Output:
Chunk ID: 73b57a
Wall time: 0.0519 seconds
Process exited with code 0
Original token count: 2388
Output:
"""
Audio Polisher - Segment boundary cleanup for transcription quality.
Conservative: only trims CUT SPEECH ARTIFACTS, never silence.

Philosophy:
  - Silence at boundaries is GOOD (natural padding, prevents abrupt starts/ends)
  - Only problem: segments starting/ending with ABRUPTLY CUT SPEECH from imprecise VAD
  - We detect the artifact pattern (burst -> gap -> speech) and remove ONLY the burst
  - The gap becomes natural silence padding (~30-50ms typically)
  - Segments that already start/end with silence pass through untouched

No ML models - pure signal processing, runs in <10ms/segment.
"""
import os
import numpy as np
import soundfile as sf
from dataclasses import dataclass
from typing import Optional, Tuple, List
from pathlib import Path

@dataclass
class PolishResult:
    """Result of audio polishing for a single segment."""
    input_path: str
    output_path: str
    was_modified: bool
    start_trimmed_ms: float = 0.0
    end_trimmed_ms: float = 0.0
    start_quality: str = "clean"
    end_quality: str = "clean"
    original_duration_ms: float = 0.0
    polished_duration_ms: float = 0.0
    snr_db: float = 0.0
    rms_db: float = 0.0
    peak_db: float = 0.0
    is_clipped: bool = False
    volume_adjusted: bool = False
    volume_gain_db: float = 0.0

    @property
    def total_trimmed_ms(self):
        return self.start_trimmed_ms + self.end_trimmed_ms

    def summary(self):
        name = Path(self.input_path).name
        if not self.was_modified:
            return (f"  {name}: SKIP | {self.original_duration_ms:.0f}ms | "
                    f"SNR:{self.snr_db:.1f}dB | start:{self.start_quality} "
                    f"end:{self.end_quality}")
        parts = []
        if self.start_trimmed_ms > 0:
            parts.append(
                f"start -{self.start_trimmed_ms:.0f}ms ({self.start_quality})")
        if self.end_trimmed_ms > 0:
            parts.append(
                f"end -{self.end_trimmed_ms:.0f}ms ({self.end_quality})")
        if self.volume_adjusted:
            parts.append(f"gain +{self.volume_gain_db:.1f}dB")
        changes = " | ".join(parts)
        return (f"  {name}: POLISHED | {self.original_duration_ms:.0f}ms -> "
                f"{self.polished_duration_ms:.0f}ms | {changes} | "
                f"SNR:{self.snr_db:.1f}dB")

class AudioPolisher:
    """
    Conservative audio segment polisher for transcription quality.
    Only modifies segments where artifact patterns are clearly detected.
    Uses energy + ZCR combined heuristic.
    """

    def __init__(
        self,
        frame_ms=10.0,
        hop_ms=5.0,
        max_start_trim_ms=200.0,
        artifact_search_ms=150.0,
        min_gap_ms=15.0,            # Min gap duration to confirm artifact pattern
        max_end_trim_ms=150.0,
        min_burst_ms=10.0,          # Min duration for trailing burst to be artifact
        max_burst_ms=100.0,         # Max duration - longer = probably real speech
        energy_gap_factor=0.35,
        zcr_artifact_threshold=0.15,
        min_dynamic_range_db=6.0,
        min_silence_pad_ms=30.0,    # Desired silence padding after artifact removal
        min_remaining_ms=300.0,
        min_trim_ms=10.0,
        fade_in_ms=5.0,             # Smooth fade-in after start trim
        fade_out_ms=10.0,           # Smooth fade-out on abrupt endings
        target_rms_db=-18.0,
        snr_boost_threshold_db=12.0,
        max_gain_db=12.0,
        clipping_threshold=0.99,
        output_format="flac",
    ):
        self.frame_ms = frame_ms
        self.hop_ms = hop_ms
        self.max_start_trim_ms = max_start_trim_ms
        self.artifact_search_ms = artifact_search_ms
        self.min_gap_ms = min_gap_ms
        self.max_end_trim_ms = max_end_trim_ms
        self.min_burst_ms = min_burst_ms
        self.max_burst_ms = max_burst_ms
        self.energy_gap_factor = energy_gap_factor
        self.zcr_artifact_threshold = zcr_artifact_threshold
        self.min_dynamic_range_db = min_dynamic_range_db
        self.min_silence_pad_ms = min_silence_pad_ms
        self.min_remaining_ms = min_remaining_ms
        self.min_trim_ms = min_trim_ms
        self.fade_in_ms = fade_in_ms
        self.fade_out_ms = fade_out_ms
        self.target_rms_db = target_rms_db
        self.snr_boost_threshold_db = snr_boost_threshold_db
        self.max_gain_db = max_gain_db
        self.clipping_threshold = clipping_threshold
        self.output_format = output_format

    def _compute_frames(self, audio, sr):
        """Compute per-frame energy (dB) and zero-crossing rate."""
        frame_samples = int(self.frame_ms * sr / 1000)
        hop_samples = int(self.hop_ms * sr / 1000)
        n_frames = max(1, (len(audio) - frame_samples) // hop_samples + 1)
        ste_db = np.zeros(n_frames)
        zcr = np.zeros(n_frames)
        frame_times = np.zeros(n_frames)
        for i in range(n_frames):
            start = i * hop_samples
            end = min(start + frame_samples, len(audio))
            frame = audio[start:end]
            rms = np.sqrt(np.mean(frame ** 2))
            ste_db[i] = 20 * np.log10(rms + 1e-10)
            if len(frame) > 1:
                zcr[i] = (np.sum(np.abs(np.diff(np.sign(frame))))
                          / (2 * len(frame)))
            frame_times[i] = (start + frame_samples / 2) / sr
        return ste_db, zcr, frame_times

    def _detect_leading_artifact(self, ste_db, zcr, frame_times):
        """
        Detect CUT SPEECH artifact at segment start.

        ONLY acts when: burst (cut speech) -> gap (silence) -> real speech.
        Trims at gap START = removes burst, keeps gap as silence padding.

        Does NOT trim if segment starts with silence (that's good padding).
        Does NOT trim if segment starts with continuous speech (natural start).
        """
        search_limit = self.artifact_search_ms / 1000
        si = np.where(frame_times < search_limit)[0]
        if len(si) < 3:
            return None, "clean"

        nf = np.percentile(ste_db, 15)
        sl = np.percentile(ste_db, 85)
        dr = sl - nf
        if dr < self.min_dynamic_range_db:
            return None, "clean"

        gap_thresh = nf + self.energy_gap_factor * dr

        # KEY CHECK: does segment start with speech energy or silence?
        # If first frames are below threshold = starts with silence = GOOD, skip
        n_check = min(3, len(si))
        first_frames_energy = ste_db[si[:n_check]]
        if np.all(first_frames_energy < gap_thresh):
            return None, "clean"  # Starts with silence - natural padding, don't touch

        # Segment starts with speech energy. Look for gap pattern.
        below = ste_db[si] < gap_thresh

        gaps = []
        in_gap = False
        gs = 0
        for i in range(len(below)):
            if below[i] and not in_gap:
                in_gap = True
                gs = i
            elif not below[i] and in_gap:
                in_gap = False
                dur = (frame_times[si[i]] - frame_times[si[gs]]) * 1000
                if dur >= self.min_gap_ms:
                    gaps.append((gs, i, dur))
        if in_gap:
            dur = (frame_times[si[-1]] - frame_times[si[gs]]) * 1000
            if dur >= self.min_gap_ms:
                gaps.append((gs, len(below), dur))

        if not gaps:
            return None, "clean"  # No gap = continuous speech from start

        g = gaps[0]
        # Gap must have speech BEFORE it (the artifact burst) and AFTER it (real speech)
        if g[0] == 0:
            return None, "clean"  # Gap at start = silence, already handled above
        if g[1] >= len(si):
            return None, "marginal_kept"  # Gap extends to search boundary

        # Artifact confirmed: speech burst -> gap -> more speech
        pre = si[:g[0]]
        mzcr = np.mean(zcr[pre]) if len(pre) > 0 else 0

        # Trim at GAP START = remove burst, keep gap as silence padding
        gap_start_frame = si[g[0]]
        tt = frame_times[gap_start_frame]

        if tt * 1000 > self.max_start_trim_ms or tt * 1000 < self.min_trim_ms:
            return None, "marginal_kept" if tt * 1000 > self.max_start_trim_ms else "clean"

        # Validate: gap should provide enough silence padding
        gap_ms = g[2]
        if gap_ms < self.min_silence_pad_ms:
            # Gap is too short for comfortable padding. Still trim if artifact
            # is clearly noise-like (high ZCR), but note the tight padding.
            if mzcr <= self.zcr_artifact_threshold:
                return None, "marginal_kept"

        # High ZCR = noise artifact (confident trim)
        if mzcr > self.zcr_artifact_threshold:
            return tt, "artifact_trimmed"
        # Low ZCR but clear gap = trim if gap is substantial
        if g[2] >= self.min_gap_ms * 1.5:
            return tt, "artifact_trimmed"

        return None, "marginal_kept"

    def _detect_trailing_artifact(self, ste_db, frame_times, total_dur):
        """
        Detect CUT SPEECH artifact at segment end.

        ONLY acts when: sustained speech -> silence gap -> isolated burst at end.
        The burst is a leak from the next VAD segment. Remove it, keep the silence.

        Does NOT trim if segment ends with silence (that's good padding).
        Does NOT trim if segment ends with sustained speech (natural or cut, can't help).
        Returns (trim_time, "artifact_trimmed") or applies fade-out flag.
        """
        nf = np.percentile(ste_db, 15)
        sl = np.percentile(ste_db, 85)
        dr = sl - nf


Output:
Chunk ID: bcd30a
Wall time: 0.0505 seconds
Process exited with code 0
Original token count: 2150
Output:
        dr = sl - nf
        if dr < self.min_dynamic_range_db:
            return None, "clean", False

        thresh = nf + self.energy_gap_factor * dr
        above = ste_db > thresh
        n = len(ste_db)

        # Check if segment ends with silence (last few frames below threshold)
        n_check = min(3, n)
        if np.all(~above[-n_check:]):
            return None, "clean", False  # Ends with silence - good padding

        # Segment ends with speech energy. Two sub-cases:
        # A) Isolated trailing burst: silence -> burst -> end
        #    = artifact from next segment, trim it
        # B) Sustained speech to the end: natural or cut ending
        #    = apply fade-out to soften abrupt cut

        # Find the last above-threshold frame
        ai = np.where(above)[0]
        if len(ai) == 0:
            return None, "clean", False

        last_above = ai[-1]

        # Check if this trailing speech is ISOLATED (preceded by silence gap)
        # Walk backwards from last_above to find if there's a gap before it
        lookback = min(10, last_above)  # ~50ms at 5ms hop
        if lookback >= 3:
            preceding = above[last_above - lookback:last_above]
            sustained = np.mean(preceding)

            if sustained < 0.3:
                # Low density before last speech = isolated trailing burst
                # Find where the burst starts (first above-thresh going backward)
                burst_start = last_above
                for bi in range(last_above - 1, -1, -1):
                    if above[bi]:
                        burst_start = bi
                    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.


**Planning patch reverts and deletions**


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

-from src.romanization import normalize_roman_for_mms
+_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+")
+_APOSTROPHE_TRANSLATION = str.maketrans({
+    "’": "'",
+    "‘": "'",
+    "ʼ": "'",
+    "`": "'",
+    "´": "'",
+})
+_DASH_TRANSLATION = str.maketrans({
+    "-": " ",
+    "_": " ",
+    "/": " ",
+    "|": " ",
+})
+
+
+def normalize_roman_for_mms(text: str) -> str:
+    """Normalize romanized text to MMS token alphabet."""
+    cleaned = _PLACEHOLDER_RE.sub(" ", text or "")
+    cleaned = unicodedata.normalize("NFKD", cleaned)
+    cleaned = "".join(ch for ch in cleaned if not unicodedata.combining(ch))
+    cleaned = cleaned.translate(_APOSTROPHE_TRANSLATION)
+    cleaned = cleaned.translate(_DASH_TRANSLATION)
+    cleaned = cleaned.lower()
+    cleaned = _NON_MMS_CHARS_RE.sub(" ", cleaned)
+    return _MULTISPACE_RE.sub(" ", cleaned).strip()
*** End Patch


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


Summary: auto


**Refactoring validator imports and regex**


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,
-)
+_PLACEHOLDER_RE = re.compile(r"\[(UNK|INAUDIBLE|NO_SPEECH)\]", re.IGNORECASE)
*** End Patch


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


**Fixing placeholder stripping usage**


Input:
*** Begin Patch
*** Update File: src/validators/simple_validator.py
@@
-    text = strip_placeholders(text or "")
+    text = _PLACEHOLDER_RE.sub(" ", text 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}}


**Replacing derive_romanized call**


Input:
*** Begin Patch
*** Update File: src/validators/simple_validator.py
@@
-    # Normalize text surfaces before validation.
-    native_no_placeholders = strip_placeholders(transcription or "")
+    # Normalize text surfaces before validation.
+    native_no_placeholders = _PLACEHOLDER_RE.sub(" ", 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)
+    clean_roman = _strip_punctuation(romanized_text) if romanized_text else ""
@@
         native_ctc_score=native_ctc_score,
         roman_mms_score=roman_mms_score,
-        romanization_source=romanized.source,
+        romanization_source="model" if clean_roman else "",
*** 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 remaining derive imports**


rg -n "derive_romanized_text|normalize_roman_for_mms|strip_placeholders|romanization_source=romanized|src\.romanization" pipeline.py src/validators -S


Output:
Chunk ID: 465e67
Wall time: 0.0512 seconds
Process exited with code 0
Original token count: 133
Output:
pipeline.py:26:from src.romanization import derive_romanized_text
pipeline.py:341:                        romanized_result = derive_romanized_text(
pipeline.py:392:                                retry_romanized = derive_romanized_text(
src/validators/mms_aligner.py:44:def normalize_roman_for_mms(text: str) -> str:
src/validators/mms_aligner.py:183:                    normalized_transcription=normalize_roman_for_mms(romanized_text),
src/validators/mms_aligner.py:189:        normalized = normalize_roman_for_mms(romanized_text)


**Planning pipeline revert**


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
@@
                 batch_health = _BatchHealth()
                 for i, result in enumerate(batch_results):
                     if result.native:
-                        romanized_result = derive_romanized_text(
-                            native_text=result.native,
-                            language=lang_code,
-                            fallback_romanized=result.transcription.romanized or "",
-                        )
-                        # Make romanized deterministic across runs.
-                        result.transcription.romanized = romanized_result.text
+                        romanized = result.transcription.romanized or ""
                         validation = validate_transcription(
                             batch[i].file_path,
                             result.native,
-                            romanized_text=romanized_result.text,
+                            romanized_text=romanized,
                             language=lang_code,
                             min_duration_sec=self.config.min_segment_duration_sec,
                         )
@@
                                 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,
                                     detected_language=retry_raw.get('detected_language', ''),
                                 )
-                                retry_romanized = derive_romanized_text(
-                                    native_text=retry_output.transcription,
-                                    language=lang_code,
-                                    fallback_romanized=retry_output.romanized or "",
-                                )
-                                retry_output.romanized = retry_romanized.text
+                                retry_roman = retry_output.romanized or ""
                                 retry_val = validate_transcription(
                                     batch[i].file_path,
                                     retry_output.transcription,
-                                    romanized_text=retry_romanized.text,
+                                    romanized_text=retry_roman,
                                     language=lang_code,
                                     min_duration_sec=self.config.min_segment_duration_sec,
                                 )
*** End Patch


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


**Removing obsolete requirements**


Input:
*** Begin Patch
*** Update File: requirements.txt
@@
 pydub>=0.25.0
 python-dotenv>=1.2.0
-Unidecode>=1.3.8
-indic-transliteration>=2.3.65
*** End Patch


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


**Checking unused module**


I’ve rolled back the `derive_romanized` integration in validator/pipeline and restored model-provided romanized input for MMS scoring. Next I’ll remove the leftover romanization module/dependency traces, then implement the boundary-energy polish upgrades.


rg -n "src\.romanization|derive_romanized_text|romanization.py" -S pipeline.py src requirements.txt


Output:
Chunk ID: 63e54d
Wall time: 0.0513 seconds
Process exited with code 0
Original token count: 13
Output:
src/romanization.py:118:def derive_romanized_text(


**Removing unnecessary file**


Input:
*** Begin Patch
*** Delete File: src/romanization.py
*** End Patch


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


**Designing audio polisher enhancements**


Input:
*** Begin Patch
*** Update File: src/backend/audio_polisher.py
@@
 class PolishResult:
@@
     end_trimmed_ms: float = 0.0
+    start_silence_added_ms: float = 0.0
+    end_silence_added_ms: float = 0.0
     start_quality: str = "clean"
     end_quality: str = "clean"
@@
     def total_trimmed_ms(self):
         return self.start_trimmed_ms + self.end_trimmed_ms
@@
         if self.end_trimmed_ms > 0:
             parts.append(
                 f"end -{self.end_trimmed_ms:.0f}ms ({self.end_quality})")
+        if self.start_silence_added_ms > 0:
+            parts.append(f"start +{self.start_silence_added_ms:.0f}ms silence")
+        if self.end_silence_added_ms > 0:
+            parts.append(f"end +{self.end_silence_added_ms:.0f}ms silence")
         if self.volume_adjusted:
             parts.append(f"gain +{self.volume_gain_db:.1f}dB")
*** End Patch


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


<turn_aborted>
The user interrupted the previous turn on purpose. Any running unified exec processes were terminated. If any tools/commands were aborted, they may have partially executed; verify current state before retrying.
</turn_aborted>