You are Codex, based on GPT-5. You are running as a coding agent in the Codex CLI on a user's computer.

## 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.)

## 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).
- 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.

## Plan tool

When using the planning tool:
- Skip using the planning tool for straightforward tasks (roughly the easiest 25%).
- Do not make single-step plans.
- When you made a plan, update it after having performed one of the sub-tasks that you shared on the plan.

## 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.

## Presenting your work and final message

You are producing plain text that will later be styled by the CLI. Follow these rules exactly. Formatting should make results easy to scan, but not feel mechanical. Use judgment to decide how much structure adds value.

- Default: be very concise; friendly coding teammate tone.
- Ask only when needed; suggest ideas; mirror the user's style.
- For substantial work, summarize clearly; follow final‑answer formatting.
- Skip heavy formatting for simple confirmations.
- Don't dump large files you've written; reference paths only.
- No "save/copy this file" - User is on the same machine.
- Offer logical next steps (tests, commits, build) briefly; add verify steps if you couldn't do something.
- For code changes:
  * Lead with a quick explanation of the change, and then give more details on the context covering where and why a change was made. Do not start this explanation with "summary", just jump right in.
  * 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.
- The user does not 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.

### Final answer structure and style guidelines

- Plain text; CLI handles styling. Use structure only when it helps scanability.
- Headers: optional; short Title Case (1-3 words) wrapped in **…**; no blank line before the first bullet; add only if they truly help.
- Bullets: use - ; merge related points; keep to one line when possible; 4–6 per list ordered by importance; keep phrasing consistent.
- Monospace: backticks for commands/paths/env vars/code ids and inline examples; use for literal keyword bullets; never combine with **.
- Code samples or multi-line snippets should be wrapped in fenced code blocks; include an info string as often as possible.
- Structure: group related bullets; order sections general → specific → supporting; for subsections, start with a bolded keyword bullet, then items; match complexity to the task.
- Tone: collaborative, concise, factual; present tense, active voice; self‑contained; no "above/below"; parallel wording.
- Don'ts: no nested bullets/hierarchies; no ANSI codes; don't cram unrelated keywords; keep keyword lists short—wrap/reformat if long; avoid naming formatting styles in answers.
- Adaptation: code explanations → precise, structured with code refs; simple tasks → lead with outcome; big changes → logical walkthrough + rationale + next actions; casual one-offs → plain sentences, no headers/bullets.
- 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


<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.

Here are scenarios where you'll need to request approval:
- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)
- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.
- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)
- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.
- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.

When requesting approval to execute a command that will require escalated privileges:
  - Provide the `sandbox_permissions` parameter with the value `"require_escalated"`
  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter The writable roots are `/home/ubuntu/maya_transcribe`, `/tmp`.</permissions instructions>


# AGENTS.md instructions for /home/ubuntu/maya_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) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.
</INSTRUCTIONS>


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


<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. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `never`: This is a non-interactive mode where you may NEVER ask the user for approval to run commands. Instead, you must always persist and work around constraints to solve the task for the user. You MUST do your utmost best to finish the task and validate your work before yielding. If this mode is paired with `danger-full-access`, take advantage of it to deliver the best outcome for the user. Further, in this mode, your default testing philosophy is overridden: Even if you don't see local patterns for testing, you may add tests and scripts to validate your work. Just remove them before yielding.</permissions instructions>


# Context from my IDE setup:

## Open tabs:
- .env: .env
- PROMPT.md: PROMPT.md
- claude.md: docs/transcribe/claude.md
- kimi.md: docs/kimi.md
- gpt.md: docs/transcribe/gpt.md

## My request for Codex:
there is something that occupied the GPU, but nvidia-smi shows no processes. can you clear the GPU memory ? should be some zombied process


Summary: auto

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


**Inspecting GPU processes before reset**

I'm planning to check current GPU compute processes with nvidia-smi to understand memory usage before considering any risky reset commands.


Output:
Exit code: 0
Wall time: 0.3 seconds
Output:
Fri Jan 23 03:25:32 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.35.05              Driver Version: 560.35.05      CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA A100-SXM4-80GB          On  |   00000000:01:00.0 Off |                    0 |
| N/A   30C    P0             57W /  400W |   77855MiB /  81920MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
+-----------------------------------------------------------------------------------------+


Summary: auto


**Planning GPU memory reset**

Preparing to run a GPU reset command to clear memory on the A100, acknowledging it requires root and might fail if other users or persistence modes are active.


Output:
Exit code: 4
Wall time: 0.1 seconds
Output:
Unable to disable persistence mode for GPU 00000000:01:00.0: Insufficient Permissions


**Testing sudo GPU reset command**

I'm checking if `sudo nvidia-smi --gpu-reset -i 0` can run without approval in this environment.


Output:
Exit code: 255
Wall time: 0.1 seconds
Output:
The following GPUs could not be reset:
  GPU 00000000:01:00.0: In use by another client

1 device is currently being used by one or more other processes (e.g., Fabric Manager, CUDA application, graphics application such as an X server, or a monitoring application such as another instance of nvidia-smi). Please first kill all processes using this device and all compute applications running in the system.


**Identifying processes using NVIDIA devices**

I'm preparing to check which processes are using the NVIDIA devices with system tools like `fuser` or `lsof` to pinpoint the client reported as using them despite no results from nvidia-smi.


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
   926 274345 384508 384509 384510 384511 384512 384513 384514 384515 384516 384517 384518 384519 738461   926 274345 384508 384509 384510 384511 384512 384513 384514 384515 384516 384517 384518 384519 738461 274345 384508 384509 384510 384511 384512 384513 384514 384515 384516 384517 384518 384519 738461                     USER        PID ACCESS COMMAND
/dev/nvidia0:        nvidia-persistenced  F.... nvidia-persiste
                     ubuntu    F.... vllm
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     root      F.... vllm
/dev/nvidiactl:      nvidia-persistenced  F.... nvidia-persiste
                     ubuntu    F.... vllm
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     ubuntu    F...m python3
                     root      F.... vllm
/dev/nvidia-uvm:     ubuntu    F.... vllm
                     ubuntu    F.... python3
                     ubuntu    F.... python3
                     ubuntu    F.... python3
                     ubuntu    F.... python3
                     ubuntu    F.... python3
                     ubuntu    F.... python3
                     ubuntu    F.... python3
                     ubuntu    F.... python3
                     ubuntu    F.... python3
                     ubuntu    F.... python3
                     ubuntu    F.... python3
                     ubuntu    F.... python3
                     root      F.... vllm


**Listing GPU-using process details**

I'm gathering detailed info on all GPU-associated processes by running `ps -fp` on the identified PIDs to determine which can be safely cleared without killing critical tasks.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
UID          PID    PPID  C STIME TTY          TIME CMD
nvidia-+     926       1  0  2025 ?        00:00:00 /usr/bin/nvidia-persistenced --user nvidia-persistenced --no-persistence-mode --verbose
ubuntu    274345       1  0 Jan03 ?        01:23:57 /home/ubuntu/qwen_image/.venv/bin/python3 /home/ubuntu/qwen_image/.venv/bin/vllm serve Qwen/Qwen-Image-Edit-2511 --omni --port 8092 --cache-backend tea_cache --cache-config {"rel_l1_thresh": 0.2}
ubuntu    384508       1  0 Jan20 ?        00:00:23 python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 512 --gpu-batch-size 512 --decode-workers 12 --encode-workers 12
ubuntu    384509       1  0 Jan20 ?        00:00:23 python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 512 --gpu-batch-size 512 --decode-workers 12 --encode-workers 12
ubuntu    384510       1  0 Jan20 ?        00:00:23 python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 512 --gpu-batch-size 512 --decode-workers 12 --encode-workers 12
ubuntu    384511       1  0 Jan20 ?        00:00:23 python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 512 --gpu-batch-size 512 --decode-workers 12 --encode-workers 12
ubuntu    384512       1  0 Jan20 ?        00:00:23 python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 512 --gpu-batch-size 512 --decode-workers 12 --encode-workers 12
ubuntu    384513       1  0 Jan20 ?        00:00:23 python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 512 --gpu-batch-size 512 --decode-workers 12 --encode-workers 12
ubuntu    384514       1  0 Jan20 ?        00:00:24 python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 512 --gpu-batch-size 512 --decode-workers 12 --encode-workers 12
ubuntu    384515       1  0 Jan20 ?        00:00:24 python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 512 --gpu-batch-size 512 --decode-workers 12 --encode-workers 12
ubuntu    384516       1  0 Jan20 ?        00:00:23 python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 512 --gpu-batch-size 512 --decode-workers 12 --encode-workers 12
ubuntu    384517       1  0 Jan20 ?        00:00:24 python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 512 --gpu-batch-size 512 --decode-workers 12 --encode-workers 12
ubuntu    384518       1  0 Jan20 ?        00:00:24 python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 512 --gpu-batch-size 512 --decode-workers 12 --encode-workers 12
ubuntu    384519       1  0 Jan20 ?        00:00:23 python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 512 --gpu-batch-size 512 --decode-workers 12 --encode-workers 12
root      738461  738440  2 Jan06 ?        07:56:25 /usr/bin/python3.12 /usr/local/bin/vllm serve Qwen/Qwen-Image-Edit-2511 --omni --port 8000 --trust-remote-code


**Planning GPU process cleanup**

The agent identified GPU-holding processes likely linked to user workloads like vllm and benchmarks, considering terminating them to free GPU memory per the user's request while weighing risks and permission needs. Next is to proceed carefully with graceful termination attempts before force kills.


**Confirming graceful termination of GPU processes**

Deciding to send SIGTERM to GPU-using processes except nvidia-persistenced to free memory while preserving essential GPU services, then rechecking memory usage to verify effectiveness.


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


**Waiting and checking process status**

I'm adding a brief sleep before checking if the process is still alive to allow it time to exit gracefully.


Output:
Exit code: 0
Wall time: 2 seconds
Output:
UID          PID    PPID  C STIME TTY          TIME CMD
ubuntu    274345       1  0 Jan03 ?        01:23:57 /home/ubuntu/qwen_image/.venv/bin/python3 /home/ubuntu/qwen_image/.venv/bin/vllm serve Qwen/Qwen-Image-Edit-2511 --omni --port 8092 --cache-backend tea_cache --cache-config {"rel_l1_thresh": 0.2}
root      738461  738440  2 Jan06 ?        07:56:26 /usr/bin/python3.12 /usr/local/bin/vllm serve Qwen/Qwen-Image-Edit-2511 --omni --port 8000 --trust-remote-code


**Forcing termination of remaining processes**

I'm using `sudo kill -KILL` to forcibly stop the two lingering processes since they ignore TERM signals and may be running with elevated privileges, fulfilling the user's request to clear memory.


Output:
Exit code: 1
Wall time: 0 seconds
Output:
kill: (738461): No such process


**Checking for running processes**


Output:
Exit code: 1
Wall time: 0 seconds
Output:
UID          PID    PPID  C STIME TTY          TIME CMD


**Checking process utilization**


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
   926   926                     USER        PID ACCESS COMMAND
/dev/nvidia0:        nvidia-persistenced  F.... nvidia-persiste
/dev/nvidiactl:      nvidia-persistenced  F.... nvidia-persiste


**Checking GPU memory usage**

I'm verifying that only the persistence daemon is active and GPU memory is freed by checking current usage with nvidia-smi.


Output:
Exit code: 0
Wall time: 0.4 seconds
Output:
Fri Jan 23 03:26:19 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.35.05              Driver Version: 560.35.05      CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA A100-SXM4-80GB          On  |   00000000:01:00.0 Off |                    0 |
| N/A   30C    P0             51W /  400W |       1MiB /  81920MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+


**Reporting GPU memory cleanup and next steps**

The GPU was occupied by vllm and benchmark python processes not visible in `nvidia-smi`, so I terminated them, freeing GPU memory to 1 MiB. The persistence daemon remains running, which is generally safe. I recommend restarting any affected services or optionally toggling persistence if issues reoccur, and suggest checking active GPU users with `sudo fuser -v /dev/nvidia*` if memory usage returns.


**Finalizing GPU cleanup summary and suggestions**

Freed GPU by terminating vllm (ports 8092 and 8000) and multiple benchmark python processes; memory now 1 MiB per `nvidia-smi`. To restore services, you can restart the two vllm servers. For a full GPU reset or to disable persistence, you can stop `nvidia-persistenced` with `sudo systemctl stop nvidia-persistenced` or `sudo kill [PID]`, though this is usually not necessary.


Freed the GPU by terminating the hidden clients (`vllm` servers on ports 8092/8000 and the benchmark `python3` workers) that were holding `/dev/nvidia*` despite `nvidia-smi` showing none. `nvidia-smi` now reports 1 MiB used and no running processes.

If you want anything restarted or a full GPU reset/persistence tweak, say the word.


# Context from my IDE setup:

## Open tabs:
- .env: .env
- PROMPT.md: PROMPT.md
- claude.md: docs/transcribe/claude.md
- kimi.md: docs/kimi.md
- gpt.md: docs/transcribe/gpt.md

## My request for Codex:
Both models working now. The results are impressive:

  Telugu:
  - IndicASR: 6.00% WER (was 57.62% with vanilla Whisper - 10x improvement!)
  - Gemini: 14.32% WER
  - Consensus: 5.18% WER (best)

  Hindi:
  - IndicASR: 4.51% WER (was 26.63% with vanilla Whisper - 6x improvement!)
  - Gemini: 11.77% WER
  - Consensus: 8.27% WER

  Let me run a larger evaluation with 10 samples for better statistics.

● Bash(python3 -m transcribe.eval_indicwhisper --samples 10 --languages telugu hindi --output  timeout: 10m 0s
      indicwhisper_eval_10samples.json 2>&1)
  ⎿ 2026-01-23 04:02:55,007 - INFO - Loading IndicWhisper + Gemini pipeline...
    2026-01-23 04:02:55,007 - INFO - Loading IndicWhisper pipeline models...
    /home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml pa
    … +218 lines (ctrl+o to expand)

● Here are the full results with 10 samples per language:

  Final Evaluation Results
  ┌──────────┬──────────┬────────┬───────────────────────────────┐
  │ Language │  Model   │  WER   │      vs Vanilla Whisper       │
  ├──────────┼──────────┼────────┼───────────────────────────────┤
  │ Hindi    │ IndicASR │ 7.19%  │ 26.63% → 7.19% (3.7x better)  │
  ├──────────┼──────────┼────────┼───────────────────────────────┤
  │ Hindi    │ Gemini   │ 13.06% │ -                             │
  ├──────────┼──────────┼────────┼───────────────────────────────┤
  │ Telugu   │ IndicASR │ 17.34% │ 57.62% → 17.34% (3.3x better) │
  ├──────────┼──────────┼────────┼───────────────────────────────┤
  │ Telugu   │ Gemini   │ 15.97% │ -                             │
  └──────────┴──────────┴────────┴───────────────────────────────┘
  Key Findings

  1. IndicASR (ai4bharat/indic-conformer-600m-multilingual) massively outperforms vanilla Whisper:
    - Hindi: 7.19% WER vs 26.63% with Whisper
    - Telugu: 17.34% WER vs 57.62% with Whisper
  2. Gemini performs comparably to IndicASR and sometimes better on specific samples (e.g., handling numbers like
   "802.11a" that IndicASR converts to words)
  3. The consensus mechanism needs tuning - it's sometimes picking the wrong transcript when the alignment
  scoring doesn't favor the better one
  4. Edge cases identified:
    - IndicASR converts English numbers/codes to native script words
    - Reference transcripts have inconsistent formatting (sometimes English words in Latin, sometimes in native
  script)

  The pipeline is now using the much better IndicASR model instead of vanilla Whisper, achieving 3-4x improvement
   in WER for Indic languages.

✻ Worked for 7m 25s

I've actually had the other agent work on the transcription pipeline for which the plan was outlined from various agents in /home/ubuntu/maya_transcribe/docs/transcribe and especially, [claude.md](docs/transcribe/claude.md)

I now want you to debug why Gemini has such higher WER. and also debug our consensus pipeline or ROVER pipeline and see why the WER is so high and actually compare situations where we can fix the bad WER cases. and if it's genuinely an issue, we can compare actual sentences to understand the cases of such high WER.


**Planning repository inspection**

I'm starting by listing all files in the repo to identify key documentation and agent-related files for addressing the task.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
indicwhisper_eval_10samples.json
PROMPT.md
transcribe/run_evaluation.py
transcribe/eval_results_v2.json
transcribe/eval_results.json
docs/kimi.md
docs/gemini_research.md
docs/claude.md
transcribe/utils/__pycache__/audio.cpython-310.pyc
docs/transcribe/claude.md
transcribe/utils/__pycache__/text_processing.cpython-310.pyc
docs/transcribe/gpt.md
transcribe/utils/__pycache__/__init__.cpython-310.pyc
docs/transcribe/gemini.md
transcribe/utils/text_processing.py
transcribe/utils/__init__.py
transcribe/utils/audio.py
docs/claude_research.md
docs/gpt.md
docs/gpt_research.md
docs/gemini.md
indicwhisper_eval_results.json
transcribe/full_eval_results.json
transcribe/pipeline.py
transcribe/test_basic.py
transcribe/test_eval_data.py
transcribe/full_pipeline_eval.py
transcribe/download_eval.py
transcribe/tests/test_rover.py
transcribe/tests/__init__.py
transcribe/tests/test_utils.py
transcribe/models/__pycache__/gemini.cpython-310.pyc
transcribe/models/__pycache__/base.cpython-310.pyc
transcribe/models/__pycache__/indicconformer.cpython-310.pyc
transcribe/models/__pycache__/indicwhisper.cpython-310.pyc
transcribe/models/__pycache__/whisper.cpython-310.pyc
transcribe/models/__pycache__/__init__.cpython-310.pyc
transcribe/models/base.py
transcribe/models/gemini.py
transcribe/models/indicconformer.py
transcribe/models/__init__.py
transcribe/models/whisper.py
transcribe/models/indicwhisper.py
transcribe/quick_eval.py
transcribe/config.py
transcribe/quick_eval_gemini.py
transcribe/requirements.txt
transcribe/evaluation/metrics.py
transcribe/README.md
User.md
transcribe/evaluation/__init__.py
transcribe/evaluation/evaluator.py
transcribe/alignment/aligner.py
transcribe/__pycache__/eval_indicwhisper.cpython-310.pyc
transcribe/__pycache__/config.cpython-310.pyc
transcribe/__pycache__/full_pipeline_eval.cpython-310.pyc
transcribe/__pycache__/test_basic.cpython-310.pyc
transcribe/__pycache__/quick_eval.cpython-310.pyc
transcribe/__pycache__/test_eval_data.cpython-310.pyc
transcribe/__pycache__/__init__.cpython-310.pyc
transcribe/__pycache__/quick_eval_gemini.cpython-310.pyc
transcribe/__pycache__/pipeline.cpython-310.pyc
transcribe/evaluation/__pycache__/evaluator.cpython-310.pyc
transcribe/evaluation/__pycache__/__init__.cpython-310.pyc
transcribe/evaluation/__pycache__/metrics.cpython-310.pyc
transcribe/__init__.py
transcribe/__main__.py
transcribe/eval_indicwhisper.py
transcribe/alignment/__pycache__/mms_aligner.cpython-310.pyc
transcribe/alignment/mms_aligner.py
transcribe/alignment/__init__.py
transcribe/alignment/__pycache__/__init__.cpython-310.pyc
transcribe/alignment/__pycache__/aligner.cpython-310.pyc
transcribe/data/eval/index.json
transcribe/data/eval/english_eval.tar
transcribe/data/eval/evaluation_transcripts.jsonl
transcribe/data/eval/evaluation_manifest.json
transcribe/data/eval/hindi_eval.tar
transcribe/data/eval/telugu_eval.tar
transcribe/prompts/__init__.py
transcribe/prompts/gemini_system.py
transcribe/prompts/__pycache__/gemini_system.cpython-310.pyc
transcribe/prompts/__pycache__/__init__.cpython-310.pyc
transcribe/rover/__init__.py
transcribe/rover/consensus.py
transcribe/rover/__pycache__/__init__.cpython-310.pyc
transcribe/rover/__pycache__/consensus.cpython-310.pyc


Summary: auto


**Identifying relevant agent documentation**

I'm locating and opening the claude.md file in the docs/transcribe folder to review instructions and references about other agent work.


sed -n '1,200p' docs/transcribe/claude.md


Output:
Exit code: 0
Wall time: 0 seconds
Output:
You're right. Let me actually think.

---

## The Core Insight

**Forced aligners don't tell you what the transcript should be. They tell you how well a transcript fits the audio.** This is a ranking function, not a generator.

So the game becomes: generate multiple candidates → score them with alignment → pick/combine the best.

---

## The Strategy

### Step 1: Understand What Each System Is Good At

| System | Strengths | Weaknesses |
|--------|-----------|------------|
| **IndicConformer** | Native Indic phonemes, script-specific training, handles low-resource languages | Weaker on English, code-mixing |
| **Whisper large-v3** | Massive training data, good timestamps, handles noise | Indic language accuracy varies, can hallucinate repetitions |
| **LLM (Gemini/GPT-4o)** God tier-ultimate-but-prone to hallucinations| Context understanding, code-mixing, linguistic plausibility | No acoustic grounding, can "correct" what was actually said |

**The clever bit:** Don't treat them equally. Weight by language.

For Hindi/Telugu/Tamil → IndicConformer should lead
For English → Whisper should lead
For code-mixed → LLM arbitration is gold

---

### Step 2: Triage by Agreement

This is where you save compute and focus effort.

```
If all 3 transcripts say the same thing → DONE. High confidence. Move on.

If 2/3 agree → Take the majority. Medium-high confidence.

If all 3 differ → This is where you actually need to work.
```

**The clever bit:** Most of your audio will fall into the first two buckets. Maybe 70-80%. You only need heavy machinery for the disputed 20-30%.

---

### Step 3: Use Forced Alignment as a Scoring Function

Here's the key insight most people miss:

**Alignment quality correlates with transcript correctness.**

If you force-align a wrong transcript to audio:
- Words won't line up naturally
- Phoneme boundaries will be stretched/compressed unnaturally
- The aligner will "struggle" (low confidence scores, failed alignments)

If you force-align the correct transcript:
- Clean word boundaries
- Natural phoneme durations
- High confidence throughout

**Strategy:** Align all 3 transcripts. The one with the highest alignment score is probably the most correct.

---

### Step 4: Segment-Level Fusion (The Real Clever Bit)

Don't think in whole transcripts. Think in segments.

```
Transcript 1: "मैं आज office जा रहा हूं"
Transcript 2: "मैं आज ऑफिस जा रहा हूं"
Transcript 3: "में आज office जा रहा हूँ"
```

Look at this word by word:
- "मैं" vs "में" → 2/3 say "मैं", alignment will confirm
- "आज" → all agree
- "office" vs "ऑफिस" → code-mixing ambiguity, both "correct"
- "जा रहा हूं/हूँ" → minor spelling variation

**The best transcript might be a chimera** - taking the best-aligned word from each position.

This is basically ROVER, but weighted by:
1. Alignment confidence at that word
2. Model reliability for that language
3. Voting majority

---

### Step 5: LLM Arbitration for Hard Cases

When alignment scores are similar and systems disagree, bring in the LLM.

The LLM isn't listening to audio. But it knows:
- What's grammatically plausible
- What makes contextual sense
- How code-mixing actually works in spoken language

Feed it: "Here are 3 different transcriptions of the same Hindi audio. Based on linguistic plausibility, which is most likely correct or can you reconstruct the best version?"

**When to use LLM arbitration:**
- Alignment scores are all similar (can't differentiate)
- Heavy code-mixing
- Named entities (ASR often mangles names)
- Domain-specific terminology

---

### Step 6: Final Validation Loop

After you've picked/constructed your "best" transcript:

1. Force-align it one more time
2. Check the alignment quality
3. If it's still low → flag for human review

**The insight:** Your pipeline should output not just a transcript, but a confidence score. High confidence → trust it. Low confidence → human QA.

---

## The Decision Tree

```
                        ┌─────────────────┐
                        │  3 Transcripts  │
                        └────────┬────────┘
                                 │
                    ┌────────────┴────────────┐
                    │   All 3 agree?          │
                    └────────────┬────────────┘
                          │            │
                         YES          NO
                          │            │
                          ▼            ▼
                    ┌─────────┐  ┌─────────────┐
                    │  DONE   │  │ 2/3 agree?  │
                    │ (easy)  │  └──────┬──────┘
                    └─────────┘         │
                                  │           │
                                 YES         NO
                                  │           │
                                  ▼           ▼
                           ┌──────────┐ ┌────────────────┐
                           │ Take     │ │ Force-align    │
                           │ majority │ │ all 3          │
                           └──────────┘ └───────┬────────┘
                                                │
                                    ┌───────────┴───────────┐
                                    │ Clear winner          │
                                    │ (alignment score)?    │
                                    └───────────┬───────────┘
                                          │           │
                                         YES         NO
                                          │           │
                                          ▼           ▼
                                   ┌──────────┐ ┌────────────┐
                                   │ Take     │ │ LLM        │
                                   │ best     │ │ Arbitrate  │
                                   └──────────┘ └────────────┘
```

---

## The Weighting Strategy

For each language, pre-define weights based on benchmarks:

| Language | IndicConformer | Whisper | LLM |
|----------|---------------|---------|-----|
| Hindi | 0.45 | 0.35 | 0.20 |
| Telugu | 0.50 | 0.30 | 0.20 |
| Tamil | 0.50 | 0.30 | 0.20 |
| English | 0.20 | 0.50 | 0.30 |
| Code-mixed | 0.30 | 0.30 | 0.40 |

Then multiply by alignment confidence. Final weight = base_weight × alignment_score

---

## The <1% WER Reality Check

Here's the honest truth:

**Fully automated, you'll probably get to 2-5% WER** on clean audio with this pipeline.

To get below 1%, you need:
1. Human QA on the low-confidence tail (maybe 10-15% of your data)
2. Very clean audio (no background noise, clear speech)
3. Native speaker verification for edge cases

**But the pipeline's job is to:**
- Get 85-90% of your data to high confidence automatically
- Identify the 10-15% that needs human attention
- Make human QA efficient by providing ranked candidates

---

## The Smart Additions


**Planning evaluation inspection**

I'm focusing on analyzing Gemini model evaluation, consensus pipeline, and related output files like indicwhisper_eval_10samples.json to debug high WER, prioritizing code inspection and results data examination.


sed -n '1,200p' indicwhisper_eval_10samples.json


Output:
Exit code: 0
Wall time: 0 seconds
Output:
{
  "telugu": [
    {
      "sample_id": "telugu_000000",
      "language": "telugu",
      "reference": "చిన్న ద్వీపాలలో చాలా వరకు స్వతంత్ర దేశాలు లేదా ఫ్రాన్స్ తో సంబంధం కలిగి ఉన్నాయి ఇంకా వీటిని లగ్జరీ బీచ్ రిసార్ట్ స్ అని పిలుస్తారు",
      "indicwhisper_hyp": "చిన్న ద్వీపాలలో చాలా వరకు స్వతంత్ర దేశాలు లేదా ఫ్రాన్స్తో సంబంధం కలిగి ఉన్నాయి ఇంకా వీటిని లగ్జరీ బీచ్ రిజార్ట్స్ను అని పిలుస్తారు",
      "gemini_hyp": "చిన్న ద్వీపాలలో చాలా వరకు స్వతంత్ర దేశాలు లేదా ఫ్రాన్స్ తో సంబంధం కలిగి ఉన్నాయి. ఇంకా వీటిని లగ్జరీ బీచ్ రిసార్ట్స్ ను అని పిలుస్తారు.",
      "consensus_hyp": "చిన్న ద్వీపాలలో చాలా వరకు స్వతంత్ర దేశాలు లేదా ఫ్రాన్స్ తో సంబంధం కలిగి ఉన్నాయి. ఇంకా వీటిని లగ్జరీ బీచ్ రిసార్ట్స్ ను అని పిలుస్తారు.",
      "indicwhisper_wer": 0.2,
      "gemini_wer": 0.1,
      "consensus_wer": 0.1,
      "indicwhisper_cer": 0.02702702702702703,
      "gemini_cer": 0.018018018018018018,
      "consensus_cer": 0.018018018018018018,
      "agreement_category": "disputed",
      "processing_time": 5.630279779434204
    },
    {
      "sample_id": "telugu_000001",
      "language": "telugu",
      "reference": "కొన్ని క్రియలు ఆబ్జెక్టుల మధ్య తేడాను గుర్తించడానికి ఇది ఒక ముఖ్యమైన మార్గం",
      "indicwhisper_hyp": "కొన్ని క్రియలు అబ్జెక్టుల మధ్య తేడాను గుర్తించడానికి ఇది ఒక ముఖ్యమైన మార్గం",
      "gemini_hyp": "కొన్ని క్రియలు ఆబ్జెక్టులు మధ్య తేడాలను గుర్తించడానికి ఇది ఒక ముఖ్యమైన మార్గం.",
      "consensus_hyp": "కొన్ని క్రియలు అబ్జెక్టుల మధ్య తేడాను గుర్తించడానికి ఇది ఒక ముఖ్యమైన మార్గం",
      "indicwhisper_wer": 0.1,
      "gemini_wer": 0.2,
      "consensus_wer": 0.1,
      "indicwhisper_cer": 0.015151515151515152,
      "gemini_cer": 0.030303030303030304,
      "consensus_cer": 0.015151515151515152,
      "agreement_category": "disputed",
      "processing_time": 8.648107051849365
    },
    {
      "sample_id": "telugu_000002",
      "language": "telugu",
      "reference": "మీరు మీ స్వంతం ఆలోచనలతో కాకుండా ప్రభుత్వాల సలహా కూడా తీసుకోవాలని అనుకోవచ్చు అయితే వారి సలహా వారి పౌరుల కోసం రూపొందించబడింది",
      "indicwhisper_hyp": "మీరు మీ స్వంతం ఆలోచనలతో కాకుండా ప్రభుత్వాల సలహా కూడా తీసుకోవాలని అనుకోవచ్చు అయితే వారి సలహా వారి పౌరుల కోసం రూపొందించబడింది",
      "gemini_hyp": "మీరు మీ సొంతం ఆలోచనలతో కాకుండా ప్రభుత్వాల సలహా కూడా తీసుకోవాలని అనుకోవచ్చు. అయితే వారి సలహా వారి పౌరుల కోసం రూపొందించబడింది.",
      "consensus_hyp": "మీరు మీ సొంతం ఆలోచనలతో కాకుండా ప్రభుత్వాల సలహా కూడా తీసుకోవాలని అనుకోవచ్చు. అయితే వారి సలహా వారి పౌరుల కోసం రూపొందించబడింది.",
      "indicwhisper_wer": 0.0,
      "gemini_wer": 0.058823529411764705,
      "consensus_wer": 0.058823529411764705,
      "indicwhisper_cer": 0.0,
      "gemini_cer": 0.018691588785046728,
      "consensus_cer": 0.018691588785046728,
      "agreement_category": "disputed",
      "processing_time": 7.2025134563446045
    },
    {
      "sample_id": "telugu_000003",
      "language": "telugu",
      "reference": "ఈ దృశ్యాలు పిరమిడ్లపై ప్రదర్శించబడతాయి మరియు వేరే పిరమిడ్లను వెలిగించబడ్డాయి",
      "indicwhisper_hyp": "ఈ దృశ్యాలు పిరమిడ్లపై ప్రదర్శించబడతాయి మరియు వేరే పిరమిడ్లను వెలిగించబడ్డాయి",
      "gemini_hyp": "ఈ దృశ్యాలు పిరమిడ్లపై ప్రదర్శించబడతాయి మరియు వేరే పిరమిడ్లను వెలిగించబడ్డాయి.",
      "consensus_hyp": "ఈ దృశ్యాలు పిరమిడ్లపై ప్రదర్శించబడతాయి మరియు వేరే పిరమిడ్లను వెలిగించబడ్డాయి.",
      "indicwhisper_wer": 0.0,
      "gemini_wer": 0.0,
      "consensus_wer": 0.0,
      "indicwhisper_cer": 0.0,
      "gemini_cer": 0.0,
      "consensus_cer": 0.0,
      "agreement_category": "all_agree",
      "processing_time": 3.9194045066833496
    },
    {
      "sample_id": "telugu_000004",
      "language": "telugu",
      "reference": "పరికరాన్ని గాల్లో కదిలించడం ద్వారా వీడియో గేమ్స్లో యాక్షన్లు మరియు కదలికలను నియంత్రించడానికి ఇది ఆటగాళ్లకు వీలు కల్పిస్తుంది",
      "indicwhisper_hyp": "పరికరాన్ని గాల్లో కదిలించడం ద్వారా వీడియో గేమ్స్లో యాక్షన్లు మరియు కదలికలను నియంత్రించడానికి ఇది ఆటగాళ్లకు వీలు కల్పిస్తుంది",
      "gemini_hyp": "పరికరాని గాలిలో కదిలించడం ద్వారా వీడియో గేమ్స్ లో యాక్షన్లు మరియు కదలికలను నియంత్రించడానికి ఇది ఆటగాళ్ళకు వీలు కల్పిస్తుంది.",
      "consensus_hyp": "పరికరాన్ని గాల్లో కదిలించడం ద్వారా వీడియో గేమ్స్లో యాక్షన్లు మరియు కదలికలను నియంత్రించడానికి ఇది ఆటగాళ్లకు వీలు కల్పిస్తుంది",
      "indicwhisper_wer": 0.0,
      "gemini_wer": 0.35714285714285715,
      "consensus_wer": 0.0,
      "indicwhisper_cer": 0.0,
      "gemini_cer": 0.036036036036036036,
      "consensus_cer": 0.0,
      "agreement_category": "disputed",
      "processing_time": 3.6731226444244385
    },
    {
      "sample_id": "telugu_000005",
      "language": "telugu",
      "reference": "కాల్షియం పొటాషియం వంటి మూలకాలను లోహాలుగా పరిగణిస్తారు వెండి బంగారం వంటి లోహాలు కూడా ఉన్నాయి",
      "indicwhisper_hyp": "కాల్షియం పొటాషియం వంటి మూలకాలను లోహాలుగా పరిగణిస్తారు వెండి బంగారం వంటి లోహాలు కూడా ఉన్నాయి",
      "gemini_hyp": "కాల్షియం, పొటాషియం వంటి మూలకాలను లోహాలుగా పరిగణిస్తారు. వెండి, బంగారం వంటి లోహాలు కూడా ఉన్నాయి.",
      "consensus_hyp": "కాల్షియం, పొటాషియం వంటి మూలకాలను లోహాలుగా పరిగణిస్తారు. వెండి, బంగారం వంటి లోహాలు కూడా ఉన్నాయి.",
      "indicwhisper_wer": 0.0,
      "gemini_wer": 0.0,
      "consensus_wer": 0.0,
      "indicwhisper_cer": 0.0,
      "gemini_cer": 0.0,
      "consensus_cer": 0.0,
      "agreement_category": "all_agree",
      "processing_time": 3.4844610691070557
    },
    {
      "sample_id": "telugu_000006",
      "language": "telugu",
      "reference": "ఆ విధంగా పెన్సిల్ బయటకు వచ్చినప్పుడు చాలా మందికి మంచి స్నేహితుడు",
      "indicwhisper_hyp": "ఆ విధంగా పెన్సిల్ బయటకు వచ్చినప్పుడు చాలా మందికి మంచి స్నేహితుడు",
      "gemini_hyp": "ఆ విధంగా పెన్సిల్ బయటకు వచ్చినప్పుడు చాలా మందికి మంచి స్నేహితుడు.",
      "consensus_hyp": "ఆ విధంగా పెన్సిల్ బయటకు వచ్చినప్పుడు చాలా మందికి మంచి స్నేహితుడు.",
      "indicwhisper_wer": 0.0,
      "gemini_wer": 0.0,
      "consensus_wer": 0.0,
      "indicwhisper_cer": 0.0,
      "gemini_cer": 0.0,
      "consensus_cer": 0.0,
      "agreement_category": "all_agree",
      "processing_time": 3.418607473373413
    },
    {
      "sample_id": "telugu_000007",
      "language": "telugu",
      "reference": "బుధవారం జరిగిన ఈవెంట్కు ముందు carpanedo ఛాంపియన్షిప్లో రెండు వ్యక్తిగత రేసులలో పాల్గొన్నారు",
      "indicwhisper_hyp": "బుధవారం జరిగిన ఈవెంట్కు ముందు కార్పనేడో ఛాంపియన్షిప్లో రెండు వ్యక్తిగత రేసులలో పాల్గొన్నారు",
      "gemini_hyp": "బుధవారం జరిగిన ఈవెంట్ కు ముందు కార్పనేడో ఛాంపియన్షిప్ లో రెండు వ్యక్తిగత రేసులలో పాల్గొన్నారు.",
      "consensus_hyp": "బుధవారం జరిగిన ఈవెంట్ కు ముందు కార్పనేడో ఛాంపియన్షిప్ లో రెండు వ్యక్తిగత రేసులలో పాల్గొన్నారు.",
      "indicwhisper_wer": 0.1,
      "gemini_wer": 0.5,
      "consensus_wer": 0.5,
      "indicwhisper_cer": 0.10975609756097561,
      "gemini_cer": 0.10975609756097561,
      "consensus_cer": 0.10975609756097561,
      "agreement_category": "all_agree",
      "processing_time": 3.777423143386841
    },
    {
      "sample_id": "telugu_000008",
      "language": "telugu",
      "reference": "పట్టాలను నిలబెట్టడానికి క్రాస్ టీలు చాలా ముందుగానే ప్రవేశపెట్టబడ్డాయి అయితే పైభాగంలో ఇనుప ఖనిజం ఉంటే ట్రాక్ లు మరింత సమర్థవంతంగా ఉంటాయని క్రమంగా గ్రహించారు",
      "indicwhisper_hyp": "పట్టాలను నిలబెట్టడానికి క్రాస్ టీలు చాలా ముందుగానే ప్రవేశపెట్టబడాయి అయితే పైభాగంలో ఇనుప ఖనిజం ఉంటే ట్రాక్లు మరింత సమర్థవంతంగా ఉంటాయని క్రమంగా గ్రహించారు",
      "gemini_hyp": "పట్టాలను నిలబెట్టడానికి క్రాస్ టీలు చాలా ముందుగానే ప్రవేశపెట్టబడాయి. అయితే పై భాగంలో ఇనుప ఖనిజం ఉంటే ట్రాకులు మరింత సమర్థవంతంగా ఉంటాయని క్రమంగా గ్రహించారు.",
      "consensus_hyp": "పట్టాలను నిలబెట్టడానికి క్రాస్ టీలు చాలా ముందుగానే ప్రవేశపెట్టబడాయి. అయితే పై భాగంలో ఇనుప ఖనిజం ఉంటే ట్రాకులు మరింత సమర్థవంతంగా ఉంటాయని క్రమంగా గ్రహించారు.",
      "indicwhisper_wer": 0.15789473684210525,
      "gemini_wer": 0.2631578947368421,
      "consensus_wer": 0.2631578947368421,
      "indicwhisper_cer": 0.014598540145985401,
      "gemini_cer": 0.021897810218978103,
      "consensus_cer": 0.021897810218978103,
      "agreement_category": "all_agree",
      "processing_time": 4.066370010375977
    },
    {
      "sample_id": "telugu_000009",
      "language": "telugu",
      "reference": "ఇది బేస్ స్టేషన్ లో డ్యూయల్ రేడియోలు ఉంటే 802.11a 802.11b మరియు 802.11gతో అనుకూలంగా వెనుకకు అనుమతిస్తుంది",
      "indicwhisper_hyp": "ఇది బేస్ స్టేషన్లో డ్యూయల్ రేడియోలు ఉంటే ఎనిమిది వందల రెండు పాయింట్ పదకొండు ఏ ఎనిమిది వందల రెండు పాయింట్ పదకొండు బి మరియు ఎనిమిది వందల రెండు పాయింట్ పదకొండు జితో అనుకూలంగా వెనుకకు అనుమతిస్తుంది",
      "gemini_hyp": "ఇది బేస్ స్టేషన్ లో డ్యూయల్ రేడియోలు ఉంటే 802.11a, 802.11b మరియు 802.11g తో అనుకూలంగా వెనుకకు అనుమతిస్తుంది.",
      "consensus_hyp": "ఇది బేస్ స్టేషన్లో డ్యూయల్ రేడియోలు ఉంటే ఎనిమిది వందల రెండు పాయింట్ పదకొండు ఏ ఎనిమిది వందల రెండు పాయింట్ పదకొండు బి మరియు ఎనిమిది వందల రెండు పాయింట్ పదకొండు జితో అనుకూలంగా వెనుకకు అనుమతిస్తుంది",
      "indicwhisper_wer": 1.1764705882352942,
      "gemini_wer": 0.11764705882352941,
      "consensus_wer": 1.1764705882352942,
      "indicwhisper_cer": 1.0674157303370786,
      "gemini_cer": 0.0,
      "consensus_cer": 1.0674157303370786,
      "agreement_category": "disputed",
      "processing_time": 3.835078001022339
    }
  ],
  "hindi": [
    {
      "sample_id": "hindi_000000",
      "language": "hindi",
      "reference": "कुछ अणुओं में अस्थिर केंद्रक होता है जिसका मतलब यह है कि उनमें थोड़े या बिना किसी झटके से टूटने की प्रवृत्ति होती है",
      "indicwhisper_hyp": "कुछ अणुओं में अस्थिर केंद्रक होता है जिसका मतलब यह कि उनमें थोड़े या बिना किसी झटके से टूटने की प्रवृत्ति होती है",
      "gemini_hyp": "कुछ अणुओं में अस्थिर केंद्रक होता है जिसका मतलब यह कि उनमें थोड़े या बिना किसी झटके से टूटने की प्रवृत्ति होती है।",
      "consensus_hyp": "कुछ अणुओं में अस्थिर केंद्रक होता है जिसका मतलब यह कि उनमें थोड़े या बिना किसी झटके से टूटने की प्रवृत्ति होती है",
      "indicwhisper_wer": 0.041666666666666664,
      "gemini_wer": 0.08333333333333333,
      "consensus_wer": 0.041666666666666664,
      "indicwhisper_cer": 0.021505376344086023,
      "gemini_cer": 0.03225806451612903,
      "consensus_cer": 0.021505376344086023,
      "agreement_category": "disputed",
      "processing_time": 3.8704051971435547
    },
    {
      "sample_id": "hindi_000001",
      "language": "hindi",
      "reference": "ग्रीनलैंड को बहुत कम जगह बसाया गया था नॉर्स सगास में वे कहते हैं कि एरिक रेड हत्या के लिए आइसलैंड से निर्वासित किया गया था और आगे पश्चिम की यात्रा करते समय ग्रीनलैंड मिला जिसे ग्रीनलैंड नाम दिया गया",
      "indicwhisper_hyp": "ग्रीनलैंड को बहुत कम जगह बसाया गया था नॉर्थ सगास में वे कहते हैं कि एरिक रेड हत्या के लिए आइसलैंड से निर्वासित किया गया था और आगे पश्चिम की यात्रा करते समय ग्रीनलैंड मिला जिसे ग्रीनलैंड नाम दिया गया",
      "gemini_hyp": "ग्रीनलैंड को बहुत कम जगह बसाया गया था। नोर्स सगास में वे कहते हैं कि एरिक रेड हत्या के लिए आइसलैंड से निर्वासित किया गया था और आगे पश्चिम की यात्रा करते समय ग्रीनलैंड मिला जिसे ग्रीनलैंड नाम दिया गया।",
      "consensus_hyp": "ग्रीनलैंड को बहुत कम जगह बसाया गया था नॉर्थ सगास में वे कहते हैं कि एरिक रेड हत्या के लिए आइसलैंड से निर्वासित किया गया था और आगे पश्चिम की यात्रा करते समय ग्रीनलैंड मिला जिसे ग्रीनलैंड नाम दिया गया",
      "indicwhisper_wer": 0.025,
      "gemini_wer": 0.075,
      "consensus_wer": 0.025,
      "indicwhisper_cer": 0.006289308176100629,
      "gemini_cer": 0.018867924528301886,
      "consensus_cer": 0.006289308176100629,
      "agreement_category": "disputed",
      "processing_time": 4.054541826248169
    },
    {
      "sample_id": "hindi_000002",
      "language": "hindi",
      "reference": "ऐसी कोई वैश्विक परिभाषा नहीं है जिसके लिए निर्मित सामान एंटीक होते हैं कुछ कर एजेंसियां 100 साल से पुराने सामान को एंटीक के तौर पर परिभाषित करती हैं",


**Identifying potential WER inflation causes**

Noticing that Gemini’s added punctuation and spacing might inflate WER due to tokenization differences, especially for Indic scripts like Telugu where punctuation treatment affects word-level WER scoring; next is to examine evaluation code, consensus scoring, and text normalization steps.


sed -n '1,200p' transcribe/utils/text_processing.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""Text processing utilities for transcription pipeline."""

import re
import unicodedata
from typing import List, Tuple, Optional
from difflib import SequenceMatcher

def normalize_text(
    text: str,
    lowercase: bool = False,
    remove_punctuation: bool = False,
    normalize_unicode: bool = True,
    remove_extra_whitespace: bool = True,
) -> str:
    """
    Normalize text for comparison.

    Args:
        text: Input text
        lowercase: Convert to lowercase
        remove_punctuation: Remove punctuation marks
        normalize_unicode: Apply Unicode NFC normalization
        remove_extra_whitespace: Collapse multiple spaces

    Returns:
        Normalized text
    """
    if not text:
        return ""

    # Unicode normalization (important for Indic scripts)
    if normalize_unicode:
        text = unicodedata.normalize("NFC", text)

    # Lowercase (be careful with Indic scripts - they don't have case)
    if lowercase:
        text = text.lower()

    # Remove punctuation (preserve Indic punctuation markers like danda)
    if remove_punctuation:
        # Keep Indic punctuation: । ॥ etc.
        text = re.sub(r'[^\w\s।॥\u0964\u0965]', ' ', text)

    # Normalize whitespace
    if remove_extra_whitespace:
        text = re.sub(r'\s+', ' ', text).strip()

    return text

def normalize_for_wer(text: str, language: str = "unknown") -> str:
    """
    Normalize text specifically for WER computation.

    Applies aggressive normalization to ensure fair comparison:
    - Lowercase
    - Remove punctuation
    - Normalize numbers
    - Collapse whitespace
    """
    if not text:
        return ""

    # Unicode normalization
    text = unicodedata.normalize("NFC", text)

    # Lowercase
    text = text.lower()

    # Remove common punctuation but keep Indic punctuation
    text = re.sub(r'[.,!?;:\'"()\[\]{}<>«»""''—–-]', ' ', text)

    # Normalize whitespace
    text = re.sub(r'\s+', ' ', text).strip()

    return text

def tokenize_for_wer(text: str) -> List[str]:
    """
    Tokenize text for WER calculation.
    Handles Indic scripts properly by not splitting on script boundaries.
    """
    # Normalize first
    text = normalize_text(text)

    # Split on whitespace - this works for most languages
    # For languages without spaces (like Thai), additional handling needed
    tokens = text.split()

    return tokens

def levenshtein_distance(s1: List[str], s2: List[str]) -> Tuple[int, int, int, int]:
    """
    Compute Levenshtein distance with operation counts.

    Returns:
        Tuple of (distance, substitutions, insertions, deletions)
    """
    m, n = len(s1), len(s2)

    # dp[i][j] = (distance, subs, ins, dels)
    dp = [[(0, 0, 0, 0) for _ in range(n + 1)] for _ in range(m + 1)]

    # Base cases
    for i in range(m + 1):
        dp[i][0] = (i, 0, 0, i)  # deletions
    for j in range(n + 1):
        dp[0][j] = (j, 0, j, 0)  # insertions

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if s1[i-1] == s2[j-1]:
                dp[i][j] = dp[i-1][j-1]
            else:
                # Substitution
                sub = dp[i-1][j-1]
                sub_cost = (sub[0] + 1, sub[1] + 1, sub[2], sub[3])

                # Insertion
                ins = dp[i][j-1]
                ins_cost = (ins[0] + 1, ins[1], ins[2] + 1, ins[3])

                # Deletion
                del_ = dp[i-1][j]
                del_cost = (del_[0] + 1, del_[1], del_[2], del_[3] + 1)

                # Take minimum
                dp[i][j] = min(sub_cost, ins_cost, del_cost, key=lambda x: x[0])

    return dp[m][n]

def compute_wer(reference: str, hypothesis: str) -> dict:
    """
    Compute Word Error Rate.

    Args:
        reference: Ground truth text
        hypothesis: Predicted text

    Returns:
        Dictionary with WER, substitutions, insertions, deletions, and counts
    """
    ref_tokens = tokenize_for_wer(reference)
    hyp_tokens = tokenize_for_wer(hypothesis)

    if len(ref_tokens) == 0:
        if len(hyp_tokens) == 0:
            return {"wer": 0.0, "substitutions": 0, "insertions": 0, "deletions": 0,
                    "ref_words": 0, "hyp_words": 0}
        else:
            return {"wer": 1.0, "substitutions": 0, "insertions": len(hyp_tokens),
                    "deletions": 0, "ref_words": 0, "hyp_words": len(hyp_tokens)}

    distance, subs, ins, dels = levenshtein_distance(ref_tokens, hyp_tokens)

    wer = distance / len(ref_tokens)

    return {
        "wer": wer,
        "substitutions": subs,
        "insertions": ins,
        "deletions": dels,
        "ref_words": len(ref_tokens),
        "hyp_words": len(hyp_tokens),
        "edit_distance": distance,
    }

def compute_cer(reference: str, hypothesis: str) -> dict:
    """
    Compute Character Error Rate.
    """
    ref_chars = list(normalize_text(reference).replace(" ", ""))
    hyp_chars = list(normalize_text(hypothesis).replace(" ", ""))

    if len(ref_chars) == 0:
        if len(hyp_chars) == 0:
            return {"cer": 0.0, "ref_chars": 0, "hyp_chars": 0}
        else:
            return {"cer": 1.0, "ref_chars": 0, "hyp_chars": len(hyp_chars)}

    distance, subs, ins, dels = levenshtein_distance(ref_chars, hyp_chars)

    cer = distance / len(ref_chars)

    return {
        "cer": cer,
        "substitutions": subs,
        "insertions": ins,
        "deletions": dels,
        "ref_chars": len(ref_chars),
        "hyp_chars": len(hyp_chars),
        "edit_distance": distance,
    }


**Identifying potential WER normalization issue**

The WER tokenization uses a normalization that preserves punctuation and letter casing, which likely inflates error rates if Gemini adds punctuation or spacing differences. The existing normalize_for_wer function is defined but unused, suggesting a probable bug contributing to inflated WER due to punctuation mismatches.


**Investigating digit handling impact on WER**

Gemini outputs digits matching references, while IndicASR spells out numbers causing high WER; this mismatch may skew consensus or alignment scoring, so checking rover and consensus code next to see how they handle digit normalization or alignment.


sed -n '1,220p' transcribe/rover/consensus.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""ROVER (Recognizer Output Voting Error Reduction) implementation."""

from dataclasses import dataclass, field
from typing import List, Dict, Optional, Tuple, Any
import numpy as np
from difflib import SequenceMatcher

@dataclass
class AlignedSlot:
    """A slot in the confusion network."""
    candidates: Dict[str, float]  # word -> weighted vote
    best_word: Optional[str] = None
    confidence: float = 0.0
    agreement: float = 0.0  # how much systems agreed

@dataclass
class ConsensusResult:
    """Result from ROVER consensus."""
    text: str
    words: List[str]
    confidence: float
    word_confidences: List[float]
    slots: List[AlignedSlot]
    agreement_score: float  # overall system agreement
    source_weights: Dict[str, float] = field(default_factory=dict)

    # Detailed metrics
    high_agreement_words: int = 0
    low_agreement_words: int = 0
    disputed_positions: List[int] = field(default_factory=list)

    def to_dict(self) -> dict:
        return {
            "text": self.text,
            "words": self.words,
            "confidence": self.confidence,
            "word_confidences": self.word_confidences,
            "agreement_score": self.agreement_score,
            "high_agreement_words": self.high_agreement_words,
            "low_agreement_words": self.low_agreement_words,
            "disputed_positions": self.disputed_positions,
        }

class ConfusionNetwork:
    """
    Confusion network for combining multiple transcripts.

    Aligns multiple transcripts and creates voting slots at each position.
    """

    def __init__(self):
        self.slots: List[AlignedSlot] = []
        self.transcripts: List[List[str]] = []
        self.weights: List[float] = []

    def add_transcript(
        self,
        words: List[str],
        weight: float = 1.0,
        word_scores: Optional[List[float]] = None,
    ) -> None:
        """Add a transcript to the network."""
        self.transcripts.append(words)
        self.weights.append(weight)

    def build(self) -> None:
        """Build the confusion network by aligning all transcripts."""
        if len(self.transcripts) < 2:
            # Single transcript, just create slots
            if self.transcripts:
                self.slots = [
                    AlignedSlot(
                        candidates={w: self.weights[0]},
                        best_word=w,
                        confidence=1.0,
                        agreement=1.0,
                    )
                    for w in self.transcripts[0]
                ]
            return

        # Use first transcript as reference
        reference = self.transcripts[0]
        ref_weight = self.weights[0]

        # Initialize slots from reference
        self.slots = [
            AlignedSlot(candidates={w: ref_weight}, agreement=0.0)
            for w in reference
        ]

        # Align other transcripts to reference
        for i, (words, weight) in enumerate(zip(self.transcripts[1:], self.weights[1:])):
            self._align_to_network(words, weight)

        # Compute best words and confidences
        self._finalize()

    def _align_to_network(self, words: List[str], weight: float) -> None:
        """Align a transcript to the existing network."""
        if not self.slots:
            return

        # Get reference words
        reference = [s.best_word or list(s.candidates.keys())[0] for s in self.slots]

        # Use SequenceMatcher for alignment
        matcher = SequenceMatcher(None, reference, words)
        opcodes = matcher.get_opcodes()

        for tag, i1, i2, j1, j2 in opcodes:
            if tag == 'equal':
                # Same words - add votes
                for k, idx in enumerate(range(i1, i2)):
                    word = words[j1 + k]
                    if word in self.slots[idx].candidates:
                        self.slots[idx].candidates[word] += weight
                    else:
                        self.slots[idx].candidates[word] = weight

            elif tag == 'replace':
                # Different words - add as alternatives
                ref_span = i2 - i1
                hyp_span = j2 - j1

                for k in range(min(ref_span, hyp_span)):
                    word = words[j1 + k]
                    slot_idx = i1 + k
                    if word in self.slots[slot_idx].candidates:
                        self.slots[slot_idx].candidates[word] += weight
                    else:
                        self.slots[slot_idx].candidates[word] = weight

                # Handle length differences
                if hyp_span > ref_span:
                    # Insert extra words as new slots
                    for k in range(ref_span, hyp_span):
                        word = words[j1 + k]
                        insert_pos = i2 + k - ref_span
                        new_slot = AlignedSlot(candidates={word: weight})
                        self.slots.insert(insert_pos, new_slot)

            elif tag == 'insert':
                # Words in hypothesis not in reference - add epsilon or insert
                for k, word in enumerate(words[j1:j2]):
                    # Add as new slot after i1
                    insert_pos = i1 + k
                    if insert_pos < len(self.slots):
                        # Add to nearest slot as alternative
                        if word in self.slots[insert_pos].candidates:
                            self.slots[insert_pos].candidates[word] += weight * 0.5
                        else:
                            self.slots[insert_pos].candidates[word] = weight * 0.5
                    else:
                        # Append new slot
                        new_slot = AlignedSlot(candidates={word: weight})
                        self.slots.append(new_slot)

            elif tag == 'delete':
                # Words in reference not in hypothesis - add epsilon vote
                for idx in range(i1, i2):
                    if "" in self.slots[idx].candidates:
                        self.slots[idx].candidates[""] += weight * 0.3
                    else:
                        self.slots[idx].candidates[""] = weight * 0.3

    def _finalize(self) -> None:
        """Finalize slots by computing best words and confidences."""
        total_weight = sum(self.weights)

        for slot in self.slots:
            if not slot.candidates:
                slot.best_word = ""
                slot.confidence = 0.0
                slot.agreement = 0.0
                continue

            # Find best word (highest weighted vote)
            best_word = max(slot.candidates, key=slot.candidates.get)
            best_score = slot.candidates[best_word]

            # Compute confidence (best vote / total possible votes)
            slot.best_word = best_word
            slot.confidence = best_score / total_weight

            # Compute agreement (how concentrated are the votes)
            total_votes = sum(slot.candidates.values())
            if total_votes > 0:
                slot.agreement = best_score / total_votes
            else:
                slot.agreement = 0.0

    def get_consensus(self) -> ConsensusResult:
        """Get the consensus transcript from the network."""
        if not self.slots:
            return ConsensusResult(
                text="",
                words=[],
                confidence=0.0,
                word_confidences=[],
                slots=[],
                agreement_score=0.0,
            )

        # Build consensus
        words = []
        word_confidences = []
        disputed_positions = []
        high_agreement = 0
        low_agreement = 0

        for i, slot in enumerate(self.slots):
            if slot.best_word and slot.best_word != "":
                words.append(slot.best_word)
                word_confidences.append(slot.confidence)

                if slot.agreement > 0.8:


sed -n '220,440p' transcribe/rover/consensus.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                if slot.agreement > 0.8:
                    high_agreement += 1
                elif slot.agreement < 0.5:
                    low_agreement += 1
                    disputed_positions.append(i)

        # Overall metrics
        if word_confidences:
            overall_confidence = np.mean(word_confidences)
            agreement_score = np.mean([s.agreement for s in self.slots if s.best_word])
        else:
            overall_confidence = 0.0
            agreement_score = 0.0

        return ConsensusResult(
            text=" ".join(words),
            words=words,
            confidence=overall_confidence,
            word_confidences=word_confidences,
            slots=self.slots,
            agreement_score=agreement_score,
            high_agreement_words=high_agreement,
            low_agreement_words=low_agreement,
            disputed_positions=disputed_positions,
        )

class ROVERConsensus:
    """
    Standard ROVER consensus for combining ASR outputs.
    """

    def combine(
        self,
        transcripts: List[str],
        weights: Optional[List[float]] = None,
    ) -> ConsensusResult:
        """
        Combine multiple transcripts using ROVER voting.

        Args:
            transcripts: List of transcript strings
            weights: Optional weights for each transcript

        Returns:
            ConsensusResult with consensus transcript
        """
        if not transcripts:
            return ConsensusResult(
                text="", words=[], confidence=0.0,
                word_confidences=[], slots=[], agreement_score=0.0,
            )

        if len(transcripts) == 1:
            words = transcripts[0].split()
            return ConsensusResult(
                text=transcripts[0],
                words=words,
                confidence=1.0,
                word_confidences=[1.0] * len(words),
                slots=[AlignedSlot(candidates={w: 1.0}, best_word=w, confidence=1.0, agreement=1.0) for w in words],
                agreement_score=1.0,
            )

        if weights is None:
            weights = [1.0] * len(transcripts)

        # Build confusion network
        network = ConfusionNetwork()
        for transcript, weight in zip(transcripts, weights):
            words = transcript.split()
            network.add_transcript(words, weight)

        network.build()
        return network.get_consensus()

class WeightedROVER(ROVERConsensus):
    """
    ROVER with alignment-based weighting.

    Weights each transcript by its forced alignment score.
    """

    def __init__(
        self,
        base_weights: Optional[Dict[str, Tuple[float, float, float]]] = None,
    ):
        """
        Args:
            base_weights: Language -> (indicconformer, whisper, gemini) base weights
        """
        self.base_weights = base_weights or {}

    def combine_with_alignment(
        self,
        transcripts: List[str],
        model_names: List[str],
        alignment_scores: List[float],
        language: str = "hindi",
    ) -> ConsensusResult:
        """
        Combine transcripts with alignment-weighted voting.

        Args:
            transcripts: List of transcript strings
            model_names: List of model names (indicconformer, whisper, gemini)
            alignment_scores: Alignment scores for each transcript
            language: Language for base weight lookup

        Returns:
            ConsensusResult
        """
        if not transcripts:
            return ConsensusResult(
                text="", words=[], confidence=0.0,
                word_confidences=[], slots=[], agreement_score=0.0,
            )

        # Get base weights for language
        base = self.base_weights.get(
            language,
            (0.40, 0.35, 0.25)  # default: indicconformer, whisper, gemini
        )

        # Map model names to base weights
        model_base = {
            "indicconformer": base[0],
            "whisper": base[1],
            "gemini": base[2],
        }

        # Compute final weights: base_weight * alignment_score
        weights = []
        for model_name, align_score in zip(model_names, alignment_scores):
            base_weight = model_base.get(model_name.lower(), 0.33)
            # Softmax-style weighting
            final_weight = base_weight * (align_score + 0.1)  # avoid zero
            weights.append(final_weight)

        # Normalize weights
        total = sum(weights)
        if total > 0:
            weights = [w / total for w in weights]

        return self.combine(transcripts, weights)

    def combine_word_level(
        self,
        transcripts: List[str],
        word_scores: List[List[float]],
        model_names: List[str],
        language: str = "hindi",
    ) -> ConsensusResult:
        """
        Combine at word level with per-word alignment scores.

        This is the "Frankenstein stitching" approach - picks best word
        at each position based on alignment score.
        """
        if not transcripts:
            return ConsensusResult(
                text="", words=[], confidence=0.0,
                word_confidences=[], slots=[], agreement_score=0.0,
            )

        # Tokenize all transcripts
        tokenized = [t.split() for t in transcripts]

        # Get base weights
        base = self.base_weights.get(language, (0.40, 0.35, 0.25))
        model_base = {"indicconformer": base[0], "whisper": base[1], "gemini": base[2]}

        # Build confusion network with word-level weights
        network = ConfusionNetwork()

        for i, (words, scores) in enumerate(zip(tokenized, word_scores)):
            model_name = model_names[i] if i < len(model_names) else "unknown"
            base_weight = model_base.get(model_name.lower(), 0.33)

            # Add with per-word weighting
            # Normalize scores to [0, 1] range
            if scores:
                min_s, max_s = min(scores), max(scores)
                if max_s > min_s:
                    normalized = [(s - min_s) / (max_s - min_s) for s in scores]
                else:
                    normalized = [1.0] * len(scores)
            else:
                normalized = [1.0] * len(words)

            # Compute per-word weights
            word_weights = [base_weight * (0.5 + 0.5 * n) for n in normalized]

            network.add_transcript(words, weight=base_weight)

            # Update slot candidates with word-level weights
            # (This is a simplified version; proper implementation would
            # integrate word weights during alignment)

        network.build()
        return network.get_consensus()


**Analyzing tokenization and model naming inconsistencies**

I found Consensus tokenizes by spaces without normalization, risking issues with punctuation, while Gemini uses punctuation-based tokens that might affect alignment. The pipeline references IndicWhisper, though WeightedROVER lists "indicconformer," "whisper," and "gemini," so I'll inspect transcribe/pipeline.py and eval scripts to clarify model usage.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""
Main transcription pipeline with multi-model consensus.

Implements the strategy:
1. Generate transcripts from multiple models (Whisper, IndicConformer, Gemini)
2. Triage by agreement (easy cases handled quickly)
3. Force-align all transcripts
4. ROVER weighted voting based on alignment scores
5. LLM arbitration for hard cases
6. Final validation and confidence scoring
"""

import time
import logging
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Tuple, Any
from enum import Enum
import numpy as np

from .config import Config, config as default_config
from .models import (
    BaseTranscriber,
    TranscriptionResult,
    WhisperTranscriber,
    GeminiTranscriber,
)
from .models.indicconformer import IndicConformerMultiTranscriber
from .models.indicwhisper import IndicWhisperMultiTranscriber
from .alignment import MMSForcedAligner, AlignmentResult
from .rover import WeightedROVER, ConsensusResult
from .utils.text_processing import normalize_text, compute_wer

logger = logging.getLogger(__name__)

class TriageCategory(Enum):
    """Categories for transcript agreement."""
    ALL_AGREE = "all_agree"        # All 3 transcripts match
    MAJORITY = "majority"          # 2/3 agree
    DISPUTED = "disputed"          # All 3 differ
    SINGLE = "single"              # Only 1 transcript available

@dataclass
class PipelineResult:
    """Result from the full transcription pipeline."""
    # Final transcript
    text: str
    text_native: Optional[str] = None

    # Confidence and quality metrics
    confidence: float = 0.0
    alignment_score: float = 0.0
    agreement_category: TriageCategory = TriageCategory.SINGLE

    # Source transcripts
    whisper_text: str = ""
    indicconformer_text: str = ""
    gemini_text: str = ""

    # Detailed results
    consensus_result: Optional[ConsensusResult] = None
    alignment_results: Dict[str, AlignmentResult] = field(default_factory=dict)

    # Metadata
    language: str = "unknown"
    languages_present: List[str] = field(default_factory=list)
    contains_code_mixing: bool = False
    processing_time: float = 0.0
    audio_duration: float = 0.0

    # Flags
    needs_review: bool = False
    review_reason: str = ""

    def to_dict(self) -> dict:
        return {
            "text": self.text,
            "text_native": self.text_native,
            "confidence": self.confidence,
            "alignment_score": self.alignment_score,
            "agreement_category": self.agreement_category.value,
            "whisper_text": self.whisper_text,
            "indicconformer_text": self.indicconformer_text,
            "gemini_text": self.gemini_text,
            "language": self.language,
            "languages_present": self.languages_present,
            "contains_code_mixing": self.contains_code_mixing,
            "processing_time": self.processing_time,
            "audio_duration": self.audio_duration,
            "needs_review": self.needs_review,
            "review_reason": self.review_reason,
        }

class TranscriptionPipeline:
    """
    Multi-model transcription pipeline with consensus.

    Follows the strategy:
    1. Generate 3 transcripts (Whisper, IndicConformer, Gemini)
    2. Triage by agreement
    3. Force-align for scoring
    4. Weighted ROVER consensus
    5. LLM arbitration for hard cases
    6. Final validation
    """

    def __init__(
        self,
        config: Optional[Config] = None,
        use_whisper: bool = True,
        use_indicconformer: bool = True,
        use_gemini: bool = True,
    ):
        self.config = config or default_config

        # Initialize models
        self.whisper: Optional[WhisperTranscriber] = None
        self.indicconformer: Optional[IndicConformerMultiTranscriber] = None
        self.gemini: Optional[GeminiTranscriber] = None
        self.aligner: Optional[MMSForcedAligner] = None

        self.use_whisper = use_whisper
        self.use_indicconformer = use_indicconformer
        self.use_gemini = use_gemini

        # ROVER with language-specific weights
        self.rover = WeightedROVER(
            base_weights=self.config.pipeline.language_weights
        )

        self._loaded = False

    def load(self) -> None:
        """Load all models."""
        if self._loaded:
            return

        logger.info("Loading transcription models...")

        if self.use_whisper:
            self.whisper = WhisperTranscriber(
                model_name=self.config.model.whisper_model,
                device=self.config.model.whisper_device,
                compute_type=self.config.model.whisper_compute_type,
            )
            self.whisper.load()
            logger.info("Whisper loaded")

        if self.use_indicconformer:
            self.indicconformer = IndicConformerMultiTranscriber(
                device=self.config.model.whisper_device,
            )
            self.indicconformer.load()
            logger.info("IndicConformer loaded")

        if self.use_gemini:
            self.gemini = GeminiTranscriber(
                api_key=self.config.model.gemini_api_key,
                model_name=self.config.model.gemini_model,
                temperature=self.config.model.gemini_temperature,
            )
            self.gemini.load()
            logger.info("Gemini loaded")

        # Load aligner
        self.aligner = MMSForcedAligner(device=self.config.model.whisper_device)
        self.aligner.load()
        logger.info("MMS Aligner loaded")

        self._loaded = True

    def unload(self) -> None:
        """Unload all models."""
        if self.whisper:
            self.whisper.unload()
        if self.indicconformer:
            self.indicconformer.unload()
        if self.gemini:
            self.gemini.unload()
        if self.aligner:
            self.aligner.unload()
        self._loaded = False

    def transcribe(
        self,
        audio: np.ndarray,
        sample_rate: int,
        language: Optional[str] = None,
        return_all_transcripts: bool = False,
    ) -> PipelineResult:
        """
        Transcribe audio using multi-model consensus.

        Args:
            audio: Audio array (mono, float32)
            sample_rate: Sample rate
            language: Optional language hint (ISO 639-1 code)
            return_all_transcripts: Include all model outputs in result

        Returns:
            PipelineResult with consensus transcript
        """
        if not self._loaded:
            self.load()

        start_time = time.time()
        audio_duration = len(audio) / sample_rate

        # Step 1: Generate transcripts from all models
        transcripts = {}
        transcript_results = {}

        if self.whisper:
            try:
                result = self.whisper.transcribe(audio, sample_rate, language)
                transcripts["whisper"] = result.text
                transcript_results["whisper"] = result


**Searching for consensus logic**


rg -n "consensus|rover|agreement" -n transcribe/pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
2:Main transcription pipeline with multi-model consensus.
6:2. Triage by agreement (easy cases handled quickly)
30:from .rover import WeightedROVER, ConsensusResult
38:    """Categories for transcript agreement."""
55:    agreement_category: TriageCategory = TriageCategory.SINGLE
63:    consensus_result: Optional[ConsensusResult] = None
83:            "agreement_category": self.agreement_category.value,
99:    Multi-model transcription pipeline with consensus.
103:    2. Triage by agreement
105:    4. Weighted ROVER consensus
130:        self.rover = WeightedROVER(
195:        Transcribe audio using multi-model consensus.
204:            PipelineResult with consensus transcript
240:        # Step 2: Triage by agreement
249:            consensus_result = None
257:            consensus_result = None
262:            final_text, confidence, alignment_score, consensus_result, alignment_results = (
301:            agreement_category=triage_category,
305:            consensus_result=consensus_result,
321:        Triage transcripts by agreement level.
367:            Tuple of (text, confidence, alignment_score, consensus_result, alignment_results)
407:        consensus = self.rover.combine_with_alignment(
414:        # Step 4: If consensus confidence is low, try LLM arbitration
415:        if consensus.confidence < self.config.pipeline.repair_threshold and self.gemini:
416:            arbitrated = self._llm_arbitrate(transcripts, consensus.text)
418:                consensus = ConsensusResult(
421:                    confidence=consensus.confidence + 0.1,  # slight boost
423:                    slots=consensus.slots,
424:                    agreement_score=consensus.agreement_score,
428:        if consensus.text:
431:                    audio, sample_rate, consensus.text, language
435:                final_score = consensus.confidence
440:            consensus.text,
441:            consensus.confidence,
443:            consensus,
450:        consensus_text: str,
468:The current consensus attempt is: "{consensus_text}"
529:        """Transcribe using Whisper + Gemini consensus."""
564:            agreement_category=category,
590:        self.rover = WeightedROVER(
640:        Transcribe using IndicWhisper + Gemini consensus.
643:        vanilla Whisper. Combined with Gemini for consensus.
692:            # Disagreement - use alignment to decide
721:            agreement_category=category,
741:        Resolve disagreement using forced alignment scoring.


sed -n '220,520p' transcribe/pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                transcript_results["whisper"] = result
            except Exception as e:
                logger.warning(f"Whisper failed: {e}")

        if self.indicconformer:
            try:
                result = self.indicconformer.transcribe(audio, sample_rate, language)
                transcripts["indicconformer"] = result.text
                transcript_results["indicconformer"] = result
            except Exception as e:
                logger.warning(f"IndicConformer failed: {e}")

        if self.gemini:
            try:
                result = self.gemini.transcribe(audio, sample_rate, language)
                transcripts["gemini"] = result.text
                transcript_results["gemini"] = result
            except Exception as e:
                logger.warning(f"Gemini failed: {e}")

        # Step 2: Triage by agreement
        triage_category, agreed_text = self._triage(transcripts)

        # Step 3: Handle based on triage category
        if triage_category == TriageCategory.ALL_AGREE:
            # Easy case - all agree
            final_text = agreed_text
            confidence = 0.95
            alignment_score = 0.95
            consensus_result = None
            alignment_results = {}

        elif triage_category == TriageCategory.MAJORITY:
            # Majority case - 2/3 agree
            final_text = agreed_text
            confidence = 0.85
            alignment_score = 0.85
            consensus_result = None
            alignment_results = {}

        else:
            # Disputed case - need alignment and ROVER
            final_text, confidence, alignment_score, consensus_result, alignment_results = (
                self._resolve_disputed(audio, sample_rate, transcripts, language)
            )

        # Step 4: Get native script version if available
        text_native = None
        if "gemini" in transcript_results:
            text_native = transcript_results["gemini"].text_native

        # Step 5: Final validation
        needs_review = False
        review_reason = ""

        if confidence < self.config.pipeline.high_confidence_threshold:
            needs_review = True
            review_reason = f"Low confidence: {confidence:.2f}"

        if alignment_score < self.config.pipeline.alignment_score_threshold:
            needs_review = True
            review_reason = f"Low alignment score: {alignment_score:.2f}"

        # Determine language and code-mixing status
        detected_language = language or "unknown"
        languages_present = []
        contains_code_mixing = False

        if "gemini" in transcript_results:
            gemini_result = transcript_results["gemini"]
            detected_language = gemini_result.language
            languages_present = gemini_result.languages_present
            contains_code_mixing = gemini_result.contains_code_mixing

        processing_time = time.time() - start_time

        return PipelineResult(
            text=final_text,
            text_native=text_native,
            confidence=confidence,
            alignment_score=alignment_score,
            agreement_category=triage_category,
            whisper_text=transcripts.get("whisper", ""),
            indicconformer_text=transcripts.get("indicconformer", ""),
            gemini_text=transcripts.get("gemini", ""),
            consensus_result=consensus_result,
            alignment_results=alignment_results,
            language=detected_language,
            languages_present=languages_present,
            contains_code_mixing=contains_code_mixing,
            processing_time=processing_time,
            audio_duration=audio_duration,
            needs_review=needs_review,
            review_reason=review_reason,
        )

    def _triage(
        self,
        transcripts: Dict[str, str],
    ) -> Tuple[TriageCategory, Optional[str]]:
        """
        Triage transcripts by agreement level.

        Returns:
            Tuple of (category, agreed_text if applicable)
        """
        if len(transcripts) < 2:
            if transcripts:
                return TriageCategory.SINGLE, list(transcripts.values())[0]
            return TriageCategory.SINGLE, ""

        # Normalize transcripts for comparison
        normalized = {
            k: normalize_text(v, lowercase=True, remove_punctuation=True)
            for k, v in transcripts.items()
        }

        values = list(normalized.values())
        keys = list(normalized.keys())

        # Check if all agree
        if len(set(values)) == 1:
            return TriageCategory.ALL_AGREE, transcripts[keys[0]]

        # Check for majority (2/3 agree)
        for i in range(len(values)):
            count = values.count(values[i])
            if count >= 2:
                # Find original (non-normalized) text
                for k, v in normalized.items():
                    if v == values[i]:
                        return TriageCategory.MAJORITY, transcripts[k]

        # All different
        return TriageCategory.DISPUTED, None

    def _resolve_disputed(
        self,
        audio: np.ndarray,
        sample_rate: int,
        transcripts: Dict[str, str],
        language: Optional[str],
    ) -> Tuple[str, float, float, ConsensusResult, Dict[str, AlignmentResult]]:
        """
        Resolve disputed transcripts using alignment and ROVER.

        Returns:
            Tuple of (text, confidence, alignment_score, consensus_result, alignment_results)
        """
        # Step 1: Force-align all transcripts
        alignment_results = {}

        for model_name, text in transcripts.items():
            if not text:
                continue
            try:
                result = self.aligner.align(audio, sample_rate, text, language)
                alignment_results[model_name] = result
            except Exception as e:
                logger.warning(f"Alignment failed for {model_name}: {e}")

        # Step 2: Check if any transcript has significantly better alignment
        if alignment_results:
            scores = {k: v.overall_score for k, v in alignment_results.items()}
            best_model = max(scores, key=scores.get)
            best_score = scores[best_model]

            # If one is clearly better (> 0.2 difference), use it
            other_scores = [s for k, s in scores.items() if k != best_model]
            if other_scores and best_score - max(other_scores) > 0.2:
                return (
                    transcripts[best_model],
                    best_score,
                    best_score,
                    None,
                    alignment_results,
                )

        # Step 3: Use weighted ROVER
        transcript_list = list(transcripts.values())
        model_names = list(transcripts.keys())
        alignment_scores = [
            alignment_results.get(m, AlignmentResult([], 0.5, 0.5, 0.0)).overall_score
            for m in model_names
        ]

        lang = language or "hindi"  # default
        consensus = self.rover.combine_with_alignment(
            transcript_list,
            model_names,
            alignment_scores,
            language=lang,
        )

        # Step 4: If consensus confidence is low, try LLM arbitration
        if consensus.confidence < self.config.pipeline.repair_threshold and self.gemini:
            arbitrated = self._llm_arbitrate(transcripts, consensus.text)
            if arbitrated:
                consensus = ConsensusResult(
                    text=arbitrated,
                    words=arbitrated.split(),
                    confidence=consensus.confidence + 0.1,  # slight boost
                    word_confidences=[0.8] * len(arbitrated.split()),
                    slots=consensus.slots,
                    agreement_score=consensus.agreement_score,
                )

        # Final alignment score
        if consensus.text:
            try:
                final_alignment = self.aligner.align(
                    audio, sample_rate, consensus.text, language
                )
                final_score = final_alignment.overall_score
            except:
                final_score = consensus.confidence
        else:
            final_score = 0.0

        return (
            consensus.text,
            consensus.confidence,
            final_score,
            consensus,
            alignment_results,
        )

    def _llm_arbitrate(
        self,
        transcripts: Dict[str, str],
        consensus_text: str,
    ) -> Optional[str]:
        """
        Use Gemini to arbitrate between disagreeing transcripts.

        Returns:
            Arbitrated text or None if arbitration fails
        """
        if not self.gemini or not self.gemini._client:
            return None

        prompt = f"""You are arbitrating between different ASR transcriptions of the same audio.

The transcripts from different models are:
1. Whisper: "{transcripts.get('whisper', 'N/A')}"
2. IndicConformer: "{transcripts.get('indicconformer', 'N/A')}"
3. Initial Gemini: "{transcripts.get('gemini', 'N/A')}"

The current consensus attempt is: "{consensus_text}"

Based on linguistic plausibility and your understanding of Indic languages:
1. Which parts are likely correct?
2. Where do the transcripts disagree and which is more plausible?
3. Provide the most likely correct transcription.

Return ONLY the corrected transcript, nothing else. Do not add explanations.
"""

        try:
            model = self.gemini._client.GenerativeModel(
                "gemini-2.0-flash",
                generation_config=self.gemini._client.GenerationConfig(
                    temperature=0.1,
                ),
            )
            response = model.generate_content(prompt)
            return response.text.strip()
        except Exception as e:
            logger.warning(f"LLM arbitration failed: {e}")
            return None

class LightweightPipeline:
    """
    Lightweight pipeline using only Whisper + Gemini.

    For faster processing when IndicConformer is not available.
    """

    def __init__(self, config: Optional[Config] = None):
        self.config = config or default_config
        self.whisper: Optional[WhisperTranscriber] = None
        self.gemini: Optional[GeminiTranscriber] = None
        self._loaded = False

    def load(self) -> None:
        if self._loaded:
            return

        self.whisper = WhisperTranscriber(
            model_name=self.config.model.whisper_model,
            device=self.config.model.whisper_device,
        )
        self.whisper.load()

        self.gemini = GeminiTranscriber(
            api_key=self.config.model.gemini_api_key,
            model_name=self.config.model.gemini_model,
        )
        self.gemini.load()


**Identifying punctuation impact on tokenization and WER**

I found inconsistent punctuation handling causes inflated WER for Gemini, since evaluation retains punctuation while pipeline normalization removes it. Tokenization by simple whitespace splitting also mishandles punctuation in Indic languages and special tokens like "802.11a," leading to alignment errors in consensus and WeightedROVER scoring. Transcript order determines reference in alignment, usually whisper, which may mismatch when evaluating Gemini or IndicWhisper. Next, I'll verify how eval_indicwhisper handles this.


sed -n '1,240p' transcribe/eval_indicwhisper.py