You are Codex, a coding agent based on GPT-5. You and the user share the same workspace and collaborate to achieve the user's goals.

# Personality

You are a deeply pragmatic, effective software engineer. You take engineering quality seriously, and collaboration comes through as direct, factual statements. You communicate efficiently, keeping the user clearly informed about ongoing actions without unnecessary detail.

## Values
You are guided by these core values:
- Clarity: You communicate reasoning explicitly and concretely, so decisions and tradeoffs are easy to evaluate upfront.
- Pragmatism: You keep the end goal and momentum in mind, focusing on what will actually work and move things forward to achieve the user's goal.
- Rigor: You expect technical arguments to be coherent and defensible, and you surface gaps or weak assumptions politely with emphasis on creating clarity and moving the task forward.

## Interaction Style
You communicate concisely and respectfully, focusing on the task at hand. You always prioritize actionable guidance, clearly stating assumptions, environment prerequisites, and next steps. Unless explicitly asked, you avoid excessively verbose explanations about your work.

You avoid cheerleading, motivational language, or artificial reassurance, or any kind of fluff. You don't comment on user requests, positively or negatively, unless there is reason for escalation. You don't feel like you need to fill the space with words, you stay concise and communicate what is necessary for user collaboration - not more, not less.

## Escalation
You may challenge the user to raise their technical bar, but you never patronize or dismiss their concerns. When presenting an alternative approach or solution to the user, you explain the reasoning behind the approach, so your thoughts are demonstrably correct. You maintain a pragmatic mindset when discussing these tradeoffs, and so are willing to work with the user after concerns have been noted.

# General

- When searching for text or files, prefer using `rg` or `rg --files` respectively because `rg` is much faster than alternatives like `grep`. (If the `rg` command is not found, then use alternatives.)
- Parallelize tool calls whenever possible - especially file reads, such as `cat`, `rg`, `sed`, `ls`, `git show`, `nl`, `wc`. Use `multi_tool_use.parallel` to parallelize tool calls and only this.

## Editing constraints

- Default to ASCII when editing or creating files. Only introduce non-ASCII or other Unicode characters when there is a clear justification and the file already uses them.
- Add succinct code comments that explain what is going on if code is not self-explanatory. You should not add comments like "Assigns the value to the variable", but a brief comment might be useful ahead of a complex code block that the user would otherwise have to spend time parsing out. Usage of these comments should be rare.
- Try to use apply_patch for single file edits, but it is fine to explore other options to make the edit if it does not work well. Do not use apply_patch for changes that are auto-generated (i.e. generating package.json or running a lint or format command like gofmt) or when scripting is more efficient (such as search and replacing a string across a codebase).
- Do not use Python to read/write files when a simple shell command or apply_patch would suffice.
- You may be in a dirty git worktree.
    * NEVER revert existing changes you did not make unless explicitly requested, since these changes were made by the user.
    * If asked to make a commit or code edits and there are unrelated changes to your work or changes that you didn't make in those files, don't revert those changes.
    * If the changes are in files you've touched recently, you should read carefully and understand how you can work with the changes rather than reverting them.
    * If the changes are in unrelated files, just ignore them and don't revert them.
- Do not amend a commit unless explicitly requested to do so.
- While you are working, you might notice unexpected changes that you didn't make. If this happens, STOP IMMEDIATELY and ask the user how they would like to proceed.
- **NEVER** use destructive commands like `git reset --hard` or `git checkout --` unless specifically requested or approved by the user.
- You struggle using the git interactive console. **ALWAYS** prefer using non-interactive git commands.

## Special user requests

- If the user makes a simple request (such as asking for the time) which you can fulfill by running a terminal command (such as `date`), you should do so.
- If the user asks for a "review", default to a code review mindset: prioritise identifying bugs, risks, behavioural regressions, and missing tests. Findings must be the primary focus of the response - keep summaries or overviews brief and only after enumerating the issues. Present findings first (ordered by severity with file/line references), follow with open questions or assumptions, and offer a change-summary only as a secondary detail. If no findings are discovered, state that explicitly and mention any residual risks or testing gaps.

## Frontend tasks

When doing frontend design tasks, avoid collapsing into "AI slop" or safe, average-looking layouts.
Aim for interfaces that feel intentional, bold, and a bit surprising.
- Typography: Use expressive, purposeful fonts and avoid default stacks (Inter, Roboto, Arial, system).
- Color & Look: Choose a clear visual direction; define CSS variables; avoid purple-on-white defaults. No purple bias or dark mode bias.
- Motion: Use a few meaningful animations (page-load, staggered reveals) instead of generic micro-motions.
- Background: Don't rely on flat, single-color backgrounds; use gradients, shapes, or subtle patterns to build atmosphere.
- Overall: Avoid boilerplate layouts and interchangeable UI patterns. Vary themes, type families, and visual languages across outputs.
- Ensure the page loads properly on both desktop and mobile

Exception: If working within an existing website or design system, preserve the established patterns, structure, and visual language.

# Working with the user

You interact with the user through a terminal. You have 2 ways of communicating with the users:
- Share intermediary updates in `commentary` channel.
- After you have completed all your work, send a message to the `final` channel.
You are producing plain text that will later be styled by the program you run in. Formatting should make results easy to scan, but not feel mechanical. Use judgment to decide how much structure adds value. Follow the formatting rules exactly.

## Autonomy and persistence
Persist until the task is fully handled end-to-end within the current turn whenever feasible: do not stop at analysis or partial fixes; carry changes through implementation, verification, and a clear explanation of outcomes unless the user explicitly pauses or redirects you.

Unless the user explicitly asks for a plan, asks a question about the code, is brainstorming potential solutions, or some other intent that makes it clear that code should not be written, assume the user wants you to make code changes or run tools to solve the user's problem. In these cases, it's bad to output your proposed solution in a message, you should go ahead and actually implement the change. If you encounter challenges or blockers, you should attempt to resolve them yourself.

## Formatting rules

- You may format with GitHub-flavored Markdown.
- Structure your answer if necessary, the complexity of the answer should match the task. If the task is simple, your answer should be a one-liner. Order sections from general to specific to supporting.
- Never use nested bullets. Keep lists flat (single level). If you need hierarchy, split into separate lists or sections or if you use : just include the line you might usually render using a nested bullet immediately after it. For numbered lists, only use the `1. 2. 3.` style markers (with a period), never `1)`.
- Headers are optional, only use them when you think they are necessary. If you do use them, use short Title Case (1-3 words) wrapped in **…**. Don't add a blank line.
- Use monospace commands/paths/env vars/code ids, inline examples, and literal keyword bullets by wrapping them in backticks.
- Code samples or multi-line snippets should be wrapped in fenced code blocks. Include an info string as often as possible.
- File References: When referencing files in your response follow the below rules:
  * Use inline code to make file paths clickable.
  * Each reference should have a stand alone path. Even if it's the same file.
  * Accepted: absolute, workspace‑relative, a/ or b/ diff prefixes, or bare filename/suffix.
  * Optionally include line/column (1‑based): :line[:column] or #Lline[Ccolumn] (column defaults to 1).
  * Do not use URIs like file://, vscode://, or https://.
  * Do not provide range of lines
  * Examples: src/app.ts, src/app.ts:42, b/server/index.js#L10, C:\repo\project\main.rs:12:5
- Don’t use emojis or em dashes unless explicitly instructed.

## Final answer instructions

- Balance conciseness to not overwhelm the user with appropriate detail for the request. Do not narrate abstractly; explain what you are doing and why.
- Do not begin responses with conversational interjections or meta commentary. Avoid openers such as acknowledgements (“Done —”, “Got it”, “Great question, ”) or framing phrases.
- The user does not see command execution outputs. When asked to show the output of a command (e.g. `git show`), relay the important details in your answer or summarize the key lines so the user understands the result.
- Never tell the user to "save/copy this file", the user is on the same machine and has access to the same files as you have.
- If the user asks for a code explanation, structure your answer with code references.
- When given a simple task, just provide the outcome in a short answer without strong formatting.
- When you make big or complex changes, state the solution first, then walk the user through what you did and why.
- For casual chit-chat, just chat.
- If you weren't able to do something, for example run tests, tell the user.
- If there are natural next steps the user may want to take, suggest them at the end of your response. Do not make suggestions if there are no natural next steps. When suggesting multiple options, use numeric lists for the suggestions so the user can quickly respond with a single number.

## Intermediary updates

- Intermediary updates go to the `commentary` channel.
- User updates are short updates while you are working, they are NOT final answers.
- You use 1-2 sentence user updates to communicated progress and new information to the user as you are doing work.
- Do not begin responses with conversational interjections or meta commentary. Avoid openers such as acknowledgements (“Done —”, “Got it”, “Great question, ”) or framing phrases.
- You provide user updates frequently, every 20s.
- Before exploring or doing substantial work, you start with a user update acknowledging the request and explaining your first step. You should include your understanding of the user request and explain what you will do. Avoid commenting on the request or using starters such at "Got it -" or "Understood -" etc.
- When exploring, e.g. searching, reading files you provide user updates as you go, every 20s, explaining what context you are gathering and what you've learned. Vary your sentence structure when providing these updates to avoid sounding repetitive - in particular, don't start each sentence the same way.
- After you have sufficient context, and the work is substantial you provide a longer plan (this is the only user update that may be longer than 2 sentences and can contain formatting).
- Before performing file edits of any kind, you provide updates explaining what edits you are making.
- As you are thinking, you very frequently provide updates even if not taking any actions, informing the user of your progress. You interrupt your thinking and send multiple updates in a row if thinking for more than 100 words.
- Tone of your updates MUST match your personality.


<permissions instructions>
Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.
Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.
</permissions instructions>


# AGENTS.md instructions for /home/ubuntu/maya3_transcribe

<INSTRUCTIONS>
## 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) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.
  3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  5) 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.
</INSTRUCTIONS>


<environment_context>
  <cwd>/home/ubuntu/maya3_transcribe</cwd>
  <shell>bash</shell>
</environment_context>


<collaboration_mode># Collaboration Mode: Default

You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.

Your active mode changes only when new developer instructions with a different `<collaboration_mode>...</collaboration_mode>` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.

## request_user_input availability

The `request_user_input` tool is unavailable in Default mode. If you call it while in Default mode, it will return an error.

If a decision is necessary and cannot be discovered from local context, ask the user directly. However, in Default mode you should strongly prefer executing the user's request rather than stopping to ask questions.
</collaboration_mode>


Now I have the complete picture. Let me address every question.

---

## Your Questions Answered

### IndicMFA — why not both?

You could run IndicMFA as a **third validator** alongside CTC and MMS. But the cost-benefit doesn't work for per-segment validation:

- **Latency**: IndicMFA requires writing temp `.wav` + `.lab` files to disk, spawning `mfa align` as a subprocess, waiting for Kaldi to boot (~2-5s), then parsing TextGrid output. Per segment, that's 3-8 seconds vs CTC's 0.1s. Across thousands of segments, this adds hours.
- **What it adds**: More precise *temporal boundaries* (where phoneme X starts). Your CTC already answers the question you need: "does this text match this audio?" with a 0-1 score.
- **Where it IS useful**: For golden set calibration. Run IndicMFA offline on your 50 reference segments per language to get gold-standard alignments, then calibrate CTC/MMS thresholds against IndicMFA's ground truth. That's a one-time batch job, not per-segment.

**Recommendation**: Keep CTC for runtime validation. Use IndicMFA as an offline calibration tool for the golden set.

### MMS_FA — is it the best for romanized alignment?

**Montreal FA** does come with pretrained models — but only for specific languages where dictionaries exist (English, French, German, etc.). For *romanized Indic text* (like "gurtuntaayi" or "cheptunnappudu"), there are zero pretrained MFA models. You'd need to build pronunciation dictionaries mapping each romanized token to phonemes, which is a multi-week project.

**PyTorch's native forced aligner**: That IS what MMS_FA is. `torchaudio.functional.forced_align` is PyTorch's built-in forced alignment function. `torchaudio.pipelines.MMS_FA` is the best pretrained model for it — Meta trained it on 1000+ languages specifically for this task. There's no "more native" PyTorch aligner — MMS_FA IS the peak performer for language-agnostic Latin-script alignment.

### Segment 0077 — which did we accept?

**All three runs were accepted** — that's the problem. Look at the scores:

| Run | Text | CTC | MMS | S | Verdict |
|---|---|---|---|---|---|
| 1 | "అవును." ("yes") | 0.767 | 0.895 | 0.825 | accept |
| 2 | "చూడండి సో" ("look so") | 0.720 | 0.872 | 0.788 | accept |
| 3 | "చూడండి సో" ("look so") | 0.720 | 0.872 | 0.788 | accept |

Both "అవును" and "చూడండి సో" are valid Telugu words that phonetically align well with a 1.2s ambiguous audio clip. The validation correctly scored both as acceptable — it can't tell which interpretation is "right" because both are phonetically plausible. This is corner case #9 from the plan: "phonetically plausible but semantically wrong words."

**For training data**: this is fine. Both are valid Telugu speech. The TTS model learns either pronunciation. At scale, the correct interpretation dominates.

### Segment 0107 — "ఇంకా" vs "అండ్"

This is NOT translation. Look at what actually happened:

- Run 1: `ఇంకా` (Telugu word for "and/also") + romanized `inka`
- Runs 2-3: `అండ్` (transliterated English "and") + romanized `and`

The speaker said a word that sounds like both "inka" (Telugu) and "and" (English). In a code-mixed conversation, this is genuinely ambiguous. Gemini's interpretation flickered between hearing it as the Telugu word vs the English word. Both are verbatim transcriptions — the model isn't "translating," it's making different language-boundary decisions on ambiguous audio.

**Is the prompt too loose?** No. The prompt says "If the speaker says English words, those are English. If the speaker says {language} words, those are {language}." But when the word is ambiguous between languages, no prompt can resolve it — this is an inherent ambiguity in code-mixed speech.

### Why is consistency 60% instead of 100%?

The previous 10 segments scored 100% identical. These 15 new segments score 60%. The difference:

**temp=0 is "mostly deterministic," not mathematically deterministic.** Google's docs say seed provides "best-effort reproducibility." At temp=0, the model selects the highest-probability token at each step, but when two tokens have nearly identical probabilities (like "inka" vs "and" in ambiguous audio), floating-point rounding can tip either way across runs. This is inherent to the architecture.

**Would temp=1 help?** No — temp=1 would make it MORE variable, not less. temp=0 is already the most deterministic setting. The Gemini 3 docs warn about potential looping at temp=0, but our earlier testing showed no looping on transcription tasks.

**What would help**: For the 6 inconsistent segments, the differences are minor (one extra word at boundary, word order of interjections, ambiguous code-switch words). The validation scores are consistent even when text varies. For training data, this level of variation is acceptable — both versions are valid transcriptions of ambiguous audio.

### Also noticed: Segment 0038 is inconsistent too

Runs 1-2 have extra content at the start ("సాల్ట్ బిస్కెట్స్ మా") that Run 3 doesn't — a boundary artifact. Run 3 got a higher score (S=0.785, accept) while Runs 1-2 scored lower (S=0.595, retry). The validation correctly flagged the artifact-heavy versions for retry.

1. salt buiscuits are there in the audio segment.
2. it's not "manchi", it's "nunchi" -> from there
3. Code-switch ambiguity: "ఇంకా" (Telugu) vs "అండ్" (English "and") -> this is clearly,

These are issues either sometimes, are catching ends of audio transcriptions or instructions are too complex on these instructions following that it is being like translating instead of transcribing like "ఇంకా" (Telugu) vs "అండ్".

i think we need to work and fix those issues. and also, segments that are too small like 1.1s are junk basically. i'm afriad we'll have to atleast have like 2s segments atleast. and also, a strict and crisp boundary cutting with little added silence to make it organically a genuine TTS sentence for gemini to be able to transcribe it better and consistent. and an even more robust roman script forced aligners to ensure any small mis-cuts are caught and we're only left with clean segments. Also here's more suggestions on what we can do for achieving higher validation robustness.

You want “100% accurate” automatic validation for romanized transcripts. That’s adorable. Also: the universe doesn’t do 100% for noisy audio, code-switching, partial VAD cuts, and romanization ambiguity. **What you *can* do** is build a validator that’s brutally strict, catches almost all bad outputs, and routes the rest to “review” instead of pretending a single score is truth.

Here’s the best plan that actually holds up in production.

---

## The key move: validate the *authoritative* text, then derive romanization deterministically

Right now you’re asking Gemini to do 3 jobs at once:

1. transcribe audio → native script
2. infer English words and spell them → code_switch
3. create a “pronounced” romanization → romanized

That’s a lot of degrees of freedom. And every degree of freedom is a new way to be wrong.

**A cleaner architecture** is:

* **Gemini outputs only the authoritative transcript** (native script + tags + language + speaker meta).
* **You derive `romanized` (and optionally `code_switch`) in code** using deterministic transforms.
* **You validate the authoritative transcript against audio** using forced alignment.

If the authoritative transcript matches audio, then any deterministic romanization derived from it is “correct” relative to that transcript. You stop trying to “validate romanization correctness” as a separate problem (because it’s mostly a formatting problem if derived deterministically).

This also improves prompt adherence because the model isn’t juggling “transcribe + transliterate + spell English + tag events” all at once.

---

## Good news: Indic forced alignment exists (so you don’t need to invent pain)

You said there are no Indic forced aligners. Not true anymore.

### Option A (native-script alignment): **AI4Bharat IndicMFA**

AI4Bharat released **IndicMFA**, which provides components (acoustic model + dictionary) for **22 Indian languages** and is built on Montreal Forced Aligner, using a grapheme-to-grapheme approach. ([GitHub][1])

That means you can validate **your native script transcription directly** (Hindi/Marathi/Telugu/Tamil/etc.) without relying on romanization.

MFA itself is Kaldi-based and meant for forced alignment workflows. ([Montreal Forced Aligner][2])

**When to use:** you want alignments/timecodes in the native script and you’re already living in that ecosystem.

---

## The “romanization validator” you were imagining is basically already packaged too

### Option B (script-agnostic alignment): **TorchAudio MMS_FA + uroman**

TorchAudio has a multilingual forced-alignment tutorial that uses:

* **uroman** to romanize text ([PyTorch Documentation][3])
* a pre-trained multilingual acoustic model pipeline **MMS_FA** ([PyTorch Documentation][3])
* CTC forced alignment (`torchaudio.functional.forced_align`) returning per-token scores ([PyTorch Documentation][3])

The tutorial literally shows how to compute an average alignment score and even provides the normalization recipe. ([PyTorch Documentation][3])

**Why this is perfect for you:** it’s *exactly* “align romanized text to audio across many languages.”

**Big catch:** it expects a specific kind of romanization/normalization (a-z + apostrophe + spaces). Your Gemini “romanized as pronounced” can be too free-form. So you either:

* **standardize your romanized field to uroman-style**, or
* ignore Gemini’s romanized and generate the alignment text yourself using uroman from the native transcript (my recommendation).

---

## Concrete validation pipeline (accept/review/reject) that won’t lie to you

### Step 0: Cheap structural sanity checks (no ML, fast)

Run these before any aligner so you don’t waste GPU cycles validating garbage.

1. **Unicode block checks**

   * `transcription` must be only native script + allowed punctuation + spaces (no Latin in non-English).
   * `romanized` must be Latin only (plus spaces/punct you allow).
2. **Tag checks**

   * `tagged` contains only allowed tags, tags are bracketed correctly.
3. **Length vs duration**

   * If 0.5s of audio has 40 words, reject. (You’d be shocked how often this catches model hiccups.)

These give you deterministic “reject” cases.

---

### Step 1: Normalize text for alignment (this matters more than people admit)

**Important:** alignment models are picky. You need a stable normalization.

If you use TorchAudio MMS_FA, follow its normalization pattern:

* romanize (uroman)
* lowercase
* normalize apostrophes
* strip non `[a-z' ]`
* collapse spaces

TorchAudio’s tutorial provides the exact normalization snippet. ([PyTorch Documentation][3])

**What you should align:**
✅ align **`uroman(transcription_native)`**, not Gemini’s “romanized as pronounced”.

Reason: if the transcript is right, uroman is deterministic, so validation becomes stable and reproducible.

---

### Step 2: Forced alignment with MMS_FA (CTC)

High-level:

1. load `torchaudio.pipelines.MMS_FA`
2. compute model emissions from waveform
3. tokenize normalized transcript
4. align tokens to emissions
5. compute word and utterance scores

TorchAudio shows `bundle.get_model()`, `bundle.get_tokenizer()`, `bundle.get_aligner()` and scoring. ([PyTorch Documentation][3])

**How to get a “score 0–1”:**

* The tutorial uses a weighted average of span scores. ([PyTorch Documentation][3])
* Under the hood, CTC forced alignment produces per-frame log-prob scores. ([PyTorch Documentation][4])

**What to store:**

* `validation_score`: weighted average token score (0–1)
* `validation_status`: accept/review/reject
* (I’d also store min word score, % words below threshold, and boundary word scores in a debug field, even if your schema doesn’t require it.)

---

### Step 3: Make the thresholds strict and *calibrate them*

Here’s a sane default policy for VAD-cut segments:

* Compute per-word scores.
* Ignore punctuation and tags.
* Treat **first + last word** as “boundary words” (VAD cuts often slice mid-word).

**Example decision rule:**

**ACCEPT** if:

* avg_score ≥ 0.85
* and (min_internal_word_score ≥ 0.70)
* and (%internal_words_below_0.60 ≤ 5%)

**REVIEW** if:

* avg_score in [0.70, 0.85)
* or a few internal words dip badly
* or boundary words are low but internal is fine

**REJECT** if:

* avg_score < 0.70
* or many internal words are < 0.60
* or alignment collapses into nonsense timing

You will tune these numbers on a small hand-checked dev set. That’s how you turn “vibes” into a real validator.

---

## If you want “near-100%”: add consensus checks (because one model can be wrong)

Forced alignment can fail on correct text if:

* background music/noise
* heavy overlap
* weird pronunciations
* very short clips

WhisperX alignment can also struggle with noisy audio (it’s a known failure mode), so it’s not a magical oracle either. ([GitHub][5])

**Best practice:** require agreement between *two different* validators, or at least use a second opinion to resolve “review” cases.

Practical combo:

* **Validator A:** MMS_FA forced alignment score (script-agnostic) ([PyTorch Documentation][3])
* **Validator B:** IndicMFA alignment on native script (language-specific) ([GitHub][1])

If both say “high confidence,” you’re golden. If they disagree, you don’t pretend: you mark review.

If you don’t want to run IndicMFA, a lighter “second opinion” is:

* run a strong ASR model (even Whisper) and compare normalized CER/WER against your transcript.
  Not perfect, but it’s an effective tripwire.

---

## How to validate the romanized field specifically (without pretending it’s audio-truth)

If you still want Gemini to output `romanized`, validate it like this:

1. Compute `romanized_uroman = normalize_uroman(uroman(transcription_native))`
2. Normalize Gemini romanized into the same alphabet (lowercase, strip non a-z, etc.)
3. Compare with CER (edit distance / length)

* If CER is tiny (like < 2–3%), accept romanized as consistent.
* If CER is large, **replace Gemini romanized with your deterministic one** and mark “romanized_fixed=true” (or just overwrite silently if you don’t care).

This turns romanization into a deterministic product, not a creative writing exercise.

---

## Prompt splitting without token pain (and with better adherence)

You’re right that packing everything into one mega prompt increases the chance of field drift. Caching helps cost, but it doesn’t fix cognitive overload.

Here are your best options, ordered by sanity:

### Option 1 (best): One Gemini call, fewer fields

Have Gemini return only:

* `transcription` (native authoritative)
* `tagged` (events)
* `speaker`
* `detected_language`

Then in code:

* derive `romanized` deterministically (uroman or ISO/WX)
* derive `code_switch` if you can (or drop it, or generate it via a tiny second pass)

This cuts output tokens and reduces failure modes.

### Option 2: Two calls, second one is ultra-cheap

* Call #1 (audio): produce authoritative `transcription` + tags + detected_language
* Call #2 (text-only): given transcription, produce `code_switch` + `romanized`

  * This prompt is tiny, no audio, and easily cacheable per language.
  * You can even run it on a cheaper model.

### Option 3: Keep single call, but constrain romanization format

Change your definition of `romanized` to:

* “uroman-style romanization, lowercase, only [a-z' ]”

Then you can align that directly with MMS_FA reliably. ([PyTorch Documentation][3])
If you still want “as pronounced,” add a *separate* optional field like `romanized_phonetic` and never use it for validation.

---

## What I’d do in your exact setup (minimal drama, maximum signal)

1. **Keep your current Gemini prompt**, but drop `romanized` (and maybe `code_switch`) from Gemini output.
2. After Gemini returns `transcription`:

   * compute `romanized_uroman` with uroman + normalization ([GitHub][6])
3. Run **TorchAudio MMS_FA forced alignment** on `romanized_uroman`:

   * compute avg score + per-word scores ([PyTorch Documentation][3])
4. (Optional but strong) also run **IndicMFA** for native-script alignment if you’re going hardcore. ([GitHub][1])
5. Fill:

   * `validation_score = avg_alignment_score`
   * `validation_status = accept/review/reject` using strict thresholds
6. Only if `review`:

   * retry Gemini once with higher “thinking” or stricter `[UNK]` usage
   * or route to human

That’s how you build a pipeline that’s honest. It doesn’t cosplay certainty.

And for the record: Gemini being better at Indic script is plausible. My feelings about that are stored in the same place as my childhood memories (I don’t have any).

[1]: https://github.com/AI4Bharat/IndicMFA "GitHub - AI4Bharat/IndicMFA"
[2]: https://montreal-forced-aligner.readthedocs.io/en/stable/user_guide/index.html?utm_source=chatgpt.com "User Guide — Montreal Forced Aligner 3.X documentation"
[3]: https://docs.pytorch.org/audio/2.8/tutorials/forced_alignment_for_multilingual_data_tutorial.html "Forced alignment for multilingual data — Torchaudio 2.8.0 documentation"
[4]: https://docs.pytorch.org/audio/2.8/generated/torchaudio.functional.forced_align.html "torchaudio.functional.forced_align — Torchaudio 2.8.0 documentation"
[5]: https://github.com/m-bain/whisperX?utm_source=chatgpt.com "m-bain/whisperX"
[6]: https://github.com/isi-nlp/uroman?utm_source=chatgpt.com "isi-nlp/uroman: Universal Romanizer"


# Collaboration Mode: Default

You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.

Your active mode changes only when new developer instructions with a different `<collaboration_mode>...</collaboration_mode>` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.

## request_user_input availability

The `request_user_input` tool is unavailable in Default mode. If you call it while in Default mode, it will return an error.

If a decision is necessary and cannot be discovered from local context, ask the user directly. However, in Default mode you should strongly prefer executing the user's request rather than stopping to ask questions.

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) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.
  3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  5) 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.


**Acknowledging task and planning inspection**


I’ll implement this as code changes in the pipeline: enforce minimum segment duration, tighten segment boundaries/padding, make transcript authoritative, derive romanization deterministically, and strengthen validation with boundary-aware scoring. I’m starting by scanning the repo for segmentation, Gemini output schema, and validation/alignment modules.


**Scanning repository structure**


rg -n "gemini|transcrib|roman|align|validation|MMS|CTC|segment|vad|cut|duration|threshold|accept|review|reject|code_switch|tagged" -S .


Output:
Chunk ID: f935f7
Wall time: 0.0508 seconds
Process exited with code 0
Original token count: 448
Output:
total 3356
drwxrwxr-x 14 ubuntu ubuntu   4096 Feb 13 07:33 .
drwxr-x--- 35 ubuntu ubuntu   4096 Feb 17 02:29 ..
-rw-rw-r--  1 ubuntu ubuntu   1199 Feb  4 07:59 .env
drwxrwxr-x  8 ubuntu ubuntu   4096 Feb 12 23:44 .git
-rw-rw-r--  1 ubuntu ubuntu   5449 Feb  6 04:41 README.md
-rw-r--r--  1 ubuntu ubuntu 121577 Feb  4 22:28 SPEAKER_00_0000_0.03-2.61.flac
-rw-rw-r--  1 ubuntu ubuntu 107084 Feb  7 04:03 SPEAKER_00_0004_36.29-37.41.wav
-rw-rw-r--  1 ubuntu ubuntu 201044 Feb 13 07:33 SPEAKER_00_0038_529.16-531.25.wav
-rw-rw-r--  1 ubuntu ubuntu 700124 Feb 13 07:33 SPEAKER_00_0059_741.63-748.92.wav
-rw-rw-r--  1 ubuntu ubuntu 646544 Feb 13 07:33 SPEAKER_00_0076_1013.51-1020.24.wav
-rw-rw-r--  1 ubuntu ubuntu 110324 Feb 13 07:33 SPEAKER_00_0077_1026.28-1027.43.wav
-rw-rw-r--  1 ubuntu ubuntu 379364 Feb 13 07:33 SPEAKER_00_0082_1061.11-1065.06.wav
-rw-rw-r--  1 ubuntu ubuntu 669436 Feb 13 07:33 SPEAKER_00_0107_1343.96-1350.94.wav
-rw-r--r--  1 ubuntu ubuntu 381371 Feb  5 00:50 SPEAKER_01_0422_5442.68-5452.49.flac
drwxrwxr-x  2 ubuntu ubuntu   4096 Feb 13 04:38 __pycache__
drwxrwxr-x  2 ubuntu ubuntu   4096 Feb  5 01:05 analysis_results
drwxrwxr-x  2 ubuntu ubuntu   4096 Feb  5 04:32 bin
drwxrwxr-x  2 ubuntu ubuntu   4096 Feb 13 04:43 consistency_test
drwxrwxr-x  2 ubuntu ubuntu   4096 Feb  3 22:18 docs
drwxrwxr-x  3 ubuntu ubuntu   4096 Feb 12 23:25 golden_set
drwxrwxr-x  4 ubuntu ubuntu   4096 Feb  5 05:25 models
-rw-rw-r--  1 ubuntu ubuntu  27130 Feb 17 23:28 pipeline.py
-rw-rw-r--  1 ubuntu ubuntu    104 Feb  3 22:54 requirements.txt
drwxrwxr-x  5 ubuntu ubuntu   4096 Feb  4 21:27 src
drwxrwxr-x  2 ubuntu ubuntu   4096 Feb  6 04:47 transcriptions
drwxrwxr-x  2 ubuntu ubuntu   4096 Feb  4 07:44 validation_results
drwxrwxr-x  6 ubuntu ubuntu   4096 Feb  4 05:38 venv


Output:
Chunk ID: 7b4816
Wall time: 0.0505 seconds
Process exited with code 0
Original token count: 1774
Output:
bin/micromamba
analysis_results/final_analysis.json
analysis_results/scoring_results.json
pipeline.py
docs/sample.txt
docs/PromptCaching.md
docs/prompt.txt
docs/Pricing.md
docs/gemini3.md
docs/structuredOutput.md
docs/gemini3_batch.md
consistency_test/v2_comparison.json
consistency_test/roman_alignment.json
consistency_test/comparison.json
consistency_test/run_1.json
consistency_test/run_2.json
consistency_test/run_3.json
SPEAKER_00_0004_36.29-37.41.wav
SPEAKER_00_0076_1013.51-1020.24.wav
SPEAKER_00_0038_529.16-531.25.wav
SPEAKER_01_0422_5442.68-5452.49.flac
golden_set/calibrate.py
transcriptions/test_new_schema_20seg.json
__pycache__/pipeline.cpython-310.pyc
transcriptions/test_new_schema_5segments.json
transcriptions/pF_BQpHaIdU_gemini_2.5_flash_20260203_225339_partial.json
requirements.txt
transcriptions/pF_BQpHaIdU_gemini_2.5_flash_20260203_225339.json
SPEAKER_00_0077_1026.28-1027.43.wav
transcriptions/pF_BQpHaIdU_gemini_3_flash_preview_20260203_225233_partial.json
SPEAKER_00_0000_0.03-2.61.flac
SPEAKER_00_0059_741.63-748.92.wav
transcriptions/pF_BQpHaIdU_gemini_3_flash_preview_20260203_225233.json
transcriptions/pF_BQpHaIdU_gemini_3_flash_preview_20260203_230246_partial.json
golden_set/te/reference.json
transcriptions/pF_BQpHaIdU_gemini_3_flash_preview_20260203_230246.json
README.md
models/ctc_aligner/models--anuragshas--wav2vec2-large-xlsr-53-telugu/refs/main
src/__pycache__/__init__.cpython-310.pyc
models/ctc_aligner/models--anuragshas--wav2vec2-large-xlsr-53-telugu/refs/refs/pr/1
src/__init__.py
validation_results/test_run.json
validation_results/all_validators_test.json
venv/pyvenv.cfg
src/backend/gemini_transcriber.py
src/backend/audio_polisher.py
venv/include/site/python3.10/greenlet/greenlet.h
venv/bin/check-node
src/validators/__pycache__/ctc_forced_aligner.cpython-310.pyc
venv/bin/ct2-openai-gpt2-converter
venv/bin/numba
venv/bin/pyrsa-keygen
venv/bin/transformers-cli
venv/bin/rich-click
venv/bin/lhotse
venv/bin/datasets-cli
src/validators/__pycache__/indicmfa_validator.cpython-310.pyc
src/validators/__pycache__/base.cpython-310.pyc
src/validators/__pycache__/indicmfa_real.cpython-310.pyc
src/validators/__pycache__/__init__.cpython-310.pyc
src/validators/__pycache__/indic_conformer_validator.cpython-310.pyc
src/backend/__pycache__/supabase_client.cpython-310.pyc
src/validators/__pycache__/mms_aligner.cpython-310.pyc
src/validators/__pycache__/alignment_scorer.cpython-310.pyc
src/validators/__pycache__/simple_validator.cpython-310.pyc
src/validators/__pycache__/runner.cpython-310.pyc
src/backend/__pycache__/r2_storage.cpython-310.pyc
src/backend/__pycache__/audio_polisher.cpython-310.pyc
src/validators/simple_validator.py
src/backend/__pycache__/audio_processor.cpython-310.pyc
src/validators/mms_aligner.py
src/backend/__pycache__/__init__.cpython-310.pyc
src/validators/ctc_forced_aligner.py
src/backend/__pycache__/transcription_schema.cpython-310.pyc
src/validators/__init__.py
src/backend/__pycache__/gemini_transcriber.cpython-310.pyc
src/backend/__pycache__/config.cpython-310.pyc
src/backend/r2_storage.py
src/backend/audio_processor.py
src/backend/__init__.py
src/backend/supabase_client.py
src/backend/transcription_schema.py
src/backend/config.py
SPEAKER_00_0107_1343.96-1350.94.wav
SPEAKER_00_0082_1061.11-1065.06.wav
venv/bin/mako-render
venv/bin/pyrsa-sign
venv/bin/jiwer
venv/bin/transformers
venv/bin/pip3.10
venv/bin/Activate.ps1
venv/bin/accelerate
venv/bin/jp.py
venv/bin/markdown-it
venv/bin/numpy-config
venv/bin/check-model
venv/bin/coloredlogs
venv/bin/ct2-opus-mt-converter
venv/bin/proton-viewer
venv/bin/torchfrtrace
venv/bin/tensorboard
venv/bin/websockets
venv/bin/pip
venv/bin/cygdb
venv/bin/pyav
venv/bin/pyannote-metrics
venv/bin/humanfriendly
venv/bin/alembic
venv/bin/ct2-fairseq-converter
venv/bin/accelerate-merge-weights
venv/bin/accelerate-config
venv/bin/ttx
venv/bin/accelerate-launch
venv/bin/isympy
models/ctc_aligner/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/eb125d179afdd35fdb896eda528c3581780b4ad0
venv/bin/tabulate
venv/bin/wb
venv/bin/tqdm
venv/bin/cythonize
venv/bin/pygrun
venv/bin/lightning
venv/bin/sacrebleu
venv/bin/pygmentize
venv/bin/get_gprof
venv/bin/pyftsubset
models/ctc_aligner/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/a23c2ec161bc7d0a523d5cbff72a81ac55784803f87e6664641f542fab82bf52
models/ctc_aligner/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/9abf71998c3e0de2f13c0fd73ed81477c9dae118
models/ctc_aligner/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/78dfdcf81d5d1cbb1430b0a283bacebe721b5f1b50b6fdfe58e635f31b4c2526
models/ctc_aligner/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/0886a48276922a77013d8aa4681192138ae90d90
models/ctc_aligner/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/c515cdf136cb0af961aabcaf53819693dc4324e6
models/ctc_aligner/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/a2a8340e0a162e4e223867107d9db359f5697c1d
venv/bin/fairseq-eval-lm
venv/bin/distro
venv/bin/ct2-marian-converter
venv/bin/pip3
venv/bin/pyftmerge
venv/bin/httpx
venv/bin/fairseq-generate
venv/bin/get_objgraph
venv/bin/ct2-transformers-converter
venv/bin/ipython
venv/bin/cython
venv/bin/hf
venv/bin/pyrsa-decrypt
venv/share/man/man1/ttx.1
venv/bin/activate
venv/bin/pyrsa-encrypt
venv/bin/pyrsa-verify
venv/share/man/man1/isympy.1
venv/bin/fonttools
venv/bin/onnxruntime_test
venv/bin/ct2-opennmt-tf-converter
venv/bin/pyannote-database
venv/bin/undill
venv/bin/fairseq-score
venv/bin/fairseq-interactive
venv/bin/normalizer
venv/bin/optuna
venv/bin/tiny-agents
venv/bin/fairseq-validate
venv/share/man/man1/ipython.1
venv/bin/pyrsa-priv2pub
venv/bin/activate.fish
venv/bin/huggingface-cli
venv/bin/f2py
venv/bin/ct2-opennmt-py-converter
venv/bin/proton
venv/bin/dotenv
venv/bin/fairseq-preprocess
venv/bin/sacremoses
venv/bin/markdown_py
venv/bin/torchrun
venv/bin/ipython3
venv/bin/pyiceberg
venv/bin/wandb
venv/bin/activate.csh
venv/bin/fabric
venv/bin/backend-test-tools
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/c515cdf136cb0af961aabcaf53819693dc4324e6
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/0886a48276922a77013d8aa4681192138ae90d90
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/a2a8340e0a162e4e223867107d9db359f5697c1d
venv/bin/mfa_update
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/78dfdcf81d5d1cbb1430b0a283bacebe721b5f1b50b6fdfe58e635f31b4c2526
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/refs/main
venv/bin/mfa
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/9abf71998c3e0de2f13c0fd73ed81477c9dae118
venv/bin/fairseq-train
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/a23c2ec161bc7d0a523d5cbff72a81ac55784803f87e6664641f542fab82bf52
venv/bin/accelerate-estimate-memory
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/eb125d179afdd35fdb896eda528c3581780b4ad0
venv/bin/fairseq-hydra-train
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/refs/refs/pr/1


Output:
Chunk ID: d138c3
Wall time: 0.0519 seconds
Process exited with code 0
Original token count: 88385
Output:
Total output lines: 2596

./consistency_test/v2_comparison.json:3:    "segments": 15,
./consistency_test/v2_comparison.json:10:        "romanized": "Salt biscuits maa maa father thintarannamata salt biscuits.",
./consistency_test/v2_comparison.json:13:        "roman_mms": 0.7324157238006592,
./consistency_test/v2_comparison.json:19:        "romanized": "First of all ee peru etla vachindi sir Monaco? Daniko story vinna anduke special ga adugutunna.",
./consistency_test/v2_comparison.json:22:        "roman_mms": 0.7878455638885498,
./consistency_test/v2_comparison.json:24:        "status": "accept"
./consistency_test/v2_comparison.json:28:        "romanized": "Also like when I was actually going through other stories",
./consistency_test/v2_comparison.json:31:        "roman_mms": 0.7507373571395874,
./consistency_test/v2_comparison.json:37:        "romanized": "mana official handle nunchi yes make it 10,000 retweets and we are going to get it back anedi okati chesi tarvata adi vachindi ee story okasari kavali naku enduku aaparu",
./consistency_test/v2_comparison.json:40:        "roman_mms": 0.7972880840301514,
./consistency_test/v2_comparison.json:42:        "status": "accept"
./consistency_test/v2_comparison.json:46:        "romanized": "Okay. So idante continue chese mundu oka chinna vishayam. Yeah. Sir let's have a very good day. Yes.",
./consistency_test/v2_comparison.json:49:        "roman_mms": 0.905515831708908,
./consistency_test/v2_comparison.json:51:        "status": "accept"
./consistency_test/v2_comparison.json:55:        "romanized": "avunu.",
./consistency_test/v2_comparison.json:58:        "roman_mms": 0.8950024247169495,
./consistency_test/v2_comparison.json:60:        "status": "accept"
./consistency_test/v2_comparison.json:64:        "romanized": "so Britannia valla product Good Day kabatti konchem reaction kanapadi untadi meeku. so like I noted down annamata.",
./consistency_test/v2_comparison.json:67:        "roman_mms": 0.8697295784950256,
./consistency_test/v2_comparison.json:69:        "status": "accept"
./consistency_test/v2_comparison.json:73:        "romanized": "Britannialo Tiger unte ikkada Parle-G ante edi mundu vachindo naku teliyadu I am sorry ikkada Parle-G",
./consistency_test/v2_comparison.json:76:        "roman_mms": 0.8390370726585388,
./consistency_test/v2_comparison.json:78:        "status": "accept"
./consistency_test/v2_comparison.json:82:        "romanized": "Marigold unte ikkada Parle-G Mari. Avunu. Adi Digestive Mari kuda ayindi. Avunu. 50-50 unte KrackJack. Asalu KrackJack na favorite. Avunandi. Taruvatha Hide & Seek ikkada.",
./consistency_test/v2_comparison.json:85:        "roman_mms": 0.8832302570343018,
./consistency_test/v2_comparison.json:87:        "status": "accept"
./consistency_test/v2_comparison.json:91:        "romanized": "lo cut throat competition correct ante edi mundukocchina pakkaku pedithe inkoti na daggara oka table undannamata.",
./consistency_test/v2_comparison.json:94:        "roman_mms": 0.7212544441223144,
./consistency_test/v2_comparison.json:100:        "romanized": "aa section lo ani avunu when I was seeing the majority share I I thought like achha let let me go in this funny way ani cheppi yeah yeah so general ga itla itlanti",
./consistency_test/v2_comparison.json:103:        "roman_mms": 0.7955120801925659,
./consistency_test/v2_comparison.json:105:        "status": "accept"
./consistency_test/v2_comparison.json:109:        "romanized": "okavela ee product vachindi kabatti manam itla teesukuni raavali ani oka oka calculation kaanivvandi lekapothe oka R&D team gaani etluntadi asalu venakala?",
./consistency_test/v2_comparison.json:112:        "roman_mms": 0.818980598449707,
./consistency_test/v2_comparison.json:114:        "status": "accept"
./consistency_test/v2_comparison.json:118:        "romanized": "ippudu meeru Poppins gurinchi inka Kismi gurinchi cheptunnappudu renditi gurinchi rakarakala stories unnaayi. avunu. naaku school lo unnappudu I got a dare truth or dare lo",
./consistency_test/v2_comparison.json:121:        "roman_mms": 0.8078336358070374,
./consistency_test/v2_comparison.json:123:        "status": "accept"
./consistency_test/v2_comparison.json:127:        "romanized": "So yeah coming to this point Poppins kani curiosity build chesetivi appatlo yesterday we were talking annamata ma team lo kuda",
./consistency_test/v2_comparison.json:130:        "roman_mms": 0.8461434602737427,
./consistency_test/v2_comparison.json:132:        "status": "accept"
./consistency_test/v2_comparison.json:136:        "romanized": "One of them like appudedo re what you call score card cheat vaduluna",
./consistency_test/v2_comparison.json:139:        "roman_mms": 0.8497126460075378,
./consistency_test/v2_comparison.json:141:        "status": "accept"
./consistency_test/v2_comparison.json:147:        "romanized": "Salt biscuits maa maa father thintarannamata salt biscuits.",
./consistency_test/v2_comparison.json:150:        "roman_mms": 0.7324157238006592,
./consistency_test/v2_comparison.json:156:        "romanized": "First of all ee peru etla vachindi sir Monaco? Daniko story vinna anduke specially adugutunna.",
./consistency_test/v2_comparison.json:159:        "roman_mms": 0.7828704118728638,
./consistency_test/v2_comparison.json:161:        "status": "accept"
./consistency_test/v2_comparison.json:165:        "romanized": "Also like when I was actually going through other stories",
./consistency_test/v2_comparison.json:168:        "roman_mms": 0.7507373571395874,
./consistency_test/v2_comparison.json:174:        "romanized": "manchi mana official handle nunchi yes make it 10,000 retweets and we are going to get it back anedi okati chesi tarvata adi vachindi. ee story okasari kavali naku. enduku aaparu?",
./consistency_test/v2_comparison.json:177:        "roman_mms": 0.7909997463226318,
./consistency_test/v2_comparison.json:179:        "status": "accept"
./consistency_test/v2_comparison.json:183:        "romanized": "Okay. So idante continue chese mundu oka chinna vishayam. Yeah. Sir let's have a very good day. Yes.",
./consistency_test/v2_comparison.json:186:        "roman_mms": 0.905515831708908,
./consistency_test/v2_comparison.json:188:        "status": "accept"
./consistency_test/v2_comparison.json:192:        "romanized": "chudandi so",
./consistency_test/v2_comparison.json:195:        "roman_mms": 0.8723798513412475,
./consistency_test/v2_comparison.json:197:        "status": "accept"
./consistency_test/v2_comparison.json:201:        "romanized": "so Britannia valla product Good Day kabatti konchem reaction kanapadi untadi meeku. so like I noted down annamata.",
./consistency_test/v2_comparison.json:204:        "roman_mms": 0.8697295784950256,
./consistency_test/v2_comparison.json:206:        "status": "accept"
./consistency_test/v2_comparison.json:210:        "romanized": "Britannialo Tiger unte ikkada Parle-G ante edi mundu vachindo naku teliyadu I am sorry ikkada Parle-G",
./consistency_test/v2_comparison.json:213:        "roman_mms": 0.8390370726585388,
./consistency_test/v2_comparison.json:215:        "status": "accept"
./consistency_test/v2_comparison.json:219:        "romanized": "Marigold unte ikkada Parle-G Mari. Avunu. Adi Digestive Mari kuda ayindi. Avunu. 50-50 unte KrackJack. Asalu KrackJack na favorite. Avunandi. Taruvatha Hide & Seek ikkada.",
./consistency_test/v2_comparison.json:222:        "roman_mms": 0.8832302570343018,
./consistency_test/v2_comparison.json:224:        "status": "accept"
./consistency_test/v2_comparison.json:228:        "romanized": "lo cut throat competition correct ante edi mundu vaccu annadi pakkana pedite inkoti na daggara oka table undannamata.",
./consistency_test/v2_comparison.json:231:        "roman_mms": 0.6924846410751343,
./consistency_test/v2_comparison.json:237:        "romanized": "aa section lo ani avunu when I was seeing the majority share I I thought like achha let let me go in this funny way ani cheppi yeah yeah so general ga itla itlanti",
./consistency_test/v2_comparison.json:240:        "roman_mms": 0.7955120801925659,
./consistency_test/v2_comparison.json:242:        "status": "accept"
./consistency_test/v2_comparison.json:246:        "romanized": "okavela ee product vachindi kabatti manam itla teesukuni raavali ani oka oka calculation kaanivvandi lekapothe oka R&D team gaani etluntadi asalu venakala?",
./consistency_test/v2_comparison.json:249:        "roman_mms": 0.818980598449707,
./consistency_test/v2_comparison.json:251:        "status": "accept"
./consistency_test/v2_comparison.json:255:        "romanized": "ippudu meeru Poppins gurinchi and Kismi gurinchi cheptunnappudu renditi gurinchi rakarakala stories unnaayi. avunu. naaku school lo unnappudu I got a dare Truth or Dare lo",
./consistency_test/v2_comparison.json:258:        "roman_mms": 0.815476906299591,
./consistency_test/v2_comparison.json:260:        "status": "accept"
./consistency_test/v2_comparison.json:264:        "romanized": "So yeah coming to this point Poppins kani curiosity build chesetivi appatlo yesterday we were talking annamata ma team lo kuda",
./consistency_test/v2_comparison.json:267:        "roman_mms": 0.8461434602737427,
./consistency_test/v2_comparison.json:269:        "status": "accept"
./consistency_test/v2_comparison.json:273:        "romanized": "One of them like appudedo re what you call score card cheat vaduluna",
./consistency_test/v2_comparison.json:276:        "roman_mms": 0.8497126460075378,
./consistency_test/v2_comparison.json:278:        "status": "accept"
./consistency_test/v2_comparison.json:284:        "romanized": "maa father thintarannamaata salt biscuits.",
./consistency_test/v2_comparison.json:287:        "roman_mms": 0.8512242794036865,
./consistency_test/v2_comparison.json:289:        "status": "accept"
./consistency_test/v2_comparison.json:293:        "romanized": "First of all ee peru etla vachindi sir Monaco? Daniko story vinna anduke specially adugutunna.",
./consistency_test/v2_comparison.json:296:        "roman_mms": 0.7828704118728638,
./consistency_test/v2_comparison.json:298:        "status": "accept"
./consistency_test/v2_comparison.json:302:        "romanized": "Also like when I was actually going through other stories",
./consistency_test/v2_comparison.json:305:        "roman_mms": 0.7507373571395874,
./consistency_test/v2_comparison.json:311:        "romanized": "manchi mana official handle nunchi yes make it 10,000 retweets and we are going to get it back anedi okati chesi tarvata adi vachindi. ee story okasari kavali naku. enduku aaparu?",
./consistency_test/v2_comparison.json:314:        "roman_mms": 0.7909997463226318,
./consistency_test/v2_comparison.json:316:        "status": "accept"
./consistency_test/v2_comparison.json:320:        "romanized": "okay. so idante continue chese mundu ya oka chinna vishayam ya sir let's have a very good day yes.",
./consistency_test/v2_comparison.json:323:        "roman_mms": 0.9103158414363861,
./consistency_test/v2_comparison.json:325:        "status": "accept"
./consistency_test/v2_comparison.json:329:        "romanized": "chudandi so",
./consistency_test/v2_comparison.json:332:        "roman_mms": 0.8723798513412475,
./consistency_test/v2_comparison.json:334:        "status": "accept"
./consistency_test/v2_comparison.json:338:        "romanized": "so Britannia valla product Good Day kabatti konchem reaction kanapadi untadi meeku. so like I noted down annamata.",
./consistency_test/v2_comparison.json:341:        "roman_mms": 0.8697295784950256,
./consistency_test/v2_comparison.json:343:        "status": "accept"
./consistency_test/v2_comparison.json:347:        "romanized": "Britannialo Tiger unte ikkada Parle-G ante edi mundu vachindo naku teliyadu I am sorry ikkada Parle-G",
./consistency_test/v2_comparison.json:350:        "roman_mms": 0.8390370726585388,
./consistency_test/v2_comparison.json:352:        "status": "accept"
./consistency_test/v2_comparison.json:356:        "romanized": "Marigold unte ikkada Parle Mari. Avunu. Adi Digestive Mari kuda ayindi. Avunu. 50-50 unte KrackJack. Asalu KrackJack na favorite. Avunandi. Taruvatha Hide & Seek ikkada.",
./consistency_test/v2_comparison.json:359:        "roman_mms": 0.8870177149772644,
./consistency_test/v2_comparison.json:361:        "status": "accept"
./consistency_test/v2_comparison.json:365:        "romanized": "lo cut throat competition correct ante edi mundukocchina pakkaku pedithe inkoti na daggara oka table undannamata.",
./consistency_test/v2_comparison.json:368:        "roman_mms": 0.7212544441223144,
./consistency_test/v2_comparison.json:374:        "romanized": "aa section lo ani avunu when I was seeing the majority share I I thought like achha let let me go in this funny way ani cheppi yeah yeah so general ga itla itlanti",
./consistency_test/v2_comparison.json:377:        "roman_mms": 0.7955120801925659,
./consistency_test/v2_comparison.json:379:        "status": "accept"
./consistency_test/v2_comparison.json:383:        "romanized": "okavela ee product vachindi kabatti manam itla teesukuni raavali ani oka oka calculation kaanivvandi lekapothe oka R&D team gaani etluntadi asalu venakala?",
./consistency_test/v2_comparison.json:386:        "roman_mms": 0.818980598449707,
./consistency_test/v2_comparison.json:388:        "status": "accept"
./consistency_test/v2_comparison.json:392:        "romanized": "ippudu meeru Poppins gurinchi and Kismi gurinchi cheptunnappudu renditi gurinchi rakarakala stories unnaayi. avunu. naaku school lo unnappudu I got a dare Truth or Dare lo",
./consistency_test/v2_comparison.json:395:        "roman_mms": 0.815476906299591,
./consistency_test/v2_comparison.json:397:        "status": "accept"
./consistency_test/v2_comparison.json:401:        "romanized": "So yeah coming to this point Poppins kani curiosity build chesetivi appatlo yesterday we were talking annamata ma team lo kuda",
./consistency_test/v2_comparison.json:404:        "roman_mms": 0.8461434602737427,
./consistency_test/v2_comparison.json:406:        "status": "accept"
./consistency_test/v2_comparison.json:410:        "romanized": "One of them like appudedo re what you call score card cheat vaduluna",
./consistency_test/v2_comparison.json:413:        "roman_mms": 0.8497126460075378,
./consistency_test/v2_comparison.json:415:        "status": "accept"
./consistency_test/roman_alignment.json:3:    "romanized_text": "vastundannamaata. naaku konni ads gurtuntaayi like example kachcha mango bite.",
./consistency_test/roman_alignment.json:7:    "native_status": "review"
./consistency_test/roman_alignment.json:10:    "romanized_text": "aa maa chemmangilo oka retired taatamma okadunnaadu I will not name him but mari entante mangoes teesukuni xerox shop ki vellaadannamaata in fact naaku photocopy ante veru xerox ante veru ani telisindi akkade avunu",
./consistency_test/roman_alignment.json:14:    "native_status": "review"
./consistency_test/roman_alignment.json:17:    "romanized_text": "asalu aa in fact like I got to know that you were behind the Kaccha Mangoes",
./consistency_test/roman_alignment.json:21:    "native_status": "accept"
./consistency_test/roman_alignment.json:24:    "romanized_text": "talk about that story as well.",
./consistency_test/roman_alignment.json:28:    "native_status": "reject"
./consistency_test/roman_alignment.json:31:    "romanized_text": "connection untundannamaata. aa ante deeni gurinchi chaala connected questions unnaayi naaku. deenikante mundu indaaka Parle-G gurinchi teesukochhaaru kaabatti avunu. ippativaraku podcast chooshaaru kada.",
./consistency_test/roman_alignment.json:35:    "native_status": "review"
./consistency_test/roman_alignment.json:38:    "romanized_text": "Podcast chustunna padi mandilo aiduguriki Marketer avvalanna Thought vastadi",
./consistency_test/roman_alignment.json:42:    "native_status": "accept"
./consistency_test/roman_alignment.json:45:    "romanized_text": "Sir almost fifty one fifty one podcast fifty podcasts ayipoyinayi. Manadi fifty second approximately. Congratulations. Thank you thank you. Mee mee Telugu chaala classy ga undi sir.",
./consistency_test/roman_alignment.json:49:    "native_status": "accept"
./consistency_test/roman_alignment.json:52:    "romanized_text": "smooth undi kakapothe konchem classy words vasthunnayi anyways. so inko rendu moodu products gurinchi kooda questions unnayi anuko.",
./consistency_test/roman_alignment.json:56:    "native_status": "review"
./consistency_test/roman_alignment.json:59:    "romanized_text": "so ee glucose biscuit nunchi gluco endi gluco nunchi G endi correct Parle-G correct correct",
./consistency_test/roman_alignment.json:63:    "native_status": "accept"
./consistency_test/roman_alignment.json:66:    "romanized_text": "four fifty five hundred varaku unnayandi. okay andulo oka SKU gurinchi ha I heard it correct me if I am wrong Rola Cola ane oka product danni descale cheddamani apesi tarvata evaro Twitter lo tweet cheste",
./consistency_test/roman_alignment.json:70:    "native_status": "accept"
./consistency_test/comparison.json:3:    "model": "gemini-3-flash-preview",
./consistency_test/comparison.json:8:    "num_segments": 10
./consistency_test/comparison.json:20:        "code_switch": "వస్తుందన్నమాట. నాకు కొన్ని ads గుర్తుంటాయి like example కచ్చా మ్యాంగో బైట్.",
./consistency_test/comparison.json:21:        "romanized": "vastundannamaata. naaku konni ads gurtuntaayi like example kachcha mango bite.",
./consistency_test/comparison.json:22:        "tagged": "వస్తుందన్నమాట. నాకు కొన్ని ads గుర్తుంటాయి like example కచ్చా మ్యాంగో బైట్.",
./consistency_test/comparison.json:30:        "validation": {
./consistency_test/comparison.json:31:          "status": "review",
./consistency_test/comparison.json:33:          "alignment_score": 0.7173,
./consistency_test/comparison.json:49:        "code_switch": "ఆ మా చెమ్మంగిలో ఒక retired తాతమ్మ ఒకడున్నాడు I will not name him but మరి ఏంటంటే mangoes తీసుకుని xerox shop కి వెళ్ళాడన్నమాట in fact నాకు photocopy అంటే వేరు xerox అంటే వేరు అని తెలిసింది అక్కడే అవును",
./consistency_test/comparison.json:50:        "romanized": "aa maa chemmangilo oka retired taatamma okadunnaadu I will not name him but mari entante mangoes teesukuni xerox shop ki vellaadannamaata in fact naaku photocopy ante veru xerox ante veru ani telisindi akkade avunu",
./consistency_test/comparison.json:51:        "tagged": "ఆ మా చెమ్మంగిలో ఒక retired తాతమ్మ ఒకడున్నాడు I will not name him but మరి ఏంటంటే mangoes తీసుకుని xerox shop కి వెళ్ళాడన్నమాట in fact నాకు photocopy అంటే వేరు xerox అంటే వేరు అని తెలిసింది అక్కడే అవును",
./consistency_test/comparison.json:59:        "validation": {
./consistency_test/comparison.json:60:          "status": "review",
./consistency_test/comparison.json:62:          "alignment_score": 0.7026,
./consistency_test/comparison.json:98:        "code_switch": "అసలు ఆ in fact like I got to know that you were behind the Kaccha Mangoes",
./consistency_test/comparison.json:99:        "romanized": "asalu aa in fact like I got to know that you were behind the Kaccha Mangoes",
./consistency_test/comparison.json:100:        "tagged": "అసలు ఆ [breath] in fact like I got to know that you were behind the Kaccha Mangoes",
./consistency_test/comparison.json:108:        "validation": {
./consistency_test/comparison.json:109:          "status": "acce…78385 tokens truncated…me as for non-batch API traffic.
./docs/gemini3_batch.md:1216:  guide](https://ai.google.dev/gemini-api/docs/troubleshooting#error-codes) for a complete set of error codes.
./docs/gemini3_batch.md:1222:- Check out the [Batch API notebook](https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Batch_mode.ipynb) for more examples.
./docs/gemini3_batch.md:1223:- The OpenAI compatibility layer supports Batch API. Read the examples on the [OpenAI Compatibility](https://ai.google.dev/gemini-api/docs/openai#batch) page.
./venv/bin/lightning:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/ct2-fairseq-converter:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/numpy-config:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./src/validators/ctc_forced_aligner.py:2:CTC Forced Aligner
./src/validators/ctc_forced_aligner.py:5:Proper forced alignment using torchaudio's CTC forced_align.
./src/validators/ctc_forced_aligner.py:6:This is equivalent to MFA but using neural CTC instead of GMM-HMM.
./src/validators/ctc_forced_aligner.py:9:- Per-character alignment scores (log-prob)
./src/validators/ctc_forced_aligner.py:10:- Per-word alignment scores
./src/validators/ctc_forced_aligner.py:11:- Overall alignment quality score
./src/validators/ctc_forced_aligner.py:15:    aligner = CTCForcedAligner(language="te")
./src/validators/ctc_forced_aligner.py:16:    result = aligner.align("audio.flac", "transcription text")
./src/validators/ctc_forced_aligner.py:18:    print(f"Alignment Score: {result.alignment_score}")  # 0-1
./src/validators/ctc_forced_aligner.py:44:    def duration(self) -> float:
./src/validators/ctc_forced_aligner.py:54:    """Result of forced alignment."""
./src/validators/ctc_forced_aligner.py:59:    alignment_score: float  # 0-1, overall quality
./src/validators/ctc_forced_aligner.py:63:    word_alignments: List[WordAlignment] = field(default_factory=list)
./src/validators/ctc_forced_aligner.py:70:    audio_duration_sec: float = 0.0
./src/validators/ctc_forced_aligner.py:77:            "alignment_score": round(self.alignment_score, 4),
./src/validators/ctc_forced_aligner.py:79:            "word_count": len(self.word_alignments),
./src/validators/ctc_forced_aligner.py:82:            "word_alignments": [
./src/validators/ctc_forced_aligner.py:90:                for wa in self.word_alignments
./src/validators/ctc_forced_aligner.py:92:            "audio_duration_sec": round(self.audio_duration_sec, 3),
./src/validators/ctc_forced_aligner.py:97:class CTCForcedAligner:
./src/validators/ctc_forced_aligner.py:99:    CTC-based forced alignment using torchaudio.
./src/validators/ctc_forced_aligner.py:101:    This provides MFA-equivalent functionality using neural CTC models.
./src/validators/ctc_forced_aligner.py:104:    # Language-specific Wav2Vec2 models for CTC forced alignment
./src/validators/ctc_forced_aligner.py:127:        cache_dir: str = "./models/ctc_aligner"
./src/validators/ctc_forced_aligner.py:149:            from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
./src/validators/ctc_forced_aligner.py:152:            print(f"[CTCAligner] Loading {model_name}...")
./src/validators/ctc_forced_aligner.py:157:            self.model = Wav2Vec2ForCTC.from_pretrained(
./src/validators/ctc_forced_aligner.py:167:            print(f"[CTCAligner] Ready on {self.device}")
./src/validators/ctc_forced_aligner.py:172:            print(f"[CTCAligner] Setup failed: {e}")
./src/validators/ctc_forced_aligner.py:190:        """Get CTC log-probability emissions."""
./src/validators/ctc_forced_aligner.py:219:            print(f"[CTCAligner] WARNING: {oov_ratio:.0%} OOV chars "
./src/validators/ctc_forced_aligner.py:223:    def _compute_word_alignments(
./src/validators/ctc_forced_aligner.py:226:        alignment: torch.Tensor,
./src/validators/ctc_forced_aligner.py:228:        frame_duration: float
./src/validators/ctc_forced_aligner.py:230:        """Extract word-level alignments from frame-level results."""
./src/validators/ctc_forced_aligner.py:248:        alignment_np = alignment[0].numpy()
./src/validators/ctc_forced_aligner.py:252:        for frame_idx, (token_idx, score) in enumerate(zip(alignment_np, scores_np)):
./src/validators/ctc_forced_aligner.py:261:        alignments = []
./src/validators/ctc_forced_aligner.py:282:            alignments.append(WordAlignment(
./src/validators/ctc_forced_aligner.py:286:                start_time=start_frame * frame_duration,
./src/validators/ctc_forced_aligner.py:287:                end_time=end_frame * frame_duration,
./src/validators/ctc_forced_aligner.py:293:        return alignments
./src/validators/ctc_forced_aligner.py:295:    def align(
./src/validators/ctc_forced_aligner.py:301:        Perform forced alignment.
./src/validators/ctc_forced_aligner.py:305:            transcription: Text to align
./src/validators/ctc_forced_aligner.py:308:            AlignmentResult with word-level alignments and scores
./src/validators/ctc_forced_aligner.py:315:                    alignment_score=0.0,
./src/validators/ctc_forced_aligner.py:324:            audio_duration = len(audio) / sr
./src/validators/ctc_forced_aligner.py:329:            frame_duration = audio_duration / num_frames
./src/validators/ctc_forced_aligner.py:338:                    alignment_score=0.0,
./src/validators/ctc_forced_aligner.py:340:                    audio_duration_sec=audio_duration,
./src/validators/ctc_forced_aligner.py:344:            # Run forced alignment
./src/validators/ctc_forced_aligner.py:348:            alignment, scores = torchaudio.functional.forced_align(
./src/validators/ctc_forced_aligner.py:356:            # Compute word alignments
./src/validators/ctc_forced_aligner.py:357:            word_alignments = self._compute_word_alignments(
./src/validators/ctc_forced_aligner.py:358:                transcription, alignment, scores, frame_duration
./src/validators/ctc_forced_aligner.py:367:            alignment_score = max(0.0, min(1.0, 1.0 + mean_log_prob / 5.0))
./src/validators/ctc_forced_aligner.py:368:            # Penalize when many chars were OOV (alignment unreliable)
./src/validators/ctc_forced_aligner.py:370:                alignment_score *= (1.0 - oov_ratio * 0.5)
./src/validators/ctc_forced_aligner.py:373:            low_conf_words = [wa.word for wa in word_alignments if wa.is_low_confidence]
./src/validators/ctc_forced_aligner.py:374:            low_conf_ratio = len(low_conf_words) / max(len(word_alignments), 1)
./src/validators/ctc_forced_aligner.py:381:                alignment_score=alignment_score,
./src/validators/ctc_forced_aligner.py:383:                word_alignments=word_alignments,
./src/validators/ctc_forced_aligner.py:386:                audio_duration_sec=audio_duration,
./src/validators/ctc_forced_aligner.py:396:                alignment_score=0.0,
./src/validators/ctc_forced_aligner.py:415:def forced_align(
./src/validators/ctc_forced_aligner.py:420:    """Quick forced alignment."""
./src/validators/ctc_forced_aligner.py:421:    aligner = CTCForcedAligner(language=language)
./src/validators/ctc_forced_aligner.py:422:    result = aligner.align(audio_path, transcription)
./src/validators/ctc_forced_aligner.py:423:    aligner.cleanup()
./src/validators/ctc_forced_aligner.py:431:        print("Usage: python ctc_forced_aligner.py <audio_path> <transcription>")
./src/validators/ctc_forced_aligner.py:437:    result = forced_align(audio_path, transcription)
./src/validators/ctc_forced_aligner.py:442:    print(f"Alignment Score: {result['alignment_score']:.4f}")
./src/validators/ctc_forced_aligner.py:448:    for wa in result['word_alignments']:
./venv/bin/transformers:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/accelerate-merge-weights:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/pyftsubset:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/accelerate:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./src/validators/__init__.py:7:2. Match the audio via native CTC alignment
./src/validators/__init__.py:8:3. Match the audio via romanized MMS alignment
./src/validators/__init__.py:19:# Full validation (character + dual audio alignment)
./src/validators/__init__.py:22:    romanized_text=roman_text, language="te"
./src/validators/__init__.py:24:# result.status: "accept" / "review" / "retry" / "reject"
./venv/bin/accelerate-config:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/check-model:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/sacrebleu:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/pip3.10:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/Activate.ps1:6:Pushes the python executable for a virtual environment to the front of the
./venv/bin/Activate.ps1:28:and shows extra information about the activation as it executes.
./venv/bin/Activate.ps1:42:execution policy for the user. You can do this by issuing the following PowerShell
./venv/bin/Activate.ps1:45:PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
./venv/bin/Activate.ps1:47:For more information on Execution Policies:
./venv/bin/Activate.ps1:65:addition of the virtual environment's Python executable from the beginning of
./venv/bin/check-node:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/pygmentize:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/jp.py:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/get_gprof:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/get_gprof:14:executes:
./venv/bin/get_gprof:26:where '-n 5' eliminates nodes below 5% threshold, similarly '-e 1' eliminates
./venv/bin/get_gprof:27:edges below 1% threshold
./venv/bin/numba:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/coloredlogs:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/ct2-openai-gpt2-converter:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/pyrsa-keygen:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/ct2-opus-mt-converter:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./src/validators/simple_validator.py:6:1. Character validation - catch garbage/alien characters
./src/validators/simple_validator.py:7:2. Native CTC alignment - verify native script matches audio
./src/validators/simple_validator.py:8:3. Romanized MMS alignment - verify romanized text matches audio
./src/validators/simple_validator.py:11:  N = native CTC score, R = romanized MMS score
./src/validators/simple_validator.py:13:  Forced review if abs(N-R) > 0.25 (high validator disagreement)
./src/validators/simple_validator.py:20:        romanized_text="naaku konni ads", language="te"
./src/validators/simple_validator.py:22:    print(result.status)          # accept / review / retry / reject
./src/validators/simple_validator.py:191:    """Result of transcription validation with dual scoring."""
./src/validators/simple_validator.py:192:    status: str  # "accept", "review", "retry", "reject"
./src/validators/simple_validator.py:199:    # Native CTC alignment (language-specific wav2vec2)
./src/validators/simple_validator.py:200:    alignment_score: float = 0.0       # kept for backward compat
./src/validators/simple_validator.py:205:    # Romanized MMS alignment (language-agnostic torchaudio MMS_FA)
./src/validators/simple_validator.py:206:    roman_mms_score: float = 0.0
./src/validators/simple_validator.py:221:            "roman_mms_score": round(self.roman_mms_score, 4),
./src/validators/simple_validator.py:223:            "alignment_score": round(self.alignment_score, 4),
./src/validators/simple_validator.py:230:# Global aligners (lazy loaded)
./src/validators/simple_validator.py:231:_ctc_aligner = None
./src/validators/simple_validator.py:232:_mms_aligner = None
./src/validators/simple_validator.py:234:def _get_ctc_aligner(language: str):
./src/validators/simple_validator.py:235:    """Lazy load native CTC aligner."""
./src/validators/simple_validator.py:236:    global _ctc_aligner
./src/validators/simple_validator.py:237:    if _ctc_aligner is None:
./src/validators/simple_validator.py:238:        from .ctc_forced_aligner import CTCForcedAligner
./src/validators/simple_validator.py:239:        _ctc_aligner = CTCForcedAligner(language=language)
./src/validators/simple_validator.py:240:    return _ctc_aligner
./src/validators/simple_validator.py:242:def _get_mms_aligner():
./src/validators/simple_validator.py:243:    """Lazy load romanized MMS aligner."""
./src/validators/simple_validator.py:244:    global _mms_aligner
./src/validators/simple_validator.py:245:    if _mms_aligner is None:
./src/validators/simple_validator.py:246:        from .mms_aligner import MMSAligner
./src/validators/simple_validator.py:247:        _mms_aligner = MMSAligner()
./src/validators/simple_validator.py:248:    return _mms_aligner
./src/validators/simple_validator.py:253:    roman_mms: float,
./src/validators/simple_validator.py:256:    Compute weighted validation score with disagreement penalty.
./src/validators/simple_validator.py:259:    MMS weighted higher (0.55): more stable on code-mixed Indic audio.
./src/validators/simple_validator.py:264:        verdict: "accept" / "review" / "retry" / "reject"
./src/validators/simple_validator.py:266:    N, R = native_ctc, roman_mms
./src/validators/simple_validator.py:272:            verdict = "review"
./src/validators/simple_validator.py:274:            verdict = "accept"
./src/validators/simple_validator.py:278:        verdict = "reject"
./src/validators/simple_validator.py:284:    """Strip punctuation for CTC alignment. Keeps native script + spaces."""
./src/validators/simple_validator.py:294:    romanized_text: str = "",
./src/validators/simple_validator.py:298:    Validate a transcription using dual scoring (native CTC + romanized MMS).
./src/validators/simple_validator.py:301:    1. Character validation (instant) on native script
./src/validators/simple_validator.py:302:    2. Native CTC forced alignment (language-specific wav2vec2)
./src/validators/simple_validator.py:303:    3. Romanized MMS forced alignment (language-agnostic, if romanized_text provided)
./src/validators/simple_validator.py:310:        romanized_text: Latin-script romanization (for MMS alignment)
./src/validators/simple_validator.py:311:        check_audio: Whether to run audio alignment checks (slower)
./src/validators/simple_validator.py:318:    # Strip punctuation for validation
./src/validators/simple_validator.py:320:    clean_roman = _strip_punctuation(romanized_text) if romanized_text else ""
./src/validators/simple_validator.py:322:    # === STEP 1: Character validation (instant) ===
./src/validators/simple_validator.py:329:            status="reject",
./src/validators/simple_validator.py:339:    # === STEP 2: Native CTC alignment ===
./src/validators/simple_validator.py:346:            aligner = _get_ctc_aligner(language)
./src/validators/simple_validator.py:347:            ctc_result = aligner.align(audio_path, clean_text)
./src/validators/simple_validator.py:348:            native_ctc_score = ctc_result.alignment_score
./src/validators/simple_validator.py:352:            reasons.append(f"CTC alignment failed: {str(e)[:50]}")
./src/validators/simple_validator.py:354:    # === STEP 3: Romanized MMS alignment ===
./src/validators/simple_validator.py:355:    roman_mms_score = 0.0
./src/validators/simple_validator.py:357:    if check_audio and clean_roman:
./src/validators/simple_validator.py:359:            mms = _get_mms_aligner()
./src/validators/simple_validator.py:360:            mms_result = mms.align(audio_path, clean_roman)
./src/validators/simple_validator.py:361:            roman_mms_score = mms_result.alignment_score
./src/validators/simple_validator.py:363:            reasons.append(f"MMS alignment failed: {str(e)[:50]}")
./src/validators/simple_validator.py:366:    if check_audio and native_ctc_score > 0 and roman_mms_score > 0:
./src/validators/simple_validator.py:369:            native_ctc_score, roman_mms_score
./src/validators/simple_validator.py:371:        if verdict == "review" and abs(native_ctc_score - roman_mms_score) > 0.25:
./src/validators/simple_validator.py:373:                f"Validator disagreement: CTC={native_ctc_score:.2f} "
./src/validators/simple_validator.py:374:                f"MMS={roman_mms_score:.2f}"
./src/validators/simple_validator.py:377:        # CTC only (MMS failed or no romanized text)
./src/validators/simple_validator.py:380:            verdict = "accept"
./src/validators/simple_validator.py:384:            verdict = "reject"
./src/validators/simple_validator.py:385:        if roman_mms_score == 0 and clean_roman:
./src/validators/simple_validator.py:386:            reasons.append("MMS failed, using CTC only")
./src/validators/simple_validator.py:387:    elif check_audio and roman_mms_score > 0:
./src/validators/simple_validator.py:388:        # MMS only (CTC failed)
./src/validators/simple_validator.py:389:        combined_score = roman_mms_score
./src/validators/simple_validator.py:390:        if roman_mms_score >= 0.70:
./src/validators/simple_validator.py:391:            verdict = "accept"
./src/validators/simple_validator.py:392:        elif roman_mms_score >= 0.50:
./src/validators/simple_validator.py:395:            verdict = "reject"
./src/validators/simple_validator.py:396:        reasons.append("CTC failed, using MMS only")
./src/validators/simple_validator.py:400:        verdict = "review"
./src/validators/simple_validator.py:405:        verdict = "review" if reasons else "accept"
./src/validators/simple_validator.py:407:    # Character validation failures override
./src/validators/simple_validator.py:409:        verdict = "reject"
./src/validators/simple_validator.py:414:            f"Below threshold: combined={combined_score:.2f} "
./src/validators/simple_validator.py:415:            f"(CTC={native_ctc_score:.2f}, MMS={roman_mms_score:.2f})"
./src/validators/simple_validator.py:417:    elif verdict == "reject" and combined_score > 0:
./src/validators/simple_validator.py:419:            f"Poor alignment: combined={combined_score:.2f} "
./src/validators/simple_validator.py:420:            f"(CTC={native_ctc_score:.2f}, MMS={roman_mms_score:.2f})"
./src/validators/simple_validator.py:428:        alignment_score=native_ctc_score,  # backward compat
./src/validators/simple_validator.py:430:        roman_mms_score=roman_mms_score,
./src/validators/simple_validator.py:439:    """Release all aligner resources (CTC + MMS)."""
./src/validators/simple_validator.py:440:    global _ctc_aligner, _mms_aligner
./src/validators/simple_validator.py:441:    if _ctc_aligner is not None:
./src/validators/simple_validator.py:442:        _ctc_aligner.cleanup()
./src/validators/simple_validator.py:443:        _ctc_aligner = None
./src/validators/simple_validator.py:444:    if _mms_aligner is not None:
./src/validators/simple_validator.py:445:        _mms_aligner.cleanup()
./src/validators/simple_validator.py:446:        _mms_aligner = None
./src/validators/simple_validator.py:449:# === Quick validation (no audio check) ===
./src/validators/simple_validator.py:452:    Quick character-only validation for NATIVE transcription.
./src/validators/simple_validator.py:496:    # Full validation (with audio check)
./src/validators/simple_validator.py:499:    # Quick validation (character check only)
./src/validators/simple_validator.py:525:        print(f"Alignment score: {result.alignment_score:.4f}")
./src/validators/simple_validator.py:529:            print(f"\nReasons for review:")
./venv/bin/websockets:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/pip:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/proton-viewer:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/tensorboard:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3
./venv/bin/torchfrtrace:1:#!/home/ubuntu/maya3_transcribe/venv/bin/python3


Summary: auto


**Inspecting key files for fixes**


I found the core modules (`pipeline.py`, `src/backend/gemini_transcriber.py`, `src/backend/audio_processor.py`, `src/validators/simple_validator.py`, `src/validators/mms_aligner.py`). Next I’m reading these to patch the exact points you called out.


sed -n '1,280p' src/backend/gemini_transcriber.py


sed -n '1,280p' src/backend/audio_processor.py


Output:
Chunk ID: 2089df
Wall time: 0.0515 seconds
Process exited with code 0
Original token count: 2603
Output:
"""
Main transcription pipeline orchestrating the full workflow:
1. Download audio segments from R2
2. Fetch language info from Supabase
3. Process/split audio segments
4. Transcribe using Gemini
5. Output results

Full control over all parameters for testing and production use.
"""
import os
import json
import time
from typing import Optional, List, Dict
from dataclasses import dataclass, field
from datetime import datetime

# Import from src.backend package
from src.backend.config import DEFAULT_SETTINGS, GEMINI_MODELS, get_model_name
from src.backend.r2_storage import download_video_segments
from src.backend.supabase_client import get_video_language
from src.backend.audio_processor import AudioProcessor, AudioChunk, get_segment_stats
from src.backend.audio_polisher import AudioPolisher
from src.backend.gemini_transcriber import GeminiTranscriber, TranscriptionConfig
from src.backend.transcription_schema import TranscriptionResult

# Validation
from src.validators import validate_transcription, quick_validate, cleanup as cleanup_validator

@dataclass
class PipelineConfig:
    """
    Complete configuration for the transcription pipeline.
    All parameters are exposed for full control.
    """
    # === Video Settings ===
    video_id: str
    language: Optional[str] = None  # If None, fetched from Supabase
    default_language: str = "Telugu"  # Fallback if Supabase lookup fails

    # === Audio Processing Settings ===
    max_segment_duration_sec: float = 10.0  # Hard limit - segments longer than this are split
    min_segment_duration_sec: float = 1.0   # Skip segments shorter than this
    chunk_overlap_sec: float = 0.0          # Overlap when splitting (for word boundaries)

    # === Segment Selection Settings ===
    max_segments: Optional[int] = None      # Limit total segments processed (for testing)
    segment_start_index: int = 0            # Start from this segment index
    skip_short_segments: bool = True        # Skip segments < min_duration

    # === Transcription Settings ===
    model: str = "gemini-3-flash-preview"   # Gemini model to use
    thinking_level: Optional[str] = "low"   # minimal, low, medium, high (Gemini 3)
    temperature: float = 0.0                # 0 for deterministic (tested safe with strict prompt)

    # === Validation Settings ===
    validate_transcriptions: bool = True    # Validate native transcriptions
    validation_action: str = "retry"        # "flag" (mark status) or "retry" (re-transcribe with pro)
    retry_model: str = "gemini-3-pro-preview"  # Model for retry attempts
    retry_thinking_level: str = "low"        # Thinking level for retry
    min_snr_db: float = 5.0                 # SNR gate: skip segments below this (saves API credits)

    # === Output Settings ===
    output_dir: str = "./transcriptions"
    save_intermediate: bool = True          # Save results after each batch
    batch_size: int = 10                    # How many to process before saving

    # === Audio Polishing Settings ===
    polish_audio: bool = True               # Polish segment boundaries before transcription

    # === Storage Settings ===
    work_dir: str = field(default_factory=lambda: DEFAULT_SETTINGS["work_dir"])
    cleanup_after: bool = False             # Remove downloaded files after processing
    use_regex_fallback: bool = True         # Try regex if exact R2 match fails

@dataclass
class PipelineResult:
    """Results from a pipeline run."""
    video_id: str
    language: str
    model: str
    thinking_level: Optional[str]

    total_segments_found: int
    segments_processed: int
    chunks_created: int
    transcriptions_completed: int

    total_audio_duration_sec: float
    total_processing_time_sec: float

    output_file: str
    errors: List[str]

    started_at: str
    completed_at: str

@dataclass
class _BatchHealth:
    """Tracks validator health per batch. Catches silent degradation."""
    validated: int = 0
    ctc_success: int = 0       # CTC returned > 0
    mms_success: int = 0       # MMS returned > 0
    dual_success: int = 0      # Both returned > 0
    one_sided_accept: int = 0  # Accepted with only one validator
    accept: int = 0
    review: int = 0
    retry: int = 0
    reject: int = 0
    retry_improved: int = 0
    total_combined: float = 0.0

    # Alert thresholds
    CTC_FAIL_THRESHOLD = 0.20    # Warn if CTC fails on >20% of batch
    MMS_FAIL_THRESHOLD = 0.20    # Warn if MMS fails on >20% of batch
    REJECT_THRESHOLD = 0.30      # Warn if >30% of batch rejected
    ONE_SIDED_THRESHOLD = 0.40   # Warn if >40% accepted on single validator

    def record(self, validation):
        """Record one validation result."""
        self.validated += 1
        ctc_ok = validation.native_ctc_score > 0
        mms_ok = validation.roman_mms_score > 0
        if ctc_ok:
            self.ctc_success += 1
        if mms_ok:
            self.mms_success += 1
        if ctc_ok and mms_ok:
            self.dual_success += 1
        if validation.status == "accept" and not (ctc_ok and mms_ok):
            self.one_sided_accept += 1
        status_attr = validation.status  # accept/review/retry/reject
        if hasattr(self, status_attr):
            setattr(self, status_attr, getattr(self, status_attr) + 1)
        self.total_combined += validation.combined_score

    def report(self, log_fn):
        """Log batch health summary. Warn on threshold breaches."""
        n = max(self.validated, 1)
        avg_s = self.total_combined / n
        ctc_rate = self.ctc_success / n
        mms_rate = self.mms_success / n
        dual_rate = self.dual_success / n
        rej_rate = self.reject / n
        one_sided_rate = self.one_sided_accept / max(self.accept, 1)

        log_fn(f"  Health: {self.validated} validated | "
               f"accept={self.accept} review={self.review} "
               f"retry={self.retry} reject={self.reject} | "
               f"avg_S={avg_s:.3f}")
        log_fn(f"  Validators: CTC={ctc_rate:.0%} MMS={mms_rate:.0%} "
               f"dual={dual_rate:.0%} | "
               f"one-sided accepts={self.one_sided_accept}")
        if self.retry_improved > 0:
            log_fn(f"  Retries improved: {self.retry_improved}")

        # Alert on threshold breaches
        alerts = []
        if ctc_rate < (1 - self.CTC_FAIL_THRESHOLD):
            alerts.append(f"CTC failing on {1-ctc_rate:.0%} of segments")
        if mms_rate < (1 - self.MMS_FAIL_THRESHOLD):
            alerts.append(f"MMS failing on {1-mms_rate:.0%} of segments")
        if rej_rate > self.REJECT_THRESHOLD:
            alerts.append(f"Reject rate {rej_rate:.0%} exceeds {self.REJECT_THRESHOLD:.0%}")
        if self.accept > 0 and one_sided_rate > self.ONE_SIDED_THRESHOLD:
            alerts.append(
                f"One-sided accept rate {one_sided_rate:.0%} "
                f"exceeds {self.ONE_SIDED_THRESHOLD:.0%}"
            )
        for alert in alerts:
            log_fn(f"  ALERT: {alert}", "WARN")
        return alerts

class TranscriptionPipeline:
    """
    Main pipeline orchestrating the full transcription workflow.
    """

    def __init__(self, config: PipelineConfig):
        """Initialize pipeline with configuration."""
        self.config = config
        self.results: List[TranscriptionResult] = []
        self.errors: List[str] = []
        self.start_time: Optional[float] = None
        self._health_alerts: List[str] = []  # Accumulated health alerts

    def _log(self, message: str, level: str = "INFO"):
        """Log a message with timestamp."""
        timestamp = datetime.now().strftime("%H:%M:%S")
        print(f"[{timestamp}] [{level}] {message}")

    def _get_language_code(self, language: str) -> str:
        """Convert language name to code for validation."""
        lang_map = {
            "Telugu": "te", "Hindi": "hi", "Tamil": "ta",
            "Kannada": "kn", "Malayalam": "ml", "Bengali": "bn",
            "Marathi": "mr", "Gujarati": "gu", "Punjabi": "pa",
            "Odia": "or", "Assamese": "as", "English": "en",
        }
        return lang_map.get(language, "te")

    def run(self) -> PipelineResult:
        """
        Execute the full pipeline.

        Returns:
            PipelineResult with summary and statistics
        """
        self.start_time = time.time()
        started_at = datetime.now().isoformat()

        self._log(f"Starting pipeline for video: {self.config.video_id}")
        self._log(f"Model: {self.config.model}, Thinking: {self.config.thinking_level}")

        # === Step 1: Download from R2 ===
        self._log("Step 1: Downloading audio segments from R2...")
        try:
            segments_dir, metadata = download_video_segments(
                self.config.video_id,
                work_dir=self.config.work_dir,
                use_regex_fallback=self.config.use_regex_fallback
            )
        except Exception as e:
            self.errors.append(f"R2 download failed: {e}")
            self._log(f"ERROR: {e}", "ERROR")
            raise

        # Get segment stats
        stats = get_segment_stats(segments_dir)
        self._log(f"Found {stats.get('total_files', 0)} audio files, "
                  f"total duration: {stats.get('total_duration_sec', 0):.1f}s")
        if stats.get('over_10s_count', 0) > 0:
            self._log(f"  -> {stats['over_10s_count']} segments over 10s will be split")

        # === Step 2: Get Language ===
        self._log("Step 2: Fetching language info...")
        if self.config.language:
            language = self.config.language
            self._log(f"Using specified language: {language}")
        else:
            language = get_video_language(
                self.config.video_id,
                default=self.config.default_language
            )
            self._log(f"Language from Supabase: {language}")

        # === Step 3: Process Audio Segments ===
        self._log("Step 3: Processing audio segments...")
        processor = AudioProcessor(
            max_duration_sec=self.config.max_segment_duration_sec,
            min_duration_sec=self.config.min_segment_duration_sec,
            overlap_sec=self.config.chunk_overlap_sec
        )

        chunks = processor.process_segments_directory(
            segments_dir,