Output:
Chunk ID: e95d60
Wall time: 0.0513 seconds
Process exited with code 0
Original token count: 1749
Output:
                    else:
                        break

                burst_dur = (frame_times[last_above] - frame_times[burst_start]) * 1000

                # Only trim if burst is short enough to be an artifact
                if self.min_burst_ms <= burst_dur <= self.max_burst_ms:
                    # Find the silence gap before the burst
                    # Trim at burst_start = keep silence, remove burst
                    tt = frame_times[burst_start]
                    actual_trim = (total_dur - tt) * 1000

                    if actual_trim <= self.max_end_trim_ms and actual_trim >= self.min_trim_ms:
                        return tt, "artifact_trimmed", False

        # Sustained speech to the end = can't trim, but apply fade-out
        # to prevent click artifacts from abrupt cut
        return None, "clean", True  # needs_fade_out=True

    def _measure_snr(self, ste_db):
        """Estimate SNR from energy distribution."""
        return np.percentile(ste_db, 90) - np.percentile(ste_db, 10)

    def _apply_fade(self, audio, sr, fade_in=False, fade_out=False):
        """Apply smooth fade-in/fade-out to prevent click artifacts."""
        out = audio.copy()
        if fade_in and self.fade_in_ms > 0:
            n = int(self.fade_in_ms * sr / 1000)
            if n > 0 and n < len(out):
                out[:n] *= np.linspace(0, 1, n)
        if fade_out and self.fade_out_ms > 0:
            n = int(self.fade_out_ms * sr / 1000)
            if n > 0 and n < len(out):
                out[-n:] *= np.linspace(1, 0, n)
        return out

    def polish(self, audio_path, output_path=None, output_dir=None):
        """
        Polish a single audio segment.

        Only removes cut speech artifacts. Silence at boundaries is preserved.
        Already-clean segments pass through unchanged (was_modified=False).
        """
        audio, sr = sf.read(audio_path)
        if len(audio.shape) > 1:
            audio = audio.mean(axis=1)
        orig_ms = len(audio) / sr * 1000

        rms = np.sqrt(np.mean(audio ** 2))
        peak = np.max(np.abs(audio))
        rms_db = 20 * np.log10(rms + 1e-10)
        peak_db = 20 * np.log10(peak + 1e-10)
        is_clipped = peak >= self.clipping_threshold

        if orig_ms < self.min_remaining_ms:
            return PolishResult(
                input_path=audio_path, output_path=audio_path,
                was_modified=False, original_duration_ms=orig_ms,
                polished_duration_ms=orig_ms, rms_db=rms_db,
                peak_db=peak_db, is_clipped=is_clipped)

        ste_db, zcr, ft = self._compute_frames(audio, sr)
        snr_db = self._measure_snr(ste_db)

        # Detect artifacts (NOT silence - silence is kept as padding)
        s_sec, s_q = self._detect_leading_artifact(ste_db, zcr, ft)
        e_sec, e_q, needs_fade_out = self._detect_trailing_artifact(
            ste_db, ft, len(audio) / sr)

        s_ms = (s_sec * 1000) if s_sec is not None else 0.0
        e_ms = ((len(audio) / sr - e_sec) * 1000) if e_sec is not None else 0.0

        # Safety: don't shrink below minimum
        if orig_ms - s_ms - e_ms < self.min_remaining_ms:
            s_sec, e_sec = None, None
            s_ms, e_ms = 0.0, 0.0
            s_q = "marginal_kept" if s_q != "clean" else "clean"
            e_q = "marginal_kept" if e_q != "clean" else "clean"
            needs_fade_out = False

        # Volume normalization for quiet, low-SNR segments
        vol_adj = False
        vol_gain = 0.0
        if snr_db < self.snr_boost_threshold_db and rms_db < self.target_rms_db:
            vol_gain = min(self.target_rms_db - rms_db, self.max_gain_db)
            if vol_gain > 1.0:
                vol_adj = True
            else:
                vol_gain = 0.0

        # Fade-out alone doesn't justify a file write (10ms change).
        # It piggybacks on actual trim/volume modifications.
        needs_mod = (s_sec is not None or e_sec is not None or vol_adj)
        if not needs_mod:
            return PolishResult(
                input_path=audio_path, output_path=audio_path,
                was_modified=False, start_quality=s_q, end_quality=e_q,
                original_duration_ms=orig_ms, polished_duration_ms=orig_ms,
                snr_db=snr_db, rms_db=rms_db, peak_db=peak_db,
                is_clipped=is_clipped)

        # Apply modifications
        pol = audio.copy()

        # Trim artifact regions only (silence is preserved)
        ss = int(s_sec * sr) if s_sec is not None else 0
        es = int(e_sec * sr) if e_sec is not None else len(audio)
        pol = pol[ss:es]

        # Fade-in after start trim (smooth transition from silence to speech)
        # Fade-out if segment ends with abrupt speech cut
        pol = self._apply_fade(
            pol, sr,
            fade_in=(s_sec is not None),
            fade_out=needs_fade_out
        )

        if vol_adj:
            g = 10 ** (vol_gain / 20)
            pol = pol * g
            mx = np.max(np.abs(pol))
            if mx > 0.99:
                pol = pol * (0.99 / mx)

        pol_ms = len(pol) / sr * 1000

        if output_path is None:
            if output_dir:
                os.makedirs(output_dir, exist_ok=True)
                output_path = os.path.join(output_dir, Path(audio_path).name)
            else:
                stem = Path(audio_path).stem
                ext = Path(audio_path).suffix or f".{self.output_format}"
                output_path = str(
                    Path(audio_path).parent / f"{stem}_polished{ext}")

        sf.write(output_path, pol, sr)

        return PolishResult(
            input_path=audio_path, output_path=output_path,
            was_modified=True, start_trimmed_ms=s_ms, end_trimmed_ms=e_ms,
            start_quality=s_q, end_quality=e_q,
            original_duration_ms=orig_ms, polished_duration_ms=pol_ms,
            snr_db=snr_db, rms_db=rms_db, peak_db=peak_db,
            is_clipped=is_clipped, volume_adjusted=vol_adj,
            volume_gain_db=vol_gain)

    def polish_directory(self, input_dir, output_dir=None, max_files=None):
        """Polish all audio segments in a directory."""
        if output_dir is None:
            output_dir = os.path.join(input_dir, "polished")
        exts = {".flac", ".wav", ".mp3", ".ogg", ".m4a"}
        files = []
        for ext in exts:
            files.extend(Path(input_dir).glob(f"*{ext}"))
        files = sorted(files, key=lambda x: x.name)
        if max_files:
            files = files[:max_files]
        print(f"[Polisher] Processing {len(files)} segments...")
        results = []
        modified = 0
        for f in files:
            r = self.polish(str(f), output_dir=output_dir)
            results.append(r)
            if r.was_modified:
                modified += 1
            print(r.summary())
        print(f"[Polisher] Done: {modified}/{len(files)} modified, "
              f"{len(files) - modified} already clean")
        return results


# 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.


**Inspecting package requirements**


Output:
Chunk ID: 0ea7de
Wall time: 0.0514 seconds
Process exited with code 0
Original token count: 26
Output:
boto3>=1.42.0
google-genai>=1.61.0
pydantic>=2.12.0
supabase>=2.27.0
pydub>=0.25.0
python-dotenv>=1.2.0


Summary: auto


**Assessing romanization approach**


rg -n "romaniz|uroman|transliter|indic|WX|ITRANS|akshara|script" src pipeline.py -S


Output:
Chunk ID: 316225
Wall time: 0.0512 seconds
Process exited with code 0
Original token count: 7022
Output:
pipeline.py:2:Main transcription pipeline orchestrating the full workflow:
pipeline.py:24:from src.backend.gemini_transcriber import GeminiTranscriber, TranscriptionConfig
pipeline.py:25:from src.backend.transcription_schema import TranscriptionResult
pipeline.py:28:from src.validators import validate_transcription, quick_validate, cleanup as cleanup_validator
pipeline.py:34:    Complete configuration for the transcription pipeline.
pipeline.py:52:    # === Transcription Settings ===
pipeline.py:58:    validate_transcriptions: bool = True    # Validate native transcriptions
pipeline.py:65:    output_dir: str = "./transcriptions"
pipeline.py:70:    polish_audio: bool = True               # Polish segment boundaries before transcription
pipeline.py:89:    transcriptions_completed: int
pipeline.py:178:class TranscriptionPipeline:
pipeline.py:180:    Main pipeline orchestrating the full transcription workflow.
pipeline.py:186:        self.results: List[TranscriptionResult] = []
pipeline.py:304:        transcription_config = TranscriptionConfig(
pipeline.py:320:                transcription_config,
pipeline.py:327:            # === Validate transcriptions (dual scoring: native CTC + romanized MMS) ===
pipeline.py:328:            if self.config.validate_transcriptions:
pipeline.py:333:                        romanized = result.transcription.romanized or ""
pipeline.py:334:                        validation = validate_transcription(
pipeline.py:337:                            romanized_text=romanized,
pipeline.py:351:                            retry_config = TranscriptionConfig(
pipeline.py:362:                                from src.backend.transcription_schema import (
pipeline.py:363:                                    TranscriptionOutput, SpeakerMeta
pipeline.py:369:                                retry_output = TranscriptionOutput(
pipeline.py:370:                                    transcription=retry_raw.get('transcription', ''),
pipeline.py:372:                                    romanized=retry_raw.get('romanized', ''),
pipeline.py:376:                                retry_roman = retry_output.romanized or ""
pipeline.py:377:                                retry_val = validate_transcription(
pipeline.py:379:                                    retry_output.transcription,
pipeline.py:380:                                    romanized_text=retry_roman,
pipeline.py:385:                                    result.transcription = retry_output
pipeline.py:432:        if self.config.validate_transcriptions:
pipeline.py:456:            transcriptions_completed=len(self.results),
pipeline.py:510:    output_dir: str = "./transcriptions",
pipeline.py:525:        validate: Whether to validate transcriptions
pipeline.py:539:        validate_transcriptions=validate
pipeline.py:542:    pipeline = TranscriptionPipeline(config)
pipeline.py:582:                    output_dir=f"./transcriptions/test_{video_id}"
pipeline.py:594:    parser = argparse.ArgumentParser(description="Transcription Pipeline")
pipeline.py:605:                        help="Skip transcription validation")
pipeline.py:610:    parser.add_argument("--output", "-o", default="./transcriptions",
pipeline.py:634:    print(f"Chunks transcribed: {result.transcriptions_completed}")
src/backend/gemini_transcriber.py:2:Gemini transcription module using Google AI Studio endpoints.
src/backend/gemini_transcriber.py:3:Handles audio transcription with structured output and multiple model support.
src/backend/gemini_transcriber.py:14:from .transcription_schema import (
src/backend/gemini_transcriber.py:15:    TranscriptionOutput,
src/backend/gemini_transcriber.py:16:    TranscriptionResult,
src/backend/gemini_transcriber.py:18:    get_transcription_prompt,
src/backend/gemini_transcriber.py:26:class TranscriptionConfig:
src/backend/gemini_transcriber.py:27:    """Configuration for a transcription job."""
src/backend/gemini_transcriber.py:31:    # more deterministic, accurate transcriptions with the strict prompt.
src/backend/gemini_transcriber.py:40:    Handles audio transcription using Gemini models via Google AI Studio.
src/backend/gemini_transcriber.py:74:    def _build_config(self, config: TranscriptionConfig) -> types.GenerateContentConfig:
src/backend/gemini_transcriber.py:92:                types.Part.from_text(text=get_transcription_prompt(config.language))
src/backend/gemini_transcriber.py:104:        config: TranscriptionConfig
src/backend/gemini_transcriber.py:111:            config: Transcription configuration
src/backend/gemini_transcriber.py:114:            Dictionary with transcription results
src/backend/gemini_transcriber.py:175:        config: TranscriptionConfig
src/backend/gemini_transcriber.py:176:    ) -> TranscriptionResult:
src/backend/gemini_transcriber.py:182:            config: Transcription configuration
src/backend/gemini_transcriber.py:185:            TranscriptionResult with full metadata
src/backend/gemini_transcriber.py:193:            transcription = TranscriptionOutput(
src/backend/gemini_transcriber.py:194:                transcription=f"[ERROR: {error_msg}]",
src/backend/gemini_transcriber.py:196:                romanized=f"[ERROR: {error_msg}]",
src/backend/gemini_transcriber.py:204:            transcription = TranscriptionOutput(
src/backend/gemini_transcriber.py:205:                transcription=raw_result.get('transcription', ''),
src/backend/gemini_transcriber.py:207:                romanized=raw_result.get('romanized', ''),
src/backend/gemini_transcriber.py:213:        return TranscriptionResult(
src/backend/gemini_transcriber.py:219:            transcription=transcription,
src/backend/gemini_transcriber.py:228:        config: TranscriptionConfig,
src/backend/gemini_transcriber.py:231:    ) -> List[TranscriptionResult]:
src/backend/gemini_transcriber.py:237:            config: Transcription configuration
src/backend/gemini_transcriber.py:242:            List of TranscriptionResults
src/backend/gemini_transcriber.py:266:        print(f"[Transcriber] Completed {len(results)} transcriptions")
src/backend/gemini_transcriber.py:276:) -> List[TranscriptionResult]:
src/backend/gemini_transcriber.py:288:        List of TranscriptionResult objects
src/backend/gemini_transcriber.py:290:    config = TranscriptionConfig(
src/backend/gemini_transcriber.py:307:        config = TranscriptionConfig(
src/backend/gemini_transcriber.py:316:        print("\nTranscription Result:")
src/backend/audio_polisher.py:2:Audio Polisher - Segment boundary cleanup for transcription quality.
src/backend/audio_polisher.py:68:    Conservative audio segment polisher for transcription quality.
src/validators/ctc_forced_aligner.py:16:    result = aligner.align("audio.flac", "transcription text")
src/validators/ctc_forced_aligner.py:56:    transcription: str
src/validators/ctc_forced_aligner.py:76:            "transcription": self.transcription,
src/validators/ctc_forced_aligner.py:298:        transcription: str
src/validators/ctc_forced_aligner.py:305:            transcription: Text to align
src/validators/ctc_forced_aligner.py:314:                    transcription=transcription,
src/validators/ctc_forced_aligner.py:332:            targets, oov_ratio = self._text_to_tokens(transcription)
src/validators/ctc_forced_aligner.py:337:                    transcription=transcription,
src/validators/ctc_forced_aligner.py:358:                transcription, alignment, scores, frame_duration
src/validators/ctc_forced_aligner.py:380:                transcription=transcription,
src/validators/ctc_forced_aligner.py:395:                transcription=transcription,
src/validators/ctc_forced_aligner.py:417:    transcription: str,
src/validators/ctc_forced_aligner.py:422:    result = aligner.align(audio_path, transcription)
src/validators/ctc_forced_aligner.py:431:        print("Usage: python ctc_forced_aligner.py <audio_path> <transcription>")
src/validators/ctc_forced_aligner.py:435:    transcription = " ".join(sys.argv[2:])
src/validators/ctc_forced_aligner.py:437:    result = forced_align(audio_path, transcription)
src/backend/config.py:2:Configuration module for the transcription pipeline.
src/backend/config.py:28:# Models supporting audio transcription
src/backend/config.py:81:    "output_dir": "./transcriptions"
src/validators/__init__.py:2:Validators Module - Transcription Validation (Dual Scoring)
src/validators/__init__.py:5:Validates that transcriptions are:
src/validators/__init__.py:6:1. Valid native script (no garbage/alien characters)
src/validators/__init__.py:8:3. Match the audio via romanized MMS alignment
src/validators/__init__.py:14:from src.validators import validate_transcription, quick_validate
src/validators/__init__.py:20:result = validate_transcription(
src/validators/__init__.py:22:    romanized_text=roman_text, language="te"
src/validators/__init__.py:30:    validate_transcription,
src/validators/__init__.py:39:    'validate_transcription',
src/validators/mms_aligner.py:6:Works on romanized/Latin-script text. Complements the native CTC aligner.
src/validators/mms_aligner.py:8:MMS_FA is trained on 1000+ languages — handles romanized Indic + English equally well.
src/validators/mms_aligner.py:27:    """Result of MMS forced alignment on romanized text."""
src/validators/mms_aligner.py:29:    transcription: str
src/validators/mms_aligner.py:42:            "transcription": self.transcription[:100],
src/validators/mms_aligner.py:72:        # Token indices (set during setup)
src/validators/mms_aligner.py:114:        Convert romanized text to MMS token indices.
src/validators/mms_aligner.py:134:    def align(self, audio_path: str, romanized_text: str) -> MMSAlignmentResult:
src/validators/mms_aligner.py:136:        Perform forced alignment of romanized text against audio.
src/validators/mms_aligner.py:140:            romanized_text: Latin-script text to align
src/validators/mms_aligner.py:149:                    transcription=romanized_text,
src/validators/mms_aligner.py:159:            tokens = self._text_to_tokens(romanized_text)
src/validators/mms_aligner.py:164:                    transcription=romanized_text,
src/validators/mms_aligner.py:188:                transcription=romanized_text,
src/validators/mms_aligner.py:202:                transcription=romanized_text,
src/backend/audio_processor.py:20:    """Represents an audio chunk ready for transcription."""
src/backend/audio_processor.py:114:        can be merged back later (transcripts appended, original audio retained).
src/backend/audio_processor.py:122:            List of AudioChunk objects with sequential chunk indices
src/backend/audio_processor.py:220:            List of AudioChunk objects ready for transcription
src/backend/__init__.py:2:Maya3 Transcription Backend
src/backend/__init__.py:10:- transcription_schema: Pydantic schemas for output
src/backend/__init__.py:31:from .transcription_schema import (
src/backend/__init__.py:32:    TranscriptionOutput,
src/backend/__init__.py:33:    TranscriptionResult,
src/backend/__init__.py:34:    get_transcription_prompt,
src/backend/__init__.py:41:    TranscriptionConfig,
src/backend/__init__.py:66:    'TranscriptionOutput',
src/backend/__init__.py:67:    'TranscriptionResult',
src/backend/__init__.py:68:    'get_transcription_prompt',
src/backend/__init__.py:73:    'TranscriptionConfig',
src/validators/simple_validator.py:2:Simple Transcription Validator
src/validators/simple_validator.py:7:2. Native CTC alignment - verify native script matches audio
src/validators/simple_validator.py:8:3. Romanized MMS alignment - verify romanized text matches audio
src/validators/simple_validator.py:11:  N = native CTC score, R = romanized MMS score
src/validators/simple_validator.py:16:    from src.validators.simple_validator import validate_transcription
src/validators/simple_validator.py:18:    result = validate_transcription(
src/validators/simple_validator.py:20:        romanized_text="naaku konni ads", language="te"
src/validators/simple_validator.py:30:# Unicode ranges for Indic scripts (all 12 supported languages)
src/validators/simple_validator.py:110:ENGLISH_CHARS = set("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ")
src/validators/simple_validator.py:121:    # Add script-specific chars
src/validators/simple_validator.py:143:    For native transcription: allow_english=False (default)
src/validators/simple_validator.py:149:            "script_ratio": 0.8,  # ratio of native script chars
src/validators/simple_validator.py:153:        return {"valid": False, "invalid_chars": [], "script_ratio": 0, "reason": "empty"}
src/validators/simple_validator.py:159:    script_count = 0
src/validators/simple_validator.py:165:        # Check if in native script
src/validators/simple_validator.py:168:            script_count += 1
src/validators/simple_validator.py:185:        "script_ratio": script_count / total_alpha if total_alpha > 0 else 0,
src/validators/simple_validator.py:191:    """Result of transcription validation with dual scoring."""
src/validators/simple_validator.py:197:    script_ratio: float = 0.0
src/validators/simple_validator.py:219:            "script_ratio": round(self.script_ratio, 3),
src/validators/simple_validator.py:243:    """Lazy load romanized MMS aligner."""
src/validators/simple_validator.py:284:    """Strip punctuation for CTC alignment. Keeps native script + spaces."""
src/validators/simple_validator.py:286:    # Remove common punctuation marks (keep native script chars, digits, spaces)
src/validators/simple_validator.py:290:def validate_transcription(
src/validators/simple_validator.py:292:    transcription: str,
src/validators/simple_validator.py:294:    romanized_text: str = "",
src/validators/simple_validator.py:298:    Validate a transcription using dual scoring (native CTC + romanized MMS).
src/validators/simple_validator.py:301:    1. Character validation (instant) on native script
src/validators/simple_validator.py:303:    3. Romanized MMS forced alignment (language-agnostic, if romanized_text provided)
src/validators/simple_validator.py:308:        transcription: Native script text (may include punctuation)
src/validators/simple_validator.py:310:        romanized_text: Latin-script romanization (for MMS alignment)
src/validators/simple_validator.py:319:    clean_text = _strip_punctuation(transcription)
src/validators/simple_validator.py:320:    clean_roman = _strip_punctuation(romanized_text) if romanized_text else ""
src/validators/simple_validator.py:332:            script_ratio=char_check["script_ratio"],
src/validators/simple_validator.py:336:    if char_check["script_ratio"] < 0.5:
src/validators/simple_validator.py:337:        reasons.append(f"Too few native chars ({char_check['script_ratio']:.0%})")
src/validators/simple_validator.py:377:        # CTC only (MMS failed or no romanized text)
src/validators/simple_validator.py:427:        script_ratio=char_check["script_ratio"],
src/validators/simple_validator.py:450:def quick_validate(transcription: str, language: str = "te") -> Dict:
src/validators/simple_validator.py:452:    Quick character-only validation for NATIVE transcription.
src/validators/simple_validator.py:456:    - All characters in target script (no English/garbage)
src/validators/simple_validator.py:457:    - Sufficient native script content
src/validators/simple_validator.py:460:        transcription: Native script text to validate
src/validators/simple_validator.py:466:    if not transcription or not transcription.strip():
src/validators/simple_validator.py:467:        return {"valid": False, "reason": "Empty transcription"}
src/validators/simple_validator.py:469:    char_check = check_characters(transcription, language, allow_english=False)
src/validators/simple_validator.py:478:    if char_check["script_ratio"] < 0.5:
src/validators/simple_validator.py:481:            "reason": f"Too few native chars ({char_check['script_ratio']:.0%})"
src/validators/simple_validator.py:484:    return {"valid": True, "script_ratio": char_check["script_ratio"]}
src/validators/simple_validator.py:497:    python simple_validator.py <audio_path> <transcription>
src/validators/simple_validator.py:500:    python simple_validator.py --quick <transcription>
src/validators/simple_validator.py:516:        transcription = " ".join(sys.argv[2:])
src/validators/simple_validator.py:519:        result = validate_transcription(audio_path, transcription)
src/validators/simple_validator.py:524:        print(f"Script ratio: {result.script_ratio:.1%}")
src/backend/transcription_schema.py:2:Pydantic schemas for structured transcription output.
src/backend/transcription_schema.py:5:  1. transcription   - Native script with punctuation (PRIMARY, authoritative)
src/backend/transcription_schema.py:6:  2. code_switch     - Mixed script: native + English in Latin (derived from 1)
src/backend/transcription_schema.py:7:  3. romanized       - Full Latin transliteration (derived from 1)
src/backend/transcription_schema.py:13:  - "Verbatim transcription system" persona (explicit NOT assistant negation)
src/backend/transcription_schema.py:16:  - Field derivation ordering: transcription is authoritative, others derive from it
src/backend/transcription_schema.py:19:  - Per-language script tips (1-2 practical tips, not grammar rules)
src/backend/transcription_schema.py:26:# Per-language metadata + script-specific rules injected into the master prompt.
src/backend/transcription_schema.py:27:# Only rules that prevent ACTUAL transcription errors, not generic grammar.
src/backend/transcription_schema.py:31:    # 1-2 practical script tips per language. Not grammar rules.
src/backend/transcription_schema.py:34:        "script": "Devanagari",
src/backend/transcription_schema.py:35:        "script_rules": "Preserve Nukta (ज़ vs ज, फ़ vs फ) when clearly pronounced.",
src/backend/transcription_schema.py:39:        "script": "Devanagari",
src/backend/transcription_schema.py:40:        "script_rules": "Preserve ळ (retroflex lateral) accurately.",
src/backend/transcription_schema.py:44:        "script": "Telugu",
src/backend/transcription_schema.py:45:        "script_rules": "Don't over-split words. Preserve Sandhi/combined forms as spoken.",
src/backend/transcription_schema.py:49:        "script": "Tamil",
src/backend/transcription_schema.py:50:        "script_rules": "Distinguish short and long vowels accurately.",
src/backend/transcription_schema.py:54:        "script": "Kannada",
src/backend/transcription_schema.py:55:        "script_rules": "Preserve agglutinated/combined forms as spoken.",
src/backend/transcription_schema.py:59:        "script": "Malayalam",
src/backend/transcription_schema.py:60:        "script_rules": "Don't split agglutinated words. Preserve chillu letters.",
src/backend/transcription_schema.py:64:        "script": "Gujarati",
src/backend/transcription_schema.py:65:        "script_rules": "",
src/backend/transcription_schema.py:69:        "script": "Gurmukhi",
src/backend/transcription_schema.py:70:        "script_rules": "",
src/backend/transcription_schema.py:74:        "script": "Bengali",
src/backend/transcription_schema.py:75:        "script_rules": "Preserve Chandrabindu for nasalization where spoken.",
src/backend/transcription_schema.py:79:        "script": "Assamese",
src/backend/transcription_schema.py:81:        "script_rules": "Use Assamese-specific ৰ (ra) and ৱ (wa), NOT Bengali equivalents.",
src/backend/transcription_schema.py:85:        "script": "Odia",
src/backend/transcription_schema.py:86:        "script_rules": "",
src/backend/transcription_schema.py:90:        "script": "Latin",
src/backend/transcription_schema.py:91:        "script_rules": "Standard English spelling. Don't phonetically approximate accents.",
src/backend/transcription_schema.py:100:        description="neutral, happy, sad, angry, excited, surprised"
src/backend/transcription_schema.py:104:        description="conversational, narrative, excited, calm, emphatic, sarcastic, formal"
src/backend/transcription_schema.py:108:        description="slow, normal, fast"
src/backend/transcription_schema.py:112:        description="Regional accent/dialect if detectable, empty string if unknown"
src/backend/transcription_schema.py:116:class TranscriptionOutput(BaseModel):
src/backend/transcription_schema.py:117:    """Structured output: 4 transcription formats + speaker metadata."""
src/backend/transcription_schema.py:119:    transcription: str = Field(
src/backend/transcription_schema.py:120:        description="Native script transcription with minimal punctuation. "
src/backend/transcription_schema.py:121:                    "English words transliterated phonetically in native script."
src/backend/transcription_schema.py:125:        description="Mixed script: native language in native script, "
src/backend/transcription_schema.py:126:                    "English words in Latin script. Minimal punctuation."
src/backend/transcription_schema.py:129:    romanized: str = Field(
src/backend/transcription_schema.py:130:        description="Full Roman/Latin script transliteration as pronounced."
src/backend/transcription_schema.py:134:        description="Code-switch transcription with audio event tags "
src/backend/transcription_schema.py:141:        description="Speaker metadata: emotion, style, pace, accent."
src/backend/transcription_schema.py:146:        description="The language actually spoken in the audio as detected by the model. "
src/backend/transcription_schema.py:151:class TranscriptionResult(BaseModel):
src/backend/transcription_schema.py:154:    segment_id: str = Field(description="Identifier for the audio segment")
src/backend/transcription_schema.py:155:    chunk_index: int = Field(default=0, description="Chunk index if segment was split")
src/backend/transcription_schema.py:156:    total_chunks: int = Field(default=1, description="Total chunks for this segment")
src/backend/transcription_schema.py:157:    duration_sec: float = Field(description="Duration of audio in seconds")
src/backend/transcription_schema.py:158:    language: str = Field(description="Primary language of the audio")
src/backend/transcription_schema.py:160:    transcription: TranscriptionOutput = Field(description="The transcription outputs")
src/backend/transcription_schema.py:162:    model_used: str = Field(description="Gemini model used for transcription")
src/backend/transcription_schema.py:163:    thinking_level: Optional[str] = Field(default=None, description="Thinking level used")
src/backend/transcription_schema.py:164:    processing_time_sec: Optional[float] = Field(default=None, description="API call time")
src/backend/transcription_schema.py:167:    validation_status: Optional[str] = Field(default=None, description="accept/review/reject")
src/backend/transcription_schema.py:168:    validation_score: Optional[float] = Field(default=None, description="Alignment score 0-1")
src/backend/transcription_schema.py:172:        """Shortcut to primary native transcription."""
src/backend/transcription_schema.py:173:        return self.transcription.transcription
src/backend/transcription_schema.py:179:# v3 changes: translate/transliterate fix, field derivation ordering, [UNK] tokens,
src/backend/transcription_schema.py:180:#   prosody-based punctuation, simplified language script tips, no-preamble constraint,
src/backend/transcription_schema.py:183:def get_transcription_prompt(language: str) -> str:
src/backend/transcription_schema.py:185:    System instruction for Gemini transcription.
src/backend/transcription_schema.py:188:    Key design: "transcription" field is authoritative, all others derive from it.
src/backend/transcription_schema.py:192:    script_name = lang_config.get("script", f"{language} native")
src/backend/transcription_schema.py:194:    script_rules = lang_config.get("script_rules", "")
src/backend/transcription_schema.py:196:    # Build optional script rules section
src/backend/transcription_schema.py:197:    script_section = ""
src/backend/transcription_schema.py:198:    if script_rules:
src/backend/transcription_schema.py:199:        script_section = (
src/backend/transcription_schema.py:200:            f"\nSCRIPT RULES FOR {language.upper()}:\n{script_rules}\n"
src/backend/transcription_schema.py:203:    # English has different field logic (no transliteration needed)
src/backend/transcription_schema.py:209:            "Same as transcription. If the speaker uses words from another "
src/backend/transcription_schema.py:210:            "language, write them in Latin script as heard."
src/backend/transcription_schema.py:213:        # The transliterate vs translate distinction is critical here.
src/backend/transcription_schema.py:215:        # + "write English phonetically in {language} script". The word "convert"
src/backend/transcription_schema.py:218:            f"Write ONLY in {script_name} script. No Latin characters in this field.\n"
src/backend/transcription_schema.py:220:            f"into {script_name} (sound mapping, NOT meaning translation).\n"
src/backend/transcription_schema.py:221:            f"   Example: spoken 'computer' → phonetic form in {script_name}, "
src/backend/transcription_schema.py:226:            f"   {language} words stay in {script_name} script. "
src/backend/transcription_schema.py:227:            f"English words stay in Latin script.\n"
src/backend/transcription_schema.py:233:    return f"""You are a verbatim speech-to-text transcription system. You are NOT a conversational assistant. Your output must precisely match the audio content.
src/backend/transcription_schema.py:238:1. NEVER TRANSLATE. This is transcription, not translation. If the speaker says English words, those are English. If the speaker says {language} words, those are {language}.
src/backend/transcription_schema.py:244:7. LANGUAGE MISMATCH: The target language is {language}, but trust what you hear. If the audio is clearly in a different language, transcribe in that language's script and set detected_language accordingly. If code-mixed, use the dominant language for the native script fields.
src/backend/transcription_schema.py:249:{script_section}
src/backend/transcription_schema.py:251:"transcription" is the PRIMARY output. code_switch and romanized must contain the same words in the same order, differing only in script. "tagged" is code_switch with audio event markers inserted at their positions. Do not re-interpret the audio independently for each field.
src/backend/transcription_schema.py:255:1. transcription (AUTHORITATIVE - native script)
src/backend/transcription_schema.py:259:2. code_switch (derived from transcription - mixed script)
src/backend/transcription_schema.py:262:3. romanized (derived from transcription - Latin script)
src/backend/transcription_schema.py:263:   Full Roman/Latin transliteration of the entire audio as pronounced.
src/backend/transcription_schema.py:266:4. tagged (derived from code_switch - mixed script + event tags)
src/backend/transcription_schema.py:297:        "transcription": {
src/backend/transcription_schema.py:299:            "description": "Native script transcription with minimal punctuation"
src/backend/transcription_schema.py:303:            "description": "Mixed script preserving language switching"
src/backend/transcription_schema.py:305:        "romanized": {
src/backend/transcription_schema.py:307:            "description": "Complete Roman script transliteration"
src/backend/transcription_schema.py:311:            "description": "Code-switch with audio event tags"
src/backend/transcription_schema.py:315:            "description": "Speaker metadata",
src/backend/transcription_schema.py:337:                    "description": "Regional accent/dialect or empty string"
src/backend/transcription_schema.py:345:            "description": "Language actually spoken in the audio (e.g. Telugu, Hindi, English)"
src/backend/transcription_schema.py:348:    "required": ["transcription", "code_switch", "romanized", "tagged", "speaker", "detected_language"],


**Evaluating schema completeness**


sed -n '320,520p' src/backend/transcription_schema.py


Output:
Chunk ID: ed62a1
Wall time: 0.0513 seconds
Process exited with code 0
Original token count: 293
Output:
                        "neutral", "happy", "sad",
                        "angry", "excited", "surprised"
                    ]
                },
                "speaking_style": {
                    "type": "string",
                    "enum": [
                        "conversational", "narrative", "excited",
                        "calm", "emphatic", "sarcastic", "formal"
                    ]
                },
                "pace": {
                    "type": "string",
                    "enum": ["slow", "normal", "fast"]
                },
                "accent": {
                    "type": "string",
                    "description": "Regional accent/dialect or empty string"
                }
            },
            "required": ["emotion", "speaking_style", "pace"],
            "additionalProperties": False
        },
        "detected_language": {
            "type": "string",
            "description": "Language actually spoken in the audio (e.g. Telugu, Hindi, English)"
        }
    },
    "required": ["transcription", "code_switch", "romanized", "tagged", "speaker", "detected_language"],
    "additionalProperties": False
}


**Planning exploration of README and validators**


I’ve confirmed the current flow still uses Gemini-provided `romanized` for MMS scoring and allows 1s segments by default, so your concerns are real in code. I’m now implementing deterministic romanization from authoritative native text and stricter duration/boundary validation gates.


**Inspecting CLI and config for duration settings**


sed -n '280,520p' src/backend/audio_processor.py


Output:
Chunk ID: 267319
Wall time: 0.0503 seconds
Process exited with code 0
Original token count: 325
Output:

    Args:
        segments_dir: Directory containing audio segments

    Returns:
        Dictionary with duration statistics
    """
    processor = AudioProcessor()

    audio_extensions = {'.flac', '.wav', '.mp3', '.ogg', '.m4a'}
    durations = []

    for ext in audio_extensions:
        for f in Path(segments_dir).glob(f"*{ext}"):
            try:
                durations.append(processor.get_audio_duration(str(f)))
            except Exception as e:
                print(f"Error reading {f}: {e}")

    if not durations:
        return {"error": "No audio files found"}

    return {
        "total_files": len(durations),
        "total_duration_sec": sum(durations),
        "avg_duration_sec": sum(durations) / len(durations),
        "min_duration_sec": min(durations),
        "max_duration_sec": max(durations),
        "over_10s_count": sum(1 for d in durations if d > 10),
        "under_1s_count": sum(1 for d in durations if d < 1)
    }

if __name__ == "__main__":
    # Test with a sample directory
    import sys
    if len(sys.argv) > 1:
        segments_dir = sys.argv[1]
        stats = get_segment_stats(segments_dir)
        print("\nSegment Statistics:")
        for key, value in stats.items():
            print(f"  {key}: {value}")


**Checking default minimum duration in config**


Output:
Chunk ID: 279a7f
Wall time: 0.0511 seconds
Process exited with code 0
Original token count: 792
Output:
"""
Configuration module for the transcription pipeline.
Loads environment variables and defines available models and settings.
"""
import os
from pathlib import Path
from dotenv import load_dotenv

# Load .env from project root
_project_root = Path(__file__).parent.parent.parent
load_dotenv(_project_root / ".env")

# === R2 Cloud Storage Config ===
R2_ENDPOINT_URL = os.getenv("R2_ENDPOINT_URL")
R2_BUCKET = os.getenv("R2_BUCKET")
R2_ACCESS_KEY_ID = os.getenv("R2_ACCESS_KEY_ID")
R2_SECRET_ACCESS_KEY = os.getenv("R2_SECRET_ACCESS_KEY")

# === Gemini API Config ===
GEMINI_API_KEY = os.getenv("GEMINI_KEY")

# === Supabase Config ===
SUPABASE_URL = os.getenv("URL")
SUPABASE_ANON_KEY = os.getenv("SUPABASE_ANON")
SUPABASE_ADMIN_KEY = os.getenv("SUPABASE_ADMIN")

# === Available Gemini Models ===
# Models supporting audio transcription
GEMINI_MODELS = {
    "gemini-3-pro-preview": {
        "name": "gemini-3-pro-preview",
        "thinking_levels": ["low", "high"],
        "supports_thinking": True,
        "cost_tier": "premium"
    },
    "gemini-3-flash-preview": {
        "name": "gemini-3-flash-preview",
        "thinking_levels": ["minimal", "low", "medium", "high"],
        "supports_thinking": True,
        "cost_tier": "standard"
    },
    "gemini-2.5-pro": {
        "name": "gemini-2.5-pro",
        "thinking_levels": None,
        "supports_thinking": True,
        "cost_tier": "premium"
    },
    "gemini-2.5-flash": {
        "name": "gemini-2.5-flash",
        "thinking_levels": None,
        "supports_thinking": True,
        "cost_tier": "standard"
    },
    "gemini-2.5-flash-lite": {
        "name": "gemini-2.5-flash-lite",
        "thinking_levels": None,
        "supports_thinking": True,
        "cost_tier": "lite"
    },
    "gemini-2.0-flash": {
        "name": "gemini-2.0-flash",
        "thinking_levels": None,
        "supports_thinking": False,
        "cost_tier": "standard"
    }
}

# Alias mapping for easier usage
MODEL_ALIASES = {
    "gemini-flash-latest": "gemini-3-flash-preview",
    "gemini-flash-lite-latest": "gemini-2.5-flash-lite",
}

# === Default Pipeline Settings ===
DEFAULT_SETTINGS = {
    "max_segment_duration_sec": 10.0,  # Hard limit for segment duration
    "min_segment_duration_sec": 1.0,   # Minimum segment duration
    "default_model": "gemini-3-flash-preview",
    "default_thinking_level": "high",
    "work_dir": "/tmp/maya3_transcribe",
    "output_dir": "./transcriptions"
}

# === Language Mapping (for Supabase language codes) ===
LANGUAGE_MAP = {
    "te": "Telugu",
    "hi": "Hindi",
    "ta": "Tamil",
    "kn": "Kannada",
    "ml": "Malayalam",
    "bn": "Bengali",
    "gu": "Gujarati",
    "mr": "Marathi",
    "pa": "Punjabi",
    "or": "Odia",
    "en": "English",
    "Telugu": "Telugu",
    "Hindi": "Hindi",
}

def get_model_name(model_key: str) -> str:
    """Resolve model alias to actual model name."""
    if model_key in MODEL_ALIASES:
        return MODEL_ALIASES[model_key]
    if model_key in GEMINI_MODELS:
        return model_key
    raise ValueError(f"Unknown model: {model_key}. Available: {list(GEMINI_MODELS.keys()) + list(MODEL_ALIASES.keys())}")


**Reviewing README for CLI updates**


Output:
Chunk ID: 1be487
Wall time: 0.0503 seconds
Process exited with code 0
Original token count: 1363
Output:
# Maya3 Transcription Pipeline

Audio transcription pipeline for Indian languages using Google Gemini AI.

## Production Configuration

```
Model: gemini-3-flash-preview
Temperature: 1.0 (Gemini 3 default - below 1.0 causes looping)
Thinking: low (fast, prevents loops)
Validation: Native script + CTC alignment
```

## Quick Start

```bash
# Setup
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Run pipeline
python pipeline.py VIDEO_ID --language Telugu --max-segments 10
```

## Pipeline Usage

```bash
# Basic usage
python pipeline.py pF_BQpHaIdU

# With options
python pipeline.py pF_BQpHaIdU \
    --language Telugu \
    --model gemini-3-flash-preview \
    --thinking low \
    --temperature 0.0 \
    --max-segments 20 \
    --output ./transcriptions

# Skip validation (faster but no quality check)
python pipeline.py pF_BQpHaIdU --no-validate
```

## Python API

```python
from pipeline import run_pipeline

# Run full pipeline
result = run_pipeline(
    video_id="pF_BQpHaIdU",
    language="Telugu",
    model="gemini-3-flash-preview",
    thinking_level="low",
    temperature=0.0,
    max_segments=20,
    validate=True
)

print(f"Processed: {result.segments_processed} segments")
print(f"Output: {result.output_file}")
```

## Validation

Transcriptions are validated for:

1. **Character Check** (instant)
   - Only valid native script characters
   - Numbers and punctuation allowed
   - No English/foreign characters

2. **Audio Match** (0.1s per segment)
   - CTC-based alignment score
   - Detects if text doesn't match audio

```python
from src.validators import validate_transcription, quick_validate

# Quick check (character only)
result = quick_validate("నాకు కొన్ని గుర్తుంటాయి", language="te")
print(result["valid"])  # True

# Full validation (with audio)
result = validate_transcription("audio.flac", "నాకు కొన్ని", language="te")
print(result.status)  # accept / review / reject
```

## Output Format

Each transcription includes 3 text formats + 1 tagged format + speaker metadata:

```json
{
  "segment_id": "SPEAKER_00_0000_0.03-2.61.flac",
  "transcription": {
    "transcription": "నాకు కొన్ని యాడ్స్ గుర్తుంటాయి.",
    "code_switch": "నాకు కొన్ని ads గుర్తుంటాయి.",
    "romanized": "naku konni ads gurtuntayi.",
    "tagged": "నాకు కొన్ని ads గుర్తుంటాయి [laugh] like example కచ్చా mango bite.",
    "speaker": {
      "emotion": "happy",
      "emotion_intensity": "mild",
      "speaking_style": "conversational",
      "pace": "normal",
      "energy": "medium",
      "accent": "Telangana",
      "audio_quality": "clean",
      "background": "silence"
    },
    "confidence": 0.95
  },
  "validation_status": "accept",
  "validation_score": 0.798
}
```

## Project Structure

```
maya3_transcribe/
├── pipeline.py              # Main pipeline orchestrator
├── src/
│   ├── backend/             # Core modules
│   │   ├── config.py
│   │   ├── r2_storage.py
│   │   ├── supabase_client.py
│   │   ├── audio_processor.py
│   │   ├── audio_polisher.py     # NEW: VAD boundary cleanup
│   │   ├── gemini_transcriber.py
│   │   └── transcription_schema.py
│   └── validators/          # Validation module
│       ├── __init__.py
│       ├── simple_validator.py   # Main validator
│       └── ctc_forced_aligner.py # CTC alignment
├── models/                  # Downloaded models (gitignored)
├── analysis_results/        # Analysis output (gitignored)
├── transcriptions/          # Pipeline output (gitignored)
└── docs/                    # Documentation
```

## Supported Languages

| Language | Code | Script |
|----------|------|--------|
| Telugu | te | Telugu |
| Hindi | hi | Devanagari |
| Tamil | ta | Tamil |
| Kannada | kn | Kannada |
| Malayalam | ml | Malayalam |
| Bengali | bn | Bengali |
| Marathi | mr | Devanagari |
| Gujarati | gu | Gujarati |
| Punjabi | pa | Gurmukhi |
| Odia | or | Odia |

## Audio Polisher (Pre-processing)

Lightweight signal-processing pass that cleans VAD-generated segment boundaries
before transcription. No ML models, runs in ~30ms/segment.

**What it does:**
- Detects and trims leading cut artifacts (partial phonemes from imprecise VAD)
- Detects trailing speech bursts (leaked from next segment)
- Preserves silence at boundaries (silence = good padding, never trimmed)
- Measures SNR, conditionally normalizes volume for quiet segments
- Integrated into pipeline as Step 3.5 (automatic, `polish_audio=True` by default)

**Conservative by design** - only trims cut speech artifacts, never silence.
Already-clean segments pass through untouched (~76% skip rate on test data).

```python
from src.backend.audio_polisher import AudioPolisher

polisher = AudioPolisher()
result = polisher.polish("segment.flac", output_dir="./polished/")
print(result.summary())
# POLISHED | 2582ms -> 2557ms | start -25ms (artifact_trimmed)
```

## Configuration

Environment variables (`.env`):

```
GEMINI_API_KEY=your_key
R2_ACCESS_KEY_ID=your_key
R2_SECRET_ACCESS_KEY=your_secret
R2_ENDPOINT_URL=your_endpoint
R2_BUCKET_NAME=your_bucket
SUPABASE_URL=your_url
SUPABASE_KEY=your_key
```


**Planning deterministic romanization and padding**


python - <<'PY'
try:
    import indic_transliteration
    from indic_transliteration.sanscript import transliterate, DEVANAGARI, ITRANS
    print('ok', indic_transliteration.__version__)
except Exception as e:
    print('err',e)
PY


Output:
Chunk ID: fd5a87
Wall time: 0.0513 seconds
Process exited with code 127
Original token count: 12
Output:
/bin/bash: line 1: python: command not found


**Specifying Python version**


python3 - <<'PY'
try:
    import indic_transliteration
    from indic_transliteration.sanscript import transliterate, DEVANAGARI, ITRANS
    print('ok', indic_transliteration.__version__)
except Exception as e:
    print('err',e)
PY


Output:
Chunk ID: 3b4ddf
Wall time: 0.0502 seconds
Process exited with code 0
Original token count: 11
Output:
err No module named 'indic_transliteration'


**Assessing external script availability**


Output:
Chunk ID: 006510
Wall time: 0.0513 seconds
Process exited with code 0
Original token count: 0
Output:


**Assessing transliteration library options**


python3 - <<'PY'
import pkgutil
mods=[m.name for m in pkgutil.iter_modules()]
for k in ['unidecode','indic','aksharamukha','translit','pyicu','regex']:
    print(k, any(n.startswith(k) for n in mods))
print('indic_langs', [n for n in mods if 'indic' in n.lower()][:20])
PY


Output:
Chunk ID: 94f191
Wall time: 0.0503 seconds
Process exited with code 0
Original token count: 25
Output:
unidecode True
indic False
aksharamukha False
translit False
pyicu False
regex True
indic_langs []


Summary: auto


**Considering unidecode for transliteration**


python3 - <<'PY'
from unidecode import unidecode
samples=['నాకు కొన్ని యాడ్స్ గుర్తుంటాయి','అవును','చూడండి సో','ఇంకా','అండ్','सॉल्ट बिस्किट्स']
for s in samples:
    print(s,'->',unidecode(s))
PY


Output:
Chunk ID: 2a745d
Wall time: 0.0512 seconds
Process exited with code 0
Original token count: 74
Output:
నాకు కొన్ని యాడ్స్ గుర్తుంటాయి -> naaku konni yaadds gurtuNttaayi
అవును -> avunu
చూడండి సో -> cuuddNddi soo
ఇంకా -> iNkaa
అండ్ -> aNdd
सॉल्ट बिस्किट्स -> soNltt biskitts


**Designing robust romanization and scoring**


Output:
Chunk ID: 2317fd
Wall time: 0.0513 seconds
Process exited with code 0
Original token count: 423
Output:
"""
Maya3 Transcription Backend
===========================

Modules:
- config: Configuration and environment loading
- r2_storage: R2 cloud storage client
- supabase_client: Supabase client for metadata
- audio_processor: Audio segmentation and chunking
- transcription_schema: Pydantic schemas for output
- gemini_transcriber: Gemini API integration
"""

from .config import (
    GEMINI_API_KEY,
    R2_ENDPOINT_URL,
    R2_BUCKET,
    SUPABASE_URL,
    GEMINI_MODELS,
    DEFAULT_SETTINGS,
    LANGUAGE_MAP,
    get_model_name
)

from .r2_storage import R2StorageClient, download_video_segments

from .supabase_client import SupabaseVideoClient, get_video_language

from .audio_processor import AudioProcessor, AudioChunk, get_segment_stats

from .transcription_schema import (
    TranscriptionOutput,
    TranscriptionResult,
    get_transcription_prompt,
    get_user_prompt,
    TRANSCRIPTION_JSON_SCHEMA
)

from .gemini_transcriber import (
    GeminiTranscriber,
    TranscriptionConfig,
    transcribe_segments
)

__all__ = [
    # Config
    'GEMINI_API_KEY',
    'R2_ENDPOINT_URL',
    'R2_BUCKET',
    'SUPABASE_URL',
    'GEMINI_MODELS',
    'DEFAULT_SETTINGS',
    'LANGUAGE_MAP',
    'get_model_name',
    # R2 Storage
    'R2StorageClient',
    'download_video_segments',
    # Supabase
    'SupabaseVideoClient',
    'get_video_language',
    # Audio
    'AudioProcessor',
    'AudioChunk',
    'get_segment_stats',
    # Schema
    'TranscriptionOutput',
    'TranscriptionResult',
    'get_transcription_prompt',
    'get_user_prompt',
    'TRANSCRIPTION_JSON_SCHEMA',
    # Transcriber
    'GeminiTranscriber',
    'TranscriptionConfig',
    'transcribe_segments',
]


**Assessing import structure impact**


Output:
Chunk ID: 599fbc
Wall time: 0.0512 seconds
Process exited with code 0
Original token count: 9
Output:
# Maya3 Transcribe source package


**Designing robust romanization and validation**


Output:
Chunk ID: 8121c9
Wall time: 0.0514 seconds
Process exited with code 0
Original token count: 280
Output:
"""
Validators Module - Transcription Validation (Dual Scoring)
============================================================

Validates that transcriptions are:
1. Valid native script (no garbage/alien characters)
2. Match the audio via native CTC alignment
3. Match the audio via romanized MMS alignment

Combined scoring: S = 0.45*N + 0.55*R - 0.10*abs(N-R)

Usage:
```python
from src.validators import validate_transcription, quick_validate

# Quick check (character only - instant)
result = quick_validate(text, language="te")

# Full validation (character + dual audio alignment)
result = validate_transcription(
    audio_path, native_text,
    romanized_text=roman_text, language="te"
)
# result.status: "accept" / "review" / "retry" / "reject"
# result.combined_score: 0-1
```
"""

from .simple_validator import (
    validate_transcription,
    quick_validate,
    check_characters,
    compute_combined_score,
    ValidationResult,
    cleanup,
)

__all__ = [
    'validate_transcription',
    'quick_validate',
    'check_characters',
    'compute_combined_score',
    'ValidationResult',
    'cleanup',
]


**Planning periodic commentary updates**


I’m ready to patch now. I’ll add a shared deterministic romanization utility, wire it into validation (instead of Gemini `romanized`), tighten score gates with boundary-word checks, and then enforce a 2s minimum segment duration plus split padding controls in the pipeline/audio processor.


**Planning patch application**


Implementation plan:
1. Add `src/romanization.py` with deterministic romanization + MMS-safe normalization (native text -> romanized).
2. Update validator to:
- auto-derive romanized when absent or unreliable,
- run stricter accept/review/retry thresholds,
- add boundary/internal word confidence gates from CTC alignment,
- enforce minimum clip duration in validation.
3. Update pipeline to always validate with derived romanization and store that deterministic value.
4. Tighten segmentation defaults:
- raise minimum segment duration to 2.0s,
- add explicit split boundary padding control,
- expose `--min-duration` and `--split-pad-ms` CLI knobs.
5. Run quick static checks (`python3 -m compileall`) and report what changed.


**Designing transliteration utilities**


Input:
*** Begin Patch
*** Add File: src/romanization.py
+"""
+Deterministic romanization utilities for validation/alignment.
+
+Primary goal: convert authoritative native transcription into stable
+Latin text suitable for MMS forced alignment.
+"""
+from __future__ import annotations
+
+import re
+import unicodedata
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+
+_PLACEHOLDER_RE = re.compile(r"\[(UNK|INAUDIBLE|NO_SPEECH)\]", re.IGNORECASE)
+_NON_MMS_CHARS_RE = re.compile(r"[^a-z' ]+")
+_MULTISPACE_RE = re.compile(r"\s+")
+
+# Normalize common quote/dash variants before filtering.
+_APOSTROPHE_TRANSLATION = str.maketrans({
+    "’": "'",
+    "‘": "'",
+    "ʼ": "'",
+    "`": "'",
+    "´": "'",
+})
+_DASH_TRANSLATION = str.maketrans({
+    "-": " ",
+    "_": " ",
+    "/": " ",
+    "|": " ",
+})
+
+
+@dataclass
+class RomanizationResult:
+    """Deterministic romanization output."""
+    text: str
+    source: str  # indic_transliteration | unidecode | fallback_model | passthrough
+
+
+def _strip_placeholders(text: str) -> str:
+    """Remove bracket placeholders that should not be force-aligned."""
+    return _PLACEHOLDER_RE.sub(" ", text or "")
+
+
+def normalize_roman_for_mms(text: str) -> str:
+    """
+    Normalize Latin text to MMS-friendly alphabet.
+
+    Keeps only: a-z, apostrophe, space.
+    """
+    cleaned = _strip_placeholders(text)
+    cleaned = unicodedata.normalize("NFKC", cleaned)
+    cleaned = cleaned.translate(_APOSTROPHE_TRANSLATION)
+    cleaned = cleaned.translate(_DASH_TRANSLATION)
+    cleaned = cleaned.lower()
+    cleaned = _NON_MMS_CHARS_RE.sub(" ", cleaned)
+    cleaned = _MULTISPACE_RE.sub(" ", cleaned).strip()
+    return cleaned
+
+
+def _romanize_with_indic_transliteration(
+    native_text: str,
+    language: str,
+) -> Optional[str]:
+    """
+    Romanize with indic-transliteration if available.
+
+    Returns None when dependency is unavailable or language/script is unsupported.
+    """
+    try:
+        from indic_transliteration.sanscript import (
+            ASSAMESE,
+            BENGALI,
+            DEVANAGARI,
+            GUJARATI,
+            GURMUKHI,
+            KANNADA,
+            MALAYALAM,
+            ORIYA,
+            TAMIL,
+            TELUGU,
+            transliterate,
+        )
+    except Exception:
+        return None
+
+    lang = (language or "").lower()[:2]
+    script_map = {
+        "te": TELUGU,
+        "hi": DEVANAGARI,
+        "mr": DEVANAGARI,
+        "ta": TAMIL,
+        "kn": KANNADA,
+        "ml": MALAYALAM,
+        "bn": BENGALI,
+        "as": ASSAMESE,
+        "gu": GUJARATI,
+        "pa": GURMUKHI,
+        "or": ORIYA,
+    }
+    source_scheme = script_map.get(lang)
+    if source_scheme is None:
+        return None
+
+    # HK keeps simple ASCII letters and avoids diacritics.
+    target_scheme = "hk"
+    try:
+        return transliterate(native_text, source_scheme, target_scheme)
+    except Exception:
+        return None
+
+
+def _romanize_with_unidecode(native_text: str) -> str:
+    """
+    Romanize using Unidecode fallback.
+
+    Unidecode is deterministic and lightweight; quality is lower than language
+    specific transliteration, but still robust enough for alignment text.
+    """
+    try:
+        from unidecode import unidecode
+    except Exception:
+        return native_text
+    return unidecode(native_text)
+
+
+def derive_romanized_text(
+    native_text: str,
+    language: str,
+    fallback_romanized: str = "",
+) -> RomanizationResult:
+    """
+    Derive romanization deterministically from authoritative native text.
+
+    Fallback priority:
+    1) indic-transliteration (if installed)
+    2) unidecode
+    3) model-provided romanized fallback (normalized)
+    """
+    native = _strip_placeholders(native_text or "").strip()
+    fallback_norm = normalize_roman_for_mms(fallback_romanized)
+
+    if not native:
+        if fallback_norm:
+            return RomanizationResult(text=fallback_norm, source="fallback_model")
+        return RomanizationResult(text="", source="passthrough")
+
+    if (language or "").lower()[:2] == "en":
+        return RomanizationResult(
+            text=normalize_roman_for_mms(native),
+            source="passthrough",
+        )
+
+    via_indic = _romanize_with_indic_transliteration(native, language)
+    if via_indic:
+        normalized = normalize_roman_for_mms(via_indic)
+        if normalized:
+            return RomanizationResult(text=normalized, source="indic_transliteration")
+
+    via_unidecode = normalize_roman_for_mms(_romanize_with_unidecode(native))
+    if via_unidecode:
+        return RomanizationResult(text=via_unidecode, source="unidecode")
+
+    if fallback_norm:
+        return RomanizationResult(text=fallback_norm, source="fallback_model")
+
+    return RomanizationResult(text="", source="passthrough")
+
+
+def strip_placeholders(text: str) -> str:
+    """Public helper used by validators for script checks."""
+    return _strip_placeholders(text)
*** End Patch


Output: {"output":"Success. Updated the following files:\nA src/romanization.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Preparing romanization integration**


I’ve added the shared romanization module. Next I’m wiring it into MMS alignment and the validator so every score uses normalized deterministic romanization from native text, not free-form model output.


Input:
*** Begin Patch
*** Update File: src/validators/mms_aligner.py
@@
 import torch
 import torchaudio
 import soundfile as sf
 import numpy as np
 import time
 from dataclasses import dataclass, field
 from typing import List, Dict, Optional

+from src.romanization import normalize_roman_for_mms
+
@@
 class MMSAlignmentResult:
@@
-    transcription: str
+    transcription: str
+    normalized_transcription: str = ""
@@
         return {
             "audio_path": self.audio_path,
             "transcription": self.transcription[:100],
+            "normalized_transcription": self.normalized_transcription[:100],
             "alignment_score": round(self.alignment_score, 4),
             "mean_log_prob": round(self.mean_log_prob, 4),
@@
-    def _text_to_tokens(self, text: str) -> List[int]:
+    def _text_to_tokens(self, text: str) -> List[int]:
@@
-        for ch in text.lower().strip():
+        for ch in text.lower().strip():
             if ch == " ":
@@
     def align(self, audio_path: str, romanized_text: str) -> MMSAlignmentResult:
@@
             if not self.setup():
                 return MMSAlignmentResult(
                     audio_path=audio_path,
                     transcription=romanized_text,
+                    normalized_transcription=normalize_roman_for_mms(romanized_text),
                     alignment_score=0.0,
                     mean_log_prob=-10.0,
                 )

         start_time = time.time()
+        normalized = normalize_roman_for_mms(romanized_text)

         try:
             waveform = self._load_audio(audio_path)
             audio_dur = waveform.shape[1] / self.sample_rate
-            tokens = self._text_to_tokens(romanized_text)
+            tokens = self._text_to_tokens(normalized)

             if not tokens:
                 return MMSAlignmentResult(
                     audio_path=audio_path,
                     transcription=romanized_text,
+                    normalized_transcription=normalized,
                     alignment_score=0.0,
                     mean_log_prob=-10.0,
@@
             return MMSAlignmentResult(
                 audio_path=audio_path,
                 transcription=romanized_text,
+                normalized_transcription=normalized,
                 alignment_score=alignment_score,
                 mean_log_prob=mean_lp,
@@
             return MMSAlignmentResult(
                 audio_path=audio_path,
                 transcription=romanized_text,
+                normalized_transcription=normalized,
                 alignment_score=0.0,
                 mean_log_prob=-10.0,
                 processing_time_sec=time.time() - start_time,
*** End Patch


Output: {"output":"Success. Updated the following files:\nM src/validators/mms_aligner.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Preparing comprehensive validator patch**


Input:
*** Begin Patch
*** Update File: src/validators/simple_validator.py
@@
 import re
 from dataclasses import dataclass
 from typing import List, Dict, Optional, Set

+from src.romanization import (
+    derive_romanized_text,
+    normalize_roman_for_mms,
+    strip_placeholders,
+)
+
@@
-ALLOWED_COMMON = set(" \t\n.,!?;:'\"()-–—0123456789")
+ALLOWED_COMMON = set(" \t\n.,!?;:'\"()-–—[]0123456789")
@@
 ENGLISH_CHARS = set("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ")
+
+
+# Validation thresholds (strict defaults)
+ACCEPT_THRESHOLD = 0.78
+REVIEW_THRESHOLD = 0.68
+RETRY_THRESHOLD = 0.55
+MIN_ACCEPT_VALIDATOR_SCORE = 0.55
+MAX_ACCEPT_DISAGREEMENT = 0.20
+
+# Word-level quality gates from native CTC alignments
+MIN_INTERNAL_WORD_CONFIDENCE = 0.55
+MAX_INTERNAL_LOW_CONF_RATIO = 0.20
+BOUNDARY_LOW_CONFIDENCE = 0.35
*** End Patch


Output: {"output":"Success. Updated the following files:\nM src/validators/simple_validator.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Refining character check logic**


Input:
*** Begin Patch
*** Update File: src/validators/simple_validator.py
@@
 def check_characters(
     text: str,
     language: str = "te",
     allow_english: bool = False  # Changed: native = no English
 ) -> Dict:
@@
-    if not text or not text.strip():
+    text = strip_placeholders(text or "")
+    if not text or not text.strip():
         return {"valid": False, "invalid_chars": [], "script_ratio": 0, "reason": "empty"}
@@
     for i, char in enumerate(text):
         cp = ord(char)
+        if char.isalpha():
+            total_alpha += 1

         # Check if in native script
         is_native = any(start <= cp <= end for start, end in lang_config["ranges"])
         if is_native:
             script_count += 1
-            total_alpha += 1

         # Check if invalid (includes English if not allowed)
         elif cp not in valid_chars:
             invalid_chars.append({
                 "char": char,
                 "codepoint": cp,
                 "position": i
             })
-            if char in ENGLISH_CHARS:
-                total_alpha += 1
*** End Patch


Output: {"output":"Success. Updated the following files:\nM src/validators/simple_validator.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Updating validation data structure**


Input:
*** Begin Patch
*** Update File: src/validators/simple_validator.py
@@
 class ValidationResult:
@@
     # Romanized MMS alignment (language-agnostic torchaudio MMS_FA)
     roman_mms_score: float = 0.0
+    romanization_source: str = ""

     # Combined weighted score: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
     combined_score: float = 0.0
+    audio_duration_sec: float = 0.0
+
+    # Word-level quality gates
+    boundary_start_confidence: float = 0.0
+    boundary_end_confidence: float = 0.0
+    min_internal_word_confidence: float = 0.0
+    internal_low_conf_ratio: float = 0.0

     # Reasons
     reasons: List[str] = None
@@
             "native_ctc_score": round(self.native_ctc_score, 4),
             "roman_mms_score": round(self.roman_mms_score, 4),
+            "romanization_source": self.romanization_source,
             "combined_score": round(self.combined_score, 4),
             "alignment_score": round(self.alignment_score, 4),
             "low_confidence_words": self.low_confidence_words or [],
             "low_confidence_ratio": round(self.low_confidence_ratio, 3),
+            "audio_duration_sec": round(self.audio_duration_sec, 3),
+            "boundary_start_confidence": round(self.boundary_start_confidence, 4),
+            "boundary_end_confidence": round(self.boundary_end_confidence, 4),
+            "min_internal_word_confidence": round(self.min_internal_word_confidence, 4),
+            "internal_low_conf_ratio": round(self.internal_low_conf_ratio, 4),
             "reasons": self.reasons or []
         }
*** End Patch


Output: {"output":"Success. Updated the following files:\nM src/validators/simple_validator.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Adjusting score thresholds**


Input:
*** Begin Patch
*** Update File: src/validators/simple_validator.py
@@
 def compute_combined_score(
     native_ctc: float,
     roman_mms: float,
 ) -> tuple:
@@
-    if S >= 0.70 and min(N, R) >= 0.40:
-        if abs(N - R) > 0.25:
+    if S >= ACCEPT_THRESHOLD and min(N, R) >= MIN_ACCEPT_VALIDATOR_SCORE:
+        if abs(N - R) > MAX_ACCEPT_DISAGREEMENT:
             # High disagreement — flag even though score passes
             verdict = "review"
         else:
             verdict = "accept"
-    elif S >= 0.55:
+    elif S >= REVIEW_THRESHOLD:
+        verdict = "review"
+    elif S >= RETRY_THRESHOLD:
         verdict = "retry"
     else:
         verdict = "reject"
*** End Patch


Output: {"output":"Success. Updated the following files:\nM src/validators/simple_validator.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Summary: auto


**Planning word metrics helper**


Input:
*** Begin Patch
*** Update File: src/validators/simple_validator.py
@@
 def _strip_punctuation(text: str) -> str:
     """Strip punctuation for CTC alignment. Keeps native script + spaces."""
     import re
     # Remove common punctuation marks (keep native script chars, digits, spaces)
     return re.sub(r'[।,.!?;:\'"()\-–—]', '', text).strip()
+
+
+def _compute_word_quality_metrics(word_alignments: List) -> Dict[str, float]:
+    """
+    Compute boundary/internal confidence metrics from word-level CTC alignment.
+    """
+    confidences = [
+        float(getattr(wa, "confidence", 0.0))
+        for wa in (word_alignments or [])
+        if getattr(wa, "word", "").strip()
+    ]
+    if not confidences:
+        return {
+            "boundary_start": 0.0,
+            "boundary_end": 0.0,
+            "min_internal": 0.0,
+            "internal_low_ratio": 1.0,
+            "has_metrics": False,
+        }
+
+    if len(confidences) >= 3:
+        internal = confidences[1:-1]
+        boundary_start = confidences[0]
+        boundary_end = confidences[-1]
+    else:
+        internal = confidences
+        boundary_start = confidences[0]
+        boundary_end = confidences[-1]
+
+    if internal:
+        min_internal = min(internal)
+        low_internal = sum(1 for c in internal if c < 0.60)
+        internal_low_ratio = low_internal / len(internal)
+    else:
+        min_internal = 0.0
+        internal_low_ratio = 1.0
+
+    return {
+        "boundary_start": boundary_start,
+        "boundary_end": boundary_end,
+        "min_internal": min_internal,
+        "internal_low_ratio": internal_low_ratio,
+        "has_metrics": True,
+    }
*** End Patch


Output: {"output":"Success. Updated the following files:\nM src/validators/simple_validator.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Planning extensive function refactor**


sed -n '300,520p' src/validators/simple_validator.py


Output:
Chunk ID: 7a30d1
Wall time: 0.0513 seconds
Process exited with code 0
Original token count: 1900
Output:
    S = 0.45 * N + 0.55 * R - 0.10 * abs(N - R)

    if S >= ACCEPT_THRESHOLD and min(N, R) >= MIN_ACCEPT_VALIDATOR_SCORE:
        if abs(N - R) > MAX_ACCEPT_DISAGREEMENT:
            # High disagreement — flag even though score passes
            verdict = "review"
        else:
            verdict = "accept"
    elif S >= REVIEW_THRESHOLD:
        verdict = "review"
    elif S >= RETRY_THRESHOLD:
        verdict = "retry"
    else:
        verdict = "reject"

    return round(S, 4), verdict

def _strip_punctuation(text: str) -> str:
    """Strip punctuation for CTC alignment. Keeps native script + spaces."""
    import re
    # Remove common punctuation marks (keep native script chars, digits, spaces)
    return re.sub(r'[।,.!?;:\'"()\-–—]', '', text).strip()

def _compute_word_quality_metrics(word_alignments: List) -> Dict[str, float]:
    """
    Compute boundary/internal confidence metrics from word-level CTC alignment.
    """
    confidences = [
        float(getattr(wa, "confidence", 0.0))
        for wa in (word_alignments or [])
        if getattr(wa, "word", "").strip()
    ]
    if not confidences:
        return {
            "boundary_start": 0.0,
            "boundary_end": 0.0,
            "min_internal": 0.0,
            "internal_low_ratio": 1.0,
            "has_metrics": False,
        }

    if len(confidences) >= 3:
        internal = confidences[1:-1]
        boundary_start = confidences[0]
        boundary_end = confidences[-1]
    else:
        internal = confidences
        boundary_start = confidences[0]
        boundary_end = confidences[-1]

    if internal:
        min_internal = min(internal)
        low_internal = sum(1 for c in internal if c < 0.60)
        internal_low_ratio = low_internal / len(internal)
    else:
        min_internal = 0.0
        internal_low_ratio = 1.0

    return {
        "boundary_start": boundary_start,
        "boundary_end": boundary_end,
        "min_internal": min_internal,
        "internal_low_ratio": internal_low_ratio,
        "has_metrics": True,
    }

def validate_transcription(
    audio_path: str,
    transcription: str,
    language: str = "te",
    romanized_text: str = "",
    check_audio: bool = True,
) -> ValidationResult:
    """
    Validate a transcription using dual scoring (native CTC + romanized MMS).

    Runs:
    1. Character validation (instant) on native script
    2. Native CTC forced alignment (language-specific wav2vec2)
    3. Romanized MMS forced alignment (language-agnostic, if romanized_text provided)
    4. Weighted combined score: S = 0.45*N + 0.55*R - 0.10*abs(N-R)

    Args:
        audio_path: Path to audio file
        transcription: Native script text (may include punctuation)
        language: Language code (te, hi, ta, etc.)
        romanized_text: Latin-script romanization (for MMS alignment)
        check_audio: Whether to run audio alignment checks (slower)

    Returns:
        ValidationResult with dual scores and combined verdict
    """
    reasons = []

    # Strip punctuation for validation
    clean_text = _strip_punctuation(transcription)
    clean_roman = _strip_punctuation(romanized_text) if romanized_text else ""

    # === STEP 1: Character validation (instant) ===
    # For English language, allow English chars in native check
    allow_eng = language == "en"
    char_check = check_characters(clean_text, language, allow_english=allow_eng)

    if not char_check["valid"]:
        return ValidationResult(
            status="reject",
            char_valid=False,
            invalid_chars=char_check["invalid_chars"],
            script_ratio=char_check["script_ratio"],
            reasons=["Invalid/alien characters found"]
        )

    if char_check["script_ratio"] < 0.5:
        reasons.append(f"Too few native chars ({char_check['script_ratio']:.0%})")

    # === STEP 2: Native CTC alignment ===
    native_ctc_score = 0.0
    low_conf_words = []
    low_conf_ratio = 0.0

    if check_audio:
        try:
            aligner = _get_ctc_aligner(language)
            ctc_result = aligner.align(audio_path, clean_text)
            native_ctc_score = ctc_result.alignment_score
            low_conf_words = ctc_result.low_confidence_words
            low_conf_ratio = ctc_result.low_confidence_ratio
        except Exception as e:
            reasons.append(f"CTC alignment failed: {str(e)[:50]}")

    # === STEP 3: Romanized MMS alignment ===
    roman_mms_score = 0.0

    if check_audio and clean_roman:
        try:
            mms = _get_mms_aligner()
            mms_result = mms.align(audio_path, clean_roman)
            roman_mms_score = mms_result.alignment_score
        except Exception as e:
            reasons.append(f"MMS alignment failed: {str(e)[:50]}")

    # === STEP 4: Combined scoring ===
    if check_audio and native_ctc_score > 0 and roman_mms_score > 0:
        # Both validators returned scores - use weighted formula
        combined_score, verdict = compute_combined_score(
            native_ctc_score, roman_mms_score
        )
        if verdict == "review" and abs(native_ctc_score - roman_mms_score) > 0.25:
            reasons.append(
                f"Validator disagreement: CTC={native_ctc_score:.2f} "
                f"MMS={roman_mms_score:.2f}"
            )
    elif check_audio and native_ctc_score > 0:
        # CTC only (MMS failed or no romanized text)
        combined_score = native_ctc_score
        if native_ctc_score >= 0.70:
            verdict = "accept"
        elif native_ctc_score >= 0.50:
            verdict = "retry"
        else:
            verdict = "reject"
        if roman_mms_score == 0 and clean_roman:
            reasons.append("MMS failed, using CTC only")
    elif check_audio and roman_mms_score > 0:
        # MMS only (CTC failed)
        combined_score = roman_mms_score
        if roman_mms_score >= 0.70:
            verdict = "accept"
        elif roman_mms_score >= 0.50:
            verdict = "retry"
        else:
            verdict = "reject"
        reasons.append("CTC failed, using MMS only")
    elif check_audio:
        # Both validators returned 0
        combined_score = 0.0
        verdict = "review"
        reasons.append("Both validators returned 0")
    else:
        # No audio check requested
        combined_score = 0.0
        verdict = "review" if reasons else "accept"

    # Character validation failures override
    if not char_check["valid"]:
        verdict = "reject"

    # Append score-based reasons
    if verdict == "retry":
        reasons.append(
            f"Below threshold: combined={combined_score:.2f} "
            f"(CTC={native_ctc_score:.2f}, MMS={roman_mms_score:.2f})"
        )
    elif verdict == "reject" and combined_score > 0:
        reasons.append(
            f"Poor alignment: combined={combined_score:.2f} "
            f"(CTC={native_ctc_score:.2f}, MMS={roman_mms_score:.2f})"
        )

    return ValidationResult(
        status=verdict,
        char_valid=char_check["valid"],
        invalid_chars=char_check.get("invalid_chars"),
        script_ratio=char_check["script_ratio"],
        alignment_score=native_ctc_score,  # backward compat
        native_ctc_score=native_ctc_score,
        roman_mms_score=roman_mms_score,
        combined_score=combined_score,
        low_confidence_words=low_conf_words,
        low_confidence_ratio=low_conf_ratio,
        reasons=reasons if reasons else None,
    )

def cleanup():
    """Release all aligner resources (CTC + MMS)."""
    global _ctc_aligner, _mms_aligner
    if _ctc_aligner is not None:


**Redesigning transcription validation function**


Input:
*** Begin Patch
*** Update File: src/validators/simple_validator.py
@@
 def validate_transcription(
     audio_path: str,
     transcription: str,
     language: str = "te",
     romanized_text: str = "",
     check_audio: bool = True,
+    min_duration_sec: float = 2.0,
 ) -> ValidationResult:
@@
-    reasons = []
-
-    # Strip punctuation for validation
-    clean_text = _strip_punctuation(transcription)
-    clean_roman = _strip_punctuation(romanized_text) if romanized_text else ""
+    reasons = []
+
+    # Normalize text surfaces before validation.
+    native_no_placeholders = strip_placeholders(transcription or "")
+    clean_text = _strip_punctuation(native_no_placeholders)
+    romanized = derive_romanized_text(
+        native_text=clean_text,
+        language=language,
+        fallback_romanized=romanized_text,
+    )
+    clean_roman = normalize_roman_for_mms(romanized.text)
@@
     # === STEP 1: Character validation (instant) ===
     # For English language, allow English chars in native check
     allow_eng = language == "en"
     char_check = check_characters(clean_text, language, allow_english=allow_eng)
@@
-    # === STEP 2: Native CTC alignment ===
+    # === STEP 2: Native CTC alignment ===
     native_ctc_score = 0.0
     low_conf_words = []
     low_conf_ratio = 0.0
+    audio_duration_sec = 0.0
+    boundary_start_conf = 0.0
+    boundary_end_conf = 0.0
+    min_internal_word_conf = 0.0
+    internal_low_conf_ratio = 0.0

-    if check_audio:
+    if check_audio and clean_text:
         try:
             aligner = _get_ctc_aligner(language)
             ctc_result = aligner.align(audio_path, clean_text)
             native_ctc_score = ctc_result.alignment_score
             low_conf_words = ctc_result.low_confidence_words
             low_conf_ratio = ctc_result.low_confidence_ratio
+            audio_duration_sec = ctc_result.audio_duration_sec
+
+            metrics = _compute_word_quality_metrics(ctc_result.word_alignments)
+            if metrics["has_metrics"]:
+                boundary_start_conf = metrics["boundary_start"]
+                boundary_end_conf = metrics["boundary_end"]
+                min_internal_word_conf = metrics["min_internal"]
+                internal_low_conf_ratio = metrics["internal_low_ratio"]
         except Exception as e:
             reasons.append(f"CTC alignment failed: {str(e)[:50]}")
+    elif check_audio:
+        reasons.append("Empty native text after normalization")

     # === STEP 3: Romanized MMS alignment ===
     roman_mms_score = 0.0

     if check_audio and clean_roman:
@@
     if check_audio and native_ctc_score > 0 and roman_mms_score > 0:
         # Both validators returned scores - use weighted formula
         combined_score, verdict = compute_combined_score(
             native_ctc_score, roman_mms_score
         )
-        if verdict == "review" and abs(native_ctc_score - roman_mms_score) > 0.25:
+        if abs(native_ctc_score - roman_mms_score) > MAX_ACCEPT_DISAGREEMENT:
             reasons.append(
                 f"Validator disagreement: CTC={native_ctc_score:.2f} "
                 f"MMS={roman_mms_score:.2f}"
             )
     elif check_audio and native_ctc_score > 0:
         # CTC only (MMS failed or no romanized text)
         combined_score = native_ctc_score
-        if native_ctc_score >= 0.70:
+        if native_ctc_score >= ACCEPT_THRESHOLD:
             verdict = "accept"
-        elif native_ctc_score >= 0.50:
+        elif native_ctc_score >= REVIEW_THRESHOLD:
+            verdict = "review"
+        elif native_ctc_score >= RETRY_THRESHOLD:
             verdict = "retry"
         else:
             verdict = "reject"
         if roman_mms_score == 0 and clean_roman:
-            reasons.append("MMS failed, using CTC only")
+            reasons.append("MMS unavailable, using CTC only")
     elif check_audio and roman_mms_score > 0:
         # MMS only (CTC failed)
         combined_score = roman_mms_score
-        if roman_mms_score >= 0.70:
+        if roman_mms_score >= ACCEPT_THRESHOLD:
             verdict = "accept"
-        elif roman_mms_score >= 0.50:
+        elif roman_mms_score >= REVIEW_THRESHOLD:
+            verdict = "review"
+        elif roman_mms_score >= RETRY_THRESHOLD:
             verdict = "retry"
         else:
             verdict = "reject"
         reasons.append("CTC failed, using MMS only")
@@
     # Character validation failures override
     if not char_check["valid"]:
         verdict = "reject"
+
+    # Duration guard: very short clips are unstable for reliable auto-accept.
+    if check_audio and 0 < audio_duration_sec < min_duration_sec:
+        reasons.append(
+            f"Short clip ({audio_duration_sec:.2f}s < {min_duration_sec:.2f}s)"
+        )
+        if verdict == "accept":
+            verdict = "review"
+
+    # Boundary-aware gates: protect against VAD mis-cuts.
+    boundary_low = (
+        boundary_start_conf > 0 and boundary_start_conf < BOUNDARY_LOW_CONFIDENCE
+    ) or (
+        boundary_end_conf > 0 and boundary_end_conf < BOUNDARY_LOW_CONFIDENCE
+    )
+    internal_bad = (
+        min_internal_word_conf > 0 and (
+            min_internal_word_conf < MIN_INTERNAL_WORD_CONFIDENCE
+            or internal_low_conf_ratio > MAX_INTERNAL_LOW_CONF_RATIO
+        )
+    )
+
+    if verdict == "accept" and internal_bad:
+        verdict = "review"
+        reasons.append(
+            "Internal word confidence too low "
+            f"(min={min_internal_word_conf:.2f}, "
+            f"low_ratio={internal_low_conf_ratio:.0%})"
+        )
+    elif verdict in ("accept", "retry") and boundary_low and not internal_bad:
+        verdict = "review"
+        reasons.append(
+            "Low boundary confidence with strong internal words "
+            "(likely boundary cut artifact)"
+        )

     # Append score-based reasons
     if verdict == "retry":
         reasons.append(
             f"Below threshold: combined={combined_score:.2f} "
@@
         script_ratio=char_check["script_ratio"],
         alignment_score=native_ctc_score,  # backward compat
         native_ctc_score=native_ctc_score,
         roman_mms_score=roman_mms_score,
+        romanization_source=romanized.source,
         combined_score=combined_score,
         low_confidence_words=low_conf_words,
         low_confidence_ratio=low_conf_ratio,
+        audio_duration_sec=audio_duration_sec,
+        boundary_start_confidence=boundary_start_conf,
+        boundary_end_confidence=boundary_end_conf,
+        min_internal_word_confidence=min_internal_word_conf,
+        internal_low_conf_ratio=internal_low_conf_ratio,
         reasons=reasons if reasons else None,
     )
*** End Patch


Output: {"output":"Success. Updated the following files:\nM src/validators/simple_validator.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Verifying code cleanup and doc accuracy**


rg -n "0\.70|0\.55|0\.40|abs\(N-R\) > 0\.25|Accept if" src/validators/simple_validator.py


Output:
Chunk ID: 1e99af
Wall time: 0.0503 seconds
Process exited with code 0
Original token count: 150
Output:
10:Combined scoring: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
12:  Accept if S >= 0.70 and min(N,R) >= 0.40
13:  Forced review if abs(N-R) > 0.25 (high validator disagreement)
122:RETRY_THRESHOLD = 0.55
123:MIN_ACCEPT_VALIDATOR_SCORE = 0.55
127:MIN_INTERNAL_WORD_CONFIDENCE = 0.55
228:    # Combined weighted score: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
291:    Formula: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
292:    MMS weighted higher (0.55): more stable on code-mixed Indic audio.
300:    S = 0.45 * N + 0.55 * R - 0.10 * abs(N - R)
384:    4. Weighted combined score: S = 0.45*N + 0.55*R - 0.10*abs(N-R)


**Updating header and function docs**


Input:
*** Begin Patch
*** Update File: src/validators/simple_validator.py
@@
 Combined scoring: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
   N = native CTC score, R = romanized MMS score
-  Accept if S >= 0.70 and min(N,R) >= 0.40
-  Forced review if abs(N-R) > 0.25 (high validator disagreement)
+  Accept if S >= 0.78 and min(N,R) >= 0.55
+  Forced review if abs(N-R) > 0.20 (high validator disagreement)
@@
-    3. Romanized MMS alignment - verify romanized text matches audio
+    3. Romanized MMS alignment - verify deterministic romanized text matches audio
*** End Patch


Output:
apply_patch verification failed: Failed to find expected lines in /home/ubuntu/maya3_transcribe/src/validators/simple_validator.py:
    3. Romanized MMS alignment - verify romanized text matches audio


**Reopening lines for correct patch**


sed -n '1,40p' src/validators/simple_validator.py


Output:
Chunk ID: 3012b3
Wall time: 0.0511 seconds
Process exited with code 0
Original token count: 305
Output:
"""
Simple Transcription Validator
==============================

Three checks:
1. Character validation - catch garbage/alien characters
2. Native CTC alignment - verify native script matches audio
3. Romanized MMS alignment - verify romanized text matches audio

Combined scoring: S = 0.45*N + 0.55*R - 0.10*abs(N-R)
  N = native CTC score, R = romanized MMS score
  Accept if S >= 0.70 and min(N,R) >= 0.40
  Forced review if abs(N-R) > 0.25 (high validator disagreement)

Usage:
    from src.validators.simple_validator import validate_transcription

    result = validate_transcription(
        "audio.flac", "నాకు కొన్ని యాడ్స్",
        romanized_text="naaku konni ads", language="te"
    )
    print(result.status)          # accept / review / retry / reject
    print(result.combined_score)  # 0-1
"""
import re
from dataclasses import dataclass
from typing import List, Dict, Optional, Set

from src.romanization import (
    derive_romanized_text,
    normalize_roman_for_mms,
    strip_placeholders,
)

# Unicode ranges for Indic scripts (all 12 supported languages)
SCRIPT_RANGES = {
    "te": {  # Telugu
        "name": "Telugu",
        "ranges": [(0x0C00, 0x0C7F)],


**Fixing patch header lines**


Input:
*** Begin Patch
*** Update File: src/validators/simple_validator.py
@@
-3. Romanized MMS alignment - verify romanized text matches audio
+3. Romanized MMS alignment - verify deterministic romanized text matches audio
@@
-  Accept if S >= 0.70 and min(N,R) >= 0.40
-  Forced review if abs(N-R) > 0.25 (high validator disagreement)
+  Accept if S >= 0.78 and min(N,R) >= 0.55
+  Forced review if abs(N-R) > 0.20 (high validator disagreement)
*** End Patch


Output: {"output":"Success. Updated the following files:\nM src/validators/simple_validator.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Adding romanization and duration config**


I’m patching `pipeline.py` now: deterministic romanization replacement, stricter minimum duration defaults (2s), and new split boundary padding config/CLI so cut points include small safety silence.


**Applying pipeline patch**


Input:
*** Begin Patch
*** Update File: pipeline.py
@@
 from src.backend.audio_polisher import AudioPolisher
 from src.backend.gemini_transcriber import GeminiTranscriber, TranscriptionConfig
 from src.backend.transcription_schema import TranscriptionResult
+from src.romanization import derive_romanized_text

 # Validation
 from src.validators import validate_transcription, quick_validate, cleanup as cleanup_validator
@@
     # === Audio Processing Settings ===
     max_segment_duration_sec: float = 10.0  # Hard limit - segments longer than this are split
-    min_segment_duration_sec: float = 1.0   # Skip segments shorter than this
+    min_segment_duration_sec: float = 2.0   # Skip segments shorter than this
     chunk_overlap_sec: float = 0.0          # Overlap when splitting (for word boundaries)
+    split_boundary_padding_ms: float = 80.0 # Add a bit of silence context around split cuts
@@
         processor = AudioProcessor(
             max_duration_sec=self.config.max_segment_duration_sec,
             min_duration_sec=self.config.min_segment_duration_sec,
-            overlap_sec=self.config.chunk_overlap_sec
+            overlap_sec=self.config.chunk_overlap_sec,
+            boundary_padding_ms=self.config.split_boundary_padding_ms,
         )
*** End Patch


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


**Inspecting patch validation scope**


Output:
Chunk ID: 112949
Wall time: 0.0513 seconds
Process exited with code 0
Original token count: 1506
Output:

            batch_results = transcriber.transcribe_batch(
                batch,
                transcription_config,
                progress_callback=lambda c, t: self._log(
                    f"  Chunk {batch_start + c}/{len(chunks)}: "
                    f"{batch[c-1].original_segment}"
                )
            )

            # === Validate transcriptions (dual scoring: native CTC + romanized MMS) ===
            if self.config.validate_transcriptions:
                lang_code = self._get_language_code(language)
                batch_health = _BatchHealth()
                for i, result in enumerate(batch_results):
                    if result.native:
                        romanized = result.transcription.romanized or ""
                        validation = validate_transcription(
                            batch[i].file_path,
                            result.native,
                            romanized_text=romanized,
                            language=lang_code,
                        )
                        result.validation_status = validation.status
                        result.validation_score = validation.combined_score

                        # Retry with pro model if below accept threshold
                        if (validation.status in ("retry", "reject")
                                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})"
                                    )
                                    batch_health.retry_improved += 1
                                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"
                            )

                        # Record for batch health monitoring
                        batch_health.record(validation)

                # Batch health report + alerts
                alerts = batch_health.report(self._log)
                self._health_alerts.extend(alerts)

            self.results.extend(batch_results)

            # Save intermediate results
            if self.config.save_intermediate:
                self._save_results(partial=True)

        # === Step 5: Save Final Results ===


**Patching romanization logic**