momentry_core/docs_v1.0/issues_2026-06-21.md

Issue Report: 2026-06-21

Issue 1: Worker Process Stuck

Description

Worker process (PID 58279) started on Fri10PM was stuck and not processing new jobs. Last log entry dated 2026-06-20 06:52.

Symptoms

• Jobs triggered via API returned "Processing triggered" but never executed
• Redis keys for new jobs were not created
• Progress API returned empty response
• Worker logs showed old timestamps

Resolution

• Killed stuck worker: kill 58279
• Restarted worker: cd /Users/accusys/momentry_core && ./target/release/momentry worker
• New worker PID: 52908

Root Cause (Suspected)

• Worker process running for extended period without proper cleanup
• Possible Redis connection timeout or job queue corruption

Recommendation

• Add worker health check mechanism
• Implement automatic worker restart on inactivity timeout
• Add logging for job queue polling status

---

Issue 2: Face/YOLO Processor Failure - Missing OpenCV

Description

Face and YOLO processors failed with ModuleNotFoundError: No module named 'cv2'

Error Log

[ERROR] Processor face failed for job d8acb03870f0cc9b14e01f14a7bf24d6: Failed to run "/Users/accusys/momentry_core/scripts/face_processor.py"
[ERROR] Processor yolo failed for job d8acb03870f0cc9b14e01f14a7bf24d6: Failed to run "/Users/accusys/momentry_core/scripts/yolo_processor.py"

Python Test Result

python3 /Users/accusys/momentry_core/scripts/face_processor.py --help
Traceback (most recent call last):
  File ".../face_processor.py", line 25, in <module>
    import cv2
ModuleNotFoundError: No module named 'cv2'

Resolution

pip3 install opencv-python

Recommendation

• Add Python dependency check in worker startup
• Document required Python packages in README
• Add requirements.txt with all processor dependencies

---

Issue 3: Redis Prefix Configuration Confusion

Description

Two different Redis namespaces exist:

• momentry: - Production server (port 3002)
• momentry_dev: - Playground server (port 3003)

Impact

• Jobs triggered on production server not visible to playground worker
• Progress data stored in different namespaces
• API proxy needs to match correct prefix

Current Setup

Production Server (port 3002): Redis prefix "momentry:"
Playground Server (port 3003): Redis prefix "momentry_dev:"

Recommendation

• Document Redis prefix configuration clearly
• Add environment variable for Redis prefix selection
• Consider using same prefix for development simplicity

---

Issue 4: Progress API Behavior

Description

GET /api/v1/progress/:file_uuid returns empty response when:

1. No job exists for the file
2. Job is complete (all processors finished)
3. Worker is stuck/not processing

Expected Behavior (from docs)

{
  "file_uuid": "...",
  "overall_progress": 71,
  "processors": [
    {"processor_type": "asr", "status": "complete", "progress": 100},
    {"processor_type": "yolo", "status": "running", "progress": 65}
  ]
}

Actual Behavior

• Returns empty response (no output) when job complete or missing
• Frontend cannot distinguish between "not started" vs "completed"

Recommendation

• Return explicit status for completed jobs (e.g., {"overall_progress": 100, "status": "completed"})
• Return 404 when job not found (file never processed)
• Add status field to response: pending, running, completed, failed

---

Issue 5: Frontend Status Display Bug

Description

Frontend showed "處理中" (processing) status for Gamma Carry file but:

• Database status: registered (not processed)
• No job in Redis
• No progress data

Cause

Frontend code sets f.status = 'processing' immediately after process trigger, without verifying job creation:

// LibraryView.vue line 463
if (result.success) {
  f.status = 'processing'  // Sets status prematurely
  pollProgress(f.file_uuid)
}

Impact

• User sees "processing" status but actual processing never started
• Misleading UI feedback

Recommendation

• Verify job creation before setting status
• Check Redis job key existence
• Poll progress API and set status based on actual response
• Handle case when progress API returns empty (job not created)

---

Test Results Summary

File: Gamma Carry Saves the World..mp4

• UUID: d8acb03870f0cc9b14e01f14a7bf24d6
• Processing triggered: 2026-06-21 12:13

Processor Results

Processor	Status	Output
cut	✓ Complete	4825 frames
asr	✓ Complete	0 segments
face	✗ Failed	Missing cv2
yolo	✗ Failed	Missing cv2
ocr	- Not run	Dependency failed
pose	- Not run	Dependency failed

Redis Keys Created

momentry:job:d8acb03870f0cc9b14e01f14a7bf24d6
momentry:progress:d8acb03870f0cc9b14e01f14a7bf24d6
momentry:job:d8acb03870f0cc9b14e01f14a7bf24d6:processor:cut
momentry:job:d8acb03870f0cc9b14e01f14a7bf24d6:processor:asr
momentry:job:d8acb03870f0cc9b14e01f14a7bf24d6:processor:face
momentry:job:d8acb03870f0cc9b14e01f14a7bf24d6:processor:yolo

API Test Results

API	Status	Note
POST /api/v1/file/:uuid/process	✓ Works	Job created
GET /api/v1/file/:uuid/processor-counts	✓ Works	Returns correct counts
GET /api/v1/progress/:uuid	Partial	Empty when complete/missing
GET /api/v1/jobs	- Not tested	No response via proxy

---

Recommended Actions

Immediate

1. Install OpenCV: pip3 install opencv-python
2. Add worker health monitoring
3. Fix progress API to return status for completed jobs

Short-term

1. Add Python dependency validation in worker
2. Document Redis prefix configuration
3. Improve frontend status verification

Long-term

1. Add requirements.txt for processor scripts
2. Implement worker auto-restart mechanism
3. Add comprehensive logging for job lifecycle
4. Create integration tests for processing pipeline

---

Report generated: 2026-06-21 12:15 Reporter: momentry_studio development session

---

Issue 6: ASRX Processor Field Name Mismatch (NEW - 2026-06-22)

Description

ASRX processor fails with KeyError: 'start' when processing ASR output.

Error Log

[DEBUG] [ASRX/stderr] KeyError: 'start'
[INFO] [ASRX] Result: 0 segments
[INFO] Processor asrx completed and verified (0 chunks, 0 frames)

Root Cause

ASR JSON output format changed:

• Old format: {"start": ..., "end": ..., "text": ...}
• Current format: {"start_time": ..., "end_time": ..., "start_frame": ..., "end_frame": ..., "text": ...}

asrx_processor.py expects old format:

# Line 137-138
start_sec = seg["start"]  # KeyError: 'start'
end_sec = seg["end"]      # KeyError: 'end'

ASR Output Structure (Current)

{
  "start_time": 217.27,
  "end_time": 224.96,
  "start_frame": 5209,
  "end_frame": 5394,
  "text": "..."
}

File Locations

• ASR output: /Users/accusys/momentry/output/{uuid}.asr.json
• ASRX script: /Users/accusys/momentry_core/scripts/asrx_processor.py
• Error line: Line 137

Impact

• ASRX produces 0 segments even when ASR has valid segments
• No speaker diarization data generated
• File status shows "completed" but asrx output is missing

Test Case: Charade_YouTube_24fps.mp4

• UUID: c36f35685177c981aa139b66bbbccc5b
• ASR segments: 1779 (has valid data)
• ASRX segments: 0 (failed silently)
• Status: completed (misleading)

Fix Required

Update asrx_processor.py to prioritize frame-based positioning:

# Line 136-149 fix: Use start_frame/end_frame if available
for seg in result.get("segments", []):
    # Prefer frame-based (precise)
    if "start_frame" in seg and "end_frame" in seg:
        start_frame = seg["start_frame"]
        end_frame = seg["end_frame"]
        start_sec = start_frame / fps
        end_sec = end_frame / fps
    else:
        # Fallback to time-based
        start_sec = seg.get("start", seg.get("start_time", 0))
        end_sec = seg.get("end", seg.get("end_time", 0))
        start_frame = int(start_sec * fps)
        end_frame = int(end_sec * fps)
    
    output_result["segments"].append({
        "start_time": start_sec,
        "end_time": end_sec,
        "start_frame": start_frame,
        "end_frame": end_frame,
        "text": seg.get("text", ""),
        "speaker_id": seg.get("speaker_id", seg.get("speaker", "")),
        "language": seg.get("language", ""),
        "lang_prob": seg.get("lang_prob", 0.0),
        "quality": seg.get("quality", 0.0),
    })

Key Points

1. Frame-based is precise - use start_frame / end_frame directly when available
2. Time is auxiliary - derive from frames: start_time = start_frame / fps
3. Backward compatible - fallback to start/end or start_time/end_time for older ASR output

Recommendation

1. Make asrx_processor.py backward compatible with both formats
2. Add validation: check if asrx segments > 0 before marking complete
3. Log error when ASRX returns 0 segments from non-empty ASR input
4. Add unit test for ASR/ASRX format compatibility