**Analyzing segment contamination**

I'm considering that the training may filter out overlaps, but in this case, it’s about sequential segments. I should mention that the micro-contamination stage might catch some issues, but it only checks segments longer than 4 seconds, and here the segment duration is 5.3 seconds. It seems that the mean similarity for the segment compared to the centroid might be low, but because the segment label is SPEAKER_00, the centroid could be skewed.

New logic should help catch both directions and short segments, although it may lead to an increase in false splits. We've implemented conservative anchors and require both sides to be assigned and match the original speaker for control. I just need to run this on a batch!


**Evaluating segment analysis**

I'm considering how often we encounter mismatches between the first 1.5 seconds and whole segment labels. It seems non-trivial based on the earlier observations. Sure, I could measure this by scanning those segments, as we flagged 9 out of 79 in the previous video. Some segments were low-quality, not all indicating true multi-speaker situations.

Now, if the completed segments don't get reprocessed or repaired, their pipeline outputs just stay as they are. I wonder if we should add an "audit/repair" job and reprocess that subset. Would you like me to create a script for that?

**Considering privacy and pipeline needs**

I need to think about privacy and note that we used their R2, which is fine. I should provide clear instructions here. It's worth mentioning that we can't load pyannote in this container, but thankfully that patch isn't necessary. For a complete pipeline rerun, the environment must have pyannote and the HF token.

Now, let’s outline the key points: completed videos are unaffected by the code change, but action is needed; this issue isn’t isolated. The recommendation is to audit completed tars for multi-speaker segments. I can also offer to write a script to list completed tars, scan them, and produce a report. Would that help?


Nothing “automatic” happens to already-COMPLETED videos: the tarballs already in R2 stay exactly as they are. This patch only changes future pipeline runs (or any explicit reprocessing you do).

This is not a one-off bug class. The failure mode we fixed is systematic:
- Old centroid gate assumed segments start with the segment’s label (`pre≈original`, `post≈other`). Your clip is the inverse (`pre≈other`, `post≈original`), which was getting rejected even when the change was real.
- On short segments, the adaptive “relative valley” threshold can get too conservative (collapses toward `severe_th`), so we don’t even *propose* a split.

What to do about completed videos depends on how aggressive you want to be:
- **Cheap + safe:** run an *audit* over completed tarballs to identify “likely multi-speaker” segments (windowed embedding scan like we did here), then **exclude/quarantine** those segments downstream without touching R2.
- **Repair in place:** for flagged segments, **split the FLAC inside the tar** (using the segment’s own audio + filename timestamps), update `metadata.json`, and re-upload a corrected tar to R2.
- **Full reprocess:** rerun the pipeline for affected video_ids and overwrite outputs (best quality, most expensive).

If you tell me which path you want (audit-only vs auto-repair), I can add a script that scans R2 completed tars and produces a report (and optionally rewrites the tarballs).


are these identifyable with pre-existing metadata files, or do we have to process all the audio samples again


Summary: auto

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


**Assessing multi-speaker detection**

I need to explore whether pre-existing metadata can help identify multi-speaker segments without guessing. Upon examining the structure, I noticed that the metadata file offers segments with details like start/end times and speaker identifiers. However, it lacks evidence of speaker changes within segments, limiting reliable detection. I could use heuristics, but it seems the metadata won’t suffice for identifying multi-speaker segments unless there’s more detailed logging available, which doesn’t seem to exist here.


**Reprocessing audio segments**

I need to process the flagged audio segments again, but I can skip full diarization. Instead, I can run embeddings on each individual segment file or the beginning parts to check for contamination. Since the tar file already has segmented audio clips, I can easily reprocess them. I’ll scan each segment for internal speaker changes using embeddings and compare those to centroids from other segments. This approach means I don’t have to rerun the entire pipeline, but I still need the audio for verification.


**Analyzing audio segments for R2**

Now that R2 is complete, I have access to tar files with segment audio. I’m considering whether to download the entire tar or analyze the segments using range requests, but streaming might be complicated. If pyannote data isn’t installed, I can't run diarization, but I can still scan with the embeddings model that's available. I should prioritize segments based on heuristics, like length and quality stats, while keeping an eye on flagged segments, especially where speakers appear inconsistently. However, audio processing is necessary for verification, though I only need a light pass on the existing segments.


**Evaluating metadata for speaker changes**

I should mention that while I can look at the metadata's overlap fields, they only show separate segments and don’t capture sequential speaker changes. The micro_contamination counts might provide some insight, but they aren’t reliable. So, I can directly answer that we can’t identify multi-speaker segments solely based on metadata. For this specific case, the metadata shows the segment is 'usable' with no indicated unusable reasons, and overlap begins after 204.54, meaning there’s no evidence of within-segment change.


Metadata alone isn’t enough to reliably identify this class.

- In the completed tar, `metadata.json` only records *final* segments (`start/end/speaker/status`). If a segment wasn’t split, there’s no field that says “speaker changes inside this segment”.
- The summary fields you *do* have (`quality_stats.split_segments`, `chunk_reassignment_detail`, overlaps, etc.) are aggregate and won’t tell you “segment X has 2 speakers”. In your example, `SPEAKER_00_0021_199.00-204.34.flac` is marked `usable` with no `unusable_reason`, and nothing in metadata flags the within-segment switch.

The good news: you don’t need to re-run full diarization on the original audio to find these.
- Because the tar already contains `segments/*.flac`, you can scan the **existing segment audio clips** with a lightweight embedding/window pass (like we did) to detect within-clip speaker switches and/or “wrong-label-at-start” contamination.
- That still “processes audio again”, but it’s cheap and scoped (operate on each 3–10s segment file), not the whole source recording and not the full pipeline.


# Context from my IDE setup:

## Open tabs:
- .env: .env

## My request for Codex:
give more info. i will note what you are saying, and then do this during whenever i do a full run on those samples. tell me how can i do a quick run like how you did. give info here in chat


Summary: auto


**Analyzing segments with Python script**

I need to run a Python script to analyze a single segment by building centroids from all segments in the extracted folder. I'll slide windows on the target to identify the best speaker and their score. Additionally, I want to loop through all segments to flag wrong starts by comparing the first and second best speakers against labels while detecting changes within the clips. I might reference specific files like `src/r2_client.py` and `src/unified_embeddings.py`, including line numbers for clarity, but remember not to reveal any sensitive keys or ask the user to paste them.


**Debugging video segments**

I’m crafting a response for a quick debug involving a single video and segment. First, I need to download a tar file and set up the environment with the necessary configurations. After that, I'll extract the contents and run a sliding window check. This involves computing per-speaker centroids for 8-second segments while analyzing target 1-second windows. If the best speaker differs from the label for more than a certain percentage, I’ll flag it. I’ll also need to account for any intro skipped in the metadata to map segment times accurately.


**Crafting response with scripts**

I’m thinking I shouldn’t include large code blocks, but providing them might be acceptable since the user asked for information. I’ll include two scripts: one for a minimal single segment and another for a broader scan. I want to mention performance aspects, like using the GPU if available, or that it’ll be slower otherwise. There are edge cases to consider, like tar files being at the root or under a prefix. Additionally, I’ll remind the user about pip install instructions for SpeechBrain and ensure no secrets are printed. I'll organize everything into clear bullets and code blocks for readability.


**Formatting code snippets**

I need to follow the formatting guidelines, using monospace for commands and possibly including headers. I want to provide one or two snippets that could be slightly longer but shouldn’t be huge code blocks. Since the user requested information in the chat, I’ll keep the snippets minimal and straight to the point. I’ll have one snippet for pulling the tar and inspecting a segment, and another for scanning the entire tar for multi-speaker segments. Also, I’ll include a requirement to patch `torchaudio` in the script.


**Interpreting output results**

I need to discuss how to interpret the output for the best speaker. If the best speaker changes frequently and the margins are high, that indicates it’s likely a multi-speaker situation. I should provide specific thresholds: for instance, a margin greater than 0.05 and a similarity score above 0.45. I also want to caution that the centroids from segments might be contaminated, but they should still be reliable enough for practical use. Alright, let’s finalize this response!


You can do a “quick run” entirely from the already-uploaded cleaned tar (no full re-diarization, no original audio needed). What I did was:

1) download the `VIDEO_ID.tar` from R2
2) extract `segments/*.flac` + `metadata.json`
3) build per-speaker centroids from the existing `segments/` files
4) slide a 1.0s window over the target segment and see if the “best matching speaker” changes

### 1) Download the tar from R2
This repo’s `R2Client` may apply `R2_PREFIX`; for our debugging we want “no prefix” (tars are often at bucket root). Run:

```bash
source .env

python3 - <<'PY'
from pathlib import Path
from src.r2_client import R2Client, R2Config

video_id = "--jp1imJD9w"
remote_key = f"{video_id}.tar"
out = Path("tmp/r2debug") / remote_key
out.parent.mkdir(parents=True, exist_ok=True)

base = R2Client(bucket_type="production")  # bucket=1-cleaned-data
cfg = R2Config(
    endpoint_url=base.config.endpoint_url,
    access_key_id=base.config.access_key_id,
    secret_access_key=base.config.secret_access_key,
    bucket=base.bucket,
    prefix="",  # IMPORTANT: bypass R2_PREFIX
)
r2 = R2Client(config=cfg, bucket_type="custom")

print("Downloading", f"s3://{r2.bucket}/{remote_key}")
assert r2.download_file(remote_key, str(out))
print("Saved", out)
PY
```

### 2) Extract tar (segments + metadata)
```bash
mkdir -p tmp/r2debug/extracted
tar -xf tmp/r2debug/--jp1imJD9w.tar -C tmp/r2debug/extracted
```

### 3) Quick “is this segment multi-speaker?” check (sliding windows)
This prints the best speaker per 1s window (hop 0.25s). If it flips speakers with decent margins, the segment is contaminated.

```bash
python3 - <<'PY'
import torch
import torchaudio
if not hasattr(torchaudio, "list_audio_backends"):
    torchaudio.list_audio_backends = lambda: ["soundfile", "sox"]

from pathlib import Path
from collections import defaultdict
from speechbrain.inference.speaker import EncoderClassifier

video_id = "--jp1imJD9w"
seg_dir = Path(f"tmp/r2debug/extracted/{video_id}/segments")
target = seg_dir / "SPEAKER_00_0021_199.00-204.34.flac"

device = "cuda" if torch.cuda.is_available() else "cpu"
model = EncoderClassifier.from_hparams(
    source="speechbrain/spkrec-ecapa-voxceleb",
    savedir="tmp/r2debug/models/ecapa",
    run_opts={"device": device},
)

def load16k(path: Path):
    wav, sr = torchaudio.load(str(path))
    if wav.shape[0] > 1:
        wav = wav.mean(dim=0, keepdim=True)
    if sr != 16000:
        wav = torchaudio.functional.resample(wav, sr, 16000)
    return wav, 16000

def embed(wav):
    with torch.no_grad():
        return model.encode_batch(wav.to(device)).detach().cpu().squeeze()

def cos(a, b):
    return float(torch.nn.functional.cosine_similarity(a, b, dim=0))

# Build per-speaker centroids from ALL existing segment clips
speaker_embs = defaultdict(list)
for f in seg_dir.glob("*.flac"):
    speaker = "_".join(f.name.split("_")[:2])  # SPEAKER_00
    wav, sr = load16k(f)
    clip_len = min(wav.shape[1], int(sr * 8.0))
    start = (wav.shape[1] - clip_len) // 2
    speaker_embs[speaker].append(embed(wav[:, start : start + clip_len]))
centroids = {spk: torch.stack(v).mean(dim=0) for spk, v in speaker_embs.items()}

wav, sr = load16k(target)
dur = wav.shape[1] / sr

win_sec = 1.0
hop_sec = 0.25
win = int(sr * win_sec)
hop = int(sr * hop_sec)

prev = None
for start in range(0, wav.shape[1] - win + 1, hop):
    w = wav[:, start : start + win]
    e = embed(w)
    sims = sorted(((spk, cos(e, c)) for spk, c in centroids.items()), key=lambda x: x[1], reverse=True)
    best, second = sims[0], sims[1]
    t0, t1 = start / sr, (start + win) / sr
    if prev is None or best[0] != prev:
        print(f"{t0:5.2f}-{t1:5.2f} best={best[0]}({best[1]:.3f}) 2nd={second[0]}({second[1]:.3f})")
    prev = best[0]
PY
```

How to interpret:
- If you see something like `best=SPEAKER_01` early, then `best=SPEAKER_00` later (with non-trivial margins), that’s exactly “two speakers inside one cleaned chunk”.
- To map the segment’s `199.00-204.34` back to source time, add `intro_skipped` from `metadata.json`.

If you want, I can give you a follow-up one-liner version that scans *every* `segments/*.flac` in a tar and outputs a CSV of “likely multi-speaker segments” (still using only the tar’s segment clips, not original audio).