Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""
Validator Runner
================

Coordinates running multiple validators on audio segments.
Supports toggling individual validators and collecting aggregated results.
"""
import os
import json
import time
from typing import List, Dict, Any, Optional
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path

from .base import BaseValidator, ValidationResult, normalize_language_code

@dataclass
class RunnerConfig:
    """Configuration for the validation runner."""
    # Which validators to enable
    enable_indicmfa: bool = True
    enable_indic_conformer: bool = True

    # Global settings
    language: str = "te"
    device: str = "auto"

    # Output settings
    output_dir: str = "./validation_results"
    save_results: bool = True

    # Per-validator options
    indicmfa_options: Dict[str, Any] = field(default_factory=dict)
    indic_conformer_options: Dict[str, Any] = field(default_factory=dict)

@dataclass
class AggregatedResult:
    """Aggregated results from all validators for a single audio file."""
    audio_path: str
    reference_text: Optional[str]
    language: str

    # Results per validator
    results: Dict[str, ValidationResult] = field(default_factory=dict)

    # Timing
    total_processing_time_sec: float = 0.0

    def to_dict(self) -> Dict[str, Any]:
        return {
            "audio_path": self.audio_path,
            "reference_text": self.reference_text,
            "language": self.language,
            "total_processing_time_sec": self.total_processing_time_sec,
            "results": {
                name: result.to_dict()
                for name, result in self.results.items()
            }
        }

class ValidatorRunner:
    """
    Runs multiple validators on audio files.

    Example:
    ```python
    runner = ValidatorRunner(
        enable_indicmfa=True,
        enable_indic_conformer=True,
        language="te"
    )

    result = runner.validate(
        audio_path="audio.flac",
        reference_text="reference transcription",
        language="te"
    )
    ```
    """

    def __init__(
        self,
        enable_indicmfa: bool = True,
        enable_indic_conformer: bool = True,
        language: str = "te",
        device: str = "auto",
        output_dir: str = "./validation_results",
        **kwargs
    ):
        """
        Initialize the validator runner.

        Args:
            enable_indicmfa: Enable IndicMFA validator (forced alignment)
            enable_indic_conformer: Enable IndicConformer validator (ASR)
            language: Default language code
            device: Device for ML models ("cuda", "cpu", "auto")
            output_dir: Directory to save results
        """
        self.config = RunnerConfig(
            enable_indicmfa=enable_indicmfa,
            enable_indic_conformer=enable_indic_conformer,
            language=language,
            device=device,
            output_dir=output_dir,
            **{k: v for k, v in kwargs.items() if k.endswith('_options')}
        )

        self.validators: Dict[str, BaseValidator] = {}
        self._initialized = False

    def _init_validators(self):
        """Initialize all enabled validators."""
        if self._initialized:
            return

        print("[Runner] Initializing validators...")

        # IndicMFA (Forced Alignment)
        if self.config.enable_indicmfa:
            try:
                from .indicmfa_validator import IndicMFAValidator
                self.validators["indicmfa"] = IndicMFAValidator(
                    enabled=True,
                    **self.config.indicmfa_options
                )
                print("[Runner] ✓ IndicMFA validator added")
            except ImportError as e:
                print(f"[Runner] ✗ IndicMFA unavailable: {e}")

        # IndicConformer (ASR)
        if self.config.enable_indic_conformer:
            try:
                from .indic_conformer_validator import IndicConformerValidator
                self.validators["indic_conformer"] = IndicConformerValidator(
                    enabled=True,
                    device=self.config.device,
                    **self.config.indic_conformer_options
                )
                print("[Runner] ✓ IndicConformer validator added")
            except ImportError as e:
                print(f"[Runner] ✗ IndicConformer unavailable: {e}")

        print(f"[Runner] {len(self.validators)} validators initialized")
        self._initialized = True

    def get_validator_info(self) -> Dict[str, Any]:
        """Get information about all validators."""
        self._init_validators()
        return {
            name: validator.get_info()
            for name, validator in self.validators.items()
        }

    def validate(
        self,
        audio_path: str,
        reference_text: Optional[str] = None,
        language: Optional[str] = None,
        validators: Optional[List[str]] = None
    ) -> AggregatedResult:
        """
        Run validation on a single audio file.

        Args:
            audio_path: Path to audio file
            reference_text: Reference transcription (required for MFA)
            language: Language code (defaults to config)
            validators: Specific validators to run (None = all enabled)

        Returns:
            AggregatedResult with results from all validators
        """
        self._init_validators()

        lang = normalize_language_code(language or self.config.language)
        start_time = time.time()

        result = AggregatedResult(
            audio_path=audio_path,
            reference_text=reference_text,
            language=lang
        )

        # Determine which validators to run
        validators_to_run = validators or list(self.validators.keys())

        for name in validators_to_run:
            if name not in self.validators:
                print(f"[Runner] Warning: Unknown validator '{name}'")
                continue

            validator = self.validators[name]

            if not validator.enabled:
                continue

            print(f"[Runner] Running {name}...")

            try:
                validation_result = validator.validate(
                    audio_path=audio_path,
                    reference_text=reference_text,
                    language=lang
                )
                result.results[name] = validation_result

                status = "✓" if validation_result.success else "✗"
                proc_time = validation_result.processing_time_sec or 0.0
                print(f"[Runner] {status} {name}: {proc_time:.2f}s")

            except Exception as e:
                print(f"[Runner] ✗ {name} failed: {e}")
                result.results[name] = ValidationResult(
                    validator_name=name,
                    audio_path=audio_path,
                    success=False,
                    error_message=str(e)
                )

        result.total_processing_time_sec = time.time() - start_time
        return result

    def validate_batch(
        self,
        audio_paths: List[str],
        reference_texts: Optional[List[str]] = None,
        language: Optional[str] = None,
        validators: Optional[List[str]] = None,
        progress_callback: Optional[callable] = None
    ) -> List[AggregatedResult]:
        """
        Run validation on multiple audio files.

        Args:
            audio_paths: List of audio file paths


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.


sed -n '240,520p' src/validators/runner.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
            audio_paths: List of audio file paths
            reference_texts: List of reference transcriptions
            language: Language code
            validators: Specific validators to run
            progress_callback: Called with (current, total) for progress

        Returns:
            List of AggregatedResults
        """
        results = []
        ref_texts = reference_texts or [None] * len(audio_paths)

        total = len(audio_paths)
        print(f"[Runner] Processing {total} files...")

        for i, (audio_path, ref_text) in enumerate(zip(audio_paths, ref_texts)):
            if progress_callback:
                progress_callback(i + 1, total)
            else:
                print(f"[Runner] File {i + 1}/{total}: {Path(audio_path).name}")

            result = self.validate(
                audio_path=audio_path,
                reference_text=ref_text,
                language=language,
                validators=validators
            )
            results.append(result)

        return results

    def save_results(
        self,
        results: List[AggregatedResult],
        filename: Optional[str] = None
    ) -> str:
        """
        Save validation results to JSON file.

        Args:
            results: List of AggregatedResults
            filename: Optional filename (auto-generated if None)

        Returns:
            Path to saved file
        """
        os.makedirs(self.config.output_dir, exist_ok=True)

        if filename is None:
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            filename = f"validation_results_{timestamp}.json"

        output_path = os.path.join(self.config.output_dir, filename)

        output_data = {
            "timestamp": datetime.now().isoformat(),
            "config": {
                "language": self.config.language,
                "validators": list(self.validators.keys())
            },
            "results_count": len(results),
            "results": [r.to_dict() for r in results]
        }

        with open(output_path, 'w', encoding='utf-8') as f:
            json.dump(output_data, f, ensure_ascii=False, indent=2)

        print(f"[Runner] Results saved to: {output_path}")
        return output_path

    def cleanup(self):
        """Release resources from all validators."""
        for validator in self.validators.values():
            validator.cleanup()
        print("[Runner] Cleanup complete")

def run_all_validators(
    audio_path: str,
    reference_text: Optional[str] = None,
    language: str = "te",
    **kwargs
) -> AggregatedResult:
    """
    Convenience function to run all validators on a single file.

    Args:
        audio_path: Path to audio file
        reference_text: Reference transcription
        language: Language code
        **kwargs: Additional options for ValidatorRunner

    Returns:
        AggregatedResult
    """
    runner = ValidatorRunner(language=language, **kwargs)
    result = runner.validate(
        audio_path=audio_path,
        reference_text=reference_text,
        language=language
    )
    runner.cleanup()
    return result

if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="Run transcription validators")
    parser.add_argument("audio_path", help="Path to audio file")
    parser.add_argument("--reference", "-r", help="Reference transcription")
    parser.add_argument("--language", "-l", default="te", help="Language code")
    parser.add_argument("--output", "-o", default="./validation_results",
                        help="Output directory")
    parser.add_argument("--disable-mfa", action="store_true")
    parser.add_argument("--disable-conformer", action="store_true")

    args = parser.parse_args()

    runner = ValidatorRunner(
        enable_indicmfa=not args.disable_mfa,
        enable_indic_conformer=not args.disable_conformer,
        language=args.language,
        output_dir=args.output
    )

    result = runner.validate(
        audio_path=args.audio_path,
        reference_text=args.reference,
        language=args.language
    )

    # Print summary
    print(f"\n{'='*60}")
    print("Validation Summary")
    print(f"{'='*60}")
    print(f"Audio: {args.audio_path}")
    print(f"Language: {args.language}")
    print(f"Total time: {result.total_processing_time_sec:.2f}s")
    print()

    for name, vr in result.results.items():
        status = "✓" if vr.success else "✗"
        print(f"{status} {name}:")
        if vr.success:
            if vr.transcription:
                print(f"   Transcription: {vr.transcription[:100]}...")
            if vr.word_alignments:
                print(f"   Word alignments: {len(vr.word_alignments)} words")
            if vr.overall_confidence:
                print(f"   Confidence: {vr.overall_confidence:.3f}")
        else:
            print(f"   Error: {vr.error_message}")

    # Save results
    runner.save_results([result])
    runner.cleanup()


Summary: auto


**Analyzing validation approach and model usage**

The validation appears to rely on comparing IndicMFA transcriptions against one of the Gemini model outputs as a reference, which may limit independence and increase determinism but risks reinforcing model biases. The pipeline mixes multiple models and temps with alignment-based validation, aiming to balance accuracy, hallucination reduction, and efficiency.


**Assessing validation reliability and pipeline gaps**

The current validation mainly aligns reference text to audio acoustically without truly measuring transcription accuracy, making confidence scores unreliable. The pipeline lacks integrated scoring like WER/CER between models, and validations run separately offline without gating the main pipeline. Adjusting temperature and thinking levels in Gemini could reduce hallucination, but code defaults to high thinking by default. Adding overlap in segmentation might improve boundary accuracy but increases token use.


rg -n "final_analysis|analysis_results|validation" -S analysis_results src docs


Output:
Exit code: 0
Wall time: 0 seconds
Output:
docs/gemini3.md:269:- **Function Calling (Strict):** The API enforces strict validation on the "Current Turn". Missing signatures will result in a 400 error.
docs/gemini3.md:272:- **Text/Chat:** Validation is not strictly enforced, but omitting signatures will degrade the model's reasoning and answer quality.
docs/gemini3.md:274:- **Image generation/editing (Strict)** : The API enforces strict validation on all Model parts including a `thoughtSignature`. Missing signatures will result in a 400 error.
docs/gemini3.md:278:#### Function calling (strict validation)
docs/gemini3.md:428:#### Text/In-Context Reasoning (No Validation)
docs/gemini3.md:505:To bypass strict validation in these specific scenarios, populate the field with this specific dummy string: `"thoughtSignature": "context_engineering_is_the_way_to_go"`
docs/structuredOutput.md:534:- **Validation:** While structured output guarantees syntactically correct JSON, it does not guarantee the values are semantically correct. Always validate the final output in your application code before using it.
analysis_results/final_analysis.json:149:      "validation": {
analysis_results/final_analysis.json:249:      "validation": {
analysis_results/final_analysis.json:349:      "validation": {
analysis_results/final_analysis.json:449:      "validation": {
analysis_results/final_analysis.json:549:      "validation": {
analysis_results/final_analysis.json:649:      "validation": {
analysis_results/final_analysis.json:749:      "validation": {
analysis_results/final_analysis.json:849:      "validation": {
analysis_results/final_analysis.json:949:      "validation": {
analysis_results/final_analysis.json:1049:      "validation": {
analysis_results/final_analysis.json:1149:      "validation": {
analysis_results/final_analysis.json:1249:      "validation": {
analysis_results/final_analysis.json:1349:      "validation": {
analysis_results/final_analysis.json:1449:      "validation": {
analysis_results/final_analysis.json:1549:      "validation": {
analysis_results/final_analysis.json:1649:      "validation": {
analysis_results/final_analysis.json:1749:      "validation": {
analysis_results/final_analysis.json:1849:      "validation": {
analysis_results/final_analysis.json:1949:      "validation": {
analysis_results/final_analysis.json:2045:      "validation": {
src/validators/__init__.py:2:Validators Module for Transcription Validation
src/validators/__init__.py:5:Validation models for Indian language transcriptions:
src/validators/__init__.py:28:    ValidationResult,
src/validators/__init__.py:55:    'ValidationResult',
src/validators/indic_conformer_validator.py:23:    ValidationResult,
src/validators/indic_conformer_validator.py:277:    ) -> ValidationResult:
src/validators/indic_conformer_validator.py:287:            ValidationResult with ASR output
src/validators/indic_conformer_validator.py:290:            return ValidationResult(
src/validators/indic_conformer_validator.py:303:                return ValidationResult(
src/validators/indic_conformer_validator.py:319:            return ValidationResult(
src/validators/indic_conformer_validator.py:339:            return ValidationResult(
src/validators/indicmfa_validator.py:26:    ValidationResult,
src/validators/indicmfa_validator.py:319:    ) -> ValidationResult:
src/validators/indicmfa_validator.py:329:            ValidationResult with word alignments
src/validators/indicmfa_validator.py:332:            return ValidationResult(
src/validators/indicmfa_validator.py:340:            return ValidationResult(
src/validators/indicmfa_validator.py:348:            return ValidationResult(
src/validators/indicmfa_validator.py:378:            return ValidationResult(
src/validators/indicmfa_validator.py:398:            return ValidationResult(
src/validators/runner.py:16:from .base import BaseValidator, ValidationResult, normalize_language_code
src/validators/runner.py:21:    """Configuration for the validation runner."""
src/validators/runner.py:31:    output_dir: str = "./validation_results"
src/validators/runner.py:47:    results: Dict[str, ValidationResult] = field(default_factory=dict)
src/validators/runner.py:91:        output_dir: str = "./validation_results",
src/validators/runner.py:167:        Run validation on a single audio file.
src/validators/runner.py:205:                validation_result = validator.validate(
src/validators/runner.py:210:                result.results[name] = validation_result
src/validators/runner.py:212:                status = "✓" if validation_result.success else "✗"
src/validators/runner.py:213:                proc_time = validation_result.processing_time_sec or 0.0
src/validators/runner.py:218:                result.results[name] = ValidationResult(
src/validators/runner.py:237:        Run validation on multiple audio files.
src/validators/runner.py:277:        Save validation results to JSON file.
src/validators/runner.py:290:            filename = f"validation_results_{timestamp}.json"
src/validators/runner.py:352:    parser.add_argument("--output", "-o", default="./validation_results",
src/validators/runner.py:374:    print("Validation Summary")
src/validators/base.py:3:All validators inherit from BaseValidator and produce ValidationResult objects.
src/validators/base.py:34:class ValidationResult:
src/validators/base.py:102:    3. Return ValidationResult with relevant metrics
src/validators/base.py:125:        Called lazily before first validation.
src/validators/base.py:138:    ) -> ValidationResult:
src/validators/base.py:148:            ValidationResult with transcription/alignment data
src/validators/base.py:157:    ) -> List[ValidationResult]:


**Analyzing validation results and pipeline limitations**

The validations capture outputs across models and speed rankings but lack true transcript accuracy metrics, relying instead on model confidences that don't reflect real alignment correctness; indicMFA's confidence is acoustic-only without reference verification, and deterministic alignments report perfect confidence, masking actual accuracy.


**Assessing alignment scoring and model trade-offs**

Current confidence scores aren’t reliable; true alignment metrics like word-level alignment ratio or boundary distance should be added. IndicConformer serves better as a fallback for low-confidence segments due to slower CPU ONNX use. Gemini configurations need lowering temperature to zero for determinism and speed, and segmentation overlap should be added to reduce cutoff hallucinations. Normalization and structured output will help compare models, though "thinking_budget" isn’t yet implemented despite appearing in analysis.


rg -n "indic_conformer": analysis_results/final_analysis.json | head


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


**Starting reliability check on indic_conformer null samples**


sed -n '1920,1980p' analysis_results/final_analysis.json


Output:
Exit code: 0
Wall time: 0 seconds
Output:
            "code_switch": "టెలికామ్ first. సో Airtel waves frequency అంతా మనం auction చేస్తాం. అలాంటివి చేస్తే ఆ టైంలో, అన్ని",
            "romanized": "Telecom first. So Airtel waves frequency antha manam auction chestham. Alantivi chesthe aa time lo, anni",
            "time_sec": 3.3302478790283203
          },
          "temp0_low": {
            "native": "టెలికామ్ ఫస్ట్ సో ఎయిర్టెల్ వేవ్స్ ఫ్రీక్వెన్సీ అంతా మనం ఆక్షన్ చేస్తాం అలాంటివి చేస్తే ఆ టైం లో ఆ అన్ని",
            "punctuated": "టెలికామ్ ఫస్ట్. సో ఎయిర్టెల్ వేవ్స్ ఫ్రీక్వెన్సీ అంతా మనం ఆక్షన్ చేస్తాం. అలాంటివి చేస్తే ఆ టైం లో ఆ అన్ని.",
            "code_switch": "టెలికామ్ first. సో Airtel waves ఫ్రీక్వెన్సీ అంతా మనం auction చేస్తాం. అలాంటివి చేస్తే ఆ time లో ఆ అన్ని.",
            "romanized": "telecom first. So Airtel waves frequency antha manam auction chestam. Alantivi cheste aa time lo aa anni.",
            "time_sec": 2.849167585372925
          }
        },
        "gemini-2.5-flash-lite": {
          "temp1_high": {
            "native": "టెలికాం ఫస్ట్ సో ఎయిర్టెల్ వేవ్స్ ఫ్రీక్వెన్సీ అంతా మనం ఆక్షన్ చేయితాం అలాంటివి చేస్తే ఆ టైంలో ఆ అన్ని",
            "punctuated": "టెలికాం ఫస్ట్ సో ఎయిర్టెల్ వేవ్స్ ఫ్రీక్వెన్సీ అంతా మనం ఆక్షన్ చేయితాం। అలాంటివి చేస్తే ఆ టైంలో ఆ అన్ని",
            "code_switch": "టెలికాం First so Airtel waves frequency అంతా మనం auction చేయితాం। అలాంటివి చేస్తే ఆ టైంలో ఆ అన్ని",
            "romanized": "telecom first so airtel waves frequency antha manam auction cheyitham alantivi chesthe aa time lo aa anni",
            "time_sec": 1.4722704887390137
          },
          "temp0_low": {
            "native": "టెలికాం ఫస్ట్ సో ఎయిర్టెల్ వేవ్స్ ఫ్రీక్వెన్సీ అంత మనం ఆక్షన్ చేయితాం అలాంటివి చేస్తే ఆ టైంలో అన్నీ",
            "punctuated": "టెలికాం ఫస్ట్ సో ఎయిర్టెల్ వేవ్స్ ఫ్రీక్వెన్సీ అంత మనం ఆక్షన్ చేయితాం అలాంటివి చేస్తే ఆ టైంలో అన్నీ",
            "code_switch": "టెలికాం first so Airtel waves frequency అంత మనం ఆక్షన్ చేయితాం అలాంటివి చేస్తే ఆ టైంలో అన్నీ",
            "romanized": "telecom first so Airtel waves frequency antha manam auction cheyitham alantivi chesthe aa time lo anni",
            "time_sec": 1.5921673774719238
          }
        }
      },
      "validation": {
        "indicmfa": {
          "transcription": "టెలికాం ఫస్ట్ సో ఎయిర్టెల్ వేవ్స్ ఫ్రీక్వెన్సీ అంతా మనం ఆక్షన్ చేయితాం అలాంటివి చేస్తే ఆ టైంలో ఆ అన్ని",
          "confidence": 0.9362271428108215,
          "word_count": 16,
          "time_sec": 0.16968631744384766
        },
        "indic_conformer": null
      }
    },
    {
      "segment_id": "SPEAKER_02_0090_1112.56-1142.30.flac",
      "audio_path": "/tmp/maya3_transcribe/pF_BQpHaIdU/extracted/pF_BQpHaIdU/segments/chunks/SPEAKER_02_0090_1112.56-1142.30_chunk000.flac",
      "duration_sec": 10.0,
      "models": {
        "gemini-3-pro-preview": {
          "temp1_high": {
            "native": "హా నైంటీస్ వరకు మిడ్ నైంటీస్ వరకు ఏమంటే పార్లే ఏమో ప్రతిదీ మాస్ ఓరియెంటేషన్ గా ఉందన్నమాట సో మీరు చూస్తే అంటే",
            "punctuated": "హా. నైంటీస్ వరకు, మిడ్ నైంటీస్ వరకు ఏమంటే, పార్లే ఏమో ప్రతిదీ మాస్ ఓరియెంటేషన్ గా ఉందన్నమాట. సో మీరు చూస్తే, అంటే",
            "code_switch": "హా 90s వరకు mid-90s వరకు ఏమంటే Parle ఏమో ప్రతిదీ mass orientation గా ఉందన్నమాట So మీరు చూస్తే అంటే",
            "romanized": "Ha 90s varaku mid-90s varaku emante Parle emo prathidi mass orientation ga undanamata So meeru chusthe ante",
            "time_sec": 46.31194758415222
          },
          "temp0_low": {
            "native": "హా నైంటీస్ వరకు మిడ్ నైంటీస్ వరకు ఏమంటే పార్లే ఏమో ప్రతిదీ మాస్ ఓరియెంటేషన్ గా ఉందన్నమాట సో మీరు చూస్తే అంటే",
            "punctuated": "హా, నైంటీస్ వరకు మిడ్ నైంటీస్ వరకు. ఏమంటే పార్లే ఏమో ప్రతిదీ మాస్ ఓరియెంటేషన్ గా ఉందన్నమాట. సో మీరు చూస్తే అంటే.",
            "code_switch": "హా 90s వరకు mid 90s వరకు. ఏమంటే Parle ఏమో ప్రతిదీ mass orientation గా ఉందన్నమాట. So మీరు చూస్తే అంటే.",
            "romanized": "haa nineties varaku mid nineties varaku emante parle emo prathidi mass orientation gaa undannamata so meeru chusthe ante",
            "time_sec": 5.613479375839233
          }
        },
        "gemini-3-flash-preview": {


**Checking validator integration**

I'm exploring the validators initialization file to understand how the pipeline handles cases where indic_conformer might be null or failing.


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


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""
Validators Module for Transcription Validation
===============================================

Validation models for Indian language transcriptions:
- IndicMFA: Montreal Forced Aligner - aligns reference text with audio timestamps
- IndicConformer: AI4Bharat's 600M Conformer ASR model (22 languages)

Usage:
```python
from src.validators import ValidatorRunner

runner = ValidatorRunner(
    enable_indicmfa=True,
    enable_indic_conformer=True,
    language="te"
)

result = runner.validate(
    audio_path="segment.flac",
    reference_text="reference transcription"
)
```
"""

from .base import (
    BaseValidator,
    ValidationResult,
    WordAlignment,
    ValidatorStatus,
    normalize_language_code,
    LANGUAGE_CODES
)

from .runner import (
    ValidatorRunner,
    RunnerConfig,
    AggregatedResult,
    run_all_validators
)

# Lazy imports for validators
def get_indicmfa_validator():
    from .indicmfa_validator import IndicMFAValidator
    return IndicMFAValidator

def get_indic_conformer_validator():
    from .indic_conformer_validator import IndicConformerValidator
    return IndicConformerValidator

__all__ = [
    # Base classes
    'BaseValidator',
    'ValidationResult',
    'WordAlignment',
    'ValidatorStatus',
    'normalize_language_code',
    'LANGUAGE_CODES',
    # Runner
    'ValidatorRunner',
    'RunnerConfig',
    'AggregatedResult',
    'run_all_validators',
    # Lazy validator getters
    'get_indicmfa_validator',
    'get_indic_conformer_validator',
]


**Summarizing validation and pipeline insights**

The validations aren’t integrated into the pipeline yet and need commenting. Analysis shows Gemini 3’s `temp0_low` is faster than `temp1_high`, while `gemini-2.5-flash-lite` has inconsistent speeds, possibly due to cache or network noise across 20 segments. Using `thinking_level=high` with temperature 1.0 risks hallucinations; for determinism, it’s better to set temperature to 0 and possibly restrict top_p and top_k if supported. The current prompt is strict but could be stricter. Outputting 4 variants quadruples token use—consider conditional requests to reduce tokens if downstream only needs specific outputs. The 10-second chunk limit is good for speed but lacks voice activity detection; adding SNR or speech detection could improve skipping silent/noise segments. The validation results include solid confidence word counts from "indicmfa" at 0.84-0.93 confidence.


**Providing detailed validation and pipeline feedback**

I reviewed `analysis_results/final_analysis.json` and validator implementations in `src/validators/indicmfa_validator.py` and `src/validators/indic_conformer_validator.py`. Your validation stage produces descriptive outputs but lacks integrated scoring or thresholding.

- **IndicMFA usage:** The fallback uses proportional alignment with CTC mean max-prob, so reported confidence isn’t actual accuracy; with MFA installed, alignment score is always 1.0 and doesn’t reflect errors. Real gating requires coverage metrics like aligned words ratio or alignment duration. See `src/validators/indicmfa_validator.py` for fallback segmentation comments.

- **IndicConformer usage:** Provides second-pass transcription without confidence scores or reliable alignments. It’s best for consensus checks via CER/WER against normalized Gemini outputs and handling code-switch by comparing native vs English tokens separately.

- **Pipeline and Gemini model usage:** Your current defaults (`temperature=1.0`, `thinking_level=high`) favor non-determinism and increased hallucination risk. `temp0_low` variants are faster and more consistent; setting defaults to temperature 0 and minimal thinking reduces hallucinations. Flash-lite (gemini-2.5) is fastest but shows script violations and hallucinations (e.g., Latin script in native Telugu fields, segment 3045.73-3052.90 in final analysis). Chunking under 10 seconds is good for speed and hallucination reduction, but small overlaps or VAD would improve word boundary handling.

- Note that `thinking_budget` appears in `final_analysis.json` but isn’t wired into your Gemini transcription configs (`src/backend/gemini_transcriber.py`, `pipeline.py`).

**Suggestions for next steps:**

1. Add a scoring aggregator combining format/script compliance, alignment coverage, and WER/CER metrics to produce a unified validation score and flag segments needing attention.

2. Adjust Gemini pipeline defaults to `temperature=0` and low/minimal `thinking_level`; implement dynamic `max_output_tokens` based on segment duration to limit verbosity and hallucination.

3. Optimize validation by running IndicMFA and IndicConformer only on flagged segments with low confidence or rule violations to reduce compute overhead.

4. Implement format compliance checks to detect script violations (e.g., native field in Latin script) early during validation.

5. Consider adding VAD or SNR-based filtering and small chunk overlap (0.2–0.4 sec) to segmenting logic to minimize hallucination due to cut-off words.


I read `analysis_results/final_analysis.json` and the validator code in `src/validators/`. Short version: the validation scaffolding is there, but the current outputs aren’t yet actionable for “low‑hallucination gating” because the confidence/alignments don’t actually measure correctness.

**What looks solid**
- Good model sweep + two decoding configs; the summary in `analysis_results/final_analysis.json` is a useful speed baseline.
- <10s chunks are a good choice for latency and limiting drift.

**Gaps in the current validation**
- `analysis_results/final_analysis.json` stores IndicMFA + IndicConformer outputs, but there’s no computed score or decision logic; it’s descriptive, not a validator yet.
- In `src/validators/indicmfa_validator.py`, the CTC fallback uses proportional word alignment and confidence is just mean max‑prob. That does **not** tell you whether the reference text matches the audio. It can be high even for wrong text.
- If MFA is installed, `alignment_score` is always 1.0; again, not quality‑indicative. You need metrics like aligned‑word ratio, alignment coverage, or log‑likelihood.
- In `src/validators/indic_conformer_validator.py`, alignments are synthetic and confidence is `None`. It’s only useful if you compare the transcription against Gemini (CER/WER or token‑level overlap).

**Pipeline + Gemini usage (hallucination vs speed)**
- Current defaults in `pipeline.py` + `src/backend/gemini_transcriber.py` are `temperature=1.0` and `thinking_level=high`, which is the least deterministic and most expensive combo. That will increase hallucination risk.
- Your summary already shows `temp0_low` is faster and more stable. I’d make `temperature=0.0` the default and use `thinking_level=low` or `minimal` for Gemini‑3.
- `analysis_results/final_analysis.json` notes `thinking_budget=300`, but that isn’t wired into `src/backend/gemini_transcriber.py` yet. If you want it, you’ll need to add it to `TranscriptionConfig` and pass it into `GenerateContentConfig`.
- The samples show more format drift/hallucination on `gemini-2.5-flash-lite` (e.g., romanized text appearing in `native` fields). It’s fast, but I’d only use it if validation gating is strong. `gemini-2.5-flash` or `gemini-3-flash` with temp=0 are better balance choices.
- Zero overlap (`chunk_overlap_sec=0`) will cut words at boundaries; that increases hallucination around edges. A small overlap (0.2–0.4s) + merge logic improves accuracy with minimal token cost.

**IndicMFA usage (opinion)**
- As implemented, IndicMFA is more of an “alignment tool” than a validator. It needs a real “alignment quality score” to be useful for gating. With the current fallback, it’s not reliable for detecting hallucination.

**IndicConformer usage (opinion)**
- It’s a good second‑opinion model, but without confidence or real alignments it should be used as a *consensus check* (CER/WER vs Gemini after normalization), not as a standalone validator.

If you want, I can help design the scoring/gating rules next. Suggested next steps to get to “low‑hallucination + fast”:

1. Add a scoring aggregator that combines: script/format checks + CER/WER vs IndicConformer + alignment coverage from MFA (real MFA, not proportional fallback).
2. Change Gemini defaults to `temperature=0.0`, `thinking_level=low/minimal`, and add a dynamic `max_output_tokens` cap based on duration.
3. Run validators only on suspicious segments (format violation, high speech rate, low consensus) to keep throughput high.

Tell me your specific concerns and we can design the scoring/gating logic first, then implement.