role: user

type: text

text:
<user_query>
@\home\ubuntu\.cursor\projects\home-ubuntu-transcripts\agent-transcripts\d0be19b0-5479-4b7c-bb93-6fbe803d474b\d0be19b0-5479-4b7c-bb93-6fbe803d474b.jsonl

the other agent is taking care of the fleet deployment and validations. meanwhile go through the code and tell me, why are we packing the videos as microshards and video=videoID format. a typical path is finalsftdata/final-export/production/microshards/lang=as/video=--7aNzpAuPw/--7aNzpAuPw_as_0000/ and it has
audio.tar
application/x-tar
Standard
215.04 kB
11 Mar 2026 23:26:34
PDT

audio_index.parquet
--
Standard
2.59 kB
11 Mar 2026 23:26:35
PDT

manifest.json
--
Standard
885 B
11 Mar 2026 23:26:35
PDT

metadata.parquet
--
Standard
14.04 kB
11 Mar 2026 23:26:32
PDT

werent we supposed to pack the segments based on their languages. like there are certain segments in each video specifically that are not native language. like a video that is totally in telugu, might have segments of speaker that spoke in english in the middle and i wanted to split them. are we making microshards into both languages? or what's happening here. explain.
</user_query>


role: assistant

text:
I’m tracing the final export path layout and the language-routing logic in code so I can tell you exactly whether mixed-language segments are being split or just grouped under the video’s primary language.

**Inspecting code and transcripts**

I need to examine the code for the microshard/video format and language split logic. It seems I might need to read a transcript file, which is a JSONL format from another agent. The user provided a specific path, so I should inspect that. I’ll look for terms like microshard, final-export, lang=, and video= in the code. While the README might have relevant information, I can't grep through current MD files or create new ones. I’ll need to follow instructions closely!
**Updating documentation process**

It looks like this is about updating documentation. I should probably avoid using grep on the docs since it's not necessary. Instead, I'll likely use `rg` for searching, which might be more efficient. I can utilize `multi_tool_use.parallel` to search multiple patterns and read the transcript. Even though the developer suggests using Subagent for broader exploration, this task is more specific, so direct `rg` searches should work fine. I need to check the open transcript path and provide a commentary update.


text:
I found the packing code path; now I’m reading the worker and reference-query logic to separate “storage grouping” from “per-segment language labeling,” because those are not the same thing here.

**Inspecting microshard creation code**

I need to come up with a short title that’s six words or fewer. It’s allowed, so I’ll make that a priority. Then, I’ll focus on inspecting the code related to microshard creation. That seems like it will require a detailed look to ensure everything’s functioning correctly. I want to make sure nothing is overlooked, so I'll be thorough in this analysis. Alright, let’s get started on that!


text:
I’ve confirmed microshards are created after grouping kept segments by `segment_language`; next I’m checking the pack artifact layout itself so I can explain why you see `audio.tar`, `metadata.parquet`, and `audio_index.parquet` together under a `lang=.../video=.../microshard_id` path.

**Clarifying export artifacts**

I think I need to look into the final_export_common to explain what audio.tar and audio_index.parquet are all about. It feels like I should also check out lines beyond what I initially saw. Additionally, examining final_export_config could help with prefixes. I might want to validate the final_export_run to better understand expectations too. All in all, diving into these details will give me a clearer picture.


text: I found the key normalization step: the export doesn’t blindly use the video language. It synthesizes `segment_language` first, then the worker groups on that value. I’m reading that exact SQL now so I can tell you which language wins when a segment is mixed or mislabeled upstream.


text:
Yes, the code is meant to split by segment language, not just by the video’s overall language. But it does that in a 2-stage way:

1. Stage A creates microshards per `(video_id, segment_language, chunk_index)`.
2. Stage B later compacts those microshards into final language-only shards across many videos.

So `video=videoID` is not replacing language-based packing. It’s just keeping the Stage A output traceable to one source video.

The key part is in `src/final_export_reference_store.py`, where `segment_language` is resolved per segment, not per video:

```273:281:src/final_export_reference_store.py
SELECT
    *,
    coalesce(
        nullif(tx_detected_language, ''),
        nullif(gemini_lang, ''),
        nullif(corrected_language, ''),
        nullif(queue_language, '')
    ) AS segment_language
FROM read_parquet('{canonical_path}')
```

Then `src/final_export_worker.py` groups exported rows by that `segment_language` and uploads each group to a language-specific prefix:

```223:286:src/final_export_worker.py
segment_language = str(row.get("segment_language") or "").strip().lower()
if not segment_language:
    drop_counts["transcript_row_but_missing_required_fields"] += 1
    continue
if segment_language not in self.config.supported_languages:
    drop_counts["language_unsupported"] += 1
    continue

payload = build_export_segment_payload(
    video_id=video_id,
    canonical_row=row,
    polished_segment=polished,
    run_id=self.config.run_id,
    worker_id=self.config.worker_id,
    exported_at=datetime.now(timezone.utc).isoformat(),
)
grouped_metadata[segment_language].append(payload["metadata_row"])
grouped_audio[segment_language].append(payload["audio_row"])

...

microshard_id = f"{video_id}_{language}_{chunk_index:04d}"

...

base_prefix=(
    f"{self.config.microshard_prefix}/lang={language}/video={video_id}/{microshard_id}"
)
```

So for your specific question:

- If a Telugu video has Telugu segments and some English segments in the middle, and upstream labeling marks those English segments as `en`, then this exporter will produce both:
  - `.../microshards/lang=te/video=<video_id>/...`
  - `.../microshards/lang=en/video=<video_id>/...`
- If you only see `.../lang=as/video=--7aNzpAuPw/...`, that does not prove the whole video was only Assamese. It only proves that one microshard exists for Assamese. Any other language for that same video would be under a different top-level `lang=...` branch, not beside it.
- If the mixed-language segments were not labeled differently upstream, they will stay under the fallback language for that segment, which can end up being the video language.
- If a segment has no usable language or an unsupported one, it gets dropped rather than packed.

That last point is the important caveat: this export stage does not do fresh language detection from audio. It trusts the resolved `segment_language` already present in the reference data. So mixed-language splitting only happens if those upstream fields are already correct.

Why the `video=videoID` level exists:
- Stage A never mixes videos inside a microshard.
- That makes retries, auditing, and per-video validation much safer.
- Stage B is where cross-video mixing happens, but only within one language.

That’s also why the pack contains these four files:

```299:338:src/final_export_common.py
metadata_path = pack_dir / "metadata.parquet"
audio_tar_path = pack_dir / "audio.tar"
audio_index_path = pack_dir / "audio_index.parquet"
manifest_path = pack_dir / manifest_name

pq.write_table(pa.Table.from_pylist(metadata_rows), metadata_path, compression="zstd")

audio_index_rows: list[dict[str, Any]] = []
member_names: list[str] = []
sum_flac_bytes = 0
with tarfile.open(audio_tar_path, "w") as tf:
    for item in audio_rows:
        member_name = item["tar_member_name"]
        flac_bytes = item["flac_bytes"]
        info = tarfile.TarInfo(name=member_name)
        info.size = len(flac_bytes)
        tf.addfile(info, io.BytesIO(flac_bytes))
        member_names.append(member_name)
        sum_flac_bytes += len(flac_bytes)
        audio_index_rows.append(
            {
                "segment_id": item["segment_id"],
                "video_id": item["video_id"],
                "tar_member_name": member_name,
                "flac_size_bytes": len(flac_bytes),
                "flac_sha256": item["flac_sha256"],
                "audio_duration_s": item["audio_duration_s"],
            }
        )
```

In practice:
- `metadata.parquet`: per-segment text, language, metrics, provenance
- `audio.tar`: the FLAC bytes bundled together to avoid tiny-object explosion
- `audio_index.parquet`: maps each segment to its tar member, size, sha
- `manifest.json`: counts and hashes for audit

One more subtle but important distinction: each metadata row stores both `segment_language` and `video_language`. So an English segment inside a Telugu video can be exported with `segment_language='en'` while still carrying `video_language='te'` in metadata. That is exactly how mixed-language videos are supposed to survive cleanly through the pipeline.

So the short answer is:

- Yes, the design is segment-language aware.
- No, `video=videoID` does not mean “packed only by video language.”
- A single video can absolutely create microshards in multiple languages.
- But whether that actually happens depends entirely on the upstream `segment_language` resolution, not on new detection at export time.

This matches what the live pipeline had already observed in a real run, where one video produced separate `kn`, `en`, and `te` microshards in [Fleet deploy status](d0be19b0-5479-4b7c-bb93-6fbe803d474b).

If you want, I can next trace one specific video ID through the reference parquet logic and tell you exactly why it landed under `lang=as` and whether it should also have emitted `en` or another language.

**Clarifying video metadata**

I'm thinking about Telugu videos with English segments. In that case, the metadata rows of the English microshard will indicate `segment_language='en'` while `video_language='te'`. That’s an important distinction to make! I could also mention how the `segment_language` is prioritized based on the precedence from the reference store, which computes using `coalesce(tx_detected_language, gemini_lang, corrected_language, queue_language)` from the canonical view. That adds clarity to the process.
**Citing code references**

I need to think about how to cite code properly using code fences with start:end:path and content. I might need to cite specific references like 272-285 from the reference store and 223-286 from the worker. For the final output, I should include actual content, possibly snippets, along with the relevant code lines. It looks like I have the line numbers, so I’ll aim to cite code blocks without any language tags, just to keep it clear.
**Clarifying language segments**

I'm thinking about how the `segment_language` is set within a video path. If it remains in Telugu, it won't export properly. It's crucial to note that the exporter doesn't inspect the audio for overrides, relying instead on precomputed labels. I should consider mentioning whether English is supported and convey that unsupported languages get dropped. However, I might not need to overcomplicate this. I want to reassure that if a segment isn't in the list of supported languages, it won't be exported.
**Explaining microshard structure**

Yes, I've realized that under the paths like `microshards/lang=en/video=<video_id>/` and `microshards/lang=te/video=...`, the prefix starts with `lang`. If someone only checks the `lang=as/video=...`, it won’t show other languages, which is pretty crucial to mention! If a user looks at a path like `.../microshards/lang=as/video=--7aNzpAuPw/...`, that just shows there's an Assamese microshard. To find out if English microshards exist, they need to check under `.../microshards/lang=*/video=--7aNzpAuPw/` or query `final_export_microshards`.
**Clarifying microshard evidence**

I think I could mention that in a previous fleet transcript, one video produced microshards in languages like `kn`, `en`, and `te`, which shows that multiple language microshards exist. This information came from the transcript, and I could potentially cite the [Fleet deploy state](uuid) for support. However, since the user asked for a code explanation, I need to focus on providing the relevant code details instead of just citing the transcript.
**Considering evidence and transcripts**

I think evidence could help here. One agent observed a real video producing three microshards in `kn`, `en`, and `te`, which aligns with the code. Since the transcript is accessible, I wonder if I can quote it or just note the previous run. I should avoid relying too heavily on it, as it might be outdated. I can mention this matches another agent's observation of the video. Also, I need to confirm if `video=` is necessary, considering the database already has `video_id`.
**Inferring microshard structure**

I can infer that the `microshard_id` follows the format `{video_id}_{language}_{chunk_index:04d}`. The manifest includes `source_video_ids_sample: [video_id]`, and the database retains information like `video_id`, `language`, `run_id`, and `chunk_index`. The compactor’s `_flush_buffer` generates final shards by language, intentionally organizing the output to handle one video at a time in stage A while allowing stage B to compact across videos. This setup likely aids in fault isolation and enabling precise validation. I might also check `commit_microshard_consumption`, although it might not be necessary.


text:
<user_query>
but where does cross-video mixing happens in this scenario ?
</user_query>


text:
<user_query>
but, i dont see a prefix named shards/, all i see are microshards. does this phaseB occurs differently ? or seperately, or is it planned to be happening now ? what's been there ?
</user_query>


text: I’m checking the Stage B entrypoints and deploy script now so I can tell you whether final shards are supposed to appear from a separate worker process or only later as a drain/compaction step.


text:
Cross-video mixing happens only in Phase B, inside the compactor. It is a separate worker, not part of the Stage A video spooler.

Stage A only does:

- one source video at a time
- split that video’s kept segments by `segment_language`
- upload intermediate packs under `.../microshards/lang=.../video=.../...`

Stage B is where it starts pulling many microshards of the same language, regardless of source video, buffering them together, and writing a final shard under `.../shards/lang=.../...`.

You can see that directly here:

```140:157:src/final_export_compactor.py
new_jobs = await self.db.claim_microshards_for_language(
    worker_id=self.config.worker_id,
    run_id=self.config.run_id,
    language=language,
    limit=self.config.compactor_claim_limit,
)
if not new_jobs:
    exhausted = True
else:
    self.stats.jobs_claimed += len(new_jobs)
    for job in new_jobs:
        claimed_jobs[job.microshard_id] = job
        buffer.extend(self._load_microshard_rows(job))

...

if len(buffer) >= self.config.final_shard_target_rows:
    await self._flush_buffer(language, buffer, self.config.final_shard_target_rows)
```

And when it flushes, it explicitly acknowledges multiple videos in one shard:

```221:243:src/final_export_compactor.py
source_counts = Counter(item.microshard_id for item in selected)
video_ids = sorted({item.video_id for item in selected})
artifacts = build_pack_artifacts(
    pack_dir=pack_dir,
    manifest_name="manifest.json",
    metadata_rows=metadata_rows,
    audio_rows=audio_rows,
    manifest_payload={
        "shard_id": shard_id,
        "run_id": self.config.run_id,
        "language": language,
        "segment_count": len(selected),
        "video_count": len(video_ids),
        "source_microshard_count": len(source_counts),
        "source_video_ids_sample": video_ids[:32],
        ...
    },
)
keys = self._upload_pack(
    bucket=self.config.output_bucket,
    base_prefix=f"{self.config.shard_prefix}/lang={language}/{shard_id}",
    artifacts=artifacts,
)
```

So to your second question: if you do not see a `shards/` prefix, then Phase B has not produced output for that run/prefix yet.

From the code, that can happen for a few reasons:

1. You are only running Stage A workers.
   The launcher defaults to `video`, not `compact`.

```4:12:scripts/run_final_export_worker.sh
STAGE="${FINAL_EXPORT_STAGE:-video}"

case "${STAGE}" in
  video)
    exec python -m src.final_export_video_main
    ;;
  compact|compactor)
    exec python -m src.final_export_compact_main
```

2. The deploy script also defaults to video-stage unless `--stage compact` is passed.

```17:18:scripts/deploy_final_export.sh
STAGE="${STAGE:-video}"
DRY_RUN="false"
```

3. Even if compactor workers are running, they only emit a final shard when a language buffer reaches the threshold.
   Default is `15000` rows per final shard.

```31:33:scripts/deploy_final_export.sh
FINAL_EXPORT_FINAL_SHARD_ROWS="${FINAL_EXPORT_FINAL_SHARD_ROWS:-15000}"
FINAL_EXPORT_MICROSHARD_TARGET_ROWS="${FINAL_EXPORT_MICSHARD_TARGET_ROWS:-5000}"
FINAL_EXPORT_ALLOW_PARTIAL_SHARDS="${FINAL_EXPORT_ALLOW_PARTIAL_SHARDS:-false}"
```

4. Partial flush is disabled by default.
   So if a language has, say, only 8,000 rows accumulated so far, you will still see only microshards and no final `shards/` output yet.

That means:

- `microshards/` = Stage A intermediate output
- `shards/` = Stage B compacted output
- if only `microshards/` exists, then either:
  - Stage B is not running
  - or it is running but has not yet reached shard flush threshold
  - or it is waiting for an explicit end-of-run drain with `allow_partial_shards=true`

So the answer is: yes, Phase B is separate. It does not happen “inside” the video worker. If all you see is `microshards/`, then what has happened so far is likely only Stage A, or Stage B has not flushed anything yet.

If you want, I can next tell you from the current deploy/runtime setup whether your live fleet is actually running any `compact` workers at all, or whether everything so far is only producing Stage A microshards.


text:
<user_query>
okay so the other agent, wants to finish all Stage A first, then go to stage B ? i mean the other agent will have to launch stageB once stageA is finished as a seperate run right ?
</user_query>


text:
Yes, that’s how it looks.

From the code and the prior run context, the big production fleet was almost certainly doing Stage A only, because deployment defaults to `video` stage:

```4:12:scripts/run_final_export_worker.sh
STAGE="${FINAL_EXPORT_STAGE:-video}"

case "${STAGE}" in
  video)
    exec python -m src.final_export_video_main
    ;;
  compact|compactor)
    exec python -m src.final_export_compact_main
```

And the deploy script also defaults to `video` unless someone explicitly passes `--stage compact`:

```17:18:scripts/deploy_final_export.sh
STAGE="${STAGE:-video}"
DRY_RUN="false"
```

So the practical answer is:

- Yes, Stage B has to be launched separately.
- No, it should not be a separate `run_id` if it is meant to consume the Stage A microshards from this production batch.
- It is a separate worker fleet/process, but for the same logical export run.

That last part is important because the compactor only claims microshards for the matching `run_id`:

```440:474:src/final_export_db.py
async def claim_microshards_for_language(
    self,
    *,
    worker_id: str,
    run_id: str,
    language: str,
    limit: int,
) -> list[FinalExportMicroshardJob]:
    ...
    WHERE status = 'pending'
      AND run_id = $2
      AND language = $3
      AND consumed_rows < row_count
```

So if the other agent launches Stage B later with a new run id, it will see nothing. To compact the existing microshards, Stage B must use the same `FINAL_EXPORT_RUN_ID` as Stage A.

So the clean mental model is:

- Stage A run: produce `microshards/`
- Stage B deployment: separate compactor workers
- Same `run_id`
- Same output family/prefix
- Stage B writes `shards/`

If the other agent’s intention is “finish all Stage A first, then compact,” that is valid. But the launch command should be something like the same deployment flow with `--stage compact`, not a brand new export run.

One more nuance: Stage B does not strictly need to wait for Stage A to fully finish. It can run concurrently. But if they want simpler operations, they can do:
1. finish Stage A
2. launch compactor workers with same `run_id`
3. optionally do a final drain pass with `allow_partial_shards=true`

So your wording is almost right, with one correction:

- separate deployment: yes
- separate run: no

If you want, I can give you the exact env/command shape the other agent should use to launch Stage B against the already-produced microshards.

**Analyzing user query**

I need to address the user's question carefully, considering if tools are necessary. It seems like I'll reference the agent transcript and the related code. Based on the transcript, it looks like there was validation for strict validators in both microshards and final shards during the pilot.

I should note that we want to avoid overstepping what we can claim based on this evidence. Additionally, I'll mention that the deploy script defaults to stage video, indicating that it likely involves Stage A workers unless specified otherwise.
**Clarifying the stage deployment**

The user's question is about whether the other agent needs to finish all of Stage A before moving on to Stage B. Based on my understanding of the code, it looks like the production fleet runs all of Stage A first. Stage B operates as a separate worker mode, but it can use the same run_id with a `--stage compact` deployment.

It doesn't require a different run_id, just the same `FINAL_EXPORT_RUN_ID`. Stage B can launch either after Stage A is complete or even while it's still running, given that the setting of allow_partial_shards is false and target is 15000.
**Explaining stage deployment nuances**

If the agent intends to finish Stage A before running Stage B, they would launch separate compact workers afterward, likely using the same run_id and output prefix—definitely not a separate data run. It’s essential to caution that if they opt for a new run_id, the compactor won't recognize the microshards since it filters by run_id. I should clarify that while it’s a separate deployment, it shouldn’t be treated as a separate run_id.

Also, I can reference the `claim_microshards_for_language` for run_id filtering in the code, while noting the default deployment stage as `STAGE=video` and that the compactor claims microshards by the same run_id.


text:
<user_query>
i see. here is what the other agent said.

Stage B (Language Compactor) Plan:
The pipeline is two-stage by design:
Stage A (current): Per-video processing - download audio, polish, filter, create per-video microshards. Our 240 workers are doing this now.
Stage B: Language compaction - takes all the microshards from Stage A and packs them into final language-level shards (e.g., all Hindi segments into Hindi shards, all Tamil into Tamil shards).
Stage B runs AFTER Stage A completes because it needs all microshards available to properly pack by language. The deploy script already supports --stage compact and the compactor code is in src/final_export_compactor.py / src/final_export_compact_main.py. The compactor claims microshards by language via final_export_language_leases in Supabase. Each language gets its own lease, so 2-4 workers per language can run in parallel safely. We can reuse the same Docker image with --stage compact.
The plan: Once Stage A finishes, we keep a subset of workers (~40-60) for Stage B compaction, destroy the rest. Quick and clean. Let me add this to TODO and resume monitoring.
</user_query>


text:
I’m verifying the compactor lease behavior before answering, because the “2-4 workers per language” part may not match what the code actually enforces.

**Evaluating concurrency in agents**

I need to determine if another agent's statement about concurrency is correct. I might have to inspect the code to see if Stage B can run concurrently and if there can be one worker per language. I should verify whether "2-4 workers per language can run in parallel safely" is true; it may not be valid if language leases allow only one worker at a time. I’ll check the compactor and database for language leases while paying attention to any caveats.


text: I’ve confirmed the lease semantics: compaction is serialized per language. So the broad plan is fine, but one part of the other agent’s description needs correction.


text:
Mostly yes, with 2 important corrections.

The good part:
- Stage B is a separate deployment/process.
- It should be launched after Stage A if they want the simpler operational model.
- It must use the same `FINAL_EXPORT_RUN_ID` as Stage A, otherwise it will not see those microshards.

The correction:
- The code does not allow “2-4 workers per language”.
- It allows only one active lease per language at a time.

You can see that in the lease table logic: the row is keyed by `language`, and the compactor inserts or updates that single lease row.

```354:404:src/final_export_db.py
async def acquire_language_lease(
    self,
    *,
    worker_id: str,
    run_id: str,
    lease_seconds: int,
    languages: list[str] | None = None,
) -> Optional[str]:
    ...
    WITH candidates AS (
        SELECT language, min(created_at) AS oldest_created_at
        FROM final_export_microshards
        WHERE status = 'pending'
          AND consumed_rows < row_count
          {language_filter_sql}
        GROUP BY language
        ORDER BY oldest_created_at, language
        LIMIT 20
    )
    SELECT language
    FROM candidates
    ...
    INSERT INTO final_export_language_leases (
        language, claimed_by, claimed_at, heartbeat_at, lease_expires_at, updated_at
    )
    VALUES ($1, $2, now(), now(), now() + make_interval(secs => $3), now())
    ON CONFLICT (language) DO UPDATE SET
        claimed_by = EXCLUDED.claimed_by,
        claimed_at = EXCLUDED.claimed_at,
        heartbeat_at = EXCLUDED.heartbeat_at,
        lease_expires_at = EXCLUDED.lease_expires_at,
        updated_at = now()
    WHERE final_export_language_leases.lease_expires_at < now()
       OR final_export_language_leases.claimed_by = EXCLUDED.claimed_by
```

And the supported languages list is only 12 total:

```25:40:src/config.py
LANGUAGE_MAP: dict[str, tuple[str, str, str]] = {
    "hi": ("Hindi", "Devanagari", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "mr": ("Marathi", "Devanagari", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "te": ("Telugu", "Telugu", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "ta": ("Tamil", "Tamil", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "kn": ("Kannada", "Kannada", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "ml": ("Malayalam", "Malayalam", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "gu": ("Gujarati", "Gujarati", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "pa": ("Punjabi", "Gurmukhi", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "bn": ("Bengali", "Bengali", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "as": ("Assamese", "Assamese", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "or": ("Odia", "Odia", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "en": ("English", "Latin", "Standard English transcription rules apply."),
}
SUPPORTED_LANGUAGES = list(LANGUAGE_MAP.keys())
```

So the real takeaway is:

- Separate Stage B run: yes
- Same `run_id`: yes
- Wait until Stage A is done: reasonable and simple
- Keep `40-60` workers for Stage B: no, that is far more than this code can actually use efficiently

With this implementation, the practical ceiling for concurrent compaction is roughly:
- one worker per active language
- so about `<= 12` useful compactor workers
- maybe a couple extra for lease failover, but not 40-60

Also, one more nuance: if they run Stage B with `FINAL_EXPORT_ALLOW_PARTIAL_SHARDS=false`, some leftover rows may remain as pending microshards until the very end. So the normal finish pattern should be:

1. Run Stage B compaction with the same `run_id`
2. Let it write full shards
3. Do one final drain pass with `allow_partial_shards=true`

So yes, the other agent’s overall sequencing idea is sound, but the “2-4 workers per language” and “40-60 workers for Stage B” parts do not match the current code. A much more realistic Stage B fleet is around `8-12` workers, depending on how many languages actually have pending microshards.

**Clarifying deployment details**

I need to respond concisely, mentioning that Stage B allows for separate deployments with the same run_id. It doesn’t need to wait, even though that might be operationally simple. Instead of 2-4 workers per language, we can have one active lease per language, maximizing parallelism at about 12 languages. While I might suggest keeping 40-60 workers, that could lead to many idle unless we're filtering languages. Ultimately, each worker processes one language at a time, and splitting languages isn’t necessary.
**Addressing user query**

The user asked about an agent mentioning 40-60 for Stage B, and I need to correct any inaccuracies. My response could say, "Mostly yes, but there are two corrections." I’ll include a code block for one lease per language and note the count of supported languages. I'll mention that Stage B can launch after Stage A, and if only microshards are visible, then Stage B hasn’t been launched. Finally, I’ll touch on the plan needing a final drain pass while keeping it concise.


text:
<user_query>
what is the final shard is going to look like. can you give me a schema example of it. like how are the segments planned and the metadata files. i will also plan other datasets to be of the same schema.
</user_query>


text: I’m pulling the exact row schemas from the pack builder so I can give you the final shard layout as it is actually written, not a guessed summary.


text: I’ve got the row schema; I’m pulling the manifest fields too so I can give you a complete dataset contract for `metadata.parquet`, `audio_index.parquet`, `audio.tar`, and `manifest.json`.


text:
A final shard is basically the same pack format as a microshard, but compacted across many source videos for one language.

Path shape:
- `finalsftdata/final-export/production/shards/lang=<language>/<shard_id>/`
- Example:
  - `finalsftdata/final-export/production/shards/lang=hi/hi_shard_1710200000_000123/`

Inside that folder, it will contain exactly 4 files:
- `metadata.parquet`
- `audio.tar`
- `audio_index.parquet`
- `manifest.json`

The compactor writes it like this:

```216:243:src/final_export_compactor.py
shard_id = f"{language}_shard_{int(time.time())}_{self.stats.packs_uploaded + 1:06d}"
pack_dir = self._work_root / "final_shards" / language / shard_id
metadata_rows = [item.metadata_row for item in selected]
audio_rows = self._load_audio_rows(selected)

source_counts = Counter(item.microshard_id for item in selected)
video_ids = sorted({item.video_id for item in selected})
artifacts = build_pack_artifacts(
    pack_dir=pack_dir,
    manifest_name="manifest.json",
    metadata_rows=metadata_rows,
    audio_rows=audio_rows,
    manifest_payload={
        "shard_id": shard_id,
        "run_id": self.config.run_id,
        "language": language,
        "segment_count": len(selected),
        "video_count": len(video_ids),
        "source_microshard_count": len(source_counts),
        "source_video_ids_sample": video_ids[:32],
        "worker_id": self.config.worker_id,
        "created_at": datetime.now(timezone.utc).isoformat(),
    },
)
keys = self._upload_pack(
    bucket=self.config.output_bucket,
    base_prefix=f"{self.config.shard_prefix}/lang={language}/{shard_id}",
    artifacts=artifacts,
)
```

## What a final shard means
- One final shard contains segments from only one `segment_language`.
- It can mix many `video_id`s.
- Default target size is `15000` segments per shard.
- Leftovers are held back unless a final drain pass enables partial shards.

So this is the intended semantic contract:
- shard-level language purity: yes
- cross-video mixing: yes
- one row per exported segment: yes
- audio bytes bundled in one tar: yes

## `metadata.parquet` schema
This is the main training/analytics table. Each row is one segment.

The code writes these exact columns:

```253:287:src/final_export_common.py
metadata_row = {
    "video_id": video_id,
    "segment_id": segment_id,
    "parent_segment_file": str(
        canonical_row.get("parent_segment_file") or polished_segment.trim_meta.original_file
    ),
    "is_split_part": bool(
        canonical_row.get("is_split_segment", polished_segment.trim_meta.was_split)
    ),
    "split_index": int(
        canonical_row.get("split_index_from_id") or polished_segment.trim_meta.split_index or 0
    ),
    "segment_language": str(canonical_row.get("segment_language") or ""),
    "video_language": str(canonical_row.get("corrected_language") or canonical_row.get("queue_language") or ""),
    "youtube_audio_language": str(canonical_row.get("youtube_audio_language") or ""),
    "youtube_default_language": str(canonical_row.get("youtube_default_language") or ""),
    "transcription_native": transcription_native,
    "transcription_romanized": transcription_romanized,
    "transcription_mixed": transcription_mixed,
    "transcription_tagged": transcription_tagged,
    "has_audio_tag": has_audio_tag,
    "duration_s": metrics["duration_s"],
    "sample_rate_hz": metrics["sample_rate_hz"],
    "rms_dbfs": metrics["rms_dbfs"],
    "peak_dbfs": metrics["peak_dbfs"],
    "zero_crossing_rate": metrics["zero_crossing_rate"],
    "silence_fraction": metrics["silence_fraction"],
    "chars_per_sec": metrics["chars_per_sec"],
    "words_per_sec": metrics["words_per_sec"],
    "tx_quality_score": canonical_row.get("tx_quality_score"),
    "final_bucket": str(canonical_row.get("final_bucket") or ""),
    "audio_sha256": flac_sha256,
    "flac_size_bytes": len(polished_segment.flac_bytes),
    "meta_information": meta_information,
}
```

### Meaning of the important fields
- `video_id`: YouTube/source video id.
- `segment_id`: unique exported segment id. This is the join key to `audio_index.parquet`.
- `parent_segment_file`: original unsplit parent segment name.
- `is_split_part`: whether this row came from a split segment.
- `split_index`: split piece index.
- `segment_language`: final per-segment language. This is the shard language too.
- `video_language`: original/fallback video-level language.
- `transcription_native`: native-script text if available, else fallback.
- `transcription_romanized`: romanized text if available, else fallback.
- `transcription_mixed`: canonical mixed-script/plain transcript.
- `transcription_tagged`: transcript with event tags.
- `duration_s`, `sample_rate_hz`, `rms_dbfs`, `peak_dbfs`, `silence_fraction`, etc.: audio-derived metrics from the polished FLAC actually shipped.
- `audio_sha256`: per-segment FLAC checksum.
- `meta_information`: JSON string with provenance and rich metadata.

## `meta_information` JSON schema
This is a JSON string column, not a nested parquet struct right now.

It contains these top-level objects:

```145:179:src/final_export_common.py
payload = {
    "replay_provenance": {
        "segment_file": canonical_row.get("segment_file", ""),
        "parent_segment_file": canonical_row.get("parent_segment_file", ""),
        "is_split_segment": bool(canonical_row.get("is_split_segment", False)),
        "split_index_from_id": canonical_row.get("split_index_from_id"),
        "original_start_ms": canonical_row.get("original_start_ms"),
        "original_end_ms": canonical_row.get("original_end_ms"),
        "trimmed_start_ms": canonical_row.get("trimmed_start_ms"),
        "trimmed_end_ms": canonical_row.get("trimmed_end_ms"),
        "leading_pad_ms": canonical_row.get("leading_pad_ms"),
        "trailing_pad_ms": canonical_row.get("trailing_pad_ms"),
    },
    "source_row_provenance": raw_tx_row or {},
    "transcript_provenance": {
        "canonical_transcription": canonical_row.get("transcription", ""),
        "canonical_tagged": canonical_row.get("tagged", ""),
        "raw_transcription": (raw_tx_row or {}).get("transcription", ""),
        "raw_tagged": (raw_tx_row or {}).get("tagged", ""),
    },
    "variant_provenance": variant_row or {},
    "validation_provenance": validation_row or {},
    "language_evidence": {
        "segment_language": canonical_row.get("segment_language", ""),
        "tx_detected_language": canonical_row.get("tx_detected_language", ""),
        "gemini_lang": canonical_row.get("gemini_lang", ""),
        "corrected_language": canonical_row.get("corrected_language", ""),
        "queue_language": canonical_row.get("queue_language", ""),
        "youtube_audio_language": canonical_row.get("youtube_audio_language", ""),
        "youtube_default_language": canonical_row.get("youtube_default_language", ""),
    },
    "video_metadata": video_metadata or {},
    "export_provenance": export_provenance,
}
```

### Practical example of one `meta_information`
Something like this:

```json
{
  "replay_provenance": {
    "segment_file": "chunk_000_SPEAKER_00_0008_56.78-59.95.flac",
    "parent_segment_file": "chunk_000_SPEAKER_00_0008_56.78-59.95.flac",
    "is_split_segment": false,
    "split_index_from_id": 0,
    "original_start_ms": 56780,
    "original_end_ms": 59950,
    "trimmed_start_ms": 56630,
    "trimmed_end_ms": 60100,
    "leading_pad_ms": 150,
    "trailing_pad_ms": 150
  },
  "source_row_provenance": {
    "transcription": "hello everyone",
    "tagged": "hello everyone",
    "detected_language": "en",
    "quality_score": 0.97,
    "speaker_emotion": "neutral",
    "speaker_style": "conversational",
    "speaker_pace": "normal",
    "speaker_accent": null
  },
  "transcript_provenance": {
    "canonical_transcription": "hello everyone",
    "canonical_tagged": "hello everyone",
    "raw_transcription": "hello everyone",
    "raw_tagged": "hello everyone"
  },
  "variant_provenance": {
    "input_script_profile": "latin",
    "native_script_text": "hello everyone",
    "romanized_text": "hello everyone",
    "processing_route": "local_skip_fully_roman",
    "validation_errors": ""
  },
  "validation_provenance": {
    "final_validation_source": "recover_v2",
    "final_has_validation": true,
    "final_bucket": "some-source-bucket",
    "lid_consensus": true,
    "lid_agree_count": 3,
    "consensus_lang": "en",
    "conformer_multi_ctc_normalized": 0.93,
    "mms_confidence": 0.88
  },
  "language_evidence": {
    "segment_language": "en",
    "tx_detected_language": "en",
    "gemini_lang": "en",
    "corrected_language": "te",
    "queue_language": "te",
    "youtube_audio_language": "te",
    "youtube_default_language": "te"
  },
  "video_metadata": {
    "youtube_audio_language": "te",
    "youtube_default_language": "te",
    "channel_id": "UCxxxx",
    "channel_title": "Example Channel",
    "title": "Example Video",
    "description": "...",
    "tags": "tag1,tag2"
  },
  "export_provenance": {
    "run_id": "production-20260312",
    "worker_id": "final-export-video-123",
    "exported_at": "2026-03-12T10:20:30.000000+00:00",
    "audio_sha256_type": "flac_sha256"
  }
}
```

## `audio_index.parquet` schema
This is the lookup table for locating the segment audio inside the tar.

Each row has:

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

That comes from:

```327:335:src/final_export_common.py
audio_index_rows.append(
    {
        "segment_id": item["segment_id"],
        "video_id": item["video_id"],
        "tar_member_name": member_name,
        "flac_size_bytes": len(flac_bytes),
        "flac_sha256": item["flac_sha256"],
        "audio_duration_s": item["audio_duration_s"],
    }
)
```

## `audio.tar`
This is just the packed FLAC payloads.

Each tar member is one segment audio file:
- member name is `segment_id` if it already ends with `.flac`
- otherwise `segment_id + ".flac"`

So consumers should:
1. read `metadata.parquet`
2. join on `segment_id` to `audio_index.parquet`
3. open `audio.tar`
4. extract `tar_member_name`

## `manifest.json` schema
This is the shard-level audit file. It includes both semantic info and integrity info.

Shard-specific fields added by compactor:
- `shard_id`
- `run_id`
- `language`
- `segment_count`
- `video_count`
- `source_microshard_count`
- `source_video_ids_sample`
- `worker_id`
- `created_at`

Pack-level fields added by `build_pack_artifacts`:
- `metadata_row_count`
- `audio_index_row_count`
- `audio_tar_member_count`
- `sum_flac_bytes`
- `segment_id_set_sha256`
- `metadata_sha256`
- `audio_index_sha256`
- `audio_tar_sha256`
- `metadata_size_bytes`
- `audio_index_size_bytes`
- `audio_tar_size_bytes`

Example:

```json
{
  "shard_id": "hi_shard_1710200000_000123",
  "run_id": "production-20260312",
  "language": "hi",
  "segment_count": 15000,
  "video_count": 892,
  "source_microshard_count": 117,
  "source_video_ids_sample": ["abc123", "def456"],
  "worker_id": "final-export-compact-01",
  "created_at": "2026-03-12T23:00:00+00:00",
  "metadata_row_count": 15000,
  "audio_index_row_count": 15000,
  "audio_tar_member_count": 15000,
  "sum_flac_bytes": 3849201934,
  "segment_id_set_sha256": "...",
  "metadata_sha256": "...",
  "audio_index_sha256": "...",
  "audio_tar_sha256": "...",
  "metadata_size_bytes": 22839123,
  "audio_index_size_bytes": 1293811,
  "audio_tar_size_bytes": 3851042816
}
```

## If you want other datasets to match this schema
I would strongly recommend preserving this exact contract:

- same 4-file shard structure
- same `segment_id` join key
- same `metadata.parquet` core columns
- same `audio_index.parquet` shape
- same `meta_information` JSON envelope
- same `manifest.json` integrity fields

That gives you:
- uniform ingestion
- common integrity validation
- common shard download logic
- easy inter-dataset joins

The best reusable “base schema” is:

- identity: `video_id`, `segment_id`, `parent_segment_file`
- segmentation: `is_split_part`, `split_index`
- language: `segment_language`, `video_language`, `youtube_audio_language`, `youtube_default_language`
- text: `transcription_native`, `transcription_romanized`, `transcription_mixed`, `transcription_tagged`
- audio metrics: `duration_s`, `sample_rate_hz`, `rms_dbfs`, `peak_dbfs`, `zero_crossing_rate`, `silence_fraction`, `chars_per_sec`, `words_per_sec`
- integrity: `audio_sha256`, `flac_size_bytes`
- provenance: `meta_information`

One recommendation if you are designing future datasets from scratch:
- keep `meta_information` as a JSON string for compatibility now
- but also consider a future v2 with some of its most-used fields promoted to top-level parquet columns for query speed

If you want, I can turn this into a clean reusable “dataset contract” table with field names, types, nullability, and semantic rules so your other datasets can follow it exactly.

**Drafting code citations**

I’m considering including code blocks to showcase actual lines showing the path and manifest payload. It seems reasonable to add 2-3 code citations, possibly one for compactor flush, another for metadata_row fields, and one for the meta_information JSON. However, the final answer can mostly be in prose. I also need to remember not to use nested bullets; I want to keep the sections with short, flat bullet points. Let’s get started on this draft!


text:
<user_query>
what 12 languages are we processing rn.
</user_query>


text:
These 12:

- `hi` = Hindi
- `mr` = Marathi
- `te` = Telugu
- `ta` = Tamil
- `kn` = Kannada
- `ml` = Malayalam
- `gu` = Gujarati
- `pa` = Punjabi
- `bn` = Bengali
- `as` = Assamese
- `or` = Odia
- `en` = English

They come from `SUPPORTED_LANGUAGES`:

```25:40:src/config.py
LANGUAGE_MAP: dict[str, tuple[str, str, str]] = {
    "hi": ("Hindi", "Devanagari", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "mr": ("Marathi", "Devanagari", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "te": ("Telugu", "Telugu", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "ta": ("Tamil", "Tamil", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "kn": ("Kannada", "Kannada", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "ml": ("Malayalam", "Malayalam", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "gu": ("Gujarati", "Gujarati", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "pa": ("Punjabi", "Gurmukhi", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "bn": ("Bengali", "Bengali", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "as": ("Assamese", "Assamese", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "or": ("Odia", "Odia", "Don't over-split words. Preserve Sandhi/combined forms as spoken."),
    "en": ("English", "Latin", "Standard English transcription rules apply."),
}
SUPPORTED_LANGUAGES = list(LANGUAGE_MAP.keys())
```

And Stage A only keeps rows whose resolved `segment_language` is in that set. So anything outside these 12 gets dropped as unsupported.