Output:
Exit code: 0
Wall time: 13.4 seconds
Output:
[CTCAligner] Loading anuragshas/wav2vec2-large-xlsr-53-telugu...
[CTCAligner] Ready on cuda
[MMSAligner] Ready on cuda (29 labels, SR=16000)
count 22
R>=0.7: 20/22 (90.9%)
R>=0.75: 18/22 (81.8%)
R>=0.8: 11/22 (50.0%)
R>=0.85: 9/22 (40.9%)
R>=0.9: 3/22 (13.6%)
R_zero 1
N_nonzero 0
/home/ubuntu/maya3_transcribe/venv/lib/python3.10/site-packages/transformers/configuration_utils.py:309: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.
  warnings.warn(
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 334, in align
    targets, oov_ratio = self._text_to_tokens(transcription)
ValueError: too many values to unpack (expected 2)


# 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.


**Identifying calibration and comparison steps**


rg -n "0\.70|retry|reject|review|combined|weights|0\.45|0\.55|abs\(N-R\)|min\(N,R\)|validation_action" -S src pipeline.py golden_set docs README.md


rg -n "unit test|pytest|tests|assert" -S src golden_set pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
README.md:8:Model: gemini-3-flash-preview
README.md:35:    --model gemini-3-flash-preview \
README.md:54:    model="gemini-3-flash-preview",
README.md:87:print(result.status)  # accept / review / reject
docs/sample.txt:15:    model = "gemini-3-flash-preview"
docs/PromptCaching.md:18:| Gemini 3 Flash Preview | 1024            |
docs/PromptCaching.md:19:| Gemini 3 Pro Preview   | 4096            |
docs/Pricing.md:40:## Gemini 3 Pro Preview
docs/Pricing.md:42:*`gemini-3-pro-preview`*
docs/Pricing.md:45:[Try it in Google AI Studio](https://aistudio.google.com?model=gemini-3-pro-preview)
docs/Pricing.md:76:## Gemini 3 Flash Preview
docs/Pricing.md:78:*`gemini-3-flash-preview`*
docs/Pricing.md:81:[Try it in Google AI Studio](https://aistudio.google.com?model=gemini-3-flash-preview)
docs/Pricing.md:112:## Gemini 3 Pro Image Preview 🍌
docs/Pricing.md:114:*`gemini-3-pro-image-preview`*
docs/Pricing.md:117:[Try it in Google AI Studio](https://aistudio.google.com?model=gemini-3-pro-image-preview)
docs/Pricing.md:121:[Gemini 3 Pro](https://ai.google.dev/gemini-api/docs/pricing#gemini-3-pro-preview).
docs/Pricing.md:123:Preview models may change before becoming stable and have more restrictive rate
docs/Pricing.md:213:## Gemini 2.5 Flash Preview
docs/Pricing.md:215:*`gemini-2.5-flash-preview-09-2025`*
docs/Pricing.md:218:[Try it in Google AI Studio](https://aistudio.google.com?model=gemini-2.5-flash-preview-09-2025)
docs/Pricing.md:220:The latest model based on the 2.5 Flash model. 2.5 Flash Preview is best for
docs/Pricing.md:275:## Gemini 2.5 Flash-Lite Preview
docs/Pricing.md:277:*`gemini-2.5-flash-lite-preview-09-2025`*
docs/Pricing.md:280:[Try it in Google AI Studio](https://aistudio.google.com?model=gemini-2.5-flash-lite-preview-09-2025)
docs/Pricing.md:307:*`gemini-2.5-flash-native-audio-preview-12-2025`*
docs/Pricing.md:310:[Try it in Google AI Studio](https://aistudio.google.com/app/live#gemini-2.5-flash-native-audio-preview-12-2025)
docs/Pricing.md:316:Preview models may change before becoming stable and have more restrictive rate
docs/Pricing.md:336:Preview models may change before becoming stable and have more restrictive rate
docs/Pricing.md:358:## Gemini 2.5 Flash Preview TTS
docs/Pricing.md:360:*`gemini-2.5-flash-preview-tts`*
docs/Pricing.md:368:Preview models may change before becoming stable and have more restrictive rate
docs/Pricing.md:387:## Gemini 2.5 Pro Preview TTS
docs/Pricing.md:389:*`gemini-2.5-pro-preview-tts`*
docs/Pricing.md:397:Preview models may change before becoming stable and have more restrictive rate
docs/Pricing.md:430:| Input price | Free of charge | $0.10 (text / image / video) $0.70 (audio) |
docs/Pricing.md:500:Preview models may change before becoming stable and have more restrictive rate
docs/Pricing.md:512:*`veo-3.1-generate-preview`, `veo-3.1-fast-generate-preview`*
docs/Pricing.md:520:Preview models may change before becoming stable and have more restrictive rate
docs/Pricing.md:588:## Gemini Robotics-ER 1.5 Preview
docs/Pricing.md:590:*`gemini-robotics-er-1.5-preview`*
docs/Pricing.md:593:[Try it in Google AI Studio](https://aistudio.google.com?model=gemini-robotics-er-1.5-preview)
docs/Pricing.md:617:## Gemini 2.5 Computer Use Preview
docs/Pricing.md:619:*`gemini-2.5-computer-use-preview-10-2025`*
docs/Pricing.md:668:| [Google Search](https://ai.google.dev/gemini-api/docs/google-search#pricing) | 500 RPD free (limit shared for Flash and Flash-Lite). Not available for Pro. | 1,500 RPD free (limit shared for Flash and Flash-Lite). Then $35 / 1,000 grounded prompts See the [Gemini 3 Pro Preview](https://ai.google.dev/gemini-api/docs/pricing#gemini-3-pro-preview) pricing section for Gemini 3 specific Search pricing. |
docs/Pricing.md:672:| [Computer use](https://ai.google.dev/gemini-api/docs/computer-use) | Not available | See [Gemini 2.5 Computer Use Preview](https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-computer-use-preview-10-2025) pricing table. |
docs/Pricing.md:693:| [Gemini Deep Research Agent](https://ai.google.dev/gemini-api/docs/deep-research) | All model inference is charged at standard [Gemini 3 Pro](https://ai.google.dev/gemini-api/docs/pricing#gemini-3-pro-preview) list rates, including input, output, and intermediate input / reasoning tokens generated during agentic loops. | Tool usage fees apply per existing pricing structure, maintaining standard distinctions for Search Grounding (retrieved tokens excluded) versus Url_context / File Search (retrieved tokens included in all other tools). |
docs/gemini3.md:2:[Try Gemini 3 Pro](https://aistudio.google.com?model=gemini-3-pro-preview) [Try Gemini 3 Flash](https://aistudio.google.com?model=gemini-3-flash-preview) [Try Nano Banana Pro](https://aistudio.google.com?model=gemini-3-pro-image-preview)
docs/gemini3.md:15:        model="gemini-3-pro-preview",
docs/gemini3.md:29:        model: "gemini-3-pro-preview",
docs/gemini3.md:40:    curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-preview:generateContent" \
docs/gemini3.md:61:All Gemini 3 models are currently in preview.
docs/gemini3.md:65:| **gemini-3-pro-preview** | 1M / 64k | Jan 2025 | $2 / $12 (\<200k tokens) $4 / $18 (\>200k tokens) |
docs/gemini3.md:66:| **gemini-3-flash-preview** | 1M / 64k | Jan 2025 | $0.50 / $3 |
docs/gemini3.md:67:| **gemini-3-pro-image-preview** | 65k / 32k | Jan 2025 | $2 (Text Input) / $0.134 (Image Output)\*\* |
docs/gemini3.md:111:        model="gemini-3-pro-preview",
docs/gemini3.md:127:        model: "gemini-3-pro-preview",
docs/gemini3.md:140:    curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-preview:generateContent" \
docs/gemini3.md:184:        model="gemini-3-pro-preview",
docs/gemini3.md:212:        model: "gemini-3-pro-preview",
docs/gemini3.md:238:    curl "https://generativelanguage.googleapis.com/v1alpha/models/gemini-3-pro-preview:generateContent" \
docs/gemini3.md:295:For `gemini-3-pro-image-preview`, thought signatures are critical for conversational editing. When you ask the model to modify an image it relies on the `thoughtSignature` from the previous turn to understand the composition and logic of the original image.
docs/gemini3.md:526:        model="gemini-3-pro-preview",
docs/gemini3.md:557:        model: "gemini-3-pro-preview",
docs/gemini3.md:577:    curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-preview:generateContent" \
docs/gemini3.md:627:        model="gemini-3-pro-image-preview",
docs/gemini3.md:654:        model: "gemini-3-pro-image-preview",
docs/gemini3.md:678:    curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
docs/gemini3.md:726:        model="gemini-3-flash-preview",
docs/gemini3.md:759:        model: "gemini-3-flash-preview",
docs/gemini3.md:794:    MODEL="gemini-3-flash-preview"
docs/gemini3.md:869:      model="gemini-3-flash-preview",
docs/gemini3.md:916:      model="gemini-3-flash-preview",
docs/gemini3.md:956:      model: 'gemini-3-flash-preview',
docs/gemini3.md:1009:      model: 'gemini-3-flash-preview',
docs/gemini3.md:1037:    curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:generateContent" \
docs/gemini3.md:1105:3. **Is there a free tier for Gemini 3?** Gemini 3 Flash `gemini-3-flash-preview` has a free tier in the Gemini API. You can try both Gemini 3 Pro and Flash for free in Google AI Studio, but currently, there is no free tier available for `gemini-3-pro-preview` in the Gemini API.
docs/gemini3.md:1119:- Get started with the [Gemini 3 Cookbook](https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Get_started.ipynb#templateParams=%7B%22MODEL_ID%22%3A+%22gemini-3-pro-preview%22%7D)
pipeline.py:53:    model: str = "gemini-3-flash-preview"   # Gemini model to use
pipeline.py:59:    validation_action: str = "retry"        # "flag" (mark status) or "retry" (re-transcribe with pro)
pipeline.py:60:    retry_model: str = "gemini-2.5-pro-preview-05-06"  # Model for retry attempts
pipeline.py:61:    retry_thinking_level: str = "medium"    # Thinking level for retry
pipeline.py:258:                        result.validation_score = validation.combined_score
pipeline.py:260:                        # Retry with pro model if validation says "retry"
pipeline.py:261:                        if (validation.status == "retry"
pipeline.py:262:                                and self.config.validation_action == "retry"):
pipeline.py:265:                                f"{self.config.retry_model} "
pipeline.py:266:                                f"(S={validation.combined_score:.2f})", "WARN"
pipeline.py:268:                            retry_config = TranscriptionConfig(
pipeline.py:269:                                model=self.config.retry_model,
pipeline.py:270:                                thinking_level=self.config.retry_thinking_level,
pipeline.py:274:                            retry_raw = transcriber.transcribe_audio(
pipeline.py:275:                                batch[i].file_path, retry_config
pipeline.py:277:                            if not retry_raw.get("error"):
pipeline.py:278:                                # Re-validate the retry result
pipeline.py:282:                                speaker_data = retry_raw.get('speaker')
pipeline.py:286:                                retry_output = TranscriptionOutput(
pipeline.py:287:                                    transcription=retry_raw.get('transcription', ''),
pipeline.py:288:                                    code_switch=retry_raw.get('code_switch', ''),
pipeline.py:289:                                    romanized=retry_raw.get('romanized', ''),
pipeline.py:290:                                    tagged=retry_raw.get('tagged', ''),
pipeline.py:293:                                retry_roman = retry_output.romanized or ""
pipeline.py:294:                                retry_val = validate_transcription(
pipeline.py:296:                                    retry_output.transcription,
pipeline.py:297:                                    romanized_text=retry_roman,
pipeline.py:301:                                if retry_val.combined_score > validation.combined_score:
pipeline.py:302:                                    result.transcription = retry_output
pipeline.py:303:                                    result.model_used = retry_config.model
pipeline.py:304:                                    result.thinking_level = retry_config.thinking_level
pipeline.py:305:                                    validation = retry_val
pipeline.py:308:                                        f"S={retry_val.combined_score:.2f} "
pipeline.py:309:                                        f"({retry_val.status})"
pipeline.py:314:                                        f"S={retry_val.combined_score:.2f}"
pipeline.py:318:                            result.validation_score = validation.combined_score
pipeline.py:320:                        if validation.status in ("reject", "retry"):
pipeline.py:324:                                f"(S={validation.combined_score:.2f}, "
pipeline.py:414:    model: str = "gemini-3-flash-preview",
pipeline.py:506:    parser.add_argument("--model", "-m", default="gemini-3-flash-preview",
golden_set/calibrate.py:38:            cfg = TranscriptionConfig(model="gemini-3-flash-preview",thinking_level="low",temperature=0.0,language=lang)
golden_set/calibrate.py:44:            results.append({"file":seg["file"],"cer":round(cer,4),"ctc":val.native_ctc_score,"mms":val.roman_mms_score,"S":val.combined_score,"verdict":val.status})
golden_set/calibrate.py:45:            print(f"  {seg['file']}: CER={cer:.2%} S={val.combined_score:.2f} ({val.status})")
golden_set/calibrate.py:48:            results.append({"file":seg["file"],"ctc":val.native_ctc_score,"mms":val.roman_mms_score,"S":val.combined_score,"verdict":val.status})
golden_set/calibrate.py:49:            print(f"  {seg['file']}: S={val.combined_score:.2f} ({val.status}) CTC={val.native_ctc_score:.2f} MMS={val.roman_mms_score:.2f}")
docs/structuredOutput.md:48:    ingredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons
docs/structuredOutput.md:53:        model="gemini-3-flash-preview",
docs/structuredOutput.md:94:    ingredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons
docs/structuredOutput.md:99:      model: "gemini-3-flash-preview",
docs/structuredOutput.md:139:      ingredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons
docs/structuredOutput.md:183:            "gemini-3-flash-preview",
docs/structuredOutput.md:195:    curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:generateContent" \
docs/structuredOutput.md:202:              { "text": "Please extract the recipe from the following text.\nThe user wants to make delicious chocolate chip cookies.\nThey need 2 and 1/4 cups of all-purpose flour, 1 teaspoon of baking soda,\n1 teaspoon of salt, 1 cup of unsalted butter (softened), 3/4 cup of granulated sugar,\n3/4 cup of packed brown sugar, 1 teaspoon of vanilla extract, and 2 large eggs.\nFor the best part, they will need 2 cups of semisweet chocolate chips.\nFirst, preheat the oven to 375°F (190°C). Then, in a small bowl, whisk together the flour,\nbaking soda, and salt. In a large bowl, cream together the butter, granulated sugar, and brown sugar\nuntil light and fluffy. Beat in the vanilla and eggs, one at a time. Gradually beat in the dry\ningredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons\nonto ungreased baking sheets and bake for 9 to 11 minutes." }
docs/structuredOutput.md:286:        "Gradually beat in the dry ingredients until just combined.",
docs/structuredOutput.md:312:        model="gemini-3-flash-preview",
docs/structuredOutput.md:338:      model: "gemini-3-flash-preview",
docs/structuredOutput.md:352:| **Preview:** This is a feature available only for the Gemini 3 series models, `gemini-3-pro-preview` and `gemini-3-flash-preview`.
docs/structuredOutput.md:374:        model="gemini-3-pro-preview",
docs/structuredOutput.md:405:        model: "gemini-3-pro-preview",
docs/structuredOutput.md:425:    curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-preview:generateContent" \
docs/structuredOutput.md:510:| Gemini 3 Pro Preview | ✔ |
docs/structuredOutput.md:511:| Gemini 3 Flash Preview | ✔ |
docs/structuredOutput.md:540:- **Schema complexity:** The API may reject very large or deeply nested schemas. If you encounter errors, try simplifying your schema by shortening property names, reducing nesting, or limiting the number of constraints.
docs/gemini3_batch.md:51:        model="models/gemini-3-flash-preview",
docs/gemini3_batch.md:83:        model: 'gemini-3-flash-preview',
docs/gemini3_batch.md:94:    curl https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:batchGenerateContent \
docs/gemini3_batch.md:275:        model="gemini-3-flash-preview",
docs/gemini3_batch.md:288:        model: 'gemini-3-flash-preview',
docs/gemini3_batch.md:301:    curl https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:batchGenerateContent \
docs/gemini3_batch.md:463:        model="models/gemini-3-flash-preview",
docs/gemini3_batch.md:565:        model: 'gemini-3-flash-preview',
docs/gemini3_batch.md:1010:        model="gemini-3-pro-image-preview",
docs/gemini3_batch.md:1097:            model: 'gemini-3-pro-image-preview',
docs/gemini3_batch.md:1178:    curl https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:batchGenerateContent \
src/backend/transcription_schema.py:45:        "script_rules": "Don't over-split words. Preserve Sandhi/combined forms as spoken.",
src/backend/transcription_schema.py:55:        "script_rules": "Preserve agglutinated/combined forms as spoken.",
src/backend/transcription_schema.py:161:    validation_status: Optional[str] = Field(default=None, description="accept/review/reject")
src/backend/transcription_schema.py:231:CRITICAL RULES (violations cause rejection):
src/backend/gemini_transcriber.py:28:    model: str = "gemini-3-flash-preview"
src/backend/gemini_transcriber.py:272:    model: str = "gemini-3-flash-preview",
src/backend/gemini_transcriber.py:307:            model="gemini-3-flash-preview",
src/backend/config.py:30:    "gemini-3-pro-preview": {
src/backend/config.py:31:        "name": "gemini-3-pro-preview",
src/backend/config.py:36:    "gemini-3-flash-preview": {
src/backend/config.py:37:        "name": "gemini-3-flash-preview",
src/backend/config.py:70:    "gemini-flash-latest": "gemini-3-flash-preview",
src/backend/config.py:78:    "default_model": "gemini-3-flash-preview",
src/backend/audio_polisher.py:70:    Uses energy + ZCR combined heuristic.
src/validators/__init__.py:10:Combined scoring: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
src/validators/__init__.py:24:# result.status: "accept" / "review" / "retry" / "reject"
src/validators/__init__.py:25:# result.combined_score: 0-1
src/validators/__init__.py:33:    compute_combined_score,
src/validators/__init__.py:42:    'compute_combined_score',
src/validators/simple_validator.py:10:Combined scoring: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
src/validators/simple_validator.py:12:  Accept if S >= 0.70 and min(N,R) >= 0.40
src/validators/simple_validator.py:13:  Forced review if abs(N-R) > 0.25 (high validator disagreement)
src/validators/simple_validator.py:22:    print(result.status)          # accept / review / retry / reject
src/validators/simple_validator.py:23:    print(result.combined_score)  # 0-1
src/validators/simple_validator.py:192:    status: str  # "accept", "review", "retry", "reject"
src/validators/simple_validator.py:208:    # Combined weighted score: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
src/validators/simple_validator.py:209:    combined_score: float = 0.0
src/validators/simple_validator.py:222:            "combined_score": round(self.combined_score, 4),
src/validators/simple_validator.py:251:def compute_combined_score(
src/validators/simple_validator.py:258:    Formula: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
src/validators/simple_validator.py:259:    MMS weighted higher (0.55): more stable on code-mixed Indic audio.
src/validators/simple_validator.py:263:        (combined_score, verdict)
src/validators/simple_validator.py:264:        verdict: "accept" / "review" / "retry" / "reject"
src/validators/simple_validator.py:267:    S = 0.45 * N + 0.55 * R - 0.10 * abs(N - R)
src/validators/simple_validator.py:269:    if S >= 0.70 and min(N, R) >= 0.40:
src/validators/simple_validator.py:272:            verdict = "review"
src/validators/simple_validator.py:275:    elif S >= 0.55:
src/validators/simple_validator.py:276:        verdict = "retry"
src/validators/simple_validator.py:278:        verdict = "reject"
src/validators/simple_validator.py:304:    4. Weighted combined score: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
src/validators/simple_validator.py:314:        ValidationResult with dual scores and combined verdict
src/validators/simple_validator.py:329:            status="reject",
src/validators/simple_validator.py:368:        combined_score, verdict = compute_combined_score(
src/validators/simple_validator.py:371:        if verdict == "review" and abs(native_ctc_score - roman_mms_score) > 0.25:
src/validators/simple_validator.py:378:        combined_score = native_ctc_score
src/validators/simple_validator.py:379:        if native_ctc_score >= 0.70:
src/validators/simple_validator.py:382:            verdict = "retry"
src/validators/simple_validator.py:384:            verdict = "reject"
src/validators/simple_validator.py:387:        combined_score = 0.0
src/validators/simple_validator.py:388:        verdict = "review" if reasons else "accept"
src/validators/simple_validator.py:392:        verdict = "reject"
src/validators/simple_validator.py:395:    if verdict == "retry":
src/validators/simple_validator.py:397:            f"Below threshold: combined={combined_score:.2f} "
src/validators/simple_validator.py:400:    elif verdict == "reject" and combined_score > 0:
src/validators/simple_validator.py:402:            f"Poor alignment: combined={combined_score:.2f} "
src/validators/simple_validator.py:414:        combined_score=combined_score,
src/validators/simple_validator.py:512:            print(f"\nReasons for review:")


Output:
Exit code: 1
Wall time: 0 seconds
Output:


Summary: auto


**Evaluating scoring and bug impact**


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    40	    default_language: str = "Telugu"  # Fallback if Supabase lookup fails
    41
    42	    # === Audio Processing Settings ===
    43	    max_segment_duration_sec: float = 10.0  # Hard limit - segments longer than this are split
    44	    min_segment_duration_sec: float = 1.0   # Skip segments shorter than this
    45	    chunk_overlap_sec: float = 0.0          # Overlap when splitting (for word boundaries)
    46
    47	    # === Segment Selection Settings ===
    48	    max_segments: Optional[int] = None      # Limit total segments processed (for testing)
    49	    segment_start_index: int = 0            # Start from this segment index
    50	    skip_short_segments: bool = True        # Skip segments < min_duration
    51
    52	    # === Transcription Settings ===
    53	    model: str = "gemini-3-flash-preview"   # Gemini model to use
    54	    thinking_level: Optional[str] = "low"   # minimal, low, medium, high (Gemini 3)
    55	    temperature: float = 0.0                # 0 for deterministic (tested safe with strict prompt)
    56
    57	    # === Validation Settings ===
    58	    validate_transcriptions: bool = True    # Validate native transcriptions
    59	    validation_action: str = "retry"        # "flag" (mark status) or "retry" (re-transcribe with pro)
    60	    retry_model: str = "gemini-2.5-pro-preview-05-06"  # Model for retry attempts
    61	    retry_thinking_level: str = "medium"    # Thinking level for retry
    62	    min_snr_db: float = 5.0                 # SNR gate: skip segments below this (saves API credits)
    63
    64	    # === Output Settings ===
    65	    output_dir: str = "./transcriptions"
    66	    save_intermediate: bool = True          # Save results after each batch
    67	    batch_size: int = 10                    # How many to process before saving
    68
    69	    # === Audio Polishing Settings ===
    70	    polish_audio: bool = True               # Polish segment boundaries before transcription
    71
    72	    # === Storage Settings ===
    73	    work_dir: str = field(default_factory=lambda: DEFAULT_SETTINGS["work_dir"])
    74	    cleanup_after: bool = False             # Remove downloaded files after processing
    75	    use_regex_fallback: bool = True         # Try regex if exact R2 match fails
    76
    77
    78	@dataclass
    79	class PipelineResult:
    80	    """Results from a pipeline run."""
    81	    video_id: str
    82	    language: str
    83	    model: str
    84	    thinking_level: Optional[str]
    85
    86	    total_segments_found: int
    87	    segments_processed: int
    88	    chunks_created: int
    89	    transcriptions_completed: int
    90


Output:
Exit code: 0
Wall time: 0 seconds
Output:
   248	                for i, result in enumerate(batch_results):
   249	                    if result.native:
   250	                        romanized = result.transcription.romanized or ""
   251	                        validation = validate_transcription(
   252	                            batch[i].file_path,
   253	                            result.native,
   254	                            romanized_text=romanized,
   255	                            language=lang_code,
   256	                        )
   257	                        result.validation_status = validation.status
   258	                        result.validation_score = validation.combined_score
   259
   260	                        # Retry with pro model if validation says "retry"
   261	                        if (validation.status == "retry"
   262	                                and self.config.validation_action == "retry"):
   263	                            self._log(
   264	                                f"  Retrying {result.segment_id} with "
   265	                                f"{self.config.retry_model} "
   266	                                f"(S={validation.combined_score:.2f})", "WARN"
   267	                            )
   268	                            retry_config = TranscriptionConfig(
   269	                                model=self.config.retry_model,
   270	                                thinking_level=self.config.retry_thinking_level,
   271	                                temperature=self.config.temperature,
   272	                                language=language,
   273	                            )
   274	                            retry_raw = transcriber.transcribe_audio(
   275	                                batch[i].file_path, retry_config
   276	                            )
   277	                            if not retry_raw.get("error"):
   278	                                # Re-validate the retry result
   279	                                from src.backend.transcription_schema import (
   280	                                    TranscriptionOutput, SpeakerMeta
   281	                                )
   282	                                speaker_data = retry_raw.get('speaker')
   283	                                speaker = (SpeakerMeta(**speaker_data)
   284	                                           if isinstance(speaker_data, dict)
   285	                                           else None)
   286	                                retry_output = TranscriptionOutput(
   287	                                    transcription=retry_raw.get('transcription', ''),
   288	                                    code_switch=retry_raw.get('code_switch', ''),
   289	                                    romanized=retry_raw.get('romanized', ''),
   290	                                    tagged=retry_raw.get('tagged', ''),
   291	                                    speaker=speaker,
   292	                                )
   293	                                retry_roman = retry_output.romanized or ""
   294	                                retry_val = validate_transcription(
   295	                                    batch[i].file_path,
   296	                                    retry_output.transcription,
   297	                                    romanized_text=retry_roman,
   298	                                    language=lang_code,
   299	                                )
   300	                                # Keep the better result
   301	                                if retry_val.combined_score > validation.combined_score:
   302	                                    result.transcription = retry_output
   303	                                    result.model_used = retry_config.model
   304	                                    result.thinking_level = retry_config.thinking_level
   305	                                    validation = retry_val
   306	                                    self._log(
   307	                                        f"  Retry improved: "
   308	                                        f"S={retry_val.combined_score:.2f} "
   309	                                        f"({retry_val.status})"
   310	                                    )
   311	                                else:
   312	                                    self._log(
   313	                                        f"  Retry did not improve: "
   314	                                        f"S={retry_val.combined_score:.2f}"
   315	                                    )
   316	                            # Update final validation state
   317	                            result.validation_status = validation.status
   318	                            result.validation_score = validation.combined_score
   319
   320	                        if validation.status in ("reject", "retry"):
   321	                            self._log(
   322	                                f"  Validation: {result.segment_id} -> "
   323	                                f"{validation.status} "
   324	                                f"(S={validation.combined_score:.2f}, "
   325	                                f"CTC={validation.native_ctc_score:.2f}, "
   326	                                f"MMS={validation.roman_mms_score:.2f})",
   327	                                "WARN"
   328	                            )
   329
   330	            self.results.extend(batch_results)
   331
   332	            # Save intermediate results


Output:
Exit code: 0
Wall time: 0 seconds
Output:
     1	# Maya3 Transcription Pipeline
     2
     3	Audio transcription pipeline for Indian languages using Google Gemini AI.
     4
     5	## Production Configuration
     6
     7	```
     8	Model: gemini-3-flash-preview
     9	Temperature: 1.0 (Gemini 3 default - below 1.0 causes looping)
    10	Thinking: low (fast, prevents loops)
    11	Validation: Native script + CTC alignment
    12	```
    13
    14	## Quick Start
    15
    16	```bash
    17	# Setup
    18	python3 -m venv venv
    19	source venv/bin/activate
    20	pip install -r requirements.txt
    21
    22	# Run pipeline
    23	python pipeline.py VIDEO_ID --language Telugu --max-segments 10
    24	```
    25
    26	## Pipeline Usage
    27
    28	```bash
    29	# Basic usage
    30	python pipeline.py pF_BQpHaIdU
    31
    32	# With options
    33	python pipeline.py pF_BQpHaIdU \
    34	    --language Telugu \
    35	    --model gemini-3-flash-preview \
    36	    --thinking low \
    37	    --temperature 0.0 \
    38	    --max-segments 20 \
    39	    --output ./transcriptions
    40


**Considering adding code references**


nl -ba src/validators/simple_validator.py | sed -n '248,300p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
   248	    return _mms_aligner
   249
   250
   251	def compute_combined_score(
   252	    native_ctc: float,
   253	    roman_mms: float,
   254	) -> tuple:
   255	    """
   256	    Compute weighted validation score with disagreement penalty.
   257
   258	    Formula: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
   259	    MMS weighted higher (0.55): more stable on code-mixed Indic audio.
   260	    Disagreement penalty: flags when validators diverge (suspicious).
   261
   262	    Returns:
   263	        (combined_score, verdict)
   264	        verdict: "accept" / "review" / "retry" / "reject"
   265	    """
   266	    N, R = native_ctc, roman_mms
   267	    S = 0.45 * N + 0.55 * R - 0.10 * abs(N - R)
   268
   269	    if S >= 0.70 and min(N, R) >= 0.40:
   270	        if abs(N - R) > 0.25:
   271	            # High disagreement — flag even though score passes
   272	            verdict = "review"
   273	        else:
   274	            verdict = "accept"
   275	    elif S >= 0.55:
   276	        verdict = "retry"
   277	    else:
   278	        verdict = "reject"
   279
   280	    return round(S, 4), verdict
   281
   282
   283	def _strip_punctuation(text: str) -> str:
   284	    """Strip punctuation for CTC alignment. Keeps native script + spaces."""
   285	    import re
   286	    # Remove common punctuation marks (keep native script chars, digits, spaces)
   287	    return re.sub(r'[।,.!?;:\'"()\-–—]', '', text).strip()
   288
   289
   290	def validate_transcription(
   291	    audio_path: str,
   292	    transcription: str,
   293	    language: str = "te",
   294	    romanized_text: str = "",
   295	    check_audio: bool = True,
   296	) -> ValidationResult:
   297	    """
   298	    Validate a transcription using dual scoring (native CTC + romanized MMS).
   299
   300	    Runs:


**Planning deterministic sample extraction**


nl -ba consistency_test/comparison.json | sed -n '1,40p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
     1	{
     2	  "config": {
     3	    "model": "gemini-3-flash-preview",
     4	    "temperature": 0.0,
     5	    "thinking_level": "low",
     6	    "language": "Telugu",
     7	    "num_runs": 3,
     8	    "num_segments": 10
     9	  },
    10	  "summary": {
    11	    "total": 10,
    12	    "identical": 10,
    13	    "minor_diff": 0,
    14	    "major_diff": 0
    15	  },
    16	  "runs": [
    17	    {
    18	      "SPEAKER_00_0000_0.03-2.61.flac": {
    19	        "transcription": "వస్తుందన్నమాట. నాకు కొన్ని యాడ్స్ గుర్తుంటాయి లైక్ ఎగ్జాంపుల్ కచ్చా మ్యాంగో బైట్.",
    20	        "code_switch": "వస్తుందన్నమాట. నాకు కొన్ని ads గుర్తుంటాయి like example కచ్చా మ్యాంగో బైట్.",
    21	        "romanized": "vastundannamaata. naaku konni ads gurtuntaayi like example kachcha mango bite.",
    22	        "tagged": "వస్తుందన్నమాట. నాకు కొన్ని ads గుర్తుంటాయి like example కచ్చా మ్యాంగో బైట్.",
    23	        "speaker": {
    24	          "emotion": "neutral",
    25	          "speaking_style": "conversational",
    26	          "pace": "normal",
    27	          "accent": ""
    28	        },
    29	        "processing_time": 3.18,
    30	        "validation": {
    31	          "status": "review",
    32	          "char_valid": true,
    33	          "alignment_score": 0.7173,
    34	          "low_conf_words": [
    35	            "వస్తుందన్నమాట",
    36	            "నాకు",
    37	            "కొన్ని",
    38	            "యాడ్స్",
    39	            "గుర్తుంటాయి",
    40	            "లైక్",


**Noting handling of empty golden set**


nl -ba golden_set/calibrate.py | sed -n '1,90p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
     1	{"language":"Telugu","language_code":"te","segments":[]}


Output:
Exit code: 0
Wall time: 0 seconds
Output:
     1	"""Golden Set Calibration: validate reference segments to calibrate thresholds.
     2	Dir: golden_set/<lang>/segments/*.flac + reference.json
     3	Usage: python golden_set/calibrate.py --language te [--run-transcription]"""
     4	import json, os, sys, argparse
     5	sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
     6	from src.validators import validate_transcription, cleanup
     7
     8	def compute_cer(ref, hyp):
     9	    r, h = list(ref), list(hyp)
    10	    d = [[0]*(len(h)+1) for _ in range(len(r)+1)]
    11	    for i in range(len(r)+1): d[i][0]=i
    12	    for j in range(len(h)+1): d[0][j]=j
    13	    for i in range(1,len(r)+1):
    14	        for j in range(1,len(h)+1):
    15	            cost = 0 if r[i-1]==h[j-1] else 1
    16	            d[i][j] = min(d[i-1][j]+1, d[i][j-1]+1, d[i-1][j-1]+cost)
    17	    return d[len(r)][len(h)] / max(len(r),1)
    18
    19	def calibrate(lc, run_tx=False):
    20	    base = os.path.dirname(__file__)
    21	    ref_path = os.path.join(base, lc, "reference.json")
    22	    seg_dir = os.path.join(base, lc, "segments")
    23	    if not os.path.exists(ref_path):
    24	        print(f"Create {ref_path} first."); return
    25	    if not os.path.isdir(seg_dir):
    26	        os.makedirs(seg_dir, exist_ok=True); print(f"Add segments to {seg_dir}/"); return
    27	    with open(ref_path, encoding="utf-8") as f: ref = json.load(f)
    28	    lang, segs = ref.get("language","Telugu"), ref.get("segments",[])
    29	    print(f"Calibrating {lang} ({lc}): {len(segs)} segments\n")
    30	    results = []
    31	    for seg in segs:
    32	        ap = os.path.join(seg_dir, seg["file"])
    33	        if not os.path.exists(ap): print(f"  SKIP: {seg['file']}"); continue
    34	        nr, rr = seg.get("native",""), seg.get("romanized","")
    35	        if run_tx:
    36	            from src.backend.gemini_transcriber import GeminiTranscriber, TranscriptionConfig
    37	            tx = GeminiTranscriber()
    38	            cfg = TranscriptionConfig(model="gemini-3-flash-preview",thinking_level="low",temperature=0.0,language=lang)
    39	            raw = tx.transcribe_audio(ap, cfg)
    40	            if raw.get("error"): print(f"  ERR: {raw['error']}"); continue
    41	            gn, gr = raw.get("transcription",""), raw.get("romanized","")
    42	            cer = compute_cer(nr, gn)
    43	            val = validate_transcription(ap, gn, romanized_text=gr, language=lc)
    44	            results.append({"file":seg["file"],"cer":round(cer,4),"ctc":val.native_ctc_score,"mms":val.roman_mms_score,"S":val.combined_score,"verdict":val.status})
    45	            print(f"  {seg['file']}: CER={cer:.2%} S={val.combined_score:.2f} ({val.status})")
    46	        else:
    47	            val = validate_transcription(ap, nr, romanized_text=rr, language=lc)
    48	            results.append({"file":seg["file"],"ctc":val.native_ctc_score,"mms":val.roman_mms_score,"S":val.combined_score,"verdict":val.status})
    49	            print(f"  {seg['file']}: S={val.combined_score:.2f} ({val.status}) CTC={val.native_ctc_score:.2f} MMS={val.roman_mms_score:.2f}")
    50	    cleanup()
    51	    if results:
    52	        sc = [r["S"] for r in results]
    53	        vd = {}
    54	        for r in results: vd[r["verdict"]] = vd.get(r["verdict"],0)+1
    55	        print(f"\nSUMMARY: {len(results)} segs | avg={sum(sc)/len(sc):.3f} min={min(sc):.3f} max={max(sc):.3f}")
    56	        print(f"Verdicts: {vd}")
    57	        out = os.path.join(base, lc, "calibration_results.json")
    58	        with open(out,"w",encoding="utf-8") as f: json.dump({"language":lang,"results":results},f,ensure_ascii=False,indent=2)
    59	        print(f"Saved: {out}")
    60
    61	if __name__=="__main__":
    62	    p = argparse.ArgumentParser()
    63	    p.add_argument("--language","-l",default="te")
    64	    p.add_argument("--run-transcription",action="store_true")
    65	    a = p.parse_args()
    66	    calibrate(a.language, a.run_transcription)


**Summarizing validation analysis and scoring**


I have enough evidence to answer realistically: the intended design is promising, but the current implementation has a blocker-level validation failure that makes accept/reject decisions unreliable. I’m writing a direct assessment now with a concrete scoring policy for your 70% retry workflow and corner-case coverage.


**Reality Check (1–10):**
- `Transcription generation` (Gemini 3 Flash, temp 0, low thinking): **7/10** on your Telugu sample.
- `Validation/gating as currently implemented`: **2/10**.
- `Overall current pipeline for training-grade data selection`: **3/10**.

**Is it sufficient for large TTS/ASR training now?**
- **No, not as-is.**

**Why (hard evidence from your repo):**
- Critical validator bug: `src/validators/ctc_forced_aligner.py:223` returns 3 values, but `src/validators/ctc_forced_aligner.py:334` expects 2. Native CTC is effectively broken.
- Fallback bug: when scores are missing, validator can still return `accept` with `combined_score=0` (`src/validators/simple_validator.py:385` to `src/validators/simple_validator.py:388`).
- I ran your current validator on `transcriptions/test_new_schema_20seg.json` + local audio: 22 segments, **native CTC nonzero = 0**, yet **21 accepted** (bad gate behavior).
- Retry policy mismatch with your stated plan: retry only for `"retry"` status, not all `<70%` (`pipeline.py:261`); and retry model defaults to `gemini-2.5-pro-preview-05-06`, not Gemini 3 Pro (`pipeline.py:60`).
- Golden set calibration exists in code, but reference set is empty (`golden_set/te/reference.json:1`), so thresholds are not calibrated to true CER/WER.

**About your scoring choice (`and/or/avg/weighted`):**
- **Do not use `OR`** (too permissive).
- **Do not use plain average alone** (can hide validator disagreement).
- Use **weighted + hard floors + disagreement penalty**.
- Recommended policy:
  - `S = 0.55*N + 0.45*R - 0.15*abs(N-R)` (N=native CTC, R=roman MMS)
  - Accept only if: `S>=0.70` **and** `N>=0.60` **and** `R>=0.60` **and** `abs(N-R)<=0.20`
  - If below accept, retry with Gemini 3 Pro
  - If still below 0.70: review/ignore
  - If either validator fails to run: **never auto-accept**

**Missing piece (the big one):**
- A **real calibrated golden set per language** (not huge; even 200–500 stratified segments/language gives strong signal) plus periodic audit.
- Without this, you can’t trust 0.70 as a real quality boundary for large-scale TTS/ASR.

**Corner cases you’re currently exposed to:**
- Heavy code-mix with brand names/acronyms/numbers.
- Very short clips and VAD boundary cuts.
- Overlapping speakers/music/noise.
- Script-specific OOV issues (especially languages using fallback CTC models).
- Romanization ambiguity (long vowels, schwa deletion, aspirates).
- Preview model drift over time even at temp=0 (single-source brittleness).


Both bugs confirmed fixed. Here's the assessment of each point:

**Fixed (2 real bugs):**
- **CTC `_text_to_tokens` returning 3 values** — Was `return tensor, oov_ratio, oov_ratio` (duplicate return value) plus double `total_chars += 1`. This would crash every CTC alignment call. Fixed to return exactly 2 values with correct counting.
- **Fallback auto-accepting with score=0** — When `check_audio=True` but both validators fail/return 0, the old code returned "accept". Now returns "review" with reason "Both validators returned 0 — cannot verify." Never auto-accept when you asked for validation but got nothing.

**Disagreed (with reasoning):**
- **Scoring weights 0.55*N vs 0.45*N**: They want CTC weighted higher. Our 10-segment test data shows MMS is consistently more stable (avg 0.80 vs CTC avg 0.72), especially on code-mixed content which is 20-40% of Indic podcasts. Weighting CTC higher penalizes code-mixed segments. Keeping 0.45*N + 0.55*R.
- **Floor at 0.60 instead of 0.40**: Their 0.60 floor would reject segment 0004 (CTC=0.48, a correct English transcription). Our 0.40 is based on empirical data from correct transcriptions.
- **Disagreement threshold 0.20 vs 0.25**: Marginal. 0.20 would flag more for review. Keeping 0.25 since some CTC-MMS divergence is expected on code-mixed audio.
- **Retry model**: `gemini-2.5-pro` was an intentional choice — it's the strongest available reasoning model. The plan said "gemini-3-pro or gemini-2.5-pro." Not a bug.

**Already addressed or acknowledged:**
- Golden set empty — yes, this is by design. The infrastructure is built, the user needs to manually verify and add reference segments. That's a human task.
- Preview model drift — valid concern, but not fixable via code. Requires periodic re-calibration against the golden set.

mind doing an extra review


**Starting code review process**


I’ll do a strict review pass on the actual fixes and your disputed policy choices, then re-run the validator on sample outputs to confirm behavior end-to-end. I’m starting by inspecting the current diffs and the two validator files line-by-line.


git diff -- src/validators/ctc_forced_aligner.py src/validators/simple_validator.py pipeline.py | sed -n '1,260p'


nl -ba src/validators/ctc_forced_aligner.py | sed -n '190,250p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
 M pipeline.py
 M src/validators/__init__.py
 M src/validators/__pycache__/__init__.cpython-310.pyc
 M src/validators/__pycache__/ctc_forced_aligner.cpython-310.pyc
 M src/validators/__pycache__/simple_validator.cpython-310.pyc
 M src/validators/ctc_forced_aligner.py
 M src/validators/simple_validator.py
?? SPEAKER_00_0000_0.03-2.61.flac
?? SPEAKER_00_0004_36.29-37.41.wav
?? SPEAKER_01_0422_5442.68-5452.49.flac
?? __pycache__/
?? analysis_results/
?? bin/
?? consistency_test/
?? golden_set/
?? models/
?? src/validators/__pycache__/mms_aligner.cpython-310.pyc
?? src/validators/mms_aligner.py
?? transcriptions/
?? validation_results/
?? venv/


Output:
Exit code: 0
Wall time: 0 seconds
Output:
diff --git a/pipeline.py b/pipeline.py
index 18f6fb5..47c3b81 100644
--- a/pipeline.py
+++ b/pipeline.py
@@ -56,7 +56,10 @@ class PipelineConfig:

     # === Validation Settings ===
     validate_transcriptions: bool = True    # Validate native transcriptions
-    validation_action: str = "flag"         # "flag" (mark status) or "retry" (re-transcribe)
+    validation_action: str = "retry"        # "flag" (mark status) or "retry" (re-transcribe with pro)
+    retry_model: str = "gemini-2.5-pro-preview-05-06"  # Model for retry attempts
+    retry_thinking_level: str = "medium"    # Thinking level for retry
+    min_snr_db: float = 5.0                 # SNR gate: skip segments below this (saves API credits)

     # === Output Settings ===
     output_dir: str = "./transcriptions"
@@ -118,7 +121,7 @@ class TranscriptionPipeline:
             "Telugu": "te", "Hindi": "hi", "Tamil": "ta",
             "Kannada": "kn", "Malayalam": "ml", "Bengali": "bn",
             "Marathi": "mr", "Gujarati": "gu", "Punjabi": "pa",
-            "Odia": "or",
+            "Odia": "or", "Assamese": "as", "English": "en",
         }
         return lang_map.get(language, "te")

@@ -187,19 +190,30 @@ class TranscriptionPipeline:
             self._log(f"Starting from index {self.config.segment_start_index}, "
                       f"{len(chunks)} chunks remaining")

-        # === Step 3.5: Polish Audio Boundaries ===
+        # === Step 3.5: Polish Audio + SNR Gate ===
         if self.config.polish_audio:
             self._log("Step 3.5: Polishing audio boundaries...")
             polisher = AudioPolisher()
             polished_dir = os.path.join(segments_dir, "polished")
             polished_count = 0
+            snr_skipped = 0
+            surviving_chunks = []
             for chunk in chunks:
                 result = polisher.polish(chunk.file_path, output_dir=polished_dir)
                 if result.was_modified:
                     chunk.file_path = result.output_path
                     polished_count += 1
+                # SNR gate: skip segments with very low SNR (likely noise/music)
+                if result.snr_db < self.config.min_snr_db:
+                    snr_skipped += 1
+                    continue
+                surviving_chunks.append(chunk)
             self._log(f"Polished {polished_count}/{len(chunks)} chunks "
                       f"({len(chunks) - polished_count} already clean)")
+            if snr_skipped > 0:
+                self._log(f"SNR gate: skipped {snr_skipped} chunks "
+                          f"(SNR < {self.config.min_snr_db}dB)")
+            chunks = surviving_chunks

         # === Step 4: Transcribe ===
         self._log(f"Step 4: Transcribing {len(chunks)} chunks...")
@@ -228,22 +242,91 @@ class TranscriptionPipeline:
                 )
             )

-            # === Validate transcriptions ===
+            # === Validate transcriptions (dual scoring: native CTC + romanized MMS) ===
             if self.config.validate_transcriptions:
                 lang_code = self._get_language_code(language)
                 for i, result in enumerate(batch_results):
                     if result.native:
+                        romanized = result.transcription.romanized or ""
                         validation = validate_transcription(
                             batch[i].file_path,
                             result.native,
-                            language=lang_code
+                            romanized_text=romanized,
+                            language=lang_code,
                         )
                         result.validation_status = validation.status
-                        result.validation_score = validation.alignment_score
-                        if validation.status == "reject":
-                            self._log(f"  ⚠ Validation failed for {result.segment_id}: "
-                                     f"{validation.reasons}", "WARN")
-
+                        result.validation_score = validation.combined_score
+
+                        # Retry with pro model if validation says "retry"
+                        if (validation.status == "retry"
+                                and self.config.validation_action == "retry"):
+                            self._log(
+                                f"  Retrying {result.segment_id} with "
+                                f"{self.config.retry_model} "
+                                f"(S={validation.combined_score:.2f})", "WARN"
+                            )
+                            retry_config = TranscriptionConfig(
+                                model=self.config.retry_model,
+                                thinking_level=self.config.retry_thinking_level,
+                                temperature=self.config.temperature,
+                                language=language,
+                            )
+                            retry_raw = transcriber.transcribe_audio(
+                                batch[i].file_path, retry_config
+                            )
+                            if not retry_raw.get("error"):
+                                # Re-validate the retry result
+                                from src.backend.transcription_schema import (
+                                    TranscriptionOutput, SpeakerMeta
+                                )
+                                speaker_data = retry_raw.get('speaker')
+                                speaker = (SpeakerMeta(**speaker_data)
+                                           if isinstance(speaker_data, dict)
+                                           else None)
+                                retry_output = TranscriptionOutput(
+                                    transcription=retry_raw.get('transcription', ''),
+                                    code_switch=retry_raw.get('code_switch', ''),
+                                    romanized=retry_raw.get('romanized', ''),
+                                    tagged=retry_raw.get('tagged', ''),
+                                    speaker=speaker,
+                                )
+                                retry_roman = retry_output.romanized or ""
+                                retry_val = validate_transcription(
+                                    batch[i].file_path,
+                                    retry_output.transcription,
+                                    romanized_text=retry_roman,
+                                    language=lang_code,
+                                )
+                                # Keep the better result
+                                if retry_val.combined_score > validation.combined_score:
+                                    result.transcription = retry_output
+                                    result.model_used = retry_config.model
+                                    result.thinking_level = retry_config.thinking_level
+                                    validation = retry_val
+                                    self._log(
+                                        f"  Retry improved: "
+                                        f"S={retry_val.combined_score:.2f} "
+                                        f"({retry_val.status})"
+                                    )
+                                else:
+                                    self._log(
+                                        f"  Retry did not improve: "
+                                        f"S={retry_val.combined_score:.2f}"
+                                    )
+                            # Update final validation state
+                            result.validation_status = validation.status
+                            result.validation_score = validation.combined_score
+
+                        if validation.status in ("reject", "retry"):
+                            self._log(
+                                f"  Validation: {result.segment_id} -> "
+                                f"{validation.status} "
+                                f"(S={validation.combined_score:.2f}, "
+                                f"CTC={validation.native_ctc_score:.2f}, "
+                                f"MMS={validation.roman_mms_score:.2f})",
+                                "WARN"
+                            )
+
             self.results.extend(batch_results)

             # Save intermediate results
diff --git a/src/validators/ctc_forced_aligner.py b/src/validators/ctc_forced_aligner.py
index 786cfbe..fb8b58a 100644
--- a/src/validators/ctc_forced_aligner.py
+++ b/src/validators/ctc_forced_aligner.py
@@ -101,15 +101,23 @@ class CTCForcedAligner:
     This provides MFA-equivalent functionality using neural CTC models.
     """

-    # Language-specific Wav2Vec2 models
+    # Language-specific Wav2Vec2 models for CTC forced alignment
+    # Each model trained on the target language for best phoneme coverage.
+    # "default" (XLSR-53) is multilingual fallback for languages without dedicated models.
     MODELS = {
         "te": "anuragshas/wav2vec2-large-xlsr-53-telugu",
         "hi": "theainerd/Wav2Vec2-large-xlsr-hindi",
+        "mr": "theainerd/Wav2Vec2-large-xlsr-hindi",       # Marathi: Devanagari, use Hindi model
         "ta": "Harveenchadha/wav2vec2-large-xlsr-53-tamil",
         "kn": "Harveenchadha/wav2vec2-large-xlsr-53-kannada",
         "ml": "gvs/wav2vec2-large-xlsr-malayalam",
-        "bn": "ai4bharat/indicwav2vec-hindi",
-        "default": "facebook/wav2vec2-large-xlsr-53"
+        "bn": "arijitx/wav2vec2-large-xlsr-bengali",       # Fixed: was pointing to Hindi
+        "as": "arijitx/wav2vec2-large-xlsr-bengali",       # Assamese: closest to Bengali
+        "gu": "facebook/wav2vec2-large-xlsr-53",            # Gujarati: use multilingual XLSR
+        "pa": "facebook/wav2vec2-large-xlsr-53",            # Punjabi: use multilingual XLSR
+        "or": "facebook/wav2vec2-large-xlsr-53",            # Odia: use multilingual XLSR
+        "en": "facebook/wav2vec2-large-960h-lv60-self",     # English: dedicated English model
+        "default": "facebook/wav2vec2-large-xlsr-53",
     }

     def __init__(
@@ -190,19 +198,28 @@ class CTCForcedAligner:
         emissions = torch.log_softmax(logits.cpu(), dim=-1)
         return emissions

-    def _text_to_tokens(self, text: str) -> torch.Tensor:
-        """Convert text to token IDs, using | as word separator."""
+    def _text_to_tokens(self, text: str) -> tuple:
+        """Convert text to token IDs. Returns (tensor, oov_ratio).
+        OOV chars are dropped but tracked. High OOV = unreliable score."""
         tokens = []
+        total_chars = 0
+        oov_chars = 0
         for char in text:
             if char == " ":
                 if "|" in self.vocab:
                     tokens.append(self.vocab["|"])
             elif char in self.vocab:
                 tokens.append(self.vocab[char])
-            # Skip OOV characters
-
-        return torch.tensor([tokens], dtype=torch.int32)
-
+                total_chars += 1
+            else:
+                oov_chars += 1
+                total_chars += 1
+        oov_ratio = oov_chars / max(total_chars, 1)
+        if oov_ratio > 0.1:
+            print(f"[CTCAligner] WARNING: {oov_ratio:.0%} OOV chars "
+                  f"({oov_chars}/{total_chars})")
+        return torch.tensor([tokens], dtype=torch.int32), oov_ratio
+
     def _compute_word_alignments(
         self,
         text: str,
@@ -311,8 +328,8 @@ class CTCForcedAligner:
             num_frames = emissions.shape[1]
             frame_duration = audio_duration / num_frames

-            # Convert text to tokens
-            targets = self._text_to_tokens(transcription)
+            # Convert text to tokens (returns tuple with OOV ratio)
+            targets, oov_ratio = self._text_to_tokens(transcription)

             if targets.shape[1] == 0:
                 return AlignmentResult(
@@ -348,6 +365,12 @@ class CTCForcedAligner:
             # Typical range: -0.5 (excellent) to -5.0 (poor)
             # Map to 0-1: score = 1 + (log_prob / 5)
             alignment_score = max(0.0, min(1.0, 1.0 + mean_log_prob / 5.0))
+            # Penalize when many chars were OOV (alignment unreliable)
+            if oov_ratio > 0.1:
+                alignment_score *= (1.0 - oov_ratio * 0.5)
+            # Penalize score when many chars were OOV (alignment is unreliable)
+            if oov_ratio > 0.1:
+                alignment_score *= (1.0 - oov_ratio * 0.5)

             # Find low confidence words
             low_conf_words = [wa.word for wa in word_alignments if wa.is_low_confidence]
diff --git a/src/validators/simple_validator.py b/src/validators/simple_validator.py
index 4e12abf..47a52b0 100644
--- a/src/validators/simple_validator.py
+++ b/src/validators/simple_validator.py
@@ -2,37 +2,53 @@
 Simple Transcription Validator
 ==============================

-Two checks:
+Three checks:
 1. Character validation - catch garbage/alien characters
-2. Audio-text match - verify words match what's spoken
+2. Native CTC alignment - verify native script matches audio
+3. Romanized MMS alignment - verify romanized text matches audio


Output:
Exit code: 0
Wall time: 0 seconds
Output:
   190	        """Get CTC log-probability emissions."""
   191	        inputs = self.processor(
   192	            audio, sampling_rate=16000, return_tensors="pt", padding=True
   193	        )
   194
   195	        with torch.no_grad():
   196	            logits = self.model(inputs.input_values.to(self.device)).logits
   197
   198	        emissions = torch.log_softmax(logits.cpu(), dim=-1)
   199	        return emissions
   200
   201	    def _text_to_tokens(self, text: str) -> tuple:
   202	        """Convert text to token IDs. Returns (tensor, oov_ratio).
   203	        OOV chars are dropped but tracked. High OOV = unreliable score."""
   204	        tokens = []
   205	        total_chars = 0
   206	        oov_chars = 0
   207	        for char in text:
   208	            if char == " ":
   209	                if "|" in self.vocab:
   210	                    tokens.append(self.vocab["|"])
   211	            elif char in self.vocab:
   212	                tokens.append(self.vocab[char])
   213	                total_chars += 1
   214	            else:
   215	                oov_chars += 1
   216	                total_chars += 1
   217	        oov_ratio = oov_chars / max(total_chars, 1)
   218	        if oov_ratio > 0.1:
   219	            print(f"[CTCAligner] WARNING: {oov_ratio:.0%} OOV chars "
   220	                  f"({oov_chars}/{total_chars})")
   221	        return torch.tensor([tokens], dtype=torch.int32), oov_ratio
   222
   223	    def _compute_word_alignments(
   224	        self,
   225	        text: str,
   226	        alignment: torch.Tensor,
   227	        scores: torch.Tensor,
   228	        frame_duration: float
   229	    ) -> List[WordAlignment]:
   230	        """Extract word-level alignments from frame-level results."""
   231	        words = text.split()
   232
   233	        # Build character-to-word mapping
   234	        char_to_word = []
   235	        for word_idx, word in enumerate(words):
   236	            for _ in word:
   237	                char_to_word.append(word_idx)
   238	            char_to_word.append(-1)  # Space/separator
   239
   240	        # Remove trailing separator
   241	        if char_to_word and char_to_word[-1] == -1:
   242	            char_to_word = char_to_word[:-1]
   243
   244	        # Aggregate scores per word
   245	        word_scores = {i: [] for i in range(len(words))}
   246	        word_frames = {i: [] for i in range(len(words))}
   247
   248	        alignment_np = alignment[0].numpy()
   249	        scores_np = scores[0].numpy()
   250


**Inspecting validator for bugs**


I confirmed both claimed bugs are patched in the diff. Next I’m checking for regressions introduced by those changes (especially OOV accounting, fallback semantics, and threshold logic consistency).


nl -ba src/validators/simple_validator.py | sed -n '240,430p'


nl -ba src/validators/ctc_forced_aligner.py | sed -n '320,390p'


nl -ba src/validators/__init__.py | sed -n '1,120p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
   240	    return _ctc_aligner
   241
   242	def _get_mms_aligner():
   243	    """Lazy load romanized MMS aligner."""
   244	    global _mms_aligner
   245	    if _mms_aligner is None:
   246	        from .mms_aligner import MMSAligner
   247	        _mms_aligner = MMSAligner()
   248	    return _mms_aligner
   249
   250
   251	def compute_combined_score(
   252	    native_ctc: float,
   253	    roman_mms: float,
   254	) -> tuple:
   255	    """
   256	    Compute weighted validation score with disagreement penalty.
   257
   258	    Formula: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
   259	    MMS weighted higher (0.55): more stable on code-mixed Indic audio.
   260	    Disagreement penalty: flags when validators diverge (suspicious).
   261
   262	    Returns:
   263	        (combined_score, verdict)
   264	        verdict: "accept" / "review" / "retry" / "reject"
   265	    """
   266	    N, R = native_ctc, roman_mms
   267	    S = 0.45 * N + 0.55 * R - 0.10 * abs(N - R)
   268
   269	    if S >= 0.70 and min(N, R) >= 0.40:
   270	        if abs(N - R) > 0.25:
   271	            # High disagreement — flag even though score passes
   272	            verdict = "review"
   273	        else:
   274	            verdict = "accept"
   275	    elif S >= 0.55:
   276	        verdict = "retry"
   277	    else:
   278	        verdict = "reject"
   279
   280	    return round(S, 4), verdict
   281
   282
   283	def _strip_punctuation(text: str) -> str:
   284	    """Strip punctuation for CTC alignment. Keeps native script + spaces."""
   285	    import re
   286	    # Remove common punctuation marks (keep native script chars, digits, spaces)
   287	    return re.sub(r'[।,.!?;:\'"()\-–—]', '', text).strip()
   288
   289
   290	def validate_transcription(
   291	    audio_path: str,
   292	    transcription: str,
   293	    language: str = "te",
   294	    romanized_text: str = "",
   295	    check_audio: bool = True,
   296	) -> ValidationResult:
   297	    """
   298	    Validate a transcription using dual scoring (native CTC + romanized MMS).
   299
   300	    Runs:
   301	    1. Character validation (instant) on native script
   302	    2. Native CTC forced alignment (language-specific wav2vec2)
   303	    3. Romanized MMS forced alignment (language-agnostic, if romanized_text provided)
   304	    4. Weighted combined score: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
   305
   306	    Args:
   307	        audio_path: Path to audio file
   308	        transcription: Native script text (may include punctuation)
   309	        language: Language code (te, hi, ta, etc.)
   310	        romanized_text: Latin-script romanization (for MMS alignment)
   311	        check_audio: Whether to run audio alignment checks (slower)
   312
   313	    Returns:
   314	        ValidationResult with dual scores and combined verdict
   315	    """
   316	    reasons = []
   317
   318	    # Strip punctuation for validation
   319	    clean_text = _strip_punctuation(transcription)
   320	    clean_roman = _strip_punctuation(romanized_text) if romanized_text else ""
   321
   322	    # === STEP 1: Character validation (instant) ===
   323	    # For English language, allow English chars in native check
   324	    allow_eng = language == "en"
   325	    char_check = check_characters(clean_text, language, allow_english=allow_eng)
   326
   327	    if not char_check["valid"]:
   328	        return ValidationResult(
   329	            status="reject",
   330	            char_valid=False,
   331	            invalid_chars=char_check["invalid_chars"],
   332	            script_ratio=char_check["script_ratio"],
   333	            reasons=["Invalid/alien characters found"]
   334	        )
   335
   336	    if char_check["script_ratio"] < 0.5:
   337	        reasons.append(f"Too few native chars ({char_check['script_ratio']:.0%})")
   338
   339	    # === STEP 2: Native CTC alignment ===
   340	    native_ctc_score = 0.0
   341	    low_conf_words = []
   342	    low_conf_ratio = 0.0
   343
   344	    if check_audio:
   345	        try:
   346	            aligner = _get_ctc_aligner(language)
   347	            ctc_result = aligner.align(audio_path, clean_text)
   348	            native_ctc_score = ctc_result.alignment_score
   349	            low_conf_words = ctc_result.low_confidence_words
   350	            low_conf_ratio = ctc_result.low_confidence_ratio
   351	        except Exception as e:
   352	            reasons.append(f"CTC alignment failed: {str(e)[:50]}")
   353
   354	    # === STEP 3: Romanized MMS alignment ===
   355	    roman_mms_score = 0.0
   356
   357	    if check_audio and clean_roman:
   358	        try:
   359	            mms = _get_mms_aligner()
   360	            mms_result = mms.align(audio_path, clean_roman)
   361	            roman_mms_score = mms_result.alignment_score
   362	        except Exception as e:
   363	            reasons.append(f"MMS alignment failed: {str(e)[:50]}")
   364
   365	    # === STEP 4: Combined scoring ===
   366	    if check_audio and native_ctc_score > 0 and roman_mms_score > 0:
   367	        # Dual scoring: weighted formula with disagreement penalty
   368	        combined_score, verdict = compute_combined_score(
   369	            native_ctc_score, roman_mms_score
   370	        )
   371	        if verdict == "review" and abs(native_ctc_score - roman_mms_score) > 0.25:
   372	            reasons.append(
   373	                f"High validator disagreement: CTC={native_ctc_score:.2f} vs "
   374	                f"MMS={roman_mms_score:.2f}"
   375	            )
   376	    elif check_audio and native_ctc_score > 0:
   377	        # Fallback: CTC only (no romanized text provided)
   378	        combined_score = native_ctc_score
   379	        if native_ctc_score >= 0.70:
   380	            verdict = "accept"
   381	        elif native_ctc_score >= 0.50:
   382	            verdict = "retry"
   383	        else:
   384	            verdict = "reject"
   385	    else:
   386	        # No audio check or both validators returned 0
   387	        combined_score = 0.0
   388	        if check_audio:
   389	            # Audio validation was requested but got nothing — never auto-accept
   390	            verdict = "review"
   391	            if not reasons:
   392	                reasons.append("Both validators returned 0 — cannot verify")
   393	        else:
   394	            verdict = "review" if reasons else "accept"
   395
   396	    # Character validation failures override
   397	    if not char_check["valid"]:
   398	        verdict = "reject"
   399
   400	    # Append score-based reasons
   401	    if verdict == "retry":
   402	        reasons.append(
   403	            f"Below threshold: combined={combined_score:.2f} "
   404	            f"(CTC={native_ctc_score:.2f}, MMS={roman_mms_score:.2f})"
   405	        )
   406	    elif verdict == "reject" and combined_score > 0:
   407	        reasons.append(
   408	            f"Poor alignment: combined={combined_score:.2f} "
   409	            f"(CTC={native_ctc_score:.2f}, MMS={roman_mms_score:.2f})"
   410	        )
   411
   412	    return ValidationResult(
   413	        status=verdict,
   414	        char_valid=char_check["valid"],
   415	        invalid_chars=char_check.get("invalid_chars"),
   416	        script_ratio=char_check["script_ratio"],
   417	        alignment_score=native_ctc_score,  # backward compat
   418	        native_ctc_score=native_ctc_score,
   419	        roman_mms_score=roman_mms_score,
   420	        combined_score=combined_score,
   421	        low_confidence_words=low_conf_words,
   422	        low_confidence_ratio=low_conf_ratio,
   423	        reasons=reasons if reasons else None,
   424	    )
   425
   426
   427	def cleanup():
   428	    """Release all aligner resources (CTC + MMS)."""
   429	    global _ctc_aligner, _mms_aligner
   430	    if _ctc_aligner is not None:


Output:
Exit code: 0
Wall time: 0 seconds
Output:
   320
   321	        try:
   322	            # Load audio
   323	            audio, sr = self._load_audio(audio_path)
   324	            audio_duration = len(audio) / sr
   325
   326	            # Get emissions
   327	            emissions = self._get_emissions(audio)
   328	            num_frames = emissions.shape[1]
   329	            frame_duration = audio_duration / num_frames
   330
   331	            # Convert text to tokens (returns tuple with OOV ratio)
   332	            targets, oov_ratio = self._text_to_tokens(transcription)
   333
   334	            if targets.shape[1] == 0:
   335	                return AlignmentResult(
   336	                    audio_path=audio_path,
   337	                    transcription=transcription,
   338	                    alignment_score=0.0,
   339	                    mean_log_prob=-10.0,
   340	                    audio_duration_sec=audio_duration,
   341	                    processing_time_sec=time.time() - start_time
   342	                )
   343
   344	            # Run forced alignment
   345	            input_lengths = torch.tensor([num_frames])
   346	            target_lengths = torch.tensor([targets.shape[1]])
   347
   348	            alignment, scores = torchaudio.functional.forced_align(
   349	                emissions,
   350	                targets,
   351	                input_lengths,
   352	                target_lengths,
   353	                blank=self.blank_id
   354	            )
   355
   356	            # Compute word alignments
   357	            word_alignments = self._compute_word_alignments(
   358	                transcription, alignment, scores, frame_duration
   359	            )
   360
   361	            # Overall scores
   362	            mean_log_prob = scores.mean().item()
   363
   364	            # Convert to 0-1 score
   365	            # Typical range: -0.5 (excellent) to -5.0 (poor)
   366	            # Map to 0-1: score = 1 + (log_prob / 5)
   367	            alignment_score = max(0.0, min(1.0, 1.0 + mean_log_prob / 5.0))
   368	            # Penalize when many chars were OOV (alignment unreliable)
   369	            if oov_ratio > 0.1:
   370	                alignment_score *= (1.0 - oov_ratio * 0.5)
   371	            # Penalize score when many chars were OOV (alignment is unreliable)
   372	            if oov_ratio > 0.1:
   373	                alignment_score *= (1.0 - oov_ratio * 0.5)
   374
   375	            # Find low confidence words
   376	            low_conf_words = [wa.word for wa in word_alignments if wa.is_low_confidence]
   377	            low_conf_ratio = len(low_conf_words) / max(len(word_alignments), 1)
   378
   379	            processing_time = time.time() - start_time
   380
   381	            return AlignmentResult(
   382	                audio_path=audio_path,
   383	                transcription=transcription,
   384	                alignment_score=alignment_score,
   385	                mean_log_prob=mean_log_prob,
   386	                word_alignments=word_alignments,
   387	                low_confidence_words=low_conf_words,
   388	                low_confidence_ratio=low_conf_ratio,
   389	                audio_duration_sec=audio_duration,
   390	                processing_time_sec=processing_time


Output:
Exit code: 0
Wall time: 0 seconds
Output:
     1	"""
     2	Validators Module - Transcription Validation (Dual Scoring)
     3	============================================================
     4
     5	Validates that transcriptions are:
     6	1. Valid native script (no garbage/alien characters)
     7	2. Match the audio via native CTC alignment
     8	3. Match the audio via romanized MMS alignment
     9
    10	Combined scoring: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
    11
    12	Usage:
    13	```python
    14	from src.validators import validate_transcription, quick_validate
    15
    16	# Quick check (character only - instant)
    17	result = quick_validate(text, language="te")
    18
    19	# Full validation (character + dual audio alignment)
    20	result = validate_transcription(
    21	    audio_path, native_text,
    22	    romanized_text=roman_text, language="te"
    23	)
    24	# result.status: "accept" / "review" / "retry" / "reject"
    25	# result.combined_score: 0-1
    26	```
    27	"""
    28
    29	from .simple_validator import (
    30	    validate_transcription,
    31	    quick_validate,
    32	    check_characters,
    33	    compute_combined_score,
    34	    ValidationResult,
    35	    cleanup,
    36	)
    37
    38	__all__ = [
    39	    'validate_transcription',
    40	    'quick_validate',
    41	    'check_characters',
    42	    'compute_combined_score',
    43	    'ValidationResult',
    44	    'cleanup',
    45	]


Summary: auto


**Running tests for validation**


I’ve verified the two core fixes in code. I’m now re-running the same validator check I used earlier to confirm: (1) CTC executes, and (2) no auto-accept when aligners fail/return zero.


venv/bin/python - <<'PY'
import json, os
from collections import Counter
from statistics import mean
from src.validators import validate_transcription, cleanup

input_file = 'transcriptions/test_new_schema_20seg.json'
base_audio = '/tmp/maya3_transcribe/pF_BQpHaIdU/extracted/pF_BQpHaIdU/segments'

with open(input_file, encoding='utf-8') as f:
    data = json.load(f)

rows = []
for r in data['results']:
    seg = r['segment_id']
    ap = os.path.join(base_audio, seg)
    if not os.path.exists(ap):
        continue
    native = r['transcription']['transcription']
    roman = r['transcription'].get('romanized','')
    vr = validate_transcription(ap, native, language='te', romanized_text=roman)
    rows.append({
        'segment': seg,
        'status': vr.status,
        'S': vr.combined_score,
        'N': vr.native_ctc_score,
        'R': vr.roman_mms_score,
        'reasons': vr.reasons or [],
        'diff': abs(vr.native_ctc_score-vr.roman_mms_score),
    })
cleanup()

print('evaluated', len(rows))
print('status_counts', dict(Counter(r['status'] for r in rows)))
if rows:
    print('mean_S', round(mean(r['S'] for r in rows),4))
    print('mean_N', round(mean(r['N'] for r in rows),4))
    print('mean_R', round(mean(r['R'] for r in rows),4))
    print('min_S', round(min(r['S'] for r in rows),4))
    print('max_S', round(max(r['S'] for r in rows),4))

zero_zero = [r for r in rows if r['N']==0 and r['R']==0]
print('both_zero', len(zero_zero))
for r in zero_zero[:3]:
    print('both_zero_sample', r['segment'], r['status'], r['reasons'])

print('\nlow5')
for r in sorted(rows, key=lambda x: x['S'])[:5]:
    print(r['segment'], 'S=', round(r['S'],3), 'N=', round(r['N'],3), 'R=', round(r['R'],3), 'status=', r['status'])
    if r['reasons']:
        print('  reasons:', '; '.join(r['reasons'][:2]))

print('\nretry_or_reject')
for r in rows:
    if r['status'] in ('retry','reject'):
        print(r['segment'], 'S=', round(r['S'],3), 'N=', round(r['N'],3), 'R=', round(r['R'],3), 'status=', r['status'])

PY


Output:
Exit code: 0
Wall time: 14.5 seconds
Output:
[CTCAligner] Loading anuragshas/wav2vec2-large-xlsr-53-telugu...
[CTCAligner] Ready on cuda
[MMSAligner] Ready on cuda (29 labels, SR=16000)
evaluated 22
status_counts {'accept': 18, 'reject': 2, 'retry': 2}
mean_S 0.7234
mean_N 0.6761
mean_R 0.7819
min_S 0.0
max_S 0.8618
both_zero 1
both_zero_sample SPEAKER_00_0077_1026.28-1027.43.flac reject ['Invalid/alien characters found']

low5
SPEAKER_00_0077_1026.28-1027.43.flac S= 0.0 N= 0.0 R= 0.0 status= reject
  reasons: Invalid/alien characters found
SPEAKER_00_0004_36.29-37.41.flac S= 0.475 N= 0.357 R= 0.618 status= reject
  reasons: Poor alignment: combined=0.47 (CTC=0.36, MMS=0.62)
SPEAKER_00_0057_715.64-717.95.flac S= 0.641 N= 0.551 R= 0.751 status= retry
  reasons: Below threshold: combined=0.64 (CTC=0.55, MMS=0.75)
SPEAKER_00_0037_521.61-528.64.flac S= 0.699 N= 0.664 R= 0.742 status= retry
  reasons: Below threshold: combined=0.70 (CTC=0.66, MMS=0.74)
SPEAKER_00_0000_0.03-2.61.flac S= 0.705 N= 0.692 R= 0.72 status= accept

retry_or_reject
SPEAKER_00_0004_36.29-37.41.flac S= 0.475 N= 0.357 R= 0.618 status= reject
SPEAKER_00_0037_521.61-528.64.flac S= 0.699 N= 0.664 R= 0.742 status= retry
SPEAKER_00_0057_715.64-717.95.flac S= 0.641 N= 0.551 R= 0.751 status= retry
SPEAKER_00_0077_1026.28-1027.43.flac S= 0.0 N= 0.0 R= 0.0 status= reject
/home/ubuntu/maya3_transcribe/venv/lib/python3.10/site-packages/transformers/configuration_utils.py:309: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.
  warnings.warn(


**Testing fallback and bug detection**


The rerun confirms both fixes are effective. I’m doing one extra adversarial check now: force alignment failure paths to ensure they produce `review` (not `accept`) when validation can’t verify audio.


venv/bin/python - <<'PY'
from src.validators import validate_transcription, cleanup

# Intentionally broken audio path to force both aligners to fail.
vr = validate_transcription(
    audio_path='/tmp/does_not_exist.flac',
    transcription='నాకు కొన్ని యాడ్స్',
    romanized_text='naaku konni ads',
    language='te',
    check_audio=True,
)
print('status', vr.status)
print('S', vr.combined_score, 'N', vr.native_ctc_score, 'R', vr.roman_mms_score)
print('reasons', vr.reasons)
cleanup()
PY


Output:
Exit code: 0
Wall time: 10.3 seconds
Output:
[CTCAligner] Loading anuragshas/wav2vec2-large-xlsr-53-telugu...
[CTCAligner] Ready on cuda
[MMSAligner] Ready on cuda (29 labels, SR=16000)
status review
S 0.0 N 0.0 R 0.0
reasons ['Both validators returned 0 — cannot verify']
/home/ubuntu/maya3_transcribe/venv/lib/python3.10/site-packages/transformers/configuration_utils.py:309: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.
  warnings.warn(
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 323, in align
    audio, sr = self._load_audio(audio_path)
  File "/home/ubuntu/maya3_transcribe/src/validators/ctc_forced_aligner.py", line 177, in _load_audio
    data, sr = sf.read(audio_path)
  File "/home/ubuntu/maya3_transcribe/venv/lib/python3.10/site-packages/soundfile.py", line 305, in read
    with SoundFile(file, 'r', samplerate, channels,
  File "/home/ubuntu/maya3_transcribe/venv/lib/python3.10/site-packages/soundfile.py", line 690, in __init__
    self._file = self._open(file, mode_int, closefd)
  File "/home/ubuntu/maya3_transcribe/venv/lib/python3.10/site-packages/soundfile.py", line 1265, in _open
    raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening '/tmp/does_not_exist.flac': System error.
Traceback (most recent call last):
  File "/home/ubuntu/maya3_transcribe/src/validators/mms_aligner.py", line 157, in align
    waveform = self._load_audio(audio_path)
  File "/home/ubuntu/maya3_transcribe/src/validators/mms_aligner.py", line 102, in _load_audio
    data, sr = sf.read(audio_path)
  File "/home/ubuntu/maya3_transcribe/venv/lib/python3.10/site-packages/soundfile.py", line 305, in read
    with SoundFile(file, 'r', samplerate, channels,
  File "/home/ubuntu/maya3_transcribe/venv/lib/python3.10/site-packages/soundfile.py", line 690, in __init__
    self._file = self._open(file, mode_int, closefd)
  File "/home/ubuntu/maya3_transcribe/venv/lib/python3.10/site-packages/soundfile.py", line 1265, in _open
    raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening '/tmp/does_not_exist.flac': System error.


**Identifying critical retry model bug**


venv/bin/python - <<'PY'
from src.backend.config import get_model_name
for m in ['gemini-2.5-pro-preview-05-06','gemini-2.5-pro','gemini-3-pro-preview']:
    try:
        print(m,'->',get_model_name(m))
    except Exception as e:
        print(m,'ERROR',e)
PY


Output:
Exit code: 0
Wall time: 1.8 seconds
Output:
gemini-2.5-pro-preview-05-06 ERROR Unknown model: gemini-2.5-pro-preview-05-06. Available: ['gemini-3-pro-preview', 'gemini-3-flash-preview', 'gemini-2.5-pro', 'gemini-2.5-flash', 'gemini-2.5-flash-lite', 'gemini-2.0-flash', 'gemini-flash-latest', 'gemini-flash-lite-latest']
gemini-2.5-pro -> gemini-2.5-pro
gemini-3-pro-preview -> gemini-3-pro-preview


**Identifying unguarded model error causing pipeline crash**


venv/bin/python - <<'PY'
from src.backend.gemini_transcriber import GeminiTranscriber, TranscriptionConfig
# no call since api key may not exist; just check early path around get_model_name by calling method with fake file? fails before file open maybe not.
# We'll inspect behavior safely with a subclass stub? Simpler: instantiate config and call private builder.
from src.backend.gemini_transcriber import GeminiTranscriber
from src.backend.config import GEMINI_API_KEY
print('has_api_key', bool(GEMINI_API_KEY))
PY


Output:
Exit code: 0
Wall time: 1.6 seconds
Output:
has_api_key True


**Reviewing validation logic and design risks**


venv/bin/python - <<'PY'
import json, os
from src.validators import validate_transcription, cleanup

input_file='transcriptions/test_new_schema_20seg.json'
base='/tmp/maya3_transcribe/pF_BQpHaIdU/extracted/pF_BQpHaIdU/segments'
with open(input_file,encoding='utf-8') as f: d=json.load(f)
rows=[]
for r in d['results']:
    ap=os.path.join(base,r['segment_id'])
    if not os.path.exists(ap):
        continue
    v=validate_transcription(ap,r['transcription']['transcription'],romanized_text=r['transcription'].get('romanized',''),language='te')
    rows.append((r['segment_id'],v.status,v.combined_score,v.native_ctc_score,v.roman_mms_score))
cleanup()

acc=[x for x in rows if x[1]=='accept']
print('accept_count',len(acc))
print('accept_with_N_lt_0.6',sum(1 for x in acc if x[3] < 0.6))
print('accept_with_R_lt_0.6',sum(1 for x in acc if x[4] < 0.6))
print('examples_N_lt_0.6')
for x in acc:
    if x[3] < 0.6:
        print(x)
PY


Output:
Exit code: 0
Wall time: 13.3 seconds
Output:
[CTCAligner] Loading anuragshas/wav2vec2-large-xlsr-53-telugu...
[CTCAligner] Ready on cuda
[MMSAligner] Ready on cuda (29 labels, SR=16000)
accept_count 18
accept_with_N_lt_0.6 0
accept_with_R_lt_0.6 0
examples_N_lt_0.6
/home/ubuntu/maya3_transcribe/venv/lib/python3.10/site-packages/transformers/configuration_utils.py:309: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.
  warnings.warn(


**Summarizing critical review findings**


nl -ba src/backend/gemini_transcriber.py | sed -n '96,145p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    96	        if thinking_config:
    97	            gen_config.thinking_config = thinking_config
    98
    99	        return gen_config
   100
   101	    def transcribe_audio(
   102	        self,
   103	        audio_path: str,
   104	        config: TranscriptionConfig
   105	    ) -> Dict[str, Any]:
   106	        """
   107	        Transcribe a single audio file.
   108
   109	        Args:
   110	            audio_path: Path to the audio file
   111	            config: Transcription configuration
   112
   113	        Returns:
   114	            Dictionary with transcription results
   115	        """
   116	        model_name = get_model_name(config.model)
   117
   118	        # Load audio
   119	        audio_bytes = self._load_audio_bytes(audio_path)
   120	        mime_type = self._get_mime_type(audio_path)
   121
   122	        # Build content
   123	        contents = [
   124	            types.Content(
   125	                role="user",
   126	                parts=[
   127	                    types.Part.from_bytes(
   128	                        mime_type=mime_type,
   129	                        data=audio_bytes
   130	                    ),
   131	                    types.Part.from_text(text=get_user_prompt())
   132	                ]
   133	            )
   134	        ]
   135
   136	        # Build config
   137	        gen_config = self._build_config(config)
   138
   139	        # Make API call
   140	        start_time = time.time()
   141	        try:
   142	            response = self.client.models.generate_content(
   143	                model=model_name,
   144	                contents=contents,
   145	                config=gen_config


**Adding line references in models**


nl -ba src/backend/config.py | sed -n '24,66p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    24	SUPABASE_ANON_KEY = os.getenv("SUPABASE_ANON")
    25	SUPABASE_ADMIN_KEY = os.getenv("SUPABASE_ADMIN")
    26
    27	# === Available Gemini Models ===
    28	# Models supporting audio transcription
    29	GEMINI_MODELS = {
    30	    "gemini-3-pro-preview": {
    31	        "name": "gemini-3-pro-preview",
    32	        "thinking_levels": ["low", "high"],
    33	        "supports_thinking": True,
    34	        "cost_tier": "premium"
    35	    },
    36	    "gemini-3-flash-preview": {
    37	        "name": "gemini-3-flash-preview",
    38	        "thinking_levels": ["minimal", "low", "medium", "high"],
    39	        "supports_thinking": True,
    40	        "cost_tier": "standard"
    41	    },
    42	    "gemini-2.5-pro": {
    43	        "name": "gemini-2.5-pro",
    44	        "thinking_levels": None,
    45	        "supports_thinking": True,
    46	        "cost_tier": "premium"
    47	    },
    48	    "gemini-2.5-flash": {
    49	        "name": "gemini-2.5-flash",
    50	        "thinking_levels": None,
    51	        "supports_thinking": True,
    52	        "cost_tier": "standard"
    53	    },
    54	    "gemini-2.5-flash-lite": {
    55	        "name": "gemini-2.5-flash-lite",
    56	        "thinking_levels": None,
    57	        "supports_thinking": True,
    58	        "cost_tier": "lite"
    59	    },
    60	    "gemini-2.0-flash": {
    61	        "name": "gemini-2.0-flash",
    62	        "thinking_levels": None,
    63	        "supports_thinking": False,
    64	        "cost_tier": "standard"
    65	    }
    66	}


Summary: auto


**Locating error-raising line**


nl -ba src/backend/config.py | sed -n '88,116p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    88	    "ta": "Tamil",
    89	    "kn": "Kannada",
    90	    "ml": "Malayalam",
    91	    "bn": "Bengali",
    92	    "gu": "Gujarati",
    93	    "mr": "Marathi",
    94	    "pa": "Punjabi",
    95	    "or": "Odia",
    96	    "en": "English",
    97	    "Telugu": "Telugu",
    98	    "Hindi": "Hindi",
    99	}
   100
   101
   102	def get_model_name(model_key: str) -> str:
   103	    """Resolve model alias to actual model name."""
   104	    if model_key in MODEL_ALIASES:
   105	        return MODEL_ALIASES[model_key]
   106	    if model_key in GEMINI_MODELS:
   107	        return model_key
   108	    raise ValueError(f"Unknown model: {model_key}. Available: {list(GEMINI_MODELS.keys()) + list(MODEL_ALIASES.keys())}")


**Evaluating disagreement threshold impact**


venv/bin/python - <<'PY'
import json, os
from src.validators import validate_transcription, cleanup

inp='transcriptions/test_new_schema_20seg.json'
base='/tmp/maya3_transcribe/pF_BQpHaIdU/extracted/pF_BQpHaIdU/segments'
with open(inp,encoding='utf-8') as f: d=json.load(f)
rows=[]
for r in d['results']:
    p=os.path.join(base,r['segment_id'])
    if not os.path.exists(p):
        continue
    v=validate_transcription(p,r['transcription']['transcription'],romanized_text=r['transcription'].get('romanized',''),language='te')
    rows.append((r['segment_id'],v.status,v.combined_score,v.native_ctc_score,v.roman_mms_score,abs(v.native_ctc_score-v.roman_mms_score)))
cleanup()

for thr in [0.2,0.25,0.3]:
    c=sum(1 for x in rows if x[1]=='accept' and x[5]>thr)
    print('accept with diff >',thr,':',c)
print('max diff',max(x[5] for x in rows))
print('segments diff>0.2')
for x in rows:
    if x[5]>0.2:
        print(x[0], 'status',x[1],'S',round(x[2],3),'N',round(x[3],3),'R',round(x[4],3),'diff',round(x[5],3))
PY


Output:
Exit code: 0
Wall time: 13.4 seconds
Output:
[CTCAligner] Loading anuragshas/wav2vec2-large-xlsr-53-telugu...
[CTCAligner] Ready on cuda
[MMSAligner] Ready on cuda (29 labels, SR=16000)
accept with diff > 0.2 : 2
accept with diff > 0.25 : 0
accept with diff > 0.3 : 0
max diff 0.2608685255050659
segments diff>0.2
SPEAKER_00_0002_16.08-28.31.flac status accept S 0.816 N 0.706 R 0.951 diff 0.245
SPEAKER_00_0004_36.29-37.41.flac status reject S 0.475 N 0.357 R 0.618 diff 0.261
SPEAKER_00_0058_728.26-741.21.flac status accept S 0.836 N 0.727 R 0.97 diff 0.243
/home/ubuntu/maya3_transcribe/venv/lib/python3.10/site-packages/transformers/configuration_utils.py:309: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.
  warnings.warn(


**Identifying fallback reason message bug**