job (segmentation + tar + optional R2 upload).

    This runs in a background thread and should not block GPU processing.
    """
    start_time = time.time()
    result = ExportResult(video_id=job.video_id, success=False)

    try:
        # Step 1: Segment audio (parallel ffmpeg)
        if job.original_audio_path.exists():
            seg_start = time.time()
            result.segments_exported = segment_audio_parallel(
                audio_path=job.original_audio_path,
                segments=job.segments,
                output_dir=job.output_dir,
                intro_offset=job.intro_offset,
                max_workers=8,
                output_format="flac"
            )
            result.export_time_sec = time.time() - seg_start
            logger.info(f"[{job.video_id}] Segmented {result.segments_exported} clips in {result.export_time_sec:.1f}s")
        else:
            logger.warning(f"[{job.video_id}] Original audio not found: {job.original_audio_path}")

        # Step 2: Remove original audio from output dir if not needed in TAR
        # (Original is already preserved in R2 source bucket)
        if not job.include_original_in_tar:
            original_in_output = job.output_dir / f"{job.video_id}_original.flac"
            if original_in_output.exists():
                original_in_output.unlink()
                logger.debug(f"[{job.video_id}] Removed _original.flac from TAR (source in R2)")

        # Step 3: Create tar archive
        tar_path = job.output_dir.parent / f"{job.video_id}.tar"
        with tarfile.open(tar_path, "w") as tar:
            tar.add(job.output_dir, arcname=job.video_id)
        result.tar_size_bytes = tar_path.stat().st_size if tar_path.exists() else 0

        # Step 4: R2 upload (if enabled)
        if job.r2_upload_enabled and job.r2_upload_key:
            from src.r2_client import R2Client

            upload_start = time.time()
            r2 = R2Client(bucket_type=job.r2_bucket_type)
            upload_ok = r2.upload_file(str(tar_path), job.r2_upload_key)
            result.r2_upload_time_sec = time.time() - upload_start

            if not upload_ok:
                raise RuntimeError(f"R2 upload failed: {job.r2_upload_key}")

            logger.info(f"[{job.video_id}] Uploaded to R2 in {result.r2_upload_time_sec:.1f}s")

            # Delete local tar after successful upload
            try:
                tar_path.unlink()
            except:
                pass

        result.success = True

    except Exception as e:
        result.error = str(e)
        logger.error(f"[{job.video_id}] Export failed: {e}")

    return result

class BackgroundExportManager:
    """
    Manages background export jobs with proper lifecycle handling.

    Usage:
        manager = BackgroundExportManager(max_workers=4)
        manager.start()

        # Submit jobs (non-blocking)
        manager.submit(job1)
        manager.submit(job2)

        # At shutdown, wait for completion
        results = manager.shutdown(wait=True, timeout=300)
    """

    def __init__(self, max_workers: int = 4):
        self.max_workers = max_workers
        self.executor: Optional[ThreadPoolExecutor] = None
        self.pending_futures: Dict[str, Future] = {}
        self.results: Dict[str, ExportResult] = {}
        self.lock = Lock()
        self._started = False


Output:
Exit code: 0
Wall time: 0 seconds
Output:
        self._started = False

    def start(self):
        """Start the background export manager."""
        if self._started:
            return
        self.executor = ThreadPoolExecutor(
            max_workers=self.max_workers,
            thread_name_prefix="export_worker"
        )
        self._started = True
        logger.info(f"BackgroundExportManager started with {self.max_workers} workers")

    def submit(self, job: ExportJob) -> bool:
        """
        Submit an export job to run in background.

        Returns True if submitted successfully, False otherwise.
        """
        if not self._started or not self.executor:
            logger.error("ExportManager not started")
            return False

        try:
            future = self.executor.submit(run_export_job, job)
            with self.lock:
                self.pending_futures[job.video_id] = future
            logger.debug(f"Submitted export job: {job.video_id}")
            return True
        except Exception as e:
            logger.error(f"Failed to submit export job: {e}")
            return False

    def get_pending_count(self) -> int:
        """Get number of pending export jobs."""
        with self.lock:
            return len(self.pending_futures)

    def collect_completed(self) -> List[ExportResult]:
        """Collect results from completed jobs (non-blocking)."""
        completed = []
        with self.lock:
            done_ids = []
            for video_id, future in self.pending_futures.items():
                if future.done():
                    try:
                        result = future.result(timeout=0)
                        self.results[video_id] = result
                        completed.append(result)
                    except Exception as e:
                        result = ExportResult(video_id=video_id, success=False, error=str(e))
                        self.results[video_id] = result
                        completed.append(result)
                    done_ids.append(video_id)

            for vid in done_ids:
                del self.pending_futures[vid]

        return completed

    def shutdown(self, wait: bool = True, timeout: float = 300) -> Dict[str, ExportResult]:
        """
        Shutdown the export manager.

        Args:
            wait: If True, wait for pending jobs to complete
            timeout: Maximum time to wait for pending jobs

        Returns:
            Dict of video_id -> ExportResult for all jobs
        """
        if not self._started:
            return self.results

        logger.info(f"Shutting down ExportManager (pending: {len(self.pending_futures)})")

        if wait and self.pending_futures:
            # Wait for pending jobs with timeout
            pending_list = list(self.pending_futures.values())
            start = time.time()

            for future in as_completed(pending_list, timeout=timeout):
                try:
                    result = future.result(timeout=1)
                    with self.lock:
                        self.results[result.video_id] = result
                except Exception as e:
                    logger.error(f"Export job failed during shutdown: {e}")

            elapsed = time.time() - start
            logger.info(f"Waited {elapsed:.1f}s for {len(pending_list)} export jobs")

        # Shutdown executor
        if self.executor:
            self.executor.shutdown(wait=False)
            self.executor = None

        self._started = False
        return self.results

# Global export manager instance (initialized by massive_process.py)
_EXPORT_MANAGER: Optional[BackgroundExportManager] = None

def get_export_manager() -> Optional[BackgroundExportManager]:
    """Get the global export manager instance."""
    return _EXPORT_MANAGER

def init_export_manager(max_workers: int = 4) -> BackgroundExportManager:
    """Initialize the global export manager."""
    global _EXPORT_MANAGER
    if _EXPORT_MANAGER is None:
        _EXPORT_MANAGER = BackgroundExportManager(max_workers=max_workers)
        _EXPORT_MANAGER.start()
    return _EXPORT_MANAGER

def shutdown_export_manager(wait: bool = True, timeout: float = 300) -> Dict[str, ExportResult]:
    """Shutdown the global export manager and return results."""
    global _EXPORT_MANAGER
    if _EXPORT_MANAGER:
        results = _EXPORT_MANAGER.shutdown(wait=wait, timeout=timeout)
        _EXPORT_MANAGER = None
        return results
    return {}


**Investigating export manager usage**

I’m looking into the export manager that’s defined in src/export.py. It’s created by init_export_manager, which we call in the main process of massive_process.py. I’m wondering if the worker processes are also importing src.export and using get_export_manager. To clarify the situation, I plan to search for usage of get_export_manager in the pipeline. This might shed more light on how everything’s interconnected.


rg -n "get_export_manager\(|init_export_manager\(|shutdown_export_manager\(" -S pipeline.py src | head -n 80


Output:
Exit code: 0
Wall time: 0 seconds
Output:
pipeline.py:979:            export_manager = get_export_manager()
src/export.py:325:def get_export_manager() -> Optional[BackgroundExportManager]:
src/export.py:330:def init_export_manager(max_workers: int = 4) -> BackgroundExportManager:
src/export.py:339:def shutdown_export_manager(wait: bool = True, timeout: float = 300) -> Dict[str, ExportResult]:


Output:
Exit code: 0
Wall time: 0 seconds
Output:
            'total_speech': round(sum(s['duration'] for s in vad_segments), 2),
        },
        'sample_clips': {spk: [Path(p).name for p in paths] for spk, paths in sample_clips.items()},
        'timing': {k: round(v, 2) for k, v in timings.items()},
        'timing_total': round(total_time, 2),
        'compute_stats': compute_stats,
        'config': {
            'model': MODELS._model_name,
            'merge_threshold': config.cluster_merge_threshold,
            'vad_workers': config.vad_workers,
            'embedding_batch_size': config.embedding_batch_size,
            'min_segment_duration': config.min_segment_duration,
            # v6.1 config
            'min_tts_duration': config.min_tts_duration,
            'min_snr_db': config.min_snr_db,
            'filter_by_quality': config.filter_by_quality,
            # v6.3 chunk reassignment config
            'chunk_reassignment_enabled': config.enable_chunk_reassignment,
            'chunk_reassignment_threshold': config.chunk_reassignment_threshold,
            'chunk_reassignment_severe': config.chunk_reassignment_severe,
            # v6.7 music detection config
            'music_detection_enabled': config.enable_music_detection,
            'music_ratio_clean': config.music_ratio_clean,
            'music_ratio_demucs': config.music_ratio_demucs,
            # v6.8 intro skip + audio config
            'auto_intro_skip': config.auto_intro_skip,
            'preserve_original_audio': config.preserve_original_audio,
            # v7.9 boundary refinement
            'boundary_refinement_enabled': config.enable_boundary_refinement,
        },
        'processed_at': datetime.now().isoformat(),
        'pipeline_version': 'v7.9-vad-boundary-refinement',
        # === v7.5: Add download_meta for database storage ===
        'download_meta': {
            'native_sample_rate': metadata.get('original_sample_rate'),
            'channels': metadata.get('channels', 1),
            'acodec': metadata.get('acodec', 'unknown'),
            'abr': metadata.get('abr'),
            'format': metadata.get('format'),
            'duration': metadata.get('original_duration'),
        },
        # v6.4 cache stats
        'embedding_cache_stats': cache_stats if 'cache_stats' in dir() else {},
    }

    # Save metadata JSON
    with open(output_dir / "metadata.json", 'w') as f:
        json.dump(result, f, indent=2)

    # === v6.8+: Cleanup 16kHz processing file if original is preserved ===
    # Keep only the high-quality original for final export
    if config.preserve_original_audio and metadata.get('original_audio_preserved'):
        trimmed_file = Path(audio_path)
        original_file = Path(metadata.get('original_audio_path', ''))
        if trimmed_file.exists() and original_file.exists():
            trimmed_size_mb = trimmed_file.stat().st_size / (1024 * 1024)
            trimmed_file.unlink()
            logger.info(f"🧹 Cleaned up 16kHz processing file ({trimmed_size_mb:.1f}MB), keeping original quality")

    # ========================================
    # STAGE 10: Export (Segmentation + Package + Upload)
    # Can run in background to avoid blocking GPU
    # ========================================
    export_enabled = getattr(config, 'r2_upload_enabled', False) or getattr(config, 'segment_audio', False)
    background_export = getattr(config, 'background_export', False)

    if export_enabled:
        log_stage(10, "Export", "START")

        upload_prefix = (getattr(config, 'r2_upload_prefix', '') or '').strip().strip('/')
        remote_key = f"{video_id}.tar" if not upload_prefix else f"{upload_prefix}/{video_id}.tar"
        original_audio_path = Path(metadata.get('original_audio_path', ''))
        intro_offset = metadata.get('intro_skipped', 0.0)

        if background_export:
            # === BACKGROUND EXPORT MODE ===
            # Submit job to background manager, don't block GPU
            from src.export import ExportJob, get_export_manager

            export_manager = get_export_manager()
            if export_manager:
                job = ExportJob(
                    video_id=video_id,
                    output_dir=output_dir,
                    original_audio_path=original_audio_path,
                    segments=segments,
                    intro_offset=intro_offset,
                    r2_upload_enabled=getattr(config, 'r2_upload_enabled', False),
                    r2_upload_key=remote_key,
                    r2_bucket_type=getattr(config, 'r2_upload_bucket_type', 'production'),
                    include_original_in_tar=getattr(config, 'include_original_in_tar', False),
                )
                export_manager.submit(job)
                log_stage(10, "Export", "QUEUED", f"background job submitted")
                result['export_mode'] = 'background'
                result['r2_upload_key'] = remote_key
            else:
                logger.warning("Background export manager not initialized, falling back to sync")
                background_export = False  # Fall through to sync mode

        if not background_export:
            # === SYNCHRONOUS EXPORT MODE ===
            from src.r2_client import R2Client
            from src.export import segment_audio_parallel

            with COMPUTE.monitor_stage("Export") as metrics:
                # Step 1: Segment original audio (parallel ffmpeg)
                segments_exported = 0
                if getattr(config, 'segment_audio', True) and original_audio_path.exists():
                    seg_start = time.time()
                    segments_exported = segment_audio_parallel(
                        audio_path=original_audio_path,
                        segments=segments,
                        output_dir=output_dir,
                        intro_offset=intro_offset,
                        max_workers=8,
                        output_format="flac"
                    )
                    seg_time = time.time() - seg_start
                    logger.info(f"📦 Segmented {segments_exported} clips in {seg_time:.1f}s")

                # Step 1.5: Remove original audio from output (source is preserved in R2)
                if not getattr(config, 'include_original_in_tar', False):
                    original_in_output = output_dir / f"{video_id}_original.flac"
                    if original_in_output.exists():
                        original_in_output.unlink()
                        logger.debug(f"Removed _original.flac from output (source in R2)")

                # Step 2: Create tar archive
                tar_path = output_dir.parent / f"{video_id}.tar"
                with tarfile.open(tar_path, "w") as tar:
                    tar.add(output_dir, arcname=video_id)
                tar_size_bytes = tar_path.stat().st_size if tar_path.exists() else 0

                # Step 3: Upload to R2 (if enabled)
                upload_ok = True
                if getattr(config, 'r2_upload_enabled', False):
                    r2 = R2Client(bucket_type=getattr(config, 'r2_upload_bucket_type', 'production'))
                    if getattr(config, 'r2_upload_skip_if_exists', False) and r2.exists(remote_key):
                        logger.info(f"R2 upload skipped (exists): s3://{r2.bucket}/{remote_key}")
                    else:
                        upload_ok = r2.upload_file(str(tar_path), remote_key)
                        if not upload_ok:
                            raise RuntimeError(f"R2 upload failed for s3://{r2.bucket}/{remote_key}")

                    # Delete local tar after upload
                    if getattr(config, 'r2_upload_delete_local_tar', True):
                        try:
                            tar_path.unlink()
                        except Exception:
                            pass

            timings['export'] = metrics.duration
            compute_stats['export'] = {'cpu': metrics.cpu_percent_avg, 'gpu': metrics.gpu_percent_avg}
            result['export'] = {
                'segments_exported': segments_exported,
                'tar_size_bytes': int(tar_size_bytes),
                'r2_upload_key': remote_key if getattr(config, 'r2_upload_enabled', False) else None,
            }

            log_stage(10, "Export", "DONE", f"{timings['export']:.1f}s | {segments_exported} segs | {tar_size_bytes/(1024**2):.1f}MB")


**Evaluating export processes**

I'm thinking about how each worker processes multiple videos concurrently, so exports usually complete. However, if a worker crashes, some exports might not finish, and that's potentially acceptable. The error message suggests that worker terminations by Kubernetes could disrupt background exports; I want to address that. Our end-to-end test for R2 uploads failed, so I’m considering adjusting the test to run synchronous exports when `--max-videos` is 1. We might also want to change the default to avoid background exports in the test.


**Analyzing export tasks in queue mode**

This bug might also appear in supabase queue mode, where workers run continuously and don’t exit after each video—though they can be restarted. I think a better fix would be to use a background export manager within the main process rather than in the worker. Since the pipeline runs in the worker process, I want to offload the export to background threads that aren’t tied to the GPU process. It seems that the worker should run indefinitely, processing export tasks without issues.


**Reviewing end-to-end test scenarios**

For our end-to-end tests involving `--max-videos`, the worker process exits quickly, which disrupts background exports and leads to upload failures. This means our method of launching an args-mode instance with `--max-videos 1` for testing is flawed if background exports are used. I could disable background export for this test, or set the max videos higher to allow background exports to finish. A simpler approach would be to run E2E tests synchronously by not passing `--background-export`, as it defaults to false in massive_process.py.


**Addressing export management in workers**

Since the export manager operates in the worker process's thread pool, I need to trigger it within that context. It might be an option to modify the pipeline to store export futures in results, but that seems overly complex. A simpler fix would be to call `shutdown_export_manager(wait=True)` in the `gpu_worker` and `gpu_worker_supabase` functions before shutting down, ensuring pending exports finish. This should prevent interpreter shutdown while exports are still running and help complete R2 uploads. Also, to avoid thread pool issues, worker processes should handle the initialization of the export manager upon first use rather than the parent.


**Modifying export manager initialization**

The pipeline expects an export manager, and if it's set to None, it defaults to synchronous mode. If I remove the `init_export_manager` from the main process, background exports won’t occur unless the worker initializes it. I could adjust the pipeline to call `init_export_manager` inside the worker if it’s None, creating a safe thread pool after forking. Alternatively, keeping the initialization in the main process raises safety concerns if forked. I’ll review where `init_export_manager` is called in massive_process.py and consider initializing it in each worker instead. We also need to shut down the export manager properly to avoid lost uploads.


rg -n "PipelineConfig|load_config|config_overrides" massive_process.py | head -n 80


Output:
Exit code: 0
Wall time: 0 seconds
Output:
201:    config_overrides: Dict
269:            **config_overrides
429:    config_overrides: dict,
494:        for key, value in config_overrides.items():
680:    config_overrides: Optional[Dict] = None,
691:    config_overrides = config_overrides or {}
693:    workers_per_gpu = config_overrides.get('workers_per_gpu', 1)
735:                    output_dir, config_overrides, lease_duration_sec,
881:    config_overrides: Optional[Dict] = None
890:        config_overrides: Optional config parameter overrides
895:    config_overrides = config_overrides or {}
897:    workers_per_gpu = config_overrides.get('workers_per_gpu', 1)
923:    workers_per_gpu = config_overrides.pop('workers_per_gpu', 1)
940:                args=(worker_id, gpu_id, video_queue, result_queue, stop_signal, output_dir, config_overrides),
1198:    config_overrides = {
1233:            config_overrides=config_overrides,
1244:            config_overrides=config_overrides


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    num_speakers: int = 0
    usable_percentage: float = 0.0
    output_path: str = ""
    stage_timings: Dict[str, float] = field(default_factory=dict)
    source_type: str = ""
    r2_upload_key: str = ""
    gpu_id: int = -1
    worker_id: int = -1

@dataclass
class BenchmarkStats:
    """Aggregate statistics from benchmark run."""
    total_videos: int = 0
    successful: int = 0
    failed: int = 0
    total_audio_duration_sec: float = 0.0
    total_audio_hours: float = 0.0
    total_process_time_sec: float = 0.0
    throughput_videos_per_min: float = 0.0
    throughput_hours_audio_per_hour: float = 0.0  # Hours of audio processed per hour of wall time
    avg_process_time_sec: float = 0.0
    avg_rtf: float = 0.0
    parallel_efficiency: float = 0.0
    gpus_used: int = 0
    gpu_count: int = 0
    workers_per_gpu: int = 1
    start_time: str = ""
    end_time: str = ""
    start_timestamp: str = ""
    end_timestamp: str = ""
    wall_time_sec: float = 0.0

def gpu_worker(
    worker_id: int,
    gpu_id: int,
    video_queue: Queue,
    result_queue: Queue,
    stop_signal: Value,
    output_dir: str,
    config_overrides: Dict
):
    """
    GPU Worker Process - Runs complete pipeline on assigned GPU with prefetch.

    Each worker:
    1. Sets CUDA_VISIBLE_DEVICES to its assigned GPU
    2. Loads all models ONCE into GPU memory
    3. Prefetches next video(s) while processing current one
    4. Reports results back to main process

    OPTIMIZATION: Download prefetch hides 15-20s latency per video
    """
    # CRITICAL: Set GPU and suppress warnings before ANY torch imports
    os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
    os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
    os.environ['NNPACK_DISABLE'] = '1'
    os.environ['TORCH_CPP_LOG_LEVEL'] = 'ERROR'
    import warnings
    warnings.filterwarnings('ignore', message='.*NNPACK.*')
    warnings.filterwarnings('ignore', category=FutureWarning)
    warnings.filterwarnings('ignore', category=UserWarning)

    # Setup worker logging
    worker_logger = logging.getLogger(f"Worker-{worker_id}")
    worker_logger.setLevel(logging.INFO)

    worker_logger.info(f"Starting on GPU {gpu_id}")

    try:
        # Load .env inside the worker as well (spawned processes may not have env populated)
        if load_dotenv is not None:
            try:
                load_dotenv(dotenv_path=Path("/workspace/maya3_data/.env"), override=False)
            except Exception:
                pass

        # Now import torch and pipeline (after CUDA_VISIBLE_DEVICES is set)
        import torch

        # Verify GPU assignment
        if not torch.cuda.is_available():
            worker_logger.error("CUDA not available!")
            return

        gpu_name = torch.cuda.get_device_name(0)  # Now 0 is our assigned GPU
        vram_gb = torch.cuda.get_device_properties(0).total_memory / 1e9
        worker_logger.info(f"GPU: {gpu_name} ({vram_gb:.1f}GB)")

        # Aggressively silence speechbrain before any imports
        for name in ['speechbrain', 'speechbrain.utils', 'speechbrain.utils.checkpoints',
                     'speechbrain.utils.fetching', 'speechbrain.utils.parameter_transfer']:
            sb_logger = logging.getLogger(name)
            sb_logger.setLevel(logging.CRITICAL)
            sb_logger.handlers = []  # Remove all handlers
            sb_logger.propagate = False

        from src.logger import silence_verbose_loggers
        silence_verbose_loggers()

        # Import pipeline components
        from src.config import Config
        from src.models import MODELS
        from pipeline import process_single_video

        # Create config with overrides and gpu_id
        config = Config(
            output_dir=output_dir,
            **config_overrides
        )
        config.gpu_id = gpu_id  # For PipelineLogger

        # Load models ONCE for this worker
        MODELS.load_all(config)

        # PREFETCH OPTIMIZATION: Download next video while processing current
        # Uses a background thread to hide download latency (15-20s per video)
        from threading import Thread
        from queue import Queue as ThreadQueue, Empty as ThreadEmpty
        from src.download import download_audio

        prefetch_queue = ThreadQueue(maxsize=2)  # Buffer of 2 pre-downloaded videos
        prefetch_stop = [False]  # Use list for mutability in closure

        def prefetch_worker():
            """Background thread that pre-downloads videos."""
            while not prefetch_stop[0]:
                try:
                    # Get next URL from main queue (non-blocking short timeout)
                    try:
                        url = video_queue.get(timeout=1.0)
                    except:
                        continue

                    if url is None:  # Poison pill
                        prefetch_queue.put((None, None, None))
                        break

                    # Download audio
                    try:
                        audio_path, metadata = download_audio(url, config)
                        prefetch_queue.put((url, audio_path, metadata))
                        worker_logger.debug(f"Prefetched: {url}")
                    except Exception as e:
                        # Put URL with error marker
                        prefetch_queue.put((url, None, str(e)))

                except Exception as e:
                    if not prefetch_stop[0]:
                        worker_logger.debug(f"Prefetch error: {e}")

        # Start prefetch thread
        prefetch_thread = Thread(target=prefetch_worker, daemon=True)
        prefetch_thread.start()
        worker_logger.info("Prefetch thread started")

        # Process videos from prefetch queue
        videos_processed = 0
        while not stop_signal.value:
            try:
                # Get pre-downloaded video from prefetch queue
                try:
                    video_url, audio_path, metadata_or_error = prefetch_queue.get(timeout=30.0)
                except ThreadEmpty:
                    worker_logger.warning("Prefetch queue timeout, checking main queue")
                    continue

                if video_url is None:  # Poison pill
                    worker_logger.info("Received stop signal")
                    break

                # Check if download failed
                if audio_path is None:
                    worker_logger.error(f"Prefetch failed for {video_url}: {metadata_or_error}")
                    result_queue.put(VideoResult(
                        video_url=video_url,
                        success=False,
                        error=f"Download failed: {metadata_or_error}",
                        gpu_id=gpu_id,
                        worker_id=worker_id


**Evaluating export manager integration**

I'm thinking we can improve the setup by removing the `init_export_manager` call from the main function. Instead, we could add `gpu_worker` and `gpu_worker_supabase` before the processing loop. If background export is enabled, we can call `init_export_manager(max_workers=...)`.

Now, we need to decide on `max_workers`; it seems `config_overrides` has the `background_export_workers` attribute. In the worker, after creating the config, we should check if the background export attribute exists before making the call. I need to inspect the `src/config.py` to be sure.


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 260
Output:
#!/usr/bin/env python3
"""
Configuration for Fast Pipeline v6.0 - Adaptive Compute-Aware

Key improvements:
- Auto-detects system resources (nproc, vCPUs, GPU vRAM)
- Adapts worker counts and batch sizes to available compute
- Optimized for micro-level speaker detection (0.4s events)
"""

import os
from dataclasses import dataclass, field
from typing import Optional

@dataclass
class Config:
    """
    Pipeline configuration with adaptive compute settings.

    Flow (per instructions.md):
    1. Download (max parallel workers)
    2. Quick VAD (parallel, get speech outline)
    3. Chunk at silence boundaries (VAD-aware)
    4. OSD (mark overlaps as unusable FIRST)
    5. Frame-level segmentation (17ms resolution for micro-changes)
    6. Embeddings + Conservative clustering
    7. Output metadata JSON only
    """

    # === AUTHENTICATION ===
    # NOTE: Must be provided via environment variable HF_TOKEN
    # Never hard-code tokens in repo (HF gated model access + security).
    hf_token: str = os.environ.get('HF_TOKEN', '')

    # === INPUT SOURCE (YouTube vs R2) ===
    # Default behavior remains YouTube URLs. For benchmark/testing you can pass
    # bare 11-char video IDs and set input_source='r2' to stream originals from
    # the R2 source bucket (bucket name: "test") without YouTube rate limits.
    input_source: str = "youtube"  # 'youtube' | 'r2'

    # === R2 SOURCE (input) ===
    # When input_source='r2', download.py will:
    # - Resolve video_id -> object key via HEAD (NO bucket listing)
    # - Generate a presigned GET URL
    # - ffmpeg stream-extract audio
    #
    # Prefix/extension knobs are optional; env overrides exist too:
    # - R2_SOURCE_PREFIX / R2_SOURCE_PREFIXES
    # - R2_SOURCE_EXTENSIONS
    r2_source_prefix: str = ""          # e.g. "podcasts/" (optional)
    r2_source_extensions: str = ""      # e.g. ".webm,.mp4" (optional)
    r2_presign_ttl_sec: int = 3600      # Presigned URL TTL (seconds)

    # Avoid any YouTube metadata calls during R2 runs (rate-limit safe).
    # When False, R2 downloads fall back to duration-based dynamic intro skip.
    fetch_youtube_metadata_for_r2: bool = True

    # === R2 OUTPUT (upload processed artifacts) ===
    # When enabled, pipeline will package each video's output directory into a .tar
    # and upload it to the R2 production bucket (1-cleaned-data) by default.
    r2_upload_enabled: bool = False
    r2_upload_bucket_type: str = "production"  # maps to 1-cleaned-data in src/r2_client.py
    r2_upload_prefix: str = ""                 # e.g. "benchmarks/rtx4090/2026-01-03T16-00Z"
    r2_upload_skip_if_exists: bool = False     # avoid overwriting if True
    r2_upload_delete_local_tar: bool = True    # free local disk after upload

    # === AUDIO SEGMENTATION ===
    # segment_audio: Cut original FLAC into individual speaker segment files
    # 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 l…3192 chars truncated… cause: Fixed thresholds + create_new_on_ambiguous triggered on natural variation
    #
    # NEW GATES (see unified_embeddings.py):
    # 1. Persistence gate: Require ≥N consecutive low-sim chunks
    # 2. Relative valley gate: Per-segment adaptive threshold (outlier detection)
    # 3. Same-speaker centroid gate: Reject if both sides match original speaker
    # 4. No-new-speaker-unless-proven: Keep original speaker on ambiguous (key fix!)
    enable_chunk_reassignment: bool = True  # RE-ENABLED with validation gates
    chunk_reassignment_threshold: float = 0.30  # Normal threshold (per-segment adaptive overrides)
    chunk_reassignment_severe: float = 0.15  # Severe circuit-breaker (only extreme changes)
    chunk_reassignment_min_speech: float = 0.6  # Min speech ratio for eligibility
    chunk_reassignment_min_portion: float = 1.0  # LOWERED: 1.0s to catch "yeah/ok" (was 2.0s)

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

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

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

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

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

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

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

    # === v7.0: Detection Early-Exit ===
    # DISABLED: Early-exit samples only 10% of chunks, which can miss music contamination.
    # For correctness, we now analyze ALL chunks. To speed up, increase music_batch_size
    # or enable use_hybrid_detection (INA screening + PANNs on suspicious chunks).
    music_early_exit: bool = False  # DISABLED for correctness (was True)
    music_early_exit_sample_ratio: float = 0.10  # (Not used when early_exit=False)
    music_early_exit_threshold: float = 0.05    # (Not used when early_exit=False)

    # === OUTPUT ===
    output_dir: str = "data/fast_output_v6"
    generate_sample_clips: bool = False  # CHANGED: metadata-only by default
    clips_per_speaker: int = 3

    # === POST-PROCESSING (Adjacent merge) ===
    # max_silence_gap:
    # Post-processing "adjacent merge" threshold for SAME-speaker segments.
    # If two consecutive segments are labeled the same speaker and the time gap
    # between them is <= this value, we merge them into one continuous clip.
    #
    # NOTE: merging *includes* the gap (usually silence/breath) inside the clip.
    # 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)


Summary: auto


**Evaluating docker-compose risks**

Our docker-compose configuration uses a setting that might be risky. The comment suggests it's not recommended, yet it could still be beneficial since our production pipeline might need `--background-export` for long-running worker processes. However, if a worker process exits, background tasks might not complete. Initializing the export manager before the fork might also be unsafe, especially if threads created before forking lead to issues. I need to assess the implications carefully.


**Reassessing export manager initialization**

In `massive_process.py`, I notice we're initializing the export manager before forking, which isn't safe for long-running workers since the thread pool is created in the parent and inherited by child processes. They all share the same export manager, which could lead to issues, even if we haven't seen them due to lighter tasks. To enhance reliability, I think we should move the initialization to each worker after the fork and ensure proper shutdown. Following the configuration comment, disabling background export by default for stability seems wise—it could be simpler and more reliable.


**Reevaluating export strategy**

The user mentioned focusing on TAR creation with segments and metadata, but without including the original FLAC files. They also suggested that possibly using synchronous export could achieve good throughput with their setup of multi-GPU workers. Since we now use background export in docker-compose for production, I think we should reconsider our approach. Defaulting to synchronous export might enhance reliability, ensuring each process finishes exporting before marking Supabase as complete. While background export aims to prevent GPU idleness, it could introduce unnecessary complexity, especially concerning throughput.


**Considering export options**

Since the user is scaling across multiple machines and prioritizing reliability, I think the best approach is to use synchronous export without background processes until we’re more certain about performance. Even though they might want maximum throughput with several GPUs, it seems feasible. Evaluating the time needed for GPU compute, export segmentation, and R2 uploads suggests that the overhead could be manageable. I propose disabling background export in production by default or making it optional, but we need to ensure their existing pipeline runs smoothly.


**Refining background export management**

I need to modify `smoke_test.sh` to default to synchronous export without `--background-export`, though the script aims to test background export as well. I could use an option like `BACKGROUND_EXPORT=1` to allow background testing if needed, but let’s keep it simple. Additionally, we should update the `docker-compose.yml` to remove `--background-export` from default settings for reliability, aligning with recommendations for stability around multiprocessing. Keeping background export can cause issues due to process forking, so I intend to ensure reliable synchronous exports and evaluate configurations that prioritize stability.


**Reassessing export manager handling**

It seems that having the export manager at the main level doesn't make much sense anymore. In the Supabase queue mode, since each worker is a separate process, the export manager should be initialized within each worker. This means we need to remove the initialization and shutdown code from the main process and manage it inside the worker instead. I want to ensure that even if workers exit abruptly, we handle exports as best as possible, and we'll need to adjust our local end-to-end test accordingly.


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 341
Output:
        traceback.print_exc()

def gpu_worker_supabase(
    worker_id: int,
    gpu_id: int,
    result_queue,
    stop_signal,
    output_dir: str,
    config_overrides: dict,
    lease_duration_sec: int = 900,
    poll_interval_sec: int = 10,
    max_videos: int = 0  # 0 = unlimited
):
    """
    GPU worker that claims videos from Supabase queue.

    Instead of reading from a file queue, this worker:
    1. Claims the next PENDING video from Supabase
    2. Processes it
    3. Updates Supabase with results (COMPLETED/FAILED)
    4. Repeats until stop signal or no more videos
    """
    # Set GPU for this process
    os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)

    # Worker-specific logging
    worker_logger = logging.getLogger(f"Worker-{worker_id}")
    worker_logger.setLevel(logging.INFO)

    # Get pod ID for unique worker identification
    pod_id = os.environ.get('RUNPOD_POD_ID', os.environ.get('HOSTNAME', 'local'))
    supabase_worker_id = f"gpu-{gpu_id}-{pod_id}"

    worker_logger.info(f"Starting worker {supabase_worker_id} on GPU {gpu_id}")

    # Initialize analytics to None (will be set in try block)
    analytics = None

    try:
        # Aggressively silence speechbrain before any imports
        for name in ['speechbrain', 'speechbrain.utils', 'speechbrain.utils.checkpoints',
                     'speechbrain.utils.fetching', 'speechbrain.utils.parameter_transfer']:
            sb_logger = logging.getLogger(name)
            sb_logger.setLevel(logging.CRITICAL)
            sb_logger.handlers = []  # Remove all handlers
            sb_logger.propagate = False

        from src.logger import silence_verbose_loggers
        silence_verbose_loggers()

        # Import pipeline (loads models)
        from pipeline import process_single_video
        from src.config import Config
        from src.models import MODELS
        from src.download import download_audio

        # Initialize Supabase client
        supabase = SupabaseClient()

        # Initialize analytics reporter (uses same Supabase credentials)
        # Background thread handles async reporting - zero overhead to main processing
        analytics = create_reporter(
            gpu_id=gpu_id,
            supabase_url=os.environ.get('URL') or os.environ.get('SUPABASE_URL'),
            supabase_key=os.environ.get('SUPABASE_ADMIN'),
            machine_id=pod_id,
            enabled=True
        )
        analytics.session_start()
        worker_logger.info(f"Analytics initialized: {analytics.worker_id}")

        # Build config with gpu_id for pipeline logging
        config = Config(output_dir=output_dir)
        for key, value in config_overrides.items():
            if hasattr(config, key):
                setattr(config, key, value)
        config.gpu_id = gpu_id  # For PipelineLogger

        # Load models once
        MODELS.load_all(config)

        # Process loop
        videos_processed = 0
        consecutive_empty = 0
        max_consecutive_empty = 6  # Stop after 1 minute of no videos

        while not stop_signal.value:
            # Check max videos limit
            if max_videos > 0 and videos_processed >= max_videos:
                worker_logger.info(f"Reached max videos limit ({max_videos})")
                break

            # Claim next video from Supabase
            video = supabase.claim_next_video(
                worker_id=supabase_worker_id,
                lease_duration_sec=lease_duration_sec
            )

            if not video:
                consecutive_empty += 1
                if consecutive_empty >= max_consecutive_empty:
                    worker_logger.info("No more pending videos, stopping worker")
                    break
                worker_logger.debug(f"No pending videos, sleeping {poll_interval_sec}s...")
                time.sleep(poll_interval_sec)
                continue

            consecutive_empty = 0
            video_id = video['youtube_id']
            video_url = video['youtube_url']

            # Report video pick to analytics (non-blocking)
            analytics.pick(video_id)

            # Fetch full video record to get chapters
            full_video = supabase.get_video(video_id)
            if not full_video:
                full_video = video  # Fallback to claim result

            worker_logger.info(f"Processing: {video_id} - {(full_video.get('title') or '')[:50]}")
            has_chapters = full_video.get('has_chapters', False)
            if has_chapters:
                worker_logger.info(f"   Chapters: {full_video.get('chapter_count', 0)} from Supabase")
            start_time = time.time()

            try:
                # Process video with Supabase metadata (chapters, title, etc.)
                result = process_single_video(video_url, config, supabase_metadata=full_video)
                process_time = time.time() - start_time

                # Extract results for DB update
                quality_stats = result.get('quality_stats', {…3613 chars truncated…=gpu_id,
                    worker_id=worker_id
                )

            except Exception as e:
                process_time = time.time() - start_time
                error_msg = str(e)
                error_type = type(e).__name__
                worker_logger.error(f"Failed: {video_id} - {error_msg}")
                traceback.print_exc()

                # Report failure to analytics (non-blocking)
                analytics.fail(
                    video_id=video_id,
                    stage="processing",
                    error_type=error_type,
                    message=error_msg,
                    stack_trace=traceback.format_exc()
                )

                # Update Supabase with failure
                supabase.update_status(video_id, STATUS_FAILED, {
                    'last_error': error_msg[:1000],  # Truncate long errors
                    'last_error_type': type(e).__name__,
                    'processing_meta': {
                        'worker_id': supabase_worker_id,
                        'gpu_id': gpu_id,
                        'failed_at': datetime.now().isoformat(),
                    },
                })

                video_result = VideoResult(
                    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)

        # 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})"
                )


**Adjusting export manager settings**

I’m creating a threadpool in the worker process, but I need to clear the inherited global export manager from the parent to prevent cross-fork issues. Since I'm removing init_export_manager from the parent, that should handle it. I still want to call init just to be cautious. I also need to update the massive_process.py main by removing init_export_manager and ensuring there are no conflicts, especially since all runs spawn processes. I’ll also check the config for any necessary adjustments.


**Implementing code changes**

I’m planning to make specific code changes. First, in massive_process.py, I need to remove the initialization block for export_manager in the main function and adjust for the shutdown block accordingly. Next, in gpu_worker, I’ll import init_export_manager only if config.background_export is True, then call it and possibly log that action. Lastly, at the worker shutdown, before exiting, I’ll import and call shutdown_export_manager with a timeout to ensure any pending jobs linked to video_id are completed, even if it delays the exit slightly.


**Ensuring proper worker shutdown**

In production, the worker rarely exits, but it’s fine to flush if the stop_signal is set. In gpu_worker_supabase, after setting the config and loading models, I’ll check if config.background_export is True before calling init_export_manager. At the end, before calling analytics.shutdown, I should call shutdown_export_manager—probably before analytics.shutdown. I have to consider that export tasks might take time due to dependencies like R2Client and boto3 threads, and that the supabase worker uses analytics reporter with background threads. I'll ensure the background export manager is not created in the Config __init__.


**Verifying R2 upload functionality**

After fixing massive_process, I need to rerun the local end-to-end test to confirm that R2 uploads work correctly. In the last run, the upload failed, leaving the tar in the output since it wasn't deleted. The smoke script currently doesn't check for upload success, just the tar contents. To improve this, I'll add a check to ensure it prints "Uploaded to R2" or verifies the existence of the remote key. While our immediate focus is on answering user questions and providing a deployment plan, fixing this bug is essential for reliability.


**Addressing reliability issues**

I noticed that the earlier handoff summary mentioned fixing the shutdown event queue, but we’ve discovered a new bug. Fixing this is important for reliability and aligns with the user's expectation for "non-failure." I also want to be cautious about secrets printed from `vastai show instance --raw`; even if the user said it's okay, I should avoid mentioning them further and not run that command again without filtering. I've updated the smoke_test.sh patch, so now I’ll move on to fixing massive_process.py and implement the necessary patch.


Input:
*** Begin Patch
*** Update File: massive_process.py
@@
 def gpu_worker(
@@
     try:
@@
         # Create config with overrides and gpu_id
         config = Config(
             output_dir=output_dir,
             **config_overrides
         )
         config.gpu_id = gpu_id  # For PipelineLogger

+        # Background export must be initialized inside the worker process.
+        # Initializing thread pools in the parent process before fork leads to
+        # unreliable behavior and can drop uploads on worker exit.
+        if getattr(config, 'background_export', False):
+            try:
+                from src.export import init_export_manager
+                init_export_manager(max_workers=int(getattr(config, 'background_export_workers', 4) or 4))
+            except Exception as e:
+                worker_logger.warning(f"Failed to init background export manager, falling back to sync export: {e}")
+                config.background_export = False
+
         # Load models ONCE for this worker
         MODELS.load_all(config)
@@
         # Stop prefetch thread
         prefetch_stop[0] = True
         prefetch_thread.join(timeout=5.0)
+
+        # Flush background exports on shutdown (best-effort).
+        if getattr(config, 'background_export', False):
+            try:
+                from src.export import shutdown_export_manager
+                shutdown_export_manager(wait=True, timeout=600)
+            except Exception as e:
+                worker_logger.warning(f"Background export shutdown error: {e}")
         worker_logger.info(f"Shutting down. Processed {videos_processed} videos.")

     except Exception as e:
         worker_logger.error(f"Worker failed: {e}")
         traceback.print_exc()
@@
 def gpu_worker_supabase(
@@
     # Initialize analytics to None (will be set in try block)
     analytics = None

     try:
@@
         # Build config with gpu_id for pipeline logging
         config = Config(output_dir=output_dir)
         for key, value in config_overrides.items():
             if hasattr(config, key):
                 setattr(config, key, value)
         config.gpu_id = gpu_id  # For PipelineLogger

+        # Background export must be initialized inside the worker process.
+        if getattr(config, 'background_export', False):
+            try:
+                from src.export import init_export_manager
+                init_export_manager(max_workers=int(getattr(config, 'background_export_workers', 4) or 4))
+            except Exception as e:
+                worker_logger.warning(f"Failed to init background export manager, falling back to sync export: {e}")
+                config.background_export = False
+
         # Load models once
         MODELS.load_all(config)
@@
             # Clear GPU cache between videos
             MODELS.clear_cache(aggressive=True)

-        # Graceful shutdown - flush remaining analytics events
+        # Flush background exports on shutdown (best-effort).
+        if getattr(config, 'background_export', False):
+            try:
+                from src.export import shutdown_export_manager
+                shutdown_export_manager(wait=True, timeout=600)
+            except Exception as e:
+                worker_logger.warning(f"Background export shutdown error: {e}")
+
+        # 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()
@@
     # Initialize background export manager if enabled
-    export_manager = None
-    if args.background_export:
-        from src.export import init_export_manager
-        export_manager = init_export_manager(max_workers=args.export_workers)
-        logger.info(f"Background export enabled ({args.export_workers} workers)")
+    # NOTE: Background export manager is initialized inside each worker process
+    # (after fork). Initializing thread pools in the parent process is unsafe.

     # Run!
     if supabase_mode:
         # Supabase distributed queue mode
         stats = run_supabase_queue(
@@
     else:
         # Traditional file-based mode
         stats = run_massive_process(
             video_urls=video_urls,
             gpu_ids=gpu_ids,
             output_dir=args.output,
             config_overrides=config_overrides
         )
-
-    # Wait for background exports to complete (if enabled)
-    if args.background_export:
-        from src.export import shutdown_export_manager
-        logger.info("Waiting for background exports to complete...")
-        export_results = shutdown_export_manager(wait=True, timeout=600)
-
-        export_success = sum(1 for r in export_results.values() if r.success)
-        export_failed = sum(1 for r in export_results.values() if not r.success)
-        total_segments = sum(r.segments_exported for r in export_results.values())
-        total_export_time = sum(r.export_time_sec for r in export_results.values())
-        total_upload_time = sum(r.r2_upload_time_sec for r in export_results.values())
-
-        logger.info(f"Background exports: {export_success}/{len(export_results)} successful")
-        logger.info(f"  Total segments: {total_segments}")
-        logger.info(f"  Export time: {total_export_time:.1f}s")
-        logger.info(f"  Upload time: {total_upload_time:.1f}s")
-
-        if export_failed > 0:
-            logger.warning(f"  Failed exports: {export_failed}")
-            for vid, r in export_results.items():
-                if not r.success:
-                    logger.warning(f"    {vid}: {r.error}")

     # Exit with appropriate code
     if stats.failed > 0:
         sys.exit(2 if stats.successful > 0 else 1)
     sys.exit(0)
*** End Patch


Output: {"output":"Success. Updated the following files:\nM massive_process.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Considering E2E tests**

I need to rerun the E2E test to confirm that the R2 upload works after changing massive_process.py. Earlier, we generated a massive output of 7,253 lines because of downloading a large PANNs model and the `wget` download progress. I'll think about reducing output by disabling music detection or using the `--no-music-detection` option, but we might want to keep it for the full pipeline. However, for a smoke test, it’s crucial to run the pipeline completely, even if that means more noise.


**Exploring E2E run optimizations**

To rerun the E2E test efficiently and confirm the R2 upload works, I can choose a video ID that doesn't need PANNs downloads again since they're already cached. However, the container resets with each run, so caches may not persist if we're using `--rm` without volumes, which means everything will redownload. I realize we didn’t mount `/root/.cache`, so everything downloaded during the previous run will be lost. To save time, I should mount the model cache volume to `/root/.cache`.


**Updating smoke test plans**

In the smoke_test.sh, I could mount `-v model-cache:/root/.cache`, but I’d like to keep things simple for now. Alternatively, I can disable music detection by adding `--no-music-detection` to extra arguments. This would avoid the PANNs download, making the run faster and producing less output, but I still want to test music detection since it's part of the pipeline. We already processed the pipeline and created the tar, but R2 upload failed due to a background export. I should verify the code changes and consider rerunning while reducing output.


**Reviewing R2 upload process**

The export manager's R2 upload utilizes boto3 and likely still uses a thread pool. We call shutdown_export_manager when a worker exits, which waits for any pending export jobs to finish. However, there's a chance we could encounter an error if the worker exits before the export job is submitted. We call shutdown_export_manager after the prefetch thread join when the worker receives a stop signal, so that’s good. Still, there’s an issue where `export_manager.submit(job)` returns a boolean that we ignore. We need to ensure that init has been called before the pipeline uses get_export_manager.


**Preparing to rerun E2E test**

I've confirmed we call init right after creating the config, before loading the models. Now, I want to rerun the E2E test using the same video ID `--E8hBLmJvw` to check if the upload works without errors. To reduce output, I could set the environment variable `VIDEO_ID` to something different, but we also have the option to disable music detection by passing `--no-music-detection` to massive_process. I plan to run the E2E script with R2_UPLOAD=1, redirecting the output to a file, then tailing it to look for any "Upload failed" errors. This will help manage the output efficiently.


sudo bash -lc 'cd /home/ubuntu/maya3 && E2E=1 VIDEO_ID=--E8hBLmJvw R2_UPLOAD=1 GPU_DEVICE=0 bash scripts/docker/smoke_test.sh > /tmp/maya3_smoke_e2e.log 2>&1; echo "exit_code=$?"; tail -n 120 /tmp/maya3_smoke_e2e.log'


Output:
Exit code: 0
Wall time: 311.9 seconds
Output:
exit_code=0
218650K .......... .......... .......... .......... .......... 68%  402K 2m9s
218700K .......... .......... .......... .......... .......... 68%  578K 2m9s
218750K .......... .......... .......... .......... .......... 68% 1004K 2m9s
218800K .......... .......... .......... .......... .......... 68%  918K 2m9s
218850K .......... .......... .......... .......... .......... 68% 1.06M 2m8s
218900K .......... .......... .......... .......... .......... 68% 1014K 2m8s
218950K .......... .......... .......... .......... .......... 68%  827K 2m8s
219000K .......... .......... .......... .......... .......... 68% 1.16M 2m8s
219050K .......... .......... .......... .......... .......... 68%  830K 2m8s
219100K .......... .......... .......... .......... .......... 68%  407K 2m8s
219150K .......... .......... .......... .......... .......... 68%  818K 2m8s
219200K .......... .......... .......... .......... .......... 68% 1.24M 2m8s
219250K .......... .......... .......... .......... .......... 68% 1.38M 2m8s
219300K .......... .......... .......... .......... .......... 68%  987K 2m8s
219350K .......... .......... .......... .......... .......... 68%  888K 2m8s
219400K .......... .......... .......... .......... .......... 68%  651K 2m8s
219450K .......... .......... .......... .......... .......... 68%  810K 2m8s
219500K .......... .......... .......... .......... .......... 68% 1.08M 2m8s
219550K .......... .......... .......... .......... .......... 68% 1.33M 2m8s
219600K .......... .......... .......... .......... .......... 68%  999K 2m7s
219650K .......... .......... .......... .......... .......... 68% 1.76M 2m7s
219700K .......... .......... .......... .......... .......... 68%  883K 2m7s
219750K .......... .......... .......... .......... .......... 68% 2.64M 2m7s
219800K .......... .......... .......... .......... .......... 68% 1.02M 2m7s
219850K .......... .......... .......... .......... .......... 68%  899K 2m7s
219900K .......... .......... .......... .......... ..[GPU0] 7/10 MusicDetecti 243.5s
........ 68%  609K 2m7s
219950K .......... .......... .......... .......... .......... 68%  954K 2m7s
220000K .......... .......... .......... .......... .......... 68% 1.38M 2m7s
220050K .......... .......... .......... .......... .......... 68%  440K 2m7s
220100K .......... .......... .......... .......... .......... 68% 1.02M 2m7s
220150K .......... .......... .......... .......... .......... 68% 1.41M 2m7s
220200K .......... .......... .......... .......... .......... 68%  919K 2m7s
220250K .......... .......... .......... .......... .......... 68% 1.52M 2m7s
220300K .......... .......... .......... .......... .......... 68% 1011K 2m6s
220350K .......... .......... .......... .......... .......... 68%  807K 2m6s
220400K ........[GPU0] 8/10 Finalize       0.5s
[GPU0] 9/10 Output         0.0s
.. .........[GPU0] DONE   --E8hBLmJvw 292.2s | 4.7min | 1 spk | 30%
. .......... .......... .......... 68% 1.21M 2m6s
220450K .......... .......... .......... .......... .......... 68% 1.17M 2m6s
220500K .......... .......... .......... .......... .......... 68% 1.30M 2m6s
220550K .......... .......... .......... .......... .......... 68%  573K 2m6s
220600K .......... .......... .......... .......... .......... 69%  756K 2m6s
220650K .......... .......... .......... .......... .......... 69%  845K 2m6s
220700K .......... .......... .......... .......... .........19:39:36 | Worker-0     | INFO     | Done: --E8hBLmJvw | 292.6s | 1 speakers | 29.7% usable
19:39:36 | MassiveCompute | INFO     | [1/1] OK: --E8hBLmJvw (292.6s on GPU 0)
. 69%  881K 2m6s
220750K .......... .......... .......... .......... .......... 69% 1.53M 2m6s
220800K .......... .......... .......... .......... .......... 69% 1.46M 2m6s
220850K .......... .......... .......... .......... .......... 69% 1.80M 2m6s
220900K .......... .......... .......... .......... .......... 69% 1005K 2m6s
220950K .......... .......... .......... .......... .......... 69% 9.68M 2m6s
221000K .......... .......... .......... .......... .......... 69% 1.80M 2m5s
221050K .......... .......... .......... .......... .......... 69% 1.18M 2m5s
221100K .......... .......... .......... .......... .......... 69%  827K 2m5s
221150K .......... ..........19:39:36 | Worker-0     | INFO     | Received stop signal
 .......... .......... .......... 69% 1.14M 2m5s
221200K .......... .......... .......... .......... .......... 69% 1.24M 2m5s
221250K .......... .......... .......... .......... .......... 69% 1.79M 2m5s
221300K .......... .......... .......... .......... .......... 69% 1.51M 2m5s
221350K .......... .......... .......... .......... .......... 69% 1013K 2m5s
221400K .......... .......... .......... .......... .......... 69%  192M 2m5s
221450K .......... .......... .......... .......... .......... 69% 4.54M 2m5s
221500K .......... .......... .......... .......... .......... 69% 1.14M 2m5s
221550K .......... .......... .......... .......... .......... 69% 15.1M 2m5s
221600K .......... .......... .......... .......... .......... 69% 1.79M 2m5s
221650K .......... .......... .......... .......... .......... 69%  754K 2m4s
221700K .......... .......... .......... .......... .......... 69%  645K 2m4s
221750K .......... .......... .......... .......... .......... 69%  271M 2m4s
221800K .......... .......... .......... .......... .......... 69%  204M 2m4s
221850K .......... .......... .......... .......... .......... 69%  960K 2m4s
221900K .......... .......... .......... .......... .......... 69%  148M 2m4s
221950K .......... .......... .......... .......... .......... 69% 2.64M 2m4s
222000K .......... .......... .......... .......... .......... 69% 1.13M 2m4s
222050K .......... .......... .......... .......... ........19:39:37 | Worker-0     | INFO     | Shutting down. Processed 1 videos.
.. 69% 1.92M 2m4s
222100K .......... .......... .......... .......... .......... 69% 3.04M 2m4s
222150K .......... .......... .......... .......... .......... 69% 1.35M 2m4s
222200K .......... .......... .......... .......... .......... 69% 1.41M 2m4s
222250K .......... .......... .......... .......... .......... 69% 1.58M 2m4s
222300K .......... .......... .......... .......... .......... 69% 1.44M 2m3s
222350K .......... .......... .......... .......... .......... 69% 2.23M 2m3s
222400K .......... .......... .......... .......... .......... 69% 1.83M 2m3s
222450K .......... ........19:39:37 | MassiveCompute | INFO     |
======================================================================
19:39:37 | MassiveCompute | INFO     | BENCHMARK RESULTS
19:39:37 | MassiveCompute | INFO     | ======================================================================
19:39:37 | MassiveCompute | INFO     | Videos: 1/1 successful
19:39:37 | MassiveCompute | INFO     | Failed: 0
19:39:37 | MassiveCompute | INFO     | Wall time: 306.7s (5.1 min)
19:39:37 | MassiveCompute | INFO     | Total audio: 0.08 hours
19:39:37 | MassiveCompute | INFO     | ----------------------------------------------------------------------
19:39:37 | MassiveCompute | INFO     | THROUGHPUT:
19:39:37 | MassiveCompute | INFO     |    Videos/min: 0.20
19:39:37 | MassiveCompute | INFO     |    Audio hours/wall hour: 0.93x realtime
19:39:37 | MassiveCompute | INFO     |    Parallel efficiency: 95.4%
19:39:37 | MassiveCompute | INFO     | ----------------------------------------------------------------------
19:39:37 | MassiveCompute | INFO     | Avg process time/video: 292.6s
19:39:37 | MassiveCompute | INFO     | ----------------------------------------------------------------------
19:39:37 | MassiveCompute | INFO     | AVG PER-STAGE TIME (successful videos):
19:39:37 | MassiveCompute | INFO     |    music_detection         243.48s  ( 91.8%)
19:39:37 | MassiveCompute | INFO     |    unified_embeddings        6.52s  (  2.5%)
19:39:37 | MassiveCompute | INFO     |    diarization               5.64s  (  2.1%)
19:39:37 | MassiveCompute | INFO     |    download                  3.04s  (  1.1%)
19:39:37 | MassiveCompute | INFO     |    vad                       2.51s  (  0.9%)
19:39:37 | MassiveCompute | INFO     |    micro_contamination       1.00s  (  0.4%)
19:39:37 | MassiveCompute | INFO     |    load_buffer               0.53s  (  0.2%)
19:39:37 | MassiveCompute | INFO     |    chunking                  0.50s  (  0.2%)
19:39:37 | MassiveCompute | INFO     |    quality_filter            0.50s  (  0.2%)
19:39:37 | MassiveCompute | INFO     |    segment_embeddings        0.50s  (  0.2%)
19:39:37 | MassiveCompute | INFO     |    chunk_reassignment        0.50s  (  0.2%)
19:39:37 | MassiveCompute | INFO     |    finalization              0.50s  (  0.2%)
19:39:37 | MassiveCompute | INFO     |    clustering                0.00s  (  0.0%)
19:39:37 | MassiveCompute | INFO     | ======================================================================
19:39:37 | MassiveCompute | INFO     | Results saved to: output/benchmark_results.json
.. .......... .......... .......... 69% 2.05M 2m3s
222500K .......... .......... .==> Verifying TAR does not contain _original.flac: ./output/--1yBpF537M.tar
OK: _original.flac not present in TAR
==> Done


Summary: auto


**Evaluating tar verification**

I found a tar file named `--1yBpF537M.tar`, which seems like it might have been left over from a previous operation. This raises a concern since the smoke test is possibly checking the wrong tar file. It needs to verify the specific video ID's tar file or ensure the remote file exists if the local one is deleted. I should consider disabling the deletion during the test or using a config option to keep the local tar for verification. Our video ID does start with `--`, so the path looks fine for verification.


**Analyzing verification and downloads**

If we don't find the file because it was deleted, we might verify it remotely by listing the keys; however, the script isn't very robust right now. I've noticed a huge `wget` progress output, which seems to run in the background while logs are active. This could be the download for the PANNs model, which takes a long time. I wonder if the music detection stage finishes before the download is complete—maybe it triggers the model download but doesn’t wait for it to finish before proceeding.


**Checking logs for uploads**

I’m puzzled about why the tail shows `wget` progress after the stage has completed and after verifying the tar. The tail output includes some `wget` progress lines followed by stage updates, which might just be glimpses. Now I need to check if the background export uploaded successfully since the tail output doesn't indicate it. It doesn’t show "Uploaded to R2" or any export logs, likely because it only captured the last 120 lines. I'll grep the log for relevant messages and notice that the smoke test seems to have processed a different video ID.


**Updating verification processes**

The log shows that it processed `--E8hBLmJvw`, indicating that the verification is incorrect. I need to update `smoke_test.sh` to ensure it verifies the correct tar file. If `R2_UPLOAD=1` and the tar was deleted, we'll have to check if the remote key exists.

In the E2E script, we can grab both `VIDEO_ID` and `upload_prefix` for verification. After the container run, if `VIDEO_ID` is set, I can compute the expected tar path but need to clarify how to handle video IDs versus URLs accurately.


**Optimizing tar handling and verification**

I can parse the `VIDEO_ID` to get the correct `video_id_for_tar` by checking for '://'. If it contains that, I'll extract based on the `v` parameter or use the last path segment. The extraction logic seems straightforward for both forms of IDs. After running the container, I’ll check if the expected tar exists and ensure it doesn’t include the _original version. If it's missing and `R2_UPLOAD=1`, I’ll verify the remote key exists.

However, if it’s zero, I’ll issue a warning. Checking the tar contents is also necessary, even if it means downloading it, but I might want to skip deletion for testing. Maybe I can run two passes: first to verify the tar content without uploading and then to check for the remote upload. Ultimately, I’ll patch `smoke_test.sh` to ensure it verifies the correct tar based on the specified `VIDEO_ID`.


**Simplifying tar checks**

I can check the tar and verify the remote existence as needed, but keeping things simple is key. I should also handle cases where the video ID and tar file names start with '--', ensuring that `tar -tf` works properly. I might need to ensure that quoting is done correctly to avoid issues. So, it makes sense to patch the script accordingly to accommodate these details while maintaining clarity and functionality.