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


# AGENTS.md instructions for /home/ubuntu/maya3

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


<environment_context>
  <cwd>/home/ubuntu/maya3</cwd>
  <approval_policy>on-request</approval_policy>
  <sandbox_mode>workspace-write</sandbox_mode>
  <network_access>restricted</network_access>
  <shell>bash</shell>
</environment_context>


<environment_context>
  <approval_policy>never</approval_policy>
  <sandbox_mode>danger-full-access</sandbox_mode>
  <network_access>enabled</network_access>
  <shell>bash</shell>
</environment_context>


# Context from my IDE setup:

## Open tabs:
- .gitignore: .gitignore
- reset_for_production.py: scripts/reset_for_production.py
- requirements.txt: requirements.txt
- Dockerfile: Dockerfile
- models.py: src/models.py

## My request for Codex:
Logs for instance 29758334
Search Filter
ex. Rate Limit Error
Line Count
1000
Thu Jan  8 04:34:19 UTC 2026
Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease
Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease
Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
Reading package lists...
Reading package lists...
Building dependency tree...
Reading state information...
curl is already the newest version (7.81.0-1ubuntu1.21).
less is already the newest version (590-1ubuntu0.22.04.3).
locales is already the newest version (2.35-0ubuntu3.11).
openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
software-properties-common is already the newest version (0.99.22.9).
sudo is already the newest version (1.9.9-1ubuntu2.5).
tmux is already the newest version (3.2a-4ubuntu0.2).
wget is already the newest version (1.21.2-2ubuntu1.1).
0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
Reading package lists...
Building dependency tree...
Reading state information...
git is already the newest version (1:2.34.1-1ubuntu1.15).
rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
rm: cannot remove '/etc/forward_port2': No such file or directory
Code not found, cloning...
Cloning into '/tmp/code'...
Server listening on 0.0.0.0 port 22.
Server listening on :: port 22.
Thu Jan  8 04:34:24 UTC 2026
Warning: Permanently added 'ssh7.vast.ai' (ED25519) to the list of known hosts.
/usr/bin/python: No module named src.worker

And

Logs for instance 29758333
Search Filter
ex. Rate Limit Error
Line Count
1000
Thu Jan  8 04:34:24 UTC 2026
Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease
Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease
Hit:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
Reading package lists...
Reading package lists...
Building dependency tree...
Reading state information...
curl is already the newest version (7.81.0-1ubuntu1.21).
less is already the newest version (590-1ubuntu0.22.04.3).
locales is already the newest version (2.35-0ubuntu3.11).
openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
software-properties-common is already the newest version (0.99.22.9).
sudo is already the newest version (1.9.9-1ubuntu2.5).
tmux is already the newest version (3.2a-4ubuntu0.2).
wget is already the newest version (1.21.2-2ubuntu1.1).
0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
Reading package lists...
Building dependency tree...
Reading state information...
git is already the newest version (1:2.34.1-1ubuntu1.15).
rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
rm: cannot remove '/etc/forward_port2': No such file or directory
Code not found, cloning...
Cloning into '/tmp/code'...
Server listening on 0.0.0.0 port 22.
Server listening on :: port 22.
Thu Jan  8 04:34:29 UTC 2026
Warning: Permanently added 'ssh8.vast.ai' (ED25519) to the list of known hosts.
/usr/bin/python: No module named src.worker

delete the current last 2 instances and Instance ID: 29758333 and Instance ID: 29758334, and create 2 new instances with proper code details as well to avoid the startup error as `/usr/bin/python: No module named src.worker`

[GPU5] 5/9  Embeddings     3.5s  | 303 chunks
Checkpoint path: /root/panns_data/Cnn14_mAP=0.431.pth
04:39:16 | Worker-3     | INFO     | Processing: a7E_Gjk54qo - Sohni Mahiwal Dey Ishq Da Kharrak | Qissa Goi Podc
[GPU1] PICK   a7E_Gjk54qo
[GPU5] 6/9  Clustering     0.5s
[GPU3] 1/9  Download      33.4s
[GPU5] 6/9  MicroContami   0.0s
[GPU0] 1/9  Download      12.7s
04:39:18 | FastPipelineV6.Models | ERROR    | ❌ Failed to load PANNs: Error(s) in loading state_dict for Cnn14:
	size mismatch for fc_audioset.weight: copying a param with shape torch.Size([527, 2048]) from checkpoint, the shape in current model is torch.Size([235, 2048]).
	size mismatch for fc_audioset.bias: copying a param with shape torch.Size([527]) from checkpoint, the shape in current model is torch.Size([235]).
[GPU5] 1/9  Download      20.1s
[GPU2] 5/9  Embeddings     2.8s  | 640 chunks
[GPU2] 6/9  Clustering     0.5s
[GPU7] 1/9  Download      41.2

can you also fix this issue with music model. and also, i've just checked the files are not being uploaded to R2. debug why that is happening as well.

can you check R2 for where the files are being uploaded. are they being uploaded at all ? i cant seem to find files in R2. check and debug vast's logs as well.

and one more issue i found in logs is, although not sure if the worker is retrying after this, will leave it up to you.
[GPU2] 6/9  Clustering     0.0s
04:58:49 | Worker-9     | INFO     | Processing: ZOxuMyEtbWo - What customer crave | PART - 03 | Magic 20 தமிழ்
[GPU4] PICK   ZOxuMyEtbWo
[GPU6] 1/9  Download       2.8s
[GPU2] 6/9  MicroContami   0.0s
04:58:49 | Worker-0     | ERROR    | Worker failed: The read operation timed out
Traceback (most recent call last):
  File "/workspace/maya3_data/massive_process.py", line 536, in gpu_worker_supabase
    full_video = supabase.get_video(video_id)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/maya3_data/src/supabase_client.py", line 380, in get_video
    result = self._request('GET', 'videos', params=params)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/maya3_data/src/supabase_client.py", line 105, in _request
    with urlopen(req, timeout=30) as resp:
         ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/urllib/request.py", line 216, in urlopen
    return opener.open(url, data, timeout)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/urllib/request.py", line 519, in open
    response = self._open(req, data)
               ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/urllib/request.py", line 536, in _open
    result = self._call_chain(self.handle_open, protocol, protocol +
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/urllib/request.py", line 496, in _call_chain
    result = func(*args)
             ^^^^^^^^^^^
  File "/usr/lib/python3.11/urllib/request.py", line 1391, in https_open
    return self.do_open(http.client.HTTPSConnection, req,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/urllib/request.py", line 1352, in do_open
    r = h.getresponse()
        ^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/http/client.py", line 1374, in getresponse
    response.begin()
  File "/usr/lib/python3.11/http/client.py", line 318, in begin
    version, status, reason = self._read_status()
                              ^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/http/client.py", line 279, in _read_status
    line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/socket.py", line 705, in readinto
    return self._sock.recv_into(b)
           ^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ssl.py", line 1278, in recv_into
    return self.read(nbytes, buffer)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ssl.py", line 1134, in read
    return self._sslobj.read(len, buffer)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TimeoutError: The read operation timed out
[GPU2] 7/9  MusicDetecti   1.0s
[GPU2] 8/9  Finalize       0.0s

most importantly, please please please push a fix or update to identify the machine from supabase's analytics to current vast-ai's machine as for us to understand which machine is what. currently we are pushing docker IDs and there is no way for us to backtrack or propograte to map the Vast-ai's instances with the analytics's instances IDs, or whatever. check what instnace ID is being pushed and update it to push current unique ID which we shall have for identification or, push vast's machine ID or isntance ID or whatever to have unified map.

Steps.
1. Fix all these issues.
2. delete all the data from supabase from analytics table and reset entire videosIDs to pending so everything starts from beginning.
3. i didnt get any success with recycle command before. i hope there is an option to tell the instance to repull the image and restart, with newer code changes. if not i'd prefer delete the instances and create new ones.
4. quick suggestions for new instances, clusters of 8*3090s, 8*4090s and avoid chinese as their internet is slower, use any region to deploy these as long as they have proper disk, internet, and reliability.
5. deploy into one instance, wait for deployment logs, check running status, and validate if R2 uploads are being done peroperly and everything is alright, then deploy into 9 more totalling 10 instances and 80GPUs running.
6. if any of these fail, i will monitor with UI and will delete them so we can chose a diff instance.


Summary: auto


**Assessing user requests**

I need to respond as Codex CLI in the specified repo, and the user has a large request involving fixing multiple code issues, operational tasks like deleting Vast.ai instances, checking R2, and more. I realize we may not interact directly with Vast.ai or Supabase unless we have the necessary scripts or API keys. I can focus on updating code to identify machines from analytics, propose commands, and check the repo for existing scripts to assist the user. Following the AGENTS instructions will be key here.


**Analyzing environment context and issues**

I see there are skills like "skill-creator" and "skill-installer," but the user didn't mention those, so I'll focus elsewhere. They shared two environment contexts. The second one indicates that approval is set to "never," and sandbox mode allows full access with enabled network access. I'll assume this current environment is correct, which means we can run commands freely.

The main issue seems to be a startup error with Python: "No module named src.worker." This could arise from an incorrect Python path or running the wrong environment when starting instances on Vast.


**Investigating code and architecture**

It seems the code might be looking for directories like /workspace or /tmp/code, so I'll check the Dockerfile, requirements, and scripts in the repo.

There's also an issue with the music model related to loading the PANNs. It seems there's a dimension mismatch in the fully connected layer between the pretrained checkpoint (527 classes) and the current model (235 classes). They might use a Cnn14 model designed for a subset. To resolve this, I can either adjust the output size or load the state_dict with `strict=False` to ignore the mismatch.


**Troubleshooting upload and connection issues**

I need to investigate where PANNs are being loaded in the code.

There's also an issue with uploads to Cloudflare R2. It seems they can't find files, which could be due to wrong bucket prefixes, missing environment variables, or regional settings. I'll check the R2 client code in src/r2_client.py to figure out what's going wrong and implement logging to track any failures.

For Vast logs, I'll also need to adjust the code to enhance logging. Lastly, the Supabase read operation is timing out, so I'll implement retries with exponential backoff and possibly increase the timeout.


**Planning necessary updates and tasks**

The user mentioned the need for the `supabase_client` to retry on timeouts. I'll check the implementation details in massive_process.py and supabase_client.py.

One crucial task is to update the system to identify the Vast instance in Supabase analytics. Currently, they push Docker IDs, but we need to capture and send the Vast instance or machine ID. I can implement a function to gather this info from environment variables or metadata. We can also construct a composite ID of the instance ID, hostname, and Docker ID.

Additionally, the user asked for a reset script for Supabase, though we'd need appropriate API keys to run it. I'll ensure our environment is set to connect with Supabase and handle potentially destructive operations with care. Since we're expected to manage both code fixes and instructional guides, I could break tasks down—fixing code issues, managing Vast instances, and creating the Supabase reset script. But I'll need to assess the availability of the `vast` CLI and ensure we're ready to proceed!


**Clarifying task and planning steps**

The developer mentioned that root AGENTS are included, but the user provided custom instructions, which may represent the content of an AGENTS.md file. I should look for this file in the repo, likely at /home/ubuntu/maya3. Even if the user has given clear instructions, it’s still good to check for additional AGENTS files that may contain nested instructions.

Given the complexity of tasks I need to handle, I’ll create a structured plan with steps such as investigating code failures, fixing imports and loading issues, debugging uploads, and managing Supabase resets. I’ll ensure to mark each step as completed sequentially. Since I can run commands with full access, I’ll start by using `ls` and `find` to inspect the repository and gather data, setting up my plan first with the update_plan tool.


Explanation: Tackle the runtime failures first (worker import, PANNs load, R2 uploads, Supabase timeouts), then add a robust machine/instance identifier for analytics and provide reset/deploy scripts.


Output: Plan updated


rg -n "src\.worker|python -m src\.worker|No module named src\.worker" -S . || true


rg -n "PANNs|Cnn14|fc_audioset" -S src scripts . || true


rg -n "R2|cloudflare|s3|boto3|upload" -S src scripts . || true


rg -n "supabase" -S src scripts . || true


ls -la src && find src -maxdepth 2 -type f -name "*worker*" -o -name "worker*.py" -o -name "*vast*" -o -name "*r2*" -o -name "*s3*"


sed -n '1,200p' scripts/reset_for_production.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 384
drwxrwxr-x 13 ubuntu ubuntu   4096 Jan  8 04:16 .
drwxr-x--- 38 ubuntu ubuntu   4096 Jan  8 04:52 ..
drwxrwxr-x  3 ubuntu ubuntu   4096 Jan  8 00:45 .cache
drwx------  2 ubuntu ubuntu   4096 Jan  8 04:44 .claude
drwxrwxr-x  7 ubuntu ubuntu   4096 Jan  8 04:29 dashboard-ui
drwxrwxr-x  3 ubuntu ubuntu   4096 Jan  8 00:32 data
-rw-rw-r--  1 ubuntu ubuntu   1950 Jan  6 22:23 docker-compose.yml
-rw-rw-r--  1 ubuntu ubuntu   4064 Jan  8 01:50 Dockerfile
-rw-rw-r--  1 ubuntu ubuntu    213 Jan  6 22:23 .dockerignore
-rw-rw-r--  1 ubuntu ubuntu   3704 Jan  6 22:23 .env
-rw-rw-r--  1 ubuntu ubuntu    470 Jan  6 22:23 .env.example
drwxrwxr-x  9 ubuntu ubuntu   4096 Jan  8 05:08 .git
-rw-rw-r--  1 ubuntu ubuntu    568 Jan  8 04:16 .gitignore
-rwxrwxr-x  1 ubuntu ubuntu  49067 Jan  8 00:31 massive_process.py
-rw-rw-r--  1 ubuntu ubuntu 131296 Jan  8 01:02 metadata.json
drwxr-xr-x  4 root   root     4096 Jan  8 02:27 output
-rw-rw-r--  1 ubuntu ubuntu  67119 Jan  8 01:06 pipeline.py
drwxrwxr-x  2 ubuntu ubuntu   4096 Jan  7 23:43 __pycache__
-rw-rw-r--  1 ubuntu ubuntu   3654 Jan  8 00:31 README.md
-rw-rw-r--  1 ubuntu ubuntu    623 Jan  8 02:14 requirements.txt
drwxrwxr-x  5 ubuntu ubuntu   4096 Jan  8 02:30 scripts
drwxrwxr-x  3 ubuntu ubuntu   4096 Jan  8 04:57 src
-rw-rw-r--  1 ubuntu ubuntu  30615 Jan  6 22:23 vast_ai.md
-rw-rw-r--  1 ubuntu ubuntu  19445 Jan  6 22:23 vast_ai_sdk.md
drwxrwxr-x  2 ubuntu ubuntu   4096 Jan  8 04:16 .vercel
drwxrwxr-x  2 ubuntu ubuntu   4096 Jan  6 22:31 .xdg_run


Output:
Exit code: 0
Wall time: 0 seconds
Output:
./src/worker_pools.py:12:    from src.worker_pools import GlobalVADPool, DownloadPool


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 111
Output:
./pipeline.py:6:- PANNs CNN14 for music/instrument detection in 1.5s chunks
./pipeline.py:109:# === v7.4 FEATURE: Hybrid Music Detection (INA + PANNs) ===
./pipeline.py:110:# Fast two-stage detection: INA screens everything, PANNs verifies suspicious only
./pipeline.py:395:    # On 16GB GPUs (T4), overlapping PANNs model load with embedding inference can spike VRAM and OOM.
./pipeline.py:537:    # STAGE 7: MUSIC DETECTION (v7.4 - Hybrid INA+PANNs)
./pipeline.py:538:    # Two-stage: Fast INA screening + PANNs verification (suspicious only)
./pipeline.py:544:                # === v7.4: HYBRID DETECTION (INA + PANNs) ===
./pipeline.py:546:                    logger.info("⚡ Using HYBRID music detection (INA + PANNs)")
./pipeline.py:589:                               f"PANNs skipped {hybrid_stats['panns_skip_ratio']*100:.0f}%")
./pipeline.py:592:                    # === Original PANNs-only detection ===
./pipeline.py:600:                    logger.info(f"   PANNs-only: {num_music_chunks} chunks analyzed")
./pipeline.py:624:                    logger.info(f"   PANNs fallback: {num_music_chunks} chunks analyzed")
src/hybrid_music_detection.py:13:Stage 2: PANNs CNN14 (DETAILED, only when needed)
src/hybrid_music_detection.py:21:- Before: PANNs on 100% of chunks → 16s
src/hybrid_music_detection.py:22:- After: INA on 100% (fast) + PANNs on 10% (suspicious) → ~4s
src/hybrid_music_detection.py:25:| INA Result | PANNs Run? | Final Decision |
src/hybrid_music_detection.py:29:| music      | YES        | PANNs decides  |
src/hybrid_music_detection.py:30:| noise      | YES        | PANNs decides  |
src/hybrid_music_detection.py:51:    # Stage 2: PANNs settings (only for suspicious segments)
src/hybrid_music_detection.py:57:    panns_music_threshold: float = 0.20  # PANNs music prob threshold
src/hybrid_music_detection.py:58:    panns_noise_threshold: float = 0.25  # PANNs noise prob threshold
src/hybrid_music_detection.py:85:    # Stage 2 (PANNs) results - only populated if INA flagged suspicious
src/hybrid_music_detection.py:99:    Two-stage music detection: Fast INA screening + PANNs verification.
src/hybrid_music_detection.py:159:            Number of suspicious chunks that required PANNs verification
src/hybrid_music_detection.py:168:        logger.info("   Stage 2: PANNs CNN14 (verification, suspicious only)")
src/hybrid_music_detection.py:203:            # Decide if PANNs verification needed
src/hybrid_music_detection.py:207:                # Clean speech or silence - no PANNs needed
src/hybrid_music_detection.py:216:        # === STAGE 2: PANNs verification (only suspicious chunks) ===
src/hybrid_music_detection.py:222:            logger.info(f"   [Stage 2] PANNs verification: {panns_time:.1f}s ({len(suspicious_chunks)} chunks)")
src/hybrid_music_detection.py:224:            logger.warning("   ⚠ PANNs model not provided, suspicious chunks unverified")
src/hybrid_music_detection.py:250:        logger.info(f"   PANNs skipped: {self._stats['panns_skip_ratio']*100:.1f}% of chunks")
src/hybrid_music_detection.py:327:        Run PANNs on suspicious chunks only.
src/hybrid_music_detection.py:337:        # Prepare audio chunks for PANNs (requires 32kHz)
src/hybrid_music_detection.py:364:        # Run PANNs inference in batches
src/hybrid_music_detection.py:399:                        # PANNs cleared it - INA was false positive
src/hybrid_music_detection.py:403:                logger.error(f"PANNs batch failed: {e}")
src/hybrid_music_detection.py:442:        # PANNs results (only from chunks that ran PANNs)
src/hybrid_music_detection.py:565:    logger.info(f"   ⚡ PANNs skipped: {stats['panns_skip_ratio']*100:.1f}% of chunks")
src/models.py:118:        self.panns_model = None  # v6.7: PANNs CNN14 for music detection
src/models.py:120:        self._panns_loaded = False  # Separate flag for optional PANNs
src/models.py:287:        Load PANNs CNN14 model for music detection (lazy loading).
src/models.py:300:            logger.info("✅ PANNs CNN14 already loaded (hot inference)")
src/models.py:303:        logger.info("Loading PANNs CNN14 for music detection...")
src/models.py:312:                logger.warning("CUDA not available, using CPU for PANNs")
src/models.py:325:            logger.info(f"✅ PANNs CNN14 loaded on {device} in {elapsed:.1f}s")
src/models.py:330:            logger.error(f"❌ PANNs not installed. Run: pip install panns-inference")
src/models.py:334:            logger.error(f"❌ Failed to load PANNs: {e}")
src/music_detection.py:2:"""Music Detection Module - PANNs CNN14 for TTS quality.
src/music_detection.py:153:        logger.info("⚡ MUSIC DETECTION CACHE (PANNs CNN14) - v7.0")
src/music_detection.py:212:        logger.info(f"   [TIMING] PANNs inference: {(time.time()-t0)*1000:.0f}ms")
src/music_detection.py:384:        Run PANNs inference and extract music, noise, and speech probabilities.
src/music_detection.py:412:                logger.error(f"PANNs batch failed: {e}")
src/config.py:198…590 chars truncated…CTION (v7.4 - Hybrid INA+PANNs) ===
./src/config.py:199:    # Two-stage detection: Fast INA screening + PANNs verification
./src/config.py:201:    # Stage 2: PANNs CNN14 (only on suspicious segments)
./src/config.py:203:    use_hybrid_detection: bool = False  # DISABLED: Run PANNs on EVERYTHING for strict music detection
./src/config.py:205:    music_batch_size: int = 128  # Batch size for PANNs inference (v7.5: optimized for A100)
./src/config.py:233:    # or enable use_hybrid_detection (INA screening + PANNs on suspicious chunks).
src/compute.py:236:        # PANNs CNN14 is heavier: ~1GB for batch=64
./src/compute.py:236:        # PANNs CNN14 is heavier: ~1GB for batch=64
./src/music_detection.py:2:"""Music Detection Module - PANNs CNN14 for TTS quality.
./src/music_detection.py:153:        logger.info("⚡ MUSIC DETECTION CACHE (PANNs CNN14) - v7.0")
./src/music_detection.py:212:        logger.info(f"   [TIMING] PANNs inference: {(time.time()-t0)*1000:.0f}ms")
./src/music_detection.py:384:        Run PANNs inference and extract music, noise, and speech probabilities.
./src/music_detection.py:412:                logger.error(f"PANNs batch failed: {e}")
./src/hybrid_music_detection.py:13:Stage 2: PANNs CNN14 (DETAILED, only when needed)
./src/hybrid_music_detection.py:21:- Before: PANNs on 100% of chunks → 16s
./src/hybrid_music_detection.py:22:- After: INA on 100% (fast) + PANNs on 10% (suspicious) → ~4s
./src/hybrid_music_detection.py:25:| INA Result | PANNs Run? | Final Decision |
./src/hybrid_music_detection.py:29:| music      | YES        | PANNs decides  |
./src/hybrid_music_detection.py:30:| noise      | YES        | PANNs decides  |
./src/hybrid_music_detection.py:51:    # Stage 2: PANNs settings (only for suspicious segments)
./src/hybrid_music_detection.py:57:    panns_music_threshold: float = 0.20  # PANNs music prob threshold
./src/hybrid_music_detection.py:58:    panns_noise_threshold: float = 0.25  # PANNs noise prob threshold
./src/hybrid_music_detection.py:85:    # Stage 2 (PANNs) results - only populated if INA flagged suspicious
./src/hybrid_music_detection.py:99:    Two-stage music detection: Fast INA screening + PANNs verification.
./src/hybrid_music_detection.py:159:            Number of suspicious chunks that required PANNs verification
./src/hybrid_music_detection.py:168:        logger.info("   Stage 2: PANNs CNN14 (verification, suspicious only)")
./src/hybrid_music_detection.py:203:            # Decide if PANNs verification needed
./src/hybrid_music_detection.py:207:                # Clean speech or silence - no PANNs needed
./src/hybrid_music_detection.py:216:        # === STAGE 2: PANNs verification (only suspicious chunks) ===
./src/hybrid_music_detection.py:222:            logger.info(f"   [Stage 2] PANNs verification: {panns_time:.1f}s ({len(suspicious_chunks)} chunks)")
./src/hybrid_music_detection.py:224:            logger.warning("   ⚠ PANNs model not provided, suspicious chunks unverified")
./src/hybrid_music_detection.py:250:        logger.info(f"   PANNs skipped: {self._stats['panns_skip_ratio']*100:.1f}% of chunks")
./src/hybrid_music_detection.py:327:        Run PANNs on suspicious chunks only.
./src/hybrid_music_detection.py:337:        # Prepare audio chunks for PANNs (requires 32kHz)
./src/hybrid_music_detection.py:364:        # Run PANNs inference in batches
./src/hybrid_music_detection.py:399:                        # PANNs cleared it - INA was false positive
./src/hybrid_music_detection.py:403:                logger.error(f"PANNs batch failed: {e}")
./src/hybrid_music_detection.py:442:        # PANNs results (only from chunks that ran PANNs)
./src/hybrid_music_detection.py:565:    logger.info(f"   ⚡ PANNs skipped: {stats['panns_skip_ratio']*100:.1f}% of chunks")
./README.md:29:| 7. Music Detection | INA + PANNs hybrid detection |
./src/models.py:118:        self.panns_model = None  # v6.7: PANNs CNN14 for music detection
./src/models.py:120:        self._panns_loaded = False  # Separate flag for optional PANNs
./src/models.py:287:        Load PANNs CNN14 model for music detection (lazy loading).
./src/models.py:300:            logger.info("✅ PANNs CNN14 already loaded (hot inference)")
./src/models.py:303:        logger.info("Loading PANNs CNN14 for music detection...")
./src/models.py:312:                logger.warning("CUDA not available, using CPU for PANNs")
./src/models.py:325:            logger.info(f"✅ PANNs CNN14 loaded on {device} in {elapsed:.1f}s")
./src/models.py:330:            logger.error(f"❌ PANNs not installed. Run: pip install panns-inference")
./src/models.py:334:            logger.error(f"❌ Failed to load PANNs: {e}")
./Dockerfile:65:ENV PANNS_MODEL_PATH=/root/panns_data/Cnn14_mAP=0.431.pth
./Dockerfile:73:# 1. Download PANNs CNN14 model (~312MB)
./Dockerfile:74:RUN wget -q --show-progress -O /root/panns_data/Cnn14_mAP=0.431.pth \
./Dockerfile:75:    "https://zenodo.org/record/3987831/files/Cnn14_mAP%3D0.431.pth?download=1"


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 462
Output:
./requirements.txt:36:boto3
./pipeline.py:170:    total_stages = 10 if getattr(config, 'r2_upload_enabled', False) else 9
./pipeline.py:873:        'source_url': metadata.get('source_url'),  # R2/YouTube URL for re-download
./pipeline.py:979:    export_enabled = getattr(config, 'r2_upload_enabled', False) or getattr(config, 'segment_audio', False)
./pipeline.py:985:        upload_prefix = (getattr(config, 'r2_upload_prefix', '') or '').strip().strip('/')
./pipeline.py:986:        remote_key = f"{video_id}.tar" if not upload_prefix else f"{upload_prefix}/{video_id}.tar"
./pipeline.py:1003:                    r2_upload_enabled=getattr(config, 'r2_upload_enabled', False),
./pipeline.py:1004:                    r2_upload_key=remote_key,
./pipeline.py:1005:                    r2_bucket_type=getattr(config, 'r2_upload_bucket_type', 'production'),
./pipeline.py:1007:                    delete_output_dir_after_upload=getattr(config, 'r2_upload_delete_local_output_dir', False),
./pipeline.py:1012:                result['r2_upload_key'] = remote_key
./pipeline.py:1019:            from src.r2_client import R2Client
./pipeline.py:1038:                # Step 1.5: Remove original audio from output (source is preserved in R2)
./pipeline.py:1043:                        logger.debug(f"Removed _original.flac from output (source in R2)")
./pipeline.py:1051:                # Step 3: Upload to R2 (if enabled)
./pipeline.py:1052:                upload_ok = True
./pipeline.py:1053:                if getattr(config, 'r2_upload_enabled', False):
./pipeline.py:1054:                    r2 = R2Client(bucket_type=getattr(config, 'r2_upload_bucket_type', 'production'))
./pipeline.py:1055:                    if getattr(config, 'r2_upload_skip_if_exists', False) and r2.exists(remote_key):
./pipeline.py:1056:                        logger.info(f"R2 upload skipped (exists): s3://{r2.bucket}/{remote_key}")
./pipeline.py:1058:                        upload_ok = r2.upload_file(str(tar_path), remote_key)
./pipeline.py:1059:                        if not upload_ok:
./pipeline.py:1060:                            raise RuntimeError(f"R2 upload failed for s3://{r2.bucket}/{remote_key}")
./pipeline.py:1062:                    # Delete local tar after upload
./pipeline.py:1063:                    if getattr(config, 'r2_upload_delete_local_tar', True):
./pipeline.py:1069:                    # Delete per-video output dir after successful upload (prevents disk fill).
./pipeline.py:1070:                    if getattr(config, 'r2_upload_enabled', False) and getattr(config, 'r2_upload_delete_local_output_dir', False):
./pipeline.py:1082:                'r2_upload_key': remote_key if getattr(config, 'r2_upload_enabled', False) else None,
./pipeline.py:1100:    # Summary (after optional upload so timing_total is end-to-end)
./vast_ai.md:498:--env '-e VAR1=value1 -e VAR2=value2 -e SECRET=xxx'
./vast_ai.md:906:vastai copy s3.101:/bucket/data C.<instance_id>:/workspace/
./vast_ai.md:938:  "result_url": "https://s3.amazonaws.com/vast.ai/instance_logs/abc123.log",
./vast_ai.md:952:  --dst s3://mybucket/results \
./vast_ai.md:967:- **Cloudflare Tunnels:** Secure HTTPS access via `*.trycloudflare.com`
./vast_ai.md:1008:  "result_url": "https://s3.amazonaws.com/vast.ai/instance_logs/xyz.log"
scripts/deploy/deploy_cluster.sh:88:# Test R2 connection
scripts/deploy/deploy_cluster.sh:90:from src.r2_client import R2Client
scripts/deploy/deploy_cluster.sh:91:r2 = R2Client(bucket_type='source')
scripts/deploy/deploy_cluster.sh:92:print(f'R2 OK - Bucket: {r2.bucket}')
scripts/deploy/deploy_cluster.sh:101:echo "  $SSH_CMD 'cd /workspace/maya3_data && .venv/bin/python massive_process.py --supabase-queue --r2-upload'"
scripts/deploy/deploy_all.py:82:    --r2-upload \
scripts/deploy/start_workers.sh:34:    --r2-upload \
scripts/find_valid_videos.py:2:"""Find COMPLETED videos that have R2 source and local TAR files for validation."""
scripts/find_valid_videos.py:13:from src.r2_client import R2Client
scripts/find_valid_videos.py:16:r2 = R2Client(bucket_type='source')
scripts/find_valid_videos.py:18:# Get video IDs from R2
scripts/find_valid_videos.py:19:print("Getting videos from R2 source bucket...")
scripts/find_valid_videos.py:30:print(f"Found {len(r2_ids)} videos in R2")
scripts/find_valid_videos.py:38:# Find videos that are COMPLETED and in R2
scripts/find_valid_videos.py:45:print(f"\nCOMPLETED videos with R2 source: {len(good_videos)}")
scripts/find_valid_videos.py:49:print("VIDEOS READY FOR VALIDATION (COMPLETED + R2 source + local TAR)")
./scripts/deploy/deploy_cluster.sh:88:# Test R2 connection
./scripts/deploy/deploy_cluster.sh:90:from src.r2_client import R2Client
./scripts/deploy/deploy_cluster.sh:91:r2 = R2Client(bucket_type='source')
./scripts/deploy/deploy_cluster.sh:92:print(f'R2 OK - Bucket: {r2.bucket}')
./scripts/deploy/deploy_cluster.sh:101:echo "  $SSH_CMD 'cd /workspace/maya3_data && .venv/bin/python massive_process.py --supabase-queue --r2-upload'"
scripts/…28639 chars truncated…6:    r2_upload_delete_local_tar: bool = True    # free local disk after upload
scripts/docker/smoke_test.sh:8:R2_UPLOAD="${R2_UPLOAD:-0}"
scripts/docker/smoke_test.sh:36:echo "==> R2 connectivity smoke test"
scripts/docker/smoke_test.sh:39:  "${IMAGE_TAG}" -c "from src.r2_client import R2Client; print('source_bucket', R2Client(bucket_type='source').bucket); print('production_bucket', R2Client(bucket_type='production').bucket)"
scripts/docker/smoke_test.sh:44:  upload_prefix="smoke_test/${ts}"
scripts/docker/smoke_test.sh:56:  if [ "${R2_UPLOAD}" = "1" ]; then
scripts/docker/smoke_test.sh:58:      --r2-upload
scripts/docker/smoke_test.sh:59:      --r2-upload-prefix "${upload_prefix}"
scripts/docker/smoke_test.sh:60:      --r2-upload-skip-if-exists
scripts/docker/smoke_test.sh:74:    echo "Warning: no .tar found in ${OUTPUT_DIR} (maybe R2 upload deleted it, or export disabled)."
./src/logger.py:40:    def upload(self, video_id: str, duration: float, size_mb: float):
./src/logger.py:41:        """Log upload completion."""
./src/logger.py:116:        'R2Client',
./Dockerfile:110:# Default command - Supabase queue mode with R2 upload
./Dockerfile:112:CMD ["--supabase-queue", "--r2-upload", "--background-export", "--output", "./output"]
./README.md:3:High-throughput, GPU-accelerated audio diarization and segmentation pipeline. Processes podcast audio from R2 storage, performs speaker diarization, music detection, and outputs clean speaker-segmented data.
./README.md:14:R2 (Source Audio) → Supabase Queue → GPU Workers → R2 (Output .tar)
./README.md:23:| 1. Download | Fetch audio from R2 bucket |
./README.md:32:| 10. Export | Upload .tar to R2 |
./README.md:42:- Cloudflare R2 bucket (for storage)
./README.md:85:| `API_TOKEN` | Cloudflare R2 API token |
./README.md:121:│   ├── download.py       # R2 download
./README.md:128:│   ├── r2_client.py      # R2 storage client
./scripts/prepare_benchmark.py:4:1. Listing R2 files that exist (podcasts/*.webm)
./scripts/prepare_benchmark.py:23:from src.r2_client import R2Client
./scripts/prepare_benchmark.py:40:r2 = R2Client(bucket_type='source')  # 'test' bucket has the podcasts/
./scripts/prepare_benchmark.py:42:print(f"\n1. Listing R2 files from {r2.bucket}/podcasts/...")
./scripts/prepare_benchmark.py:68:print(f"   Found {len(all_r2_files)} files in R2")
./scripts/prepare_benchmark.py:93:# Filter to videos that are in both R2 AND Supabase
./scripts/prepare_benchmark.py:102:print(f"   Validated: {len(validated)} videos (in R2 + Supabase)")
./scripts/docker/smoke_test.sh:8:R2_UPLOAD="${R2_UPLOAD:-0}"
./scripts/docker/smoke_test.sh:36:echo "==> R2 connectivity smoke test"
./scripts/docker/smoke_test.sh:39:  "${IMAGE_TAG}" -c "from src.r2_client import R2Client; print('source_bucket', R2Client(bucket_type='source').bucket); print('production_bucket', R2Client(bucket_type='production').bucket)"
./scripts/docker/smoke_test.sh:44:  upload_prefix="smoke_test/${ts}"
./scripts/docker/smoke_test.sh:56:  if [ "${R2_UPLOAD}" = "1" ]; then
./scripts/docker/smoke_test.sh:58:      --r2-upload
./scripts/docker/smoke_test.sh:59:      --r2-upload-prefix "${upload_prefix}"
./scripts/docker/smoke_test.sh:60:      --r2-upload-skip-if-exists
./scripts/docker/smoke_test.sh:74:    echo "Warning: no .tar found in ${OUTPUT_DIR} (maybe R2 upload deleted it, or export disabled)."
./scripts/reset_for_production.py:3:Reset Supabase tables and R2 folder for fresh production run.
./scripts/reset_for_production.py:8:3. Deletes everything in R2 bucket 1-cleaned-data/cleaned/trail/
./scripts/reset_for_production.py:112:    """Delete everything in R2 bucket 1-cleaned-data/cleaned/trail/."""
./scripts/reset_for_production.py:113:    from src.r2_client import R2Client
./scripts/reset_for_production.py:115:    print("\n🪣 R2 Bucket: 1-cleaned-data")
./scripts/reset_for_production.py:120:        client = R2Client(bucket_type='production')
./scripts/reset_for_production.py:126:        # Use boto3 paginator for listing all objects
./scripts/reset_for_production.py:146:        print(f"\n🗑  Deleting {len(objects)} objects from R2...")
./scripts/reset_for_production.py:148:        # Delete in batches of 1000 (S3/R2 limit)
./scripts/reset_for_production.py:171:    parser = argparse.ArgumentParser(description='Reset Supabase and R2 for fresh production run')
./scripts/reset_for_production.py:173:    parser.add_argument('--skip-r2', action='store_true', help='Skip deleting R2 cleaned/trail/ prefix')
./scripts/reset_for_production.py:201:    # 3. Delete R2 folder
./scripts/reset_for_production.py:204:        print("STEP 3: Skip R2 deletion (--skip-r2)")
./scripts/reset_for_production.py:208:        print("STEP 3: Delete R2 cleaned/trail/ folder")
./scripts/reset_for_production.py:219:        print("   1. Run the pipeline WITHOUT --r2-upload-prefix to upload directly to 1-cleaned-data/")
./scripts/reset_for_production.py:220:        print("   2. Or use: --r2-upload-prefix '' (empty string)")


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 477
Output:
./requirements.txt:37:supabase
scripts/sync_csv_to_supabase.py:3:Sync CSVs to Supabase
scripts/sync_csv_to_supabase.py:6:then upserts to Supabase videos table.
scripts/sync_csv_to_supabase.py:10:    python scripts/sync_csv_to_supabase.py
scripts/sync_csv_to_supabase.py:13:    python scripts/sync_csv_to_supabase.py --limit 100
scripts/sync_csv_to_supabase.py:16:    python scripts/sync_csv_to_supabase.py --dry-run --limit 10
scripts/sync_csv_to_supabase.py:31:from src.supabase_client import SupabaseClient
scripts/sync_csv_to_supabase.py:75:def transform_to_supabase_schema(df: pd.DataFrame) -> list:
scripts/sync_csv_to_supabase.py:76:    """Transform merged DataFrame to Supabase schema."""
scripts/sync_csv_to_supabase.py:124:def sync_to_supabase(videos: list, batch_size: int = 1000, dry_run: bool = False) -> int:
scripts/sync_csv_to_supabase.py:125:    """Upsert videos to Supabase."""
scripts/sync_csv_to_supabase.py:132:    client = SupabaseClient()
scripts/sync_csv_to_supabase.py:142:    parser = argparse.ArgumentParser(description="Sync CSVs to Supabase")
scripts/sync_csv_to_supabase.py:160:    logger.info("CSV to Supabase Sync")
scripts/sync_csv_to_supabase.py:171:    logger.info("Transforming to Supabase schema...")
scripts/sync_csv_to_supabase.py:172:    videos = transform_to_supabase_schema(merged_df)
scripts/sync_csv_to_supabase.py:176:    total = sync_to_supabase(videos, batch_size=args.batch_size, dry_run=args.dry_run)
./pipeline.py:125:    supabase_metadata: Optional[Dict] = None,
./pipeline.py:134:    === v3: Supabase Metadata Support ===
./pipeline.py:135:    If supabase_metadata is provided, it's passed to download_audio for:
./pipeline.py:217:        # Standard download (with optional Supabase metadata for chapters)
./pipeline.py:219:            audio_path, metadata = download_audio(video_url, config, supabase_metadata=supabase_metadata)
scripts/deploy/deploy_cluster.sh:80:# Test Supabase connection
scripts/deploy/deploy_cluster.sh:82:from src.supabase_client import SupabaseClient
scripts/deploy/deploy_cluster.sh:83:client = SupabaseClient()
scripts/deploy/deploy_cluster.sh:85:print(f'Supabase OK - Pending: {stats.get(\"PENDING\", 0)}')
scripts/deploy/deploy_cluster.sh:101:echo "  $SSH_CMD 'cd /workspace/maya3_data && .venv/bin/python massive_process.py --supabase-queue --r2-upload'"
scripts/deploy/deploy_all.py:81:    --supabase-queue \
scripts/deploy/start_workers.sh:33:    --supabase-queue \
./massive_process.py:61:# Supabase client for distributed queue
./massive_process.py:63:    from src.supabase_client import SupabaseClient, STATUS_COMPLETED, STATUS_FAILED
./massive_process.py:64:    SUPABASE_AVAILABLE = True
./massive_process.py:66:    SUPABASE_AVAILABLE = False
./massive_process.py:68:# Optional: load .env for HF/R2/Supabase credentials
./massive_process.py:100:        'URL': 'Supabase project URL',
./massive_process.py:101:        'SUPABASE_ADMIN': 'Supabase service role key',
./massive_process.py:423:def gpu_worker_supabase(
./massive_process.py:435:    GPU worker that claims videos from Supabase queue.
./massive_process.py:438:    1. Claims the next PENDING video from Supabase
./massive_process.py:440:    3. Updates Supabase with results (COMPLETED/FAILED)
./massive_process.py:452:    supabase_worker_id = f"gpu-{gpu_id}-{pod_id}"
./massive_process.py:454:    worker_logger.info(f"Starting worker {supabase_worker_id} on GPU {gpu_id}")
./massive_process.py:477:        # Initialize Supabase client
./massive_process.py:478:        supabase = SupabaseClient()
./massive_process.py:480:        # Initialize analytics reporter (uses same Supabase credentials)
./massive_process.py:484:            supabase_url=os.environ.get('URL') or os.environ.get('SUPABASE_URL'),
./massive_process.py:485:            supabase_key=os.environ.get('SUPABASE_ADMIN'),
./massive_process.py:513:            # Claim next video from Supabase
./massive_process.py:514:            video = supabase.claim_next_video(
./massive_process.py:515:                worker_id=supabase_worker_id,
./massive_process.py:536:            full_video = supabase.get_video(video_id)
./massive_process.py:543:                worker_logger.info(f"   Chapters: {full_video.get('chapter_count', 0)} from Supabase")
./massive_process.py:547:                # Process video with Supabase metadata (chapters, title, etc.)
./massive_process.py:548:                result = process_single_video(video_url, config, supabase_metadata=full_video)
./massive_process.py:556:                # Update Supabase with success
./massive_process.py:571:                        'worker_id': supabase_worker_id,
./massive_process.py:582:                supabase.update_status(video_id, STATUS_COMPLETED, update_data)
./massive_process.py:635:                # Update Supabase with failure
./massive_process.py:636:                supabase.update_status(video_id, STATUS_FAILED, {
./massive_process.py:640:                        'worker_id': supabase_worker_id,
./massive_process.py:6…30149 chars truncated…check_stale_workers(client: SupabaseClient, threshold_sec: int = 600) -> list:
./scripts/monitor/lease_recovery.py:61:    from supabase import create_client
./scripts/monitor/lease_recovery.py:63:    sb = create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
./scripts/monitor/lease_recovery.py:84:    client = SupabaseClient()
./scripts/monitor/lease_recovery.py:109:    client = SupabaseClient()
./scripts/run_migration.sh:2:# Run analytics migration on Supabase
./scripts/run_migration.sh:21:sed -n '118,313p' scripts/supabase_migrations.sql | psql "$DATABASE_URL" -v ON_ERROR_STOP=1
./scripts/monitor/dashboard.py:29:from supabase import create_client
./scripts/monitor/dashboard.py:65:        self.sb = create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
./scripts/monitor/retry_failed.py:26:from supabase import create_client
./scripts/monitor/retry_failed.py:78:    sb = create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
./src/supabase_client.py:3:Supabase Client for Distributed Video Processing Queue
./src/supabase_client.py:12:    from src.supabase_client import SupabaseClient
./src/supabase_client.py:14:    client = SupabaseClient()
./src/supabase_client.py:31:logger = logging.getLogger("SupabaseClient")
./src/supabase_client.py:41:class SupabaseClient:
./src/supabase_client.py:43:    Supabase REST API client for video processing queue.
./src/supabase_client.py:59:        Initialize Supabase client.
./src/supabase_client.py:62:            url: Supabase project URL (loads from URL env var if None)
./src/supabase_client.py:63:            key: Service role key (loads from SUPABASE_ADMIN env var if None)
./src/supabase_client.py:69:        self.key = key or os.environ.get('SUPABASE_ADMIN', '')
./src/supabase_client.py:72:            logger.warning("Supabase credentials not configured. Set URL and SUPABASE_ADMIN env vars.")
./src/supabase_client.py:83:        logger.info(f"SupabaseClient initialized: {self.url}")
./src/supabase_client.py:93:        """Make HTTP request to Supabase REST API."""
./src/supabase_client.py:110:            logger.error(f"Supabase error {e.code}: {error_body}")
./src/supabase_client.py:117:        """Call Supabase RPC function."""
./src/supabase_client.py:390:        # Use Supabase's count feature
./src/supabase_client.py:457:def get_supabase_client() -> SupabaseClient:
./src/supabase_client.py:458:    """Get singleton SupabaseClient instance."""
./src/supabase_client.py:459:    return SupabaseClient()
./scripts/docker/smoke_test.sh:31:echo "==> Supabase connectivity smoke test"
./scripts/docker/smoke_test.sh:34:  "${IMAGE_TAG}" -c "from src.supabase_client import SupabaseClient; print(SupabaseClient().get_stats())"
./scripts/docker/smoke_test.sh:47:    --supabase-queue
./scripts/docker/smoke_test.sh:64:  echo "==> E2E test: process 1 Supabase-queued video on GPU ${GPU_DEVICE}"
./src/download.py:580:    supabase_metadata: Dict[str, Any] = None,
./src/download.py:703:    # === v3: Use Supabase metadata for chapter-based intro skip ===
./src/download.py:704:    # Chapter info now comes from Supabase (synced from CSV), NOT yt-dlp
./src/download.py:711:    # Use Supabase metadata if provided
./src/download.py:712:    if supabase_metadata:
./src/download.py:713:        video_title = supabase_metadata.get('title') or video_title
./src/download.py:714:        chapters = supabase_metadata.get('chapters') or []
./src/download.py:715:        has_chapters = supabase_metadata.get('has_chapters', False)
./src/download.py:716:        source_duration = supabase_metadata.get('source_duration_min', 0) * 60 if supabase_metadata.get('source_duration_min') else actual_duration
./src/download.py:719:            logger.info(f"   📖 Using Supabase chapters ({len(chapters)} chapters)")
./src/download.py:720:            # Convert chapter format if needed (Supabase stores as JSON with 'seconds' key)
./src/download.py:741:            # No chapters in Supabase, use dynamic skip
./src/download.py:742:            logger.info(f"   📊 No chapters in Supabase, using dynamic skip")
./src/download.py:747:                skip_reason = "dynamic:no_supabase_chapters"
./src/download.py:750:        # No Supabase metadata provided, use dynamic skip
./src/download.py:751:        logger.info(f"   ⚠ No Supabase metadata, using dynamic skip")
./src/download.py:875:    # Build metadata - use Supabase info if available
./src/download.py:876:    sb_chapters = (supabase_metadata.get('chapters') or []) if supabase_metadata else []
./src/download.py:954:    supabase_metadata: Dict[str, Any] = None,
./src/download.py:970:    === v3: Supabase Metadata ===
./src/download.py:971:    When supabase_metadata is provided (from distributed queue), use it for:
./src/download.py:980:        supabase_metadata: Pre-fetched metadata from Supabase (chapters, title, etc.)
./src/download.py:1022:            supabase_metadata=supabase_metadata,
./src/download.py:1044:            supabase_metadata=supabase_metadata,


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 524
drwxrwxr-x  3 ubuntu ubuntu  4096 Jan  8 04:57 .
drwxrwxr-x 13 ubuntu ubuntu  4096 Jan  8 04:16 ..
-rw-rw-r--  1 ubuntu ubuntu 16195 Jan  8 00:31 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  8 00:31 config.py
-rw-rw-r--  1 ubuntu ubuntu 13684 Jan  6 22:22 diarization.py
-rw-rw-r--  1 ubuntu ubuntu 68545 Jan  8 03:34 download.py
-rw-rw-r--  1 ubuntu ubuntu 11756 Jan  6 22:22 embeddings.py
-rw-rw-r--  1 ubuntu ubuntu 12111 Jan  8 00:31 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 13596 Jan  8 01:50 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  8 03:02 __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  8 00:31 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/worker_pools.py
src/r2_client.py
src/__pycache__/r2_client.cpython-310.pyc


Output:
Exit code: 0
Wall time: 0 seconds
Output:
# Maya3 Audio Diarization Pipeline
# Multi-GPU optimized container with PRE-BAKED MODELS
#
# Build (requires HF_TOKEN for PyAnnote models):
#   docker build --build-arg HF_TOKEN=$HF_TOKEN -t maya3-pipeline:latest .
#
# Run (8 GPUs):
#   docker run --gpus all --env-file .env maya3-pipeline:latest
#
# Run with specific GPUs:
#   docker run --gpus '"device=0,1,2,3"' --env-file .env maya3-pipeline:latest

FROM nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04

# Prevent interactive prompts
ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1
ENV PYTHONDONTWRITEBYTECODE=1

# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
    python3.11 \
    python3.11-venv \
    python3.11-dev \
    python3-pip \
    ffmpeg \
    libsndfile1 \
    libsox-fmt-all \
    sox \
    git \
    curl \
    wget \
    build-essential \
    && rm -rf /var/lib/apt/lists/*

# Set Python 3.11 as default
RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1 \
    && update-alternatives --install /usr/bin/python python /usr/bin/python3.11 1

# Set working directory
WORKDIR /workspace/maya3_data

# Create model cache directories
RUN mkdir -p /root/.cache/torch/hub \
    && mkdir -p /root/.cache/huggingface \
    && mkdir -p /ephemeral/models \
    && mkdir -p /root/panns_data \
    && mkdir -p /root/.keras/datasets

# Copy requirements first for better caching
COPY requirements.txt .

# Create venv and install dependencies
# Use torch 2.5.1 for CUDA 12.1 - compatible with latest transformers
RUN python3 -m venv .venv && \
    .venv/bin/pip install --upgrade pip && \
    .venv/bin/pip install --no-cache-dir torch==2.5.1 torchaudio==2.5.1 --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

# Environment variables for model caching
ENV HF_HOME=/root/.cache/huggingface
ENV TORCH_HOME=/root/.cache/torch
ENV CUDA_DEVICE_ORDER=PCI_BUS_ID
ENV PANNS_MODEL_PATH=/root/panns_data/Cnn14_mAP=0.431.pth

# ============================================================
# PRE-BAKE ALL MODELS (no runtime downloads!)
# ============================================================

# HF_TOKEN will be provided at runtime via .env for PyAnnote models

# 1. Download PANNs CNN14 model (~312MB)
RUN wget -q --show-progress -O /root/panns_data/Cnn14_mAP=0.431.pth \
    "https://zenodo.org/record/3987831/files/Cnn14_mAP%3D0.431.pth?download=1"

# 2. Download Silero VAD model
RUN .venv/bin/python -c "\
import torch; \
torch.hub.load('snakers4/silero-vad', 'silero_vad', force_reload=False, onnx=False, verbose=True); \
print('✅ Silero VAD pre-baked')"

# 3. Download ECAPA-TDNN (SpeechBrain)
RUN .venv/bin/python -c "\
from speechbrain.inference.speaker import EncoderClassifier; \
EncoderClassifier.from_hparams(source='speechbrain/spkrec-ecapa-voxceleb', savedir='/ephemeral/models/ecapa'); \
print('✅ ECAPA-TDNN pre-baked')"

# 4. Download inaSpeechSegmenter models (keras)
RUN .venv/bin/python -c "\
from inaSpeechSegmenter import Segmenter; \
seg = Segmenter(vad_engine='sm', detect_gender=False); \
print('✅ inaSpeechSegmenter pre-baked')"

# NOTE: PyAnnote models (speaker-diarization-community-1, 3.1) will download at runtime
# They require HF_TOKEN which is provided via .env at runtime

# ============================================================
# Copy application code (after model downloads for better caching)
# ============================================================
COPY . .

# Create output directory
RUN mkdir -p ./output

# Health check - verify GPU access
HEALTHCHECK --interval=60s --timeout=30s --start-period=60s --retries=3 \
    CMD .venv/bin/python -c "import torch; assert torch.cuda.is_available(), 'CUDA not available'" || exit 1

# Default command - Supabase queue mode with R2 upload
ENTRYPOINT ["/workspace/maya3_data/.venv/bin/python", "massive_process.py"]
CMD ["--supabase-queue", "--r2-upload", "--background-export", "--output", "./output"]


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Reset Supabase tables and R2 folder for fresh production run.

This script:
1. Resets all videos in Supabase to PENDING status
2. Clears all analytics tables (worker_heartbeats, worker_metrics, processing_events, error_logs)
3. Deletes everything in R2 bucket 1-cleaned-data/cleaned/trail/

Usage:
    python scripts/reset_for_production.py [--dry-run] [--skip-r2]
"""

import os
import sys
import argparse
from pathlib import Path

# Add project root to path
sys.path.insert(0, str(Path(__file__).parent.parent))

from dotenv import load_dotenv
load_dotenv()

def reset_videos_to_pending(dry_run: bool = False):
    """Reset all videos to PENDING status."""
    from supabase import create_client

    url = os.environ.get('URL', '')
    key = os.environ.get('SUPABASE_ADMIN', '')

    if not url or not key:
        print("❌ Missing Supabase credentials (URL, SUPABASE_ADMIN)")
        return False

    client = create_client(url, key)

    # Count videos by status first
    print("\n📊 Current video status counts:")
    for status in ['PENDING', 'CLAIMED', 'COMPLETED', 'FAILED', 'SKIPPED']:
        result = client.table('videos').select('youtube_id', count='exact').eq('status', status).execute()
        count = result.count if hasattr(result, 'count') else len(result.data)
        print(f"   {status}: {count}")

    if dry_run:
        print("\n🔍 [DRY RUN] Would reset all videos to PENDING")
        return True

    # Reset all to PENDING (minimal columns - status and claim fields)
    print("\n🔄 Resetting all videos to PENDING...")
    result = client.table('videos').update({
        'status': 'PENDING',
        'claimed_by': None,
        'claimed_at': None,
    }).neq('status', 'PENDING').execute()

    updated = len(result.data) if result.data else 0
    print(f"   ✅ Reset {updated} videos to PENDING")

    return True

def clear_analytics_tables(dry_run: bool = False):
    """Clear all analytics tables."""
    from supabase import create_client

    url = os.environ.get('URL', '')
    key = os.environ.get('SUPABASE_ADMIN', '')

    if not url or not key:
        print("❌ Missing Supabase credentials")
        return False

    client = create_client(url, key)

    tables = ['worker_heartbeats', 'worker_metrics', 'processing_events', 'error_logs']

    print("\n📊 Analytics tables:")
    for table in tables:
        try:
            result = client.table(table).select('*', count='exact').execute()
            count = result.count if hasattr(result, 'count') else len(result.data)
            print(f"   {table}: {count} rows")
        except Exception as e:
            print(f"   {table}: Error - {e}")

    if dry_run:
        print("\n🔍 [DRY RUN] Would delete all rows from analytics tables")
        return True

    print("\n🗑  Clearing analytics tables...")
    for table in tables:
        try:
            # Delete all rows (Supabase requires a filter, use id != impossible value or gt)
            result = client.table(table).delete().gte('created_at', '1970-01-01').execute()
            print(f"   ✅ Cleared {table}")
        except Exception as e:
            # Try alternative delete approach for tables with different primary keys
            try:
                if table == 'worker_heartbeats':
                    result = client.table(table).delete().neq('worker_id', '').execute()
                elif table == 'worker_metrics':
                    result = client.table(table).delete().neq('worker_id', '').execute()
                else:
                    result = client.table(table).delete().neq('id', '00000000-0000-0000-0000-000000000000').execute()
                print(f"   ✅ Cleared {table}")
            except Exception as e2:
                print(f"   ⚠  Could not clear {table}: {e2}")

    return True

def delete_r2_folder(dry_run: bool = False):
    """Delete everything in R2 bucket 1-cleaned-data/cleaned/trail/."""
    from src.r2_client import R2Client

    print("\n🪣 R2 Bucket: 1-cleaned-data")
    print("   Prefix to delete: cleaned/trail/")

    try:
        # Initialize client for production bucket with the specific prefix
        client = R2Client(bucket_type='production')

        # List objects in the folder
        objects = []
        prefix = "cleaned/trail/"

        # Use boto3 paginator for listing all objects
        paginator = client.client.get_paginator('list_objects_v2')
        for page in paginator.paginate(Bucket='1-cleaned-data', Prefix=prefix):
            if 'Contents' in page:
                objects.extend([obj['Key'] for obj in page['Contents']])

        print(f"   Found {len(objects)} objects to delete")

        if not objects:
            print("   ℹ  No objects found in cleaned/trail/")
            return True

        if dry_run:
            print(f"\n🔍 [DRY RUN] Would delete {len(objects)} objects:")
            for obj in objects[:10]:
                print(f"      - {obj}")
            if len(objects) > 10:
                print(f"      ... and {len(objects) - 10} more")
            return True

        print(f"\n🗑  Deleting {len(objects)} objects from R2...")

        # Delete in batches of 1000 (S3/R2 limit)
        batch_size = 1000
        deleted = 0
        for i in range(0, len(objects), batch_size):
            batch = objects[i:i+batch_size]
            delete_objects = {'Objects': [{'Key': key} for key in batch]}
            response = client.client.delete_objects(
                Bucket='1-cleaned-data',
                Delete=delete_objects
            )
            deleted += len(response.get('Deleted', []))
            errors = response.get('Errors', [])
            if errors:
                print(f"   ⚠  {len(errors)} errors in batch")

        print(f"   ✅ Deleted {deleted} objects from cleaned/trail/")
        return True

    except Exception as e:
        print(f"   ❌ Error: {e}")
        return False

def main():
    parser = argparse.ArgumentParser(description='Reset Supabase and R2 for fresh production run')
    parser.add_argument('--dry-run', action='store_true', help='Show what would be done without making changes')
    parser.add_argument('--skip-r2', action='store_true', help='Skip deleting R2 cleaned/trail/ prefix')
    args = parser.parse_args()

    print("=" * 60)
    print("PRODUCTION RESET SCRIPT")
    print("=" * 60)

    if args.dry_run:
        print("\n⚠  DRY RUN MODE - No changes will be made\n")
    else:
        print("\n⚠  WARNING: This will reset ALL data!")
        confirm = input("Type 'RESET' to confirm: ")
        if confirm != 'RESET':
            print("Aborted.")
            return

    # 1. Reset videos
    print("\n" + "=" * 60)
    print("STEP 1: Reset Videos to PENDING")
    print("=" * 60)
    reset_videos_to_pending(args.dry_run)

    # 2. Clear analytics
    print("\n" + "=" * 60)
    print("STEP 2: Clear Analytics Tables")
    print("=" * 60)
    clear_analytics_tables(args.dry_run)


**Creating a worker module**

I’m thinking about what to do with the `src/worker.py`. I should create this to handle calling `massive_process` or starting a worker. It looks like the current start script is outdated since `src.worker` isn't found, just an import for worker pools. So, I'll provide a new module to prevent errors. I might create a `src/worker.py` with a CLI entry point that calls `massive_process.gpu_worker`. I need to check how `massive_process.py` is invoked to align everything properly.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
MASSIVE COMPUTE - 8x H100 Parallel Processing

Designed for maximum throughput on multi-GPU clusters.
Each GPU runs an independent pipeline worker processing videos in parallel.

Usage:
    # Process videos from file (one URL per line)
    python massive_process.py --input videos.txt

    # Process from YouTube playlist or channel
    python massive_process.py --playlist "PLAYLIST_URL"

    # Benchmark mode with test videos
    python massive_process.py --benchmark --num-videos 16

    # Custom GPU selection
    python massive_process.py --input videos.txt --gpus 0,1,2,3

Architecture:
    Main Process
        │
        ├── GPU Worker 0 (CUDA_VISIBLE_DEVICES=0)
        │     └── pipeline.py (full model stack)
        │
        ├── GPU Worker 1 (CUDA_VISIBLE_DEVICES=1)
        │     └── pipeline.py (full model stack)
        │
        ... (8 workers total)
        │
        └── GPU Worker 7 (CUDA_VISIBLE_DEVICES=7)
              └── pipeline.py (full model stack)

Result: 8 videos processed in parallel → 8x throughput
"""

# CRITICAL: Suppress NNPACK warnings BEFORE any imports
# These warnings are harmless (NNPACK is CPU-only, we use GPU) but spam logs
import os
import warnings
os.environ['NNPACK_DISABLE'] = '1'  # Disable NNPACK entirely
os.environ['TORCH_CPP_LOG_LEVEL'] = 'ERROR'  # Suppress C++ warnings
warnings.filterwarnings('ignore', message='.*NNPACK.*')
warnings.filterwarnings('ignore', category=FutureWarning, module='pynvml')
warnings.filterwarnings('ignore', category=UserWarning, module='torchaudio')

import sys
import time
import json
import logging
import argparse
import signal
import traceback
from pathlib import Path
from datetime import datetime
from multiprocessing import Process, Queue, Value, Manager
from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass, asdict, field

# Supabase client for distributed queue
try:
    from src.supabase_client import SupabaseClient, STATUS_COMPLETED, STATUS_FAILED
    SUPABASE_AVAILABLE = True
except ImportError:
    SUPABASE_AVAILABLE = False

# Optional: load .env for HF/R2/Supabase credentials
try:
    from dotenv import load_dotenv  # type: ignore
except Exception:
    load_dotenv = None

# Clean logging utilities
from src.logger import silence_verbose_loggers

# Analytics reporter for distributed monitoring
from src.analytics import create_reporter, AnalyticsReporter

# Setup logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s | %(name)-12s | %(levelname)-8s | %(message)s',
    datefmt='%H:%M:%S'
)
logger = logging.getLogger("MassiveCompute")

# Global stop signal for graceful shutdown
_GLOBAL_STOP_SIGNAL = None

def validate_environment() -> bool:
    """
    Validate all required environment variables before starting workers.
    Fails fast on startup instead of runtime errors.
    """
    required = {
        'HF_TOKEN': 'Hugging Face token for model downloads',
        'URL': 'Supabase project URL',
        'SUPABASE_ADMIN': 'Supabase service role key',
    }

    r2_vars = {
        'R2_ENDPOINT_URL': 'Cloudflare R2 endpoint',
        'R2_ACCESS_KEY_ID': 'R2 access key',
        'R2_SECRET_ACCESS_KEY': 'R2 secret key',
    }

    missing = []
    for var, desc in required.items():
        if not os.environ.get(var):
            missing.append(f"  {var}: {desc}")

    # R2 vars only required if --r2-upload is used
    # Will be validated later if needed

    if missing:
        logger.error("Missing required environment variables:")
        for m in missing:
            logger.error(m)
        return False

    return True

def setup_signal_handlers(stop_signal):
    """
    Setup graceful shutdown handlers for SIGTERM and SIGINT.

    This ensures workers complete their current video before exiting,
    preventing lost work on container shutdown.
    """
    global _GLOBAL_STOP_SIGNAL
    _GLOBAL_STOP_SIGNAL = stop_signal

    def graceful_shutdown(signum, frame):
        sig_name = 'SIGTERM' if signum == signal.SIGTERM else 'SIGINT'
        logger.warning(f"Received {sig_name}, initiating graceful shutdown...")
        logger.warning("Workers will complete current videos then exit")

        if _GLOBAL_STOP_SIGNAL is not None:
            _GLOBAL_STOP_SIGNAL.value = True

    signal.signal(signal.SIGTERM, graceful_shutdown)
    signal.signal(signal.SIGINT, graceful_shutdown)

@dataclass
class VideoResult:
    """Result from processing a single video."""
    video_url: str
    video_id: str = ""
    success: bool = False
    error: str = ""
    duration_sec: float = 0.0
    process_time_sec: float = 0.0
    pipeline_time_sec: float = 0.0
    rtf: float = 0.0
    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.*')


**Considering module creation**

I’m tackling a big issue related to massive_process. I think the missing module, src.worker, isn’t really necessary. It might be more effective to create a `src/worker.py` that calls `massive_process.main()` with the command line arguments. However, I need to check if `massive_process.py` defines a main() function, particularly by looking for "if __name__ == '__main__':" to confirm it’s at the bottom. That’s a key detail!


rg -n "if __name__ == '__main__'|def main\(" massive_process.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
1058:def main():


Summary: auto


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    results_file = Path(output_dir) / "benchmark_results.json"
    with open(results_file, 'w') as f:
        json.dump({
            'stats': asdict(stats),
            'stage_avgs_sec': {k: round(v, 4) for k, v in stage_avgs.items()},
            'stage_pcts': {k: round(v, 2) for k, v in stage_pcts.items()},
            'results': [asdict(r) for r in results]
        }, f, indent=2)
    logger.info(f"Results saved to: {results_file}")

    if failed:
        logger.warning(f"\nFailed videos:")
        for r in failed:
            logger.warning(f"   {r.video_url}: {r.error}")

    return stats

def main():
    parser = argparse.ArgumentParser(
        description="MASSIVE COMPUTE - 8x H100 Parallel Processing",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
  # Process videos from file
  python massive_process.py --input videos.txt

  # Benchmark with 16 test videos
  python massive_process.py --benchmark --num-videos 16

  # Use specific GPUs
  python massive_process.py --input videos.txt --gpus 0,1,2,3

  # Custom output directory
  python massive_process.py --input videos.txt --output /data/results
        """
    )

    # Input options
    input_group = parser.add_mutually_exclusive_group(required=True)
    input_group.add_argument('--input', '-i', type=str, help='File with video URLs (one per line)')
    input_group.add_argument('--benchmark', action='store_true', help='Run benchmark with test videos')
    input_group.add_argument('--urls', nargs='+', help='Direct URL list')
    input_group.add_argument('--supabase-queue', action='store_true',
                            help='Use Supabase as distributed video queue (claims PENDING videos from DB)')

    # Processing options
    parser.add_argument('--gpus', type=str, default=None,
                       help='Comma-separated GPU IDs (default: all available)')
    parser.add_argument('--workers-per-gpu', type=int, default=1,
                       help='Number of workers per GPU (default: 1, try 2-4 for H100)')
    parser.add_argument('--num-videos', type=int, default=8,
                       help='Number of videos for benchmark mode (default: 8)')
    parser.add_argument('--output', '-o', type=str, default='data/massive_output',
                       help='Output directory (default: data/massive_output)')

    # Pipeline config overrides
    parser.add_argument('--merge-threshold', type=float, default=0.80,
                       help='Speaker merge threshold (default: 0.80)')
    parser.add_argument('--min-segment', type=float, default=0.2,
                       help='Minimum segment duration (default: 0.2s)')
    parser.add_argument('--no-music-detection', action='store_true',
                       help='Disable music detection')

    # R2 upload (processed .tar -> 1-cleaned-data)
    parser.add_argument('--r2-upload', action='store_true',
                       help='Upload per-video .tar into R2 bucket 1-cleaned-data (no deletes)')
    parser.add_argument('--r2-upload-prefix', type=str, default='',
                       help="Optional prefix within 1-cleaned-data for uploads (e.g. 'benchmarks/rtx4090/2026-01-03')")
    parser.add_argument('--r2-upload-skip-if-exists', action='store_true',
                       help='Skip upload if remote key already exists')

    # Export options
    parser.add_argument('--segment-audio', action='store_true', default=True,
                       help='Segment original audio into individual speaker clips (default: True)')
    parser.add_argument('--no-segment-audio', action='store_false', dest='segment_audio',
                       help='Disable audio segmentation')
    parser.add_argument('--background-export', action='store_true',
                       help='Run export (segmentation+tar+upload) in background threads')
    parser.add_argument('--export-workers', type=int, default=4,
                       help='Number of background export workers (default: 4)')

    # Supabase queue options
    parser.add_argument('--max-videos', type=int, default=0,
                       help='Max videos per worker in supabase-queue mode (0=unlimited)')
    parser.add_argument('--lease-duration', type=int, default=900,


**Clarifying package issues**

I’m examining the src directory, which contains .py files, but there’s no __init__.py listed. Without it, src isn’t a package, which would cause the command python -m src.worker to fail. However, I realize that Python allows for implicit namespace packages without __init__.py, so maybe src can still work as a namespace.

PEP 420 permits this if src is in sys.path, but only if there isn’t a conflicting file named src.py. Since that file is absent, src should be a namespace package, unless the worker module is missing. The error suggests that Python recognizes src but can't find worker.


**Inspecting package structure and errors**

I think it’s better to add `src/__init__.py` to create an explicit package and also include `src/worker.py` for robustness. Now, regarding the PANNs issue in FastPipelineV6.Models, it seems related to the `panns-inference` package. However, there’s an error indicating a state_dict mismatch for fc_audioset, meaning the PANNs model (Cnn14) was loaded from a checkpoint, but the current model expects an output of 235—which might relate to inaspeech categories.

I need to check where the model is defined in `src/models.py`, especially around the load_panns function.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
        # 4. ECAPA-TDNN for embeddings
        logger.info("Loading ECAPA-TDNN...")
        from speechbrain.inference.speaker import EncoderClassifier
        self.embedding_model = EncoderClassifier.from_hparams(
            source="speechbrain/spkrec-ecapa-voxceleb",
            savedir="/ephemeral/models/ecapa",
            run_opts={"device": str(device)}
        )
        logger.info("✅ ECAPA-TDNN loaded")

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

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

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

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

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

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

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

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

        Args:
            device: 'cuda' or 'cpu'

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

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

        try:
            from panns_inference import AudioTagging

            # Determine device
            if device == 'cuda' and not torch.cuda.is_available():
                device = 'cpu'
                logger.warning("CUDA not available, using CPU for PANNs")

            # === v7.4: Check for pre-baked model path (Docker) ===
            checkpoint_path = os.environ.get('PANNS_MODEL_PATH')
            if checkpoint_path and os.path.exists(checkpoint_path):
                logger.info(f"   Using pre-baked model: {checkpoint_path}")
                self.panns_model = AudioTagging(checkpoint_path=checkpoint_path, device=device)
            else:
                # Default: downloads model on first use
                self.panns_model = AudioTagging(device=device)

            self._panns_loaded = True
            elapsed = time.time() - start
            logger.info(f"✅ PANNs CNN14 loaded on {device} in {elapsed:.1f}s")

            return self.panns_model

        except ImportError as e:
            logger.error(f"❌ PANNs not installed. Run: pip install panns-inference")
            raise ImportError("panns-inference package required for music detection. "
                            "Install with: pip install panns-inference") from e
        except Exception as e:
            logger.error(f"❌ Failed to load PANNs: {e}")
            raise

# Global singleton instance
MODELS = ModelManager()


Output:
Exit code: 0
Wall time: 0 seconds
Output:
{
  "video_id": "-5fJwfA0I5E",
  "video_title": "Sri Sri Sri Adithya Parasri Swamy About sri sri Ambatraya Kshetram | Anchor Nirupama",
  "youtube_url": "https://www.youtube.com/watch?v=-5fJwfA0I5E",
  "original_duration": 519.34,
  "intro_skipped": 51.93,
  "intro_skip_reason": "dynamic:no_supabase_chapters",
  "intro_skip_matched_title": null,
  "processed_duration": 467.4,
  "sample_rate": 16000,
  "original_sample_rate": 48000,
  "original_audio_path": "output/-5fJwfA0I5E/-5fJwfA0I5E_original.flac",
  "source_url": "test/podcasts/-5fJwfA0I5E.webm",
  "source_type": "r2",
  "original_audio_preserved": true,
  "spectral_quality": {
    "claimed_sample_rate": 48000,
    "effective_sample_rate": 48000,
    "detected_nyquist": 24000.0,
    "rolloff_frequency": 24000.0,
    "is_upsampled": false,
    "original_format": "native_48k",
    "confidence": 1.0,
    "is_suitable_for_tts": true,
    "quality_issues": [],
    "high_freq_energy_ratio": 0.0001,
    "spectral_flatness": 0.0045
  },
  "audio_native_sample_rate": 48000,
  "is_native_quality": true,
  "num_speakers": 2,
  "speakers": [
    "SPEAKER_01",
    "SPEAKER_00"
  ],
  "total_segments": 214,
  "segments": [
    {
      "start": 0.0,
      "end": 0.3,


**Checking metadata details**

It looks like the metadata.json might just be showing sample output metadata instead of actual labels, which could explain the mismatch. I'm thinking about whether `panns-inference==0.0.0` was fixed or pinned, so I should check the requirements to see how everything aligns. It’s a bit of a puzzle, but I’ll figure it out!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
# Core ML frameworks
torch
torchaudio
transformers

# Audio processing
librosa
soundfile
pydub
audioread
tensorflow[and-cuda]

# Speech models and tools
speechbrain
resemblyzer
openai-whisper
demucs

# PyAnnote for diarization (requires HF token with model access)
pyannote.audio>=3.1.0

# Quality metrics
pesq
pystoi

# YouTube downloading (fallback)
yt-dlp

# Data processing
numpy
scipy
pandas
scikit-learn

# Cloud services
boto3
supabase
google-cloud-storage

# Utilities
tqdm
pyyaml
python-dotenv

# Additional audio utilities
webrtcvad
noisereduce
panns-inference

# Hybrid music detection
inaSpeechSegmenter>=0.8.0


python -c "import panns_inference, inspect; import panns_inference.models; import pkgutil, sys; print('panns_inference', getattr(panns_inference,'__version__',None)); import panns_inference.audio_tagging as at; import panns_inference.utils as u; from panns_inference import AudioTagging; import panns_inference.models; import panns_inference.utils; import panns_inference.audio_tagging; import panns_inference.inference; print('AudioTagging sig', inspect.signature(AudioTagging.__init__)); print('Cnn14 sig', inspect.signature(panns_inference.models.Cnn14.__init__));"


Output:
Exit code: 127
Wall time: 0 seconds
Output:
/bin/bash: line 1: python: command not found


**Checking Python installation**

I'm considering the possibility that Python might not be installed in this environment. I noticed there's a `/usr/bin/python`, yet the `python` command isn't found. That makes me think it could be pointing to `python3` instead. I’ll give `python3` a try to see if that works properly.


python3 -c "import sys; print(sys.version)"


Output:
Exit code: 0
Wall time: 0 seconds
Output:
3.10.12 (main, Nov  4 2025, 08:48:33) [GCC 11.4.0]


**Inspecting Python environment**

I'm noticing we have Python 3.10 available, but it seems like Python itself is version 3.11 in the logs. I want to check if panns_inference is installed by trying to run Python 3 and importing it. However, since we're outside of the virtual environment, I’m unsure if it’s set up here. I’m considering using pip to install it if needed, but I might not want to go that route just yet. Let's see if it’s already there first.


python3 -c "import panns_inference, inspect; import panns_inference.models; print('panns_inference', getattr(panns_inference,'__version__',None)); from panns_inference import AudioTagging; print('AudioTagging', AudioTagging); print('AudioTagging sig', inspect.signature(AudioTagging.__init__)); import panns_inference.models as m; print('Cnn14 sig', inspect.signature(m.Cnn14.__init__));"


Output:
Exit code: 0
Wall time: 2 seconds
Output:
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
panns_inference 0.1.0
AudioTagging <class 'panns_inference.inference.AudioTagging'>
AudioTagging sig (self, model=None, checkpoint_path=None, device='cuda')
Cnn14 sig (self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num)


**Inspecting code details**

I’m working with version 0.1.0 and need to look at the `panns_inference.inference.AudioTagging` code to find out the default `classes_num`. I think the next step is to inspect its `__init__` method. To do this, I might use `inspect.getsource`. I’m planning to run a Python snippet with the command `python3 -c "import inspect, panns_inference.inference as i; print(inspect.getsource(i.AudioTagging.__init__))"` to print the relevant lines. Let’s get started on that!


python3 - <<'PY'
import inspect
from panns_inference.inference import AudioTagging
print(inspect.getsource(AudioTagging.__init__))
PY


Output:
Exit code: 0
Wall time: 2.1 seconds
Output:
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
    def __init__(self, model=None, checkpoint_path=None, device='cuda'):
        """Audio tagging inference wrapper.
        """
        if not checkpoint_path:
            checkpoint_path='{}/panns_data/Cnn14_mAP=0.431.pth'.format(str(Path.home()))
        print('Checkpoint path: {}'.format(checkpoint_path))

        if not os.path.exists(checkpoint_path) or os.path.getsize(checkpoint_path) < 3e8:
            create_folder(os.path.dirname(checkpoint_path))
            zenodo_path = 'https://zenodo.org/record/3987831/files/Cnn14_mAP%3D0.431.pth?download=1'
            os.system('wget -O "{}" "{}"'.format(checkpoint_path, zenodo_path))

        if device == 'cuda' and torch.cuda.is_available():
            self.device = 'cuda'
        else:
            self.device = 'cpu'

        self.labels = labels
        self.classes_num = classes_num

        # Model
        if model is None:
            self.model = Cnn14(sample_rate=32000, window_size=1024,
                hop_size=320, mel_bins=64, fmin=50, fmax=14000,
                classes_num=self.classes_num)
        else:
            self.model = model

        checkpoint = torch.load(checkpoint_path, map_location=self.device)
        self.model.load_state_dict(checkpoint['model'])

        # Parallel
        if 'cuda' in str(self.device):
            self.model.to(self.device)
            print('GPU number: {}'.format(torch.cuda.device_count()))
            self.model = torch.nn.DataParallel(self.model)
        else:
            print('Using CPU.')


**Inspecting variable definitions**

I notice two variables, `labels` and `classes_num`, that have been imported from somewhere. I'm thinking they might be defined in `panns_inference.utils`. To confirm this, I'll take a closer look at the definitions of `classes_num` and see where it was declared. This should help clarify their purpose and how they fit into the larger context of the code I'm working with. Let's take that step next!


python3 - <<'PY'
import inspect
import panns_inference
import panns_inference.inference as inf
import panns_inference.utils as u
import panns_inference.metadata as md
import panns_inference
import pkgutil
import panns_inference
from panns_inference import inference

# Find where classes_num comes from
import panns_inference.inference
print('inference module path', panns_inference.inference.__file__)

# Print top of inference.py
import pathlib
path = pathlib.Path(panns_inference.inference.__file__)
print(path)
print('\n'.join(path.read_text().splitlines()[:60]))
PY


Output:
Exit code: 1
Wall time: 2.2 seconds
Output:
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
Traceback (most recent call last):
  File "<stdin>", line 4, in <module>
ModuleNotFoundError: No module named 'panns_inference.utils'


python3 - <<'PY'
import pkgutil, panns_inference, inspect, os
print('pkg', panns_inference.__file__)
for m in pkgutil.iter_modules(panns_inference.__path__):
    print(m.name)
PY


Output:
Exit code: 0
Wall time: 2.3 seconds
Output:
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
pkg /home/ubuntu/.local/lib/python3.10/site-packages/panns_inference/__init__.py
config
inference
models
pytorch_utils


python3 - <<'PY'
import pathlib, panns_inference
from panns_inference import config
path = pathlib.Path(config.__file__)
print(path)
print(path.read_text())
PY


Output:
Exit code: 0
Wall time: 2.2 seconds
Output:
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/home/ubuntu/.local/lib/python3.10/site-packages/panns_inference/config.py
import os
import numpy as np
import csv
from pathlib import Path

sample_rate = 32000

labels_csv_path = '{}/panns_data/class_labels_indices.csv'.format(str(Path.home()))

# Download labels if not exist
if not os.path.isfile(labels_csv_path):
	os.makedirs(os.path.dirname(labels_csv_path), exist_ok=True)
	os.system('wget -O "{}" "http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/class_labels_indices.csv"'.format(labels_csv_path))

# Load label
with open(labels_csv_path, 'r') as f:
    reader = csv.reader(f, delimiter=',')
    lines = list(reader)

labels = []
ids = []    # Each label has a unique id such as "/m/068hy"
for i1 in range(1, len(lines)):
    id = lines[i1][1]
    label = lines[i1][2]
    ids.append(id)
    labels.append(label)

classes_num = len(labels)

lb_to_ix = {label : i for i, label in enumerate(labels)}
ix_to_lb = {i : label for i, label in enumerate(labels)}

id_to_ix = {id : i for i, id in enumerate(ids)}
ix_to_id = {i : id for i, id in enumerate(ids)}


**Investigating class label mismatch**

I'm trying to figure out a mismatch between expected class numbers. The class_labels_indices.csv should have 527 entries, but I'm seeing 235 instead. Maybe the file got corrupted or partially downloaded? It's possible if some lines are missing. If there are network issues, that could lead to a truncated file. I noticed the Dockerfile sets up a location for checkpoints but possibly skips the label file, which might induce mismatches when loading the checkpoint.


**Exploring label file issues**

I'm digging into the root cause of the label file issue. It seems the download might be incomplete due to network timeouts or settings in `wget`. If the file is truncated, that could explain the mismatch in expected classes. The number 235 might indicate it’s an old or partial file left from another dataset. Alternatively, if there's a network error, I could end up with an HTML error page, leading to fewer lines. I’ll need to check the label file length in our home directory to see if it’s accurate.


**Addressing label file verification**

I see the checkpoint path refers to /root/panns_data where the label file should be located. If that file exists but is truncated to 235 lines, it creates a mismatch. To fix this, I need to make sure the label file is the correct length. Although we can't easily change the library's use to enforce classes_num=527, I could patch our PANNs loader. My best option is to verify the label file's length upon loading and re-download it if necessary to maintain the expected 527 entries.


**Checking label file access**

I need to ensure the label file path is correctly set by using `panns_inference.config.labels_csv_path`, which depends on the home directory of the runtime user. I can compute the full path using `os.path.join(Path.home(), 'panns_data', 'class_labels_indices.csv')`. I should check if the file exists and counts the lines, expecting a total of 528 lines. If there's a mismatch, I can re-download it using requests or urllib, but I should also consider pre-baking the CSV in the Dockerfile to prevent network issues. I’ll add a step to ensure this file is correctly included.


**Investigating label file issues**

I see that even if they pre-bake the checkpoint, it seems they didn’t manage the label file properly, so panns_inference attempts to download it at runtime. If that download fails partially, it leads to a mismatch in the number of classes. The logs indicate a shape mismatch between the checkpoint (which expects 527) and the loaded model (with only 235). This likely stems from the label file having only 235 entries, so I’ll check the file in my environment under /home/ubuntu/panns_data.


python3 - <<'PY'
from pathlib import Path
import os
p = Path.home()/'panns_data'/'class_labels_indices.csv'
print('home', Path.home())
print('path', p)
if p.exists():
    lines = p.read_text().splitlines()
    print('lines', len(lines))
    print('head', lines[:3])
    print('tail', lines[-3:])
else:
    print('missing')
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
home /home/ubuntu
path /home/ubuntu/panns_data/class_labels_indices.csv
lines 528
head ['index,mid,display_name', '0,/m/09x0r,"Speech"', '1,/m/05zppz,"Male speech, man speaking"']
tail ['524,/m/07c52,"Television"', '525,/m/06bz3,"Radio"', '526,/m/07hvw1,"Field recording"']


Summary: auto


**Evaluating PANNs Inference**

I’m considering patching the panns_inference library, but implementing our own PANNs inference might be too heavy. A simpler approach could be to check if the label CSV file exists and has a sufficient line count (at least 528) before we instantiate AudioTagging. If it's missing or too small, we can download it using Python. However, I need to remember that panns_inference.config runs at import time, which complicates things a bit. I’ll need to rearrange the process a bit!


**Implementing Load PANNs Fix**

I’m planning to update the load_panns function. First, I’ll set the path for the labels CSV using Path.home(). I need to check if the file exists and ensure its size is sufficient. If it’s missing or too small, I should download the file with retries and rename it after writing.

After that, I can import AudioTagging, and since the file will exist, the config should skip the download, which is good. However, if the file is truncated, I'll need to replace it. Checking file validity before the import is crucial for reliability. It might also be smart to update the Dockerfile to pre-download the label file, although it’s optional.


**Updating Dockerfile and Debugging R2**

I’ll update the Dockerfile to download the class_labels_indices.csv into /root/panns_data, and I might verify the success with something like `wc -l`. While build caching isn't strictly necessary, it's still a good idea.

I noticed that they're likely using Vast, which runs a startup script outside of a Docker container. So, if they’re running commands like `apt-get update`, they might not even be in the Docker environment. They appear to be running a script that clones code in the host environment.

Next, I'll check the R2 upload process using src/r2_client.py.