Output:
Exit code: 0
Wall time: 0 seconds
Output:
27:def _asr_summary(result: ASRResult) -> dict[str, Any]:
65:    End-to-end run: preprocess -> LID -> ASR ensemble -> normalize -> ROVER -> verify.
166:                # 3) ASR ensemble
187:                        det_sum = _asr_summary(det)
210:                ensemble_models = sorted({v.model for v in asr_results.values()})
211:                ensemble_backends = sorted({v.model.split(":", 1)[0] for v in asr_results.values()})
212:                ensemble_warnings: list[str] = []
213:                if len(ensemble_models) < 2:
214:                    ensemble_warnings.append("low_model_diversity")
245:                asr_summaries = {k: _asr_summary(v) for k, v in asr_results.items()}


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.


**Searching for line numbers**

I’m working on getting line numbers for various components like `Prompt.md` and the `schema_version` in my pipeline. It seems that the `rg` output isn’t showing the line number for `schema_version`, possibly because the query didn't include it clearly or it's not in the first few lines. I need to search again to find that information properly and make sure I can include it all. Let's give it another go!


rg -n "schema_version" maya_transcribe/pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
263:                    "schema_version": 1,


Summary: auto


rg -n "asr_summary" maya_transcribe/pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
27:def _asr_summary(result: ASRResult) -> dict[str, Any]:
187:                        det_sum = _asr_summary(det)
245:                asr_summaries = {k: _asr_summary(v) for k, v in asr_results.items()}
293:                    "asr_summary": asr_summaries,


**Summarizing coding changes for UI**

I need to provide a summary of the relevant comments and code changes, including file references. I'll mention the metrics and scores that need to be surfaced in the UI, referencing fields in `pipeline.py`.

Yes, we're ready for UI, but we must ensure stable IDs and note the current storage of `final.text` without punctuation. If punctuation retention is needed, updates are necessary.

Before UI, we should consider providing `results.jsonl` size, `schema_version`, and possibly removing the unused `seen` parameter from `ids.py` to avoid confusion.


**Updating parameters for clarity**

It looks like we should update certain call sites, since we're currently using `seen=None`. Removing the `seen` parameter could be beneficial for clarity, especially as the review references `ids.py` line 16. I'll change the signature to `segment_identity(src: Path, root: Path) -> SegmentIdentity` by removing the `seen` parameter.

I'll also update the call sites in `cli.py` and `pipeline.py`, since they're calling `segment_identity(src, root=...)` without using `seen`. Other code might still call it with `seen`, though. So, let's go ahead and patch this up!


Input:
*** Begin Patch
*** Update File: maya_transcribe/ids.py
@@
-def segment_identity(src: Path, root: Path, seen: dict[str, int] | None = None) -> SegmentIdentity:
+def segment_identity(src: Path, root: Path) -> SegmentIdentity:
*** End Patch


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


python3 -m compileall -q maya_transcribe


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


**Updating command guidelines**

Now that everything is stable, I should consider updating the command lines. For the pipeline run with Gemini and CTC, I’ll provide the latest commands. The earlier CTC model was only suitable for English, and for Indic, mapping is necessary. It's important to note that while the output is stable and ready for validation and manual review, it isn't ideal for UI if perfect quality is required. I'll also propose integrating results.jsonl with optional Supabase for corrections. Finally, I’ll ensure that update plans aren’t needed since the earlier one is complete.


**Preparing code review summary**

I’m planning to respond in plain text while keeping headings to a minimum. The user asked for a code review and readiness, which involves medium changes across multiple files. I’ll include bullets for file references, such as segment ID stability in `maya_transcribe/ids.py:16` and Gemini prompt language hint mapping at `maya_transcribe/language.py:80` and `maya_transcribe/gemini_asr.py:147`. I have to note the updated output schema version and summarize metrics, including that we don’t have real WER/CER numbers due to the lack of a gold set.


**Evaluating metrics and latency**

I’m considering the metrics related to response time, specifically about the tiny model with a total compute time of around 1.4 seconds and a potential CTC around 3 seconds. However, we didn’t print the `ctc_sec`, but we might compute it quickly if necessary. The user asked for scores and metrics before the UI, so I can mention our gating metrics and actual measured latency stats from samples, like 1.4 seconds for preprocessing, lid, and ASR. I'll read from the summary and results for more timing details.


cat outputs/test_run6/summary.json && echo '---' && python3 - <<'PY'
import json
from pathlib import Path
obj=json.loads(Path('outputs/test_run6/results.jsonl').read_text().splitlines()[0])
print(obj['timing'])
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
{
  "segments_total": 1,
  "counts": {
    "accept": 1,
    "human": 0,
    "reject": 0,
    "error": 0
  },
  "avg_total_sec": 1.824479534290731,
  "p50_total_sec": 1.824479534290731,
  "p90_total_sec": 1.824479534290731
}
---
{'preprocess_sec': 0.20665591303259134, 'lid_sec': 0.44643764197826385, 'asr_sec': 1.1707691121846437, 'normalize_sec': 0.00011232215911149979, 'rover_sec': 0.00018567079678177834, 'ctc_sec': 6.807968020439148e-07, 'verify_sec': 0.000125126913189888, 'total_sec': 1.824479534290731}


cat outputs/test_run_ctc3/summary.json && echo '---' && python3 - <<'PY'
import json
from pathlib import Path
obj=json.loads(Path('outputs/test_run_ctc3/results.jsonl').read_text().splitlines()[0])
print(obj['timing'])
print('ctc', obj['ctc'])
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
{
  "segments_total": 1,
  "counts": {
    "accept": 1,
    "human": 0,
    "reject": 0,
    "error": 0
  },
  "avg_total_sec": 3.4424101770855486,
  "p50_total_sec": 3.4424101770855486,
  "p90_total_sec": 3.4424101770855486
}
---
{'preprocess_sec': 0.19565186090767384, 'lid_sec': 0.4632572350092232, 'asr_sec': 0.8158344081602991, 'normalize_sec': 4.6106986701488495e-05, 'rover_sec': 0.00010219309478998184, 'ctc_sec': 1.9673667480237782, 'verify_sec': 7.338961586356163e-05, 'total_sec': 3.4424101770855486}
ctc {'model_id': 'facebook/wav2vec2-base-960h', 'best_key': 'rover', 'best_loss': 1.2419719696044922, 'second_best_key': None, 'second_best_loss': None, 'margin': None, 'scores': {'rover': {'loss': 1.2419719696044922, 'model_id': 'facebook/wav2vec2-base-960h', 'unk_ratio': 0.0, 'target_len': 16, 'token_score_mean': -0.4490797834514524, 'token_score_min': -6.753193378448486, 'token_coverage': 0.9375, 'token_logp_threshold': -3.0, 'span_count': 16, 'span_mismatch': False}, 'faster-whisper:tiny|det': {'loss': 1.2419719696044922, 'model_id': 'facebook/wav2vec2-base-960h', 'unk_ratio': 0.0, 'target_len': 16, 'token_score_mean': -0.4490797834514524, 'token_score_min': -6.753193378448486, 'token_coverage': 0.9375, 'token_logp_threshold': -3.0, 'span_count': 16, 'span_mismatch': False}, 'faster-whisper:tiny|samp_t0.2': {'loss': 1.2419719696044922, 'model_id': 'facebook/wav2vec2-base-960h', 'unk_ratio': 0.0, 'target_len': 16, 'token_score_mean': -0.4490797834514524, 'token_score_min': -6.753193378448486, 'token_coverage': 0.9375, 'token_logp_threshold': -3.0, 'span_count': 16, 'span_mismatch': False}}, 'errors': {}}


**Analyzing latency scores**

I see we've got actual latency now. With CTC scoring added, the CTC stage is around 1.97 seconds, and the total latency for tiny is about 3.44 seconds per segment. These aren't WER metrics but reflect end-to-end latencies. I'll mention CTC metrics, including loss, token coverage, and token score mean, as they highlight mismatches. Since we don't have real WER metrics yet due to no gold set, I'll confirm we can compute them once we have one. Looks like we're ready for UI!


**Finalizing output references**

I need to include a starting line when referencing files. For JSON files, should I reference line numbers, like `outputs/test_run6/summary.json:1`? I'll mention that. Since these outputs are ephemeral, I'm wondering how to handle it. The user asked about scores or metrics, so I can talk about the measured latencies. Also, I need to note that we changed the segment_id format, which may break existing references. Let's wrap this up!


**Review Fixes Landed**
- Stable, reproducible `segment_id` (always `base_id-uid`, 64-bit hash): `maya_transcribe/ids.py:16` (this will change IDs vs older runs, so older `refs.jsonl` won’t match).
- Gemini language hint now uses human-friendly names/scripts for prompting (and “Unknown → auto-detect”): `maya_transcribe/language.py:80`, `maya_transcribe/gemini_asr.py:153`.
- Gemini parsing is less brittle (salvage multi-line, strip punctuation, still records parse flags): `maya_transcribe/gemini_asr.py:100`.
- CTC scoring guardrails + alignment-coverage metrics (unk-ratio filter, token coverage/score stats): `maya_transcribe/ctc_fit.py:64`.
- Verification gate now uses CTC coverage metrics in addition to margin (flags `ctc_low_coverage` / `ctc_low_token_score` / `ctc_span_mismatch`): `maya_transcribe/verify.py:31`.
- Eval now includes oracle stats (oracle-best WER/CER + selection gap): `maya_transcribe/eval.py:50`.

**Metrics To Surface In UI (already in `results.jsonl`)**
- Primary decisioning: `verification.decision`, `verification.reasons`, `verification.scores` (`maya_transcribe/pipeline.py:263`).
- Audio/QC: `src_ffprobe.duration_sec` vs `ffprobe.duration_sec` (trim impact), `qc.rms_dbfs`, `qc.peak_dbfs`, `qc.clipping_fraction`.
- LID: `lid.predictions[]` and `verification.scores.lid`.
- Ensemble health: `ensemble.unique_models`, `ensemble.warnings` (`low_model_diversity`): `maya_transcribe/pipeline.py:293`.
- Hypotheses + comparisons: `asr[*].text`, `asr_summary`, `rover.text` + `rover.disagreements`, `ctc.scores`, and `final.text` + `final.source`.

**Are We Ready For UI?**
- Yes: the per-segment artifact is now stable (`schema_version` added) and contains enough signals to drive a reviewer UI (filter by decision/reason, diff hypotheses, highlight disagreements, show CTC fit/coverage, play audio).
- The only “must do” before trusting WER numbers is a human gold set; until then, you’ll mostly use the gate/CTC/LID/disagreement metrics to triage.