Those `2gram_repeat` flags are mostly false positives. For example, `रहा है` appearing 4 times across different clauses is normal Hindi grammar, not a hallucination. I’m tightening the scanner to only catch:
- **consecutive repeated words**
- **consecutive repeated phrases (2-4 grams)**
- **char-level stutter loops**
- obvious junk/empty outputs

And I’m making it **resumable per batch** with batch output files + aggregate-at-end.

**Improving n-gram detection**

I need to explain two-gram false positives, especially since examples show phrases like "raha hai" appearing multiple times in different clauses. The user is looking for consecutive n-grams and wants to adjust the scanner for that.

I think I should modify the logic in `scan_repetitions_streaming.py` to include flags for thresholds on consecutive repeated words and n-grams. We might also want to include checks for stuttering characters and the concept of a "global dominant token." These adjustments should help manage false positives better!
**Implementing resumable outputs**

I’m planning to create resumable outputs for each batch, storing them in `final_data/repetition_scan_batches`. For every batch, I want to write a parquet file if there are flagged rows, and add an empty JSON marker to skip completed batches.

At the end, I’ll aggregate the batch files into `final_data/repetition_flags.parquet` and the summary JSON. It might be a good idea to have a `--aggregate-only` option or always aggregate after scanning. I should benchmark processing with four workers on a 100k batch to ensure efficiency!
**Optimizing batch processing**

In the main function, I need to check for a marker file like `output_dir/batch_000001.done.json`. If it exists, I can read in the `flagged_count` and `scanned_rows` to keep track of accumulated counts. It’s simpler to just skip completed batches and aggregate at the end from the parquet files and the done JSON.

I want to track `rows_scanned` quickly using metadata from the done files. For batches without flags, I’ll write a metadata JSON only, but for flagged batches, I’ll write both parquet and metadata. Then, I'll process the rows and write outputs for each batch.
**Optimizing batch processing**

I think it's better to use `LIMIT/OFFSET`, but that could get expensive with many batches. Instead, I might try using record_batch sequentially and skip processing if a marker exists. I don't need to join the original data with each batch; I'm currently reading query joined data and using fetch_record_batch. It makes sense to benchmark with 100,000 rows using 4 workers on 1 batch after making modifications. If that’s stable, I'll start a full run with that config. I’ll consider using ReadLints too, but maybe it’s not needed after substantial edits. Let's patch this!


I tightened the rules to only catch **consecutive** repetition and added resumable per-batch outputs. Now I’m benchmarking your requested `4 workers / 100k batch` with the safer scanner.

**Evaluating testing procedure**

I need to consider benchmarking and perhaps clearing previous batch outputs since the smoke test wrote the current repetition flags and summary, but the new batch directory may be empty. It seems like I should add cleanup for the old batch directory to prepare for the smoke test. Also, I want to run this updated script with 100,000 items, using 4 workers and 1 batch — that should help ensure everything runs smoothly!


**Inspecting flagged rows**

I think I need to explain an example still. Maybe I should inspect a couple of flagged rows now to make sure that they are indeed true positives. This step seems important to validate the process and ensure we're identifying the correct cases. By looking closely at these rows, I can confirm whether everything checks out, which is crucial for maintaining accuracy in whatever I'm doing. Let’s make sure we get this right!


The earlier `2gram_repeat` example was exactly the bad kind of false positive: phrases like `रहा है` or `నాకే ఎందుకు` appearing multiple times across different clauses. That’s normal language use. I’ve changed it so we now only flag **consecutive** repetition, like `Wait wait wait wait...` or `Wah Guru Wah Guru Wah Guru...`, which are much closer to true positives.

The `4 workers / 2 duckdb threads / 100k batch` benchmark stayed stable at about `8.6 GB` RSS and `~211%` CPU, so I’m starting the full resumable scan with those settings now.

**Planning a full run**

I need to start a full run as the user suggested. They've mentioned streaming scanner and using 4 workers with a 100k batch, which is stable, so it makes sense to go ahead. I’m calculating that the process might take around 8.6 hours, which feels a bit long due to startup overhead. While we could consider increasing to 6 workers, the user is specifically asking about 4 with 100k, so I should proceed with that while explaining any necessary details to them.
**Setting up the full run**

I might mention later that we could increase to 6 workers if SSH is stable, but for now, I think it's safer to go with 4 workers and a 100k batch. Based on benchmarks, the estimated time is around 9 hours. While there's a thought about using 4 workers and 200k, the user requested 100k. I need to clear the batch directory since the benchmark wrote one batch, and I’ll add a note about examples for clarity. Let’s start the full run with stable settings.
**Preparing to run the tool**

The user didn't explicitly ask to start immediately, but their message of "let's go ahead" feels like a go-ahead. I think it’s important to provide some commentary before I initiate the tool. I'll run the command in the background to scan for repetitions. After a few batches, I'll check on progress and responsiveness since the user mentioned SSH issues. Although the benchmark shows stability at 8.6GB and 211% CPU, it may still be wise to consider lowering the load.


I tightened the detector and started the full run.

What changed:
- `2gram_repeat` is no longer “appears anywhere 4 times”.
- It now only flags **consecutive repetition**, which is what you wanted.
- So normal cases like `रहा है` appearing in different clauses are no longer treated as suspicious.
- True-positive style examples now look like:
  - `Wait wait wait wait wait...`
  - `Wah Guru Wah Guru Wah Guru...`
  - `Hoo! Hah! Hoo! Hah!...`

Scanner changes:
- new script: `scripts/scan_repetitions_streaming.py`
- resumable per-batch outputs in `final_data/repetition_scan_batches/`
- final aggregate outputs still go to:
  - `final_data/repetition_flags.parquet`
  - `final_data/repetition_summary.json`

Runtime settings now running:
- `4 workers`
- `2 duckdb threads`
- `100k` rows per batch

Current progress:
- batch 1: resume-skip
- batch 2: scanned `200,000 / 60,716,503`
- batch 3: scanned `300,000 / 60,716,503`

Why this should be safe:
- the benchmark stayed around `8.6 GB` RSS
- CPU was only about `~211%`, so it should stop killing SSH like the old version
- if interrupted, rerunning the same command resumes from completed batches

The full command now running is effectively:
```bash
python -u scripts/scan_repetitions_streaming.py --workers 4 --duckdb-threads 2 --batch-rows 100000
```

If you want, next I can just keep monitoring this and report the first meaningful analytics once a few million rows are scanned.


<user_query>
What's teh ETA of finish ?
</user_query>


I’ll read the current scanner progress and estimate ETA from the completed batches.


I have the completed batch count; now I’m calculating the actual elapsed time and ETA from that throughput.

**Calculating ETA**

I need to determine the estimated time of arrival (ETA). First, I should compute the current time, which I think is around 21:??. I already have the start time and should use the number of rows done along with the elapsed time. It seems using the current date from the system could help, maybe using Python. Actually, it might be easier to get the running seconds from the terminal header. Let’s use a tool for this computation.


Rough ETA is **about 2.5 hours** from now if the current pace holds.

Current pace:
- scanned: **1,000,000 / 60,716,503**
- throughput: about **6,667 rows/sec**
- about **400,000 rows/min**

So remaining time is about:
- **149 minutes**
- **2.49 hours**

This is still an early estimate, so it may move a bit once more batches complete, but it’s a reasonable ballpark now. If you want, I can keep checking every few batches and give you a more stable ETA trend.

**Calculating ETA for processing**

I want to provide a concise answer. I think the rough ETA is around 2.5 hours if throughput remains stable. It's worth mentioning that the first batches include a warmup or resume skip, so there could be slight fluctuations. I should note that I've scanned 1 million rows, and it's now at 1 million rows done because things changed since earlier. It might be around 400,000 rows per minute. Let’s craft that!


<user_query>
check CPU usage, and increase batch or workers ? need it to be done in under an hour.
</user_query>


I’m checking the live CPU/memory usage of the scanner and whether we can safely resume with higher parallelism without invalidating the completed batches.

**Assessing resource usage**

I need to consider tool usage in response to the user's request. It looks like I need to activate a virtual environment, but maybe this is more shell-related. I should inspect the current processes and CPU usage while being careful not to overload the system, as the user asked me to respond within an hour. Increasing the number of workers and threads might help speed things up, especially if I utilize DuckDB SQL. I need to analyze the current CPU and memory usage alongside potential batch size adjustments.
**Planning resource adjustments**

I need to be cautious because a previous user reported SSH issues when the load gets too high. First, I'll check the current CPU usage to see if there's safe headroom. I can restart the scanner with a higher number of workers while ensuring I preserve the ability to resume. Since the outputs are resumable per batch, stopping the current process to adjust settings is possible. However, I need to keep in mind that changing the batch size will affect indexing and the resume logic. It might be necessary to implement new logic based on row offsets instead of batch indices.
**Evaluating batch processing**

Currently, the meta path relies solely on batch indexing, which means I can't change the batch size while the process is running. It’s crucial that I maintain the same batch size and focus on increasing only the workers or threads to keep the indexing consistent. I can stop the process and restart with possibly 8 workers and 4 DuckDB threads since the meta files should still correspond if the batch size remains unchanged. I’ll need to confirm that the resume function depends on these meta files for accurate alignment before making any adjustments.
**Optimizing worker usage**

I'm considering whether more workers might increase the per-batch overhead for Python splitting. I think using around 8 to 10 workers seems safe. I may need to kill the current scan and restart it while keeping the same batch rows and output directory. I'll also have to check the batch progress since it might be over 1M. Using 8 workers with 4 DuckDB threads should keep memory usage under control.

If the user agrees, the best action could be just to increase the workers and threads. I should check CPU usage and current progress before proceeding.


We have a lot of headroom: only about 4 CPU cores are hot and the box still has ~75 GB free RAM. I’m going to keep the same `100k` batch size for safe resume, stop the current run, and restart at higher parallelism (`10` workers, `4` DuckDB threads).

**Evaluating scaling options**

I’m considering how to increase performance since the CPU is only 25% utilized and 74% idle, with ample memory available. I know I can’t adjust the batch size mid-run, but I can safely stop and resume with the same batch size while increasing the number of workers and threads.

I’m thinking about bumping up to maybe 8 or 10 workers with 4 threads. Each of the four workers currently uses around 7.8 GB of memory, so I must carefully assess this before proceeding.
**Assessing worker optimization**

The estimated time is around 137 minutes, and I need a 2.3x speedup. Increasing from 4 to 10 workers seems plausible, especially since I have nearly 4 cores in use. With 16 cores and 74% idle, using 10 workers along with 4 DuckDB threads might push activity to about 8-10 cores. I could consider 12 workers, but the user seems concerned about SSH.

For safety, maybe sticking with 8 workers and 4 threads would be best. If I want to get under an hour, I’d try 10 workers with 4 threads. I'd need to check the impact of the DuckDB threads while keeping everything else intact, ensuring completed batches are preserved.
**Considering batch options**

If I set max-batches to 1, it would skip batch 1 and stop, which isn’t ideal for benchmarking the new settings. I could run with a new output directory and use max-batches 1 for benchmarking, but the user wants to finish under an hour. Based on CPU metrics, I might estimate performance. It could be a good idea to do a live restart and monitor the first two new batches since resuming is safe. That way, I can gather useful data!


2100000 rows are safely checkpointed. Restarting from there with higher parallelism now.


I’ll watch the first resumed batches to see whether the new settings actually improve throughput without destabilizing the machine.


The new settings are safe and clearly faster. I’m getting one more progress snapshot so I can give you an updated ETA from actual post-resume throughput, not just the resume-skip lines.

**Calculating throughput and ETA**

I need to improve the answer about ETA, especially regarding throughput. So, I have records showing two batches took 73 seconds to process after skipping, but maybe the old numbers reflect a faster pace of 200k in that time. Batch 22 took 68 seconds, and batch 23 took 73 seconds. Since this whole process includes skips, it's tricky to pinpoint the throughput accurately. I might need to wait and see how batches 24 and 25 go to refine my ETA further.


Yes — increasing workers helped a lot, and it looks stable.

Current post-resume throughput:
- from `batch 22` to `batch 38`
- processed: `1.7M` rows
- elapsed: about `90s`
- throughput: about **1.13M rows/min**

Current progress:
- `3.8M / 60.7M` rows
- `6.3%` done

Updated ETA:
- remaining: about `56.9M` rows
- at current speed: about **50-55 minutes**

So this is now on track to finish **under an hour** if the pace holds.

What changed:
- kept `batch_rows=100000` so resume stayed valid
- increased to `10 workers`
- increased DuckDB to `4 threads`

Also important:
- system still looks safe enough
- lots of RAM headroom remains
- CPU is higher, but not in the “SSH death spiral” zone from the earlier bad implementation

I would **not** increase batch size now, because:
- changing batch size would break the current resume checkpoints
- we already got the ETA under 1 hour
- current settings are a good balance of speed and safety

If you want, I can keep monitoring and give you:
1. mid-run ETA updates
2. final analytics summary when all batches finish

**Calculating ETA and CPU usage**

I need to compute the ETA from batch22 to batch38. It looks like it takes about 68 seconds for batch22 and 158 seconds for batch38, which means I'm dealing with 1.6 million tasks in 90 seconds — that’s around 1.07 million per minute. With about 56.9 million remaining, that's about 53 minutes to go, which is under an hour. However, the CPU shows 380% usage while still being 72% idle, suggesting some processes might not be accounted for. I’ll need to provide a concise status update.


<user_query>
one quick question, i wanted to ask about the transcripts versions we just created. what happened to the versions of the transcripts where the text was mixed. for example, "'या Google पर जाकर whatsapp@support.com पर click करके भी अपने WhatsApp का access ले सकते हैं'" what happened to segments like these ? and what if the segment is english dominated, but has hindi text little in the middle ?

can you run the prompt on these examples and show how did it work  ?
</user_query>


I’m going to run the current variant prompt on a few concrete mixed-script examples so we can see exactly how it behaves.

**Evaluating API calls**

I’m considering running a direct API call for a few items and maybe testing a prompt script or using my own Python snippet. It seems like using the current cache name with `TranscriptVariantClient(api_key, cache_name=None)` and then awaiting `generate_batch(items)` could be simpler. The client is using `gemini-3-flash-preview`, which is great. I’m thinking that maybe I won’t need the cached prompt after all, so I’ll check the response next.


The live run hit a `load_dotenv()` quirk in inline Python, so I’m rerunning it with an explicit `.env` path.


Here’s how it works **right now**.

For your mixed example:

`या Google पर जाकर whatsapp@support.com पर click करके भी अपने WhatsApp का access ले सकते हैं`

the live model output was:

- `native_script_text`:
  `या गूगल पर जाकर whatsapp@support.com पर क्लिक करके भी अपने वॉट्सऐप का एक्सेस ले सकते हैं`
- `romanized_text`:
  `Ya Google par jaakar whatsapp@support.com par click karke bhi apne WhatsApp ka access le sakte hain`

So the behavior is:
- protected spans like `whatsapp@support.com` stay exactly unchanged
- in `native_script_text`, ordinary spoken English borrowings get converted into Hindi script
- in `romanized_text`, the same utterance stays in Roman letters, with Hindi also romanized

For an **English-dominant but Hindi-mixed** sentence, I tested:

`This policy अगर apply होती है then users can appeal`

with `language_code="hi"` and got:

- `native_script_text`:
  `दिस पॉलिसी अगर अप्लाई होती है देन यूजर्स कैन अपील`
- `romanized_text`:
  `This policy agar apply hoti hai then users can appeal`

So if the row is **language-hinted as Hindi**, mixed English/Hindi goes through Gemini and gets normalized as expected.

The important edge case is this:

- if `language_code="hi"` and the text has both Latin and Hindi chars:
  it is classified as `mixed_native_latin` and sent to Gemini
- if `language_code="en"`:
  the current classifier **always** treats it as `fully_roman` and skips Gemini
- if `language_code="hi"` but the text is entirely Latin:
  it is also treated as `fully_roman` and skipped locally

I also checked the local classifier behavior on examples:

- `hi` + mixed script -> `mixed_native_latin`
- `en` + mixed script -> `fully_roman`
- `hi` + all-Latin text -> `fully_roman`

So the answer to “what happened to mixed segments?” is:

- **mixed native+Latin rows in non-English languages were processed by Gemini**
- **English-labeled rows were skipped locally, even if they contain a little Hindi**
- **Hindi-labeled but fully Roman rows were also skipped locally**

That means there is a real policy choice here:
- if you want **English-dominant rows with a little Hindi inside** to still get normalized, the current `language_code == "en" -> fully_roman` shortcut is too aggressive

I can help with either of these next:
1. audit how many rows fall into that edge case
2. change the classifier so English rows with non-Latin script are not auto-skipped

**Clarifying script conversions**

I want to mention how certain segments are processed. For example, in the first example, native script converts Latin borrowings into Devanagari while preserving the protected text, like emails. The romanized version keeps common brands in Roman form. In the second example, the results show a mix of English and Hindi, where the native script converts English words phonetically into Devanagari, while the romanized keeps them Romanized.

It looks like the classification output really depends on the language code. If it's English-dominated with Hindi, the classifier might skip it entirely.


<user_query>
but there are scenarios where the entire segment is in english, but few hindi or non-english words are present. but the segment is marked as english. and these never made it to gemini. so, all fields for this segment would be originl right. in that case, we might have missed few segments to convert. but with the example as you said, "This policy अगर apply होती है then users can appeal", is incase hitned as hindi, then what worked is correct. but due to heavyness in english, dominated, but still has a few words in hindi. then the hinted might be english, but never made it to the gemini. for cases like these, we need to change the hinted to, if there is atleast one character or word of that language, even if it's less-dominant, we take the less-dominant one. Can you do a filter on these segments parallelly, and findout if they are large in number. like do a search on all transcripts, especially with hinted language is english, and script has atleast one non-english character, and the ones that never made it to gemini for romanisation or nativisation.

i believe the count was

Shards	1,024/1,024 (0 failed)
Rows processed	60,716,547 (exact match)
Gemini rows	43,168,424 (71.1%)
Locally skipped	17,548,123 (28.9%)

so we just need to find how many of these locally skipped have that conditiuon that i mentioned. and also do a status check on our previous task.
</user_query>


I’m checking two things in parallel: the repetition scan progress, and how many `gemini_lang='en'` local-skip rows actually contain Indic script and should probably have gone to Gemini.

**Considering DuckDB and regex**

I’m thinking that DuckDB’s regex may not support Unicode properties, so it's better to use a Python script with PyArrow and DuckDB to read only the filtered subset. I only need to focus on English hinted rows and those with 'local_skip' counts. I might query from `final_cleaned_segments_with_variants` for relevant columns like `gemini_lang`, `variant_route`, and `transcription`. I'll iterate where `gemini_lang='en'` and `variant_route='local_skip_fully_roman'`, checking for non-ASCII characters or any from `Indic Unicode` ranges. Using PyArrow, it seems manageable to scan about 17.5 million rows, perhaps using `ParquetFile` for efficiency.
**Running batch queries**

I want to check the current batch's metadata, including the count and the rows per time. I think I don’t need to gather too much data. So, I’ll run the scanner status and the count script for contamination in parallel, ensuring to use commentary for clarity. This approach should keep things efficient while helping me gather the necessary information without overwhelming myself. Let's see what results I can get from that!


The repetition scan is now over halfway done and down to about a 19-minute ETA. I’m waiting on the English-skip count job, which is streaming through the full parquet.


That count job is still running; it hasn’t flushed results yet. I’m going to give it a bit more time.


Yes, this is a **real bucket**, and it’s not tiny.

**Status check**
- Repetition scan last checked at **33.3M / 60.7M rows** (`54.85%`)
- Flags so far: **69,562**
- ETA at that check: about **19 minutes**
- It’s running in resumable mode, so even if interrupted again we can resume from completed batches

**English-hinted local-skip rows with Indic script**
I checked rows where:
- `gemini_lang = 'en'`
- `variant_route = 'local_skip_fully_roman'`
- `transcription` contains at least one Indic-script character

Results:
- English local-skip total: **17,410,815**
- Of those, with Indic script present: **442,604**
- That is **2.5421%** of English local-skips
- And about **0.73%** of the full 60.7M corpus

Breakdown:
- Mixed Latin + Indic: **442,458**
- Pure Indic but mislabeled `en`: **146**

Script breakdown:
- Devanagari: **284,780**
- Telugu: **107,563**
- Kannada: **16,756**
- Tamil: **15,870**
- Malayalam: **10,069**
- Bengali: **4,721**
- Gurmukhi: **3,506**
- Gujarati: **2,075**
- Odia: **850**

This means your concern is correct:
- these rows **never went to Gemini**
- so for them:
  - `native_script_text` is empty
  - `romanized_text` is just the original transcription copied through
- in mixed cases like `... आज के बाद ...` inside English, we missed normalization for the Indic words

Examples found in this bucket:
- `so he put it to perfection. He said, "यामिनी जी आप बोलिए जो bhi bolna hai..."`
- `... planning करनी पड़ती थी because we came from a very humble...`
- `So there's no such thing... But ज़िंदगी का first rule ये होता है...`
- `house फिर थोड़ा एक घर बनाया...`
- `the sum should be way more... तो दो लोग आ रहे हैं साथ में`

So yes, this is large enough to justify a follow-up pass.

If you want, the next logical step is:
1. materialize these **442,604** rows into a subset parquet
2. re-route only that subset through Gemini
3. merge the recovered variants back into the final dataset

That would directly fix this missed edge case bucket.

**Checking task status and next steps**

I need to address the user's request regarding the status of the previous task and the count of English entries with Indic characters. It appears there are 442,604 rows, which is 2.54% of English local skips and 0.73% of the total corpus. Most of these are mixed Latin-Indics, except for a few misclassified as English. I’ll also note that the scanning is about 54.85% complete with an ETA of 19 minutes. Next, I might suggest creating a subset of these rows or discuss potential next actions.
**Summarizing route behavior and stats**

I need to provide a clear overview of the skipped rows. The native script text is empty because these rows were skipped locally, while the romanized text remains the original transcription. It's important to mention the previous task's status, which showed progress at 54.85%, with about 69,562 flags and an ETA of 19.1 minutes—though it's likely more progressed now. Among the total skips, 442,604 represent 2.54% of English local skips and 0.73% of the total corpus. I’ll avoid excessive citations here since that's unnecessary!


<user_query>
in mixed cases like ... आज के बाद ... inside English, we missed normalization for the Indic words -> explain.
and can you show me some telugu examples too. and what the heck is gurmukhi. we dont support that language. we should probably get rid of those. check again.

and also let's plan to hit gemini with these segments. plan ETA, parallelisation with parallel workers, and if current instance is sufficient for this half million job.
</user_query>


I’m pulling a few Telugu and Gurmukhi examples from that missed English-skip bucket, and I’ll also sanity-check whether Gurmukhi is actually a supported script in the current pipeline.


The current code *does* support Punjabi via `pa -> Gurmukhi`, so those Gurmukhi rows are not out-of-schema; they’re English-labeled mixed Punjabi rows that got skipped. I’m waiting on the concrete examples now.


I’m running one Telugu mixed example and one Gurmukhi/Punjabi mixed example through the live prompt so you can see the desired behavior if we re-route this missed bucket.

**Analyzing scan progress and efficiency**

I need to explain missed normalization with Telugu examples and a Gurmukhi explanation. I'm planning the ETA and how to parallelize for a 442k job, checking if the current instance is sufficient. I want to estimate using live data from previous retries, noting that I recovered 47k errors in about 491 seconds, which averages to around 96 rows per second. For the full dataset, this could take approximately 76.8 minutes if I can push 20k RPM. I need to double-check that RPM refers to requests, not rows.
**Estimating processing capacity**

I’m calculating the performance for processing 442,604 rows in batches of 10, which results in about 44,260 requests. At a target of 20k RPM, the total time would be around 2.2 minutes — but that seems unrealistic, especially given local machine network and API limits. I'll use past performance data: I processed 47,438 rows in 491 seconds, which gives about 580 requests per minute in total. It looks like individual request latency is too high with only 80 concurrent requests.
**Considering concurrency limits**

I'm thinking about increasing concurrency, potentially up to 400. The system seems to have headroom for it, but I'm concerned about hitting API rate limits or network conditions. If I process 442k rows at an improved rate of 5,800 rows per minute, that would take roughly 76 minutes. So, there's a balance to strike here between expanding capacity and staying within the boundaries of what's reliable.
**Running language examples**

The user asked about using hinted English alongside a few non-English words, specifically taking the less-dominant one. I think a sample with `language_code='te'` for Telugu could show the desired output well. Maybe I could also include a Gurmukhi example using `pa` to check if that's supported. It seems that providing two examples—one from Telugu and another from Punjabi/Gurmukhi—could strengthen my answer, even if the user didn't explicitly request it. Let's try this!


I’m grabbing one fresh status snapshot of the repetition scan so I can include it with the plan.

**Updating scan status**

I need to craft an answer that mentions the current scan status. The user might be interested in a status check from a previous task, so I'll need to communicate that the scan progress is about 54.85% and may take around 19 minutes, though that's potentially changed. It could be a good idea to mention when I last checked. I think doing a quick tool call for the most recent status would be helpful while I'm responding.


What I meant by “missed normalization” is:

- for rows where `gemini_lang='en'`, the classifier short-circuits to `fully_roman`
- those rows never go to Gemini
- so the pipeline writes:
  - `variant_route = local_skip_fully_roman`
  - `native_script_text = ""`
  - `romanized_text = original transcription verbatim`

That is fine for pure English, but wrong for mixed rows like:
- `So there's no such thing... But ज़िंदगी का first rule ये होता है...`
- `house फिर थोड़ा एक घर बनाया...`
- `and it goes like సొగసు చూడతరమా ... it goes like that`

In those cases, the Indic words stay untouched inside `romanized_text`, and you get no `native_script_text` at all. So yes, those rows were missed.

For Telugu, here is a real example from that missed English-skip bucket:

- source:
  `and it goes like సొగసు చూడతరమా చాలదే సమయమా పగలు మా ప్రేమతో సులువు కాదులేమ్మా it goes like that`

If I send it through the current prompt with `language_code='te'`, the live output is:

- `native_script_text`:
  `అండ్ ఇట్ గోస్ లైక్ సొగసు చూడతరమా చాలదే సమయమా పగలు మా ప్రేమతో సులువు కాదులేమ్మా ఇట్ గోస్ లైక్ దట్`
- `romanized_text`:
  `and it goes like sogasu chudatarama chalade samayama pagalu ma prematho suluvu kadulemma it goes like that`

So that bucket is definitely recoverable.

On `Gurmukhi`: that is the script used for Punjabi. The current codebase **does support it** via `pa -> Punjabi -> Gurmukhi`. I re-checked the language map and Punjabi is explicitly supported. So those `3,506` Gurmukhi-script rows are not random garbage; they are English-labeled mixed Punjabi rows that got skipped because the hint was `en`. If your product decision is “we do not want Punjabi at all”, then we should explicitly exclude that bucket. But from the current code’s point of view, Punjabi is supported.

I also ran a mixed Punjabi/Gurmukhi example live:

- source:
  `1978 in Ludhiana... ਇਕ ਬੂਟਾ ਅੰਬੀ ਦਾ`

with `language_code='pa'`, output was:

- `native_script_text`:
  `1978 ਇਨ ਲੁਧਿਆਣਾ... ਇਕ ਬੂਟਾ ਅੰਬੀ ਦਾ`
- `romanized_text`:
  `1978 in Ludhiana... Ik boota ambi da`

So the behavior is correct if we re-route it.

Counts for the missed English-skip bucket:
- total English local-skip rows: `17,410,815`
- with Indic script present: `442,604`
- mixed Latin + Indic: `442,458`
- pure Indic but mislabeled `en`: `146`

Breakdown:
- Devanagari: `284,780`
- Telugu: `107,563`
- Kannada: `16,756`
- Tamil: `15,870`
- Malayalam: `10,069`
- Bengali: `4,721`
- Gurmukhi: `3,506`
- Gujarati: `2,075`
- Odia: `850`

Important caveat:
- script detection is reliable
- language inference is not always reliable from script alone
- `Telugu/Tamil/Kannada/Malayalam/Gurmukhi/Gujarati/Odia` are unambiguous
- `Devanagari` could be `hi` or `mr`
- `Bengali` script could be `bn` or `as`

So for this reroute job, the safe rule is:
- infer by script where unambiguous
- use a deterministic fallback for ambiguous scripts, or keep them in a separate review bucket

Plan for hitting Gemini on these `442,604` rows:
- extract just this subset to a parquet
- assign target language by script
- batch size `10`
- use all `4` API keys in parallel
- run from the current instance after the repetition scan finishes

ETA on current instance:
- rows to process: `442,604`
- requests at batch size `10`: about `44,261`
- based on our earlier local retry runs:
  - conservative throughput: `~600 RPM total` -> about `74 minutes`
  - tuned higher concurrency: `~1,500-2,000 RPM total` -> about `22-30 minutes`

So yes, the **current instance is sufficient** for this half-million job. We do not need a Vast fleet for it. It is mostly network/API bound, not compute bound.

Current status of the repetition scan:
- `60,300,000 / 60,716,503` scanned
- `99.31%` complete
- ETA about `0.3 min`

If you want, once that scan finishes, I can immediately:
1. materialize the `442,604` mixed-English-skip subset
2. separate the unambiguous scripts from ambiguous ones
3. give you the exact reroute plan and kick off the local 4-key recovery pass

**Considering language exclusions**

I need to mention that if the user doesn’t want Punjabi/Gurmukhi, we could exclude 3,506 rows from the reroute job. However, the code seems to support Punjabi, so if a business rule states it’s unsupported, I’d have to leave out that script bucket. I'm thinking I could point out that these might be English-hinted rows with Gurmukhi characters.

Also, it’s important to clarify that the presence of a script character indicates the script, not always the exact language, which could be limited.
**Addressing script and language nuances**

I need to clarify that Devanagari could represent either Hindi or Marathi, while Bengali script may indicate Bengali or Assamese. For rerouting English-hinted rows, I can generally infer the target script, but that doesn’t always tell me the exact language. It’s true that the prompt's output is mainly dependent on the target script rather than specific language differences, which is a nuanced detail I should mention. Maybe I could use a default mapping from script to language for clarity.
**Considering language scripts**

I'm thinking about how we might handle character distinctions in different scripts. For languages like Telugu, Kannada, Tamil, Malayalam, Gurmukhi, Odia, and Gujarati, the distinctions seem quite clear. However, with Devanagari and Bengali, we face challenges since script-level inference is generally safer than language-level inference, which can lead to mistakes. As the user asked for a plan, I should definitely include risks and the status regarding repetition scanning. Let's structure this carefully!


<user_query>
let's kick this remaining 442604 sentences run. let's not be lineant on the RPM cause i have 80k collectively. so you might need to consider the fact about network latency bounds. so we have 44261 batches, if we do a parallelisation of 500 concurrency per API key. we get 2000 RPM total. but i dont think a single python job can do this. instead check if the old pattern matching job is done. and if yes, we can launch 4 different python processess and move it to background and launch the job with 500 concurrency. so we get done in <30minutes. and keep monitoring with frequent sleep timers until all sentences are done, and if few failed, reset them and retry.
</user_query>


I’m first checking whether the repetition scan is fully finished, and I’m also checking whether we can use existing language columns to pick better target languages for the `442,604`-row reroute set instead of guessing from script alone.

**Considering script development**

I need to decide whether to write a rerun script or use inline Python. Since the task seems complex, creating a new script might be the best option. The user didn't mention a restriction on new Python files. I should manage a long-running process in the background while monitoring it through terminal files. I'm planning to extract rows from the parquet file based on specific languages and manage ambiguous scripts accordingly. It’ll be a bit tricky, but I’ll figure it out!
**Deciding on language inference**

I think I need to move forward with script-based language inference, even though the user didn’t explicitly ask for these details. It might be a good idea to mention any ambiguous scripts and consider existing rules. Since they mentioned running the remaining sentences, I should decide on a mapping approach for languages, like Devanagari to Hindi, Bengali to bn, Gurmukhi to pa, etc. However, I’m also wondering if it would be better to use the original `queue_language` or `corrected_language` from the data.
**Considering data processing script**

I’m thinking about creating a script that handles both recovered and failed parquet files per shard or process. It might be nice to include a feature that allows it to resume by skipping outputs, although it’s not strictly necessary. I need to determine an exact subset of 442,604 and split it into four for even distribution. Using DuckDB to create the final parquet files sounds good, and I might leverage `row_number` mod 4 for that. I'll also need to inspect the language distribution in the subset for better guidance.


I’ve confirmed two things:
- the repetition scan is effectively done, so the box is free for this reroute
- the mixed-English missed bucket is real and large enough to justify a dedicated pass

I’m creating:
1. a preparation step that extracts and language-targets those `442,604` rows
2. a high-concurrency reroute worker we can launch as 4 separate Python processes, one per API key

**Planning language processing**

I’ve finished the repetition scan, and should maybe read the summary later. Now, I need to kick off the reroute. I think I’ll mention that the scan is done. For more accurate language processing, I might use `corrected_language` for non-English when supported, or else fallback to `queue_language`. I should also figure out how many unresolved items there are and compute a full subset plan, splitting the data into four parts based on the target language.
**Mapping scripts to languages**

I’ve got a plan for mapping non-Latin scripts to their preferred languages. For instance, Devanagari will link to `corrected_language` for Hindi and Marathi, and Bengali script will do the same for Bengali and Assamese. For multiple scripts, I could consider the first one or follow the match with `corrected_language` or `queue_language` if supported. Inspecting the data could help simplify choices, especially since the subset has Indic characters. I need to keep an eye on any risks of incorrect corrections while computing the counts for each method.
**Planning stats and concurrency**

I’m considering generating stats and splitting the workload into four jobs. I might create a script for high-concurrency rerouting, aiming for one process per key. Each process could handle around 500 concurrent tasks, totaling about 2000 tasks. I need to think about network latency and whether a single Python process with this concurrency can effectively manage it. Maybe 250 concurrency per process would be safer, but since the user prefers 500, I think we’ll give it a shot. Each request should take around 2-5 seconds, which sounds feasible.
**Considering cache management**

I'm pondering whether to reuse the `GEMINI_CACHE_NAME` for only the first key. However, it might be better for each key to have its own cache. Since the existing worker creates a cache per key with `TranscriptVariantCacheManager`, I could just call `ensure_cache` for each key at the start, which seems good.

If there’s a failure, I should output the failed rows to parquet for a later retry. Writing a progress file for every few completed tasks might work too, even though the user wants frequent sleep timer monitoring.

I might need to split the data into four parts using DuckDB and a selected target language. Using SQL regex for script detection could complicate things; maybe Python with pyarrow will work better? The original dataset is 442k rows, which is sizable, but DuckDB could efficiently filter the subset and include the necessary columns. Maybe I can use Python afterward to assign the target language or do that in DuckDB with a CASE statement using regex matches.
**Implementing script detection**

I need to establish actual script detection in the text. I can use `regexp_matches` in DuckDB for this. I might create a subset parquet with columns like `row_id`, `transcription`, `chosen_language_code`, and `input_script_profile`. I'm considering whether to label the script as mixed native Latin or fully native.

Using `classify_input_script` later could simplify things. I could include the original text and language, then reroute the script classification with `classify_input_script` based on the chosen language.
**Evaluating language handling**

I’m setting up a better way to determine the target language using a formula. It seems I should prioritize languages supported in non-English contexts. If `corrected_language` is available but conflicts with the script, perhaps it’s safer to rely on the script for clarity. Since the user mentioned considering less dominant scripts, I'll make sure to focus on the actual script in the text if it’s clearer. I'll need to check carefully for any ambiguities.
**Defining target language rules**

I’m defining a clear structure for identifying the target language based on character scripts. If Telugu characters are present, I'll use 'te'; if not, I check for Tamil, Kannada, Malayalam, Gujarati, Gurmukhi, Odia, and Bengali scripts sequentially. For Devanagari, I prioritize corrected and queue language in defined situations. I might need to exclude 12 stuck segments that are irrelevant and create several parts for data processing, possibly managing tasks with higher concurrency. I’ll check how to set that all up effectively!
**Planning output recovery**

I need to ensure I can output recovered and failed processes. With 442,000 rows split into parts of about 110,000 each, I'm looking at around 11,000 batches. At 500 concurrent processes, that would mean approximately 22 waves. If each wave takes 5-10 seconds, the total could range from 2 to 4 minutes, possibly up to 30 minutes with retries accounted for. Before starting, I should check that no other heavy jobs are running on terminals. I'll also confirm everything is set and exits correctly!
**Evaluating concurrency levels**

I'm figuring out how to handle 500 concurrent tasks with batch strings, which seems moderate. I'll consider using httpx AsyncClient with max_connections around 600. With 500 concurrency per process, that could lead to 2000 concurrent connections across four processes. It might be a bit intense for the local network and the API, so maybe I should scale it back to 400? But the user wants to stick to 500, so I'll keep that in mind!
**Preparing for adjustments**

I’m thinking it might be good to set a default of 500 and note that I can drop it if I encounter a 429 error. I need to monitor the situation closely, especially if the 429s are substantial. It could be helpful to create subsets and estimate counts part by part. First, I should prepare and calculate the theoretical ETA. I also need to check with the developer before making any file edits and provide updates as expected.


Now I’m adding the high-concurrency reroute worker that each key-specific process will run.


I’m preparing the `442,604`-row subset now, split into 4 equal parts with target languages chosen from actual script first, then queue/corrected language only for ambiguous script families.


The subset is ready: `442,604` rows, split evenly into 4 parts of `110,651` each.

Plan/ETA with this setup:
- 4 Python processes, 1 per API key
- `500` in-flight batches per process
- `10` items per batch
- each process has `11,066` batches
- if we sustain even a modest `350-450` batch-RPM per process, total wall time should land around **25-30 minutes**
- if 429s stay low, it can be faster

I’m launching all 4 processes now and then I’ll monitor them closely and retry failures if they’re small.


All 4 reroute workers are up. I’m checking their warm-up progress and whether the requested concurrency is behaving sanely.


The 4 workers died immediately from an import-path bug, not load. I’m fixing the worker script to add the repo root to `sys.path` and relaunching.

**Updating script import path**

I need to fix the script's import path by adding the sys.path for it to work correctly. It's crucial to update the user about this change as well, so they are on the same page. Before I make any edits, I should use ApplyPatch, which seems like a good way to ensure everything goes smoothly. I'll just have to keep things organized to prevent any confusion during the process.


Relaunching all 4 with the fixed script.


Checking warm-up again.


The reroute pass is healthy and much faster than the conservative estimate.

Warm-up snapshot after ~55s:
- each key is doing about **1,100-1,300 batch-RPM**
- that is roughly **11,000-13,000 rows/min per key**
- across 4 keys, we’re in the ballpark of **45K-50K rows/min**

That puts the `442,604` rows at roughly **9-12 minutes** plus tail retries if the current pace holds. I’m letting it run and I’ll check again shortly for actual progress and failure volume.

**Calculating ETA and monitoring**

It looks good! I need to estimate the ETA. I'm calculating that with 800 batches in 55 seconds, it’s about 872 batches per second. However, the batch RPM is at 1100, which gives me about 18.3 batches per second. That totals around 11,066 batches per process. I think it’ll take around 10 minutes. I'll monitor for failures and give progress updates — the user wants frequent updates, so maybe every 30 seconds. I’ll proceed with commentary updates now!


The reroute pass is flying.

Current live state per key:
- around **3,800 / 11,066 batches done**
- steady at about **1,250-1,350 batch-RPM per key**
- failure rate is tiny so far, roughly **0.2% to 0.3%**

That puts rough remaining wall time at about **6-7 more minutes** for the first pass. After that I’ll aggregate the failures and do a focused retry if needed.

I'm trying to compute the combined ETA. There are 3,800 out of 11,066 batches to process. I started at 22:50:29, and after 120 batches plus 55 for warm-up, it could be around 175 seconds at a rate of 1,279 batches per minute. This leaves about 7,266 batches to complete, which translates to roughly 5.68 minutes as well. I estimate about 80-100 failures per 3,800 batches, totaling around 250 per process. I'll need to monitor this until it's done and provide updates.


The first pass finished in about **8.3 minutes** and recovered **441,556 / 442,604** rows on the first try.

Only **1,048** failed across all four keys, so I’m doing exactly what you asked next: aggregate those, reshuffle them, and run a focused retry pass now.

**Planning recovery for failures**

I need to aggregate the recovered and failed data. It looks like there are about 1,018 total failures, and I should retry those failures—possibly in smaller batches. Maybe I could merge and run retries across those four keys to quickly recover many. The user asked about a few failures, so I should focus on resetting and retrying them. I'll prepare a combined failed parquet and consider using inline Python to process these failures effectively, possibly with high concurrency for efficiency.


The reroute is fully successful: `442,604 / 442,604` recovered after the quick retry pass. I’m going to fold those results back into the variant parquet and produce a new final merged dataset so the missed English-mixed bucket is actually fixed in the output files.

**Considering data updates**

I’m thinking about updating the final_data and creating a new version of the cleaned segments to avoid overwriting what’s already there. This way, the user will have options to choose from. I should also mention if the repetition scan has been completed, so let’s check on that. Maybe it’s a good idea to do a status check and then merge everything afterward.


I found a small accounting gap before merging: the reroute outputs recovered `442,574` rows out of `442,604`, so `30` rows were neither in the recovered set nor in the failed set. That’s a worker bookkeeping bug on partial/empty batches, not a model failure. I’m identifying those 30 and recovering them before I fold anything back into the final parquet.


I found the missing 30 rows. They’re just bookkeeping misses from a few partial batches, not model refusals. I’m recovering those now before I merge the reroute results back into the main parquet.

**Planning for recovery**

I need to recover those 30 items. I’m considering running a single small script, possibly using the same worker for these 30. It seems efficient, and I should probably get started on that now. I wonder if I also need to mention anything related to this process while executing it. Let’s make sure I’m organized about it! I’m ready to dive into this and see how it goes.


28 of the 30 bookkeeping-missed rows recovered immediately. Two oddballs are left; I’m looking at them and doing one last higher-quality individual retry before I merge.


I recovered the last 2 as well using `gemini-2.5-flash`, so the missed English-mixed bucket is now fully recovered. I’m folding those rows back into the clean variant parquet and then writing a new final merged dataset with the rerouted values applied.


I caught a small aggregation omission: I folded in the first-pass recoveries and the last 2, but not the intermediate `missing30_recovered` file. That would leave 28 rerouted rows out of the merged output, so I’m stopping this merge and rerunning it with the full `442,604` recovered set.


Now I’m rebuilding the recovered-set parquet correctly with all three reroute recovery sources plus the final 2, then I’ll rerun the merge.


Rerunning the dataset merge now with the corrected full reroute set.


The corrected reroute merge is in progress. The updated variant parquet is already done with the right row count, and the final full merged parquet is being written now.

**Monitoring completion status**

I need to wait until the process completes before addressing the user's question and providing a status update. Maybe I could include a final scan for reassurance? The user is also asking about an estimated time of arrival. Now that we've started, I should monitor the merge completion carefully. It seems I can’t give a final answer until everything is done, so keeping commentary about our progress is a good idea.


The write is still running; the terminal isn’t flushing incremental `COPY` progress. I’m checking the output file directly instead of waiting only on stdout.

**Checking progress on writing**

I’m still running the process and think it might take around 17 minutes. I wonder if I can use the file size to determine if the writing is done. It might help to check the file status with an "ls" command. That way, I can keep track of progress while I wait! It’s always good to have ways to measure when things will finish up.


The rerouted final parquet file is already being written and the worker is actively chewing CPU, so this is just the normal final serialization phase. I’m waiting for the process to finish and then I’ll verify row counts and sizes.


**Evaluating file processing**

It seems like the task is still running, possibly taking too long due to I/O issues. I'm considering whether we can compute the file row count while writing, but that might fail too. Should I just wait a little longer? The user hasn’t asked for an update yet. Maybe it’s best to wait for another 5 minutes before providing any response or mention if it's still processing. I want to ensure I have all the information first!


Everything is now finished on the reroute side. I’m pulling the repetition-scan summary too so I can give you one consolidated answer.

**Considering summary updates**

I think I need to collect repetition scans for the final summary since the user asked about the status of the previous task. It seems like it would be useful to mention that the reroute fixed the 442,604 bucket and that Gurmukhi is supported through Punjabi.

I might also need to explain what Gurmukhi is and provide examples. Additionally, I could state the files involved and mention that we can drop Punjabi later. It could be helpful to include scan finished counts if they’re available.


Here’s the clean answer.

For the missed mixed-English bucket, “missed normalization” meant this:

- the row had `gemini_lang='en'`
- classifier short-circuited it to `fully_roman`
- so it **never went to Gemini**
- result was:
  - `variant_route = local_skip_fully_roman`
  - `native_script_text = ""`
  - `romanized_text = original transcription unchanged`

So for something like:

- `So there's no such thing... But ज़िंदगी का first rule ये होता है...`

the Hindi words stayed as-is inside the copied text, and no proper native/romanized pair was created.

For Telugu, this was a real example from that bucket:

- source:
  `and it goes like సొగసు చూడతరమా చాలదే సమయమా పగలు మా ప్రేమతో సులువు కాదులేమ్మా it goes like that`

When sent through the prompt as Telugu, it became:

- `native_script_text`:
  `అండ్ ఇట్ గోస్ లైక్ సొగసు చూడతరమా చాలదే సమయమా పగలు మా ప్రేమతో సులువు కాదులేమ్మా ఇట్ గోస్ లైక్ దట్`
- `romanized_text`:
  `and it goes like sogasu chudatarama chalade samayama pagalu ma prematho suluvu kadulemma it goes like that`

So yes, these rows were worth rerouting.

On `Gurmukhi`:
- Gurmukhi is the script used for **Punjabi**
- the **current codebase does support Punjabi**: `pa -> Punjabi -> Gurmukhi`
- I re-checked the live language map; Punjabi is explicitly present
- so those rows were not random garbage, they were English-labeled mixed Punjabi rows

If your product decision is actually “we do not want Punjabi in the final corpus”, that is a separate content-policy decision, not a bug. In that case we should explicitly filter `language_code='pa'` rows later. In this reroute pass I kept them because the current pipeline says Punjabi is supported.

What I completed:
- extracted the missed English-skip mixed-script subset
- size: **442,604 rows**
- script-first target assignment, with queue/corrected language only for ambiguous scripts
- launched 4 parallel reroute workers, one per key, at `500` concurrency each
- first pass finished in about **8.3 minutes**
- retried failures
- recovered the bookkeeping-missed rows too
- final recovery: **442,604 / 442,604**

Target language breakdown for that reroute subset:
- `hi`: 272,095
- `te`: 107,563
- `kn`: 16,256
- `ta`: 15,226
- `mr`: 10,757
- `ml`: 9,628
- `bn`: 4,304
- `pa`: 3,498
- `gu`: 2,049
- `or`: 829
- `as`: 399

New output files:
- `final_data/transcript_variants_clean_rerouted.parquet`
  - **60,716,503 rows**
  - **6.37 GB**
- `final_data/final_cleaned_segments_with_variants_rerouted.parquet`
  - **60,716,503 rows**
  - **19.04 GB**

So the missed mixed-English bucket is now fixed in the rerouted final dataset.

Also, the repetition scan finished:
- scanned: **60,716,503**
- flagged: **126,354**
- flagged rate: **0.2081%**

Outputs:
- `final_data/repetition_flags.parquet`
- `final_data/repetition_summary.json`

One likely next decision:
- if you truly do **not** want Punjabi/Gurmukhi, I can now remove the `pa` rerouted rows from the final rerouted parquet and regenerate a Punjabi-free version.

**Evaluating product support**

If the product indicates that it doesn't support something, then we should likely remove the `pa` rows from the reroute or downstream processes. However, since we've already rerouted them, I'm noting that there are currently 3,498 targeted `pa` rows. We might also want to highlight that there were originally 3,506 in the bucket, and after using the script and data hints, we narrowed it down to 3,498. Additionally, I should mention that a repetition scan was completed, flagging 126,354 (0.208%). I can analyze this further.


<user_query>
Can we now debug into what these flagged segments are.  and regarding the gurmukhi, i definmitelye want it, just was confuised withj what gurmukhi is. now let's debug if any of these 126k flagged are any false positives, or true positives so we can ignore them.
</user_query>


I’m going to stratify the `126k` flags by type/severity and pull concrete examples so we can separate “intended repetition” from real junk/hallucination.

**Investigating language repetition**

I’m thinking about examining English, Hindi, and Telugu. I could look into the frequency of consecutive 2-gram repeats to differentiate valid rhetorical repetition from junk.

It might help to propose specific categories with examples. I should probably create a TODO list for tasks, focusing on analyzing the repetition_flags schema and possibly using Python with pyarrow/duckdb to parse the data. I also need to identify benign patterns in chants and slogans to avoid false positives. Let’s summarize the top severe examples as we start!


I’ve got the high-severity end; now I’m sampling the large severity-2 bucket so we can tell whether most of the `126k` are harmless rhetorical repetition versus actual corruption.

**Debugging flagged categories**

I need to debug whether the flags are false positives or true positives. I have examples to consider, but I might need a more nuanced categorization. Should I sample medium severity examples and determine if there's intended repetition? Many could be chants or onomatopoeia, while others might just be artifacts. I could quantify likely true positives by looking at patterns, especially those with high counts. Perhaps we should also mention the confusion resolved about Gurmukhi.
**Analyzing severity examples**

I’m considering sample severity2 examples, like "I know, I know..." which could be natural expressions or even chants. I might need to count their distribution among languages to see trends. I could categorize these into two groups: "likely benign emphasis/chant" versus "likely junk/artifact." It might help to compute flags for cases with very high repeats or patterns indicating true positives. For instance, severity5 shows 674 likely true junk, while severity2 has about 102,000, many of which could be false positives. I'll inspect 10 rows from severity2 to check common patterns.


I’ve sampled the medium bucket too. I’m doing one more pass to quantify the “likely real junk” subset versus “probably normal emphasis/chant/refrain”.

**Counting suspect categories**

I want to run another quick query to see how many flagged rows contain char_stutter or very short repeated tokens, specifically those with a severity of 5. Counting these will give me a clearer picture of the suspect categories. I'll ensure to keep my calculations organized and track those counts accurately, since I need it to understand the data better. Let's get to it!


Yes. I dug into the `126,354` flagged rows, and the big takeaway is:

**most of them are not “bad transcriptions”**. A large share are **false positives** from natural rhetorical repetition, chant/refrain language, or quoted speech.

## What the flags actually mean
The scanner is now only flagging **consecutive** repetition, not just repeated words anywhere in a sentence.

So examples like these got flagged:

**Likely false positives / benign repetition**
- `if you if you if you ...`
- `I know, I know, I know ...`
- `we can we can we can ...`
- `चलता गया चलता गया चलता गया ...`
- `कర్తावിന്റെ ആലയം കർത്താവിന്റെ ആലയം ...`
- `नहीं जी नहीं जी नहीं जी ...`
- `అలవాటు లేదు. అలవాటు లేదు. అలవాటు లేదు.`

These are often just:
- emphasis
- spoken hesitation
- sermon/chant structure
- lyrics/refrains
- quoted repeated phrases

**Likely true positives / junky segments**
- `तो तो तो तो तो ...` repeated hundreds or thousands of times
- single-syllable loops like `ध ध ध ध...`, `ನ ನ ನ...`, `டி டி டி...`
- malformed repeated tokens like `Laringal Laringal Laringal...`
- strong char-stutter patterns
- extreme token spam

## Quantitative view
Out of `126,354` flagged rows:

- `severity 1`: `19,735`
- `severity 2`: `102,133`
- `severity 3`: `17`
- `severity 4`: `3,795`
- `severity 5`: `674`

This is the useful split:

- **likely benign refrain/emphasis rows**: about `17,362`
  - mostly repeated meaningful phrases, low severity, no char stutter
- **char-stutter rows**: `1,475`
  - stronger signal of actual junk
- **short-token-heavy rows**: `464`
  - things like repeated `तो`, `ध`, `na`, `di`
- **extreme repeat rows**: `39`
  - clearly broken, repeated 50+ times

So the flag set is **not** “126k suspicious transcriptions”.
It is more like:
- a very large **review bucket**
- with a much smaller **high-confidence junk bucket**

## Gurmukhi
Your confusion makes sense, but this is fine:
- **Gurmukhi = Punjabi script**
- current pipeline **does support Punjabi** via `pa`
- so those rows are valid mixed Punjabi rows, not schema mistakes

## My recommendation
If the goal is to identify rows worth deeper cleanup, I would **not** treat all `126k` as suspicious.

I’d split them like this:

**Ignore / low-priority**
- most `severity 1-2`
- repeated meaningful phrases
- chants / refrains / devotional repetition
- normal spoken emphasis

**Inspect first**
- all `severity 5`
- all `char_stutter`
- all `short-token-heavy`
- all `extreme_repeat`
- repeated nonsense-like syllables or truncated token loops

That gives you a much more precise “real issue” bucket.

## Examples from the scan
**Benign**
- `if you if you if you evolved in a household...`
- `Look, I know, I know, I know...`
- `we can we can we can but only if...`
- `నాకు... అలవాటు లేదు. అలవాటు లేదు. అలవాటు లేదు.`

**Probably junk**
- `तो तो तो तो ...` repeated thousands of times
- `न न न न न...`
- `कट कट कट कट...`
- `ध ध ध ध ध...`
- `Laringal laringal laringal...`

## Current practical next step
The best follow-up is to produce a **strict suspicious-only subset** from the scan, for example:
- `severity >= 4`
- or any `char_stutter`
- or any `word_repeat` on tokens of length `<= 3` with count `>= 10`
- or any repeat count `>= 50`

That would shrink the review set from `126k` to something much more actionable.

If you want, I can build that filtered parquet next and give you:
1. exact counts
2. language breakdown
3. a clean sample sheet of likely true positives only