role: user

type: text

text:
<user_query>
Now that we are finished with the transcriptions and native, and romanized and mixed too. i will be running some regex and abnormal tests on the data. meanwhile can we plan on packaging the dataset as a final export option. like for example,

1-cleaned-data -> is the original segmented single speaker segments. ranging from 2s to larger as large as 50,60,or even more seconds.
I will provide you with a final parquet, but until then interim final parquet is - transcript_variants_clean.parquet: 6.34 GB and the original parquet before filtering which had like 76M segments is - /home/ubuntu/transcripts/data/transcription_results.parquet (providing just incase if any columns are dropped in the final one, you can add them for max verbosity and information retain for downstream tasks.)
We have a polishing mechanism, which you can read over here in this repo. we also have this mechanism of splitting larger cut points, to smaller ones where we have generated the transcripts for and did the validation and re-validateion and replay mechanism assuming determinism. although we did it for validation and replay phases, we didnt store those cut segments even after doing a replay. just noted the fact that they are deterministic and can be repolished, and mapped against same segment numbers and etc, but we might get new extra segments that werent part or were not transcribed.

go through this, @\home\ubuntu\.cursor\projects\home-ubuntu-transcripts\agent-transcripts\dc0af9ec-1326-4bab-a099-afacebdebf5e\dc0af9ec-1326-4bab-a099-afacebdebf5e.jsonl  and @\home\ubuntu\.cursor\projects\home-ubuntu-transcripts\agent-transcripts\bc0753dc-8127-479b-963a-fa6391d6e748\bc0753dc-8127-479b-963a-fa6391d6e748.jsonl

and once you know to repolish and tally them, do a one off/N-run verifications on 2-3 videos by polishing, just to check on this just to prove determinism.

and once we are done with polishing, we need to compute some metrics regarding the audio segments, just for metadata storing. Suggest few quality metrics for an Audio segment like SNR, RMS, and other quality metrics.

then once we have for each video, N segments, and for each segment we have all the metadata we need, like
 - videoID
- segmentID
- isSplitPart
- Transcription - {JSON of all available transcriptions formats. Roman, Native, Mixed, Tagged}
- hasAudioTag - boolean if the tagged version of transcript has a valid [tag]
- Metrics - All computed audio metrics of this segment.
- metaInformation - {create a schematic metadata information about this segment. everything we know about it. yt-declared original video language, gemini_transcribed language, current segment language}, any other details if we have, make a schematic info logging.

once we have this information for each segment, we need to bucket them. so each video has a language tag and a segment might have a different tag based on the content in that segment. cause many podcasts, even though the original was supposed to be a different language, people talk in english or other language sometimes.

now that is the current scenarios. we now need to plan to bucket things.

we need a workflow, in such a way that this process is scaled horizontally into multiple GPUs, we scale in vast. so we create a supabase table with all current final 395k rows of finalized videos that are with transctipts. and each GPU claims a video, downloads the parquet from R2(we will update with latest once we are done with filtering,. few rows will be removed, but video counts same. ), extracts, polishes and replays all teh segments, maps the current segments against the transcripts parquet, and gathers all information and buckets the segment's in their respective buckets called folder in the worker's storage. now at a given fixed number of segments like 5000 or 10000, or 20k segments count, in a bucket, we call a new function with zip the bucket and uploads to R2 into that specific language prefix, along with the respective JSONL or shard as final for that zip. can be shard or a zip, whichever's lossless compressant and easier to extract.

and if a video has 30% english, and 70% hindi segments, we fill the local storages accordingly, and if next video has 20% english and 80% telugu, we make a folder for telugu, and a watcher or a counter when the count is matched or closer or filled with max_segments per folder, we initiate the transport process, in the background with parallel boto workers as upload as in async, and the main workflow continues eiwht next video.

Plan this and ask if you have any questions or doubts.
</user_query>


role: assistant

text:
I’m going to inspect the existing polishing/export pipeline and the two prior chat transcripts first, then turn that into a concrete packaging plan with the open questions called out.

**Planning for user tasks**

I need to plan carefully since the user is asking for a structured approach. They've mentioned reviewing agent transcripts and verifying 2-3 videos, so I should gather context first. They emphasized planning and asked for questions or doubts, which suggests I shouldn't jump into action just yet. I should probably draft a plan that includes polishing the codebase and figuring out the metrics after polishing, seeking user approval before proceeding. This task feels substantial and architectural, so planning makes sense!
**Organizing workflow planning**

I need to switch to planning mode and make a commentary update before diving into substantial work. The user rules say not to create new documentation files, so I should stick to updating existing ones like README.md or progress.md if necessary, but since the user didn't request doc updates, I can focus on other tasks. They specifically asked about reviewing agent transcripts, and I might consider using a subagent for broader exploration of the codebase related to polishing. It seems I should request the switch to plan mode first!
**Preparing for mode switch**

I should consider gathering some context before switching modes. The instructions say to choose the best interaction mode beforehand, and since this task is large and architectural, it makes sense to switch soon. I can explain why I need the switch. While the tool might prompt the user directly, I want to make sure I’m satisfying their request. I plan to inspect the existing polishing/export pipeline and the previous chat transcripts first, then I'll request the mode switch!


text:
I’m gathering the current repo’s vocabulary for polishing, replay, validation, and export so the plan matches what already exists instead of inventing a parallel workflow.

**Looking into transcript analysis**

I'm considering using search terms in transcript files to find mentions of polish, replay, deterministic, and export. Since the user wants me to go through these transcript files, I think I can use rg on both files for those keywords. It might also help to search the repository for polishing mechanisms. I should think about using parallel searches and checking README segments that match relevant terms. Let's inspect the file list and plan the searches!


text:
I’ve confirmed the repo already has a strong precedent for `replay -> match historical IDs -> fill gaps` from the validation recovery work, so I’m reading the actual replay/export code next to anchor the final-export plan to those same primitives.

**Planning verification steps**

Since I need to plan, I wonder if I should include local verification as part of Phase 0. I could inspect the code and run limited tests. However, the user specifically asked me to "Plan this," so perhaps I'll focus on planning and addressing questions first. They mentioned the need to repolish and tally from transcripts, then run verifications on a couple of videos. It makes sense to prepare, but maybe I shouldn't implement or run anything just yet. I should consider verifying determinism through a small pilot. I'll need to weigh my options carefully and ensure I have the necessary data and scripts ready to go!
**Planning process checks**

I can look at terminal metadata to see existing processes—though I don't think starting the server is necessary just yet. If I need to use a shell later, I should check the terminals folder first. The user wants to ensure the virtual environment (venv) is activated before running any app commands. I should list the root directory to find the venv and terminals folder. Before that, let's read through the code in audio_polish.py, recover_loader.py, and potentially relevant scripts like build_final_cleaned_dataset.py. Since the user mentioned packaging the final export, it’s possible the script already exists in some form. I'll also check the src/variant_* scripts for any new transcript variant information.


text:
I’ve got the key replay primitive now: raw parent FLAC -> deterministic polished child IDs -> match to historical transcript rows. I’m pulling the transcript-variant and current final-dataset wiring next so the export schema can preserve everything you want without duplicating work.

**Exploring dataset scripts**

I need to check the rest of the `build_final_cleaned_dataset` relevant to the schema. It might help to search for output columns and summaries later. I should also look at other scripts like `build_gemini_refined_dataset`, `variant_worker`, `variant_provider`, and `variant_main`. The export plan may leverage transcript variant processing, so checking `variant_worker` or `build_gemini_refined_dataset` for the current transcript schema could be useful too. I'll search for terms like "native_script_text" or output columns.


text:
I can already see a likely reuse path: the transcript-variant workers already solve “claim shard -> process rows -> write shard manifests -> R2 upload”, which is very close to the export-fleet pattern you want. I’m checking those pieces and any existing determinism test hooks so the plan can be operational, not just conceptual.

**Reviewing output schema**

I need to read relevant sections on the variant worker to grasp the output schema and consider how to package R2. It might also help to look into initializing the transcript variant queue to understand the sharding approach. Running some verification sounds necessary as well. I should check if there’s a script for determinism tests and search for any existing scripts related to determinism preflight. I’ll search for "determinism" to find anything that fits.


text:
There are already unit tests around replay and audio polish, which is good, but your requested proof is stronger: repeated replay on actual raw segments/videos. I’m checking the existing tests and local data paths now so I can separate what is already covered from what still needs a real-world determinism pilot.

**Inspecting tests for determinism**

I need to check the files `tests/test_recover_loader` or `test_audio_polish` for determinism and possibly look for any existing canary tests. Running actual verifications soon is a priority. I’ll search for local raw data or scripts that might help. But before executing any application commands, using a virtual environment (venv) is crucial. I might read through the tests to ensure everything aligns properly.


text:
The repo already has the right hooks for a real replay pilot: download raw `1-cleaned-data`, extract `segments/`, replay polish, and compare regenerated IDs. I’m reading the single-video validation entrypoint and raw-R2 client now so I can turn that into a minimal determinism check and a production export workflow.

**Inspecting recovery methods**

I need to take a closer look at the `recover_worker` and the validations in `main`. It seems like the determinism proof might benefit from checking the raw download path. I should also read through the validations in `main`, especially regarding the single-video test. Additionally, it could be helpful to inspect the `src/r2_client` to better understand the extraction process. Lots to consider!


text:
I have enough design context to outline the workflow, but I also want one concrete operational note before I propose it: whether I can reuse the repo venv and local data paths for a real replay pilot on 2-3 videos. I’m checking the environment and current terminal activity first, then I’ll decide if I can include a verified pilot step in the plan.

**Checking verification process**

I think I need to inspect later parts related to the DB claim or heartbeat functions, but maybe they're not necessary in the current context. There’s a user request for a verification plan, and they might have specifically asked for a one-off or N-run verification. I could start by verifying a couple of videos, so I’ll need to check my environment settings first before executing any commands. I'll run some simple shell commands to check the necessary folders.


text:
**Considering duplicate processes**

I’m thinking about how I might need to read terminal metadata to avoid any duplicate processes. This seems important for executing app commands effectively. I’ll consider using ReadFile to check the first 10 lines of terminal files. That should give me the information I need to determine if there are overlaps in processes. It’s crucial to keep everything running smoothly, so this feels like a good approach to ensure efficiency.


text:
I’m at the point where I can either stop at design or attach a small proof run. Since you asked for replay verification specifically, I’m checking the config loading path and then I’ll see if I can run a lightweight 2-3 video, multi-run replay without touching the heavier GPU validation path.

**Evaluating data needs**

I’m reviewing the scripts running on terminals. It might be better to avoid heavy apps that are not relevant. I should inspect `final_cleaned_segments_with_variants` and consider planning references. I could also read `final_data` to get an idea if the user’s plan requires it. I need to verify 2-3 videos and check if `data/transcription_results.parquet` exists, as that might help. I wonder if I should run a small pilot phase for determinism first, pending the user's approval.
**Planning video replay**

I want to run a small, GPU-less replay of 2-3 videos on my local machine to make the response more useful. First, I need to activate the virtual environment and check if the `.env` file exists—likely, but I should avoid reading any secrets. I could run some Python commands with DuckDB to pick video IDs. However, R2 credentials may need to be loaded from the `.env`, so I might use `load_dotenv`.

I should also consider using `python -m validations.main --test-recover-local VIDEO_ID --models ''`, but I know that the models argument won’t accept "none." Finally, I’ll need to determine video IDs, maybe by querying local parquet files.
**Checking data availability**

I need to make sure that `data/transcription_results.parquet` exists, even though it might be large. It could be easier to query `recover_video_summary.csv` instead. I should inspect the file columns using Python, to see what data I have. Also, I’m thinking there may not be a need to download from R2 if the raw data is available locally. Using a small number of videos might be fine, especially since the user has questions and actual verification could take time. I'll just proceed with the attempt!
**Evaluating video verifications**

The user wants to run verifications on 2-3 videos by polishing them. I think running 5 checks per video could give us a solid comparison of the outputs. For this, custom Python could work without needing a GPU. If we need specific transaction rows, I'll check historical IDs and identify any missing transaction IDs, possibly loading from a parquet file.

Using `data/transcription_results.parquet` is a consideration if it exists. I should also ensure that I set up my environment, maybe with `duckdb` and access configurations like `.env`. I’ll need to inspect how `EnvConfig()` loads environment settings.


text: The only blocker for an immediate replay pilot is data locality: the repo config is ready, but I don’t currently see the local transcription parquet in `data/`. I’m checking the data directory now so I can tell whether the verification can be run entirely locally or whether it would need the parquet you said you’ll provide.


text:
I do have the needed local inputs after all, including the full `transcription_results.parquet`, so I’m going to pick a small/medium/large trio of videos and see if I can run a replay-only determinism check against them without involving GPU validation.

**Planning video verification**

I’m considering running actual replay verification and need to pick candidate videos. I could use known IDs or query a range from tx parquet with DuckDB. It makes sense to select two to three videos: small around 20 tx rows, medium about 100, and large close to 300. Maybe I can use the same criteria as before for canary tests? I'll check recover_video_summary and pull from transcription_results.parquet for counts. Now, let's get querying!


text:
**Evaluating transcription processes**

I might be missing completely untranscribed parent segments, which could be a problem for determinism but might be okay for direct comparisons. To match the tx corpus, I need the current IDs from all raw paths, not just tx parents. In recover_worker, it only replays tx parents. If a video has a raw parent without any transcription rows, recover won't include it. For the final export, I need to replay all raw parent segments to avoid losing any untranscribed segments. This is definitely an important finding!
**Analyzing transcription segments**

I need to compare total raw segments with unique parent segment files from tx_rows. However, tx_rows only include historical transcribed segments, so if a raw parent had all its children fail or lack tx rows, it won't show up. Since the user wants every segment in the final export after polishing, I should replay all raw parents. This realization is a great planning insight! It’s important to ensure nothing is overlooked in the export process.
**Considering video processing**

I'm thinking about downloading three videos quickly, and I'll need the environment with R2 credentials for that. A custom script might be useful, given the lack of load_dotenv in R2Client. I’m contemplating running a block for 180,000 or in the background if it takes too long. I should carefully craft the script, selecting specific videos to avoid delays.

I’ll also need to ensure that we maintain determinism in processing and output through sorting after each run for accurate comparisons.
**Inspecting segment processing**

I'm considering using `polish_all_segments(segment_paths)` because it might reorder the results. I should check if the inspect function or thread pool will return completed tasks correctly. So far, we haven't read beyond 320 lines of audio_polish. I’m wondering if I should read the rest or just make some assumptions. It’s probably best to take a closer look at `polish_all_segments` to fully understand its behavior. Let's dive into that next!


text:
I’m running the actual replay pilot now: 5 replay passes on a small/medium/large video, comparing regenerated child IDs, durations, waveform hashes, and FLAC hashes across runs, plus checking how those replayed IDs line up with the historical transcription rows.

**Explaining determinism in replay**

I'm excited that Pool.map preserves order, and I've got some ideas to refine this approach. I need to note that while the proof shows replay determinism in the current environment, it doesn't prove historical bit-for-bit outputs since the original polished FLACs aren't stored. However, I can mention that repeated local runs demonstrate that the current pipeline is deterministic. I think it might be good to include three videos and identify sizes. I'll create a custom script and ensure they're long enough—around 180 seconds! Let's get started on that.


text:
The pilot result is clean: all three videos were bit-stable across 5 replay passes, and every historical `segment_file` for those videos matched back after replay. I’m pulling the existing recovery summary now so I can fold the broader “extras / salvage / missing” picture into the export plan instead of overfitting to just those three examples.

**Analyzing parent files**

I need to explore a broader issue with raw parent files lacking transaction descendants. It's important to analyze these videos, especially since some have zero transaction descendants, which means we need to replay them for exhaustive export. I might run one more analysis on a sample of 100 videos or a couple of pilots. The user is looking for a plan, so I’ll also consider a one-off verification of a few videos. If no valid children are present in the replay, those parents could be discarded altogether.
**Planning video exports**

If the final export aims for polished segments, I need to replay all raw parents to check if they generate valid children. It could be worthwhile to compute if any discarded parents exist, though it might not be essential. The user mentioned possible extras, so maybe I should focus on videos with replay-only extras. I'll consider querying recover_summary to see their prevalence and use existing data to obtain counts. This will help in packaging the final export and ensuring the user gets accurate information on any extras or missing segments.
**Accessing summary data**

I might want to look into the data in recover_summary.json to see if it contains totals. Reading this file could provide useful information to help with my planning and analysis. It’s important to know what totals are available as they could clarify aspects of the project and guide future decisions. I’ll check it out to ensure I’m basing my actions on the most accurate data.


text:
I’ve got enough to draft the workflow now. I’m reading the exact segment-level fields the current final-data builder already emits so the proposed export schema can be an extension of that instead of a brand-new incompatible layout.

**Considering transcript inspection**

I think I need to inspect the transcript for dc0, focusing on whether summarizing the deployment separation is necessary. The user asked me to review it, and I've done some searches already. I could mention the key idea of queue separation and workflow recovery, which might be good enough.

It might help to cite both transcripts with short titles. For dc0, I could use "Recover deployment plan," and for bc0, maybe "Replay extras analysis." The user asked about what the other agent suggested, so I’ll keep that in mind as I proceed.
**Summarizing recovery paths**

I’m thinking about summarizing that the validation recovery path should be reused and that historical transaction recovery should be separated from salvage, based on transcripts. I might need to compute some candidate audio metrics for suggestions.

Researching standard metrics isn’t necessary since I can propose examples like RMS, peak dBFS, and others without needing any external resources. It’s important to note that computed metrics should be reference-free. I also want to inspect the final build script for fields and check the existing segment map columns in the export.


text:
I went through the current replay/recovery/export code and the two prior chats, and the cleanest path is to reuse the existing `replay -> match -> ledger -> pack -> upload` pattern from `validations/recover_loader.py`, `validations/recover_worker.py`, `src/variant_worker.py`, and `scripts/build_final_cleaned_dataset.py`. Conceptually, this is a merge of [Recover deploy notes](dc0af9ec-1326-4bab-a099-afacebdebf5e) and [Replay extras analysis](bc0753dc-8127-479b-963a-fa6391d6e748).

I also did the replay proof run you asked for. I ran 5 replay passes on 3 real videos and compared regenerated child IDs, final durations, waveform SHA256, and FLAC SHA256 across runs. All 3 were identical across all 5 passes, and all historical transcription IDs matched back after replay with `missing_tx_ids_after_replay = 0`.

- `--BzLQJ8LBc`: 13 raw parents, 20 tx rows, 20 replayed valid children, 20/20 matched, 0 missing, 0 extras
- `--GhkYQKjLY`: 98 raw parents, 80 tx rows, 80 replayed valid children, 80/80 matched, 0 missing, 0 extras
- `-0jC9pP9skI`: 210 raw parents, 220 tx rows, 220 replayed valid children, 220/220 matched, 0 missing, 0 extras

That is strong evidence that the current replay path is deterministic locally. What it does not prove is byte-identity against the original historical polished outputs, because those old polished child files were never stored. But it does prove that the current replay is stable enough to use as the canonical export-time segment generator.

## Recommended Workflow
The biggest architectural recommendation is this: do not make each worker download the 6.34 GB parquet for every claimed video. Download the reference snapshots once per worker at startup, keep them on local NVMe, and query them locally with DuckDB. The recover pipeline already follows this idea.

I would split the system into two lanes:

1. `export lane`
   - Claims a finalized video from a dedicated Supabase table.
   - Downloads the raw tar from `1-cleaned-data`.
   - Replays `audio_polish` on all raw parent FLACs, not just parents already known from `transcription_results`.
   - Joins replayed child IDs to:
     - final transcript parquet you will provide
     - original `data/transcription_results.parquet` for backfill columns
     - `transcription_flags.parquet`
     - validation/recover parquet
     - transcript-variants parquet
     - YouTube/video metadata
   - Computes audio metrics while the replayed waveform is already in memory.
   - Buckets exportable segments by segment language, not video language.
   - Flushes language packs to R2 asynchronously when thresholds are hit.

2. `salvage lane`
   - Handles replay-valid children that do not have a transcript row.
   - Either:
     - just records them into a salvage manifest for later retranscription, or
     - retranscribes them immediately and then feeds them back into the export lane.
   - This should stay separate from the normal export path, because transcript-backed rows and replay-only extras are different trust levels.

That separation is basically what the earlier agent was getting at: export the historical transcript-backed corpus cleanly, and treat replay-only extras as salvage candidates, not as silent drops and not as if they were already part of the final dataset.

## Worker Design
I would use four dedicated tables, modeled after the variant and recover workers:

- `final_export_queue`
  - `video_id`, `status`, `claimed_by`, `attempt_count`, `priority`, `error_message`, timestamps
- `final_export_workers`
  - heartbeat, gpu/cpu type, videos processed, segments exported, pending upload bytes, last error
- `final_export_video_ledger`
  - per-video audit counts: raw parents, replayed children, matched tx rows, replay-only extras, missing historical IDs, uploaded segments, salvage-enqueued segments
- `final_export_pack_manifests`
  - one row per uploaded pack: language, pack_id, row_count, audio_bytes, R2 keys, checksums, source videos

Per worker, keep local language buckets only. Do not globally coordinate “one Hindi bucket across all workers.” Let each worker maintain its own `lang=hi` active bucket and flush independently. That avoids distributed locking and is much simpler to scale horizontally.

Suggested flush policy:

- target `10k` segments per pack
- hard flush at `20k` segments
- hard flush at a byte threshold too, not only count
- time-based flush for low-volume languages, like every `30-60 min`
- always drain remaining buckets on worker shutdown

For artifact format, I would choose:

- `audio.tar.zst`
- `manifest.parquet`
- optional `manifest.jsonl.zst`
- `pack_manifest.json`

I would prefer `tar.zst` over zip here because it is simpler to stream, cheaper to build for lots of small FLACs, and easier to handle in batch pipelines.

## Segment Schema
I would keep hot analytical fields flat, and also store a JSON/struct mirror for maximum retention.

Core flat fields:

- `video_id`
- `segment_id`
- `parent_segment_file`
- `is_split_part`
- `split_index`
- `segment_bucket_language`
- `segment_language_source`
- `video_declared_language`
- `video_corrected_language`
- `youtube_audio_language`
- `youtube_default_language`
- `transcription_native`
- `transcription_romanized`
- `transcription_mixed`
- `transcription_tagged`
- `has_audio_tag`
- `audio_tag_list`
- `tx_quality_score`
- `final_bucket`
- `final_has_validation`
- `duration_s`
- `audio_sha256`
- `flac_sha256`

Then a `meta_information` object or JSON column with:

- source lineage
- replay metadata from `audio_polish`
- language evidence
- validation evidence
- transcript provenance
- worker/export provenance
- original columns preserved from `transcription_results.parquet`

For the transcript object, I would keep:

- `native`
- `romanized`
- `mixed`
- `tagged`
- `original_transcription`
- `variant_route`
- `variant_validation_errors`

For language bucketing, I would bucket by segment-level language, not video-level language. My recommendation:

- primary bucket = dominant `segment_language`
- `code_mixed = true/false`
- if clearly mixed but dominant language exists, still bucket under that dominant language
- if no confident dominant language, bucket under `mixed` or `und`

That lets a Hindi podcast with 30% English segments naturally produce both `lang=hi` and `lang=en` packs.

## Audio Metrics
I would compute metrics on the replayed trimmed speech chunk before final silence padding, and separately store exported duration after padding. Padding will distort quality metrics otherwise.

Good core metrics:

- `duration_s`
- `sample_rate_hz`
- `rms_dbfs`
- `peak_dbfs`
- `crest_factor_db`
- `clipping_fraction`
- `silence_fraction`
- `vad_speech_fraction`
- `dc_offset`
- `zero_crossing_rate`
- `spectral_centroid_hz`
- `spectral_bandwidth_hz`
- `spectral_rolloff_hz`
- `spectral_flatness`
- `estimated_noise_floor_dbfs`
- `estimated_snr_db`

Useful transcript-aware metrics:

- `chars_per_sec`
- `words_per_sec`
- `unk_count`
- `inaudible_count`
- `event_tag_count`

Important caveat: `SNR` here should be treated as an estimated, reference-free SNR, not true clean/noisy-pair SNR. I’d estimate it from low-energy frames as noise floor versus speech-active frames.

## One Important Design Choice
If this export lane only repolishes, computes metrics, joins existing transcript rows, and packs outputs, it is mostly CPU + disk + network bound, not GPU bound. You can still run it on Vast GPU machines, but the GPU won’t be doing much unless you also decide to retranscribe salvage extras or run heavy model-based audio metrics. So the most efficient fleet may actually be:

- CPU-heavy export workers
- smaller GPU salvage workers

## Questions
I think these are the decisions we should settle before implementation:

1. Do you want the final export to include only transcript-backed segments, or should workers also retranscribe replay-only extras in the same run?
2. Should the export include only the kept/clean subset, or all transcripted segments with `golden/redo/dispose` style quality labels attached?
3. For mixed-language segments, are you okay with dominant-language bucketing plus `code_mixed=true`, or do you want separate `mixed/` buckets?
4. Is `audio.tar.zst + manifest.parquet` acceptable as the primary artifact shape, with `jsonl.zst` as optional?
5. Do you want me to plan this around your existing GPU Vast fleet only, or optimize for a split CPU export lane plus GPU salvage lane?

Once you answer those, I can turn this into the concrete Supabase schema, pack manifest schema, and worker state machine.

**Recommending file formats**

I'm thinking of recommending the use of parquet and tar.zst formats. The parquet manifest would handle rows while the tar.zst would bundle audio. It could also be good to keep a JSONL duplicate for those who prefer text. For each completed pack, I'd suggest writing a `manifest.parquet` containing segment metadata, an optional `manifest.jsonl.zst`, an `audio.tar.zst` with named FLACs, and a `pack_manifest.json` that includes counts, checksums, and languages.
**Considering export details**

I’m thinking about the current export labeled as "max information retained." It probably includes additional valid segments, so I want to mark them with `source_status='replayed_only_no_historical_tx'`. I’m wondering if I should create transcript variants for these extras since there's no historical transcript. I could either exclude these from the public export or plan to re-transcribe and polish them later, especially since the user mentioned new extra segments that need attention.
**Proposing table structure**

I'm considering a table for the user's request to manage 395k rows of finalized videos. First, I could create a `final_export_queue` that includes columns like video_id as the primary key, status, claimed_by, attempts, and counts. Then, I’d think about adding a `final_export_workers` table for heartbeat statistics, a `final_export_pack_manifests`, and a `final_export_video_ledger` to track counts matched, extras, and quarantined videos. Maybe I can also use a `transcript_variant_*` pattern. I'll need to determine which additional columns to include.
**Proposing directory structure**

I’m thinking about suggesting a per-worker local directory structure. It could look something like `/workspace/export_run/lang=hi/active/` with subdirectories for `audio/` and a file like `manifest.parquet`. Using PyArrow datasets seems effective, and I wonder if it’d be easiest to accumulate rows in memory. For 10,000 segments, keeping a small row buffer while storing audio files on disk could be a practical approach. Let’s explore this idea further!
**Evaluating audio metrics**

I'm thinking that maybe I can skip using JSONL if Parquet format is sufficient. It seems practical to compute metrics directly from the audio array in RAM after polishing, which means I won't need a second read. I need to compile a list of core metrics, like duration, sample rate, channels, RMS, peak levels, and others. It could be crucial to calculate these from the trimmed segments instead of padded ones, as padding could distort the metrics. That way, I can store both the raw and exported audio durations.
**Proposing metadata structure**

I've realized that mentioning the estimated SNR is important since I don't have a clean reference. I could calculate the 'noise floor' from the lowest 10-20% of RMS frames and estimate the SNR using the formula: `est_snr_db = speech_rms_db - noise_floor_db`. I also think I should propose a metadata schema that includes fields like source, language, transcription, replay, validation, audio, packaging, and perhaps a history section. This might help in organizing and managing the audio data more effectively.
**Evaluating determinism**

It seems like I've found strong evidence that the current replay is deterministic locally. However, that only proves current replay determinism, and not historical byte identity, since the old polished versions were never stored. I think it's important to note that maybe the user was asking for proof of determinism. But I should be cautious and not overstate this, as there are nuances to consider. Let's clarify these points!
**Planning the Export Framework**

I need to create a concise plan for the user that balances detail and brevity. Given the complexity, I think I'll break it into 2-4 sections without overwhelming with length. I might include inline code references such as `validations/recover_loader.py` and `src/audio_polish.py`, while sidestepping full code blocks. I should mention that I haven't built the export worker yet. The naming conventions might follow a pattern like `final_export_queue` and `final_export_workers`. For storage, a structure like `r2://final-export/run=YYYYMMDD/lang=hi/pack=000123/...` could work. I'll suggest segment-level bucket packaging based on detected language.
**Structuring Language Metadata**

I want to clarify that I’ll be using the video-level `corrected_language` strictly as metadata. For the segment-level bucket, I think I should implement `segment_language = tx_detected_language`, but only if that’s present. This seems like a clean approach to ensure I'm leveraging accurate language detection without overcomplicating things. Keeping the focus on metadata helps maintain clarity in the organization, which I'm hoping will streamline the process for users in the long run.
**Considering output compression and scaling**

I should mention that using tar.zst for output pack compression is preferred over zip due to its streamability, speed, and better preservation of the directory tree. Parquet manifests can be separate, while JSONL can serve as an optional zstd mirror. It’s also important to clarify that GPUs aren't necessary for replay and metrics unless extras need re-transcription. The core export packaging is I/O and CPU bound, so expensive GPUs are only needed for heavy tasks.
**Discussing resource usage in scaling**

I want to note that while Vast instances can be utilized, it's actually the CPU, RAM, and disk throughput that matters more than GPU performance. This is an important architectural insight! I might ask the user if they’re interested in a mixed fleet, with cheaper CPU boxes for the export lane and GPU boxes reserved for the salvage lane. However, since they want to scale with multiple GPUs, I’ll mention that the export lane doesn’t benefit much from GPU unless advanced metrics or retranscription is added.
**Considering video processing efficiency**

I'm focusing on optimizing the startup process for workers by following a recovery pattern to minimize Supabase queries per video. Each worker can claim videos, but instead of downloading the large 6.34 GB parquet file for each video, I noted that downloading dated snapshots to local NVMe once at startup is more efficient. It’s crucial to avoid unnecessary downloads, as the user suggested; downloading a consolidated snapshot or implementing row group pruning could streamline this.
**Optimizing data sharding and processing**

I’m thinking about the best way to shard reference snapshots by hash or use a small `video_id` for row offsets, especially since the 6.34 GB size might be manageable if downloaded just once. I might suggest a one-time local download and DuckDB/Parquet filtering by `video_id`. If cold-starts become a problem, pre-splitting into 256 hash-partitioned shards could help. I should also address queue seeding and transition statuses, plus keep track of various ledger counts for auditing. Finally, it might be important to manage flushes if segment counts don't meet language thresholds.
**Clarifying pack management**

I need to clarify how to manage packs effectively. For example, I might want to set a condition to flush if the age exceeds 30 minutes, and ensure that shutting down really drains all active packs. It’s also important to mention that the pack manifest should include checksums and ensure contiguous `row_id` ordering. I think having deterministic ordering within the pack by `(video_id, segment_id)` is essential for maintaining consistency. All these measures should help streamline the process.
**Considering data storage**

I'm thinking about how to optimize data extraction. The idea of having `audio_tags: []` for better retention sounds good. Since the user asked for both `Transcription - {JSON...}` and `metaInformation - {...}`, I should propose using flattened columns for easy access in Parquet, which prefers that format. I’ll suggest a row schema combining flat columns and optional nested JSON to capture metadata efficiently, maybe providing both main flat columns and JSON sub-objects for convenience. This could maximize verbosity and retention!
**Addressing missing columns in Parquet**

I'm considering whether to mention missing dropped columns in the final Parquet file. It might be a good idea to join it back with the original files, like `data/transcription_results.parquet` and `transcription_flags.parquet`, to fill in those gaps in the export schema. I want to ensure that the export contains all necessary information for the user, so adding this step could enhance the accuracy and completeness of the data.
**Drafting workflow suggestions**

I want to keep the titles to six words or fewer, which is good. Maybe I should note that "the other agent’s safe suggestion was to avoid mixing historical export and salvage." I need to be clear about the pack format choice too.

A necessary correction is to not have each GPU download the same 6.34GB parquet per video; instead, download/cache the reference snapshots once per worker at startup.

If Vast GPU instances are still in use, that's fine; just be aware that CPU, disk, and network may become the bottleneck in the export lane.

I’ll keep the bullet points flat and consider numbering the workflow.
**Clarifying GPU validation**

I should mention that we didn't run GPU validation, which is important context. I think I could clarify that we only executed the replay pilot. Since the user specifically asked for a one-off polishing, we didn’t run any metrics or upload results. It’s essential to communicate this clearly so the user understands the limitations of what was done. Overall, I want to ensure they have all necessary information to assess the situation.
**Considering hardware options**

I’m weighing whether to run this lane on GPU boxes I already have or to split the workload into cheaper CPU export workers paired with smaller GPU salvage workers. This is a critical decision. Once I get clarity on that, I can create the concrete table schemas and the worker implementation plan. It feels like a good next step, and I'm eager to craft a well-thought-out solution!


text:
<user_query>
few questions and clarifications, so we know to tweak the plan accordingly. Starting,

1. We are matching the segments with filenames correct ? and if split, they shall be matched with _splitN correct ? this is how we know if there are items that dont have a transcription row.
2. i read your plan about seperate lanes. i dont think we need a salvage lane, cause goals is to dispose the segments that dont have a transcription row. as simple as that. if you still need to split the entire task into 2 or multiple async lanes, sure, but plan robust.
3. let's do a 15k segment flush. and if the current video's segments after adding to local folder bucket exceeds 15, export 15 and leave the remaining for next export.
4. choose some simpler packaging method. shards are also fine for me.
5. metadata looks good, but audio metrics looks overly added, donty you think like would i ever need all those metrics ? and doesnt this add latency ? retain most important ones. CPS, WPS, duration_s, sample_rate_hz, rms, peak, and few of zero crossing or whatever's important, or if it's not too ovberhead, can do the others too.
6. salvaging extras are not needed.
7. plan for heavy workers and max parallelisation and use GPUs if you have to for the metrics, or replays, or whatever.

design this now. and once done, we can test with ferw videos and then evaluate for failure recovery methods. and also, is there a way for me to be confident that there are no missing data in the packaged shards, by not downloading the shards, instead just looking at some metadata. like we can validate everything from jsonl/shards, but how do i know if the packaged audio segments are all existing in the packaged segments in zip or whatever how do i be assured that data is safe. plan for this too. i can maybe validate post completion of this task on few full audio segments, vs entire metadatafiles. but i cant do a full data download and unpackl and data check. you can plan for size check or whatever, but ensure nothing is missing. and once this pipeline is done, we can then look forward benchmarking the times, fleet horizontal scaling required, total GPUs, time, and whatever if all metrics are being reflected properly and as expected, all these comes.
</user_query>


text:
I’m tightening the design around your clarifications now: no salvage lane, 15k flushes, simpler pack format, minimal metrics, and strong remote integrity metadata so you can trust shard completeness without downloading audio.

**Inspecting pipeline results**

I’m considering looking into the packer code in validations/packer.py and checking the current pack manifests. It might be useful to examine audio metrics to estimate any compute overhead. I wonder if there's existing metric code I could inspect, like searching for zero crossings. But maybe I could just use my general knowledge for now. So I think my next step is to fetch the packer.


text:
## Clarifications
1. Yes. Matching is by exact `segment_file` string.
   - Unsplit child: exact raw parent filename, like `SPEAKER_00_0123_10.00-18.42.flac`
   - Split child: exact replay-derived suffix form, `...flac_split0`, `...flac_split1`, etc.
   - That is exactly how we detect `has transcription row` vs `no transcription row`.

2. Agreed: no salvage lane.
   - If replay produces a valid child ID and that ID is not present in the canonical final transcript parquet, we dispose it.
   - We still count and log it in the per-video ledger as `drop_reason=no_transcription_row`, but we do not retranscribe it.

3. `15,000` flush is fine.
   - If adding a video pushes a language bucket above `15,000`, we flush exactly `15,000` rows from the front of that bucket and keep the remainder buffered for the next shard.

4. Simpler packaging: use shards, not zip.
   - I recommend one shard = one language pack with:
   - `metadata.parquet`
   - `audio.tar`
   - `audio_index.parquet`
   - `manifest.json`
   - This is simpler than zip, easy to inspect, and lets you validate remotely without downloading the audio tar.

5. I agree on metrics. The first version should stay minimal.
   - Keep: `duration_s`, `sample_rate_hz`, `rms_dbfs`, `peak_dbfs`, `zero_crossing_rate`, `silence_fraction`, `chars_per_sec`, `words_per_sec`
   - Optional later: `snr_est_db`
   - I would not add spectral metrics in v1 unless we discover a real downstream need.

6. No extras salvage. Confirmed.

7. Heavy workers and max parallelization: yes.
   - But replay and these metrics are CPU/NVMe/network work, not really GPU work.
   - We can still run on GPU Vast boxes if that is your infra path, but I would design the compute path to saturate CPU threads and local disk, not depend on GPU kernels.

## Final Design
I would use **two internal stages**, not for salvage, but for robustness.

### Stage A: Per-video export builder
Each worker claims one finalized video and does:

1. Download raw tar from `1-cleaned-data`
2. Replay `audio_polish` on all raw parent FLACs
3. Regenerate canonical child IDs
4. Join against local reference snapshots:
   - canonical final transcript parquet you provide later
   - original `data/transcription_results.parquet` for backfill columns
   - transcript variants parquet
   - validation/recover parquet
   - metadata tables
5. Drop replayed children with no transcript row
6. Compute minimal metrics on the replayed waveform
7. Split kept segments by `segment_language`
8. Write durable **micro-shards** per `video_id + language`
9. Upload those micro-shards to R2
10. Mark video as `spooled`

Important: the worker should **not** mark a video fully exported just because it has been processed in memory. It marks it `spooled` only after its micro-shards are durably uploaded and HEAD-verified.

### Stage B: Language shard compactor
A second worker pool consumes uploaded micro-shards and builds final `15,000`-segment language shards.

For each language:

1. Claim pending micro-shards in order
2. Append rows into a language buffer
3. When buffer reaches `15,000`, flush exactly `15,000`
4. Leave the remainder in the buffer
5. Upload final shard artifacts
6. Mark source micro-shards `consumed`

This two-stage design is much more robust than buffering many videos in RAM on one machine. If a worker dies, already spooled video outputs are safe, and only in-flight work is retried.

## Why This Is Better
If we do everything in one stage and keep unflushed language buckets only on local ephemeral storage, then a worker crash can lose already-processed videos that were not yet packed into the final 15k shard.

With the two-stage design:

- video processing is durable
- packing is restartable
- final shard creation is idempotent
- crash recovery is much cleaner
- queue states are easier to reason about

## Matching Logic
The canonical export rule should be:

- Replay every raw parent in the video
- For each replay-valid child:
  - derive `segment_id`
  - exact-match that `segment_id` against the canonical final transcript parquet
- If matched: keep and enrich
- If unmatched: dispose and log

Disposition reasons to log:

- `no_transcription_row`
- `transcript_row_but_missing_required_fields`
- `language_unsupported`
- `variant_missing` if variants are required at export time
- `validation_missing` only if you decide validation is mandatory for export

## Shard Format
For each final language shard, upload a directory like:

- `lang=hi/shard_000123/metadata.parquet`
- `lang=hi/shard_000123/audio.tar`
- `lang=hi/shard_000123/audio_index.parquet`
- `lang=hi/shard_000123/manifest.json`

### `metadata.parquet`
One row per segment, with the fields you care about.

### `audio.tar`
Contains the FLAC files only, named by `segment_id.flac` or the canonical segment filename.

### `audio_index.parquet`
One row per tar member:

- `segment_id`
- `tar_member_name`
- `flac_size_bytes`
- `flac_sha256`
- `audio_duration_s`

This is the key to remote validation without downloading the tar.

### `manifest.json`
Small summary object:

- `shard_id`
- `language`
- `segment_count`
- `video_count`
- `metadata_row_count`
- `audio_index_row_count`
- `audio_tar_member_count`
- `sum_flac_bytes`
- `metadata_sha256`
- `audio_index_sha256`
- `audio_tar_sha256`
- `segment_id_set_sha256`
- `source_microshard_count`
- `source_video_ids_sample`
- `created_at`
- `worker_id`

## How You Can Be Confident Nothing Is Missing
You cannot know what is inside a tar or zip from object size alone. So if you want confidence without downloading the audio object, the pipeline must emit sidecar integrity files.

That means every final shard must satisfy:

1. `metadata.parquet row count == audio_index.parquet row count == manifest.segment_count`
2. `segment_id_set_sha256(metadata) == segment_id_set_sha256(audio_index)`
3. `sum(flac_size_bytes from audio_index) == manifest.sum_flac_bytes`
4. local `audio_tar_member_count == audio_index row count`
5. after upload, R2 `Content-Length` for each object matches the local size recorded in manifest
6. object SHA256 values in manifest match locally computed checksums before upload

With that, you can do a full corpus audit by downloading only:

- all `manifest.json`
- optionally all `audio_index.parquet`
- maybe all `metadata.parquet` if you want stronger row-level checks

You do **not** need to download `audio.tar` to know whether the expected members were packed, because `audio_index.parquet` is the authoritative member inventory built at pack time.

## Minimal Metadata Schema
I would keep this core shape.

Flat fields:

- `video_id`
- `segment_id`
- `parent_segment_file`
- `is_split_part`
- `split_index`
- `segment_language`
- `video_language`
- `youtube_audio_language`
- `youtube_default_language`
- `transcription_native`
- `transcription_romanized`
- `transcription_mixed`
- `transcription_tagged`
- `has_audio_tag`
- `duration_s`
- `sample_rate_hz`
- `rms_dbfs`
- `peak_dbfs`
- `zero_crossing_rate`
- `silence_fraction`
- `chars_per_sec`
- `words_per_sec`
- `tx_quality_score`
- `final_bucket`
- `audio_sha256`
- `flac_size_bytes`

Structured JSON column:

- `meta_information`

Inside `meta_information`:

- replay provenance
- source row provenance
- transcript provenance
- variant provenance
- validation provenance
- language evidence
- export provenance
- drop reason if not exported in micro-shard stage ledger

## Parallelization
### Stage A worker
Per video:

- prefetch next raw tar while current video is replaying
- parallel FLAC decode/polish with thread pool
- metrics computed inline from the already loaded waveform
- local DuckDB joins against predownloaded reference parquet
- background upload pool for micro-shards

Suggested concurrency on a heavy worker:

- `polish_threads = 16-24`
- `duckdb_threads = 8`
- `upload_workers = 4-8`
- `download_prefetch = 1`
- `microshard_upload_async = true`

### Stage B compactor
Mostly CPU + disk + network:

- 1 reader thread pool for pulling micro-shards
- 1 tar writer
- 2-4 upload workers
- one active buffer per language or per claimed language partition

## Failure Recovery
### Video stage
States:

- `pending`
- `claimed`
- `processing`
- `spooled`
- `failed`

A video becomes `spooled` only after all of its uploaded micro-shards have been HEAD-verified and their manifests inserted.

### Compaction stage
Micro-shard states:

- `pending`
- `claimed`
- `compacted`
- `failed`

A final shard becomes committed only after:

- all 4 objects uploaded
- object sizes verified with HEAD
- manifest inserted in DB
- source micro-shards marked `compacted`

### Crash safety
- unclaimed or stale `claimed` videos return to queue
- stale micro-shards return to compactor queue
- final shard writes are idempotent by `shard_id`
- manifest insert is `ON CONFLICT DO NOTHING`
- source micro-shards are only marked consumed after final shard commit

## What I Recommend Next
The next concrete step should be a **small end-to-end pilot** on a few videos:

1. Build the per-video micro-shard format
2. Build the final 15k shard format
3. Run 3-5 mixed-language videos through Stage A
4. Compact those into a few tiny test shards
5. Run the remote integrity audit only from manifests and `audio_index.parquet`
6. Then benchmark:
   - per-video replay time
   - per-segment metric overhead
   - micro-shard upload time
   - compaction throughput
   - total CPU saturation
   - required fleet size for full run

If you want, I can next turn this into the exact table schema and state machine for:
- `final_export_queue`
- `final_export_video_outputs`
- `final_export_microshards`
- `final_export_shards`
- `final_export_workers`

**Designing for performance**

I'm considering the R2 object count, which might involve many packs. It seems like I need a design tailored for heavy workloads and maximum parallelization, potentially requiring GPUs. As for replay and metrics, they primarily rely on CPUs, and I realize that audio metrics can efficiently use numpy without needing a GPU. It’s crucial to leverage GPU instances for NVMe, networking, and parallelization while also taking advantage of efficient CPU capabilities from Vast GPUs.
**Evaluating service samples**

I'm looking into how the periodic auditor service samples `pack_manifest.json` and cross-checks it. It checks if the manifest's row count matches the metadata parquet’s row count, verifies segment IDs, and checks that the tar object size matches the manifest tar size.

To make verification easier, I think about uploading a small file like `tar_index.parquet` or `tar_members.json.zst` to list every tar member and its size. This approach addresses the user’s concern without needing to download the entire tar.
**Considering file integrity**

I’m thinking about how to store the object’s sha256 in custom metadata. The `upload_file` function may not support it easily, so maybe using `put_object` with metadata is a better idea. However, it might be easier to upload a `.sha256` file or just include the sha256 directly in the manifest.

For a remote audit without downloading the tar, I can compare the manifest and the R2 HEAD object size. It's rare to experience object corruption at the R2 layer, but generating an `audio_index` during tar creation is important to ensure everything is intact.
**Designing pack creation sequence**

I'm considering a sequence for pack creation. First, the local bucket buffer can hold up to 15,000 segment records and FLAC files. Once the count exceeds that, I'll sort by `video_id` and `segment_id`, splitting off exactly 15,000 to flush while keeping the remainder.

Then, I'll build `metadata.parquet` and `audio_index.parquet` from those rows, and stream the corresponding FLAC bytes into `audio.tar`. As I write each member, I’ll count and hash them, ensuring that the tar member count matches the metadata and audio index row counts.

After uploading `audio.tar`, `metadata.parquet`, and `audio_index.parquet`, I'll perform a HEAD request to compare sizes. Finally, I'll delete the local bucket slice and mark the manifest as committed.

Since the user mentioned evaluating failure recovery, it makes sense to outline potential recovery methods now at a high level.
**Handling segment overflow**

I need to address what happens if the current video segments exceed 15,000. When that occurs, I should export exactly 15,000 segments and leave any remainder for future processing. This can help keep things organized and ensure that I’m not overwhelmed with too many segments at once. It’s an important detail to mention in the overall plan, as it ensures a smooth workflow while managing segment records efficiently.
**Organizing metrics for analysis**

I'm thinking about the metrics to mention for the user. I could focus on simpler metrics to reduce overhead, such as duration, sample rate, RMS, peak levels, zero-crossing rate, estimated silence, and counts per second. Maybe for the first release, I should keep it to these eight key Tier 1 metrics. The user also asked about SNR, so I should mention that it could be included later if needed. I’ll clarify the matching segments with filenames, specifically about how they relate to replayed and derived IDs.
**Clarifying transcription and processing workflow**

I’m considering how to handle the unsplit match to the raw parent filename to detect any missing transcription rows. If a valid child ID isn't found, it should be counted as a "no_tx_row." I think mentioning that the final export should be based on the filtered parquet file is important, not the old 11.9GB version. The user mentioned using an interim final parquet until later, so I’ll reiterate the plan to allow swapping the canonical input snapshot. There’s also a need for efficient workers, such as using a downloader thread and multiple decode workers without GPU for certain tasks.
**Structuring GPU considerations and benchmarking**

I’m thinking about how to approach mentioning that the GPU should be treated as incidental, rather than essential for correctness. It could be helpful to include that benchmarking will come after the pilot, and I want to clarify what needs to be measured. Keeping it concise is important, but I also need to add enough details. Maybe I can organize the content into 3-4 sections, starting with answering numbered clarifications, followed by the design. I'll mention the use of a simpler packaging method and that shards are acceptable for me.
**Designing video processing**

I’m mulling over how to handle processed videos. Each video writes to a local spool file and gets recorded as `processed_pending_pack`. The packs are assembled for upload and marked as `exported`. I’m considering an alternative where each video goes directly to R2 before the compactor builds language shards. The user wants a robust solution with multiple lanes, so maybe I should implement a two-stage process: export builds followed by language compaction. I need to be mindful not to mark a video as done until all parts are secure.
**Improving video packing process**

I’m considering the issue of pack waiting too long to fill 15k, which might lead to queue lock issues. A possible solution is to use a durable spool for processed videos. I could mark a video as `spooled` once its segments are uploaded or saved. Then, a separate compactor could pack these segments into 15k language shards. This two-stage pipeline seems more robust and aligns with the user’s request for multiple async lanes without a salvage lane. I need to design Lane A for video processing workers.
**Considering video processing**

I’m marking the video as “spooled.” For Lane B, the shard compactor workers will handle spool artifacts based on language, accumulating them until reaching 15k before uploading. This adds durability, but it could complicate things. The user wants robustness, though, so perhaps I could simplify by making each video spool artifact a tar that includes audio and metadata. After processing, the worker could write segments into local spool files, with eventual packing into final shards, ensuring durability and efficient recovery.
**Outlining processing stages**

I’m proposing two stages for handling video exports. Stage 1 involves a video export worker that manages deterministic replay, joins metrics, and uploads per-language video spool shards to R2. Each video creates a limited number of spool objects, like one for metadata and one for audio.

In Stage 2, the language compactor reads these spool objects and repacks them into final 15k language shards. This stage is focused on CPU/network operations and ensures the processed videos remain durable, even if there’s a failure later on. The user mentioned the structure can be kept robust, so we’ve removed the salvage lane for a more streamlined process with internal lanes: video processor and pack compactor. For Lane 1, I’ll only process exportable segments that contain transcript rows.
**Designing language-specific exports**

For each language in the video, I need to create export files. I could produce a `video_export_meta.parquet` file for each video, containing details of all segments with their audio paths, alongside individual audio files like `audio_dir/seg.flac`.

Alternatively, I could simplify things by uploading one `video_id.tar` that includes all exportable FLAC files and the corresponding `video_id.parquet`. However, this would require the compactor to download the entire video spool to extract segments for specific languages. To improve efficiency, it seems better to create a single spool object for each `video_id + language`, containing only the relevant segments.
**Considering proof requirements**

I'm realizing that proving the tar member contents from the R2 HEAD alone is really tricky. It seems like we can’t do it without also uploading a separate `audio_index.parquet` file that was built during the packing process. This makes me think about how communication around this needs to be clear so that others know what’s essential for validation. Let's focus on ensuring that the necessary files are readily available for proper verification!