**Planning comprehensive cleanup and archival of pipeline versions**

I’m finalizing a cleanup plan to move alternate pipelines into a new `scripts/legacy/` folder with a README listing versions, while keeping only the safest, stable CPU-based `prepare_shards.py` pipeline and possibly `fast_audio_pipeline.py` as primary. I’ll also draft a detailed supabase worker and sharding plan in documentation without implementing tables, ensuring minimal disruption and clear version selection guidance.


**Evaluating pipeline versions for safety and accuracy**

I'm weighing the trade-offs between GPU and CPU audio metrics versions, noting GPU v2's approximations reduce accuracy. The safest choice seems to be the fixed optimized pipeline with GPU metrics v1 or the CPU metrics pipeline to ensure correctness without over-optimizing.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
scripts/bulk_download.py
scripts/fetch_languages.py
scripts/fill_missing_languages.py
scripts/benchmark_optimized_pipeline.py
scripts/gpu_audio_metrics.py
scripts/gpu_audio_metrics_v2.py
scripts/prepare_shards.py
scripts/benchmark_results.json
scripts/supabase_summary.py
scripts/benchmark_gpu.py
scripts/clever_pipeline.py
scripts/gpu_audio_decode.py
scripts/PERFORMANCE_ANALYSIS.md
scripts/fast_audio_pipeline.py
scripts/normalize_languages.py
scripts/optimized_pipeline.py
scripts/fast_audio_pipeline_v2.py
scripts/__pycache__/fast_audio_pipeline.cpython-310.pyc
scripts/__pycache__/fast_audio_pipeline_v2.cpython-310.pyc
scripts/__pycache__/gpu_audio_metrics_v2.cpython-310.pyc
scripts/__pycache__/gpu_audio_processor.cpython-310.pyc
scripts/__pycache__/optimized_pipeline.cpython-310.pyc
scripts/__pycache__/gpu_audio_metrics.cpython-310.pyc
scripts/__pycache__/gpu_audio_decode.cpython-310.pyc
scripts/__pycache__/prepare_shards.cpython-310.pyc


Summary: auto

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


**Assessing audio pipeline versions**

I'm weighing CPU-based `prepare_shards.py` as the stable production path and considering `fast_audio_pipeline_v2.py` for improved throughput with optional GPU resampling, favoring scalable CPU pipelines over complex GPU ones while treating `optimized_pipeline.py` and others as experimental or archival versions.


**Finalizing pipeline organization**

I'm settling on using `prepare_shards.py` CPU path with optional GPU resample as the stable production pipeline, moving all experimental and GPU-related pipelines into a new `scripts/legacy/` folder for archival, and planning to document the best version clearly while updating references to reflect this cleanup.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
# Audio Pipeline Optimization - Benchmark Results

## Executive Summary

**14.3x throughput improvement achieved through GPU-accelerated metrics and multiprocess pipeline.**

| Metric | Original | Optimized | Improvement |
|--------|----------|-----------|-------------|
| Throughput | 26.1 seg/s, 7.6 vid/min | 27.1 seg/s, 108.3 vid/min | **14.3x** |
| 500k videos (single node) | 46 days | 3.2 days | **14.3x faster** |
| 500k videos (24h target) | 46 nodes, $1,104 | 4 nodes, $96 | **91% cost reduction** |

---

## Benchmark Results (2026-01-19)

### Test Configuration
- **Hardware**: NVIDIA A100-SXM4-80GB, 16 vCPUs
- **Test Data**: 1,500 synthetic segments (~100 videos), 8-20s duration each
- **Output**: WAV format at 16k/24k/32k/44.1k sample rates

### Performance Comparison

| Configuration | Seg/s | Vid/min | Speedup vs Original |
|---------------|-------|---------|---------------------|
| **multiprocess_gpu_batch256** | 27.1 | 108.3 | **14.3x** |
| multiprocess_gpu_batch128 | 26.2 | 104.7 | 13.8x |
| cpu_baseline (original) | 24.6 | 98.5 | 13.0x |
| multiprocess_gpu_batch64 | 19.6 | 78.6 | 10.4x |
| cpu_threading | 19.7 | 78.7 | 10.4x |

### Timing Breakdown - Before vs After

**Original Pipeline (CPU-only):**
```
FLAC Decode:     17.9 ms (31.0%)
Metrics (LUFS):  26.1 ms (45.3%)  ← BOTTLENECK
GPU Resample:     0.5 ms (0.9%)
WAV Encode:      13.1 ms (22.8%)
```

**Optimized Pipeline (GPU batch 256):**
```
FLAC Decode:     13.2%  (multiprocess)
Metrics (GPU):   30.3%  ← Reduced from 45%
Resample (GPU):  56.5%  ← New bottleneck (batch inefficiency)
WAV Encode:       0.0%  (async, not measured)
```

### GPU Metrics Speedup

The GPU LUFS computation achieved significant per-segment speedup:

| Batch Size | Time/Segment | Speedup vs pyloudnorm |
|------------|--------------|----------------------|
| 1 | 212.4ms | 0.3x (overhead) |
| 8 | 31.1ms | 2.0x |
| 32 | 10.1ms | 6.1x |
| 64 | 6.4ms | 9.6x |
| 128 | 4.8ms | 12.9x |
| **256** | **4.6ms** | **13.6x** |

**Accuracy**: LUFS difference vs CPU: 0.04 dB (within measurement tolerance)

---

## Scaling Projections for 500k Videos

### Single Node Performance

| Pipeline | Time | Cost (g5.4xlarge @ $1/hr) |
|----------|------|---------------------------|
| Original (26.1 seg/s) | 46 days | $1,102 |
| **Optimized (108.3 vid/min)** | **3.2 days** | **$77** |

### Horizontal Scaling

For 500k videos with optimized pipeline:

| Target Time | Nodes Needed | Total Cost |
|-------------|--------------|------------|
| 24 hours | 4 | $96 |
| 12 hours | 7 | $84 |
| 6 hours | 13 | $78 |

**Recommended**: 4-8 g5.4xlarge nodes for 12-24h completion at ~$96.

---

## Implementation Files

### New Files Created

1. **`scripts/gpu_audio_metrics.py`** - GPU LUFS/spectral (v1, has Python loops)
   - 13.6x speedup at batch 256
   - BUT: Python loops in LUFS gating cause GPU sync overhead

2. **`scripts/gpu_audio_metrics_v2.py`** - FULLY VECTORIZED (v2, no Python loops)
   - **2.84ms/seg** at batch 256 (v1: 4.61ms) = **1.6x faster**
   - All gating logic vectorized with tensor masks
   - Cached Hann windows, rfftfreq, K-weight coefficients
   - Single GPU sync at end of batch

3. **`scripts/gpu_audio_decode.py`** - NVIDIA DALI batch decode
   - **5.2x speedup** at batch 64
   - Note: Audio decode is CPU (GPU decode only for images/video)

4. **`scripts/optimized_pipeline.py`** - Original optimized pipeline (fixed by other agent)
   - DALI batch decode
   - GPU-batched metrics
   - Matches CPU semantics now

5. **`scripts/clever_pipeline.py`** - NEW: Encode Once, Cut Later
   - **Compute metrics ONCE** on full segment (not per-chunk)
   - **Encode full segment ONCE** to all target rates
   - **Slice WAV bytes** directly for chunks (no re-encode!)
   - Parallel decode overlapped with GPU metrics
   - Skip <3s segments BEFORE decoding

### Key Insight: WAV Byte Slicing

WAV PCM16 is uncompressed: 2 bytes/sample. Instead of:
```
decode → chunk → resample → encode (per chunk)
```
We do:
```
decode → resample (full) → encode (full) → slice bytes (instant!)
```

This eliminates redundant encoding for chunks.

### Usage

```bash
# Run GPU metrics benchmark
python3 scripts/gpu_audio_metrics.py

# Run optimized pipeline benchmark
python3 scripts/optimized_pipeline.py

# Full benchmark with scaling projections
python3 scripts/benchmark_optimized.py --videos 100
```

### Optimal Configuration

```bash
python3 scripts/prepare_shards.py \
    --language telugu \
    --gpu \
    --gpu-batch-size 256 \
    --gpu-decode-workers 16 \
    --gpu-encode-workers 16 \
    --partition-count 4 \
    --partition-index 0
```

---

## Remaining Bottlenecks

The new bottleneck is **GPU resampling** (56.5% of time), caused by:

1. **Per-segment resampling**: Each chunk resampled to 4 target rates individually
2. **Memory transfer overhead**: Small batches don't amortize GPU<->CPU transfer
3. **Multiple output rates**: 16k, 24k, 32k, 44.1k multiplies work 4x

### Potential Further Optimizations

1. **Batch resampling by target rate**: Group all 16k together, etc.
2. **Stay on GPU longer**: Keep audio in GPU memory through metrics→resample→encode
3. **CUDA streams**: Overlap compute with memory transfer
4. **Skip high-rate outputs**: Only emit 16k for training (reduces work 4x)

Expected additional speedup: 2-4x (could reach 200-400 vid/min)

---

## Cost Comparison Summary

| Scenario | Original | Optimized | Savings |
|----------|----------|-----------|---------|
| Single A100 node | $1,102 (46 days) | $77 (3.2 days) | **93%** |
| 24h completion | $1,104 (46 nodes) | $96 (4 nodes) | **91%** |
| 12h completion | $1,104 (92 nodes) | $84 (7 nodes) | **92%** |

---

## Quick Start

```bash
# 1. Test GPU metrics
cd /home/ubuntu/maya3_prepare/scripts
python3 gpu_audio_metrics.py

# 2. Run benchmark
python3 benchmark_optimized.py --videos 100

# 3. Process with optimized pipeline (when integrated)


Summary: auto


**Planning cleanup and doc update**

I'll update optimisations.md to mark the stable pipeline, archive outdated content, and refresh the structure while ensuring ASCII-only text; I'll also check for any separate Optimisation.md to consolidate references.


Output:
Exit code: 2
Wall time: 0 seconds
Output:
ls: cannot access 'Optimisation.md': No such file or directory


**Searching for missing file**

I'm searching the docs and codebase for the missing Optimisation.md file reference to locate it or confirm its absence.


Output:
Exit code: 1
Wall time: 0 seconds
Output:


**Designing pipeline cleanup and documentation**

I'm planning to archive experimental scripts into a new legacy folder and create a dedicated pipeline selection doc summarizing the recommended stable pipelines and clarifying outdated benchmarks, while minimizing disruption to current imports and functionality.


**Identifying legacy candidates based on usage**

I'm marking several unused or pipeline-specific scripts and their dependencies as candidates for legacy, but I'll verify whether shared files like `gpu_audio_metrics.py` are used elsewhere before moving them.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
scripts/gpu_audio_metrics_v2.py:422:        from gpu_audio_metrics import GPUMetricsComputer
scripts/optimized_pipeline.py:40:    from gpu_audio_metrics import GPUMetricsComputer, AudioMetrics
scripts/clever_pipeline.py:31:    from gpu_audio_metrics_v2 import GPUMetricsV2
optimisations.md:94:1. **`scripts/gpu_audio_metrics.py`** - GPU LUFS/spectral (v1, has Python loops)
optimisations.md:98:2. **`scripts/gpu_audio_metrics_v2.py`** - FULLY VECTORIZED (v2, no Python loops)
optimisations.md:137:python3 scripts/gpu_audio_metrics.py
optimisations.md:195:python3 gpu_audio_metrics.py


**Designing robust worker leasing plan**

I'm planning a worker coordination system using Postgres row-level locking and leases with TTL, fixed-size shards of 100 videos (5GB), and tables for shard allocations, video claims, heartbeats, and progress metrics to enable reliable claiming, progress tracking, deduplication, and cleanup under worker crashes. Next, I'll propose schema designs and example SQL queries.


**Designing worker orchestration flow and archiving legacy GPU pipelines**

I'm defining the worker loop to claim fixed-size shards atomically with row-level locking and transaction-safe status updates, ensuring strict no-duplicate processing and heartbeat metrics for progress and cleanup. I'm also planning to archive deprecated GPU-related pipeline files into a `scripts/legacy/` folder while preserving stable CPU and GPU baseline pipelines and updating docs accordingly. Next, I'll implement the folder creation and file moves.


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


**Moving files**


mv scripts/optimized_pipeline.py scripts/benchmark_optimized_pipeline.py scripts/gpu_audio_metrics.py scripts/gpu_audio_metrics_v2.py scripts/gpu_audio_decode.py scripts/clever_pipeline.py scripts/fast_audio_pipeline_v2.py scripts/legacy/


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


**Adding README to legacy scripts**

I'm creating a README in scripts/legacy to document the archived files and explain why they remain for reference.


Input:
*** Begin Patch
*** Add File: scripts/legacy/README.md
+# Legacy Pipeline Versions
+
+This folder contains older or experimental pipeline implementations that are
+kept for reference only. They are not part of the recommended production path.
+
+Archived files:
+- `optimized_pipeline.py` + `gpu_audio_metrics.py` + `gpu_audio_decode.py` (GPU metrics + DALI)
+- `gpu_audio_metrics_v2.py` (vectorized metrics experiment)
+- `clever_pipeline.py` (encode once, cut later experiment)
+- `benchmark_optimized_pipeline.py` (benchmark harness for the above)
+- `fast_audio_pipeline_v2.py` (parallel pipeline experiment)
+
+Recommended production pipeline (safe + stable):
+- `scripts/prepare_shards.py` (CPU pipeline, optional `--gpu` for resample-only)
+- `scripts/fast_audio_pipeline.py` (GPU resample path used by `prepare_shards.py`)
*** End Patch


Output: {"output":"Success. Updated the following files:\nA scripts/legacy/README.md\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Planning production pipeline docs update**

I'm preparing to add a concise "Production Decision" note atop optimisations.md, marking older pipelines as archived and redirecting references to legacy scripts, including updated paths and usage commands, without overhauling the whole document.


Input:
*** Begin Patch
*** Update File: optimisations.md
@@
-# Audio Pipeline Optimization - Benchmark Results
+# Audio Pipeline Optimization - Benchmark Results
+
+## Production Decision (Safe + Stable)
+
+Recommended pipeline (keep this as the only active path):
+- `scripts/prepare_shards.py` (CPU pipeline, optional `--gpu` for resample-only)
+- `scripts/fast_audio_pipeline.py` (GPU resample path used by `prepare_shards.py`)
+
+All experimental variants are archived under `scripts/legacy/` and should not
+be used for production runs.
@@
-## Implementation Files
+## Implementation Files (Archived)
@@
-1. **`scripts/gpu_audio_metrics.py`** - GPU LUFS/spectral (v1, has Python loops)
+1. **`scripts/legacy/gpu_audio_metrics.py`** - GPU LUFS/spectral (v1, has Python loops)
@@
-2. **`scripts/gpu_audio_metrics_v2.py`** - FULLY VECTORIZED (v2, no Python loops)
+2. **`scripts/legacy/gpu_audio_metrics_v2.py`** - FULLY VECTORIZED (v2, no Python loops)
@@
-3. **`scripts/gpu_audio_decode.py`** - NVIDIA DALI batch decode
+3. **`scripts/legacy/gpu_audio_decode.py`** - NVIDIA DALI batch decode
@@
-4. **`scripts/optimized_pipeline.py`** - Original optimized pipeline (fixed by other agent)
+4. **`scripts/legacy/optimized_pipeline.py`** - Original optimized pipeline (fixed by other agent)
@@
-5. **`scripts/clever_pipeline.py`** - NEW: Encode Once, Cut Later
+5. **`scripts/legacy/clever_pipeline.py`** - NEW: Encode Once, Cut Later
@@
-# Run GPU metrics benchmark
-python3 scripts/gpu_audio_metrics.py
+# Run GPU metrics benchmark (archived)
+python3 scripts/legacy/gpu_audio_metrics.py
@@
-# Run optimized pipeline benchmark
-python3 scripts/optimized_pipeline.py
+# Run optimized pipeline benchmark (archived)
+python3 scripts/legacy/optimized_pipeline.py
@@
-# Full benchmark with scaling projections
-python3 scripts/benchmark_optimized.py --videos 100
+# Full benchmark with scaling projections (archived harness)
+python3 scripts/legacy/benchmark_optimized_pipeline.py --videos 100
*** End Patch


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


**Planning worker orchestration doc**

I'll create a new detailed worker orchestration strategy file separate from the existing large training strategy, naming it `docs/worker_orchestration.md` to outline schema, worker claiming, Dockerization, and corner cases before proposing any code changes.


**Creating detailed worker orchestration plan**

I'll generate a comprehensive `docs/worker_plan.md` outlining schema design, shard claiming flow, lease handling, idempotency with deterministic shard IDs, upload strategies, failure recovery, metrics tracking, and Dockerization approach for scalable workers processing language-specific video shards.


Input:
*** Begin Patch
*** Add File: docs/worker_plan.md
+# Horizontal Worker Plan (Safe + Robust)
+
+## Goal
+
+Process videos language-by-language with fixed-size shards (e.g., 100 videos per shard)
+without duplicates or deadlocks, and with strict tracking/metrics in Supabase.
+
+Production pipeline (stable):
+- `scripts/prepare_shards.py` (CPU pipeline, optional `--gpu` for resample-only)
+- `scripts/fast_audio_pipeline.py` (GPU resample path)
+
+All experimental pipeline variants are archived in `scripts/legacy/`.
+
+---
+
+## Supabase Schema (Core Tables)
+
+### 1) `video_tasks`
+Tracks each video exactly once.
+
+```sql
+create table if not exists video_tasks (
+  video_id text primary key,
+  language text not null,
+  r2_tar_key text not null,
+  status text not null default 'pending', -- pending|claimed|processing|uploaded|completed|failed
+  shard_id uuid,
+  claimed_by text,
+  lease_expires_at timestamptz,
+  attempts int not null default 0,
+  last_error text,
+  created_at timestamptz default now(),
+  updated_at timestamptz default now()
+);
+create index if not exists video_tasks_language_status_idx
+  on video_tasks(language, status);
+```
+
+### 2) `shard_runs`
+One row per shard assignment (fixed video count).
+
+```sql
+create table if not exists shard_runs (
+  shard_id uuid primary key default gen_random_uuid(),
+  language text not null,
+  target_video_count int not null,
+  assigned_video_count int not null,
+  status text not null default 'claimed', -- claimed|processing|uploading|completed|failed|stale
+  claimed_by text,
+  lease_expires_at timestamptz,
+  shard_prefix text, -- R2 prefix (language + shard_id)
+  output_format text not null default 'flac',
+  metrics jsonb,
+  created_at timestamptz default now(),
+  updated_at timestamptz default now()
+);
+create index if not exists shard_runs_language_status_idx
+  on shard_runs(language, status);
+```
+
+### 3) `worker_heartbeats`
+Lightweight worker status/metrics.
+
+```sql
+create table if not exists worker_heartbeats (
+  worker_id text primary key,
+  hostname text,
+  instance_type text,
+  language text,
+  status text not null default 'idle', -- idle|claiming|processing|uploading|failed
+  shard_id uuid,
+  videos_assigned int default 0,
+  videos_processed int default 0,
+  segments_processed int default 0,
+  outputs_generated int default 0,
+  last_error text,
+  last_heartbeat timestamptz default now()
+);
+```
+
+### 4) Optional: `video_task_events`
+Audit trail (optional but useful).
+
+```sql
+create table if not exists video_task_events (
+  id bigserial primary key,
+  video_id text,
+  shard_id uuid,
+  worker_id text,
+  event text,
+  details jsonb,
+  created_at timestamptz default now()
+);
+```
+
+---
+
+## Claiming Strategy (No Duplicates)
+
+Use a single transaction with row locks:
+
+```sql
+begin;
+-- 1) Select N pending rows with row-level lock
+with picked as (
+  select video_id
+  from video_tasks
+  where language = $1
+    and status = 'pending'
+  order by video_id
+  limit $2
+  for update skip locked
+)
+-- 2) Mark them claimed and set lease
+update video_tasks
+set status = 'claimed',
+    claimed_by = $3,
+    lease_expires_at = now() + interval '30 minutes',
+    attempts = attempts + 1
+where video_id in (select video_id from picked)
+returning video_id;
+commit;
+```
+
+Then create a `shard_runs` row with:
+- `assigned_video_count = count(picked)`
+- `target_video_count = $2`
+- `shard_id` (UUID)
+- `shard_prefix` = `language/shard_id`
+
+**No duplicates**: `FOR UPDATE SKIP LOCKED` guarantees each video is claimed by only one worker.
+
+---
+
+## Lease + Crash Recovery
+
+- Each claim has `lease_expires_at`.
+- A background cleanup job (or worker startup) does:
+  - Any `claimed`/`processing` rows with `lease_expires_at < now()` → `pending`
+  - Mark associated `shard_runs` as `stale`
+- Re-claimed shards will regenerate the same shard_id or a new one (see below).
+
+---
+
+## Shard Idempotency (Prevent Duplicate Uploads)
+
+To avoid duplicates after worker crash:
+- **Deterministic shard_id**: hash of `language + sorted(video_ids)`
+  - Guarantees any re-claim produces the same `shard_id`
+  - Upload is idempotent (same R2 key)
+- Store `shard_id` in `shard_runs` and `video_tasks` before processing starts.
+- Upload with `shard_prefix = {language}/{shard_id}/...`.
+
+If a worker crashes after upload, a re-run overwrites the same keys instead of creating duplicates.
+
+---
+
+## Worker Flow (Per Language)
+
+1) **Claim shard**
+   - Transactionally claim N pending videos.
+   - Create `shard_runs` row.
+2) **Download**
+   - Pull each `video_id.tar` from R2.
+3) **Process**
+   - Run `prepare_shards.py` for this video set.
+   - Fixed shard size = 100 videos (not dynamic).
+4) **Upload**
+   - Upload shard tar + jsonl to R2 under `{language}/{shard_id}/`.
+5) **Finalize**
+   - Mark video_tasks = `completed`.
+   - Mark shard_runs = `completed`.
+6) **Repeat**
+   - Claim next shard.
+
+---
+
+## Metrics + Heartbeats
+
+Worker should update `worker_heartbeats` every 10-30s with:
+- `videos_processed`, `segments_processed`, `outputs_generated`
+- throughput metrics (videos/min, segments/sec)
+- `last_error` if any
+
+At shard completion, write summary stats into `shard_runs.metrics`:
+- total segments, drops, output counts by SR
+- timing breakdowns from `prepare_shards.py` (if enabled)
+
+---
+
+## Dockerization Strategy
+
+### CPU Worker (Recommended)
+Lightweight, stable, scales well.
+
+Dockerfile outline:
+```
+FROM python:3.10-slim
+RUN apt-get update && apt-get install -y libsndfile1 ffmpeg && rm -rf /var/lib/apt/lists/*
+WORKDIR /app
+COPY . /app
+RUN pip install -r requirements.txt
+ENTRYPOINT ["python", "worker.py"]
+```
+
+### GPU Worker (Optional)
+Only if `--gpu` resample is desired.
+
+```
+FROM nvidia/cuda:12.1.1-runtime-ubuntu22.04
+RUN apt-get update && apt-get install -y python3 python3-pip libsndfile1 ffmpeg
+RUN pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu121
+WORKDIR /app
+COPY . /app
+RUN pip install -r requirements.txt
+ENTRYPOINT ["python3", "worker.py", "--gpu"]
+```
+
+---
+
+## Corner Cases + Safeguards
+
+1) **Worker crash mid-shard**
+   - Lease expiry reclaims videos; deterministic shard_id avoids duplicate uploads.
+2) **Partial final shard**
+   - If fewer than N videos remain, claim all remaining and process smaller shard.
+3) **Stale claims**
+   - Cleanup job reverts `claimed` rows when lease expires.
+4) **Upload succeeds but DB update fails**
+   - Re-claim uses same shard_id; upload overwrites same key (no duplicates).
+5) **R2 upload retry**
+   - Retry with exponential backoff; keep shard_id stable.
+6) **No deadlocks**
+   - Always use `FOR UPDATE SKIP LOCKED` with deterministic ordering.
+7) **No duplicates**
+   - `video_tasks.video_id` is primary key; only one claim per row.
+
+---
+
+## Next Implementation Steps
+
+1) Create tables in Supabase (`video_tasks`, `shard_runs`, `worker_heartbeats`).
+2) Write `worker.py`:
+   - Claim shard, process using `prepare_shards.py`, upload, finalize.
+3) Add heartbeat updates and shard metrics reporting.
+4) Add a simple dashboard (Supabase + Metabase or lightweight web UI).
*** End Patch


Output: {"output":"Success. Updated the following files:\nA docs/worker_plan.md\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Clarifying legacy folder imports and cleanup**

Checked that legacy scripts remain self-contained with relative imports working if run properly, so no import changes needed but a README note on PYTHONPATH or `python -m` usage could help avoid confusion; also planning to clean up new folder to keep only best versions while moving others to legacy.


**Reviewing cleanup and planning deliverables**

The focus is now on completing cleanup with a summary of safe pipelines, providing a dockerization strategy for a resilient worker pipeline, and outlining a Supabase table plus worker plan; including notes on archived scripts and previous throughput scaling is advisable, along with a detailed summary of changes referencing git status.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
## optimisations
 M scripts/__pycache__/prepare_shards.cpython-310.pyc
 D scripts/fast_audio_pipeline_v2.py
?? docs/worker_plan.md
?? optimisations.md
?? scripts/__pycache__/fast_audio_pipeline_v2.cpython-310.pyc
?? scripts/__pycache__/gpu_audio_decode.cpython-310.pyc
?? scripts/__pycache__/gpu_audio_metrics.cpython-310.pyc
?? scripts/__pycache__/gpu_audio_metrics_v2.cpython-310.pyc
?? scripts/__pycache__/optimized_pipeline.cpython-310.pyc
?? scripts/legacy/


**Reviewing file status and updates**

The `optimisations.md` file remains untracked despite edits, likely because it's newly added at the repo root; moved pipeline files show as deleted at old paths and untracked in `scripts/legacy/`. I'll keep these move-related changes and ensure the appropriate `scripts/legacy/README.md` is added to document the archival. Next, I'll prepare the final response summarizing these cleanups, plan highlights, docker strategy, and chosen stable pipeline versions.


**Finalizing response structure and next steps**

I'm preparing the final response to explain archived experimental pipelines, production pipeline selection, documentation added, and a concise plan summary from `docs/worker_plan.md`. I'll include next steps suggestions around shard size confirmation, CPU vs GPU choice, and database migrations, plus note no tests were run. I'll mention file moves like `scripts/fast_audio_pipeline_v2.py` and keep references inline, using bullet points per guidelines.


Archived the experimental pipelines, made the stable CPU path the only “production” choice, and wrote a full horizontal‑worker plan (Supabase schema + claiming + heartbeats + dockerization). This cleans up the top‑level scripts and leaves only the safest pipeline surfaced.

Details on cleanup and where to look:
- Archived experimental scripts into `scripts/legacy/` and documented them in `scripts/legacy/README.md`.
- Marked the production‑safe pipeline at the top of `optimisations.md` and updated archived file references there.
- Added `docs/worker_plan.md` with the Supabase tables, claim/lease logic, shard idempotency, and worker Docker strategy.

Plan highlights (short version):
- Tables: `video_tasks`, `shard_runs`, `worker_heartbeats` (optional `video_task_events`).
- Claiming: single transaction with `FOR UPDATE SKIP LOCKED`, strict `pending -> claimed` state, lease expiry cleanup.
- No duplicates: deterministic `shard_id` from sorted video IDs + language so reclaims overwrite the same shard key.
- Fixed shard size: assign 100 videos (or chosen count) per shard; allow smaller final shard.
- Heartbeats: worker sends progress + throughput every 10–30s.
- Uploads: shard key uses `{language}/{shard_id}/...`, mark completed only after upload.

Safe production pipeline choice:
- `scripts/prepare_shards.py` (CPU pipeline, optional `--gpu` for resample‑only)
- `scripts/fast_audio_pipeline.py` (GPU resample path used by `prepare_shards.py`)

If you want me to proceed next, pick one:
1) Confirm the fixed shard size (100 videos or another count) and output format to bake into worker defaults.
2) I can draft `worker.py` + Dockerfiles (CPU + optional GPU).
3) I can create the Supabase migration SQL from `docs/worker_plan.md` and a seeding script for `video_tasks`.


# Context from my IDE setup:

## Open tabs:
- optimized_pipeline.py: scripts/optimized_pipeline.py
- optimisations.md: optimisations.md
- Optimisation.md: Optimisation.md
- prepare_shards.py: scripts/prepare_shards.py
- fast_audio_pipeline_v2.py: scripts/fast_audio_pipeline_v2.py

## My request for Codex:
Can you create a new folder and name it workers/ and put the new plan in it, and all scripts required for the agent to build the pipeline. so it knows what to add as context and dont waste content.


Summary: auto


**Creating new workers folder with plan and scripts**

I'm creating a new workers/ folder containing a copy of the plan as plan.md, a skeleton worker.py, schema.sql, claim.sql, and minimal supporting files like Dockerfiles and a simple CLI script to seed tasks, ensuring no existing docs are removed.


**Drafting detailed workers folder contents**

I've planned the workers/ folder to include a copied plan (README.md), schema and SQL scripts for claiming and releasing shards, a minimal worker.py skeleton with clear TODOs for claiming tasks, downloading, processing using prepare_shards helpers, plus seed_tasks.py, Dockerfiles, requirements, and config examples—providing essential scaffolding for the agent to build the full pipeline.


**Refining worker.py to include DB claim, download, and process placeholders**

I'm designing worker.py with implemented database claim and shard ID functions, plus R2 download/upload stubs and a main loop, leaving core shard processing as TODO, matching the minimal scaffold the agent needs to build the pipeline while ensuring clarity and structure.


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


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


Input:
*** Begin Patch
*** Update File: workers/README.md
@@
-# Horizontal Worker Plan (Safe + Robust)
+# Worker Plan (Safe + Robust)
@@
-Production pipeline (stable):
-- `scripts/prepare_shards.py` (CPU pipeline, optional `--gpu` for resample-only)
-- `scripts/fast_audio_pipeline.py` (GPU resample path)
-
-All experimental pipeline variants are archived in `scripts/legacy/`.
+Production pipeline (stable):
+- `scripts/prepare_shards.py` (CPU pipeline, optional `--gpu` for resample-only)
+- `scripts/fast_audio_pipeline.py` (GPU resample path)
+
+All experimental pipeline variants are archived in `scripts/legacy/`.
+
+This folder contains:
+- `schema.sql` (Supabase tables)
+- `claim_shard.sql` / `release_stale.sql` (claim + lease helpers)
+- `seed_video_tasks.py` (seed the task table)
+- `worker.py` (skeleton worker loop)
*** End Patch


Output: {"output":"Success. Updated the following files:\nM workers/README.md\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Input:
*** Begin Patch
*** Add File: workers/schema.sql
+-- Supabase schema for horizontal workers
+
+create table if not exists video_tasks (
+  video_id text primary key,
+  language text not null,
+  r2_tar_key text not null,
+  status text not null default 'pending', -- pending|claimed|processing|uploading|completed|failed
+  shard_id uuid,
+  claimed_by text,
+  lease_expires_at timestamptz,
+  attempts int not null default 0,
+  last_error text,
+  created_at timestamptz default now(),
+  updated_at timestamptz default now()
+);
+
+create index if not exists video_tasks_language_status_idx
+  on video_tasks(language, status);
+
+create table if not exists shard_runs (
+  shard_id uuid primary key,
+  language text not null,
+  target_video_count int not null,
+  assigned_video_count int not null,
+  status text not null default 'claimed', -- claimed|processing|uploading|completed|failed|stale
+  claimed_by text,
+  lease_expires_at timestamptz,
+  shard_prefix text,
+  output_format text not null default 'flac',
+  metrics jsonb,
+  created_at timestamptz default now(),
+  updated_at timestamptz default now()
+);
+
+create index if not exists shard_runs_language_status_idx
+  on shard_runs(language, status);
+
+create table if not exists worker_heartbeats (
+  worker_id text primary key,
+  hostname text,
+  instance_type text,
+  language text,
+  status text not null default 'idle', -- idle|claiming|processing|uploading|failed
+  shard_id uuid,
+  videos_assigned int default 0,
+  videos_processed int default 0,
+  segments_processed int default 0,
+  outputs_generated int default 0,
+  last_error text,
+  last_heartbeat timestamptz default now()
+);
+
+create table if not exists video_task_events (
+  id bigserial primary key,
+  video_id text,
+  shard_id uuid,
+  worker_id text,
+  event text,
+  details jsonb,
+  created_at timestamptz default now()
+);
*** End Patch


Output: {"output":"Success. Updated the following files:\nA workers/schema.sql\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Input:
*** Begin Patch
*** Add File: workers/claim_shard.sql
+-- Claim a fixed-size shard of videos (no duplicates).
+-- Parameters: language, limit, worker_id, lease_minutes
+
+begin;
+
+with picked as (
+  select video_id
+  from video_tasks
+  where language = $1
+    and status = 'pending'
+  order by video_id
+  limit $2
+  for update skip locked
+)
+update video_tasks
+set status = 'claimed',
+    claimed_by = $3,
+    lease_expires_at = now() + ($4 || ' minutes')::interval,
+    attempts = attempts + 1
+where video_id in (select video_id from picked)
+returning video_id;
+
+commit;
*** End Patch


Output: {"output":"Success. Updated the following files:\nA workers/claim_shard.sql\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Input:
*** Begin Patch
*** Add File: workers/release_stale.sql
+-- Release stale claims (lease expired).
+
+update video_tasks
+set status = 'pending',
+    claimed_by = null,
+    lease_expires_at = null
+where status in ('claimed', 'processing', 'uploading')
+  and lease_expires_at is not null
+  and lease_expires_at < now();
*** End Patch


Output: {"output":"Success. Updated the following files:\nA workers/release_stale.sql\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Input:
*** Begin Patch
*** Add File: workers/seed_video_tasks.py
+#!/usr/bin/env python3
+"""Seed video_tasks from the existing videos table (Supabase/Postgres)."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import psycopg
+
+from prepare_shards import load_env
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Seed video_tasks table.")
+    parser.add_argument("--language", required=True)
+    parser.add_argument("--limit", type=int, default=None)
+    parser.add_argument("--dry-run", action="store_true")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    env = load_env(Path(".env"))
+    db_url = env.get("DATABASE_URL")
+    if not db_url:
+        raise SystemExit("DATABASE_URL missing in .env")
+
+    with psycopg.connect(db_url) as conn:
+        with conn.cursor() as cur:
+            query = """
+                select youtube_id, r2_tar_key, language
+                from videos
+                where lower(language) = %s
+                  and r2_tar_key is not null
+                  and status = 'COMPLETED'
+            """
+            params = [args.language.lower()]
+            if args.limit:
+                query += " limit %s"
+                params.append(args.limit)
+            cur.execute(query, params)
+            rows = cur.fetchall()
+
+    if args.dry_run:
+        print(f"Would insert {len(rows)} rows for language={args.language}")
+        return
+
+    with psycopg.connect(db_url) as conn:
+        with conn.cursor() as cur:
+            for video_id, r2_tar_key, language in rows:
+                cur.execute(
+                    """
+                    insert into video_tasks (video_id, language, r2_tar_key)
+                    values (%s, %s, %s)
+                    on conflict (video_id) do nothing
+                    """,
+                    (video_id, language, r2_tar_key),
+                )
+        conn.commit()
+
+    print(f"Inserted {len(rows)} rows for language={args.language}")
+
+
+if __name__ == "__main__":
+    main()
*** End Patch


Output: {"output":"Success. Updated the following files:\nA workers/seed_video_tasks.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Input:
*** Begin Patch
*** Add File: workers/worker.py
+#!/usr/bin/env python3
+"""Shard worker skeleton for horizontal scaling.
+
+This is a minimal, safe starting point. It includes:
+- Strict claim with leases (no duplicates).
+- Deterministic shard_id.
+- R2 download helpers.
+- Placeholders for processing + upload.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import os
+import socket
+import time
+import uuid
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Iterable, Optional
+
+import psycopg
+from boto3.s3.transfer import TransferConfig
+
+from prepare_shards import (
+    load_env,
+    make_r2_client,
+    resolve_r2_key,
+)
+
+
+@dataclass(frozen=True)
+class TaskRow:
+    video_id: str
+    r2_tar_key: str
+    language: str
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Shard worker (skeleton).")
+    parser.add_argument("--language", required=True)
+    parser.add_argument("--target-videos", type=int, default=100)
+    parser.add_argument("--lease-minutes", type=int, default=30)
+    parser.add_argument("--worker-id", default=None)
+    parser.add_argument("--tmp-dir", type=Path, default=Path("/tmp/worker"))
+    parser.add_argument("--output-dir", type=Path, default=Path("/tmp/worker_output"))
+    parser.add_argument("--download-workers", type=int, default=8)
+    parser.add_argument("--output-format", choices=["flac", "wav"], default="flac")
+    parser.add_argument("--once", action="store_true", help="Process a single shard and exit.")
+    return parser.parse_args()
+
+
+def deterministic_shard_id(language: str, video_ids: list[str]) -> uuid.UUID:
+    joined = f"{language}:" + ",".join(sorted(video_ids))
+    digest = hashlib.sha1(joined.encode("utf-8")).hexdigest()
+    return uuid.uuid5(uuid.NAMESPACE_URL, digest)
+
+
+def claim_videos(
+    conn,
+    language: str,
+    limit: int,
+    worker_id: str,
+    lease_minutes: int,
+) -> list[TaskRow]:
+    with conn.cursor() as cur:
+        cur.execute(
+            """
+            with picked as (
+              select video_id
+              from video_tasks
+              where language = %s
+                and status = 'pending'
+              order by video_id
+              limit %s
+              for update skip locked
+            )
+            update video_tasks
+            set status = 'claimed',
+                claimed_by = %s,
+                lease_expires_at = now() + (%s || ' minutes')::interval,
+                attempts = attempts + 1
+            where video_id in (select video_id from picked)
+            returning video_id, r2_tar_key, language
+            """,
+            (language, limit, worker_id, lease_minutes),
+        )
+        rows = cur.fetchall()
+    return [TaskRow(*row) for row in rows]
+
+
+def create_shard_run(
+    conn,
+    shard_id: uuid.UUID,
+    language: str,
+    target_count: int,
+    assigned_count: int,
+    worker_id: str,
+    lease_minutes: int,
+    output_format: str,
+) -> None:
+    with conn.cursor() as cur:
+        cur.execute(
+            """
+            insert into shard_runs (
+              shard_id, language, target_video_count, assigned_video_count,
+              status, claimed_by, lease_expires_at, shard_prefix, output_format
+            ) values (
+              %s, %s, %s, %s,
+              'claimed', %s, now() + (%s || ' minutes')::interval, %s, %s
+            )
+            on conflict (shard_id) do nothing
+            """,
+            (
+                shard_id,
+                language,
+                target_count,
+                assigned_count,
+                worker_id,
+                lease_minutes,
+                f"{language}/{shard_id}",
+                output_format,
+            ),
+        )
+
+
+def download_tars(
+    env: dict[str, str],
+    tasks: list[TaskRow],
+    download_dir: Path,
+    download_workers: int,
+) -> dict[str, Path]:
+    download_dir.mkdir(parents=True, exist_ok=True)
+    s3 = make_r2_client(env)
+    cleaned_bucket = env.get("R2_ClEANED_BUCKET") or env.get("R2_CLEANED_BUCKET")
+    prefix = env.get("R2_PREFIX")
+    if not cleaned_bucket:
+        raise SystemExit("R2_CLEANED_BUCKET missing in .env")
+
+    config = TransferConfig(
+        multipart_threshold=8 * 1024 * 1024,
+        multipart_chunksize=64 * 1024 * 1024,
+        max_concurrency=max(4, download_workers),
+        use_threads=True,
+    )
+
+    download_paths: dict[str, Path] = {}
+    for task in tasks:
+        resolved = resolve_r2_key(s3, cleaned_bucket, task.r2_tar_key, prefix)
+        if not resolved:
+            continue
+        local_path = download_dir / f"{task.video_id}.tar"
+        if not local_path.exists():
+            s3.download_file(cleaned_bucket, resolved, str(local_path), Config=config)
+        download_paths[task.video_id] = local_path
+    return download_paths
+
+
+def update_task_status(
+    conn,
+    video_ids: Iterable[str],
+    status: str,
+    shard_id: Optional[uuid.UUID] = None,
+    error: Optional[str] = None,
+) -> None:
+    with conn.cursor() as cur:
+        cur.execute(
+            """
+            update video_tasks
+            set status = %s,
+                shard_id = %s,
+                last_error = %s,
+                updated_at = now()
+            where video_id = any(%s)
+            """,
+            (status, shard_id, error, list(video_ids)),
+        )
+
+
+def update_shard_status(conn, shard_id: uuid.UUID, status: str, metrics: Optional[dict] = None) -> None:
+    with conn.cursor() as cur:
+        cur.execute(
+            """
+            update shard_runs
+            set status = %s,
+                metrics = coalesce(%s, metrics),
+                updated_at = now()
+            where shard_id = %s
+            """,
+            (status, json.dumps(metrics) if metrics else None, shard_id),
+        )
+
+
+def process_videos_placeholder(download_paths: dict[str, Path]) -> dict:
+    """TODO: Implement processing with prepare_shards logic for claimed videos only."""
+    # Suggested approach: reuse process_segment + ShardWriter from prepare_shards.py.
+    # This placeholder returns empty metrics to keep the skeleton coherent.
+    return {"videos": len(download_paths)}
+
+
+def main() -> None:
+    args = parse_args()
+    env = load_env(Path(".env"))
+    db_url = env.get("DATABASE_URL")
+    if not db_url:
+        raise SystemExit("DATABASE_URL missing in .env")
+
+    worker_id = args.worker_id or f"{socket.gethostname()}-{os.getpid()}"
+
+    while True:
+        with psycopg.connect(db_url) as conn:
+            conn.execute("begin")
+            tasks = claim_videos(conn, args.language, args.target_videos, worker_id, args.lease_minutes)
+            if not tasks:
+                conn.execute("commit")
+                print("No pending videos; sleeping.")
+                if args.once:
+                    return
+                time.sleep(30)
+                continue
+
+            video_ids = [t.video_id for t in tasks]
+            shard_id = deterministic_shard_id(args.language, video_ids)
+            create_shard_run(
+                conn,
+                shard_id,
+                args.language,
+                args.target_videos,
+                len(tasks),
+                worker_id,
+                args.lease_minutes,
+                args.output_format,
+            )
+            update_task_status(conn, video_ids, "processing", shard_id=shard_id)
+            conn.execute("commit")
+
+        download_dir = args.tmp_dir / str(shard_id)
+        download_paths = download_tars(env, tasks, download_dir, args.download_workers)
+
+        # TODO: process videos and upload to R2.
+        metrics = process_videos_placeholder(download_paths)
+
+        with psycopg.connect(db_url) as conn:
+            update_shard_status(conn, shard_id, "completed", metrics=metrics)
+            update_task_status(conn, video_ids, "completed", shard_id=shard_id)
+            conn.commit()
+
+        print(f"Completed shard {shard_id} with {len(download_paths)} videos.")
+
+        if args.once:
+            return
+
+
+if __name__ == "__main__":
+    main()
*** End Patch


Output: {"output":"Success. Updated the following files:\nA workers/worker.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Input:
*** Begin Patch
*** Add File: workers/requirements.txt
+boto3
+psycopg[binary]
+numpy
+soundfile
+tqdm
*** End Patch


Output: {"output":"Success. Updated the following files:\nA workers/requirements.txt\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Input:
*** Begin Patch
*** Add File: workers/Dockerfile.cpu
+FROM python:3.10-slim
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libsndfile1 ffmpeg \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+COPY . /app
+RUN pip install --no-cache-dir -r workers/requirements.txt
+
+ENTRYPOINT ["python", "workers/worker.py"]
*** End Patch


Output: {"output":"Success. Updated the following files:\nA workers/Dockerfile.cpu\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Input:
*** Begin Patch
*** Add File: workers/Dockerfile.gpu
+FROM nvidia/cuda:12.1.1-runtime-ubuntu22.04
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    python3 python3-pip libsndfile1 ffmpeg \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+COPY . /app
+RUN pip install --no-cache-dir -r workers/requirements.txt
+
+ENTRYPOINT ["python3", "workers/worker.py", "--gpu"]
*** End Patch


Output: {"output":"Success. Updated the following files:\nA workers/Dockerfile.gpu\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Removing unsupported GPU flag**

I'll remove the unsupported --gpu argument from Dockerfile.gpu to prevent errors since worker.py doesn't accept it, keeping GPU support consideration for future updates.


Input:
*** Begin Patch
*** Update File: workers/Dockerfile.gpu
@@
-ENTRYPOINT ["python3", "workers/worker.py", "--gpu"]
+ENTRYPOINT ["python3", "workers/worker.py"]
*** End Patch


Output: {"output":"Success. Updated the following files:\nM workers/Dockerfile.gpu\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Fixing imports for scripts usage**

Planning to patch worker.py and seed_video_tasks to insert the scripts directory into sys.path for reliable imports of prepare_shards, avoiding import errors when running from repo root.


Input:
*** Begin Patch
*** Update File: workers/worker.py
@@
-import psycopg
-from boto3.s3.transfer import TransferConfig
-
-from prepare_shards import (
-    load_env,
-    make_r2_client,
-    resolve_r2_key,
-)
+import psycopg
+from boto3.s3.transfer import TransferConfig
+
+import sys
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT / "scripts"))
+
+from prepare_shards import (
+    load_env,
+    make_r2_client,
+    resolve_r2_key,
+)
*** End Patch


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


Input:
*** Begin Patch
*** Update File: workers/seed_video_tasks.py
@@
-import psycopg
-
-from prepare_shards import load_env
+import psycopg
+import sys
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT / "scripts"))
+
+from prepare_shards import load_env
*** End Patch


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


**Confirming imports and docs updates**

Path is correctly imported where needed, and the README will note worker.py’s use of prepare_shards in scripts alongside referencing the legacy scripts move. I'll include that worker.py is a skeleton with placeholders and confirm docs/worker_plan.md remains unchanged.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
## optimisations
 M scripts/__pycache__/prepare_shards.cpython-310.pyc
 D scripts/fast_audio_pipeline_v2.py
?? docs/worker_plan.md
?? optimisations.md
?? scripts/__pycache__/fast_audio_pipeline_v2.cpython-310.pyc
?? scripts/__pycache__/gpu_audio_decode.cpython-310.pyc
?? scripts/__pycache__/gpu_audio_metrics.cpython-310.pyc
?? scripts/__pycache__/gpu_audio_metrics_v2.cpython-310.pyc
?? scripts/__pycache__/optimized_pipeline.cpython-310.pyc
?? scripts/legacy/
?? workers/


**Summarizing worker setup and docs**

The `workers/` folder was created with schema SQL files, seed and worker scripts, and Dockerfiles alongside a README outlining the plan (copied from `docs/worker_plan.md`). The placeholder `worker.py` includes shard claiming and download logic but needs processing and upload implementation. The original pipeline wasn’t deleted but some scripts moved to `scripts/legacy`. Next steps involve completing worker processing, wiring uploads, deciding video counts, and running the seed script; `docs/worker_plan.md` remains and can be pruned or redirected if desired.


**Preparing concise final overview with next steps**

I'm focusing on a brief yet complete summary that notes `worker.py` references repo-root scripts and includes numbered next-step suggestions for clarity.


Created the `workers/` folder and staged the plan + all scaffold scripts so another agent can build the worker pipeline without extra context. The plan now lives in `workers/README.md` (copied from `docs/worker_plan.md`), and the worker scaffolding + SQL helpers are all in one place.

What’s in `workers/`:
- `workers/README.md` – full plan + context for the worker system.
- `workers/schema.sql` – Supabase tables.
- `workers/claim_shard.sql` and `workers/release_stale.sql` – locking/lease helpers.
- `workers/seed_video_tasks.py` – seeds `video_tasks` from `videos`.
- `workers/worker.py` – skeleton worker loop with deterministic shard_id + claim + download; processing/upload is a TODO placeholder.
- `workers/requirements.txt`, `workers/Dockerfile.cpu`, `workers/Dockerfile.gpu`.

Notes:
- `workers/worker.py` and `workers/seed_video_tasks.py` add `scripts/` to `sys.path` so imports from `scripts/prepare_shards.py` work when run from repo root.
- Processing/upload is intentionally left as a TODO in `workers/worker.py` so the other agent can wire in the chosen stable pipeline (CPU `prepare_shards` logic).

Suggested next steps:
1) Implement `process_videos_placeholder()` in `workers/worker.py` using `process_segment` + `ShardWriter` from `scripts/prepare_shards.py`.
2) Add upload + shard metrics update to Supabase after processing.
3) Decide `target-videos` per shard (e.g. 100) and run `workers/seed_video_tasks.py` for each language.


# Context from my IDE setup:

## Open tabs:
- worker.py: workers/worker.py
- Dockerfile.gpu: workers/Dockerfile.gpu
- optimized_pipeline.py: scripts/optimized_pipeline.py
- optimisations.md: optimisations.md
- Optimisation.md: Optimisation.md

## My request for Codex:
can you validate build, validate this workflow one step at a time.

Bash(ls -la /home/ubuntu/maya3_prepare/workers/)
  ⎿  total 172
     drwxrwxr-x 2 ubuntu ubuntu  4096 Jan 20 04:17 .
     drwxrwxr-x 9 ubuntu ubuntu  4096 Jan 20 04:00 ..
     … +18 lines (ctrl+o to expand)

● Let me verify the Python code syntax and imports work correctly.

● Bash(cd /home/ubuntu/maya3_prepare/workers && python3 -c "
      from config import WorkerConfig, EnvConfig…)
  ⎿ All imports successful!
    WorkerConfig fields: ['worker_id', 'language', 'target_videos', 'lease
    _minutes', 'heartbeat_interval_sec'] ...
    … +2 lines (ctrl+o to expand)

● Bash(python3 -c "
      from worker import ShardWorker, parse_args…)
  ⎿ worker.py imports successful!
    ShardWorker methods: ['claim_videos', 'create_shard_run', 'determinist
    ic_shard_id', 'download_videos', 'process_one_shard', 'process_shard',
     'renew_lease', 'run', 'send_heartbeat', 'start_heartbeat']

● All worker components have been implemented and verified. Here's a summary of
   what was created:

  Implemented Files
  File: worker.py
  Size: 27KB
  Purpose: Main worker orchestration with heartbeat, lease renewal, processing
    pipeline
  ────────────────────────────────────────
  File: worker_processing.py
  Size: 33KB
  Purpose: Audio processing logic adapted from prepare_shards.py
  ────────────────────────────────────────
  File: r2_uploader.py
  Size: 13KB
  Purpose: R2 upload with retry logic, progress tracking
  ────────────────────────────────────────
  File: storage_manager.py
  Size: 12KB
  Purpose: Disk monitoring, cleanup, background monitor thread
  ────────────────────────────────────────
  File: config.py
  Size: 6KB
  Purpose: Configuration management from env vars and .env file
  ────────────────────────────────────────
  File: release_stale_claims.py
  Size: 7KB
  Purpose: Recovery script for crashed worker claims
  ────────────────────────────────────────
  File: entrypoint.sh
  Size: 3KB
  Purpose: Docker entrypoint with validation and signal handling
  ────────────────────────────────────────
  File: Dockerfile.cpu
  Size: 1KB
  Purpose: Docker image for CPU workers
  ────────────────────────────────────────
  File: docker-compose.yml
  Size: 2KB
  Purpose: Local testing with docker-compose
  ────────────────────────────────────────
  File: requirements.txt
  Size: 178B
  Purpose: Python dependencies
  ────────────────────────────────────────
  File: README.md
  Size: 11KB
  Purpose: Comprehensive documentation
  Key Features Implemented

  1. No Duplicates: FOR UPDATE SKIP LOCKED in video claiming
  2. Deterministic Shard IDs: Same videos always produce same shard_id
  (idempotent uploads)
  3. Lease Renewal: Long operations renew leases to prevent timeout
  4. Heartbeat Thread: Background thread updates worker_heartbeats every 15s
  5. Storage Monitor: Background thread checks disk usage, triggers cleanup
  6. Graceful Shutdown: Signal handlers for SIGTERM/SIGINT
  7. Retry Logic: Exponential backoff for R2 uploads
  8. Progress Tracking: Real-time updates to database

  Usage

  # Build Docker image
  docker build -f workers/Dockerfile.cpu -t shard-worker .

  # Run worker
  docker run -d \
      -e LANGUAGE=telugu \
      -e DATABASE_URL="..." \
      -e R2_ENDPOINT_URL="..." \
      -e R2_ACCESS_KEY_ID="..." \
      -e R2_SECRET_ACCESS_KEY="..." \
      -e R2_CLEANED_BUCKET="..." \
      -e R2_BUCKET_SHARDS="..." \
      shard-worker

  # Scale to multiple workers
  docker-compose up -d --scale worker=20

  Your other agent can now build the Docker image and test the end-to-end
  workflow.

Any secrets if you need are in .env, one thing at a time. and remember we dont have to auto-scale directly with docker here.  i will provide infra for that. for now, we need to validate if the docker based workflow + supabase sync + analytics + no-duplicates management is working for one worker and the docker image is built, tested on few 100 videos and double checked with R2's respective bucket and re-downloading the shard and opening it to see if teh data is actually present and we are good to scale this pipeline to different machines one after another. for which i will provide further details. build, test, validate the single worker workflow end to end first from here. all secrets are in .env and you are open to do stuff to supabase. just dont make any writes to previous table. you are free to read or clone previous table of videos and other info from that table and leave new table with other status and other info column empty for this current preparateion pipeline.


**Confirming worker directory contents**

The workers directory contains only README, sql, seed, worker, requirements, and Dockerfiles—none of the user-referenced files exist here. I'll report this mismatch before proceeding with step-by-step Docker workflow validation.


**Planning full worker pipeline implementation**

The current worker.py only has a placeholder; to validate the end-to-end Docker workflow with Supabase sync and R2 upload, I need to implement full processing, leasing, heartbeat, shard writing, and uploading inside worker.py, adapting prepare_shards logic and using new Supabase tables without modifying legacy tables. I'll start step-by-step by verifying the environment and then build out the worker pipeline accordingly.


**Designing robust worker with heartbeats and lease renewals**

I'll implement worker.py to run in Docker with full Supabase sync, including status updates, heartbeat upserts, and lease renewals during processing to ensure no duplicates or deadlocks. The processing will use prepare_shards functions for CPU pipeline, shard writing, and upload, with periodic heartbeat and lease renewals to support analytics and reliable coordination.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""Distributed shard worker for horizontal scaling.

This worker:
1. Claims a batch of videos from Supabase (with row-level locking)
2. Downloads video tars from R2
3. Processes segments to generate shards
4. Uploads shards to R2
5. Updates status in Supabase
6. Cleans up local storage
7. Reports heartbeat and metrics

Key safety features:
- Deterministic shard_id (same videos = same shard = idempotent upload)
- FOR UPDATE SKIP LOCKED prevents duplicate claims
- Lease expiry allows crash recovery
- Storage monitoring prevents disk exhaustion
"""

from __future__ import annotations

import argparse
import hashlib
import json
import logging
import os
import signal
import socket
import sys
import threading
import time
import uuid
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Optional

import psycopg
from boto3.s3.transfer import TransferConfig

# Setup logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
)
logger = logging.getLogger("worker")

# Import worker modules
from config import WorkerConfig, EnvConfig
from storage_manager import StorageManager, StorageStats
from r2_uploader import R2Uploader
from worker_processing import (
    VideoProcessor,
    ChunkConfig,
    SilenceConfig,
    ProcessingStats,
)

@dataclass(frozen=True)
class TaskRow:
    """A video task from the database."""
    video_id: str
    r2_tar_key: str
    language: str

@dataclass
class WorkerState:
    """Current worker state for heartbeat reporting."""
    status: str = "idle"
    shard_id: Optional[str] = None
    videos_assigned: int = 0
    videos_processed: int = 0
    segments_processed: int = 0
    outputs_generated: int = 0
    last_error: Optional[str] = None
    processing_start: Optional[float] = None

class ShardWorker:
    """Main worker class that orchestrates the processing pipeline."""

    def __init__(
        self,
        config: WorkerConfig,
        env_config: EnvConfig,
    ):
        self.config = config
        self.env_config = env_config

        # Generate worker ID if not provided
        self.worker_id = config.worker_id or f"{socket.gethostname()}-{os.getpid()}"
        logger.info(f"Worker ID: {self.worker_id}")

        # Initialize R2 clients
        self.source_uploader = R2Uploader(
            endpoint_url=env_config.r2_endpoint_url,
            access_key_id=env_config.r2_access_key_id,
            secret_access_key=env_config.r2_secret_access_key,
            bucket=env_config.r2_cleaned_bucket,
            max_retries=config.max_retries,
            retry_delay_sec=config.retry_delay_sec,
            max_concurrency=config.download_workers,
        )

        self.shard_uploader = R2Uploader(
            endpoint_url=env_config.r2_endpoint_url,
            access_key_id=env_config.r2_access_key_id,
            secret_access_key=env_config.r2_secret_access_key,
            bucket=env_config.r2_bucket_shards,
            max_retries=config.max_retries,
            retry_delay_sec=config.retry_delay_sec,
            max_concurrency=config.upload_workers,
        )

        # Initialize storage manager
        self.storage_manager = StorageManager(
            work_dir=config.work_dir,
            output_dir=config.output_dir,
            max_usage_gb=config.max_disk_usage_gb,
            cleanup_threshold_gb=config.cleanup_threshold_gb,
            check_interval_sec=config.cleanup_check_interval_sec,
        )

        # Worker state
        self.state = WorkerState()
        self._state_lock = threading.Lock()
        self._stop_event = threading.Event()
        self._heartbeat_thread: Optional[threading.Thread] = None

        # Setup signal handlers
        signal.signal(signal.SIGTERM, self._handle_signal)
        signal.signal(signal.SIGINT, self._handle_signal)

    def _handle_signal(self, signum, frame):
        """Handle shutdown signals gracefully."""
        logger.info(f"Received signal {signum}, initiating graceful shutdown...")
        self._stop_event.set()

    def _update_state(self, **kwargs) -> None:
        """Thread-safe state update."""
        with self._state_lock:
            for key, value in kwargs.items():
                if hasattr(self.state, key):
                    setattr(self.state, key, value)

    def _get_state(self) -> dict:
        """Get current state as dict."""
        with self._state_lock:
            return {
                "status": self.state.status,
                "shard_id": self.state.shard_id,
                "videos_assigned": self.state.videos_assigned,
                "videos_processed": self.state.videos_processed,
                "segments_processed": self.state.segments_processed,
                "outputs_generated": self.state.outputs_generated,
                "last_error": self.state.last_error,
            }

    def deterministic_shard_id(self, language: str, video_ids: list[str]) -> uuid.UUID:
        """Generate deterministic shard ID from language + sorted video IDs.

        This ensures that re-processing the same videos produces the same shard_id,
        making uploads idempotent (overwrites same R2 key instead of creating duplicates).
        """
        joined = f"{language}:" + ",".join(sorted(video_ids))
        digest = hashlib.sha1(joined.encode("utf-8")).hexdigest()
        return uuid.uuid5(uuid.NAMESPACE_URL, digest)

    def claim_videos(self, conn) -> list[TaskRow]:
        """Claim a batch of pending videos with row-level locking.

        Uses FOR UPDATE SKIP LOCKED to prevent duplicate claims across workers.
        """
        with conn.cursor() as cur:
            cur.execute(
                """
                with picked as (
                    select video_id
                    from video_tasks
                    where language = %s
                      and status = 'pending'
                    order by video_id
                    limit %s
                    for update skip locked
                )
                update video_tasks
                set status = 'claimed',
                    claimed_by = %s,
                    lease_expires_at = now() + (%s || ' minutes')::interval,
                    attempts = attempts + 1,
                    updated_at = now()
                where video_id in (select video_id from picked)
                returning video_id, r2_tar_key, language
                """,
                (
                    self.config.language,
                    self.config.target_videos,
                    self.worker_id,
                    str(self.config.lease_minutes),
                ),
            )
            rows = cur.fetchall()
        return [TaskRow(*row) for row in rows]

    def create_shard_run(
        self,
        conn,
        shard_id: uuid.UUID,
        assigned_count: int,
    ) -> None:
        """Create shard_runs entry for tracking."""
        with conn.cursor() as cur:
            cur.execute(
                """
                insert into shard_runs (
                    shard_id, language, target_video_count, assigned_video_count,
                    status, claimed_by, lease_expires_at, shard_prefix, output_format
                ) values (
                    %s, %s, %s, %s,
                    'claimed', %s, now() + (%s || ' minutes')::interval, %s, %s
                )
                on conflict (shard_id) do update set
                    status = 'claimed',
                    claimed_by = excluded.claimed_by,
                    lease_expires_at = excluded.lease_expires_at,
                    updated_at = now()
                """,
                (
                    str(shard_id),
                    self.config.language,
                    self.config.target_videos,
                    assigned_count,
                    self.worker_id,
                    str(self.config.lease_minutes),
                    f"{self.config.language}/{shard_id}",
                    self.config.output_format,
                ),
            )


**Noticing unexpected changes in worker.py**

The current worker.py has complex dependencies on missing modules and differs from the originally created skeleton, indicating unexpected file changes possibly by another agent; per instructions, I'll pause and ask how to proceed.


**Confirming missing modules referenced by worker.py**

Noticing worker.py depends on missing modules and the worker folder differs from earlier; I'll check the worker directory contents and ask whether to keep the current worker.py with new modules or revert to the original skeleton.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 176
drwxrwxr-x 3 ubuntu ubuntu  4096 Jan 20 04:17 .
drwxrwxr-x 9 ubuntu ubuntu  4096 Jan 20 04:00 ..
-rw-rw-r-- 1 ubuntu ubuntu   512 Jan 20 04:00 claim_shard.sql
-rw-rw-r-- 1 ubuntu ubuntu  6280 Jan 20 04:07 config.py
-rw-rw-r-- 1 ubuntu ubuntu  1729 Jan 20 04:15 docker-compose.yml
-rw-rw-r-- 1 ubuntu ubuntu  1069 Jan 20 04:14 Dockerfile.cpu
-rw-rw-r-- 1 ubuntu ubuntu   322 Jan 20 04:01 Dockerfile.gpu
-rwxrwxr-x 1 ubuntu ubuntu  3303 Jan 20 04:14 entrypoint.sh
-rw-rw-r-- 1 ubuntu ubuntu    79 Jan 20 04:14 __init__.py
drwxrwxr-x 2 ubuntu ubuntu  4096 Jan 20 04:19 __pycache__
-rw-rw-r-- 1 ubuntu ubuntu 12553 Jan 20 04:08 r2_uploader.py
-rw-rw-r-- 1 ubuntu ubuntu 11165 Jan 20 04:17 README.md
-rw-rw-r-- 1 ubuntu ubuntu  6796 Jan 20 04:15 release_stale_claims.py
-rw-rw-r-- 1 ubuntu ubuntu   258 Jan 20 04:00 release_stale.sql
-rw-rw-r-- 1 ubuntu ubuntu   178 Jan 20 04:12 requirements.txt
-rw-rw-r-- 1 ubuntu ubuntu  1781 Jan 20 04:00 schema.sql
-rw-rw-r-- 1 ubuntu ubuntu  2095 Jan 20 04:02 seed_video_tasks.py
-rw-rw-r-- 1 ubuntu ubuntu 11822 Jan 20 04:08 storage_manager.py
-rw-rw-r-- 1 ubuntu ubuntu  6945 Jan 20 03:51 worker_plan.md
-rw-rw-r-- 1 ubuntu ubuntu 32527 Jan 20 04:10 worker_processing.py
-rw-rw-r-- 1 ubuntu ubuntu 26865 Jan 20 04:12 worker.py


**Requesting user guidance on unexpected worker files**

I found that the workers directory now contains additional files like config.py, worker_processing.py, and a modified worker.py, which I didn't create. Should I validate these existing files as the source of truth or revert to the earlier minimal worker folder before proceeding?


I need to pause: the `workers/` folder now contains a full implementation (`config.py`, `worker_processing.py`, `r2_uploader.py`, `storage_manager.py`, `entrypoint.sh`, `docker-compose.yml`, etc.) and `workers/worker.py` is totally different from the skeleton I created earlier. These are changes I didn’t make.

Per your instructions, I have to stop and ask how you want to proceed.
Do you want me to validate **this new worker implementation** (the one currently in `workers/`), or should I revert to the minimal scaffold I created and validate that instead?


# Context from my IDE setup:

## Open tabs:
- worker.py: workers/worker.py
- Dockerfile.gpu: workers/Dockerfile.gpu
- optimized_pipeline.py: scripts/optimized_pipeline.py
- optimisations.md: optimisations.md
- Optimisation.md: Optimisation.md

## My request for Codex:
yes i've had my other agent finish the scaffolding you have created previously and it now has all the end to end implementations. i want you to understand the plan, build, fix any issues if it's not as planned and fix workflow and ensure max robustness with end to end implementations.


Summary: auto


**Planning thorough worker inspection**

I'm preparing to review multiple key worker-related files and configs to verify the workflow aligns with the safe plan: using new tables without touching legacy ones, ensuring lease and heartbeat logic are correct, and avoiding duplicates or deadlocks. Next, I'll validate step-by-step including Docker build and test runs.


**Detailing validation and test strategy**

I'm outlining a multi-step plan: inspect worker code for duplicates and lease logic, verify database schema matches the new tables, run local syntax checks, then execute an end-to-end test processing around 100 videos to validate the full workflow with Supabase and R2 while respecting resource constraints. I'll also confirm config correctness for R2 bucket settings and pipeline chunking.


Explanation: Need to review worker implementation, validate schema/claiming/heartbeat, fix issues, then run stepwise build/tests including a small end-to-end worker run and shard re-download verification.

Inspect worker scaffolding (config, processing, uploader, storage, worker loop) and compare to the plan; note/fix robustness gaps

Validate local syntax/imports and Docker build/run configs; adjust if required

Run a single-worker end-to-end test with Supabase + R2 (few hundred videos), verify shard upload + re-download integrity, and report metrics


Output: Plan updated


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""Configuration management for workers."""

from __future__ import annotations

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

@dataclass
class WorkerConfig:
    """Worker configuration with defaults and environment overrides."""

    # Worker identity
    worker_id: str = ""
    language: str = ""

    # Processing settings
    target_videos: int = 100  # Videos per shard (approx 5GB)
    lease_minutes: int = 60  # Lease duration for claims
    heartbeat_interval_sec: int = 15  # Heartbeat frequency

    # Directory settings
    work_dir: Path = field(default_factory=lambda: Path("/tmp/worker"))
    output_dir: Path = field(default_factory=lambda: Path("/tmp/worker_output"))

    # Storage management
    max_disk_usage_gb: float = 150.0  # Max disk usage before cleanup
    cleanup_threshold_gb: float = 100.0  # Target after cleanup
    cleanup_check_interval_sec: int = 60  # Storage check frequency

    # Parallelism
    download_workers: int = 8
    segment_workers: int = 8
    upload_workers: int = 4

    # Output settings
    output_format: str = "flac"  # flac or wav
    target_shard_size_mb: int = 5120  # 5GB shards

    # Processing options
    chunk_enabled: bool = True
    chunk_min_sec: float = 6.0
    chunk_max_sec: float = 10.0
    chunk_threshold_sec: float = 12.0
    chunk_all_buckets: bool = False

    silence_enabled: bool = True
    silence_frame_ms: float = 50.0
    silence_hop_ms: float = 10.0
    silence_search_sec: float = 0.5
    silence_percentile: float = 20.0
    silence_db_drop: float = 20.0

    val_ratio: float = 0.01
    seed: int = 17

    # Retry settings
    max_retries: int = 3
    retry_delay_sec: float = 5.0

    # Run mode
    once: bool = False  # Process single shard and exit
    dry_run: bool = False  # Don't actually process

    @classmethod
    def from_env(cls) -> "WorkerConfig":
        """Create config from environment variables."""
        return cls(
            worker_id=os.environ.get("WORKER_ID", ""),
            language=os.environ.get("LANGUAGE", ""),
            target_videos=int(os.environ.get("TARGET_VIDEOS", "100")),
            lease_minutes=int(os.environ.get("LEASE_MINUTES", "60")),
            heartbeat_interval_sec=int(os.environ.get("HEARTBEAT_INTERVAL_SEC", "15")),
            work_dir=Path(os.environ.get("WORK_DIR", "/tmp/worker")),
            output_dir=Path(os.environ.get("OUTPUT_DIR", "/tmp/worker_output")),
            max_disk_usage_gb=float(os.environ.get("MAX_DISK_USAGE_GB", "150")),
            cleanup_threshold_gb=float(os.environ.get("CLEANUP_THRESHOLD_GB", "100")),
            cleanup_check_interval_sec=int(os.environ.get("CLEANUP_CHECK_INTERVAL_SEC", "60")),
            download_workers=int(os.environ.get("DOWNLOAD_WORKERS", "8")),
            segment_workers=int(os.environ.get("SEGMENT_WORKERS", "8")),
            upload_workers=int(os.environ.get("UPLOAD_WORKERS", "4")),
            output_format=os.environ.get("OUTPUT_FORMAT", "flac"),
            target_shard_size_mb=int(os.environ.get("TARGET_SHARD_SIZE_MB", "5120")),
            max_retries=int(os.environ.get("MAX_RETRIES", "3")),
            retry_delay_sec=float(os.environ.get("RETRY_DELAY_SEC", "5")),
            once=os.environ.get("ONCE", "").lower() in ("1", "true", "yes"),
            dry_run=os.environ.get("DRY_RUN", "").lower() in ("1", "true", "yes"),
        )

@dataclass
class EnvConfig:
    """Environment configuration from .env file."""

    # Database
    database_url: str = ""

    # R2 Storage
    r2_endpoint_url: str = ""
    r2_access_key_id: str = ""
    r2_secret_access_key: str = ""
    r2_cleaned_bucket: str = ""  # Source bucket with video tars
    r2_bucket_shards: str = ""  # Destination bucket for shards
    r2_prefix: Optional[str] = None

    @classmethod
    def from_file(cls, env_path: Path) -> "EnvConfig":
        """Load from .env file."""
        env: dict[str, str] = {}
        if env_path.exists():
            for line in env_path.read_text().splitlines():
                stripped = line.strip()
                if not stripped or stripped.startswith("#") or "=" not in stripped:
                    continue
                if stripped.startswith("export "):
                    stripped = stripped[len("export "):]
                key, value = stripped.split("=", 1)
                env[key.strip()] = value.strip().strip('"').strip("'")

        return cls(
            database_url=env.get("DATABASE_URL", ""),
            r2_endpoint_url=env.get("R2_ENDPOINT_URL", ""),
            r2_access_key_id=env.get("R2_ACCESS_KEY_ID", ""),
            r2_secret_access_key=env.get("R2_SECRET_ACCESS_KEY", ""),
            r2_cleaned_bucket=env.get("R2_CLEANED_BUCKET") or env.get("R2_ClEANED_BUCKET", ""),
            r2_bucket_shards=env.get("R2_BUCKET_SHARDS", ""),
            r2_prefix=env.get("R2_PREFIX"),
        )

    @classmethod
    def from_env_vars(cls) -> "EnvConfig":
        """Load from environment variables."""
        return cls(
            database_url=os.environ.get("DATABASE_URL", ""),
            r2_endpoint_url=os.environ.get("R2_ENDPOINT_URL", ""),
            r2_access_key_id=os.environ.get("R2_ACCESS_KEY_ID", ""),
            r2_secret_access_key=os.environ.get("R2_SECRET_ACCESS_KEY", ""),
            r2_cleaned_bucket=os.environ.get("R2_CLEANED_BUCKET") or os.environ.get("R2_ClEANED_BUCKET", ""),
            r2_bucket_shards=os.environ.get("R2_BUCKET_SHARDS", ""),
            r2_prefix=os.environ.get("R2_PREFIX"),
        )

    def validate(self) -> list[str]:
        """Validate configuration, return list of errors."""
        errors = []
        if not self.database_url:
            errors.append("DATABASE_URL is required")
        if not self.r2_endpoint_url:
            errors.append("R2_ENDPOINT_URL is required")
        if not self.r2_access_key_id:
            errors.append("R2_ACCESS_KEY_ID is required")
        if not self.r2_secret_access_key:
            errors.append("R2_SECRET_ACCESS_KEY is required")
        if not self.r2_cleaned_bucket:
            errors.append("R2_CLEANED_BUCKET is required")
        if not self.r2_bucket_shards:
            errors.append("R2_BUCKET_SHARDS is required")
        return errors


sed -n '1,260p' workers/worker_processing.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""Worker processing module - adapted from prepare_shards.py for worker use.

This module handles the core audio processing logic:
- Extract segments from video tars
- Compute audio metrics (LUFS, spectral, etc.)
- Chunk long segments
- Resample to target rates
- Write to shards

The processing logic is identical to prepare_shards.py to ensure consistent output.
"""

from __future__ import annotations

import io
import json
import math
import os
import random
import tarfile
import threading
import time
import zlib
from collections import defaultdict
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Callable, Optional
from concurrent.futures import ThreadPoolExecutor, as_completed, wait, FIRST_COMPLETED

import numpy as np
import soundfile as sf
from scipy import signal

try:
    import pyloudnorm as pyln
except ImportError:
    pyln = None

# Caches for performance
_window_cache: dict[int, np.ndarray] = {}
_freq_cache: dict[tuple[int, int], np.ndarray] = {}
_cache_lock = threading.Lock()
_lufs_local = threading.local()

@dataclass(frozen=True)
class ChunkConfig:
    """Configuration for chunking long segments."""
    enabled: bool
    min_sec: float
    max_sec: float
    threshold_sec: float
    all_buckets: bool

@dataclass(frozen=True)
class SilenceConfig:
    """Configuration for silence-aware chunk boundaries."""
    enabled: bool
    frame_sec: float
    hop_sec: float
    search_sec: float
    percentile: float
    db_drop: float

@dataclass(frozen=True)
class SegmentOutput:
    """Output segment with audio bytes and metadata."""
    sample_rate: int
    audio_bytes: bytes
    metadata: dict[str, Any]

@dataclass
class ProcessingStats:
    """Statistics from processing."""
    videos: int = 0
    segments_total: int = 0
    segments_kept: dict = None
    dropped_too_short: int = 0
    dropped_too_long: int = 0
    dropped_clipped: int = 0
    segments_chunked: int = 0
    chunks_emitted: int = 0
    native_sr_buckets: dict = None

    def __post_init__(self):
        if self.segments_kept is None:
            self.segments_kept = {16000: 0, 24000: 0, 32000: 0, 44100: 0}
        if self.native_sr_buckets is None:
            self.native_sr_buckets = {}

    def to_dict(self) -> dict:
        return {
            "videos": self.videos,
            "segments_total": self.segments_total,
            "segments_kept": self.segments_kept,
            "dropped_too_short": self.dropped_too_short,
            "dropped_too_long": self.dropped_too_long,
            "dropped_clipped": self.dropped_clipped,
            "segments_chunked": self.segments_chunked,
            "chunks_emitted": self.chunks_emitted,
            "native_sr_buckets": self.native_sr_buckets,
        }

def get_hann_window(length: int) -> np.ndarray:
    window = _window_cache.get(length)
    if window is not None:
        return window
    with _cache_lock:
        window = _window_cache.get(length)
        if window is None:
            window = np.hanning(length)
            _window_cache[length] = window
    return window

def get_rfftfreq(length: int, sr: int) -> np.ndarray:
    key = (length, sr)
    freqs = _freq_cache.get(key)
    if freqs is not None:
        return freqs
    with _cache_lock:
        freqs = _freq_cache.get(key)
        if freqs is None:
            freqs = np.fft.rfftfreq(length, d=1.0 / sr)
            _freq_cache[key] = freqs
    return freqs

def get_lufs_meter(sr: int) -> Optional[Any]:
    if pyln is None:
        return None
    meters = getattr(_lufs_local, "meters", None)
    if meters is None:
        meters = {}
        _lufs_local.meters = meters
    meter = meters.get(sr)
    if meter is None:
        meter = pyln.Meter(sr)
        meters[sr] = meter
    return meter

def normalize(value: float, min_val: float, max_val: float) -> float:
    if max_val <= min_val:
        return 0.0
    return max(0.0, min(1.0, (value - min_val) / (max_val - min_val)))

def compute_spectral_metrics(audio: np.ndarray, sr: int) -> tuple[float, float, float]:
    """Compute spectral metrics: rolloff, effective nyquist, high freq ratio."""
    if audio.size == 0:
        return 0.0, 0.0, 0.0

    max_samples = min(audio.size, sr * 5)
    window = get_hann_window(max_samples)
    chunk = audio[:max_samples] * window
    spec = np.fft.rfft(chunk)
    power = np.abs(spec) ** 2
    freqs = get_rfftfreq(max_samples, sr)
    total_energy = float(np.sum(power)) + 1e-12
    cumulative = np.cumsum(power)
    rolloff_idx = int(np.searchsorted(cumulative, 0.85 * cumulative[-1]))
    rolloff_hz = float(freqs[min(rolloff_idx, len(freqs) - 1)])

    power_db = 10.0 * np.log10(power + 1e-12)
    threshold = power_db.max() - 60.0
    above = np.where(power_db >= threshold)[0]
    effective_nyquist_hz = float(freqs[above[-1]]) if above.size > 0 else 0.0

    high_freq_energy = float(np.sum(power[freqs >= 8000.0]))
    high_freq_ratio = high_freq_energy / total_energy

    return rolloff_hz, effective_nyquist_hz, high_freq_ratio

def classify_native_bucket(effective_nyquist_hz: float) -> str:
    """Classify audio bandwidth based on effective Nyquist frequency."""
    if effective_nyquist_hz < 8000:
        return "<16k"
    if effective_nyquist_hz < 12000:
        return "16-24k"
    if effective_nyquist_hz < 16000:
        return "24-32k"
    if effective_nyquist_hz < 22050:
        return "32-44k"
    return ">=44k"

def compute_audio_metrics(audio: np.ndarray, sr: int) -> dict[str, float]:
    """Compute all audio metrics for a segment."""
    audio = audio.astype(np.float32, copy=False)
    rms = float(np.sqrt(np.mean(audio ** 2) + 1e-12))
    loudness_rms_db = 20.0 * math.log10(rms + 1e-12)
    clipping_fraction = float(np.mean(np.abs(audio) > 0.99))
    dc_offset = float(np.mean(audio))
    noise_floor = float(np.percentile(np.abs(audio), 10))
    snr_proxy = 20.0 * math.log10((rms + 1e-9) / (noise_floor + 1e-9))

    loudness_lufs = -100.0
    if pyln is not None:
        try:
            meter = get_lufs_meter(sr)
            if meter is not None:
                loudness_lufs = float(meter.integrated_loudness(audio))
        except Exception:
            loudness_lufs = -100.0

    rolloff_hz, effective_nyquist_hz, high_freq_ratio = compute_spectral_metrics(audio, sr)

    return {
        "loudness_lufs": loudness_lufs,
        "loudness_rms_db": loudness_rms_db,
        "clipping_fraction": clipping_fraction,
        "dc_offset": dc_offset,
        "snr_proxy": snr_proxy,
        "spectral_rolloff_hz": rolloff_hz,
        "effective_nyquist_hz": effective_nyquist_hz,
        "high_freq_energy_ratio": high_freq_ratio,
    }

def compute_quality_score(metrics: dict[str, float], has_music: bool) -> float:
    """Compute overall quality score."""
    score = (
        0.3 * (1.0 - metrics["clipping_fraction"])
        + 0.2 * normalize(metrics["snr_proxy"], 10, 40)
        + 0.2 * (1.0 if not has_music else 0.5)
        + 0.2 * normalize(metrics["loudness_lufs"], -40, -14)
        + 0.1 * (1.0 if abs(metrics["dc_offset"]) < 0.01 else 0.5)
    )
    return max(0.0, min(1.0, score))

def resample_audio(audio: np.ndarray, src_sr: int, dst_sr: int) -> np.ndarray:
    """Resample audio to target sample rate."""
    if src_sr == dst_sr:
        return audio
    gcd = math.gcd(src_sr, dst_sr)
    up = dst_sr // gcd
    down = src_sr // gcd
    return signal.resample_poly(audio, up, down).astype(np.float32, copy=False)

def encode_audio(audio: np.ndarray, sr: int, output_format: str) -> bytes:
    """Encode audio to bytes."""
    buf = io.BytesIO()
    if output_format == "wav":
        sf.write(buf, audio, sr, format="WAV", subtype="PCM_16")
    else:
        sf.write(buf, audio, sr, format="FLAC", subtype="PCM_16")
    return buf.getvalue()

def duration_bucket(duration_sec: float) -> Optional[tuple[str, float]]:
    """Get duration bucket and weight."""


sed -n '260,620p' workers/worker_processing.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    """Get duration bucket and weight."""
    if duration_sec < 3.0:
        return None
    if duration_sec < 6.0:
        return "3-6s", 0.3
    if duration_sec < 12.0:
        return "6-12s", 1.0
    if duration_sec < 24.0:
        return "12-24s", 0.8
    if duration_sec <= 60.0:
        return "24-60s", 0.5
    return None

def compute_frame_rms(
    audio: np.ndarray, sr: int, frame_sec: float, hop_sec: float
) -> tuple[np.ndarray, np.ndarray]:
    """Compute frame-level RMS for silence detection."""
    frame_len = max(1, int(round(frame_sec * sr)))
    hop_len = max(1, int(round(hop_sec * sr)))
    if audio.size < frame_len:
        return np.array([], dtype=np.float32), np.array([], dtype=np.float32)

    if not audio.flags["C_CONTIGUOUS"]:
        audio = np.ascontiguousarray(audio)

    num_frames = 1 + (audio.size - frame_len) // hop_len
    if num_frames <= 0:
        return np.array([], dtype=np.float32), np.array([], dtype=np.float32)

    stride = audio.strides[0]
    frames = np.lib.stride_tricks.as_strided(
        audio,
        shape=(num_frames, frame_len),
        strides=(hop_len * stride, stride),
    )
    rms = np.sqrt(np.mean(frames * frames, axis=1) + 1e-12)
    times = (np.arange(num_frames, dtype=np.float32) * hop_len + (frame_len / 2)) / sr
    return times.astype(np.float32), rms.astype(np.float32)

def find_silence_boundary(
    times: np.ndarray,
    rms: np.ndarray,
    search_start: float,
    search_end: float,
    threshold_rms: float,
    median_db: float,
    db_drop: float,
) -> Optional[float]:
    """Find optimal silence boundary for chunking."""
    if times.size == 0:
        return None
    mask = (times >= search_start) & (times <= search_end)
    if not np.any(mask):
        return None
    subset = rms[mask]
    min_idx = int(np.argmin(subset))
    min_rms = float(subset[min_idx])
    min_time = float(times[mask][min_idx])
    min_db = 20.0 * math.log10(min_rms + 1e-12)
    if min_rms <= threshold_rms or (median_db - min_db) >= db_drop:
        return min_time
    return None

def make_chunk_windows(
    audio: np.ndarray,
    sr: int,
    cfg: ChunkConfig,
    silence_cfg: SilenceConfig,
    rng: random.Random,
) -> list[tuple[float, float]]:
    """Generate chunk windows for a segment."""
    duration_sec = float(audio.shape[0] / sr)
    if not cfg.enabled or duration_sec < cfg.threshold_sec:
        return [(0.0, duration_sec)]

    if not silence_cfg.enabled:
        windows: list[tuple[float, float]] = []
        offset = 0.0
        while duration_sec - offset >= cfg.min_sec:
            remaining = duration_sec - offset
            if remaining <= cfg.max_sec:
                length = remaining
            else:
                length = rng.uniform(cfg.min_sec, cfg.max_sec)
                if remaining - length < cfg.min_sec:
                    length = remaining
            windows.append((offset, length))
            offset += length
        return windows

    times, rms = compute_frame_rms(
        audio, sr, silence_cfg.frame_sec, silence_cfg.hop_sec
    )
    if times.size == 0:
        return [(0.0, duration_sec)]

    threshold_rms = float(np.quantile(rms, silence_cfg.percentile / 100.0))
    median_db = 20.0 * math.log10(float(np.median(rms)) + 1e-12)

    windows: list[tuple[float, float]] = []
    offset = 0.0
    while duration_sec - offset > cfg.max_sec:
        remaining = duration_sec - offset
        latest_cut = duration_sec - cfg.min_sec
        if offset + cfg.min_sec >= latest_cut:
            break
        target_len = rng.uniform(cfg.min_sec, cfg.max_sec)
        if remaining - target_len < cfg.min_sec:
            target_len = remaining - cfg.min_sec
        target_end = offset + target_len
        search_low = max(offset + cfg.min_sec, target_end - silence_cfg.search_sec)
        search_high = min(offset + cfg.max_sec, target_end + silence_cfg.search_sec, latest_cut)
        if search_high <= search_low:
            search_low = offset + cfg.min_sec
            search_high = min(offset + cfg.max_sec, latest_cut)
        boundary = find_silence_boundary(
            times,
            rms,
            search_low,
            search_high,
            threshold_rms,
            median_db,
            silence_cfg.db_drop,
        )
        if boundary is None:
            if not windows:
                return [(0.0, duration_sec)]
            windows.append((offset, duration_sec - offset))
            return windows
        windows.append((offset, boundary - offset))
        offset = boundary

    windows.append((offset, duration_sec - offset))
    return windows

def make_segment_seed(base_seed: int, video_id: str, speaker: str, index: int) -> int:
    """Generate deterministic seed for a segment."""
    key = f"{video_id}-{speaker}-{index}-{base_seed}"
    return zlib.adler32(key.encode("utf-8")) & 0xFFFFFFFF

def parse_segment_filename(filename: str) -> Optional[tuple[str, float]]:
    """Parse segment filename to extract speaker and start time."""
    base = filename.split("/")[-1]
    if not base.endswith(".flac"):
        return None

    base = base[:-5]
    parts = base.split("_")
    if len(parts) < 3:
        return None

    speaker = "_".join(parts[:-2])
    time_range = parts[-1]
    if "-" not in time_range:
        return None

    try:
        start_str = time_range.split("-")[0]
        start_time = float(start_str)
    except (ValueError, IndexError):
        return None

    return speaker, start_time

def process_segment(
    video_id: str,
    language: str,
    segment_bytes: bytes,
    segment_meta: dict[str, Any],
    speaker: str,
    index: int,
    chunk_cfg: ChunkConfig,
    silence_cfg: SilenceConfig,
    rng_seed: int,
    output_format: str,
) -> tuple[list[SegmentOutput], dict[str, int]]:
    """Process a single segment and return outputs."""
    stats = {
        "dropped_too_short": 0,
        "dropped_too_long": 0,
        "dropped_clipped": 0,
        "chunks_emitted": 0,
        "segments_chunked": 0,
    }
    outputs: list[SegmentOutput] = []

    try:
        audio, sr = sf.read(io.BytesIO(segment_bytes), dtype="float32")
    except Exception:
        return outputs, stats

    if audio.ndim > 1:
        audio = np.mean(audio, axis=1)

    rng = random.Random(rng_seed)
    windows = make_chunk_windows(audio, sr, chunk_cfg, silence_cfg, rng)

    music_stats = segment_meta.get("music_stats") or {}
    has_music = bool(music_stats.get("has_music", False))

    def emit_outputs(
        audio_slice: np.ndarray,
        metrics: dict[str, float],
        sample_id: str,
        segment_index: int,
        chunk_index: Optional[int],
        start: Optional[float],
        end: Optional[float],
        original_start: Optional[float],
        original_end: Optional[float],
        target_srs: list[int],
    ) -> None:
        slice_duration = float(audio_slice.shape[0] / sr)
        bucket = duration_bucket(slice_duration)
        if bucket is None:
            if slice_duration < 3.0:
                stats["dropped_too_short"] += 1
            else:
                stats["dropped_too_long"] += 1
            return
        if metrics["clipping_fraction"] > 0.1:
            stats["dropped_clipped"] += 1
            return

        duration_bucket_name, base_weight = bucket
        sampling_weight = base_weight * (0.5 if has_music else 1.0)
        quality_score = compute_quality_score(metrics, has_music)
        effective_nyquist = metrics["effective_nyquist_hz"]

        base_metadata = {
            "id": sample_id,
            "video_id": video_id,
            "language": language,
            "segment_index": segment_index,
            "chunk_index": chunk_index,
            "start": start,
            "end": end,
            "original_start": original_start,
            "original_end": original_end,
            "duration_sec": slice_duration,
            "native_sr": sr,
            "effective_nyquist_hz": effective_nyquist,
            "spectral_rolloff_hz": metrics["spectral_rolloff_hz"],
            "high_freq_energy_ratio": metrics["high_freq_energy_ratio"],
            "is_truly_wideband": bool(effective_nyquist >= 12000),
            "native_sr_bucket": classify_native_bucket(effective_nyquist),
            "loudness_lufs": metrics["loudness_lufs"],
            "loudness_rms_db": metrics["loudness_rms_db"],
            "clipping_fraction": metrics["clipping_fraction"],
            "snr_proxy": metrics["snr_proxy"],
            "quality_score": quality_score,
            "music_prob": float(music_stats.get("music_mean", 0.0)),
            "music_stats": music_stats,
            "speaker": speaker,
            "vad_speech_regions": [[0.0, slice_duration]],
            "duration_bucket": duration_bucket_name,
            "sampling_weight": sampling_weight,
        }

        for target_sr in target_srs:
            resampled = resample_audio(audio_slice, sr, target_sr)
            resampled = np.clip(resampled, -1.0, 1.0)
            audio_bytes = encode_audio(resampled, target_sr, output_format)
            meta = dict(base_metadata)
            meta["sample_rate"] = target_sr
            meta["resample_target_sr"] = target_sr
            outputs.append(SegmentOutput(target_sr, audio_bytes, meta))

        if target_srs == [16000]:
            stats["chunks_emitted"] += 1

    sample_id_base = f"{video_id}__{speaker}_{index:04d}"
    use_chunk_suffix = len(windows) > 1
    if use_chunk_suffix:
        stats["segments_chunked"] += 1

    segment_start = segment_meta.get("start")
    segment_end = segment_meta.get("end")
    original_start = segment_meta.get("original_start")
    original_end = segment_meta.get("original_end")

    # Emit 16k outputs for all chunks
    for chunk_idx, (offset_sec, chunk_len_sec) in enumerate(windows):
        start_idx = int(round(offset_sec * sr))
        end_idx = int(round((offset_sec + chunk_len_sec) * sr))
        audio_slice = audio[start_idx:end_idx]
        if audio_slice.size == 0:
            continue
        slice_duration = float(audio_slice.shape[0] / sr)
        slice_metrics = compute_audio_metrics(audio_slice, sr)

        chunk_start = float(segment_start) + offset_sec if segment_start is not None else None
        chunk_end = chunk_start + slice_duration if chunk_start is not None else None
        chunk_original_start = (
            float(original_start) + offset_sec if original_start is not None else None
        )
        chunk_original_end = (
            chunk_original_start + slice_duration
            if chunk_original_start is not None
            else None
        )

        sample_id = sample_id_base
        chunk_index = None
        if use_chunk_suffix:
            sample_id = f"{sample_id_base}_c{chunk_idx:02d}"
            chunk_index = chunk_idx

        emit_outputs(
            audio_slice,
            slice_metrics,
            sample_id,
            index,
            chunk_index,
            chunk_start,
            chunk_end,
            chunk_original_start,
            chunk_original_end,
            [16000],
        )

    # High-rate buckets for full segment
    full_metrics = compute_audio_metrics(audio, sr)
    effective_nyquist = full_metrics["effective_nyquist_hz"]
    targets_high: list[int] = []
    if effective_nyquist >= 8000:
        targets_high.append(24000)
    if effective_nyquist >= 12000:
        targets_high.append(32000)
    if effective_nyquist >= 16000:
        targets_high.append(44100)

    if targets_high:
        if chunk_cfg.all_buckets and use_chunk_suffix:
            for chunk_idx, (offset_sec, chunk_len_sec) in enumerate(windows):
                start_idx = int(round(offset_sec * sr))
                end_idx = int(round((offset_sec + chunk_len_sec) * sr))
                audio_slice = audio[start_idx:end_idx]
                if audio_slice.size == 0:
                    continue
                slice_duration = float(audio_slice.shape[0] / sr)
                slice_metrics = compute_audio_metrics(audio_slice, sr)
                slice_effective_nyquist = slice_metrics["effective_nyquist_hz"]
                slice_targets: list[int] = []
                if slice_effective_nyquist >= 8000:
                    slice_targets.append(24000)
                if slice_effective_nyquist >= 12000:
                    slice_targets.append(32000)
                if slice_effective_nyquist >= 16000:
                    slice_targets.append(44100)
                if not slice_targets:
                    continue

                chunk_start = (
                    float(segment_start) + offset_sec if segment_start is not None else None


sed -n '620,1180p' workers/worker_processing.py