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>


on a scale of 1-10. how good do you think our current transcription system + validation works. and is it sufficient to train large TTS and ASR systems ? if not what's that missing piece. if yes, how good. and if we are missing out on any corner cases, explain them too. considering indic languages, we cannot have a consensus decision makers. single source of controlled & deterministic transcription from gemini-3-flash with thinking-low, and temperature-0 and post validation with CTC on native "and" roman MFA. anything below 70% should be retried, with gemini-3-pro and if even after retry, if still <70% ignore / mark for review. the scoring has to be either average of both indic CTC + roman MFA or (and / or / avg / weighted -ideate here. we didnt yet decide on this).  dont gaslight. think realistically and reason. a whole costly project depends on this just so you know.


# 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.


**Announcing preliminary repository review**


I’m going to review the current pipeline in this repo first so the score is grounded in what you actually have, then I’ll give a realistic 1-10 rating, readiness for large-scale TTS/ASR, and concrete gaps/corner cases.


**Planning parallel file inspection**


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 688
drwxrwxr-x 12 ubuntu ubuntu   4096 Feb  7 04:03 .
drwxr-x--- 30 ubuntu ubuntu   4096 Feb 12 15:29 ..
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  7 03:46 consistency_test
drwxrwxr-x  2 ubuntu ubuntu   4096 Feb  3 22:18 docs
-rw-rw-r--  1 ubuntu ubuntu   1199 Feb  4 07:59 .env
drwxrwxr-x  4 ubuntu ubuntu   4096 Feb  5 05:25 models
-rw-rw-r--  1 ubuntu ubuntu  18216 Feb  6 20:05 pipeline.py
drwxrwxr-x  2 ubuntu ubuntu   4096 Feb  6 04:18 __pycache__
-rw-rw-r--  1 ubuntu ubuntu   5449 Feb  6 04:41 README.md
-rw-rw-r--  1 ubuntu ubuntu    104 Feb  3 22:54 requirements.txt
-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-r--r--  1 ubuntu ubuntu 381371 Feb  5 00:50 SPEAKER_01_0422_5442.68-5452.49.flac
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:
Exit code: 0
Wall time: 0.1 seconds
Total output lines: 14027
Output:
SPEAKER_00_0004_36.29-37.41.wav
SPEAKER_01_0422_5442.68-5452.49.flac
SPEAKER_00_0000_0.03-2.61.flac
transcriptions/test_new_schema_20seg.json
transcriptions/test_new_schema_5segments.json
transcriptions/pF_BQpHaIdU_gemini_2.5_flash_20260203_225339_partial.json
transcriptions/pF_BQpHaIdU_gemini_2.5_flash_20260203_225339.json
transcriptions/pF_BQpHaIdU_gemini_3_flash_preview_20260203_225233_partial.json
transcriptions/pF_BQpHaIdU_gemini_3_flash_preview_20260203_225233.json
transcriptions/pF_BQpHaIdU_gemini_3_flash_preview_20260203_230246_partial.json
transcriptions/pF_BQpHaIdU_gemini_3_flash_preview_20260203_230246.json
src/__pycache__/__init__.cpython-310.pyc
src/__init__.py
__pycache__/pipeline.cpython-310.pyc
models/ctc_aligner/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/eb125d179afdd35fdb896eda528c3581780b4ad0
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
requirements.txt
src/backend/gemini_transcriber.py
src/backend/audio_polisher.py
src/backend/__init__.py
src/backend/transcription_schema.py
src/backend/config.py
src/backend/r2_storage.py
src/backend/supabase_client.py
src/backend/audio_processor.py
venv/pyvenv.cfg
models/ctc_aligner/models--anuragshas--wav2vec2-large-xlsr-53-telugu/refs/main
src/backend/__pycache__/supabase_client.cpython-310.pyc
src/backend/__pycache__/r2_storage.cpython-310.pyc
src/backend/__pycache__/audio_polisher.cpython-310.pyc
src/backend/__pycache__/audio_processor.cpython-310.pyc
src/backend/__pycache__/__init__.cpython-310.pyc
src/backend/__pycache__/transcription_schema.cpython-310.pyc
src/backend/__pycache__/gemini_transcriber.cpython-310.pyc
src/backend/__pycache__/config.cpython-310.pyc
README.md
venv/include/site/python3.10/greenlet/greenlet.h
src/validators/__init__.py
models/ctc_aligner/models--anuragshas--wav2vec2-large-xlsr-53-telugu/refs/refs/pr/1
src/validators/__pycache__/ctc_forced_aligner.cpython-310.pyc
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/validators/__pycache__/alignment_scorer.cpython-310.pyc
analysis_results/final_analysis.json
src/validators/__pycache__/simple_validator.cpython-310.pyc
analysis_results/scoring_results.json
src/validators/__pycache__/runner.cpython-310.pyc
venv/share/man/man1/ipython.1
src/validators/simple_validator.py
pipeline.py
venv/share/man/man1/isympy.1
src/validators/ctc_forced_aligner.py
venv/share/man/man1/ttx.1
validation_results/test_run.json
validation_results/all_validators_test.json
bin/micromamba
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/refs/main
consistency_test/roman_alignment.json
consistency_test/comparison.json
consistency_test/run_1.json
consistency_test/run_2.json
consistency_test/run_3.json
docs/Pricing.md
docs/gemini3.md
docs/structuredOutput.md
docs/gemini3_batch.md
docs/PromptCaching.md
docs/prompt.txt
docs/sample.txt
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/a2a8340e0a162e4e223867107d9db359f5697c1d
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/78dfdcf81d5d1cbb1430b0a283bacebe721b5f1b50b6fdfe58e635f31b4c2526
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/0886a48276922a77013d8aa4681192138ae90d90
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/c515cdf136cb0af961aabcaf53819693dc4324e6
venv/bin/pip3.10
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/eb125d179afdd35fdb896eda528c3581780b4ad0
venv/bin/transformers
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/a23c2ec161bc7d0a523d5cbff72a81ac55784803f87e6664641f542fab82bf52
models/alignment/models--anuragshas--wav2vec2-large-xlsr-53-telugu/blobs/9abf71998c3e0de2f13c0fd73ed81477c9dae118
venv/bin/jiwer
venv/bin/pyrsa-sign
venv/bin/mako-render
venv/bin/datasets-cli
venv/bin/lhotse
venv/bin/rich-click
venv/bin/pyrsa-keygen
venv/bin/transformers-cli
venv/bin/alembic
venv/bin/ct2-fairseq-converter
venv/bin/accelerate-merge-weights
venv/bin/accelerate-config
venv/bin/check-node
venv/bin/ct2-openai-gpt2-converter
venv/bin/numba
venv/bin/cygdb
venv/bin/pyannote-metrics
venv/bin/check-model
venv/bin/pyav
venv/bin/humanfriendly
venv/bin/coloredlogs
venv/bin/tensorboard
venv/bin/websockets
venv/bin/ct2-opus-mt-converter
venv/bin/proton-viewer
venv/bin/pip
venv/bin/get_gprof
venv/bin/pyftsubset
venv/bin/torchfrtrace
venv/bin/Activate.ps1
venv/bin/jp.py
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venv/lib/python3.10/site-packages/transformers/integrations/deep


Summary: auto


**Scoping agent constraints**


I found the core code paths (`pipeline.py`, `src/backend/*`, `src/validators/*`). Next I’m reading those plus existing analysis outputs to ground the score in actual behavior and thresholds rather than assumptions.


**Extracting initial code snippets**


sed -n '1,260p' src/backend/gemini_transcriber.py


sed -n '1,260p' src/validators/runner.py


sed -n '1,260p' src/validators/alignment_scorer.py


Output:
Exit code: 0
Wall time: 0 seconds
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 = "flag"         # "flag" (mark status) or "retry" (re-transcribe)

    # === 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

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

    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",
        }
        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,
            max_segments=self.config.max_segments,
            skip_short=self.config.skip_short_segments
        )

        # Apply start index
        if self.config.segment_start_index > 0:
            chunks = chunks[self.config.segment_start_index:]
            self._log(f"Starting from index {self.config.segment_start_index}, "
                      f"{len(chunks)} chunks remaining")

        # === Step 3.5: Polish Audio Boundaries ===
        if self.config.polish_audio:
            self._log("Step 3.5: Polishing audio boundaries...")
            polisher = AudioPolisher()
            polished_dir = os.path.join(segments_dir, "polished")
            polished_count = 0
            for chunk in chunks:
                result = polisher.polish(chunk.file_path, output_dir=polished_dir)
                if result.was_modified:
                    chunk.file_path = result.output_path
                    polished_count += 1
            self._log(f"Polished {polished_count}/{len(chunks)} chunks "
                      f"({len(chunks) - polished_count} already clean)")

        # === Step 4: Transcribe ===
        self._log(f"Step 4: Transcribing {len(chunks)} chunks...")

        transcriber = GeminiTranscriber()
        transcription_config = TranscriptionConfig(
            model=self.config.model,
            thinking_level=self.config.thinking_level,
            temperature=self.config.temperature,
            language=language
        )

        # Process in batches
        for batch_start in range(0, len(chunks), self.config.batch_size):
            batch_end = min(batch_start + self.config.batch_size, len(chunks))
            batch = chunks[batch_start:batch_end]

            self._log(f"Processing batch {batch_start + 1}-{batch_end} of {len(chunks)}")

            batch_results = transcriber.transcribe_batch(
                batch,
                transcription_config,
                progress_callback=lambda c, t: self._log(
                    f"  Chunk {batch_start + c}/{len(chunks)}: "
                    f"{batch[c-1].original_segment}"
                )
            )

            # === Validate transcriptions ===
            if self.config.validate_transcriptions:
                lang_code = self._get_language_code(language)
                for i, result in enumerate(batch_results):
                    if result.native:
                        validation = validate_transcription(
                            batch[i].file_path,
                            result.native,
                            language=lang_code
                        )
                        result.validation_status = validation.status
                        result.validation_score = validation.alignment_score
                        if validation.status == "reject":
                            self._log(f"  ⚠ Validation failed for {result.segment_id}: "
                                     f"{validation.reasons}", "WARN")

            self.results.extend(batch_results)

            # Save intermediate results
            if self.config.save_intermediate:
                self._save_results(partial=True)

        # === Step 5: Save Final Results ===
        self._log("Step 5: Saving results...")
        output_file = self._save_results(partial=False)

        # === Cleanup ===
        if self.config.validate_transcriptions:
            cleanup_validator()  # Release CTC model resources


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""
Gemini transcription module using Google AI Studio endpoints.
Handles audio transcription with structured output and multiple model support.
"""
import time
from pathlib import Path
from typing import List, Optional, Dict, Any
from dataclasses import dataclass

from google import genai
from google.genai import types

from .config import GEMINI_API_KEY, GEMINI_MODELS, get_model_name
from .transcription_schema import (
    TranscriptionOutput,
    TranscriptionResult,
    SpeakerMeta,
    get_transcription_prompt,
    get_user_prompt,
    TRANSCRIPTION_JSON_SCHEMA
)
from .audio_processor import AudioChunk

@dataclass
class TranscriptionConfig:
    """Configuration for a transcription job."""
    model: str = "gemini-3-flash-preview"
    thinking_level: Optional[str] = "low"   # minimal, low, medium, high
    # Gemini 3 docs recommend 1.0, but our testing shows 0.0 produces
    # more deterministic, accurate transcriptions with the strict prompt.
    # No looping observed across 30+ test segments.
    temperature: float = 0.0
    language: str = "Telugu"
    timeout_sec: int = 60

class GeminiTranscriber:
    """
    Handles audio transcription using Gemini models via Google AI Studio.
    """

    def __init__(self, api_key: Optional[str] = None):
        """
        Initialize the transcriber.

        Args:
            api_key: Gemini API key (defaults to env var)
        """
        self.api_key = api_key or GEMINI_API_KEY
        if not self.api_key:
            raise ValueError("GEMINI_API_KEY not set")

        self.client = genai.Client(api_key=self.api_key)

    def _get_mime_type(self, file_path: str) -> str:
        """Determine MIME type from file extension."""
        ext = Path(file_path).suffix.lower()
        mime_types = {
            '.flac': 'audio/flac',
            '.wav': 'audio/wav',
            '.mp3': 'audio/mpeg',
            '.ogg': 'audio/ogg',
            '.m4a': 'audio/mp4',
            '.aac': 'audio/aac'
        }
        return mime_types.get(ext, 'audio/flac')

    def _load_audio_bytes(self, file_path: str) -> bytes:
        """Load audio file as bytes."""
        with open(file_path, 'rb') as f:
            return f.read()

    def _build_config(self, config: TranscriptionConfig) -> types.GenerateContentConfig:
        """Build the generation config for the API call."""
        model_name = get_model_name(config.model)
        model_info = GEMINI_MODELS.get(model_name, {})

        # Build thinking config for Gemini 3 models
        thinking_config = None
        if model_info.get("supports_thinking") and config.thinking_level:
            if model_name.startswith("gemini-3"):
                thinking_config = types.ThinkingConfig(
                    thinking_level=config.thinking_level.upper()
                )

        gen_config = types.GenerateContentConfig(
            temperature=config.temperature,
            response_mime_type="application/json",
            response_json_schema=TRANSCRIPTION_JSON_SCHEMA,
            system_instruction=[
                types.Part.from_text(text=get_transcription_prompt(config.language))
            ]
        )

        if thinking_config:
            gen_config.thinking_config = thinking_config

        return gen_config

    def transcribe_audio(
        self,
        audio_path: str,
        config: TranscriptionConfig
    ) -> Dict[str, Any]:
        """
        Transcribe a single audio file.

        Args:
            audio_path: Path to the audio file
            config: Transcription configuration

        Returns:
            Dictionary with transcription results
        """
        model_name = get_model_name(config.model)

        # Load audio
        audio_bytes = self._load_audio_bytes(audio_path)
        mime_type = self._get_mime_type(audio_path)

        # Build content
        contents = [
            types.Content(
                role="user",
                parts=[
                    types.Part.from_bytes(
                        mime_type=mime_type,
                        data=audio_bytes
                    ),
                    types.Part.from_text(text=get_user_prompt())
                ]
            )
        ]

        # Build config
        gen_config = self._build_config(config)

        # Make API call
        start_time = time.time()
        try:
            response = self.client.models.generate_content(
                model=model_name,
                contents=contents,
                config=gen_config
            )
            processing_time = time.time() - start_time

            # Parse response
            if response.text:
                import json
                result = json.loads(response.text)
                result['_processing_time_sec'] = processing_time
                result['_model'] = model_name
                result['_thinking_level'] = config.thinking_level
                return result
            else:
                return {
                    "error": "Empty response from model",
                    "_processing_time_sec": processing_time,
                    "_model": model_name
                }

        except Exception as e:
            processing_time = time.time() - start_time
            return {
                "error": str(e),
                "_processing_time_sec": processing_time,
                "_model": model_name
            }

    def transcribe_chunk(
        self,
        chunk: AudioChunk,
        config: TranscriptionConfig
    ) -> TranscriptionResult:
        """
        Transcribe an AudioChunk and return structured result.

        Args:
            chunk: AudioChunk to transcribe
            config: Transcription configuration

        Returns:
            TranscriptionResult with full metadata
        """
        raw_result = self.transcribe_audio(chunk.file_path, config)

        error_msg = raw_result.get("error")

        # Handle errors
        if error_msg:
            transcription = TranscriptionOutput(
                transcription=f"[ERROR: {error_msg}]",
                code_switch=f"[ERROR: {error_msg}]",
                romanized=f"[ERROR: {error_msg}]",
                tagged=f"[ERROR: {error_msg}]",
            )
        else:
            # Parse speaker metadata if present
            speaker_data = raw_result.get('speaker')
            speaker = SpeakerMeta(**speaker_data) if isinstance(speaker_data, dict) else None

            transcription = TranscriptionOutput(
                transcription=raw_result.get('transcription', ''),
                code_switch=raw_result.get('code_switch', ''),
                romanized=raw_result.get('romanized', ''),
                tagged=raw_result.get('tagged', ''),
                speaker=speaker,
            )

        return TranscriptionResult(
            segment_id=chunk.original_segment,
            chunk_index=chunk.chunk_index,
            total_chunks=chunk.total_chunks,
            duration_sec=chunk.duration_sec,
            language=config.language,
            transcription=transcription,
            model_used=raw_result.get('_model', config.model),
            thinking_level=raw_result.get('_thinking_level'),
            processing_time_sec=raw_result.get('_processing_time_sec')
        )

    def transcribe_batch(
        self,
        chunks: List[AudioChunk],
        config: TranscriptionConfig,
        max_chunks: Optional[int] = None,
        progress_callback: Optional[callable] = None
    ) -> List[TranscriptionResult]:
        """
        Transcribe multiple audio chunks.

        Args:
            chunks: List of AudioChunks to transcribe
            config: Transcription configuration
            max_chunks: Maximum chunks to process (for testing)
            progress_callback: Optional callback(current, total) for progress updates

        Returns:
            List of TranscriptionResults
        """
        if max_chunks:
            chunks = chunks[:max_chunks]

        results = []
        total = len(chunks)

        print(f"[Transcriber] Processing {total} chunks with {config.model}...")

        for i, chunk in enumerate(chunks):
            if progress_callback:
                progress_callback(i + 1, total)
            else:
                print(f"[Transcriber] Processing {i + 1}/{total}: {chunk.original_segment} "
                      f"(chunk {chunk.chunk_index + 1}/{chunk.total_chunks})")

            result = self.transcribe_chunk(chunk, config)
            results.append(result)


Output:
Exit code: 2
Wall time: 0 seconds
Output:
sed: can't read src/validators/runner.py: No such file or directory


Output:
Exit code: 2
Wall time: 0 seconds
Output:
sed: can't read src/validators/alignment_scorer.py: No such file or directory


**Inspecting validator directory structure**


find src -maxdepth 3 -type f | rg 'validator|align|runner|ctc'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 24
drwxrwxr-x  5 ubuntu ubuntu 4096 Feb  4 21:27 .
drwxrwxr-x 12 ubuntu ubuntu 4096 Feb  7 04:03 ..
drwxrwxr-x  3 ubuntu ubuntu 4096 Feb  5 23:26 backend
-rw-rw-r--  1 ubuntu ubuntu   34 Feb  3 23:03 __init__.py
drwxrwxr-x  2 ubuntu ubuntu 4096 Feb  4 21:27 __pycache__
drwxrwxr-x  3 ubuntu ubuntu 4096 Feb  5 05:24 validators


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 44
drwxrwxr-x 3 ubuntu ubuntu  4096 Feb  5 05:24 .
drwxrwxr-x 5 ubuntu ubuntu  4096 Feb  4 21:27 ..
-rw-rw-r-- 1 ubuntu ubuntu 14507 Feb  5 04:28 ctc_forced_aligner.py
-rw-rw-r-- 1 ubuntu ubuntu   925 Feb  5 05:25 __init__.py
drwxrwxr-x 2 ubuntu ubuntu  4096 Feb  6 04:18 __pycache__
-rw-rw-r-- 1 ubuntu ubuntu 12270 Feb  6 04:16 simple_validator.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/validators/__init__.py
src/validators/ctc_forced_aligner.py
src/validators/simple_validator.py
src/validators/__pycache__/runner.cpython-310.pyc
src/validators/__pycache__/simple_validator.cpython-310.pyc
src/validators/__pycache__/alignment_scorer.cpython-310.pyc
src/validators/__pycache__/indic_conformer_validator.cpython-310.pyc
src/validators/__pycache__/__init__.cpython-310.pyc
src/validators/__pycache__/indicmfa_real.cpython-310.pyc
src/validators/__pycache__/base.cpython-310.pyc
src/validators/__pycache__/indicmfa_validator.cpython-310.pyc
src/validators/__pycache__/ctc_forced_aligner.cpython-310.pyc


**Inspecting core modules and results**


I hit a quick mismatch: only `ctc_forced_aligner.py` and `simple_validator.py` are present now (older validator modules exist only as stale `.pyc`). I’m now reading these current validator files plus your analysis JSONs to assess the real current system.


sed -n '1,260p' src/validators/__init__.py


sed -n '1,320p' src/validators/simple_validator.py


sed -n '1,320p' src/validators/ctc_forced_aligner.py


sed -n '1,260p' analysis_results/scoring_results.json


sed -n '1,260p' analysis_results/final_analysis.json


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""
Validators Module - Native Script Transcription Validation
===========================================================

Validates that transcriptions are:
1. Valid native script (no garbage/alien characters)
2. Match the audio (via CTC alignment)

Usage:
```python
from src.validators import validate_transcription, quick_validate

# Quick check (character only - instant)
result = quick_validate(text, language="te")
if not result["valid"]:
    print(f"Invalid: {result['reason']}")

# Full validation (character + audio - 0.1s)
result = validate_transcription(audio_path, text, language="te")
# result.status: "accept" / "review" / "reject"
```
"""

from .simple_validator import (
    validate_transcription,
    quick_validate,
    check_characters,
    ValidationResult,
    cleanup,
)

__all__ = [
    'validate_transcription',
    'quick_validate',
    'check_characters',
    'ValidationResult',
    'cleanup',
]


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""
Simple Transcription Validator
==============================

Two checks:
1. Character validation - catch garbage/alien characters
2. Audio-text match - verify words match what's spoken

Usage:
    from src.validators.simple_validator import validate_transcription

    result = validate_transcription("audio.flac", "transcription", language="te")
    print(result['status'])  # accept / review / reject
"""
import re
from dataclasses import dataclass
from typing import List, Dict, Optional, Set

# Unicode ranges for Indic scripts
SCRIPT_RANGES = {
    "te": {  # Telugu
        "name": "Telugu",
        "ranges": [(0x0C00, 0x0C7F)],  # Telugu block
        "allow_ascii_punct": True,
        "allow_digits": True,
    },
    "hi": {  # Hindi (Devanagari)
        "name": "Hindi",
        "ranges": [(0x0900, 0x097F)],  # Devanagari block
        "allow_ascii_punct": True,
        "allow_digits": True,
    },
    "ta": {  # Tamil
        "name": "Tamil",
        "ranges": [(0x0B80, 0x0BFF)],
        "allow_ascii_punct": True,
        "allow_digits": True,
    },
    "kn": {  # Kannada
        "name": "Kannada",
        "ranges": [(0x0C80, 0x0CFF)],
        "allow_ascii_punct": True,
        "allow_digits": True,
    },
    "ml": {  # Malayalam
        "name": "Malayalam",
        "ranges": [(0x0D00, 0x0D7F)],
        "allow_ascii_punct": True,
        "allow_digits": True,
    },
    "bn": {  # Bengali
        "name": "Bengali",
        "ranges": [(0x0980, 0x09FF)],
        "allow_ascii_punct": True,
        "allow_digits": True,
    },
}

# Common allowed characters across all languages
ALLOWED_COMMON = set(" \t\n.,!?;:'\"()-–—0123456789")

# English characters (for code-mixed text)
ENGLISH_CHARS = set("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ")

def get_valid_chars(language: str, allow_english: bool = True) -> Set[int]:
    """Get set of valid Unicode codepoints for a language."""
    valid = set()

    # Add common chars
    for c in ALLOWED_COMMON:
        valid.add(ord(c))

    # Add script-specific chars
    lang_config = SCRIPT_RANGES.get(language, SCRIPT_RANGES["te"])
    for start, end in lang_config["ranges"]:
        for cp in range(start, end + 1):
            valid.add(cp)

    # Allow English for code-mixed
    if allow_english:
        for c in ENGLISH_CHARS:
            valid.add(ord(c))

    return valid

def check_characters(
    text: str,
    language: str = "te",
    allow_english: bool = False  # Changed: native = no English
) -> Dict:
    """
    Check if text contains only valid characters for the language.

    For native transcription: allow_english=False (default)

    Returns:
        {
            "valid": True/False,
            "invalid_chars": [...],
            "script_ratio": 0.8,  # ratio of native script chars
        }
    """
    if not text or not text.strip():
        return {"valid": False, "invalid_chars": [], "script_ratio": 0, "reason": "empty"}

    valid_chars = get_valid_chars(language, allow_english)
    lang_config = SCRIPT_RANGES.get(language, SCRIPT_RANGES["te"])

    invalid_chars = []
    script_count = 0
    total_alpha = 0

    for i, char in enumerate(text):
        cp = ord(char)

        # Check if in native script
        is_native = any(start <= cp <= end for start, end in lang_config["ranges"])
        if is_native:
            script_count += 1
            total_alpha += 1

        # Check if invalid (includes English if not allowed)
        elif cp not in valid_chars:
            invalid_chars.append({
                "char": char,
                "codepoint": cp,
                "position": i
            })
            if char in ENGLISH_CHARS:
                total_alpha += 1

    return {
        "valid": len(invalid_chars) == 0,
        "invalid_chars": invalid_chars[:10],  # First 10 only
        "invalid_count": len(invalid_chars),
        "script_ratio": script_count / total_alpha if total_alpha > 0 else 0,
    }

@dataclass
class ValidationResult:
    """Result of transcription validation."""
    status: str  # "accept", "review", "reject"

    # Character check
    char_valid: bool = True
    invalid_chars: List[Dict] = None
    script_ratio: float = 0.0

    # Audio match check
    alignment_score: float = 0.0
    low_confidence_words: List[str] = None
    low_confidence_ratio: float = 0.0

    # Reasons
    reasons: List[str] = None

    def to_dict(self) -> Dict:
        return {
            "status": self.status,
            "char_valid": self.char_valid,
            "invalid_chars": self.invalid_chars or [],
            "script_ratio": round(self.script_ratio, 3),
            "alignment_score": round(self.alignment_score, 4),
            "low_confidence_words": self.low_confidence_words or [],
            "low_confidence_ratio": round(self.low_confidence_ratio, 3),
            "reasons": self.reasons or []
        }

# Global CTC aligner (lazy loaded)
_ctc_aligner = None

def _get_ctc_aligner(language: str):
    """Lazy load CTC aligner."""
    global _ctc_aligner
    if _ctc_aligner is None:
        from .ctc_forced_aligner import CTCForcedAligner
        _ctc_aligner = CTCForcedAligner(language=language)
    return _ctc_aligner

def _strip_punctuation(text: str) -> str:
    """Strip punctuation for CTC alignment. Keeps native script + spaces."""
    import re
    # Remove common punctuation marks (keep native script chars, digits, spaces)
    return re.sub(r'[।,.!?;:\'"()\-–—]', '', text).strip()

def validate_transcription(
    audio_path: str,
    transcription: str,
    language: str = "te",
    check_audio: bool = True,
    low_conf_threshold: float = 0.3,
    low_conf_ratio_threshold: float = 0.8,  # Raised from 0.5 - code-mixed is normal
    min_alignment_score: float = 0.5  # Below this = likely wrong transcription
) -> ValidationResult:
    """
    Validate a transcription (native script with punctuation).

    Punctuation is stripped before character check and CTC alignment,
    so this works with the new 'transcription' field that includes punctuation.

    Args:
        audio_path: Path to audio file
        transcription: Native script text (may include punctuation)
        language: Language code (te, hi, ta, etc.)
        check_audio: Whether to check audio-text match (slower)
        low_conf_threshold: Per-word confidence threshold
        low_conf_ratio_threshold: Max ratio of low-conf words before review

    Returns:
        ValidationResult with status and details
    """
    reasons = []

    # Strip punctuation for validation (transcription field now includes punctuation)
    clean_text = _strip_punctuation(transcription)

    # === STEP 1: Character validation (instant) ===
    # Native transcription = no English allowed
    char_check = check_characters(clean_text, language, allow_english=False)

    if not char_check["valid"]:
        return ValidationResult(
            status="reject",
            char_valid=False,
            invalid_chars=char_check["invalid_chars"],
            script_ratio=char_check["script_ratio"],
            reasons=["Invalid/alien characters found"]
        )

    # Should be mostly native script
    if char_check["script_ratio"] < 0.5:
        reasons.append(f"Too few native chars ({char_check['script_ratio']:.0%})")

    # === STEP 2: Audio-text match (0.1s) ===
    alignment_score = 0.0
    low_conf_words = []
    low_conf_ratio = 0.0

    if check_audio:
        try:
            aligner = _get_ctc_aligner(language)
            # Use punctuation-stripped text for CTC alignment
            result = aligner.align(audio_path, clean_text)

            alignment_score = result.alignment_score
            low_conf_words = result.low_confidence_words
            low_conf_ratio = result.low_confidence_ratio

            # REJECT if alignment is very poor (likely wrong transcription)
            if alignment_score < min_alignment_score:
                reasons.append(f"Poor alignment ({alignment_score:.2f} < {min_alignment_score})")

            # REVIEW if many low-confidence words (but could be code-mixed)
            elif low_conf_ratio > low_conf_ratio_threshold:
                reasons.append(f"Many low-confidence words ({low_conf_ratio:.0%})")

        except Exception as e:
            reasons.append(f"Audio check failed: {str(e)[:50]}")

    # === Determine status ===
    # Reject: invalid chars OR very poor alignment
    # Review: high low-conf ratio
    # Accept: everything else
    if not char_check["valid"]:
        status = "reject"
    elif any("Poor alignment" in r for r in reasons):
        status = "reject"
    elif len(reasons) > 0:
        status = "review"
    else:
        status = "accept"

    return ValidationResult(
        status=status,
        char_valid=char_check["valid"],
        invalid_chars=char_check.get("invalid_chars"),
        script_ratio=char_check["script_ratio"],
        alignment_score=alignment_score,
        low_confidence_words=low_conf_words,
        low_confidence_ratio=low_conf_ratio,
        reasons=reasons if reasons else None
    )

def cleanup():
    """Release CTC aligner resources."""
    global _ctc_aligner
    if _ctc_aligner is not None:
        _ctc_aligner.cleanup()
        _ctc_aligner = None

# === Quick validation (no audio check) ===
def quick_validate(transcription: str, language: str = "te") -> Dict:
    """
    Quick character-only validation for NATIVE transcription.

    Checks:
    - Not empty
    - All characters in target script (no English/garbage)
    - Sufficient native script content

    Args:
        transcription: Native script text to validate
        language: Language code (te, hi, ta, etc.)

    Returns:
        {"valid": True/False, "reason": "..."}
    """
    if not transcription or not transcription.strip():
        return {"valid": False, "reason": "Empty transcription"}

    char_check = check_characters(transcription, language, allow_english=False)