Output:
Exit code: 0
Wall time: 0 seconds
Output:
## Master Plan (Unified)
Goal: **near-zero-error accepted transcripts** via (1) diverse generation and (2) independent, audio-grounded verification; everything uncertain is rejected and routed to human correction.

### 1) Pre-QC (audio sanity checks)
- Enforce canonical format: 16kHz, mono, PCM/FLAC/WAV (adapt as needed).
- Run VAD trim (e.g., Silero VAD) to hard-trim leading/trailing silence and remove non-speech tails.
- Compute audio QC metrics and reject early if bad (precision-first): clipping %, extreme loudness, very low speech ratio, suspiciously short effective speech, etc. (Inference: exact metrics/thresholds must be tuned.)

### 2) Language ID routing (handling unknown/mixed)
- Run spoken LID on every segment:
  - Primary: **MMS-LID** (Refs 1/5).
  - Optional second opinion: **SpeechBrain VoxLingua107 ECAPA** (Refs 1/5) and/or Whisper LID (Refs 3/6) as a tie-breaker.
- Routing (starting heuristic from Ref1; tune on gold set):
  - If top-1 prob ≥ 0.90 and margin(top1-top2) ≥ 0.20 → **confident route**
  - Else → **unknown/mixed route** (process with multilingual ensemble and stricter verification).

### 3) ASR ensemble (at least 2–3 diverse systems)
**Core idea (Refs 1/5):** combine an Indic specialist + a strong multilingual generalist + a diversity model.
- **Indic segments (the 11 Indic languages):**
  - **IndicWhisper** (AI4Bharat fine-tuned Whisper) (Refs 3/5, also implied in Ref2/4).
  - **Whisper large-v3** as generalist anchor (Refs 1/3/5).
  - **IndicConformer-600M multilingual** (hybrid CTC/RNNT; diversity in failure modes) (Refs 1/4/6).
  - Optional diversity: **MMS ASR** (Refs 1/3/5) especially for lower-resource (Odia/Assamese) or when others disagree.
- **English segments:**
  - **Whisper large-v3** + **Voxtral** (Refs 1/2) (and optionally a third English ASR for diversity if needed).
- **Unknown/mixed segments:**
  - Run a multilingual-heavy set: Whisper large-v3 + IndicConformer multilingual + MMS (+ Voxtral if available) and later decide based on verification.

Decoding policy (Ref1):
- Start with deterministic/high-precision decoding (beam search, temperature=0 for Whisper-like models).
- If decoding fails/degenerates, re-decode with alternate params; treat fallbacks as lower-confidence and require stricter verification.
- Where supported, store **N-best hypotheses** (Ref6) not only 1-best.

### 4) Normalization layer (mandatory before combination/eval)
- Unicode normalize; normalize punctuation/numerals consistently; standardize whitespace.
- Use **IndicNLP** (Ref5) or equivalent for Indic script normalization so system combination isn’t dominated by trivial differences.

### 5) System combination (don’t just pick one)
- Use **ROVER / confusion-network voting** (Refs 1/4/5) to produce:
  - `combined_transcript`
  - a per-token **consensus/conflict map**
- Consider character-level voting for Indic scripts as an adaptation (Ref4) if word tokenization is unreliable.

### 6) Audio-grounded verification gate (non-negotiable)
Use at least one **independent** audio-grounded check before accepting a transcript.
- **Primary gate:** **CTC-based “fit” scoring** (Ref1) on:
  - top candidates (best per-model 1-best and/or N-best)
  - the ROVER-combined transcript
  - Choose the best-scoring candidate and compute pass/fail metrics (coverage, mean posterior, confidence dips).
- **Secondary/optional gate:** forced alignment (MFA / NeMo aligner) (Refs 1/3/6) where language resources support it cleanly.
- **Reject policy:** if alignment/CTC fit is poor, **reject the whole segment** rather than “editing” words out (precision-first; avoid silent corruption).

### 7) LLM usage (only if constrained, never as free generator)
- Only use an LLM as a **selection/tie-breaker among existing hypotheses/spans** (Refs 1/4/5).
- The LLM output is still provisional until it passes the same audio-grounded verification gate.
- Prefer audio-native models (e.g., Voxtral) over text-only models when possible (Ref1), but still enforce verification.

### 8) Reject + re-decode logic
- If a segment fails the gate:
  - Try an alternate decode (different beam/LM, add a diversity model like MMS).
  - If still failing → send to human queue (don’t “force accept”).

### 9) Human-in-the-loop for rejects
- Only humans touch the reject tail (Refs 1/2/5).
- Use an annotation UI that shows: audio + top hypotheses + disagreement highlights (Ref1) and lets the annotator pick/edit.
- If you want OSS tooling: **Shoonya** (AI4Bharat) is suggested in Ref1.

### 10) Storage schema (auditability)
Store everything needed to debug and to re-run deterministically:
- `segment_id`, source metadata, duration, audio QC metrics
- `language_probs[]` (per LID model)
- `hypotheses[]` (model, decode params, 1-best + N-best, confidences/logprobs if available)
- `normalized_hypotheses[]`
- `rover_output` + `disagreement_map`
- `verification_scores[]` (CTC/aligner metrics)
- `decision` (accept/reject/human) + `reason_codes` + timestamps

## Implementation Blueprint
### Engineering scope (minimal first, then scale)
**Phase A (strong baseline):** LID + 3-model ASR ensemble + normalization + ROVER + CTC scoring gate + reject queue.
**Phase B (quality hardening):** add N-best storage, better disagreement analytics, forced alignment where feasible, and a constrained LLM tie-breaker only for narrow cases.
**Phase C (operations):** human review UI integration, dashboards, and active learning loop.

### Orchestration
- Batch segments by language route to maximize GPU throughput (Inference).
- Run models in parallel across available GPUs; treat each model decode as an independent job with retries.
- Make everything config-driven (`models`, `decode_params`, `thresholds`, `weights`), and record config snapshots with outputs.

### Data structures (suggested)
- `Segment`: `{segment_id, source_id, path, duration_s, qc_metrics}`
- `LidResult`: `{model, probs: {lang: p}, top1, top2, margin}`
- `Hypothesis`: `{model, lang, text, nbest[], token_logprobs?, avg_logprob?, decode_params}`
- `CombineResult`: `{method: rover, text, consensus_score, disagreement_spans[]}`
- `VerifyResult`: `{method: ctc_fit|forced_align, score, coverage, pass, diagnostics}`
- `Decision`: `{status: accept|reject|human, reason_codes[], chosen_text}`

### Model integration notes (practical)
- Prefer models that can expose confidence/logprob signals; if not, lean more on independent CTC scoring + agreement.
- Keep model versions pinned (Hugging Face commit hash / NeMo checkpoint version) for reproducibility.
- Persist raw hypotheses + scores; never overwrite outputs in-place without versioning.

### System combination (ROVER)
- Use an existing implementation/toolkit where possible (Ref1/5 mention NIST SCTK; Ref3 suggests a simple script).
- Maintain per-language model weights learned on a dev set (Inference; implied by Ref1/4/5).

### Observability
- Aggregate metrics by language, duration bucket, and source video.
- Track top reject reasons (low LID confidence, high disagreement, low alignment score, audio QC failure).
- Sample a fixed % of “accepted” for spot-check human QA to estimate residual label noise (Inference).

## Validation Protocol
### Build a gold set (required before tuning gates)
- Stratified sample across:
  - all languages
  - duration buckets (1–3s, 3–10s, 10–30s, 30–60s)
  - audio conditions (clean vs mildly noisy)
  - code-switching presence (at least Hindi-English; if present in your data)
- Human-transcribe this gold set with strict guidelines (verbatim, include disfluencies if desired for training consistency).

### Evaluate components and the full pipeline
- Score each base ASR model (WER/CER) by language and condition.
- Score ensemble variants:
  - best-single-model
  - ROVER combination (with/without MMS/IndicConformer)
  - verification gate impact (precision/recall tradeoff vs gold)

### Tune thresholds and weights
- Tune:
  - LID “confident route” thresholds
  - model weights for ROVER per language
  - verification gate thresholds (CTC fit / alignment pass)
- Objective: maximize **precision** of accepted set subject to a feasible accept rate (precision-first).

### Active learning loop
- Collect rejects + hard disagreements as an “error discovery” pool.
- Human-correct a subset, then:
  - recalibrate thresholds/weights
  - optionally fine-tune the best-performing model(s) for your domain (Inference; consistent with Ref2’s iterative improvement idea)

## Do This Next (Execution Checklist)
1. Create a stratified gold set (per language + durations) and produce human transcripts.
2. Stand up the baseline pipeline: `MMS-LID → (IndicWhisper + Whisper large-v3 + IndicConformer [+ MMS]) → normalize → ROVER → CTC fit gate → accept/reject`.
3. Store *all* hypotheses + scores in an auditable schema (JSONL/Parquet) and build simple per-language dashboards.
4. Tune routing/verification thresholds on the gold set until the accepted pool hits your precision target.
5. Add a human review workflow for rejects (UI + queue); feed corrected tail back into evaluation and (optional) fine-tuning.


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.


**Creating comprehensive documentation**

I see that the user wants a master document alongside updates to Agents.md, specifically including a block diagram and an execution plan. Currently, Agents.md lacks a section on why our current plan minimizes WER, so I’ll need to add that information too. The idea seems to be creating a polished summary in master.md while ensuring that Agents.md gets updated accordingly. I’ll interpret this request as a need for clarity about offline transcription and minimal WER strategies.


**Updating documentation for clarity**

I see that Agents.md already has an execution plan under "Do This Next." I might want to rename or add a distinct "Execution Plan" section that follows a more stepwise approach, similar to what I'll include in master.md. I also need to be careful with our claims about minimal WER; it's important to avoid stating absolutes. Instead, I can present our design as a well-known approach for minimizing WER, while acknowledging that it doesn't guarantee global minimal WER without proper measurement.


**Explaining WER minimization strategies**

I can clarify how we minimize WER by addressing both random and systematic errors. Multiple models help with random errors, while language-specific models tackle systematic mismatches. Using system combination methods like ROVER helps reduce errors by achieving consensus, and a verification gate minimizes hallucinations. Offline computing enables high beam search and rescoring. I’ll also highlight the importance of data normalization and script correctness. Additionally, it seems they might want a block diagram, so I can include a mermaid and ASCII version in master.md, which I plan to title “Master Plan: Offline Near-Zero-WER Transcription for Indic + English (YouTube segments).”


**Designing the transcription pipeline**

I'm laying out the pipeline for processing audio segments, which includes steps from input audio to output transcripts. It involves pre-QC/VAD, language ID ensembles, ASR ensemble, normalization, and ROVER for system combination, along with a verification gate for forced alignment. I should also add an optional re-decode loop.

For the execution plan, I’ll break it down into phases with clear deliverables and success criteria. Additionally, I'll emphasize why this approach minimizes WER, particularly in an offline context.


**Updating documentation for clarity and accuracy**

I need to make sure that we avoid any risk of hallucination with LLMs, so it’s best not to use text-only LLM generation. LLMs should be confined to tie-breaking when audio verification is already in place.

I think I want to categorize what to measure into relevant metrics and suggest a model stack that includes several options like MMS-LID and VoxLingua107. I will update Agents.md to add sections like "Block Diagram," "Why this plan drives WER down," and an "Execution Plan." I must ensure the document doesn't get too lengthy while implementing these changes.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 56
drwxrwxr-x  3 ubuntu ubuntu  4096 Jan 13 03:28 .
drwxr-x--- 43 ubuntu ubuntu  4096 Jan 13 02:33 ..
-rw-rw-r--  1 ubuntu ubuntu 35446 Jan 13 03:38 Agents.md
drwxrwxr-x  2 ubuntu ubuntu  4096 Jan 13 03:12 docs
-rw-rw-r--  1 ubuntu ubuntu  3652 Jan 13 02:35 .env
-rw-rw-r--  1 ubuntu ubuntu  2627 Jan 13 02:35 task.md


Summary: auto


**Refining WER strategy**

I need to be cautious with the claim about achieving minimal WER. Instead, I can say this is the best-known strategy for reducing WER in an offline setting, as it accepts only segments that are both consensus-supported and audio-verified. It’s important to clarify that we don’t guarantee global minimal WER, but we can emphasize that it approaches an optimal level due to this gating.

For the execution plan, I’ll outline steps like building a gold set, implementing pipeline stages, calibrating thresholds, and more. I could summarize the timeline and deliverables in an "Execution Plan" section.


**Creating a polished master document**

I need to ensure that the additions we’re making don’t conflict with the earlier template focused on what to produce. Now, for master.md, I plan to craft a polished document with several sections:

- A title
- An "Objective" summarizing the purpose
- A "Key Idea" section focusing on Ensemble, Combination, and Verification
- A "Pipeline Block Diagram"
- A listing for model choices like LID and ASR
- An "Execution Plan" detailing phases and deliverables
- A section on why this approach drives down WER, along with acceptance criteria
- Plus, I might add something on outputs and storage while noting we aren’t constrained by compute, allowing for multiple models.

I’ll aim to keep it comprehensive but not too lengthy. I also want to briefly explain what "CTC fit scoring" means, even though we won’t implement it just yet.


**Modifying Agents.md**

I need to patch Agents.md by adding new sub-sections after the storage schema section, specifically after the reason codes. I’ll include a "Block Diagram" for the pipeline, a section about "Why this plan minimizes WER (offline)," and an "Execution Plan (phased)." However, I’m wondering if the Execution Plan would fit better after the "Implementation Blueprint."

Since the user requested a block diagram, execution plan, and reasoning around WER minimization in Agents.md, I think it makes sense to add the block diagram and WER sections under the Master Plan. I’ll place the Execution Plan within the Implementation Blueprint, making sure Agents.md remains focused on the required elements without being overwhelming.


**Patching Agents.md**

I need to ensure that Agents.md includes a block diagram, an execution plan, and an explanation of why the current plan minimizes WER, as requested by the user. I’ll patch Agents.md by locating the "### 10) Storage schema (auditability)" section and adding the new subsections after the bullet list.

To do this, I’ll apply a patch to update the file and use anchors to specify the exact lines for these new additions. This way, I can keep everything organized and aligned with the user’s requirements.


Input:
*** Begin Patch
*** Update File: Agents.md
@@
 ### 10) Storage schema (auditability)
 Store everything needed to debug and to re-run deterministically:
 - `segment_id`, source metadata, duration, audio QC metrics
 - `language_probs[]` (per LID model)
 - `hypotheses[]` (model, decode params, 1-best + N-best, confidences/logprobs if available)
 - `normalized_hypotheses[]`
 - `rover_output` + `disagreement_map`
 - `verification_scores[]` (CTC/aligner metrics)
 - `decision` (accept/reject/human) + `reason_codes` + timestamps
+
+### Block Diagram (pipeline)
+```mermaid
+flowchart TD
+  A[Audio segment] --> B[Pre-QC + VAD trim\nformat normalize]
+  B --> C[LID ensemble\nMMS-LID (+ optional VoxLingua107/Whisper)]
+
+  C -->|Confident language| D[ASR ensemble (language-specific)\nIndicWhisper + Whisper large-v3 + IndicConformer\n(+ optional MMS)]
+  C -->|Uncertain/mixed| E[ASR ensemble (multilingual-heavy)\nWhisper large-v3 + IndicConformer + MMS\n(+ Voxtral if available)]
+
+  D --> F[Normalize hypotheses\nUnicode + IndicNLP + numerals/punct]
+  E --> F
+
+  F --> G[System combination\nROVER/confusion network\n+ disagreement map]
+  G --> H[Audio-grounded verification\nCTC fit scoring\n(+ optional forced alignment)]
+
+  H -->|PASS| I[ACCEPT\nfinal transcript + scores]
+  H -->|FAIL| J[Re-decode / add model\nstricter params]
+  J --> H
+  H -->|Still FAIL| K[HUMAN REVIEW\ncorrect or discard]
+  K --> I
+```
+
+### Why this plan drives WER down (offline, no compute constraints)
+- **Stops routing errors early:** Spoken LID prevents “wrong-language/wrong-script” decodes that create catastrophic transcripts (Refs 1/5).
+- **Exploits error diversity:** Different ASR architectures fail differently; running multiple strong but diverse recognizers increases the chance the correct word sequence exists among hypotheses (Refs 1/3/5/6).
+- **Combines, not guesses:** ROVER/confusion-network voting reduces WER when recognizers disagree in different places; consensus plus a disagreement map makes errors visible and auditable (Refs 1/4/5).
+- **Verifies against the audio:** Independent CTC/forced-alignment scoring rejects fluent-but-wrong or hallucinated text (the main failure mode you can’t tolerate for training) (Refs 1/3/6).
+- **Precision-first acceptance:** When you “can’t afford mistakes,” the optimal strategy is to reject uncertainty and only keep segments that clear strict audio-grounded gates; the tail goes to humans, yielding a near-zero-error accepted set (Refs 1/2/5).
+
+### Execution Plan (phased)
+1. **Gold set + harness:** Build a stratified human-transcribed gold set; wire WER/CER reporting by language and duration bucket.
+2. **Baseline pipeline:** Implement `VAD/QC → MMS-LID → (IndicWhisper + Whisper v3 + IndicConformer [+ MMS]) → normalize → ROVER → CTC-fit gate → accept/reject`.
+3. **Calibrate:** Tune LID thresholds, ROVER weights, and verification gates on the gold set to maximize precision of accepted segments.
+4. **Human loop:** Add a review queue/UI for rejects that shows audio + top hypotheses + disagreement spans; feed corrections back into threshold tuning (and optional fine-tunes later).
+5. **Scale + monitor:** Batch by language, parallelize per-model inference, and track reject reasons and residual error via spot-check QA sampling.

 ## Implementation Blueprint
 ### Engineering scope (minimal first, then scale)
 **Phase A (strong baseline):** LID + 3-model ASR ensemble + normalization + ROVER + CTC scoring gate + reject queue.
*** End Patch


Output: {"output":"Success. Updated the following files:\nM Agents.md\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Creating master.md**

I’ve updated Agents.md with new sections, which is great! Now, I need to create master.md as a comprehensive document that doesn’t conflict with Agents.md. I’ll use Agents.md as a detailed analysis and frame master.md as an executive summary.

In master.md, I’ll include a title, an overview, a pipeline summary, and a simplified block diagram. I have to ensure the execution plan covers phases 0 to 5 with specific deliverables. Finally, I’ll be cautious with my phrasing around minimizing WER, emphasizing the overall strategy.


**Minimizing WER Process**

To minimize the achieved WER effectively, I’ll focus on gold-set-driven tuning and strict acceptance gating; that feels safe. It’s also important to include “Non-negotiables” to avoid any invention. I need to add a schema detailing “What we store” and include constraints on “How to use LLMs safely.” I might also want to add “Open questions” for further clarity, but I should keep everything concise. Let’s go ahead and create the file!


Input:
*** Begin Patch
*** Add File: master.md
+# master.md — Offline “Quality-First” Transcription Master Plan (Indic + English)
+
+## Objective (from `task.md`)
+Generate **near-zero-error** transcripts for cleaned, single-speaker YouTube segments (≈1–60s) across:
+Malayalam, Hindi, Punjabi, Tamil, Kannada, Gujarati, Telugu, Bengali, Odia, Marathi, Assamese, English (+ unknowns).
+**Compute/latency are not constraints; quality/WER is.**
+
+This document is the **execution-ready summary**. Full per-reference analysis and detailed decisions live in `Agents.md`.
+
+---
+
+## Core idea
+To minimize WER offline, don’t bet on one model. Instead:
+1) **Route correctly (spoken LID)**
+2) **Generate diverse hypotheses (ASR ensemble + N-best)**
+3) **Combine (ROVER/confusion network)**
+4) **Prove audio-text match (CTC/forced-alignment verification gate)**
+5) **Reject uncertainty + human-correct the tail**
+
+---
+
+## Pipeline block diagram
+```mermaid
+flowchart TD
+  A[Audio segment] --> B[Pre-QC + VAD trim\nformat normalize]
+  B --> C[LID ensemble\nMMS-LID (+ optional VoxLingua107/Whisper)]
+
+  C -->|Confident language| D[ASR ensemble (language-specific)\nIndicWhisper + Whisper large-v3 + IndicConformer\n(+ optional MMS)]
+  C -->|Uncertain/mixed| E[ASR ensemble (multilingual-heavy)\nWhisper large-v3 + IndicConformer + MMS\n(+ Voxtral if available)]
+
+  D --> F[Normalize hypotheses\nUnicode + IndicNLP + numerals/punct]
+  E --> F
+
+  F --> G[System combination\nROVER/confusion network\n+ disagreement map]
+  G --> H[Audio-grounded verification\nCTC fit scoring\n(+ optional forced alignment)]
+
+  H -->|PASS| I[ACCEPT\nfinal transcript + scores]
+  H -->|FAIL| J[Re-decode / add model\nstricter params]
+  J --> H
+  H -->|Still FAIL| K[HUMAN REVIEW\ncorrect or discard]
+  K --> I
+```
+
+---
+
+## Initial model stack (validate on a gold set; tune per language)
+**Spoken LID**
+- Primary: **MMS-LID** (spoken LID coverage is the key requirement)
+- Optional 2nd opinion: **SpeechBrain VoxLingua107 ECAPA** (and/or Whisper LID as tie-break)
+
+**ASR (Indic)**
+- **IndicWhisper** (Indic fine-tuned Whisper; “specialist”)
+- **Whisper large-v3** (robust multilingual “generalist”)
+- **IndicConformer-600M multilingual** (different architecture; error diversity)
+- Optional diversity: **MMS ASR** (especially when others disagree / lower-resource languages)
+
+**ASR (English)**
+- **Whisper large-v3** + **Voxtral** (treat Voxtral as “measure on your gold set” for your domains)
+
+**System combination**
+- **ROVER / confusion-network voting** (keep the disagreement map)
+
+**Audio-grounded verification**
+- Primary: **CTC “fit” scoring** (score candidates against audio; reject low-fit)
+- Optional: forced alignment where you can support it cleanly
+
+---
+
+## Why this approach is the best way to minimize WER offline (and avoid hallucinations)
+This is the strongest generally applicable strategy under “unlimited compute / offline” because it attacks the dominant error sources:
+- **Wrong routing is catastrophic:** Spoken LID prevents wrong-language decodes (script/vocabulary mismatch) that can silently poison training data.
+- **Ensembles lower error by diversity:** Different models/architectures make different mistakes; if you can afford it, generating multiple hypotheses increases the odds that at least one is correct.
+- **ROVER reduces WER when errors differ:** System combination exploits that diversity instead of “picking a winner” blindly.
+- **Audio-grounded verification is the key safety gate:** A transcript can look fluent but still be wrong; independent alignment/CTC scoring forces the text to match the audio and catches hallucination patterns.
+- **Precision-first acceptance yields near-zero-error training text:** If a segment can’t be verified confidently, rejecting it (and sending only the tail to humans) minimizes label noise, which matters more than maximizing coverage for training.
+
+Important: “minimal WER” is not a claim about global benchmarks; it is achieved **for your data** by tuning gates/weights on a **human gold set** and only accepting segments that pass strict audio-grounded checks.
+
+---
+
+## Execution plan (deliverables)
+1. **Gold set + evaluation harness (Week 1)**
+   - Build a stratified gold set (all languages + duration buckets + clean/mild noise + code-switch).
+   - Produce human transcripts; compute WER/CER per language; establish baselines for each model.
+2. **Baseline pipeline (Week 1–2)**
+   - Implement: `VAD/QC → MMS-LID → ASR ensemble → normalization → ROVER → CTC-fit verification → accept/reject`.
+   - Store full audit trail (all hypotheses + scores).
+3. **Calibration (Week 2)**
+   - Tune: LID thresholds, per-language ROVER weights, verification-gate thresholds.
+   - Goal: maximize **precision** of accepted set; accept-rate is a secondary knob.
+4. **Human reject loop (Week 2–3)**
+   - Create a review queue that shows audio + top hypotheses + disagreement spans for fast correction.
+   - Feed corrected rejects back into calibration (and optional later fine-tuning).
+5. **Scale-out + monitoring (Week 3+)**
+   - Batch by language; parallelize per-model decoding; track reject reasons and residual error via sampling.
+
+---
+
+## Output schema (minimum needed for auditability)
+For each `segment_id`, store:
+- audio QC metrics, LID probs (per LID model)
+- ASR hypotheses (per model + decode params + N-best if available)
+- normalized hypotheses
+- ROVER output + disagreement map
+- verification scores (CTC/aligner) + decision (`accept|reject|human`) + reason codes
+
+---
+
+## LLM usage policy (optional; safe mode only)
+If you use an LLM at all:
+- It must **select among existing hypotheses/spans** only (no free-form “fixing”).
+- Its output must still pass the same **audio-grounded verification gate** before acceptance.
+
*** End Patch


Output: {"output":"Success. Updated the following files:\nA master.md\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


rg -n "Block Diagram" Agents.md master.md


Output:
Exit code: 0
Wall time: 0 seconds
Output:
Agents.md:581:### Block Diagram (pipeline)


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

### 8) Reject + re-decode logic
- If a segment fails the gate:
  - Try an alternate decode (different beam/LM, add a diversity model like MMS).
  - If still failing → send to human queue (don’t “force accept”).

### 9) Human-in-the-loop for rejects
- Only humans touch the reject tail (Refs 1/2/5).
- Use an annotation UI that shows: audio + top hypotheses + disagreement highlights (Ref1) and lets the annotator pick/edit.
- If you want OSS tooling: **Shoonya** (AI4Bharat) is suggested in Ref1.

### 10) Storage schema (auditability)
Store everything needed to debug and to re-run deterministically:
- `segment_id`, source metadata, duration, audio QC metrics
- `language_probs[]` (per LID model)
- `hypotheses[]` (model, decode params, 1-best + N-best, confidences/logprobs if available)
- `normalized_hypotheses[]`
- `rover_output` + `disagreement_map`
- `verification_scores[]` (CTC/aligner metrics)
- `decision` (accept/reject/human) + `reason_codes` + timestamps

### Block Diagram (pipeline)
```mermaid
flowchart TD
  A[Audio segment] --> B[Pre-QC + VAD trim\nformat normalize]
  B --> C[LID ensemble\nMMS-LID (+ optional VoxLingua107/Whisper)]

  C -->|Confident language| D[ASR ensemble (language-specific)\nIndicWhisper + Whisper large-v3 + IndicConformer\n(+ optional MMS)]
  C -->|Uncertain/mixed| E[ASR ensemble (multilingual-heavy)\nWhisper large-v3 + IndicConformer + MMS\n(+ Voxtral if available)]

  D --> F[Normalize hypotheses\nUnicode + IndicNLP + numerals/punct]
  E --> F

  F --> G[System combination\nROVER/confusion network\n+ disagreement map]
  G --> H[Audio-grounded verification\nCTC fit scoring\n(+ optional forced alignment)]

  H -->|PASS| I[ACCEPT\nfinal transcript + scores]
  H -->|FAIL| J[Re-decode / add model\nstricter params]
  J --> H
  H -->|Still FAIL| K[HUMAN REVIEW\ncorrect or discard]
  K --> I
```

### Why this plan drives WER down (offline, no compute constraints)
- **Stops routing errors early:** Spoken LID prevents “wrong-language/wrong-script” decodes that create catastrophic transcripts (Refs 1/5).
- **Exploits error diversity:** Different ASR architectures fail differently; running multiple strong but diverse recognizers increases the chance the correct word sequence exists among hypotheses (Refs 1/3/5/6).
- **Combines, not guesses:** ROVER/confusion-network voting reduces WER when recognizers disagree in different places; consensus plus a disagreement map makes errors visible and auditable (Refs 1/4/5).
- **Verifies against the audio:** Independent CTC/forced-alignment scoring rejects fluent-but-wrong or hallucinated text (the main failure mode you can’t tolerate for training) (Refs 1/3/6).
- **Precision-first acceptance:** When you “can’t afford mistakes,” the optimal strategy is to reject uncertainty and only keep segments that clear strict audio-grounded gates; the tail goes to humans, yielding a near-zero-error accepted set (Refs 1/2/5).

### Execution Plan (phased)
1. **Gold set + harness:** Build a stratified human-transcribed gold set; wire WER/CER reporting by language and duration bucket.
2. **Baseline pipeline:** Implement `VAD/QC → MMS-LID → (IndicWhisper + Whisper v3 + IndicConformer [+ MMS]) → normalize → ROVER → CTC-fit gate → accept/reject`.
3. **Calibrate:** Tune LID thresholds, ROVER weights, and verification gates on the gold set to maximize precision of accepted segments.
4. **Human loop:** Add a review queue/UI for rejects that shows audio + top hypotheses + disagreement spans; feed corrections back into threshold tuning (and optional fine-tunes later).
5. **Scale + monitor:** Batch by language, parallelize per-model inference, and track reject reasons and residual error via spot-check QA sampling.

## Implementation Blueprint
### Engineering scope (minimal first, then scale)
**Phase A (strong baseline):** LID + 3-model ASR ensemble + normalization + ROVER + CTC scoring gate + reject queue.
**Phase B (quality hardening):** add N-best storage, better disagreement analytics, forced alignment where feasible, and a constrained LLM tie-breaker only for narrow cases.
**Phase C (operations):** human review UI integration, dashboards, and active learning loop.

### Orchestration
- Batch segments by language route to maximize GPU throughput (Inference).
- Run models in parallel across available GPUs; treat each model decode as an independent job with retries.
- Make everything config-driven (`models`, `decode_params`, `thresholds`, `weights`), and record config snapshots with outputs.

### Data structures (suggested)
- `Segment`: `{segment_id, source_id, path, duration_s, qc_metrics}`
- `LidResult`: `{model, probs: {lang: p}, top1, top2, margin}`
- `Hypothesis`: `{model, lang, text, nbest[], token_logprobs?, avg_logprob?, decode_params}`
- `CombineResult`: `{method: rover, text, consensus_score, disagreement_spans[]}`
- `VerifyResult`: `{method: ctc_fit|forced_align, score, coverage, pass, diagnostics}`
- `Decision`: `{status: accept|reject|human, reason_codes[], chosen_text}`

### Model integration notes (practical)
- Prefer models that can expose confidence/logprob signals; if not, lean more on independent CTC scoring + agreement.
- Keep model versions pinned (Hugging Face commit hash / NeMo checkpoint version) for reproducibility.
- Persist raw hypotheses + scores; never overwrite outputs in-place without versioning.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
# master.md — Offline “Quality-First” Transcription Master Plan (Indic + English)

## Objective (from `task.md`)
Generate **near-zero-error** transcripts for cleaned, single-speaker YouTube segments (≈1–60s) across:
Malayalam, Hindi, Punjabi, Tamil, Kannada, Gujarati, Telugu, Bengali, Odia, Marathi, Assamese, English (+ unknowns).
**Compute/latency are not constraints; quality/WER is.**

This document is the **execution-ready summary**. Full per-reference analysis and detailed decisions live in `Agents.md`.

---

## Core idea
To minimize WER offline, don’t bet on one model. Instead:
1) **Route correctly (spoken LID)**
2) **Generate diverse hypotheses (ASR ensemble + N-best)**
3) **Combine (ROVER/confusion network)**
4) **Prove audio-text match (CTC/forced-alignment verification gate)**
5) **Reject uncertainty + human-correct the tail**

---

## Pipeline block diagram
```mermaid
flowchart TD
  A[Audio segment] --> B[Pre-QC + VAD trim\nformat normalize]
  B --> C[LID ensemble\nMMS-LID (+ optional VoxLingua107/Whisper)]

  C -->|Confident language| D[ASR ensemble (language-specific)\nIndicWhisper + Whisper large-v3 + IndicConformer\n(+ optional MMS)]
  C -->|Uncertain/mixed| E[ASR ensemble (multilingual-heavy)\nWhisper large-v3 + IndicConformer + MMS\n(+ Voxtral if available)]

  D --> F[Normalize hypotheses\nUnicode + IndicNLP + numerals/punct]
  E --> F

  F --> G[System combination\nROVER/confusion network\n+ disagreement map]
  G --> H[Audio-grounded verification\nCTC fit scoring\n(+ optional forced alignment)]

  H -->|PASS| I[ACCEPT\nfinal transcript + scores]
  H -->|FAIL| J[Re-decode / add model\nstricter params]
  J --> H
  H -->|Still FAIL| K[HUMAN REVIEW\ncorrect or discard]
  K --> I
```

---

## Initial model stack (validate on a gold set; tune per language)
**Spoken LID**
- Primary: **MMS-LID** (spoken LID coverage is the key requirement)
- Optional 2nd opinion: **SpeechBrain VoxLingua107 ECAPA** (and/or Whisper LID as tie-break)

**ASR (Indic)**
- **IndicWhisper** (Indic fine-tuned Whisper; “specialist”)
- **Whisper large-v3** (robust multilingual “generalist”)
- **IndicConformer-600M multilingual** (different architecture; error diversity)
- Optional diversity: **MMS ASR** (especially when others disagree / lower-resource languages)

**ASR (English)**
- **Whisper large-v3** + **Voxtral** (treat Voxtral as “measure on your gold set” for your domains)

**System combination**
- **ROVER / confusion-network voting** (keep the disagreement map)

**Audio-grounded verification**
- Primary: **CTC “fit” scoring** (score candidates against audio; reject low-fit)
- Optional: forced alignment where you can support it cleanly

---

## Why this approach is the best way to minimize WER offline (and avoid hallucinations)
This is the strongest generally applicable strategy under “unlimited compute / offline” because it attacks the dominant error sources:
- **Wrong routing is catastrophic:** Spoken LID prevents wrong-language decodes (script/vocabulary mismatch) that can silently poison training data.
- **Ensembles lower error by diversity:** Different models/architectures make different mistakes; if you can afford it, generating multiple hypotheses increases the odds that at least one is correct.
- **ROVER reduces WER when errors differ:** System combination exploits that diversity instead of “picking a winner” blindly.
- **Audio-grounded verification is the key safety gate:** A transcript can look fluent but still be wrong; independent alignment/CTC scoring forces the text to match the audio and catches hallucination patterns.
- **Precision-first acceptance yields near-zero-error training text:** If a segment can’t be verified confidently, rejecting it (and sending only the tail to humans) minimizes label noise, which matters more than maximizing coverage for training.

Important: “minimal WER” is not a claim about global benchmarks; it is achieved **for your data** by tuning gates/weights on a **human gold set** and only accepting segments that pass strict audio-grounded checks.

---

## Execution plan (deliverables)
1. **Gold set + evaluation harness (Week 1)**
   - Build a stratified gold set (all languages + duration buckets + clean/mild noise + code-switch).
   - Produce human transcripts; compute WER/CER per language; establish baselines for each model.
2. **Baseline pipeline (Week 1–2)**
   - Implement: `VAD/QC → MMS-LID → ASR ensemble → normalization → ROVER → CTC-fit verification → accept/reject`.
   - Store full audit trail (all hypotheses + scores).
3. **Calibration (Week 2)**
   - Tune: LID thresholds, per-language ROVER weights, verification-gate thresholds.
   - Goal: maximize **precision** of accepted set; accept-rate is a secondary knob.
4. **Human reject loop (Week 2–3)**
   - Create a review queue that shows audio + top hypotheses + disagreement spans for fast correction.
   - Feed corrected rejects back into calibration (and optional later fine-tuning).
5. **Scale-out + monitoring (Week 3+)**
   - Batch by language; parallelize per-model decoding; track reject reasons and residual error via sampling.

---

## Output schema (minimum needed for auditability)
For each `segment_id`, store:
- audio QC metrics, LID probs (per LID model)
- ASR hypotheses (per model + decode params + N-best if available)
- normalized hypotheses
- ROVER output + disagreement map
- verification scores (CTC/aligner) + decision (`accept|reject|human`) + reason codes

---

## LLM usage policy (optional; safe mode only)
If you use an LLM at all:
- It must **select among existing hypotheses/spans** only (no free-form “fixing”).
- Its output must still pass the same **audio-grounded verification gate** before acceptance.


**Figuring out line numbers**

I need to determine approximate line numbers for new sections in Agents.md. I can use `nl -ba` to find the line numbers for insertion points. I know the Master Plan starts at line 504 and the block diagram is around line 581. I’ll check the execution plan’s location—maybe around line 612? I’ll confirm the exact line numbers using `rg -n "Execution Plan (phased)" Agents.md`.


rg -n "Execution Plan" Agents.md master.md


Output:
Exit code: 0
Wall time: 0 seconds
Output:
Agents.md:610:### Execution Plan (phased)


Output:
Exit code: 0
Wall time: 0 seconds
Output:
603:### Why this plan drives WER down (offline, no compute constraints)


rg -n "Pipeline block diagram" master.md


Output:
Exit code: 0
Wall time: 0 seconds
Output:
22:## Pipeline block diagram


Output:
Exit code: 0
Wall time: 0 seconds
Output:
81:## Execution plan (deliverables)


Output:
Exit code: 0
Wall time: 0 seconds
Output:
69:## Why this approach is the best way to minimize WER offline (and avoid hallucinations)


- Added `master.md:1` with the block diagram (`master.md:22`), execution plan (`master.md:81`), and a careful “why this minimizes WER offline” rationale (`master.md:69`).
- Updated `Agents.md:581` to include the same block diagram, plus the “why WER goes down” section (`Agents.md:603`) and a phased execution plan (`Agents.md:610`).


# Context from my IDE setup:

## Open tabs:
- master.md: master.md
- Agents.md: Agents.md
- task.md: task.md
- Reference1.md: docs/Reference1.md
- task.md: docs/task.md

## My request for Codex:
Can you create the architecture diagram as in like a block diagram, not tree so i have to put it in a visual editor. just create the diagram with lines and replace it with existing ## Pipeline block diagram with the new lines so i can directly understand the diagram without having to put it in a diff editor or compiler to view the diagram.


**Updating ASCII diagram**

The user wants me to create an architecture diagram in a block format using ASCII instead of a mermaid tree layout. They wish to replace the existing “## Pipeline block diagram” section in master.md with this new format so that it’s easily readable without needing to compile or render it. It might also be a good idea to look at updating the “### Block Diagram (pipeline)” section in Agents.md similarly. I’ll dive into this task!


**Clarifying diagram updates**

The user specifically wants to replace the existing "## Pipeline block diagram" in master.md with an ASCII representation. They mention both documents, so I'll primarily focus on updating master.md. However, since Agents.md has a similar section titled "### Block Diagram (pipeline)," it might be beneficial and consistent to update that one too, even though they didn’t explicitly ask. Since my open tabs include both files, keeping them aligned could be appreciated. I’ll proceed with updating master.md first!


**Designing the ASCII diagram**

The user specifically mentions replacing the existing "## Pipeline block diagram," so I need to focus on that singular request. While I could clarify further, it seems clear enough to proceed with implementing the changes. I’ll create an ASCII diagram with block boxes and arrows to depict various processes like branching between confident and uncertain LID states, normalization, combination, verification, loops, and human review. Since they want this for a visual editor, I’ll ensure it’s formatted in a monospaced code block without needing a compiler.


**Creating the ASCII diagram**

I’m thinking about how to design the ASCII diagram using art boxes and arrows to represent the process clearly. It starts with the audio segment and flows through various stages, including pre-QC, language identification, confident and uncertain routes with ASR ensembles, and normalizing hypotheses. I’ll include loops for the re-decode block back to the ASR ensemble. The diagram aims to provide a clear pathway for the user, ensuring it's easy to understand without further rendering tools. I’ll format this properly!


**Finalizing the ASCII diagram**

After the re-decode step, I should rerun the combination and verification processes. I can illustrate these loops with arrows pointing back to the ASR ensemble or normalization steps. It's crucial to maintain vertical alignment and use simple ASCII characters for clarity.

The user wants the diagram to fit within a specific width without requiring any rendering. I’ll include a human review stage after verification failures in the final ASCII diagram and ensure the entire diagram adheres to their request to replace the existing "## Pipeline block diagram" in master.md.

I'll proceed with the necessary updates!


Input:
*** Begin Patch
*** Update File: master.md
@@
 ## Pipeline block diagram
-```mermaid
-flowchart TD
-  A[Audio segment] --> B[Pre-QC + VAD trim\nformat normalize]
-  B --> C[LID ensemble\nMMS-LID (+ optional VoxLingua107/Whisper)]
-
-  C -->|Confident language| D[ASR ensemble (language-specific)\nIndicWhisper + Whisper large-v3 + IndicConformer\n(+ optional MMS)]
-  C -->|Uncertain/mixed| E[ASR ensemble (multilingual-heavy)\nWhisper large-v3 + IndicConformer + MMS\n(+ Voxtral if available)]
-
-  D --> F[Normalize hypotheses\nUnicode + IndicNLP + numerals/punct]
-  E --> F
-
-  F --> G[System combination\nROVER/confusion network\n+ disagreement map]
-  G --> H[Audio-grounded verification\nCTC fit scoring\n(+ optional forced alignment)]
-
-  H -->|PASS| I[ACCEPT\nfinal transcript + scores]
-  H -->|FAIL| J[Re-decode / add model\nstricter params]
-  J --> H
-  H -->|Still FAIL| K[HUMAN REVIEW\ncorrect or discard]
-  K --> I
-```
+```text
+┌───────────────────────────────┐
+│          Audio segment         │
+└───────────────┬───────────────┘
+                │
+                v
+┌───────────────────────────────┐
+│        Pre-QC + VAD trim       │
+│   (format normalize + sanity)  │
+└───────────────┬───────────────┘
+                │
+                v
+┌───────────────────────────────┐
+│        Spoken LID ensemble     │
+│  MMS-LID (+ optional 2nd vote) │
+└───────────────┬───────────────┘
+                │
+      ┌─────────┴──────────┐
+      │                    │
+      v                    v
+┌───────────────────────┐  ┌────────────────────────┐
+│ Confident language     │  │ Uncertain / mixed       │
+│ route                  │  │ route                   │
+└───────────┬───────────┘  └───────────┬────────────┘
+            │                          │
+            v                          v
+┌───────────────────────┐  ┌────────────────────────┐
+│ ASR ensemble (per-L)   │  │ ASR ensemble (multi)    │
+│ IndicWhisper           │  │ Whisper v3              │
+│ + Whisper large-v3     │  │ + IndicConformer        │
+│ + IndicConformer       │  │ + MMS (+ Voxtral opt.)  │
+│ (+ MMS optional)       │  │                        │
+└───────────┬───────────┘  └───────────┬────────────┘
+            │                          │
+            └───────────┬──────────────┘
+                        │
+                        v
+┌───────────────────────────────┐
+│     Normalize all hypotheses   │
+│ (Unicode + IndicNLP + numbers) │
+└───────────────┬───────────────┘
+                │
+                v
+┌───────────────────────────────┐
+│  System combination (ROVER)    │
+│   + disagreement map (audit)   │
+└───────────────┬───────────────┘
+                │
+                v
+┌───────────────────────────────┐
+│   Audio-grounded verification  │
+│  CTC “fit” scoring (+ aligner) │
+└───────┬───────────────┬───────┘
+        │ PASS          │ FAIL
+        v               v
+┌───────────────────┐   ┌──────────────────────────────┐
+│ ACCEPT            │   │ Re-decode / add model / retry │
+│ transcript+scores │   │ (then repeat from ASR stage)  │
+└───────────────────┘   └───────────────┬──────────────┘
+                                        │ still FAIL after retries
+                                        v
+                              ┌──────────────────────────┐
+                              │ HUMAN REVIEW (tail only)  │
+                              │ correct → ACCEPT          │
+                              │ discard → DROP            │
+                              └──────────────────────────┘
+```

 ---
*** End Patch


Output: {"output":"Success. Updated the following files:\nM master.md\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Summary: auto


**Considering diagram options**

I'm thinking about the user's request regarding the diagram. They mentioned that they want something in text format that acts like a block diagram. I could use ASCII style with `+---+` for portability. However, since we've used Unicode boxes already, should I stick with that? The user seems to want a straightforward replacement in their Markdown file without needing a separate tool. It sounds like Unicode might work, but not all viewers display it properly. I'll keep that in mind.


**Evaluating diagram updates**

I'm considering the user's preference for ASCII over the mermaid format, as they mentioned "not tree." They didn't specifically request changes to Agents.md, but for consistency, it might be wise to replace the mermaid diagrams there as well. While I want to avoid "scope creep," maintaining a uniform presentation could be helpful. They focused on the master.md document, but updating Agents.md proactively might prevent future confusion. I need to balance the minor change against the potential need for clarity.


**Updating both diagrams**

The user previously requested creation of both master.md and agents.md with diagrams. Now they’ve asked to "replace" the diagram in master.md, but it seems they might also want the same update in agents.md for consistency. They didn’t specifically mention agents.md this time, but since both were important initially, I think it’s reasonable to update it too. I’ll replace the mermaid code block in agents.md's pipeline section with the ASCII diagram, making it clearer and more readable for them. Let’s ensure they know that I've updated both documents!


**Deciding on diagram style**

The user specifically asked to "replace it" in the master.md with the existing pipeline block diagram, but I think updating both master.md and agents.md is wise. If they see the mermaid diagram in agents.md, it might not meet their expectations. Since agents.md currently has a mermaid diagram, I'll replace that with the ASCII version for consistency. I need to consider if I should stick with Unicode or switch entirely to ASCII in both documents. I'm weighing the advantages of each style!


**Choosing diagram format**

The user seems to want a straightforward representation in a visual editor, which makes me think that plain ASCII would be the best choice since Unicode box drawing might not copy well. ASCII is simpler and should render correctly across different platforms, ensuring maximum compatibility. I plan to create an ASCII diagram using plus signs and dashes while keeping a consistent width. I'll also patch agents.md by replacing its mermaid diagram with the same ASCII version to maintain uniformity in both documents. It’s all about making it clear and readable for the user!


**Updating the diagram code block**

I’m planning to use apply_patch to update the existing code block. I’ll start at the line that has ```mermaid and replace it with a new ```text block for the ASCII diagram. This change will ensure that the user gets the updated format they prefer. It's a straightforward process, so let's go ahead and patch it now! It’s important to keep everything organized and in line with what the user requested.


Input:
*** Begin Patch
*** Update File: Agents.md
@@
 ### Block Diagram (pipeline)
-```mermaid
-flowchart TD
-  A[Audio segment] --> B[Pre-QC + VAD trim\nformat normalize]
-  B --> C[LID ensemble\nMMS-LID (+ optional VoxLingua107/Whisper)]
-
-  C -->|Confident language| D[ASR ensemble (language-specific)\nIndicWhisper + Whisper large-v3 + IndicConformer\n(+ optional MMS)]
-  C -->|Uncertain/mixed| E[ASR ensemble (multilingual-heavy)\nWhisper large-v3 + IndicConformer + MMS\n(+ Voxtral if available)]
-
-  D --> F[Normalize hypotheses\nUnicode + IndicNLP + numerals/punct]
-  E --> F
-
-  F --> G[System combination\nROVER/confusion network\n+ disagreement map]
-  G --> H[Audio-grounded verification\nCTC fit scoring\n(+ optional forced alignment)]
-
-  H -->|PASS| I[ACCEPT\nfinal transcript + scores]
-  H -->|FAIL| J[Re-decode / add model\nstricter params]
-  J --> H
-  H -->|Still FAIL| K[HUMAN REVIEW\ncorrect or discard]
-  K --> I
-```
+```text
+┌───────────────────────────────┐
+│          Audio segment         │
+└───────────────┬───────────────┘
+                │
+                v
+┌───────────────────────────────┐
+│        Pre-QC + VAD trim       │
+│   (format normalize + sanity)  │
+└───────────────┬───────────────┘
+                │
+                v
+┌───────────────────────────────┐
+│        Spoken LID ensemble     │
+│  MMS-LID (+ optional 2nd vote) │
+└───────────────┬───────────────┘
+                │
+      ┌─────────┴──────────┐
+      │                    │
+      v                    v
+┌───────────────────────┐  ┌────────────────────────┐
+│ Confident language     │  │ Uncertain / mixed       │
+│ route                  │  │ route                   │
+└───────────┬───────────┘  └───────────┬────────────┘
+            │                          │
+            v                          v
+┌───────────────────────┐  ┌────────────────────────┐
+│ ASR ensemble (per-L)   │  │ ASR ensemble (multi)    │
+│ IndicWhisper           │  │ Whisper v3              │
+│ + Whisper large-v3     │  │ + IndicConformer        │
+│ + IndicConformer       │  │ + MMS (+ Voxtral opt.)  │
+│ (+ MMS optional)       │  │                        │
+└───────────┬───────────┘  └───────────┬────────────┘
+            │                          │
+            └───────────┬──────────────┘
+                        │
+                        v
+┌───────────────────────────────┐
+│     Normalize all hypotheses   │
+│ (Unicode + IndicNLP + numbers) │
+└───────────────┬───────────────┘
+                │
+                v
+┌───────────────────────────────┐
+│  System combination (ROVER)    │
+│   + disagreement map (audit)   │
+└───────────────┬───────────────┘
+                │
+                v
+┌───────────────────────────────┐
+│   Audio-grounded verification  │
+│  CTC “fit” scoring (+ aligner) │
+└───────┬───────────────┬───────┘
+        │ PASS          │ FAIL
+        v               v
+┌───────────────────┐   ┌──────────────────────────────┐
+│ ACCEPT            │   │ Re-decode / add model / retry │
+│ transcript+scores │   │ (then repeat from ASR stage)  │
+└───────────────────┘   └───────────────┬──────────────┘
+                                        │ still FAIL after retries
+                                        v
+                              ┌──────────────────────────┐
+                              │ HUMAN REVIEW (tail only)  │
+                              │ correct → ACCEPT          │
+                              │ discard → DROP            │
+                              └──────────────────────────┘
+```
*** End Patch


Output: {"output":"Success. Updated the following files:\nM Agents.md\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Updating diagram details**

It looks like the user wants me to replace the existing "## Pipeline block diagram" with new lines in master.md, and I’ve done that. I’ll let them know it’s complete and mention where the update was made. I should also mention that I updated Agents.md for consistency, which is a good detail to include. Finally, I’ll make sure to verify that master.md no longer contains any mermaid references.


rg -n "```mermaid" master.md Agents.md || true


Output:
Exit code: 2
Wall time: 0 seconds
Output:
/bin/bash: -c: line 1: unexpected EOF while looking for matching ``'
/bin/bash: -c: line 2: syntax error: unexpected end of file


rg -n "mermaid" master.md Agents.md || true


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


**Finalizing documentation update**

I’m confirming that there’s no mermaid. That’s good. I need to make sure the diagram in `master.md` is under the appropriate header and not replaced. The user might prefer not having to compile. While I could show a snippet, they can view the file directly instead. So, I’ll finalize my response by mentioning the updated `master.md` pipeline diagram to ASCII block diagram lines and the updated `Agents.md`, keeping it concise since we already updated it.


Replaced `master.md:22` `## Pipeline block diagram` with a plain-text block diagram (boxes + lines) so it’s readable directly in the file (no Mermaid/rendering needed).

Also swapped the diagram in `Agents.md:581` to the same plain-text format for consistency.


# Context from my IDE setup:

## Open tabs:
- .env: .env
- Prompt.md: Prompt.md
- master.md: master.md
- Agents.md: Agents.md
- task.md: task.md

## My request for Codex:
can you check the implementation of my other coding agent and add reviews comments. maybe if you have comments, provide them in chat here and i shall carry them to the other agent. check for code robustness, and failure cases, edge cases in perspective of youtube audio segments and minimizing WER. let's also think of a sophisticated way of evaluating this pipeline. I can maybe help with telugu and hindi, but for other languages we need a robust handler like a superior model. what shall we consider for this. maybe a higher LLM like gemini-3-pro ?

# To run this code you need to install the following dependencies:
# pip install google-genai

import base64
import os
from google import genai
from google.genai import types

def generate():
    client = genai.Client(
        api_key=os.environ.get("GEMINI_API_KEY"),
    )

    model = "gemini-3-pro-preview"
    contents = [
        types.Content(
            role="user",
            parts=[
                types.Part.from_text(text="""INSERT_INPUT_HERE"""),
            ],
        ),
    ]
    generate_content_config = types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(
            thinking_level="HIGH",
        ),
        system_instruction=[
            types.Part.from_text(text="""Instruction: You are a strict, verbatim transcription engine for Indian languages
Input: Audio file attached
Primary audio language: telugu
Task:
Listen to the audio carefully
Identify the primary spoken language as specified above
Transcribe the speech exactly as spoken and produce four separate outputs as defined below
Sometimes you won't be clear about complete transcription but you know parts of it. From that you can fix the grammar. That's how you can create the best transcription.
You know the first word and the last word. You can create the best transcription. You know the middle word and the last word. You can create the best transcription.
You need to think in terms of what you know and how you can go to the end with 100% accuracy with predicting what comes here, what would be it, what is the right fit and attaching it all together and doing the combinations

STRICT GLOBAL RULES (apply to all outputs):
Verbatim only Include all repetitions filler words stammers false starts hesitations and colloquial expressions exactly as spoken
No normalization Do not correct grammar do not fix pronunciation do not standardize spellings and do not clean up dialect accent or mixed language usage
No inference Do not add meaning structure or emphasis that is not clearly audible
Script fidelity Follow the rules of each output strictly Do not translate paraphrase or summarize
OUTPUT 1: Proper native transcription
Write everything only in the native script of the primary language
Do not use any punctuation marks at all
Even if English words are spoken write them phonetically using the native script of the primary language
Preserve repetitions fillers and stammers exactly

OUTPUT FORMAT RULES:
Clearly label the four outputs as
Output 1: Proper native transcription
Do not add explanations notes or any extra English text beyond the labels"""),
        ],
    )

    for chunk in client.models.generate_content_stream(
        model=model,
        contents=contents,
        config=generate_content_config,
    ):
        print(chunk.text, end="")

if __name__ == "__main__":
    generate()

API key is in .env as GEMINI_KEY and include prompt robustness by understanding the prompt and giving it robust instructions as per [Prompt.md](Prompt.md)

First finish the code review, then suggest evaluations for WER check for our pipeline with a superior model.


**Reviewing code and model capabilities**

I need to focus on providing a thorough code review, addressing areas like improved error handling, proper audio attachment, and using `types.Part.from_bytes` for attaching audio. Currently, there’s a placeholder instead of actual audio. The system instruction indicates an audio file should be attached, but it isn't. Also, I need to confirm if the `gemini-3-pro-preview` model supports audio input or this API since some models do. Checking the docs might help, and it looks like we do have network access for this.


**Evaluating pipeline and assumptions**

I need to assess our pipeline using a superior model, like a high-quality ASR as a teacher or a human-labeled dataset. However, I must adhere to our non-negotiables, meaning no invention and only claiming what’s backed by references. I can’t assert that gemini 3 pro has the best WER without measurements. While I should mention that Gemini models can process audio input, I'm unsure about gemini-3-pro's capabilities. I’ll also check out the `Prompt.md` for guidelines and remember that the repo is ‘maya-transcribe’ with essential files already added.


**Checking configuration and edge cases**

I need to check the `.env` file to make sure the variable names match the code. They mentioned the API key is set as `GEMINI_KEY`, but the code uses `GEMINI_API_KEY`. I also need to consider prompt robustness outlined in `Prompt.md`. Additionally, discussing edge cases like YouTube audio segments is essential, including issues with minimal WER, multi-language code-switching, transliteration, proper nouns, and potential disfluencies or background noise, even if they mentioned a single speaker. Their prompt suggests that the model should improve grammar for better transcriptions.


**Critiquing guidance on inference**

I need to think carefully about the instruction that encourages making predictions with 100% accuracy while also filling in unknown parts. This approach could lead to inference or hallucination, which contradicts our "no invention" principle and could harm WER by guessing unheard words. For training data, we should mark unclear segments as `[inaudible]` or `<unk>` instead of making guesses. While minimizing WER is important, we want precise transcripts, so critiquing this guidance is necessary to ensure the integrity of the training data.


**Evaluating complex transcription guidelines**

I need to analyze their request that involves producing proper native transcriptions without punctuation and writing English words phonetically in native script. While this might work for training ASR/TTS, it may not be practical for measuring WER against ground truth. If the goal is multilingual capability, maintaining Latin script for English is essential. They also mention multiple outputs but only define one, which is confusing. Additionally, I need to integrate language identification for code-switching and refine the system instruction handling, considering the environment variables and secret management. With multiple models suggested, I should plan an evaluation strategy that includes collecting a gold set with human transcripts and determining appropriate metrics for Indic languages.


**Checking for missing files and robustness**

I have another idea: using forced alignment or phoneme-level scoring with grapheme-to-phoneme for evaluation. However, we definitely need ground truth for that. The user wants me to check for code robustness, especially concerning YouTube audio segments and minimizing WER. I should review the code and prompt, but I can't find the `Prompt.md` file they mentioned. I'll check again using the shell command `ls -la` to see if it's listed somewhere after looking previously. If it's missing, I'll also consider using `find` to locate it.


ls -la Prompt.md || true; find . -maxdepth 2 -name 'Prompt.md' -print


Output:
Exit code: 0
Wall time: 0 seconds
Output:
-rw-rw-r-- 1 ubuntu ubuntu 2152 Jan 13 04:12 Prompt.md
./Prompt.md


Summary: auto


Output:
Exit code: 0
Wall time: 0 seconds
Output:
This is a prompt for any LLM based transcription tasks. use this when using Gemma3n or voxtral or any other LLM based actions. Feel free to adjust this if it doesnt make sense. but keep it on the same tone or restrictive tone to avoid hallucinations or content creation rather transcription.

`prompt`

`Instruction: You are a strict, verbatim transcription engine for Indian languages
Input: Audio file attached
Primary audio language: <language>
Task:
Listen to the audio carefully
Identify the primary spoken language as specified above
Transcribe the speech exactly as spoken and produce  outputs as defined below
Sometimes you won't be clear about complete transcription but you know parts of it. From that you can fix the grammar. That's how you can create the best transcription.
You know the first word and the last word. You can create the best transcription. You know the middle word and the last word. You can create the best transcription.
You need to think in terms of what you know and how you can go to the end with 100% accuracy with predicting what comes here, what would be it, what is the right fit and attaching it all together and doing the combinations

STRICT GLOBAL RULES (apply to all outputs):
Verbatim only Include all repetitions filler words stammers false starts hesitations and colloquial expressions exactly as spoken
No normalization Do not correct grammar do not fix pronunciation do not standardize spellings and do not clean up dialect accent or mixed language usage
No inference Do not add meaning structure or emphasis that is not clearly audible
Script fidelity Follow the rules of each output strictly Do not translate paraphrase or summarize
OUTPUT 1: Proper native transcription
Write everything only in the native script of the primary language
Do not use any punctuation marks at all
Even if English words are spoken write them phonetically using the native script of the primary language
Preserve repetitions fillers and stammers exactly

OUTPUT FORMAT RULES:
Clearly label the four outputs as
Output 1: Proper native transcription
Do not add explanations notes or any extra English text beyond the labels`


**Evaluating transcription performance**

I think I need to analyze Gemini’s audio transcription performance since it could vary in quality. Testing it out will help me understand how well it works in different situations. I wonder what types of audio or languages might present challenges? It’s important to get a clear idea of its capabilities and limitations. Hopefully, I can find a reliable way to assess it effectively!


**Assessing language performance**

I find that Google Speech-to-Text (STT) seems strong for many Indic languages, but we can’t make claims without testing. I’ll propose measuring it against a gold standard.

For handling unknown languages, using spoken language identification (LID) is essential. If a segment is under 3 seconds, we should treat LID as uncertain and run multiple LIDs. During code-switching, multilingual ASR might be the way to go.

For evaluation, building a gold set without native speakers can be done by hiring bilingual annotators or using a consensus approach, but true word error rate (WER) will need some human labels for accuracy.


**Prioritizing language adjustments**

I’m thinking we could start by focusing on high-value languages first.

For the prompt adjustments, I need to suggest removing grammar-fixing elements, emphasizing unknown placeholders, and ensuring no guessing or extra English is presented beyond labels. I can also provide an improved template and note the difference between "GEMINI_API_KEY" and "GEMINI_KEY," ensuring the API key is loaded correctly.

Additionally, they need to input audio with a placeholder like "INSERT_INPUT_HERE" and should feed content using `contents`, either combining text and audio or using `types.Part.from_bytes` or `types.Part.from_uri`. Of course, I must ensure not to include any sensitive information while suggesting ways to retrieve environment variables.


**Handling environment variables**

I want to begin by carefully accessing the first lines of the file, but I need to avoid printing any sensitive values. I could potentially read the variable names using the shell, but I must be cautious since commands like `rg` would print lines containing secrets, which is risky.

Instead, using Python seems safer to list variable names while masking the values. I can confirm that the key is `GEMINI_KEY` from the user statement, but it's good to double-check in case the actual file uses a different name. My plan is to parse the `.env` file in Python and only output the keys, ensuring no sensitive information is displayed.


python3 - <<'PY'
import re
from pathlib import Path
p = Path('.env')
keys = []
for line in p.read_text(encoding='utf-8').splitlines():
    line=line.strip()
    if not line or line.startswith('#'):
        continue
    m=re.match(r'([A-Za-z_][A-Za-z0-9_]*)\s*=\s*(.*)', line)
    if m:
        keys.append(m.group(1))
print('Keys in .env:')
for k in keys:
    print('-', k)
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
Keys in .env:
- HF_TOKEN
- R2_ENDPOINT_URL
- R2_BUCKET
- R2_ACCESS_KEY_ID
- R2_SECRET_ACCESS_KEY
- ACCOUNT_ID
- MAX_CONSECUTIVE_EMPTY
- DATABASE_URL
- SUPABASE_ANON
- SUPABASE_ADMIN
- PROJECT_ID
- HOST
- PORT
- DATABASE
- USER
- URL
- NGROK
- RUNPOD_KEY
- docker_pat
- HF_TOKEN
- docker_image
- R2_ENDPOINT_URL
- R2_BUCKET
- R2_ACCESS_KEY_ID
- R2_SECRET_ACCESS_KEY
- ACCOUNT_ID
- S3_API
- API_TOKEN
- WORKER_PROVIDER
- WORKER_REGION
- MAX_UTILIZATION
- R2_PREFIX
- MAX_CONSECUTIVE_EMPTY
- ORCHESTRATOR_URL
- VAST_AI
- GEMINI_KEY


**Examining code structure**

I see that in the `.env`, there's the `GEMINI_KEY`, which the code should reference or load. But there are mentions of duplicate keys, which seem irrelevant. Now, the user talked about examining another agent's code, but they only provided a snippet. There could be more in the repository. Maybe their other coding agent built an implementation pipeline, but they didn’t specify the file path. I'll look through root for Python files and see if I can find anything useful.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
Agents.md
pyproject.toml
maya_transcribe.egg-info/entry_points.txt
maya_transcribe.egg-info/PKG-INFO
maya_transcribe.egg-info/requires.txt
maya_transcribe.egg-info/SOURCES.txt
maya_transcribe.egg-info/dependency_links.txt
maya_transcribe.egg-info/top_level.txt
maya_transcribe/r2.py
maya_transcribe/rover.py
maya_transcribe/asr.py
maya_transcribe/verify.py
outputs/test_run/results.jsonl
outputs/test_run/summary.json
outputs/test_run/audio/SPEAKER_00_0000_0.03-12.15.wav
maya_transcribe/__pycache__/align.cpython-310.pyc
maya_transcribe/__pycache__/pipeline.cpython-310.pyc
maya_transcribe/__pycache__/audio.cpython-310.pyc
maya_transcribe/__pycache__/cli.cpython-310.pyc
maya_transcribe/__pycache__/lid.cpython-310.pyc
maya_transcribe/__pycache__/tar.cpython-310.pyc
maya_transcribe/__pycache__/dataset.cpython-310.pyc
maya_transcribe/__pycache__/db.cpython-310.pyc
maya_transcribe/__pycache__/textnorm.cpython-310.pyc
maya_transcribe/__pycache__/__init__.cpython-310.pyc
maya_transcribe/__pycache__/config.cpython-310.pyc
maya_transcribe/__pycache__/r2.cpython-310.pyc
maya_transcribe/__pycache__/eval.cpython-310.pyc
maya_transcribe/__pycache__/asr.cpython-310.pyc
maya_transcribe/__pycache__/io.cpython-310.pyc
maya_transcribe/__pycache__/language.cpython-310.pyc
maya_transcribe/__pycache__/rover.cpython-310.pyc
maya_transcribe/__pycache__/verify.cpython-310.pyc
maya_transcribe/db.py
maya_transcribe/dataset.py
maya_transcribe/config.py
maya_transcribe/pipeline.py
maya_transcribe/tar.py
maya_transcribe/eval.py
maya_transcribe/__init__.py
maya_transcribe/lid.py
maya_transcribe/language.py
maya_transcribe/align.py
maya_transcribe/audio.py
maya_transcribe/textnorm.py
maya_transcribe/cli.py
maya_transcribe/io.py
outputs/test_run2/results.jsonl
outputs/test_run2/refs.jsonl
outputs/test_run2/summary.json
README.md
task.md
docs/Reference1.md
docs/Reference3.md
docs/Reference5.md
docs/Reference4.md
docs/Reference6.md
docs/Reference2.md
Prompt.md
master.md
outputs/test_run2/audio/SPEAKER_00_0000_0.03-12.15.wav
data/--0C5YVdu5s/--0C5YVdu5s.tar
data/--0C5YVdu5s/--0C5YVdu5s/metadata.json
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0004_22.31-27.20.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0012_70.86-77.60.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0015_99.68-105.35.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0032_196.81-201.70.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0034_204.72-219.60.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0021_134.47-138.24.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0011_65.66-69.47.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0029_179.73-190.31.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0005_27.20-30.96.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0000_0.03-12.15.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0018_116.38-124.59.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0031_192.12-196.22.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0028_170.97-179.72.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0020_130.85-133.60.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0025_152.95-156.99.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0033_202.77-204.72.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0024_148.04-152.60.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0027_165.73-170.97.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0022_138.24-139.80.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0017_107.81-116.37.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0003_20.79-22.31.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0030_190.70-191.97.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0010_62.70-65.15.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0002_17.95-20.62.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0009_54.62-61.78.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0016_105.45-107.78.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0006_31.00-41.41.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0014_93.11-96.80.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0001_12.20-17.26.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0026_157.12-162.80.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0023_141.12-147.48.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0013_85.01-92.51.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_01_0019_124.59-129.70.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0008_51.84-53.24.flac
data/--0C5YVdu5s/--0C5YVdu5s/segments/SPEAKER_00_0007_41.48-51.04.flac


**Reviewing code package**

I'm looking into the package `maya_transcribe`, which contains several modules like asr.py, verify.py, and more. It seems there's a full implementation pipeline that the user wants to review. I think I should inspect these files to assess their robustness and look for any potential failure cases. I need to be careful as there might be secrets in the code, but probably not. I’ll quickly check the `README.md` for more context and details.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
# maya-transcribe

Offline, quality-first transcription pipeline for cleaned single-speaker audio segments (Indic + English).

## Install

```bash
python3 -m pip install -U pip
python3 -m pip install -e '.[full]'
```

If you only need R2 download + evaluation (no ASR), install the base deps:

```bash
python3 -m pip install -e .
```

## Configure

Create / update `.env` in the repo root. The CLI auto-loads it.

Minimum for R2:
- `R2_ENDPOINT_URL`
- `R2_ACCESS_KEY_ID`
- `R2_SECRET_ACCESS_KEY`
- `R2_BUCKET` (defaults to `1-cleaned-data` if unset)
- `R2_PREFIX` (optional)

## Usage

```bash
maya-transcribe --help
maya-transcribe download --video-id <VIDEO_ID> --bucket 1-cleaned-data --out data/
maya-transcribe run --segments-dir data/<VIDEO_ID>/**/segments --out outputs/<VIDEO_ID> --whisper-model large-v3
```

Evaluation (requires a reference JSONL keyed by `segment_id`):

```bash
maya-transcribe eval --results outputs/<VIDEO_ID>/results.jsonl --refs refs.jsonl
```


**Evaluating pipeline modules**

Okay, so the pipeline is implemented and now I'll take a closer look at the important modules. My job here is to assess code robustness, identify failure cases, and analyze edge cases for the YouTube segments. I also need to evaluate the design and think about the possibility of utilizing superior models like Gemini 3 Pro. First, I’ll inspect `pyproject.toml` to check the dependencies and features. Let's get to it!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
[build-system]
requires = ["setuptools>=69", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "maya-transcribe"
version = "0.1.0"
description = "Quality-first offline transcription pipeline (Indic + English)"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
  "boto3>=1.34.0",
  "jiwer>=3.0.4",
  "numpy>=1.24.0",
  "python-dotenv>=1.0.1",
  "rich>=13.7.1",
  "typer>=0.12.3",
]

[project.optional-dependencies]
supabase = ["supabase>=2.6.0"]
db = ["psycopg2-binary>=2.9.9"]
asr = [
  "faster-whisper>=1.0.3",
  "soundfile>=0.12.1",
]
torch = [
  "torch>=2.2.0",
  "torchaudio>=2.2.0",
  "transformers>=4.41.0",
]
full = [
  "supabase>=2.6.0",
  "psycopg2-binary>=2.9.9",
  "faster-whisper>=1.0.3",
  "soundfile>=0.12.1",
  "torch>=2.2.0",
  "torchaudio>=2.2.0",
  "transformers>=4.41.0",
]

[project.scripts]
maya-transcribe = "maya_transcribe.cli:app"

[tool.setuptools.packages.find]
where = ["."]
include = ["maya_transcribe*"]


**Inspecting ASR modules**

I’m looking into the pipeline that uses faster-whisper for automatic speech recognition. It seems there are no AI4Bharat models involved, which is pretty minimal. I want to check various modules like lid, text normalization, verify, and rover to see what’s implemented. My next step is to inspect the `maya_transcribe/pipeline.py` file to gather more insight. I think that should help clarify how everything works together!


sed -n '1,260p' maya_transcribe/pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 247
Output:
from __future__ import annotations

import json
import time
from dataclasses import asdict
from pathlib import Path
from typing import Any

from rich.progress import track

from maya_transcribe.asr import ASRResult, FasterWhisperASR, TransformersWhisperASR
from maya_transcribe.audio import compute_qc, ffprobe, normalize_audio
from maya_transcribe.lid import LIDResult, MMSLID
from maya_transcribe.rover import rover_combine
from maya_transcribe.textnorm import normalize_text
from maya_transcribe.verify import verify_segment

def _iter_audio_files(segments_dir: Path) -> list[Path]:
    audio_files: list[Path] = []
    for ext in ("*.wav", "*.mp3", "*.m4a", "*.flac", "*.ogg", "*.opus"):
        audio_files.extend(sorted(segments_dir.rglob(ext)))
    return sorted(set(audio_files))

def _asr_summary(result: ASRResult) -> dict[str, Any]:
    avg_logprobs = [s.avg_logprob for s in result.segments if s.avg_logprob is not None]
    no_speech_probs = [s.no_speech_prob for s in result.segments if s.no_speech_prob is not None]
    compression_ratios = [s.compression_ratio for s in result.segments if s.compression_ratio is not None]

    def mean(xs: list[float]) -> float | None:
        return (sum(xs) / len(xs)) if xs else None

    return {
        "language": result.language,
        "language_probability": result.language_probability,
        "avg_logprob_mean": mean([float(x) for x in avg_logprobs]),
        "no_speech_prob_mean": mean([float(x) for x in no_speech_probs]),
        "compression_ratio_mean": mean([float(x) for x in compression_ratios]),
    }

def run_pipeline(
    *,
    segments_dir: Path,
    out_dir: Path,
    lid_model_id: str = "facebook/mms-lid-256",
    whisper_model: str = "large-v3",
    extra_transformers_whisper_models: list[str] | None = None,
    limit: int | None = None,
    thresholds: dict[str, Any] | None = None,
) -> Path:
    """
    End-to-end run: preprocess -> LID -> ASR ensemble -> normalize -> ROVER -> verify.
    Writes `out_dir/results.jsonl` and returns that path.
    """
    thresholds = thresholds or {}
    out_dir.mkdir(parents=True, exist_ok=True)

    audio_files = _iter_audio_files(segments_dir)
    if not audio_files:
        raise RuntimeError(f"No audio files found under: {segments_dir}")
    if limit is not None:
        audio_files = audio_files[: max(0, int(limit))]

    # Instantiate models once.
    lid_backend: MMSLID | None
    try:
        lid_backend = MMSLID(model_id=lid_model_id)
    except Exception:
        lid_backend = None

    whisper_backend = FasterWhisperASR(model_size_or_path=whisper_model)

    extra_transformers_whisper_models = extra_transformers_whisper_models or []
    transformers_backends: list[TransformersWhisperASR] = []
    for model_id in extra_transformers_whisper_models:
        transformers_backends.append(TransformersWhisperASR(model_id=model_id))

    results_path = out_dir / "results.jsonl"
    summary_path = out_dir / "summary.json"

    counts = {"accept": 0, "human": 0, "reject": 0, "error": 0}
    timings_total: list[float] = []

    with results_path.open("w", encoding="utf-8") as f:
        for src in track(audio_files, description="Transcribing"):
            segment_id = src.stem
            t0 = time.perf_counter()

            try:
                # 1) Preprocess
                t_pre0 = time.perf_counter()
                wav_path = out_dir / "audio" / f"{segment_id}.wav"
                normalize_audio(src, wav_path, trim_silence=True)
                info = ffprobe(wav_path)
                qc = compute_qc(wav_path)
                t_pre1 = time.perf_counter()

                # 2) LID
                t_lid0 = time.perf_counter()
                lid: LIDResult | None = None
                if lid_backend is not None:
                    lid = lid_backend.predict(wav_path, top_k=5)
                t_lid1 = time.perf_counter()

                # Route language hint (only if confident).
                route_lang = None
                if lid and lid.predictions:
                    top1 = lid.predictions[0]
                    top2p = lid.predictions[1].prob if len(lid.predictions) > 1 else 0.0
                    if float(top1.prob) >= float(thresholds.get("lid_min_prob", 0.90)) and (
                        float(top1.prob) - float(top2p)
                    ) >= float(thresholds.get("lid_min_margin", 0.20)):
                        route_lang = top1.code

                # 3) ASR ensemble
                t_asr0 = time.perf_counter()
                asr_results: dict[str, ASRResult] = {}

                # Whisper: deterministic + a small sampling diversity pass.
                det = whisper_backend.transcribe(wav_path, language=route_lang, beam_size=5, best_of=5, temperature=0.0)
                asr_results[f"{det.model}|det"] = det
                samp = whisper_backend.transcribe(
                    wav_path, language=route_lang, beam_size=5, best_of=10, temperature=0.2
                )
                a…105 chars truncated…                  res = tf_backend.transcribe(wav_path, language=route_lang, num_beams=5, max_new_tokens=256)
                    asr_results[res.model] = res

                t_asr1 = time.perf_counter()

                # 4) Normalize hypotheses
                t_norm0 = time.perf_counter()
                norm_hyps: dict[str, str] = {}
                for k, res in asr_results.items():
                    lang_hint = res.language or route_lang
                    norm_hyps[k] = normalize_text(res.text, lang=lang_hint)
                t_norm1 = time.perf_counter()

                # 5) ROVER
                t_rover0 = time.perf_counter()
                rover = rover_combine(norm_hyps)
                t_rover1 = time.perf_counter()

                # 6) Verify
                t_ver0 = time.perf_counter()
                asr_summaries = {k: _asr_summary(v) for k, v in asr_results.items()}
                verification = verify_segment(
                    qc=asdict(qc),
                    lid=lid,
                    rover={
                        "text": rover.text,
                        "disagreement_rate": rover.disagreement_rate,
                        "skeleton_key": rover.skeleton_key,
                    },
                    asr_summaries=asr_summaries,
                    thresholds=thresholds,
                )
                t_ver1 = time.perf_counter()

                record = {
                    "segment_id": segment_id,
                    "src_path": str(src),
                    "wav_path": str(wav_path),
                    "ffprobe": asdict(info),
                    "qc": asdict(qc),
                    "lid": (
                        None
                        if lid is None
                        else {
                            "model": lid.model,
                            "predictions": [{"code": p.code, "prob": p.prob} for p in lid.predictions],
                        }
                    ),
                    "route": {"language_hint": route_lang},
                    "asr": {
                        k: {
                            "model": v.model,
                            "text": v.text,
                            "language": v.language,
                            "language_probability": v.language_probability,
                            "segments": [asdict(s) for s in v.segments],
                            "meta": v.meta,
                        }
                        for k, v in asr_results.items()
                    },
                    "normalized": norm_hyps,
                    "rover": {
                        "text": rover.text,
                        "skeleton_key": rover.skeleton_key,
                        "disagreement_rate": rover.disagreement_rate,
                        "disagreements": [
                            {"kind": d.kind, "index": d.index, "chosen": d.chosen, "options": d.options}
                            for d in rover.disagreements
                        ],
                    },
                    "verification": {
                        "decision": verification.decision,
                        "reasons": verification.reasons,
                        "scores": verification.scores,
                    },
                    "timing": {
                        "preprocess_sec": t_pre1 - t_pre0,
                        "lid_sec": t_lid1 - t_lid0,
                        "asr_sec": t_asr1 - t_asr0,
                        "normalize_sec": t_norm1 - t_norm0,
                        "rover_sec": t_rover1 - t_rover0,
                        "verify_sec": t_ver1 - t_ver0,
                        "total_sec": time.perf_counter() - t0,
                    },
                }

                f.write(json.dumps(record, ensure_ascii=False) + "\n")
                decision = verification.decision
                counts[decision] = counts.get(decision, 0) + 1
                timings_total.append(record["timing"]["total_sec"])

            except Exception as e:  # pragma: no cover
                counts["error"] += 1
                f.write(
                    json.dumps(
                        {"segment_id": segment_id, "src_path": str(src), "error": str(e)}, ensure_ascii=False
                    )
                    + "\n"
                )

    summary = {
        "segments_total": len(audio_files),
        "counts": counts,
        "avg_total_sec": (sum(timings_total) / len(timings_total)) if timings_total else None,
        "p50_total_sec": _percentile(timings_total, 0.50),
        "p90_total_sec": _percentile(timings_total, 0.90),
    }
    summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
    return results_path

def _percentile(xs: list[float], q: float) -> float | None:
    if not xs:
        return None
    xs_sorted = sorted(xs)
    idx = int(round((len(xs_sorted) - 1) * q))
    idx = max(0, min(len(xs_sorted) - 1, idx))
    return float(xs_sorted[idx])


sed -n '80,190p' maya_transcribe/pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    results_path = out_dir / "results.jsonl"
    summary_path = out_dir / "summary.json"

    counts = {"accept": 0, "human": 0, "reject": 0, "error": 0}
    timings_total: list[float] = []

    with results_path.open("w", encoding="utf-8") as f:
        for src in track(audio_files, description="Transcribing"):
            segment_id = src.stem
            t0 = time.perf_counter()

            try:
                # 1) Preprocess
                t_pre0 = time.perf_counter()
                wav_path = out_dir / "audio" / f"{segment_id}.wav"
                normalize_audio(src, wav_path, trim_silence=True)
                info = ffprobe(wav_path)
                qc = compute_qc(wav_path)
                t_pre1 = time.perf_counter()

                # 2) LID
                t_lid0 = time.perf_counter()
                lid: LIDResult | None = None
                if lid_backend is not None:
                    lid = lid_backend.predict(wav_path, top_k=5)
                t_lid1 = time.perf_counter()

                # Route language hint (only if confident).
                route_lang = None
                if lid and lid.predictions:
                    top1 = lid.predictions[0]
                    top2p = lid.predictions[1].prob if len(lid.predictions) > 1 else 0.0
                    if float(top1.prob) >= float(thresholds.get("lid_min_prob", 0.90)) and (
                        float(top1.prob) - float(top2p)
                    ) >= float(thresholds.get("lid_min_margin", 0.20)):
                        route_lang = top1.code

                # 3) ASR ensemble
                t_asr0 = time.perf_counter()
                asr_results: dict[str, ASRResult] = {}

                # Whisper: deterministic + a small sampling diversity pass.
                det = whisper_backend.transcribe(wav_path, language=route_lang, beam_size=5, best_of=5, temperature=0.0)
                asr_results[f"{det.model}|det"] = det
                samp = whisper_backend.transcribe(
                    wav_path, language=route_lang, beam_size=5, best_of=10, temperature=0.2
                )
                asr_results[f"{samp.model}|samp_t0.2"] = samp

                for tf_backend in transformers_backends:
                    res = tf_backend.transcribe(wav_path, language=route_lang, num_beams=5, max_new_tokens=256)
                    asr_results[res.model] = res

                t_asr1 = time.perf_counter()

                # 4) Normalize hypotheses
                t_norm0 = time.perf_counter()
                norm_hyps: dict[str, str] = {}
                for k, res in asr_results.items():
                    lang_hint = res.language or route_lang
                    norm_hyps[k] = normalize_text(res.text, lang=lang_hint)
                t_norm1 = time.perf_counter()

                # 5) ROVER
                t_rover0 = time.perf_counter()
                rover = rover_combine(norm_hyps)
                t_rover1 = time.perf_counter()

                # 6) Verify
                t_ver0 = time.perf_counter()
                asr_summaries = {k: _asr_summary(v) for k, v in asr_results.items()}
                verification = verify_segment(
                    qc=asdict(qc),
                    lid=lid,
                    rover={
                        "text": rover.text,
                        "disagreement_rate": rover.disagreement_rate,
                        "skeleton_key": rover.skeleton_key,
                    },
                    asr_summaries=asr_summaries,
                    thresholds=thresholds,
                )
                t_ver1 = time.perf_counter()

                record = {
                    "segment_id": segment_id,
                    "src_path": str(src),
                    "wav_path": str(wav_path),
                    "ffprobe": asdict(info),
                    "qc": asdict(qc),
                    "lid": (
                        None
                        if lid is None
                        else {
                            "model": lid.model,
                            "predictions": [{"code": p.code, "prob": p.prob} for p in lid.predictions],
                        }
                    ),
                    "route": {"language_hint": route_lang},
                    "asr": {
                        k: {
                            "model": v.model,
                            "text": v.text,
                            "language": v.language,
                            "language_probability": v.language_probability,
                            "segments": [asdict(s) for s in v.segments],
                            "meta": v.meta,
                        }
                        for k, v in asr_results.items()
                    },
                    "normalized": norm_hyps,


sed -n '1,260p' maya_transcribe/verify.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
from __future__ import annotations

from dataclasses import dataclass
from typing import Any

from maya_transcribe.lid import LIDResult

@dataclass(frozen=True)
class Verification:
    decision: str  # "accept" | "human" | "reject"
    reasons: list[str]
    scores: dict[str, Any]

def lid_confidence(lid: LIDResult | None) -> dict[str, Any]:
    if not lid or not lid.predictions:
        return {"available": False, "top1": None, "top1_prob": None, "margin": None, "confident": False}
    top1 = lid.predictions[0]
    top2_prob = lid.predictions[1].prob if len(lid.predictions) > 1 else 0.0
    margin = float(top1.prob) - float(top2_prob)
    return {
        "available": True,
        "top1": top1.code,
        "top1_prob": float(top1.prob),
        "margin": margin,
        "confident": False,
    }

def verify_segment(
    *,
    qc: dict[str, Any],
    lid: LIDResult | None,
    rover: dict[str, Any],
    asr_summaries: dict[str, Any],
    thresholds: dict[str, Any] | None = None,
) -> Verification:
    """
    Conservative verification gate (precision-first).
    """
    thresholds = thresholds or {}
    min_duration = float(thresholds.get("min_duration_sec", 0.8))
    max_clipping = float(thresholds.get("max_clipping_fraction", 0.01))
    max_disagreement = float(thresholds.get("max_rover_disagreement_rate", 0.03))
    lid_min_prob = float(thresholds.get("lid_min_prob", 0.90))
    lid_min_margin = float(thresholds.get("lid_min_margin", 0.20))

    reasons: list[str] = []
    decision = "accept"

    duration = float(qc.get("duration_sec") or 0.0)
    clipping = float(qc.get("clipping_fraction") or 0.0)
    if duration < min_duration:
        decision = "reject"
        reasons.append("too_short")
    if clipping > max_clipping:
        decision = "human" if decision != "reject" else decision
        reasons.append("clipping")

    lidc = lid_confidence(lid)
    if lidc["available"]:
        lidc["confident"] = bool(lidc["top1_prob"] >= lid_min_prob and lidc["margin"] >= lid_min_margin)
        if not lidc["confident"]:
            decision = "human" if decision != "reject" else decision
            reasons.append("lid_uncertain")
    else:
        reasons.append("lid_missing")

    disagreement_rate = float(rover.get("disagreement_rate") or 0.0)
    if disagreement_rate > max_disagreement:
        decision = "human" if decision != "reject" else decision
        reasons.append("asr_disagreement")

    # Whisper-style safety: flag if any decoder claims "no speech" with high probability.
    no_speech_probs = []
    for summary in asr_summaries.values():
        p = summary.get("no_speech_prob_mean")
        if p is None:
            continue
        no_speech_probs.append(float(p))
    if no_speech_probs and max(no_speech_probs) > 0.8:
        decision = "reject"
        reasons.append("no_speech")

    scores = {
        "duration_sec": duration,
        "clipping_fraction": clipping,
        "rover_disagreement_rate": disagreement_rate,
        "lid": lidc,
        "no_speech_prob_max": max(no_speech_probs) if no_speech_probs else None,
    }

    return Verification(decision=decision, reasons=reasons, scores=scores)


**Reviewing verification process**

I’m thinking about the verify gate, which is based on a few factors: duration, clipping, LID confidence, ROVER disagreement, and no_speech probability. It seems pretty straightforward, mostly relying on heuristics, without grounding on audio like a CTC fit. I've noticed that lid_confidence always sets "confident" to False. Then in verify_segment, it adjusts lidc["confident"] based on certain thresholds. I’ll need to review the `maya_transcribe/lid.py` file for MMS-LID to understand more.