<proposed_plan>
# 4-Day Decision-Complete Plan: Gemini Transcript Pipeline for 80M Segments

## Summary
1. Objective: produce high-fidelity multilingual transcripts from R2-hosted podcast segments for ASR/TTS training, with deterministic JSON outputs, quality scoring, and deadline-safe throughput.
2. Locked preferences from this turn: authoritative transcript will be code-mixed native scripts; preprocessing will be precision-first; OpenRouter is emergency-only with strict cap.
3. Deadline anchor: start on **February 28, 2026** and finish within **100 hours** (target completion by **March 4, 2026**).
4. Core strategy: precision-first segment polishing -> strict Gemini structured transcription -> automated quality scoring -> retry only low-confidence tail.
5. Throughput strategy: dual Gemini lanes (Vertex Batch + AI Studio realtime), OpenRouter activated only if ETA risk breaches deadline.

## Concern-to-TODO Coverage (Every Concern Addressed)
1. `TODO-01` R2 source format (`videoID.tar` with `metadata.json` + `segments/*.flac`): define a deterministic ingestion manifest keyed by `video_id`, `segment_id`, source offsets, and language hint from Supabase.
2. `TODO-02` Imperfect diarization and acknowledgment leakage: add speaker-consistency and overlap heuristics; contaminated segments are flagged and either dropped or low-priority retried.
3. `TODO-03` Abrupt starts/ends from prior VAD: run boundary optimizer to trim to nearest low-energy/VAD valleys and enforce silence padding policy.
4. `TODO-04` Support exactly 12 languages: enforce a closed language enum for hints/detection and language-specific script checks for output validation.
5. `TODO-05` Supabase language metadata usage: treat as soft hint only, never as hard transcription constraint.
6. `TODO-06` Prompt review and Gemini behavior: keep strict anti-hallucination/verbatim rules, tighten derivation rules between `transcription` and `tagged`, and reduce ambiguity on language mismatch.
7. `TODO-07` Temperature/model settings: run at `temperature=0`, `candidate_count=1`, `thinking_level=minimal`, strict JSON schema; this prioritizes determinism for transcription tasks.
8. `TODO-08` Language mismatch handling: keep expected-language hint in prompt, require `detected_language`, and accept mismatch when audio dominance differs from metadata.
9. `TODO-09` Script policy decision: authoritative `transcription` remains code-mixed with each spoken language in its own script (no forced transliteration).
10. `TODO-10` Punctuation requirement: include punctuation in authoritative output but only constrained to audible prosody (`. , ? !`) to avoid grammar hallucinations.
11. `TODO-11` Audio event tagging experiment: keep fixed controlled tag set with confidence gating and deterministic relation to `transcription`.
12. `TODO-12` Event list standardization: use exactly 10 tags: `[laugh] [cough] [sigh] [breath] [throat_clear] [sniff] [music] [applause] [noise] [speech_overlap]`.
13. `TODO-13` Speaker metadata fields: keep `emotion`, `speaking_style`, `pace`; keep `accent` as required string with empty allowed when not confident.
14. `TODO-14` Segment duration policy: drop `<2s`; target `2–10s`; permit extension to `15s` only when waiting for cleaner split point.
15. `TODO-15` Validation/scoring architecture: build multi-signal quality score (schema, script, language match, text/audio ratio, alignment/consensus).
16. `TODO-16` Indic aligner question: do not depend on MFA as primary validator for all 12 languages; use CTC-based multilingual alignment/consensus path instead.
17. `TODO-17` Romanization validation question: make romanized comparison optional via transliteration layer for metrics only; do not make romanization the primary transcript.
18. `TODO-18` Rate-limit and throughput planning: scheduler must enforce RPM/TPM token buckets and monitor ETA continuously.
19. `TODO-19` Provider order and fallback: primary Gemini lanes first; OpenRouter only for deadline protection and only within strict budget cap.
20. `TODO-20` “Don’t get stuck on validation”: ship Validation V1 fast gates first; phase Validation V2 (deeper alignments) without blocking pipeline launch.

## Public Interfaces and Type Changes
1. Prompt template becomes parameterized with `expected_language`, `language_family`, and fixed tag inventory.
2. Response schema update to `TranscriptionSchemaV2`:
```json
{
  "type": "object",
  "properties": {
    "transcription": { "type": "string" },
    "tagged": { "type": "string" },
    "speaker": {
      "type": "object",
      "properties": {
        "emotion": { "type": "string", "enum": ["neutral","happy","sad","angry","excited","surprised"] },
        "speaking_style": { "type": "string", "enum": ["conversational","narrative","excited","calm","emphatic","sarcastic","formal"] },
        "pace": { "type": "string", "enum": ["slow","normal","fast"] },
        "accent": { "type": "string" }
      },
      "required": ["emotion","speaking_style","pace","accent"],
      "additionalProperties": false
    },
    "detected_language": {
      "type": "string",
      "enum": ["hi","mr","te","ta","kn","ml","gu","pa","bn","as","or","en","mixed","unknown"]
    },
    "language_confidence": {
      "type": "string",
      "enum": ["high","medium","low"]
    }
  },
  "required": ["transcription","tagged","speaker","detected_language","language_confidence"],
  "additionalProperties": false
}
```
3. Storage contract adds:
   `segment_manifest`, `provider_attempts`, `raw_response`, `normalized_text`, `quality_score`, `quality_flags`, `retry_reason`, `final_status`.
4. Determinism invariant: stripping event tags from `tagged` must exactly equal `transcription` after whitespace normalization.
5. Compatibility rule: version outputs with `schema_version` so old rows remain queryable.

## Pipeline Design (Implementation Plan)
1. Ingestion stage:
   Build manifest from R2 tar members and Supabase metadata, producing one row per segment with expected language and source path.
2. Boundary polishing stage:
   Decode audio to mono 16k, run VAD/energy boundary checks, trim to clean boundaries, add fixed silence pad (`80–120ms`) where feasible, reject segments that remain hard-cut mid-speech.
3. Duration normalization stage:
   Keep `2–10s` preferred windows; for long segments choose nearest non-speech split in `7–10s`; extend search to `15s`; if still no acceptable split, reject for precision-first mode.
4. Transcription request stage:
   Send polished segment + strict prompt + schema to Gemini with deterministic config; include expected language hint but enforce “hear-first” rule.
5. Provider routing stage:
   Route by quota-aware scheduler across Vertex Batch and AI Studio; keep OpenRouter disabled unless ETA risk and quota saturation conditions are met.
6. Validation V1 stage:
   Run fast validators (schema pass, script consistency, punctuation limits, tag consistency, text/audio duration sanity, language hint mismatch severity).
7. Retry and escalation stage:
   Retry only failed/low-score items with bounded attempts and modified strategy (no random retries); quarantine unresolved samples.
8. Validation V2 stage (non-blocking):
   Add multilingual CTC alignment/consensus scoring for flagged subset and periodic quality audits.
9. Persist and publish stage:
   Write final accepted records and quality metrics; keep rejected/quarantined queue for later remediation cycle.

## Throughput, Scheduling, and Deadline Control
1. Required pace for 80M in 100h: `800,000 segments/hour` (`13,333 segments/min`, `222 segments/sec`).
2. AI Studio ceiling provided by you: `20k RPM` and `20M TPM`; scheduler runs at 80% safety target by default, then ramps when stable.
3. Token feasibility gate:
   Measure real per-request token usage on calibration set; if sustained token cost exceeds plan budget, reduce prompt tokens and/or enable larger Vertex Batch share.
4. Lane policy:
   Vertex Batch handles bulk backlog first; AI Studio handles continuous realtime stream and retries.
5. OpenRouter emergency policy:
   Activate only when projected ETA misses deadline and Gemini lanes are quota-bound for sustained window; enforce hard spend cap (default: max 8% of projected run cost).
6. Control loop:
   Recompute ETA every 5 minutes from observed throughput, retry rate, and remaining queue; provider weights auto-adjust to hit deadline.

## Prompt and Behavior Spec
1. Keep your strict anti-translation and anti-hallucination core.
2. Explicitly state that expected language is hint-only and `detected_language` must reflect actual dominant speech.
3. Keep code-mixed native scripts in `transcription`; never transliterate by default.
4. Keep punctuation limited to `. , ? !` from audible prosody only.
5. Keep unknown handling strict: `[UNK]`, `[INAUDIBLE]`, `[NO_SPEECH]`.
6. Keep `tagged` as a derived copy of `transcription` plus only allowed tags, with no word edits.
7. Add confidence restraint for tags and accent: omit/downgrade when uncertain rather than guessing.

## Validation and Scoring Plan
1. Quality score range: `0.00–1.00`.
2. V1 weighted components:
   schema validity, `tagged/transcription` consistency, script-language consistency, text-length vs audio-duration ratio, punctuation sanity, mismatch severity.
3. V2 components:
   ASR consensus and CTC forced-alignment confidence for flagged samples.
4. Suggested thresholds:
   accept `>=0.85`, retry `0.65–0.85`, quarantine `<0.65`.
5. Romanization for validation only:
   optional transliteration to common script for CER/WER comparison across scripts; canonical stored transcript remains native script code-mixed.

## Test Cases and Acceptance Criteria
1. Multilingual code-mix test across all 12 supported languages with English insertions.
2. Boundary edge tests:
   hard-cut start, hard-cut end, both hard-cut, clean-silence start/end.
3. Duration policy tests:
   `<2s` drop, `2–10s` pass, `>10s` split, no split until `15s`.
4. No-speech and noise-only tests returning `[NO_SPEECH]`.
5. Event-tag tests ensuring only allowed tags and deterministic derivation from transcription.
6. Speaker metadata tests for required enums and empty-accent handling.
7. Language mismatch tests where Supabase hint differs from spoken dominant language.
8. JSON schema strictness tests with reject-on-extra-fields.
9. Retry policy tests with bounded attempts and proper quarantine behavior.
10. Load test proving sustained throughput needed for deadline.
11. Acceptance targets:
    JSON validity `>=99.9%`, deterministic `tagged` derivation `>=99.5%`, deadline ETA always within remaining wall-clock after first 12h burn-in.

## Rollout Plan (100 Hours)
1. Hours 0–8:
   finalize prompt/schema v2, build calibration set, run token+quality baseline.
2. Hours 8–24:
   deploy ingestion + boundary polishing + transcription core; run pilot at scale and tune thresholds.
3. Hours 24–84:
   full production run with adaptive scheduler, continuous monitoring, and controlled retries.
4. Hours 84–96:
   reprocess low-score tail and clear retry backlog.
5. Hours 96–100:
   finalize dataset snapshots, metrics report, and quarantine list for later cleanup.

## Assumptions and Defaults
1. Code-mixed native script is authoritative (`transcription`).
2. Precision-first retention is preferred over maximizing kept audio.
3. OpenRouter is emergency-only with strict cap.
4. Prompt-level determinism (`temperature=0`) is accepted for this use case.
5. Accent can be empty string if uncertain.
6. Validation V1 is required for launch; V2 is iterative and must not block primary run.
7. Any segment that still appears mid-sentence/hard-cut after polishing is rejected rather than guessed.

## Source References
1. Gemini model + features: https://ai.google.dev/gemini-api/docs/models/gemini
2. Gemini Batch API: https://ai.google.dev/gemini-api/docs/batch-api
3. Gemini structured outputs: https://ai.google.dev/gemini-api/docs/structured-output
4. Gemini audio tokenization and limits: https://ai.google.dev/gemini-api/docs/audio
5. Vertex Gemini quotas and batch behavior: https://cloud.google.com/vertex-ai/generative-ai/docs/quotas
6. OpenRouter structured outputs: https://openrouter.ai/docs/guides/features/structured-outputs
7. OpenRouter provider routing/preferences: https://openrouter.ai/docs/features/provider-routing
8. OpenRouter rate limits: https://openrouter.ai/docs/api-reference/limits
9. NeMo Forced Aligner (CTC-based): https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/tools/nemo_forced_aligner.html
10. AI4Bharat Indic Conformer multilingual ASR: https://huggingface.co/ai4bharat/indic-conformer-600m-multilingual
11. MFA acoustic models coverage: https://mfa-models.readthedocs.io/en/latest/acoustic/index.html
12. MFA dictionaries coverage: https://mfa-models.readthedocs.io/en/latest/dictionary/index.html
13. IndicXlit transliteration: https://github.com/AI4Bharat/IndicXlit
14. Current prompt under review: [prompt.txt](/home/ubuntu/transcripts/prompt.txt)
</proposed_plan>


No listed skill is applicable for this turn (`skill-creator`/`skill-installer` are unrelated to transcript pipeline planning), so I’m proceeding with a direct architecture/spec plan.


<collaboration_mode># Collaboration Mode: Default

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

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

## request_user_input availability

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

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


i got plans from 2 other models @claude_plan.md and @gpt_plan.md . i want you to consider what these other models are suggesting. Like just go through them and i want you to

1. Go and understand everything they've trying to suggest ? and evaluate them against your plan.
2. what new points or suggestions did they mention ?
3. what points did they mention and  it is against yours ? and you need to reason hard here on adapting them and evaluating them if they're correct or you're correct.
4. based on everything please edit the plan and if you want to discuss few things, exclude them or mark those points in the plan and additionally discuss with me about them in the chat here so we can come to a conclusion and then finalize the plan as per my requirements and start the plan.

if you are confident and validate the changes with reasoning and we need to update the plan. and if some things are both true and need my involvement, ask in chat. go through line by line in each files and comapre with ours. do you ahve access to the plan you created ? or no ? let me know otherwise ?


# Collaboration Mode: Default

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

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

## request_user_input availability

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

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

Summary: auto

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


**Preparing to compare plans**


I have access to the plan I created in this thread, so I can compare all three directly. I’ll read `claude_plan.md` and `gpt_plan.md` line by line, map agreements/conflicts against my plan, then produce a revised merged plan plus the few items that need your decisions.


ls -la /home/ubuntu/transcripts && rg --files /home/ubuntu/transcripts


sed -n '1,260p' /home/ubuntu/transcripts/claude_plan.md


sed -n '1,260p' /home/ubuntu/transcripts/gpt_plan.md


Output:
Chunk ID: 64abb7
Wall time: 0.0512 seconds
Process exited with code 0
Original token count: 303
Output:
total 84
drwxrwxr-x  3 ubuntu ubuntu  4096 Feb 28 07:01 .
drwxr-x--- 49 ubuntu ubuntu  4096 Feb 28 05:14 ..
-rw-rw-r--  1 ubuntu ubuntu  1384 Feb 28 03:42 .env
-rw-rw-r--  1 ubuntu ubuntu 19280 Feb 28 07:01 claude_plan.md
drwxrwxr-x  3 ubuntu ubuntu  4096 Feb 28 03:32 docs
-rw-rw-r--  1 ubuntu ubuntu 22877 Feb 28 07:01 gpt_plan.md
-rw-rw-r--  1 ubuntu ubuntu 12314 Feb 28 03:46 instructions.md
-rw-rw-r--  1 ubuntu ubuntu  4698 Feb 28 01:34 prompt.txt
/home/ubuntu/transcripts/prompt.txt
/home/ubuntu/transcripts/instructions.md
/home/ubuntu/transcripts/claude_plan.md
/home/ubuntu/transcripts/gpt_plan.md
/home/ubuntu/transcripts/docs/sample.txt
/home/ubuntu/transcripts/docs/PromptCaching.md
/home/ubuntu/transcripts/docs/Pricing.md
/home/ubuntu/transcripts/docs/gemini3_prompting.md
/home/ubuntu/transcripts/docs/oldprompt.txt
/home/ubuntu/transcripts/docs/openrouter/sample.txt
/home/ubuntu/transcripts/docs/openrouter/structured_outputs.md
/home/ubuntu/transcripts/docs/openrouter/prompt_caching.md
/home/ubuntu/transcripts/docs/openrouter/latency and performance.md
/home/ubuntu/transcripts/docs/gemini3.md
/home/ubuntu/transcripts/docs/structuredOutput.md
/home/ubuntu/transcripts/docs/gemini3_batch.md


Output:
Chunk ID: d0621b
Wall time: 0.0515 seconds
Process exited with code 0
Original token count: 3320
Output:
Alright Bharath, I've gone through your entire message and the prompt file thoroughly. Let me address **every single concern** you raised, with reasoning for each decision. I'm organizing this as a numbered plan with clear sections.

---

## MASTER PLAN: Gemini-3-Flash Transcription Pipeline

---

### 1. TRANSCRIPTION FIELD — Code-Mixed vs Single Script

**Decision: Code-mixed (each language in its native script) ✅ — Your current approach is correct.**

**Reasoning:** Gemini's audio encoder maps phonemes → tokens, and its language model is trained with each language in its native script. When you ask it to transliterate Telugu into Roman or English into Telugu script, you're adding an extra transformation step that introduces errors. Code-mixed is the most natural output format for a multimodal model — it writes what it "hears" in the script it associates with that language. This gives you the highest fidelity transcription on the first pass.

For downstream: You mentioned you'll convert scripts later with a separate LLM call anyway. So the correct strategy is:
- **This call (expensive, has audio):** Get maximum accuracy in code-mixed native script
- **Later call (cheap, text-only):** Script normalization / transliteration as needed

**Prompt tweak:** Your prompt already says this correctly. No change needed.

---

### 2. LANGUAGE MISMATCH — Handling Expected vs Actual Language

**Decision: Pass expected language as a *hint*, but instruct Gemini to trust its ears.**

**Reasoning:** If you don't pass the language hint, Gemini occasionally misidentifies closely related languages (Hindi/Marathi, Telugu/Kannada in certain dialects). But if you pass it as a hard constraint, it force-fits transcription into the wrong language when there's a genuine mismatch.

**Strategy:**
- Pass `EXPECTED: Telugu (te-IN)` as a hint in the prompt (you already do this as "TARGET")
- Rename "TARGET" to "EXPECTED LANGUAGE" to make it less prescriptive
- Keep the `detected_language` field as your verification gate
- Add a prompt line: *"The expected language is a hint from metadata. If the audio clearly speaks a different language, trust what you hear and set detected_language accordingly."*

**Downstream use:** When `detected_language ≠ expected_language`, flag this segment. You can batch-review mismatches later. Most will be legitimate (English interview in a Telugu podcast, Hindi guest on a Kannada show).

---

### 3. TEMPERATURE AND MODEL SETTINGS

**Decision: Temperature = 0 ✅**

**Reasoning:** You're right. Despite Google's documentation about reasoning tasks, transcription is a deterministic mapping task. There's one correct transcription for any audio. Temperature = 0 minimizes hallucination and ensures reproducibility. If you reprocess the same segment, you want the same output.

**Additional settings to lock down:**
- `top_p = 1.0` (no nucleus sampling interference)
- `top_k = 1` (greedy decoding)
- `response_mime_type = "application/json"` (enforced JSON mode)
- `response_json_schema` = your schema (structured output)

---

### 4. PUNCTUATION STRATEGY

**Decision: Prosody-based punctuation, always on ✅**

**Reasoning:** You're correct — you can always strip punctuation for training, but you can't reliably add it back later without another expensive model call. Prosody-based punctuation (from audible pauses/intonation) is the right approach since Gemini can detect this from the audio signal.

**Your prompt already handles this correctly.** The rule "Insert from audible pauses/intonation only. No pause = no punctuation" is clean.

**One addition to the prompt:**
- Add: *"For numbers, write them as spoken — use digits for large numbers (2024, 500), words for small contextual numbers (ek, రెండు). Match the speaker's intent."*

This is important for TTS training later — you want numbers in the form they'll be synthesized.

---

### 5. AUDIO EVENT TAGS — Fixed List

**Decision: Curated list of 8 tags for podcast audio.**

Your current list: `[laugh] [cough] [sigh] [breath] [singing] [noise] [music] [applause]`

**My recommendation — modify to 9 tags:**

| Keep | Remove | Add |
|------|--------|-----|
| `[laugh]` | `[singing]` (rare in podcasts, and if present it's usually background music) | `[throat_clear]` (very common in podcasts) |
| `[cough]` | | `[sniff]` (common, useful for TTS naturalness) |
| `[sigh]` | | |
| `[breath]` | | |
| `[noise]` | | |
| `[music]` | | |
| `[applause]` | | |

**Final list (9):** `[laugh] [cough] [sigh] [breath] [noise] [music] [applause] [throat_clear] [sniff]`

**Reasoning:** In YouTube podcasts, throat clears and sniffs are extremely common and are useful signals for both ASR (to not hallucinate text) and TTS (for naturalness). Singing is rare in talk podcasts and overlaps confusingly with music — if someone's singing, it's either background (→ `[music]`) or not really a transcription target.

**Prompt tweak:** Add to event tags section: *"Only insert tags when the event is clearly and prominently audible. When uncertain, omit. Do NOT tag normal speech breathing — only tag [breath] for audible, notable breaths."*

This prevents Gemini from over-tagging every natural breath between words.

---

### 6. SPEAKER METADATA — Emotion, Style, Pace, Accent

**Decision on accent: Make it a required field, but allow empty string.**

**Reasoning:**
- `emotion`, `speaking_style`, `pace` — these are well-defined enums Gemini can handle confidently. Keep as required. ✅
- `accent` — Gemini can sometimes detect regional accents (Hyderabadi Telugu vs Coastal Andhra, Mumbai Hindi vs UP Hindi), but it's unreliable for fine-grained distinctions. Making it required with empty string as default means you always get the field without forcing Gemini to hallucinate an accent it's not sure about.

**Your schema already handles this correctly** — accent is a string field (not enum), allowing empty string. Keep it.

**One concern:** The `speaking_style` enum has "excited" which overlaps with the `emotion` enum's "excited". Consider changing `speaking_style` "excited" to "energetic" to reduce ambiguity:

```
"speaking_style": ["conversational", "narrative", "energetic", "calm", "emphatic", "sarcastic", "formal"]
```

---

### 7. AUDIO PREPROCESSING — Segment Boundary Cleaning

This is critical. Here's the full strategy:

**Step 1: Detect boundary quality**
- Compute RMS energy in the first 50ms and last 50ms of each segment
- If RMS < threshold (e.g., -40dB), → boundary is clean (starts/ends in silence)
- If RMS > threshold, → boundary is dirty (starts/ends mid-speech)

**Step 2: For dirty starts (segment begins mid-speech)**
- Scan forward from the start looking for the first low-energy valley (below -35dB for ≥ 80ms)
- This indicates a pause between words/sentences
- Trim everything before this point
- If no valley found within the first 40% of the segment, **discard the entire segment** — it's likely all continuous speech from a bad cut

**Step 3: For dirty ends (segment ends mid-speech)**
- Scan backward from the end looking for the last low-energy valley
- Trim everything after this point
- Same 40% discard rule

**Step 4: Silence padding**
- Add **150ms** of silence (zero-padded) to both ends
- **Why 150ms and not 100ms:** Gemini's audio encoder uses windowed frames (~25ms with 10ms hop). 150ms gives ~15 frames of clear silence context, which is enough for the model to confidently identify speech onset. 100ms is borderline — 150ms is safer with negligible cost increase.

**Step 5: Post-trim length check**
- If remaining audio < 2s after trimming → discard
- Expected loss: 20-35% of segments (your 30-40% estimate is in the right ballpark)

**Implementation:** Use `librosa` or `pydub` for energy computation. This is fast — pure signal processing, no ML inference needed. Can process thousands of segments per second.

---

### 8. SEGMENT LENGTH MANAGEMENT

**Decision:**
- **Minimum:** 2s (discard below)
- **Sweet spot:** 5–10s (best transcription quality for Gemini)
- **Hard max:** 15s
- **Preferred cut search window:** 7–12s

**Strategy for long segments (>10s):**
1. Scan for silence/low-energy points between 7s and 12s
2. If found → cut at the longest silence within that window
3. If not found → extend search to 12–15s
4. If still not found by 15s → force cut at the lowest energy point in 10–15s range
5. Each sub-segment gets the 150ms silence padding treatment

**Why 5–10s is the sweet spot:** Gemini's audio understanding is best when it has enough context to understand prosody (>3s) but not so much that the JSON output becomes very long and the model starts losing precision (>15s). 10s at ~25 tokens/second = 250 audio tokens — well within the model's attention span.

---

### 9. TRANSCRIPT VALIDATION STRATEGY

This is the hardest part. Here's my honest assessment of each approach:

**a) MFA (Montreal Forced Aligner) for Indic languages:**
- MFA has acoustic models for Hindi and a few others, but coverage for all 12 of your languages is poor. Telugu, Kannada, Malayalam, Assamese, Odia — no pretrained MFA models.
- Training MFA models requires lexicons you don't have.
- **Verdict: Not viable as a primary validation method for all 12 languages.**

**b) Cross-validation with another ASR model:**
- Use AI4Bharat's IndicWhisper or IndicConformer models as a second opinion
- These exist for most of your 12 languages
- Compute CER (Character Error Rate) between Gemini output and IndicWhisper output
- Low CER = both models agree = high confidence
- High CER = disagreement = flag for review
- **Verdict: Best practical approach. Fast inference, covers most languages.**

**c) Gemini Pro review:**
- Send the audio + Gemini Flash transcript to Gemini Pro and ask it to score accuracy
- Expensive and slow — defeats the purpose of using Flash
- **Verdict: Only for spot-checking a random sample (e.g., 0.1% of segments).**

**d) Romanization + G2P validation:**
- Libraries like `aksharamukha` or `indic-transliteration` (from AI4Bharat) can convert native scripts to Roman
- You could then use phoneme-level comparison
- But this only validates script conversion, not whether the transcription matches the audio
- **Verdict: Useful as a secondary check for script correctness, not for audio-text alignment.**

**e) Confidence scoring from output characteristics:**
- Flag segments where `detected_language ≠ expected_language`
- Flag segments with high `[UNK]` or `[INAUDIBLE]` density
- Flag segments where transcription is suspiciously short relative to audio duration (possible hallucination/omission)
- Flag segments where transcription is suspiciously long (possible hallucination/addition)
- Compute characters-per-second ratio — each language has a natural speaking rate range
- **Verdict: Free, fast, catches obvious failures. Should be your first-pass filter.**

**Recommended validation stack:**
1. **Tier 1 (free, all segments):** Heuristic scoring — length ratios, UNK density, language match, chars/sec
2. **Tier 2 (cheap, all segments):** Cross-validate with IndicWhisper CER on a per-segment basis
3. **Tier 3 (expensive, flagged segments only):** Gemini Pro re-transcription for segments that failed Tier 1+2
4. **Store all scores in Supabase** alongside segment metadata for later filtering

**Don't block on validation — run it as a parallel pipeline** after transcription completes.

---

### 10. PROMPT REFINEMENTS

After reviewing your `prompt.txt`, here are the specific tweaks I'd make:

**a)** Change "TARGET: Telugu (te-IN)" to be a template variable: `EXPECTED LANGUAGE: {language} ({locale})`

**b)** Add explicit instruction about language hint behavior:
```
The EXPECTED LANGUAGE is a metadata hint. Trust what you HEAR. If the audio
is clearly a different language, transcribe in that language and set
detected_language accordingly. The hint helps with ambiguous cases only.
```

**c)** Tighten the tagged field instruction:
```
"tagged" must be CHARACTER-IDENTICAL to "transcription" except for inserted
event tags. Do not re-listen or re-interpret. Copy transcription verbatim,
then insert tags at the positions where events occur.
```

**d)** Add number handling rule (as mentioned in #4).

**e)** Add to audio events section:
```
Do NOT tag normal inter-word breathing. Only tag [breath] for audible,
notable breaths or gasps. When uncertain about any event, omit the tag.
```

**f)** Your current prompt has a typo: "AUTHORITATIV E" → "AUTHORITATIVE"

**g)** Make the prompt a template with `{language}`, `{locale}`, `{script_name}` variables so you can generate per-language prompts programmatically from one template.

---

### 11. SCALE, RATE LIMITS & EXECUTION PLAN

Let's do the math properly.

**Token budget per request:**
- Audio tokens: ~25 tokens/sec × 8s avg = ~200 tokens
- System prompt: ~700 tokens (your prompt is long)
- User prompt: ~30 tokens
- Output JSON: ~200-250 tokens
- **Total: ~1,150 tokens per request**

**AI Studio limits:**
- 20K RPM, 20M TPM
- Effective RPM from TPM: 20M / 1,150 ≈ **17,400 RPM** (TPM is the bottleneck)
- Per day: 17,400 × 60 × 24 ≈ **25M requests/day**
- 4 days: **~100M requests** — covers your 80M with headroom


Output:
Chunk ID: a06da8
Wall time: 0.0502 seconds
Process exited with code 0
Original token count: 2838
Output:
Good. This is exactly the point where being annoyingly systematic saves you from setting money on fire with 80 million clips.

I went through every concern and turned it into decisions and TODOs. Here’s the plan I’d lock **before** implementation.

## My top-line calls

1. **Your authoritative transcript should be code-mixed in native scripts, not transliterated into one script.**
   Reason: this call is the expensive multimodal one, so it should preserve the highest-fidelity truth from audio. Transliteration is a lossy text transformation you can do later with cheaper text-only tooling. AI4Bharat IndicXlit and the Indic NLP Library already give you programmatic transliteration/romanization options later, so there is no reason to burn fidelity at the audio step. ([GitHub][1])

2. **Use the video language from Supabase only as a weak prior, never as a hard constraint.**
   The prompt should say “expected language hint,” not “target language.” If the segment is fully English inside a Telugu video, Gemini should output English and `detected_language="en"`.

3. **Keep punctuation, but do not normalize numbers/symbols into digits/special symbols in the authoritative field.**
   Use the spoken form. “2026” destroys pronunciation evidence that matters for TTS. “twenty twenty-six” or “two zero two six” preserves what was actually said.

4. **Do not add a model self-confidence field.**
   Keep `detected_language`; drop generic self-confidence. Model self-reports are noisy. External validators are better.

5. **Split the downstream dataset early into lanes.**
   One monolithic “good for ASR and TTS” bucket is how people ruin both.

   * `asr_core`: high-confidence transcript, can tolerate some mid-sentence starts
   * `tts_clean`: strict boundaries, no overlap, no tags/noise
   * `tts_expressive`: strict boundaries but allows approved event tags like laugh/cough

6. **Mainline inference should be standard API calls with inline audio bytes extracted from each tar, not per-segment file uploads.**
   Gemini supports inline audio for requests under 20 MB, which your 2 to 15 second FLAC clips should easily satisfy. The Files API is for larger/reused files, but using it per segment at 80M scale would be absurd in the special way only distributed systems can be. ([Google AI for Developers][2])

7. **Batch is a secondary lane, not the primary lane for a 4-day finish.**
   Gemini Batch is cheaper but explicitly asynchronous with a target turnaround of 24 hours; Vertex batch is also async and Cloud Storage centric. That makes batch useful for backlog/overflow, not as your only path when you have a 100-hour finish line. ([Google AI for Developers][3])

---

## Every concern you raised, converted into decisions and TODOs

### 1) Prompt structure and model settings

**Decision:** Keep one **multilingual core prompt** plus a tiny per-request language hint from Supabase.
Do **not** maintain 12 wildly different prompts unless later testing proves one language needs a special rule.

**TODO**

* Replace `TARGET: Telugu (te-IN)` with `EXPECTED_LANGUAGE_HINT: <lang> (<code>)`.
* Phrase it as: “Use this only as a weak prior. Trust the audio over the hint.”
* Move the actual JSON schema enforcement into `response_json_schema` / `responseSchema`.
* Remove the full schema block from the prompt text.

**Why:** Google explicitly recommends using the response schema in the API and warns that duplicating the schema inside the prompt can lower quality. Complex schemas also count toward input tokens and can trigger `400` errors if they get too elaborate. Gemini 3 Flash Preview supports structured output, system instructions, audio input, caching, thinking, and batch. ([Google Cloud Documentation][4])

**Model settings I’d start with**

* `model = gemini-3-flash-preview`
* `temperature = 0`
* `candidateCount = 1`
* `thinking_level = "low"` on Gemini 3 Flash
* structured JSON output via schema
* fixed `seed` only if you run on Vertex, and only as a best-effort repeatability helper

**Why:** Gemini 3 defaults to high thinking. For high-throughput instruction following, Google recommends lower thinking levels; `minimal` is even cheaper/faster but I would benchmark it only after a canary because transcription is still a multimodal fidelity task. Vertex also exposes a `seed` parameter, but determinism is still best-effort, not guaranteed. ([Google AI for Developers][5])

---

### 2) `transcription` as the source of truth

**Decision:** `transcription` should be the **authoritative, code-mixed, native-script transcript**.

**TODO**

* Keep each language in its own script.
* If a Telugu segment contains an English phrase, keep English in Latin script.
* If the whole segment is English despite Telugu metadata, output English and set `detected_language="en"`.
* Preserve fillers, repetitions, false starts, and abrupt cutoffs.
* Add only prosody-based punctuation: comma, period, question mark, exclamation point.

**Important refinement**

* **Numbers:** keep them in spoken words, not digits.
* **Symbols:** only output the spoken lexical form, not normalized symbols.

  * If the speaker says “percent,” write the word.
  * If the speaker literally spells digits, write what they said.

**Why:** This preserves audio-faithful pronunciation for ASR/TTS. Later normalization is easy. Reconstructing spoken form from digits is where people discover they accidentally trained a model to say “two thousand twenty-six” when the speaker actually said “twenty twenty-six.”

---

### 3) Language hinting and `detected_language`

**Decision:** Keep `detected_language`, but standardize it.

**TODO**

* Change `detected_language` to a **controlled set of ISO-style codes** or a very small enum-like set in practice:

  * `hi mr te ta kn ml gu pa bn as or en`
  * plus `no_speech`
  * plus `other`
* Downstream, compare `expected_language_hint` vs `detected_language`.
* Store a simple `lang_mismatch_flag` outside the model response.

**Why:** Free-form language names become messy fast: `Odia` vs `Oriya`, `Punjabi` vs `Panjabi`, etc. Standardized codes make filtering and QA sane.

**Decision on confidence:**
Do **not** ask Gemini for a generic confidence field.
Use validator scores later instead.

---

### 4) Code-mixed vs transliterated single script

**Decision:** **Code-mixed native script wins.** Hard stop.

**Why**

* It is the closest thing to an audio-grounded truth.
* Transliteration is a second interpretive task and can scramble borrowings, names, brand words, and accent-driven pronunciations.
* You already have later text-only conversion options with IndicXlit / Indic NLP. ([GitHub][1])

If you want consistency later, create:

* `transcription_authoritative`
* `transcription_romanized` (derived later)
* `transcription_normalized` (derived later)

Do **not** make the expensive audio call do all three jobs.

---

### 5) `tagged` field and event tags

**Decision:** `tagged` must be a **pure derivation** of `transcription`, not a second freeform transcription pass.

**TODO**

* Keep `tagged == transcription` when no event is present.
* Only insert tags at positions where events are clearly audible.
* Do not ask Gemini to “re-listen” or “backtrack” in the prompt. That tends to increase paraphrase risk instead of helping.

**Event set I’d use**

* `[laugh]`
* `[cough]`
* `[sigh]`
* `[breath]`
* `[clears_throat]`
* `[singing]`
* `[music]`
* `[applause]`
* `[noise]`

That is a strong enough set for podcasts without turning the model into a creative foley artist.

**Rule**

* Only if **prominent and unambiguous**
* Never stack a zoo of tags
* No guessing from context

**Why:** You want precision, not recall, on tags. False positive tags are poison for both ASR labeling and controllable TTS.

---

### 6) Speaker metadata: emotion, style, pace, accent

**Decision:** Keep:

* `emotion`
* `speaking_style`
* `pace`

Keep `accent` **optional in practice**, even if the field exists.

**TODO**

* Require empty string for accent unless confidence is high.
* Restrict to **broad regional/dialect labels**, not city-level or geo-guess cosplay.
* Do not add a geo-location field.

**Why:** Emotion/style/pace are acoustically observable enough to be useful. Accent is useful only when the model is really sure; otherwise it will happily invent a geography minor.

Gemini’s audio understanding docs explicitly position the model as capable of transcription, speaker detection/diarization-style tasks, and emotion detection. ([Google AI for Developers][2])

---

### 7) Segments are not perfectly single-speaker

**Decision:** Treat brief contamination and acknowledgements as **quality flags**, not as reasons to trust the segment blindly.

**TODO**

* If a second speaker briefly leaks in:

  * keep it for `asr_core` **only if** the main speaker still dominates and later validators pass
  * reject it from `tts_clean`
  * optionally keep for `tts_expressive` only if it is clearly not overlap but an intentional non-verbal event
* Store `overlap_suspected=true` outside the model response when diarization or validator heuristics say so.

**Why:** TTS hates overlap. ASR can survive some of it.

---

### 8) Boundary cleanup and sentence polishing before Gemini

This is the big one.

**Decision:** Use a **boundary-salvage trimmer** before inference. Do not feed Gemini raw clipped edges whenever you can avoid it.

**TODO**

1. Process by **video tar**, not by random segment.

   * lease one `videoID.tar`
   * download once
   * extract once
   * process all segments inside
2. For each segment, compute:

   * leading speech-at-boundary flag
   * trailing speech-at-boundary flag
   * short-window VAD/energy profile
3. If the segment already has clean silence at both sides, keep it.
4. If speech starts immediately at `t=0`, search the first ~1.0 to 1.5s for the earliest usable pause / low-energy trough / silence-to-speech transition and trim forward.
5. If speech ends at the last frame, search the last ~1.0 to 1.5s for the latest usable pause / speech-to-silence transition and trim backward.
6. After trim, add **~150 to 200 ms of digital silence padding** on both ends for Gemini input.
7. Store:

   * original offsets
   * trimmed offsets
   * pad applied
   * `truncated_start`
   * `truncated_end`

**My opinion on the silence amount:**
100 ms is a little too aggressive. I’d target **150 to 200 ms** of pad. It is still cheap in audio-token terms and gives cleaner edge context.

**Hard rule**

* If you cannot salvage a clean-ish start or end and the clip is clearly mid-sentence on both sides, **drop it from TTS**.
* It can still remain ASR-eligible if the transcript is strong.

This is why I want two lanes. One cleanup policy cannot serve both ASR and TTS equally well.

Gemini can accept FLAC and WEBM audio, and audio is tokenized by duration at 32 tokens/second, so adding a tiny pad is basically free. ([Google Cloud Documentation][6])

---

### 9) Segment length policy

**Decision:** Your instincts are right, but I’d make them lane-specific.

**TODO**

* **Global hard reject:** `< 2.0s`
* **ASR lane:** `2.0s to 15.0s`
* **TTS clean lane:** `2.5s to 12.0s`
* **Preferred split target:** cut around `8 to 10s`
* **Stretch limit:** up to `15s` only if the next good boundary is close

**Why**


Summary: auto


**Planning full file reading beyond truncation**


I’ve loaded both files and now I’m pulling the remaining sections with line numbers so I can do an exact line-by-line comparison and call out precise conflicts/adoptions.


wc -l /home/ubuntu/transcripts/claude_plan.md /home/ubuntu/transcripts/gpt_plan.md && nl -ba /home/ubuntu/transcripts/claude_plan.md | sed -n '1,260p'


nl -ba /home/ubuntu/transcripts/claude_plan.md | sed -n '261,520p'


nl -ba /home/ubuntu/transcripts/gpt_plan.md | sed -n '1,260p'


nl -ba /home/ubuntu/transcripts/gpt_plan.md | sed -n '261,620p'


Output:
Chunk ID: f2bc72
Wall time: 0.0511 seconds
Process exited with code 0
Original token count: 3800
Output:
  379 /home/ubuntu/transcripts/claude_plan.md
  530 /home/ubuntu/transcripts/gpt_plan.md
  909 total
     1	Alright Bharath, I've gone through your entire message and the prompt file thoroughly. Let me address **every single concern** you raised, with reasoning for each decision. I'm organizing this as a numbered plan with clear sections.
     2
     3	---
     4
     5	## MASTER PLAN: Gemini-3-Flash Transcription Pipeline
     6
     7	---
     8
     9	### 1. TRANSCRIPTION FIELD — Code-Mixed vs Single Script
    10
    11	**Decision: Code-mixed (each language in its native script) ✅ — Your current approach is correct.**
    12
    13	**Reasoning:** Gemini's audio encoder maps phonemes → tokens, and its language model is trained with each language in its native script. When you ask it to transliterate Telugu into Roman or English into Telugu script, you're adding an extra transformation step that introduces errors. Code-mixed is the most natural output format for a multimodal model — it writes what it "hears" in the script it associates with that language. This gives you the highest fidelity transcription on the first pass.
    14
    15	For downstream: You mentioned you'll convert scripts later with a separate LLM call anyway. So the correct strategy is:
    16	- **This call (expensive, has audio):** Get maximum accuracy in code-mixed native script
    17	- **Later call (cheap, text-only):** Script normalization / transliteration as needed
    18
    19	**Prompt tweak:** Your prompt already says this correctly. No change needed.
    20
    21	---
    22
    23	### 2. LANGUAGE MISMATCH — Handling Expected vs Actual Language
    24
    25	**Decision: Pass expected language as a *hint*, but instruct Gemini to trust its ears.**
    26
    27	**Reasoning:** If you don't pass the language hint, Gemini occasionally misidentifies closely related languages (Hindi/Marathi, Telugu/Kannada in certain dialects). But if you pass it as a hard constraint, it force-fits transcription into the wrong language when there's a genuine mismatch.
    28
    29	**Strategy:**
    30	- Pass `EXPECTED: Telugu (te-IN)` as a hint in the prompt (you already do this as "TARGET")
    31	- Rename "TARGET" to "EXPECTED LANGUAGE" to make it less prescriptive
    32	- Keep the `detected_language` field as your verification gate
    33	- Add a prompt line: *"The expected language is a hint from metadata. If the audio clearly speaks a different language, trust what you hear and set detected_language accordingly."*
    34
    35	**Downstream use:** When `detected_language ≠ expected_language`, flag this segment. You can batch-review mismatches later. Most will be legitimate (English interview in a Telugu podcast, Hindi guest on a Kannada show).
    36
    37	---
    38
    39	### 3. TEMPERATURE AND MODEL SETTINGS
    40
    41	**Decision: Temperature = 0 ✅**
    42
    43	**Reasoning:** You're right. Despite Google's documentation about reasoning tasks, transcription is a deterministic mapping task. There's one correct transcription for any audio. Temperature = 0 minimizes hallucination and ensures reproducibility. If you reprocess the same segment, you want the same output.
    44
    45	**Additional settings to lock down:**
    46	- `top_p = 1.0` (no nucleus sampling interference)
    47	- `top_k = 1` (greedy decoding)
    48	- `response_mime_type = "application/json"` (enforced JSON mode)
    49	- `response_json_schema` = your schema (structured output)
    50
    51	---
    52
    53	### 4. PUNCTUATION STRATEGY
    54
    55	**Decision: Prosody-based punctuation, always on ✅**
    56
    57	**Reasoning:** You're correct — you can always strip punctuation for training, but you can't reliably add it back later without another expensive model call. Prosody-based punctuation (from audible pauses/intonation) is the right approach since Gemini can detect this from the audio signal.
    58
    59	**Your prompt already handles this correctly.** The rule "Insert from audible pauses/intonation only. No pause = no punctuation" is clean.
    60
    61	**One addition to the prompt:**
    62	- Add: *"For numbers, write them as spoken — use digits for large numbers (2024, 500), words for small contextual numbers (ek, రెండు). Match the speaker's intent."*
    63
    64	This is important for TTS training later — you want numbers in the form they'll be synthesized.
    65
    66	---
    67
    68	### 5. AUDIO EVENT TAGS — Fixed List
    69
    70	**Decision: Curated list of 8 tags for podcast audio.**
    71
    72	Your current list: `[laugh] [cough] [sigh] [breath] [singing] [noise] [music] [applause]`
    73
    74	**My recommendation — modify to 9 tags:**
    75
    76	| Keep | Remove | Add |
    77	|------|--------|-----|
    78	| `[laugh]` | `[singing]` (rare in podcasts, and if present it's usually background music) | `[throat_clear]` (very common in podcasts) |
    79	| `[cough]` | | `[sniff]` (common, useful for TTS naturalness) |
    80	| `[sigh]` | | |
    81	| `[breath]` | | |
    82	| `[noise]` | | |
    83	| `[music]` | | |
    84	| `[applause]` | | |
    85
    86	**Final list (9):** `[laugh] [cough] [sigh] [breath] [noise] [music] [applause] [throat_clear] [sniff]`
    87
    88	**Reasoning:** In YouTube podcasts, throat clears and sniffs are extremely common and are useful signals for both ASR (to not hallucinate text) and TTS (for naturalness). Singing is rare in talk podcasts and overlaps confusingly with music — if someone's singing, it's either background (→ `[music]`) or not really a transcription target.
    89
    90	**Prompt tweak:** Add to event tags section: *"Only insert tags when the event is clearly and prominently audible. When uncertain, omit. Do NOT tag normal speech breathing — only tag [breath] for audible, notable breaths."*
    91
    92	This prevents Gemini from over-tagging every natural breath between words.
    93
    94	---
    95
    96	### 6. SPEAKER METADATA — Emotion, Style, Pace, Accent
    97
    98	**Decision on accent: Make it a required field, but allow empty string.**
    99
   100	**Reasoning:**
   101	- `emotion`, `speaking_style`, `pace` — these are well-defined enums Gemini can handle confidently. Keep as required. ✅
   102	- `accent` — Gemini can sometimes detect regional accents (Hyderabadi Telugu vs Coastal Andhra, Mumbai Hindi vs UP Hindi), but it's unreliable for fine-grained distinctions. Making it required with empty string as default means you always get the field without forcing Gemini to hallucinate an accent it's not sure about.
   103
   104	**Your schema already handles this correctly** — accent is a string field (not enum), allowing empty string. Keep it.
   105
   106	**One concern:** The `speaking_style` enum has "excited" which overlaps with the `emotion` enum's "excited". Consider changing `speaking_style` "excited" to "energetic" to reduce ambiguity:
   107
   108	```
   109	"speaking_style": ["conversational", "narrative", "energetic", "calm", "emphatic", "sarcastic", "formal"]
   110	```
   111
   112	---
   113
   114	### 7. AUDIO PREPROCESSING — Segment Boundary Cleaning
   115
   116	This is critical. Here's the full strategy:
   117
   118	**Step 1: Detect boundary quality**
   119	- Compute RMS energy in the first 50ms and last 50ms of each segment
   120	- If RMS < threshold (e.g., -40dB), → boundary is clean (starts/ends in silence)
   121	- If RMS > threshold, → boundary is dirty (starts/ends mid-speech)
   122
   123	**Step 2: For dirty starts (segment begins mid-speech)**
   124	- Scan forward from the start looking for the first low-energy valley (below -35dB for ≥ 80ms)
   125	- This indicates a pause between words/sentences
   126	- Trim everything before this point
   127	- If no valley found within the first 40% of the segment, **discard the entire segment** — it's likely all continuous speech from a bad cut
   128
   129	**Step 3: For dirty ends (segment ends mid-speech)**
   130	- Scan backward from the end looking for the last low-energy valley
   131	- Trim everything after this point
   132	- Same 40% discard rule
   133
   134	**Step 4: Silence padding**
   135	- Add **150ms** of silence (zero-padded) to both ends
   136	- **Why 150ms and not 100ms:** Gemini's audio encoder uses windowed frames (~25ms with 10ms hop). 150ms gives ~15 frames of clear silence context, which is enough for the model to confidently identify speech onset. 100ms is borderline — 150ms is safer with negligible cost increase.
   137
   138	**Step 5: Post-trim length check**
   139	- If remaining audio < 2s after trimming → discard
   140	- Expected loss: 20-35% of segments (your 30-40% estimate is in the right ballpark)
   141
   142	**Implementation:** Use `librosa` or `pydub` for energy computation. This is fast — pure signal processing, no ML inference needed. Can process thousands of segments per second.
   143
   144	---
   145
   146	### 8. SEGMENT LENGTH MANAGEMENT
   147
   148	**Decision:**
   149	- **Minimum:** 2s (discard below)
   150	- **Sweet spot:** 5–10s (best transcription quality for Gemini)
   151	- **Hard max:** 15s
   152	- **Preferred cut search window:** 7–12s
   153
   154	**Strategy for long segments (>10s):**
   155	1. Scan for silence/low-energy points between 7s and 12s
   156	2. If found → cut at the longest silence within that window
   157	3. If not found → extend search to 12–15s
   158	4. If still not found by 15s → force cut at the lowest energy point in 10–15s range
   159	5. Each sub-segment gets the 150ms silence padding treatment
   160
   161	**Why 5–10s is the sweet spot:** Gemini's audio understanding is best when it has enough context to understand prosody (>3s) but not so much that the JSON output becomes very long and the model starts losing precision (>15s). 10s at ~25 tokens/second = 250 audio tokens — well within the model's attention span.
   162
   163	---
   164
   165	### 9. TRANSCRIPT VALIDATION STRATEGY
   166
   167	This is the hardest part. Here's my honest assessment of each approach:
   168
   169	**a) MFA (Montreal Forced Aligner) for Indic languages:**
   170	- MFA has acoustic models for Hindi and a few others, but coverage for all 12 of your languages is poor. Telugu, Kannada, Malayalam, Assamese, Odia — no pretrained MFA models.
   171	- Training MFA models requires lexicons you don't have.
   172	- **Verdict: Not viable as a primary validation method for all 12 languages.**
   173
   174	**b) Cross-validation with another ASR model:**
   175	- Use AI4Bharat's IndicWhisper or IndicConformer models as a second opinion
   176	- These exist for most of your 12 languages
   177	- Compute CER (Character Error Rate) between Gemini output and IndicWhisper output
   178	- Low CER = both models agree = high confidence
   179	- High CER = disagreement = flag for review
   180	- **Verdict: Best practical approach. Fast inference, covers most languages.**
   181
   182	**c) Gemini Pro review:**
   183	- Send the audio + Gemini Flash transcript to Gemini Pro and ask it to score accuracy
   184	- Expensive and slow — defeats the purpose of using Flash
   185	- **Verdict: Only for spot-checking a random sample (e.g., 0.1% of segments).**
   186
   187	**d) Romanization + G2P validation:**
   188	- Libraries like `aksharamukha` or `indic-transliteration` (from AI4Bharat) can convert native scripts to Roman
   189	- You could then use phoneme-level comparison
   190	- But this only validates script conversion, not whether the transcription matches the audio
   191	- **Verdict: Useful as a secondary check for script correctness, not for audio-text alignment.**
   192
   193	**e) Confidence scoring from output characteristics:**
   194	- Flag segments where `detected_language ≠ expected_language`
   195	- Flag segments with high `[UNK]` or `[INAUDIBLE]` density
   196	- Flag segments where transcription is suspiciously short relative to audio duration (possible hallucination/omission)
   197	- Flag segments where transcription is suspiciously long (possible hallucination/addition)
   198	- Compute characters-per-second ratio — each language has a natural speaking rate range
   199	- **Verdict: Free, fast, catches obvious failures. Should be your first-pass filter.**
   200
   201	**Recommended validation stack:**
   202	1. **Tier 1 (free, all segments):** Heuristic scoring — length ratios, UNK density, language match, chars/sec
   203	2. **Tier 2 (cheap, all segments):** Cross-validate with IndicWhisper CER on a per-segment basis
   204	3. **Tier 3 (expensive, flagged segments only):** Gemini Pro re-transcription for segments that failed Tier 1+2
   205	4. **Store all scores in Supabase** alongside segment metadata for later filtering
   206
   207	**Don't block on validation — run it as a parallel pipeline** after transcription completes.
   208
   209	---
   210
   211	### 10. PROMPT REFINEMENTS
   212
   213	After reviewing your `prompt.txt`, here are the specific tweaks I'd make:
   214
   215	**a)** Change "TARGET: Telugu (te-IN)" to be a template variable: `EXPECTED LANGUAGE: {language} ({locale})`
   216
   217	**b)** Add explicit instruction about language hint behavior:
   218	```
   219	The EXPECTED LANGUAGE is a metadata hint. Trust what you HEAR. If the audio
   220	is clearly a different language, transcribe in that language and set
   221	detected_language accordingly. The hint helps with ambiguous cases only.
   222	```
   223
   224	**c)** Tighten the tagged field instruction:
   225	```
   226	"tagged" must be CHARACTER-IDENTICAL to "transcription" except for inserted
   227	event tags. Do not re-listen or re-interpret. Copy transcription verbatim,
   228	then insert tags at the positions where events occur.
   229	```
   230
   231	**d)** Add number handling rule (as mentioned in #4).
   232
   233	**e)** Add to audio events section:
   234	```
   235	Do NOT tag normal inter-word breathing. Only tag [breath] for audible,
   236	notable breaths or gasps. When uncertain about any event, omit the tag.
   237	```
   238
   239	**f)** Your current prompt has a typo: "AUTHORITATIV E" → "AUTHORITATIVE"
   240
   241	**g)** Make the prompt a template with `{language}`, `{locale}`, `{script_name}` variables so you can generate per-language prompts programmatically from one template.
   242
   243	---
   244
   245	### 11. SCALE, RATE LIMITS & EXECUTION PLAN
   246
   247	Let's do the math properly.
   248
   249	**Token budget per request:**
   250	- Audio tokens: ~25 tokens/sec × 8s avg = ~200 tokens
   251	- System prompt: ~700 tokens (your prompt is long)
   252	- User prompt: ~30 tokens
   253	- Output JSON: ~200-250 tokens
   254	- **Total: ~1,150 tokens per request**
   255
   256	**AI Studio limits:**
   257	- 20K RPM, 20M TPM
   258	- Effective RPM from TPM: 20M / 1,150 ≈ **17,400 RPM** (TPM is the bottleneck)
   259	- Per day: 17,400 × 60 × 24 ≈ **25M requests/day**
   260	- 4 days: **~100M requests** — covers your 80M with headroom


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Output:
   261
   262	**But** — sustained 17K RPM for 96 hours straight requires:
   263	- Zero downtime
   264	- Perfect parallelism
   265	- No rate limit backoff
   266	- Realistically expect 60-70% utilization → **60-70M requests in 4 days from AI Studio alone**
   267
   268	**Vertex AI Batch:**
   269	- 10B inflight tokens → 10B / 1,150 ≈ **8.7M concurrent requests** in a batch
   270	- Batch jobs run asynchronously — submit and wait
   271	- Perfect for overflow and for languages where you have large volumes
   272	- Submit batches for the highest-volume languages (Hindi, Telugu, Tamil)
   273	- **Throughput: depends on Google's backend, but 10-20M/day is realistic for batch**
   274
   275	**OpenRouter (paid fallback):**
   276	- Use ONLY if AI Studio + Vertex are both saturated
   277	- Route to `google/gemini-2.5-flash` (or whatever the flash model is available as)
   278	- Set a daily spend cap to control costs
   279
   280	**Execution strategy:**
   281
   282	| Priority | Channel | Target | Segments |
   283	|----------|---------|--------|----------|
   284	| 1 | AI Studio (real-time) | Sustain 15K RPM | ~60M in 4 days |
   285	| 2 | Vertex Batch | Batch jobs for large languages | ~15-20M |
   286	| 3 | OpenRouter | Overflow only | Remaining if any |
   287
   288	**Concurrency architecture:**
   289	- Use `asyncio` + `aiohttp` with semaphore limiting
   290	- 500-1000 concurrent connections to AI Studio
   291	- Exponential backoff on 429s
   292	- Per-language queues so you can prioritize/pause by language
   293	- Progress tracking in Supabase (segment_id → status, transcript, scores)
   294
   295	---
   296
   297	### 12. DATA FLOW ARCHITECTURE
   298
   299	```
   300	R2 (videoID.tar)
   301	  → Untar: metadata.json + segments/*.flac
   302	  → Supabase lookup: get language_id for videoID
   303	  → Audio Preprocessing:
   304	      1. RMS boundary check
   305	      2. Trim dirty boundaries
   306	      3. Re-segment if >10s (find cut points 7-12s)
   307	      4. Discard if <2s
   308	      5. Pad 150ms silence
   309	      6. Export as FLAC (keep FLAC, Gemini supports it)
   310	  → Gemini Transcription:
   311	      1. Build prompt from template (inject language)
   312	      2. Encode audio as base64 or inline_data
   313	      3. Send to AI Studio / Vertex Batch / OpenRouter
   314	      4. Parse JSON response
   315	      5. Store transcript + metadata in Supabase
   316	  → Validation (parallel/async):
   317	      1. Heuristic scoring (chars/sec, UNK density, language match)
   318	      2. IndicWhisper cross-validation CER
   319	      3. Store scores in Supabase
   320	  → Post-processing (later phase):
   321	      1. Filter by quality scores
   322	      2. Re-process low-scoring segments with Gemini Pro
   323	      3. Script normalization LLM call
   324	```
   325
   326	---
   327
   328	### 13. COMPLETE TODO CHECKLIST
   329
   330	Here's every concern mapped to a task:
   331
   332	| # | Concern | Decision | Status |
   333	|---|---------|----------|--------|
   334	| 1 | Code-mixed vs single script | Code-mixed native script ✅ | Decided |
   335	| 2 | Language mismatch handling | Pass as hint, trust ears, detected_language as gate | Decided |
   336	| 3 | Temperature setting | 0, with top_k=1, top_p=1 | Decided |
   337	| 4 | Punctuation strategy | Prosody-based, always on, add number handling | Decided |
   338	| 5 | Audio event tag list | 9 tags (replaced singing with throat_clear, added sniff) | Decided |
   339	| 6 | Tagged field derivation | Character-identical copy + tags only | Decided |
   340	| 7 | Speaker metadata fields | Keep all, change "excited" → "energetic" in style enum | Decided |
   341	| 8 | Accent field required? | Required but allow empty string (current approach) | Decided |
   342	| 9 | Audio boundary cleaning | RMS detection + trim to nearest valley + 150ms padding | Decided |
   343	| 10 | Segment length limits | Min 2s, sweet 5-10s, max 15s, cut search 7-12s | Decided |
   344	| 11 | Transcript validation - MFA | Not viable for all 12 languages | Decided |
   345	| 12 | Transcript validation - cross-ASR | IndicWhisper CER as Tier 2 | Decided |
   346	| 13 | Transcript validation - heuristic | Chars/sec, UNK density, language match as Tier 1 | Decided |
   347	| 14 | Transcript validation - Gemini Pro | Spot-check only (0.1% sample) | Decided |
   348	| 15 | Romanization validation | Aksharamukha for script check, secondary only | Decided |
   349	| 16 | Prompt template-ization | Make language/locale/script template variables | To implement |
   350	| 17 | Prompt fixes | Typo fix, language hint rewording, number rule | To implement |
   351	| 18 | Rate limit math | AI Studio ~60M, Vertex ~20M, OpenRouter overflow | Decided |
   352	| 19 | 4-day timeline feasibility | Feasible with AI Studio + Vertex parallel | Decided |
   353	| 20 | Async architecture | asyncio + semaphore + per-language queues | To implement |
   354	| 21 | R2 → preprocessing pipeline | Untar, RMS check, trim, re-segment, pad, export | To implement |
   355	| 22 | Supabase schema for results | Segment transcripts + scores + metadata | To implement |
   356	| 23 | Batch vs real-time routing | Large languages to Vertex batch, rest to AI Studio | To implement |
   357	| 24 | OpenRouter integration | Paid fallback with spend cap | To implement |
   358	| 25 | Score storage for later filtering | All validation scores in Supabase | To implement |
   359
   360	---
   361
   362	### 14. OPEN QUESTIONS FOR YOU
   363
   364	Before we move to implementation, I want your input on a few things:
   365
   366	1. **IndicWhisper for validation** — Do you already have IndicWhisper/IndicConformer models deployed, or should I plan GPU compute for running these? They need GPU inference.
   367
   368	2. **Supabase schema** — Do you want me to design the full schema for storing transcripts + scores, or do you already have a segments table?
   369
   370	3. **R2 access pattern** — Are you pulling tars from R2 to a local/cloud machine for preprocessing, or do you want to stream segments directly? This affects the preprocessing architecture.
   371
   372	4. **Vertex AI Batch** — Do you already have a GCP project with Vertex AI enabled and the batch prediction API set up?
   373
   374	5. **Priority order of languages** — Should we process all 12 languages in parallel, or do you want to prioritize certain languages (e.g., Hindi and Telugu first)?
   375
   376	6. **The `speaking_style` "excited" → "energetic" rename** — Are you fine with this, or do you want to keep it as-is?
   377
   378	---
   379
   380	That's every single concern addressed. Let me know which points you want to debate or iterate on, and then we can move to implementation.


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Output:
     1	Good. This is exactly the point where being annoyingly systematic saves you from setting money on fire with 80 million clips.
     2
     3	I went through every concern and turned it into decisions and TODOs. Here’s the plan I’d lock **before** implementation.
     4
     5	## My top-line calls
     6
     7	1. **Your authoritative transcript should be code-mixed in native scripts, not transliterated into one script.**
     8	   Reason: this call is the expensive multimodal one, so it should preserve the highest-fidelity truth from audio. Transliteration is a lossy text transformation you can do later with cheaper text-only tooling. AI4Bharat IndicXlit and the Indic NLP Library already give you programmatic transliteration/romanization options later, so there is no reason to burn fidelity at the audio step. ([GitHub][1])
     9
    10	2. **Use the video language from Supabase only as a weak prior, never as a hard constraint.**
    11	   The prompt should say “expected language hint,” not “target language.” If the segment is fully English inside a Telugu video, Gemini should output English and `detected_language="en"`.
    12
    13	3. **Keep punctuation, but do not normalize numbers/symbols into digits/special symbols in the authoritative field.**
    14	   Use the spoken form. “2026” destroys pronunciation evidence that matters for TTS. “twenty twenty-six” or “two zero two six” preserves what was actually said.
    15
    16	4. **Do not add a model self-confidence field.**
    17	   Keep `detected_language`; drop generic self-confidence. Model self-reports are noisy. External validators are better.
    18
    19	5. **Split the downstream dataset early into lanes.**
    20	   One monolithic “good for ASR and TTS” bucket is how people ruin both.
    21
    22	   * `asr_core`: high-confidence transcript, can tolerate some mid-sentence starts
    23	   * `tts_clean`: strict boundaries, no overlap, no tags/noise
    24	   * `tts_expressive`: strict boundaries but allows approved event tags like laugh/cough
    25
    26	6. **Mainline inference should be standard API calls with inline audio bytes extracted from each tar, not per-segment file uploads.**
    27	   Gemini supports inline audio for requests under 20 MB, which your 2 to 15 second FLAC clips should easily satisfy. The Files API is for larger/reused files, but using it per segment at 80M scale would be absurd in the special way only distributed systems can be. ([Google AI for Developers][2])
    28
    29	7. **Batch is a secondary lane, not the primary lane for a 4-day finish.**
    30	   Gemini Batch is cheaper but explicitly asynchronous with a target turnaround of 24 hours; Vertex batch is also async and Cloud Storage centric. That makes batch useful for backlog/overflow, not as your only path when you have a 100-hour finish line. ([Google AI for Developers][3])
    31
    32	---
    33
    34	## Every concern you raised, converted into decisions and TODOs
    35
    36	### 1) Prompt structure and model settings
    37
    38	**Decision:** Keep one **multilingual core prompt** plus a tiny per-request language hint from Supabase.
    39	Do **not** maintain 12 wildly different prompts unless later testing proves one language needs a special rule.
    40
    41	**TODO**
    42
    43	* Replace `TARGET: Telugu (te-IN)` with `EXPECTED_LANGUAGE_HINT: <lang> (<code>)`.
    44	* Phrase it as: “Use this only as a weak prior. Trust the audio over the hint.”
    45	* Move the actual JSON schema enforcement into `response_json_schema` / `responseSchema`.
    46	* Remove the full schema block from the prompt text.
    47
    48	**Why:** Google explicitly recommends using the response schema in the API and warns that duplicating the schema inside the prompt can lower quality. Complex schemas also count toward input tokens and can trigger `400` errors if they get too elaborate. Gemini 3 Flash Preview supports structured output, system instructions, audio input, caching, thinking, and batch. ([Google Cloud Documentation][4])
    49
    50	**Model settings I’d start with**
    51
    52	* `model = gemini-3-flash-preview`
    53	* `temperature = 0`
    54	* `candidateCount = 1`
    55	* `thinking_level = "low"` on Gemini 3 Flash
    56	* structured JSON output via schema
    57	* fixed `seed` only if you run on Vertex, and only as a best-effort repeatability helper
    58
    59	**Why:** Gemini 3 defaults to high thinking. For high-throughput instruction following, Google recommends lower thinking levels; `minimal` is even cheaper/faster but I would benchmark it only after a canary because transcription is still a multimodal fidelity task. Vertex also exposes a `seed` parameter, but determinism is still best-effort, not guaranteed. ([Google AI for Developers][5])
    60
    61	---
    62
    63	### 2) `transcription` as the source of truth
    64
    65	**Decision:** `transcription` should be the **authoritative, code-mixed, native-script transcript**.
    66
    67	**TODO**
    68
    69	* Keep each language in its own script.
    70	* If a Telugu segment contains an English phrase, keep English in Latin script.
    71	* If the whole segment is English despite Telugu metadata, output English and set `detected_language="en"`.
    72	* Preserve fillers, repetitions, false starts, and abrupt cutoffs.
    73	* Add only prosody-based punctuation: comma, period, question mark, exclamation point.
    74
    75	**Important refinement**
    76
    77	* **Numbers:** keep them in spoken words, not digits.
    78	* **Symbols:** only output the spoken lexical form, not normalized symbols.
    79
    80	  * If the speaker says “percent,” write the word.
    81	  * If the speaker literally spells digits, write what they said.
    82
    83	**Why:** This preserves audio-faithful pronunciation for ASR/TTS. Later normalization is easy. Reconstructing spoken form from digits is where people discover they accidentally trained a model to say “two thousand twenty-six” when the speaker actually said “twenty twenty-six.”
    84
    85	---
    86
    87	### 3) Language hinting and `detected_language`
    88
    89	**Decision:** Keep `detected_language`, but standardize it.
    90
    91	**TODO**
    92
    93	* Change `detected_language` to a **controlled set of ISO-style codes** or a very small enum-like set in practice:
    94
    95	  * `hi mr te ta kn ml gu pa bn as or en`
    96	  * plus `no_speech`
    97	  * plus `other`
    98	* Downstream, compare `expected_language_hint` vs `detected_language`.
    99	* Store a simple `lang_mismatch_flag` outside the model response.
   100
   101	**Why:** Free-form language names become messy fast: `Odia` vs `Oriya`, `Punjabi` vs `Panjabi`, etc. Standardized codes make filtering and QA sane.
   102
   103	**Decision on confidence:**
   104	Do **not** ask Gemini for a generic confidence field.
   105	Use validator scores later instead.
   106
   107	---
   108
   109	### 4) Code-mixed vs transliterated single script
   110
   111	**Decision:** **Code-mixed native script wins.** Hard stop.
   112
   113	**Why**
   114
   115	* It is the closest thing to an audio-grounded truth.
   116	* Transliteration is a second interpretive task and can scramble borrowings, names, brand words, and accent-driven pronunciations.
   117	* You already have later text-only conversion options with IndicXlit / Indic NLP. ([GitHub][1])
   118
   119	If you want consistency later, create:
   120
   121	* `transcription_authoritative`
   122	* `transcription_romanized` (derived later)
   123	* `transcription_normalized` (derived later)
   124
   125	Do **not** make the expensive audio call do all three jobs.
   126
   127	---
   128
   129	### 5) `tagged` field and event tags
   130
   131	**Decision:** `tagged` must be a **pure derivation** of `transcription`, not a second freeform transcription pass.
   132
   133	**TODO**
   134
   135	* Keep `tagged == transcription` when no event is present.
   136	* Only insert tags at positions where events are clearly audible.
   137	* Do not ask Gemini to “re-listen” or “backtrack” in the prompt. That tends to increase paraphrase risk instead of helping.
   138
   139	**Event set I’d use**
   140
   141	* `[laugh]`
   142	* `[cough]`
   143	* `[sigh]`
   144	* `[breath]`
   145	* `[clears_throat]`
   146	* `[singing]`
   147	* `[music]`
   148	* `[applause]`
   149	* `[noise]`
   150
   151	That is a strong enough set for podcasts without turning the model into a creative foley artist.
   152
   153	**Rule**
   154
   155	* Only if **prominent and unambiguous**
   156	* Never stack a zoo of tags
   157	* No guessing from context
   158
   159	**Why:** You want precision, not recall, on tags. False positive tags are poison for both ASR labeling and controllable TTS.
   160
   161	---
   162
   163	### 6) Speaker metadata: emotion, style, pace, accent
   164
   165	**Decision:** Keep:
   166
   167	* `emotion`
   168	* `speaking_style`
   169	* `pace`
   170
   171	Keep `accent` **optional in practice**, even if the field exists.
   172
   173	**TODO**
   174
   175	* Require empty string for accent unless confidence is high.
   176	* Restrict to **broad regional/dialect labels**, not city-level or geo-guess cosplay.
   177	* Do not add a geo-location field.
   178
   179	**Why:** Emotion/style/pace are acoustically observable enough to be useful. Accent is useful only when the model is really sure; otherwise it will happily invent a geography minor.
   180
   181	Gemini’s audio understanding docs explicitly position the model as capable of transcription, speaker detection/diarization-style tasks, and emotion detection. ([Google AI for Developers][2])
   182
   183	---
   184
   185	### 7) Segments are not perfectly single-speaker
   186
   187	**Decision:** Treat brief contamination and acknowledgements as **quality flags**, not as reasons to trust the segment blindly.
   188
   189	**TODO**
   190
   191	* If a second speaker briefly leaks in:
   192
   193	  * keep it for `asr_core` **only if** the main speaker still dominates and later validators pass
   194	  * reject it from `tts_clean`
   195	  * optionally keep for `tts_expressive` only if it is clearly not overlap but an intentional non-verbal event
   196	* Store `overlap_suspected=true` outside the model response when diarization or validator heuristics say so.
   197
   198	**Why:** TTS hates overlap. ASR can survive some of it.
   199
   200	---
   201
   202	### 8) Boundary cleanup and sentence polishing before Gemini
   203
   204	This is the big one.
   205
   206	**Decision:** Use a **boundary-salvage trimmer** before inference. Do not feed Gemini raw clipped edges whenever you can avoid it.
   207
   208	**TODO**
   209
   210	1. Process by **video tar**, not by random segment.
   211
   212	   * lease one `videoID.tar`
   213	   * download once
   214	   * extract once
   215	   * process all segments inside
   216	2. For each segment, compute:
   217
   218	   * leading speech-at-boundary flag
   219	   * trailing speech-at-boundary flag
   220	   * short-window VAD/energy profile
   221	3. If the segment already has clean silence at both sides, keep it.
   222	4. If speech starts immediately at `t=0`, search the first ~1.0 to 1.5s for the earliest usable pause / low-energy trough / silence-to-speech transition and trim forward.
   223	5. If speech ends at the last frame, search the last ~1.0 to 1.5s for the latest usable pause / speech-to-silence transition and trim backward.
   224	6. After trim, add **~150 to 200 ms of digital silence padding** on both ends for Gemini input.
   225	7. Store:
   226
   227	   * original offsets
   228	   * trimmed offsets
   229	   * pad applied
   230	   * `truncated_start`
   231	   * `truncated_end`
   232
   233	**My opinion on the silence amount:**
   234	100 ms is a little too aggressive. I’d target **150 to 200 ms** of pad. It is still cheap in audio-token terms and gives cleaner edge context.
   235
   236	**Hard rule**
   237
   238	* If you cannot salvage a clean-ish start or end and the clip is clearly mid-sentence on both sides, **drop it from TTS**.
   239	* It can still remain ASR-eligible if the transcript is strong.
   240
   241	This is why I want two lanes. One cleanup policy cannot serve both ASR and TTS equally well.
   242
   243	Gemini can accept FLAC and WEBM audio, and audio is tokenized by duration at 32 tokens/second, so adding a tiny pad is basically free. ([Google Cloud Documentation][6])
   244
   245	---
   246
   247	### 9) Segment length policy
   248
   249	**Decision:** Your instincts are right, but I’d make them lane-specific.
   250
   251	**TODO**
   252
   253	* **Global hard reject:** `< 2.0s`
   254	* **ASR lane:** `2.0s to 15.0s`
   255	* **TTS clean lane:** `2.5s to 12.0s`
   256	* **Preferred split target:** cut around `8 to 10s`
   257	* **Stretch limit:** up to `15s` only if the next good boundary is close
   258
   259	**Why**
   260


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   261	* Sub-2s clips are disproportionately acknowledgements, clipped fragments, or junk.
   262	* TTS likes slightly longer, more self-contained utterances.
   263	* ASR can benefit from shorter and partial segments.
   264	* With Gemini audio costing by duration and 32 tokens/sec, keeping the median clip near 8 to 10s is also good for quota headroom. ([Google AI for Developers][2])
   265
   266	---
   267
   268	### 10) Prompt changes I would make immediately
   269
   270	Here’s the prompt surgery, not the final rewritten prompt yet.
   271
   272	**TODO**
   273
   274	* Change `TARGET:` to `EXPECTED_LANGUAGE_HINT:`
   275	* Remove the giant embedded JSON schema from the prompt
   276	* Add explicit no-speech behavior:
   277
   278	  * `transcription = "[NO_SPEECH]"`
   279	  * `tagged = "[NO_SPEECH]"`
   280	  * `detected_language = "no_speech"`
   281	  * `speaker = neutral / conversational / normal / ""`
   282	* Add explicit rule:
   283
   284	  * `tagged` must copy `transcription` exactly and only insert allowed tags
   285	* Add explicit number/symbol rule:
   286
   287	  * keep spoken form, do not normalize to digits/symbols unless literally spoken that way
   288	* Add explicit script rule:
   289
   290	  * use the script of the language being spoken, not the language hint
   291	* Remove ambiguous examples that could bias output
   292	* Keep the prompt lean enough to avoid becoming your TPM bottleneck
   293
   294	**Why:**
   295	Google’s structured output docs are pretty blunt here: schema belongs in the API parameter, not repeated in the prompt, and overly complex schemas/prompts can hurt output quality or trigger errors. ([Google Cloud Documentation][4])
   296
   297	---
   298
   299	### 11) Validation strategy without getting stuck on validation
   300
   301	You explicitly said not to get trapped here, which is correct. So I’d do it in **two passes**.
   302
   303	#### Pass A: cheap validation on everything
   304
   305	Run immediately after Gemini output.
   306
   307	**TODO**
   308
   309	* JSON validity
   310	* non-empty transcript
   311	* `% special tokens` (`[UNK]`, `[INAUDIBLE]`, `[NO_SPEECH]`)
   312	* script/language plausibility check
   313	* duration vs text-length plausibility
   314	* boundary score from pre-trimmer
   315	* event-tag count sanity
   316	* `detected_language` vs metadata mismatch
   317	* overlap suspicion flag
   318	* prompt/version/model metadata persisted
   319
   320	This gives you a **provisional quality score** and lets the pipeline keep moving.
   321
   322	#### Pass B: heavy validation on low-score tail
   323
   324	Run later on the bottom slice, not on all 80M on day one.
   325
   326	**What I’d use**
   327
   328	1. **Language ID check:**
   329	   Bhashini lists audio language detection models for exactly your 12-language set: Assamese, Bengali, English, Hindi, Kannada, Gujarati, Malayalam, Marathi, Odia, Punjabi, Tamil, Telugu. ([Bhashini][7])
   330
   331	2. **Romanization for script-agnostic text comparison:**
   332	   Use IndicXlit or Indic NLP Library to convert Indic scripts to Roman form before CER/WER-style agreement checks. ([GitHub][1])
   333
   334	3. **Acoustic alignment / forced alignment:**
   335
   336	   * **NeMo Forced Aligner** is a real option because it works with CTC or hybrid CTC models and supports 14+ languages plus custom models. ([NVIDIA Docs][8])
   337	   * **AI4Bharat IndicConformer** covers all 22 official Indian languages, so it is the most relevant open ASR backbone for your validation stack. ([Hugging Face][9])
   338	   * **Torchaudio multilingual forced alignment** exists, but the relevant APIs are being deprecated, so I would not make that the foundation of a new production validator. ([PyTorch Documentation][10])
   339	   * **MFA** can tokenize/transcribe many Indic languages, but its pretrained acoustic/G2P coverage is not where I’d want it for your main 12-language validator. I would treat MFA as optional tooling, not the backbone. ([Montreal Forced Aligner][11])
   340
   341	4. **Optional external validator on the tail only:**
   342	   Google Cloud Speech-to-Text supports language-specific recognition and can return word-level confidence, so it’s a viable paid audit path for selected low-score subsets, not the whole corpus. ([Google Cloud][12])
   343
   344	5. **LLM review only on the bottom tail:**
   345	   Gemini 3 Pro or 3.1 Pro can review low-score segments, but only after cheap filters. Running a premium reviewer over all 80M would be peak human behavior.
   346
   347	---
   348
   349	### 12) What metrics I would actually store
   350
   351	**TODO**
   352	Store per segment:
   353
   354	* `video_id`
   355	* `segment_id`
   356	* `speaker_id`
   357	* `original_start_ms`, `original_end_ms`
   358	* `trimmed_start_ms`, `trimmed_end_ms`
   359	* `leading_pad_ms`, `trailing_pad_ms`
   360	* `expected_language_hint`
   361	* `detected_language`
   362	* `lang_mismatch_flag`
   363	* `transcription`
   364	* `tagged`
   365	* `speaker_emotion`, `speaker_style`, `speaker_pace`, `speaker_accent`
   366	* `num_unk`
   367	* `num_inaudible`
   368	* `num_event_tags`
   369	* `boundary_score`
   370	* `text_length_per_sec`
   371	* `overlap_suspected`
   372	* `quality_score_provisional`
   373	* `quality_score_final`
   374	* `prompt_version`
   375	* `schema_version`
   376	* `trimmer_version`
   377	* `validator_version`
   378	* `model_id`
   379	* `temperature`
   380	* `thinking_level`
   381	* provider (`aistudio`, `vertex`, `openrouter`)
   382
   383	Version everything. Otherwise three weeks from now you’ll have a giant parquet graveyard and no clue which transcripts came from which prompt.
   384
   385	---
   386
   387	## Throughput and 4-day feasibility
   388
   389	Using **your stated quota** of `20K RPM` and `20M TPM`, the target is **arithmetically doable** for 80M segments in 100 hours, but only if you keep the average input size under control.
   390
   391	The critical math is this:
   392
   393	* 20K RPM across 100 hours gives you capacity for **120M requests**
   394	* 20M TPM across 100 hours gives you **120B input tokens**
   395	* Gemini counts audio at **32 tokens per second**, so a 10s clip contributes about **320 audio tokens** before prompt text even enters the picture. ([Google AI for Developers][2])
   396
   397	That means:
   398
   399	* if your total average input stays under about **1,500 tokens/request**, 80M requests still fits
   400	* if you keep it under about **1,000 tokens/request**, RPM becomes the likely bottleneck instead of TPM, which is where you want to be
   401
   402	So yes, your current plan can finish in 4 days, **but only if**:
   403
   404	1. the prompt gets compressed
   405	2. you do not waste tokens on duplicated schema
   406	3. you keep average durations in the 8 to 10s zone
   407	4. you run very high concurrency with sane backpressure
   408
   409	---
   410
   411	## Provider strategy
   412
   413	### Primary lane: Gemini standard API / online inference
   414
   415	**Use this first.**
   416
   417	Because your segments live inside `videoID.tar`, the practical worker shape is:
   418
   419	* lease tar
   420	* download tar once
   421	* extract segments locally
   422	* trim locally
   423	* send inline audio bytes to Gemini
   424
   425	Gemini supports inline audio for requests under 20 MB, which fits your segment sizes. ([Google AI for Developers][2])
   426
   427	### Secondary lane: Vertex batch
   428
   429	**Use as overflow/backlog lane.**
   430
   431	Vertex batch wants Cloud Storage/BigQuery oriented inputs and is async. If you want batch at scale, the clean path is to mirror the **trimmed** segments you actually intend to process into GCS and reference them there. Vertex’s online inference can also use public HTTP URLs or GCS URIs for small audio files, but batch planning is much cleaner with GCS. ([Google Cloud][13])
   432
   433	### Tertiary lane: OpenRouter
   434
   435	**Overflow only.**
   436
   437	OpenRouter supports:
   438
   439	* structured outputs
   440	* BYOK/provider routing
   441	* paid usage without platform-level rate limits on paid plans
   442
   443	But for audio it requires **base64-encoded audio**; direct URLs are not supported for audio input. So it is a decent escape hatch once you already have extracted bytes, not a magical zero-work overflow path. ([OpenRouter][14])
   444
   445	---
   446
   447	## The plan I would actually execute
   448
   449	### Phase 0: lock decisions
   450
   451	* authoritative field = code-mixed native script
   452	* no transliteration in Gemini call
   453	* no generic confidence field
   454	* `detected_language` standardized
   455	* strict `tagged` derivation
   456	* `tts_clean` / `tts_expressive` / `asr_core` split
   457
   458	### Phase 1: canary
   459
   460	Run a **10k segment stratified canary** across:
   461
   462	* all 12 languages
   463	* 2s to 15s bins
   464	* clean vs clipped boundaries
   465	* low/high overlap suspicion
   466
   467	Test:
   468
   469	* current prompt vs trimmed prompt
   470	* `thinking_level=low` vs `minimal`
   471	* event-tag precision
   472	* language mismatch behavior
   473
   474	### Phase 2: lock prompt + schema + trimmer
   475
   476	After canary:
   477
   478	* freeze prompt v1
   479	* freeze schema v1
   480	* freeze boundary-salvage rules v1
   481
   482	### Phase 3: ramp production
   483
   484	* processing unit = `videoID.tar`
   485	* async online Gemini calls with inline audio
   486	* store provisional scores immediately
   487	* reject obvious junk early
   488	* keep low-score tail for later review
   489
   490	### Phase 4: overflow and audit
   491
   492	* use Vertex batch only for backlog chunks or when online throttles
   493	* use OpenRouter only if both Google lanes are constrained
   494	* run heavy validators on the bottom scoring tail, not the full firehose
   495
   496	---
   497
   498	## Final recommendations in one blunt list
   499
   500	* **Use code-mixed native script as the authoritative transcript.**
   501	* **Keep numbers as spoken words, not digits.**
   502	* **Use Supabase language as a weak prior only.**
   503	* **Keep `detected_language`; don’t add a generic confidence field.**
   504	* **Make `tagged` a pure copy-plus-tags derivation.**
   505	* **Keep accent optional/empty unless very obvious.**
   506	* **Trim boundaries aggressively before Gemini.**
   507	* **Add ~150 to 200 ms silence padding after trimming.**
   508	* **Split ASR and TTS acceptance criteria.**
   509	* **Process per tar, not per segment fetch.**
   510	* **Use inline audio for the standard path.**
   511	* **Use batch as overflow, not the main lane.**
   512	* **Version every component and store per-segment metrics.**
   513	* **Do not let validation block the first full pass.**
   514
   515	The next thing to lock is the **prompt spec v1** and the **boundary-salvage spec v1**. After that, implementation becomes mechanical instead of philosophical, which is rare and beautiful.
   516
   517	[1]: https://github.com/AI4Bharat/IndicXlit "https://github.com/AI4Bharat/IndicXlit"
   518	[2]: https://ai.google.dev/gemini-api/docs/audio "https://ai.google.dev/gemini-api/docs/audio"
   519	[3]: https://ai.google.dev/gemini-api/docs/batch-api "https://ai.google.dev/gemini-api/docs/batch-api"
   520	[4]: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/multimodal/control-generated-output "https://docs.cloud.google.com/vertex-ai/generative-ai/docs/multimodal/control-generated-output"
   521	[5]: https://ai.google.dev/gemini-api/docs/thinking "https://ai.google.dev/gemini-api/docs/thinking"
   522	[6]: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/3-flash "https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/3-flash"
   523	[7]: https://dibd-bhashini.gitbook.io/bhashini-apis/available-models-for-usage "https://dibd-bhashini.gitbook.io/bhashini-apis/available-models-for-usage"
   524	[8]: https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/tools/nemo_forced_aligner.html "https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/tools/nemo_forced_aligner.html"
   525	[9]: https://huggingface.co/ai4bharat/indic-conformer-600m-multilingual "https://huggingface.co/ai4bharat/indic-conformer-600m-multilingual"
   526	[10]: https://docs.pytorch.org/audio/stable/tutorials/forced_alignment_for_multilingual_data_tutorial.html "https://docs.pytorch.org/audio/stable/tutorials/forced_alignment_for_multilingual_data_tutorial.html"
   527	[11]: https://montreal-forced-aligner.readthedocs.io/en/v3.2.0/user_guide/workflows/train_acoustic_model.html "https://montreal-forced-aligner.readthedocs.io/en/v3.2.0/user_guide/workflows/train_acoustic_model.html"
   528	[12]: https://cloud.google.com/speech/docs/languages "https://cloud.google.com/speech/docs/languages"
   529	[13]: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/batch-prediction-api "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/batch-prediction-api"
   530	[14]: https://openrouter.ai/docs/features/structured-outputs "https://openrouter.ai/docs/features/structured-outputs"


**Planning detailed merged revision**


I’ve finished the full line-by-line read of both plans (379 + 530 lines) and identified concrete merge/reject points with file-line evidence. I’ll now draft a merged `plan_v2` that integrates the strong additions, explicitly marks conflicts, and lists only the few decisions that still need your call.


**Preparing merged plan file**