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

class MistralTokenizer(TokenizerLike):
    @classmethod
    def from_pretrained(
        cls,
        path_or_repo_id: str | Path,
        *args,
        trust_remote_code: bool = False,
        revision: str | None = None,
        download_dir: str | None = None,
        **kwargs,
    ) -> "MistralTokenizer":
        from mistral_common.protocol.instruct.validator import ValidationMode

        try:
            # Transformers v5
            from transformers.tokenization_mistral_common import MistralCommonBackend
        except ImportError:
            # Transformers v4
            from transformers.tokenization_mistral_common import (
                MistralCommonTokenizer as MistralCommonBackend,
            )

        tokenizer = MistralCommonBackend.from_pretrained(
            path_or_repo_id,
            *args,
            mode=ValidationMode.test,
            cache_dir=download_dir,
            revision="main" if revision is None else revision,
            **kwargs,
        )

        return cls(tokenizer)

    def __init__(self, tokenizer: "MistralCommonBackend") -> None:
        super().__init__()

        from mistral_common.protocol.instruct.validator import ValidationMode
        from mistral_common.tokens.tokenizers.sentencepiece import (
            SentencePieceTokenizer,
        )
        from mistral_common.tokens.tokenizers.tekken import Tekkenizer

        self.transformers_tokenizer = tokenizer
        self.mistral = tokenizer.tokenizer
        self.instruct = self.mistral.instruct_tokenizer
        self.tokenizer = self.instruct.tokenizer

        mode = self.mistral._chat_completion_request_validator._mode
        if mode != ValidationMode.test:
            raise ValueError(
                "Mistral tokenizer must be in test mode. Make sure to "
                "set `mode='ValidationMode.test'` when creating the "
                "Mistral tokenizer."
            )

        _mistral_version_str = str(self.tokenizer.version.value)
        self.version: int = int(_mistral_version_str.split("v")[-1])

        self.is_tekken = isinstance(self.tokenizer, Tekkenizer)
        self.is_spm = isinstance(self.tokenizer, SentencePieceTokenizer)
        if not (self.is_tekken or self.is_spm):
            raise TypeError(f"Unsupported tokenizer: {type(self.tokenizer)}")

        # Reverse order to ensure that the lowest token id is kept.
        self._vocab_dict = {
            self.convert_ids_to_tokens([i], skip_special_tokens=False)[0]: i
            for i in range(self.vocab_size - 1, -1, -1)
        }
        # Sort the dict for convenience
        self._vocab_dict = dict(sorted(self._vocab_dict.items(), key=lambda x: x[1]))

        # Cache special tokens for faster access.
        self._special_token_ids = self._get_special_token_ids()
        self._special_token_ids_set = set(self._special_token_ids)
        self._special_tokens = self._get_special_tokens(self._special_token_ids)
        self._special_tokens_set = set(self._special_tokens)

        # Vocab sorted by token id.
        self._vocab = self.tokenizer._vocab
        self._max_token_id = self.vocab_size - 1

    def _get_special_token_ids(self) -> list[int]:
        from mistral_common.tokens.tokenizers.sentencepiece import (
            SentencePieceTokenizer,
        )
        from mistral_common.tokens.tokenizers.tekken import Tekkenizer

        if self.is_tekken:
            assert isinstance(self.tokenizer, Tekkenizer), type(self.tokenizer)
            special_ids = {t["rank"] for t in self.tokenizer._all_special_tokens}
        elif self.is_spm:
            assert isinstance(self.tokenizer, SentencePieceTokenizer), type(
                self.tokenizer
            )
            special_ids = self.tokenizer._control_tokens
        else:
            raise ValueError(f"Unknown tokenizer type: {type(self.tokenizer)}")
        return sorted(special_ids)

    def _get_special_tokens(self, all_special_ids: list[int]) -> list[str]:
        from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy

        return [
            self.tokenizer.decode([i], special_token_policy=SpecialTokenPolicy.KEEP)
            for i in all_special_ids
        ]

    def num_special_tokens_to_add(self) -> int:
        return len(self.encode(""))

    # the following attributes are set to fit vLLM's design and are used
    # by the structured output backends.
    @property
    def all_special_tokens(self) -> list[str]:
        return self._special_tokens

    @property
    def all_special_ids(self) -> list[int]:
        return self._special_token_ids


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) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) 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.


Summary: auto


**Designing Voxtral chat prompt integration**

Planning to detect Voxtral model by class name and add a method that builds a Mistral-format chat prompt combining text and audio using `cached_tokenizer_from_config` and `encode_chat_completion`, returning token IDs and multimodal audio data.


**Planning proofread mode and pipeline integration**

I'm extending transcribe with a "proofread" mode for Voxtral that uses the chat prompt method and adding pipeline params and CLI options to run two-pass proofread, storing raw and proofread results separately with fallback logic and evaluation considerations.


rg -n "bench_app\.command\(\"eval\"" -S maya_transcribe/cli.py


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


**Searching for benchmark evaluation keyword**


rg -n "bench_eval|eval\(" -S maya_transcribe/cli.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
316:def eval(


**Clarifying ensemble strategy and eval usage**

The eval command runs top-level for proofread experiments; we'll first fix Voxtral transcription and proofread WER before deciding on ensembling with Gemma. The final ensemble choice depends on proofread improvements versus baseline Voxtral combined with Gemma tuned.


**Planning Voxtral proofread patch**

I'm preparing a patch to update Voxtral ASR with proofread mode by adding flags, modifying transcribe to accept mode, and creating a helper for Voxtral chat prompts. This will ensure Voxtral runs with TranscriptionRequest and supports proofread properly before implementing ensemble or updating results.


Input:
*** Begin Patch
*** Update File: maya_transcribe/asr.py
@@
 class VllmTranscriptionASR:
     """
     vLLM-powered speech-to-text backend for multimodal audio models (e.g., Voxtral, Gemma3n).
     """

     def __init__(
@@
         self._llm = LLM(**llm_kwargs)
         self._model_config = self._llm.model_config
         self._model_cls = get_model_cls(self._model_config)
+        self._is_voxtral = "voxtral" in self._model_cls.__name__.lower() or "voxtral" in model_id.lower()
         if not bool(getattr(self._model_cls, "supports_transcription", False)):
             raise RuntimeError(f"Model does not support transcription: {model_id}")

         self._stt_config = self._model_cls.get_speech_to_text_config(self._model_config, "transcribe")
@@
     @property
     def name(self) -> str:
         return f"vllm:{self.model_id}"

+    @property
+    def supports_proofread(self) -> bool:
+        return self._is_voxtral
+
+    def _build_voxtral_chat_prompt(self, audio: "np.ndarray", sample_rate: int, prompt_text: str) -> dict[str, Any]:
+        from mistral_common.protocol.instruct.chunk import AudioChunk, RawAudio, TextChunk
+        from mistral_common.protocol.instruct.messages import UserMessage
+        from mistral_common.protocol.instruct.request import ChatCompletionRequest
+        from mistral_common.tokens.tokenizers.audio import Audio
+        from vllm.tokenizers import cached_tokenizer_from_config
+
+        tokenizer = cached_tokenizer_from_config(self._model_config)
+        if tokenizer is None:
+            raise RuntimeError("Tokenizer unavailable for Voxtral prompt.")
+
+        audio_item = Audio(audio, sample_rate, format="wav")
+        audio_chunk = AudioChunk(input_audio=RawAudio.from_audio(audio_item))
+        request = ChatCompletionRequest(messages=[UserMessage(content=[TextChunk(text=prompt_text), audio_chunk])])
+        tokenized = tokenizer.mistral.encode_chat_completion(request)
+        audio_array = tokenized.audios[0].audio_array if tokenized.audios else audio_item.audio_array
+
+        return {
+            "prompt_token_ids": tokenized.tokens,
+            "multi_modal_data": {"audio": (audio_array, sample_rate)},
+        }
+
     def transcribe(
         self,
         audio_path: Path,
         language: str | None = None,
+        prompt: str | None = None,
+        mode: str = "transcribe",
     ) -> ASRResult:
         from maya_transcribe.audio import decode_pcm16

         sr = int(getattr(self._stt_config, "sample_rate", 16000))
         wav, _ = decode_pcm16(audio_path, sample_rate=sr, mono=True)
@@
         lang = to_whisper_lang(language)
         if lang:
             try:
                 lang = self._model_cls.validate_language(lang)
             except Exception:
                 lang = None

-        prompt = self._model_cls.get_generation_prompt(
-            audio=wav,
-            stt_config=self._stt_config,
-            model_config=self._model_config,
-            language=lang,
-            task_type="transcribe",
-            request_prompt="",
-            to_language=None,
-        )
+        prompt_text = prompt or ""
+        if mode == "proofread":
+            if not self.supports_proofread:
+                raise RuntimeError("Proofread mode requires a Voxtral model.")
+            prompt_payload = self._build_voxtral_chat_prompt(wav, sr, prompt_text)
+        else:
+            prompt_payload = self._model_cls.get_generation_prompt(
+                audio=wav,
+                stt_config=self._stt_config,
+                model_config=self._model_config,
+                language=lang,
+                task_type="transcribe",
+                request_prompt=prompt_text,
+                to_language=None,
+            )

         max_tokens = self._fixed_max_tokens
         if max_tokens is None:
             duration_sec = float(wav.shape[0]) / float(sr) if sr else 0.0
             max_tokens = max(32, int(duration_sec * self._tokens_per_second))
@@
             max_tokens=max_tokens,
             stop_token_ids=self._stop_token_ids,
         )

-        outputs = self._llm.generate([prompt], sampling_params)
+        outputs = self._llm.generate([prompt_payload], sampling_params)
*** End Patch


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


**Preparing pipeline patch with new parameters**

I'm verifying imports and tokenizer usage for consistency, then planning to update the pipeline's run function by adding parameters near its definition around line 30. Next, I'll inspect the top lines to find the precise insertion point.


sed -n '1,120p' maya_transcribe/pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
from __future__ import annotations

import json
import time
from dataclasses import asdict
from pathlib import Path
from typing import Any

from rich.progress import track

from maya_transcribe.asr import (
    ASRResult,
    FasterWhisperASR,
    IndicConformerASRRouter,
    MMSASR,
    TransformersWhisperASR,
    TransformersWhisperASRRouter,
    VllmTranscriptionASR,
)
from maya_transcribe.audio import compute_qc, ffprobe, normalize_audio
from maya_transcribe.ids import segment_identity
from maya_transcribe.language import LANG_CODE_TO_NAME
from maya_transcribe.lid import LIDResult, MMSLID
from maya_transcribe.rover import rover_combine
from maya_transcribe.textnorm import normalize_text
from maya_transcribe.verify import verify_segment

def _iter_audio_files(segments_dir: Path) -> list[Path]:
    audio_files: list[Path] = []
    for ext in ("*.wav", "*.mp3", "*.m4a", "*.flac", "*.ogg", "*.opus"):
        audio_files.extend(sorted(segments_dir.rglob(ext)))
    return sorted(set(audio_files))

def _asr_summary(result: ASRResult) -> dict[str, Any]:
    avg_logprobs = [s.avg_logprob for s in result.segments if s.avg_logprob is not None]
    no_speech_probs = [s.no_speech_prob for s in result.segments if s.no_speech_prob is not None]
    compression_ratios = [s.compression_ratio for s in result.segments if s.compression_ratio is not None]

    def mean(xs: list[float]) -> float | None:
        return (sum(xs) / len(xs)) if xs else None

    return {
        "language": result.language,
        "language_probability": result.language_probability,
        "avg_logprob_mean": mean([float(x) for x in avg_logprobs]),
        "no_speech_prob_mean": mean([float(x) for x in no_speech_probs]),
        "compression_ratio_mean": mean([float(x) for x in compression_ratios]),
    }

def _infer_lang_from_relpath(relpath: str) -> str | None:
    parts = [p.strip().lower() for p in (relpath or "").split("/") if p.strip()]
    for p in parts[:-1]:
        if p in LANG_CODE_TO_NAME:
            return p
    return None

def run_pipeline(
    *,
    segments_dir: Path,
    out_dir: Path,
    lid_model_id: str = "facebook/mms-lid-256",
    whisper_model: str = "large-v3",
    skip_whisper: bool = False,
    vllm_asr_model: str | None = None,
    vllm_gpu_memory_utilization: float | None = None,
    vllm_max_model_len: int | None = None,
    extra_transformers_whisper_models: list[str] | None = None,
    transformers_whisper_model_map: dict[str, str] | None = None,
    transformers_whisper_default_model: str | None = None,
    # MMS ASR for CTC-based diversity (per master.md plan).
    mms_asr_model: str | None = None,
    # IndicConformer for Conformer architecture diversity (per master.md plan).
    indicconformer_model_map: dict[str, str] | None = None,
    indicconformer_default_model: str | None = None,
    gemini_model: str | None = None,
    gemini_prompt_path: Path | None = None,
    gemini_max_output_tokens: int = 512,
    gemini_hard_only: bool = True,
    ctc_model: str | None = None,
    ctc_model_map: dict[str, str] | None = None,
    require_lid_backend: bool = True,
    trim_silence: bool = True,
    silence_threshold_db: float = -40.0,
    min_silence_sec: float = 0.15,
    limit: int | None = None,
    thresholds: dict[str, Any] | None = None,
) -> Path:
    """
    End-to-end run: preprocess -> LID -> ASR ensemble -> normalize -> ROVER -> verify.
    Writes `out_dir/results.jsonl` and returns that path.
    """
    thresholds = thresholds or {}
    if (ctc_model or ctc_model_map) and "require_ctc" not in thresholds:
        thresholds["require_ctc"] = True
    if (ctc_model or ctc_model_map) and "min_ctc_margin" not in thresholds:
        thresholds["min_ctc_margin"] = 0.05
    out_dir.mkdir(parents=True, exist_ok=True)

    audio_files = _iter_audio_files(segments_dir)
    if not audio_files:
        raise RuntimeError(f"No audio files found under: {segments_dir}")
    if limit is not None:
        audio_files = audio_files[: max(0, int(limit))]

    # Instantiate models once.
    lid_backend: MMSLID | None
    try:
        lid_backend = MMSLID(model_id=lid_model_id)
    except Exception as e:
        if require_lid_backend:
            raise RuntimeError(
                "Failed to initialize MMS-LID. Install torch/transformers deps (pip install -e '.[torch]') "
                "or run with require_lid_backend=False."
            ) from e
        lid_backend = None


**Adding proofreading imports and params**

I'm extending the dataclasses import to include replace, adding a default proofreading prompt constant, and updating the pipeline's run_pipeline function signature with proofreading flags. I'll also integrate these new params into the configuration output near the end.


Input:
*** Begin Patch
*** Update File: maya_transcribe/pipeline.py
@@
-import json
-import time
-from dataclasses import asdict
+import json
+import time
+from dataclasses import asdict, replace
@@
 def _infer_lang_from_relpath(relpath: str) -> str | None:
@@
     return None
+
+
+DEFAULT_VLLM_PROOFREAD_PROMPT = (
+    "Correct transcription errors using the audio. Keep the same language/script. "
+    "Do not add new content. Output only the corrected transcript.\n\nTranscript:\n{transcript}"
+)
@@
     vllm_asr_model: str | None = None,
     vllm_gpu_memory_utilization: float | None = None,
     vllm_max_model_len: int | None = None,
+    vllm_proofread: bool = False,
+    vllm_proofread_prompt: str | None = None,
*** End Patch


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


**Modifying transcribe loop to add proofread logic**

I'm preparing to patch the transcribe block for vllm_backend within run_pipeline to conditionally run proofread when enabled and supported, updating asr_results with raw and proofread keys and ensuring primary_asr_key is set only once.


**Adding fallback to raw when proofread fails**

I'm adjusting the logic to catch exceptions separately for proofread calls so that if proofread fails, the raw transcript still gets added, preserving results and errors distinctly.


Input:
*** Begin Patch
*** Update File: maya_transcribe/pipeline.py
@@
                 if vllm_backend is not None:
                     try:
-                        vllm_res = vllm_backend.transcribe(wav_path, language=route_lang)
-                        asr_results[vllm_res.model] = vllm_res
+                        vllm_res = vllm_backend.transcribe(wav_path, language=route_lang)
                     except Exception as e:
                         asr_errors[vllm_backend.name] = f"{type(e).__name__}:{e}"
+                    else:
+                        if vllm_proofread and vllm_backend.supports_proofread:
+                            prompt_tpl = vllm_proofread_prompt or DEFAULT_VLLM_PROOFREAD_PROMPT
+                            proof_prompt = prompt_tpl.format(transcript=vllm_res.text)
+                            try:
+                                proof_res = vllm_backend.transcribe(
+                                    wav_path, language=route_lang, prompt=proof_prompt, mode="proofread"
+                                )
+                                raw_key = f"{vllm_res.model}|raw"
+                                proof_key = f"{vllm_res.model}|proofread"
+                                asr_results[raw_key] = vllm_res
+                                asr_results[proof_key] = replace(proof_res, model=proof_key)
+                                if primary_asr_key is None:
+                                    primary_asr_key = proof_key
+                            except Exception as e:
+                                asr_errors[f"{vllm_backend.name}|proofread"] = f"{type(e).__name__}:{e}"
+                                asr_results[vllm_res.model] = vllm_res
+                        else:
+                            asr_results[vllm_res.model] = vllm_res
*** End Patch


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


**Confirming primary ASR key logic and planning config update**

Primary ASR key is set to the first available if none explicitly assigned, which handles cases with only raw results. Next, I'll locate the config construction to add new fields like `vllm_proofread` and `vllm_proofread_prompt` for proper inclusion in the results.


rg -n "vllm_asr_model" -n maya_transcribe/pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
74:    vllm_asr_model: str | None = None,
185:    if vllm_asr_model:
188:                model_id=vllm_asr_model,
555:                    "vllm_asr_model": vllm_asr_model,


sed -n '520,590p' maya_transcribe/pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                    "asr_summary": asr_summaries,
                    "asr_errors": asr_errors,
                    "ensemble": {
                        "unique_models": ensemble_models,
                        "unique_backends": ensemble_backends,
                        "warnings": ensemble_warnings,
                    },
                    "normalized": norm_hyps,
                    "rover": {
                        "text": rover.text,
                        "skeleton_key": rover.skeleton_key,
                        "disagreement_rate": rover.disagreement_rate,
                        "inputs": sorted(rover_hyps.keys()),
                        "filtered_out": sorted(set(rover_filtered_out)),
                        "disagreements": [
                            {"kind": d.kind, "index": d.index, "chosen": d.chosen, "options": d.options}
                            for d in rover.disagreements
                        ],
                    },
                    "ctc": ctc_info,
                    "final": {"text": final_text, "source": final_source},
                    "verification": {
                        "decision": verification.decision,
                        "reasons": verification.reasons,
                        "scores": verification.scores,
                    },
                    "config": {
                        "trim_silence": trim_silence,
                        "silence_threshold_db": silence_threshold_db,
                        "min_silence_sec": min_silence_sec,
                    "gemini_model": gemini_model,
                    "gemini_hard_only": gemini_hard_only,
                    "ctc_model": ctc_model,
                    "mms_asr_model": mms_asr_model,
                    "indicconformer_default_model": indicconformer_default_model,
                    "vllm_asr_model": vllm_asr_model,
                    "vllm_gpu_memory_utilization": vllm_gpu_memory_utilization,
                    "vllm_max_model_len": vllm_max_model_len,
                    "skip_whisper": skip_whisper,
                },
                    "timing": {
                        "preprocess_sec": t_pre1 - t_pre0,
                        "lid_sec": t_lid1 - t_lid0,
                        "asr_sec": t_asr1 - t_asr0,
                        "normalize_sec": t_norm1 - t_norm0,
                        "rover_sec": t_rover1 - t_rover0,
                        "ctc_sec": t_ctc1 - t_ctc0,
                        "verify_sec": t_ver1 - t_ver0,
                        "total_sec": time.perf_counter() - t0,
                    },
                }

                f.write(json.dumps(record, ensure_ascii=False) + "\n")
                decision = verification.decision
                counts[decision] = counts.get(decision, 0) + 1
                timings_total.append(record["timing"]["total_sec"])

            except Exception as e:  # pragma: no cover
                counts["error"] += 1
                f.write(
                    json.dumps(
                        {"segment_id": segment_id, "src_path": str(src), "error": str(e)}, ensure_ascii=False
                    )
                    + "\n"
                )

    summary = {
        "segments_total": len(audio_files),
        "counts": counts,
        "avg_total_sec": (sum(timings_total) / len(timings_total)) if timings_total else None,
        "p50_total_sec": _percentile(timings_total, 0.50),


**Correcting config indentation in patch**


Input:
*** Begin Patch
*** Update File: maya_transcribe/pipeline.py
@@
                     "config": {
                         "trim_silence": trim_silence,
                         "silence_threshold_db": silence_threshold_db,
                         "min_silence_sec": min_silence_sec,
                     "gemini_model": gemini_model,
                     "gemini_hard_only": gemini_hard_only,
                     "ctc_model": ctc_model,
                     "mms_asr_model": mms_asr_model,
                     "indicconformer_default_model": indicconformer_default_model,
                     "vllm_asr_model": vllm_asr_model,
                     "vllm_gpu_memory_utilization": vllm_gpu_memory_utilization,
                     "vllm_max_model_len": vllm_max_model_len,
+                    "vllm_proofread": vllm_proofread,
+                    "vllm_proofread_prompt": vllm_proofread_prompt,
                     "skip_whisper": skip_whisper,
                 },
*** End Patch


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


Summary: auto


**Checking dict indentation**

The config dictionary likely has inconsistent indentation, especially around the "gemini_model" and "trim_silence" keys, so I'll verify the snippet to confirm proper alignment and structure.


sed -n '540,580p' maya_transcribe/pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                    "final": {"text": final_text, "source": final_source},
                    "verification": {
                        "decision": verification.decision,
                        "reasons": verification.reasons,
                        "scores": verification.scores,
                    },
                    "config": {
                        "trim_silence": trim_silence,
                        "silence_threshold_db": silence_threshold_db,
                        "min_silence_sec": min_silence_sec,
                    "gemini_model": gemini_model,
                    "gemini_hard_only": gemini_hard_only,
                    "ctc_model": ctc_model,
                    "mms_asr_model": mms_asr_model,
                    "indicconformer_default_model": indicconformer_default_model,
                    "vllm_asr_model": vllm_asr_model,
                    "vllm_gpu_memory_utilization": vllm_gpu_memory_utilization,
                    "vllm_max_model_len": vllm_max_model_len,
                    "vllm_proofread": vllm_proofread,
                    "vllm_proofread_prompt": vllm_proofread_prompt,
                    "skip_whisper": skip_whisper,
                },
                    "timing": {
                        "preprocess_sec": t_pre1 - t_pre0,
                        "lid_sec": t_lid1 - t_lid0,
                        "asr_sec": t_asr1 - t_asr0,
                        "normalize_sec": t_norm1 - t_norm0,
                        "rover_sec": t_rover1 - t_rover0,
                        "ctc_sec": t_ctc1 - t_ctc0,
                        "verify_sec": t_ver1 - t_ver0,
                        "total_sec": time.perf_counter() - t0,
                    },
                }

                f.write(json.dumps(record, ensure_ascii=False) + "\n")
                decision = verification.decision
                counts[decision] = counts.get(decision, 0) + 1
                timings_total.append(record["timing"]["total_sec"])

            except Exception as e:  # pragma: no cover
                counts["error"] += 1


**Analyzing inconsistent indentation causing Python errors**

Indentation inconsistency between config dict keys after min_silence_sec may be due to mixing tabs and spaces, causing Python errors despite prior working state; next is to confirm exact whitespace differences using line-specific display.


**Preparing to inspect whitespace explicitly**

Planning to use sed and cat -A to reveal exact whitespace details including tabs and spaces on lines 545–560 for precise indentation analysis.


sed -n '545,565l' maya_transcribe/pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                    },$
                    "config": {$
                        "trim_silence": trim_silence,$
                        "silence_threshold_db": silence_threshold_db,$
                        "min_silence_sec": min_silence_sec,$
                    "gemini_model": gemini_model,$
                    "gemini_hard_only": gemini_hard_only,$
                    "ctc_model": ctc_model,$
                    "mms_asr_model": mms_asr_model,$
                    "indicconformer_default_model": indicconformer_de\
fault_model,$
                    "vllm_asr_model": vllm_asr_model,$
                    "vllm_gpu_memory_utilization": vllm_gpu_memory_ut\
ilization,$
                    "vllm_max_model_len": vllm_max_model_len,$
                    "vllm_proofread": vllm_proofread,$
                    "vllm_proofread_prompt": vllm_proofread_prompt,$
                    "skip_whisper": skip_whisper,$
                },$
                    "timing": {$
                        "preprocess_sec": t_pre1 - t_pre0,$
                        "lid_sec": t_lid1 - t_lid0,$
                        "asr_sec": t_asr1 - t_asr0,$


**Assessing indentation impact**

The misindentation is inside a multiline dictionary expression where Python allows flexible indentation, making it syntactically valid. Fixing it would improve readability but isn't strictly required and could be skipped to avoid unrelated changes.


**Deciding to fix indentation**

I'll adjust the config block's indentation minimally to align keys properly since we're already editing the file, improving readability without unrelated changes.


Input:
*** Begin Patch
*** Update File: maya_transcribe/pipeline.py
@@
                     "config": {
                         "trim_silence": trim_silence,
                         "silence_threshold_db": silence_threshold_db,
                         "min_silence_sec": min_silence_sec,
-                    "gemini_model": gemini_model,
-                    "gemini_hard_only": gemini_hard_only,
-                    "ctc_model": ctc_model,
-                    "mms_asr_model": mms_asr_model,
-                    "indicconformer_default_model": indicconformer_default_model,
-                    "vllm_asr_model": vllm_asr_model,
-                    "vllm_gpu_memory_utilization": vllm_gpu_memory_utilization,
-                    "vllm_max_model_len": vllm_max_model_len,
-                    "vllm_proofread": vllm_proofread,
-                    "vllm_proofread_prompt": vllm_proofread_prompt,
-                    "skip_whisper": skip_whisper,
-                },
+                        "gemini_model": gemini_model,
+                        "gemini_hard_only": gemini_hard_only,
+                        "ctc_model": ctc_model,
+                        "mms_asr_model": mms_asr_model,
+                        "indicconformer_default_model": indicconformer_default_model,
+                        "vllm_asr_model": vllm_asr_model,
+                        "vllm_gpu_memory_utilization": vllm_gpu_memory_utilization,
+                        "vllm_max_model_len": vllm_max_model_len,
+                        "vllm_proofread": vllm_proofread,
+                        "vllm_proofread_prompt": vllm_proofread_prompt,
+                        "skip_whisper": skip_whisper,
+                    },
*** End Patch


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


**Adding CLI options for proofreading**

Planning to add CLI flags for enabling proofreading by updating the `run` command in `maya_transcribe/cli.py` to pass new `vllm_proofread` and prompt parameters through to `run_pipeline`.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
        r2 = R2Client(
            endpoint_url=r2.endpoint_url,
            access_key_id=r2.access_key_id,
            secret_access_key=r2.secret_access_key,
            bucket=bucket,
        )

    out.mkdir(parents=True, exist_ok=True)
    r2_prefix = prefix if prefix is not None else settings.r2_prefix

    matches: list[str]
    if use_db and not r2_key:
        from maya_transcribe.db import fetch_video_meta

        meta = fetch_video_meta(video_id)
        if meta and meta.r2_tar_key:
            r2_key = meta.r2_tar_key

    if r2_key:
        matches = [r2_key]
    else:
        best = r2.find_video_tar_key(video_id=video_id, prefix=r2_prefix)
        if not best:
            typer.echo("No matching objects found in R2.")
            raise typer.Exit(code=2)
        matches = [best]

    if dry_run:
        for key in matches:
            typer.echo(key)
        return

    key = matches[0]
    tar_path = r2.download_to(key=key, out_dir=out / video_id)
    extract_dir = out / video_id
    safe_extract_tar(tar_path, extract_dir=extract_dir)
    segments_dir = find_segments_dir(extract_dir)
    typer.echo(f"Downloaded: {tar_path}")
    typer.echo(f"Extracted to: {extract_dir}")
    if segments_dir:
        typer.echo(f"Segments dir: {segments_dir}")

@app.command()
def preprocess(
    segments_dir: Path = typer.Option(..., exists=True, file_okay=False, help="Directory containing audio segments."),
    out: Path = typer.Option(Path("outputs/preprocessed"), help="Output directory for normalized audio + QC."),
    sample_rate: int = typer.Option(16000, help="Target sample rate."),
    trim_silence: bool = typer.Option(True, help="Trim leading/trailing silence (ffmpeg silenceremove)."),
    silence_threshold_db: float = typer.Option(-40.0, help="Silence threshold in dB for trimming."),
    min_silence_sec: float = typer.Option(0.15, help="Min silence duration for trimming."),
) -> None:
    """
    Normalize audio to 16kHz mono WAV, optionally trim silence, and write QC metrics.
    """
    out.mkdir(parents=True, exist_ok=True)

    audio_files: list[Path] = []
    for ext in ("*.wav", "*.mp3", "*.m4a", "*.flac", "*.ogg", "*.opus"):
        audio_files.extend(sorted(segments_dir.rglob(ext)))
    audio_files = sorted(set(audio_files))

    if not audio_files:
        typer.echo(f"No audio files found under: {segments_dir}")
        raise typer.Exit(code=2)

    rows = []
    norm_dir = out / "audio"
    for src in audio_files:
        ident = segment_identity(src, root=segments_dir)
        segment_id = ident.segment_id
        dst = norm_dir / f"{segment_id}.wav"
        normalize_audio(
            src,
            dst,
            sample_rate=sample_rate,
            mono=True,
            trim_silence=trim_silence,
            silence_threshold_db=silence_threshold_db,
            min_silence_sec=min_silence_sec,
        )
        info = ffprobe(dst)
        qc = compute_qc(dst, sample_rate=sample_rate)
        rows.append(
            {
                "segment_id": segment_id,
                "base_id": ident.base_id,
                "relpath": ident.relpath,
                "uid": ident.uid,
                "src_path": str(src),
                "wav_path": str(dst),
                "ffprobe": {
                    "duration_sec": info.duration_sec,
                    "sample_rate": info.sample_rate,
                    "channels": info.channels,
                },
                "qc": {
                    "duration_sec": qc.duration_sec,
                    "rms_dbfs": qc.rms_dbfs,
                    "peak_dbfs": qc.peak_dbfs,
                    "clipping_fraction": qc.clipping_fraction,


**Locating run command**


rg -n "def run\(" -n maya_transcribe/cli.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
208:def run(


sed -n '200,280p' maya_transcribe/cli.py


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

    prompt = load_prompt_template(prompt_file)
    backend = GeminiASR(config=GeminiConfig(model=model, max_output_tokens=max_output_tokens), prompt_template=prompt)
    res = backend.transcribe(audio_path=audio, language=language)
    typer.echo(res.text)

@app.command()
def run(
    segments_dir: Path = typer.Option(..., exists=True, file_okay=False, help="Directory containing audio segments."),
    out: Path = typer.Option(Path("outputs/run"), help="Output directory."),
    whisper_model: str = typer.Option("large-v3", help="Whisper model size/path for faster-whisper."),
    skip_whisper: bool = typer.Option(False, help="Skip faster-whisper decoding (use only other ASR backends)."),
    vllm_asr_model: str | None = typer.Option(
        None, help="vLLM multimodal ASR model id (e.g., mistralai/Voxtral-Mini-3B-2507)."
    ),
    vllm_gpu_memory_utilization: float | None = typer.Option(
        None, help="vLLM GPU memory utilization fraction (e.g., 0.15 for low free memory)."
    ),
    vllm_max_model_len: int | None = typer.Option(None, help="Override vLLM max_model_len to reduce KV cache size."),
    lid_model_id: str = typer.Option("facebook/mms-lid-256", help="HuggingFace MMS-LID model id."),
    require_lid_backend: bool = typer.Option(True, help="Fail if MMS-LID backend can't initialize."),
    transformers_whisper_model: list[str] = typer.Option(
        [], help="Extra whisper-family HF models to run via transformers (repeatable)."
    ),
    transformers_whisper_model_map: Path | None = typer.Option(
        None, help="JSON map {lang_code: hf_model_id} for per-language Whisper-family HF models (transformers)."
    ),
    # MMS ASR for CTC-based model diversity (per master.md plan).
    mms_asr_model: str | None = typer.Option(
        None, help="Optional MMS ASR model id (e.g., facebook/mms-1b-all) for CTC-based diversity."
    ),
    # IndicConformer for Conformer architecture diversity (per master.md plan).
    indicconformer_model_map: Path | None = typer.Option(
        None, help="JSON map {lang_code: nemo_model_name} for per-language IndicConformer models."
    ),
    indicconformer_default_model: str | None = typer.Option(
        None, help="Default IndicConformer NeMo model name for architecture diversity."
    ),
    gemini_model: str | None = typer.Option(None, help="Optional Gemini model name (adds Gemini as a hypothesis)."),
    gemini_prompt_file: Path | None = typer.Option(Path("Prompt.md"), help="Prompt file for Gemini."),
    gemini_hard_only: bool = typer.Option(True, help="Only call Gemini on 'hard' segments."),
    ctc_model: str | None = typer.Option(None, help="Optional HF CTC model id for audio-grounded fit scoring."),
    ctc_model_map: Path | None = typer.Option(None, help="JSON map {lang_code: hf_model_id} for CTC scoring."),
    require_ctc: bool = typer.Option(True, help="When CTC is enabled, require a CTC score to accept."),
    min_ctc_margin: float = typer.Option(0.05, help="Min (second-best - best) CTC loss margin to auto-accept."),
    trim_silence: bool = typer.Option(True, help="Trim leading/trailing silence (ffmpeg silenceremove)."),
    silence_threshold_db: float = typer.Option(-40.0, help="Silence threshold in dB for trimming."),
    min_silence_sec: float = typer.Option(0.15, help="Min silence duration for trimming."),
    limit: int | None = typer.Option(None, help="Process only the first N segments."),
    thresholds_json: Path | None = typer.Option(None, help="Optional JSON file of threshold overrides (verify + routing)."),
) -> None:
    """
    End-to-end pipeline: preprocess -> LID -> ASR ensemble -> ROVER -> verify.
    """
    from maya_transcribe.pipeline import run_pipeline

    ctc_map = None
    if ctc_model_map is not None:
        ctc_map = json.loads(ctc_model_map.read_text(encoding="utf-8"))
        if not isinstance(ctc_map, dict):
            raise typer.BadParameter("ctc_model_map must be a JSON object {lang_code: model_id}")

    tf_map = None
    if transformers_whisper_model_map is not None:
        tf_map = json.loads(transformers_whisper_model_map.read_text(encoding="utf-8"))
        if not isinstance(tf_map, dict):
            raise typer.BadParameter("transformers_whisper_model_map must be a JSON object {lang_code: model_id}")

    ic_map = None
    if indicconformer_model_map is not None:
        ic_map = json.loads(indicconformer_model_map.read_text(encoding="utf-8"))
        if not isinstance(ic_map, dict):
            raise typer.BadParameter("indicconformer_model_map must be a JSON object {lang_code: nemo_model_name}")

    thresholds: dict[str, object] = {}
    if thresholds_json is not None:
        loaded = json.loads(thresholds_json.read_text(encoding="utf-8"))
        if not isinstance(loaded, dict):
            raise typer.BadParameter("thresholds_json must be a JSON object")
        thresholds = loaded


**Adding proofread options to CLI commands**

I'm preparing to add `vllm_proofread` and `vllm_proofread_prompt` options to the `run` and `bench_run` commands in `cli.py`, using `typer.Option`, then passing these through to `run_pipeline` calls to enable proofread functionality with optional prompt overrides.


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
     vllm_gpu_memory_utilization: float | None = typer.Option(
         None, help="vLLM GPU memory utilization fraction (e.g., 0.15 for low free memory)."
     ),
     vllm_max_model_len: int | None = typer.Option(None, help="Override vLLM max_model_len to reduce KV cache size."),
+    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
+    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
*** End Patch


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


**Locating run_pipeline call for patching**


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
         skip_whisper=skip_whisper,
         vllm_asr_model=vllm_asr_model,
         vllm_gpu_memory_utilization=vllm_gpu_memory_utilization,
         vllm_max_model_len=vllm_max_model_len,
+        vllm_proofread=vllm_proofread,
+        vllm_proofread_prompt=vllm_proofread_prompt,
         extra_transformers_whisper_models=transformers_whisper_model,
*** End Patch


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


**Adding options to bench_run**

I'll locate the bench_run function in cli.py to add new options by patching around existing vllm options.


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
     vllm_asr_model: str | None = typer.Option(
         None, help="vLLM multimodal ASR model id (e.g., mistralai/Voxtral-Mini-3B-2507)."
     ),
     vllm_gpu_memory_utilization: float | None = typer.Option(
         None, help="vLLM GPU memory utilization fraction (e.g., 0.15 for low free memory)."
     ),
     vllm_max_model_len: int | None = typer.Option(None, help="Override vLLM max_model_len to reduce KV cache size."),
+    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
+    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
*** End Patch


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


**Preparing to patch pipeline call**


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
         skip_whisper=skip_whisper,
         vllm_asr_model=vllm_asr_model,
         vllm_gpu_memory_utilization=vllm_gpu_memory_utilization,
         vllm_max_model_len=vllm_max_model_len,
+        vllm_proofread=vllm_proofread,
+        vllm_proofread_prompt=vllm_proofread_prompt,
         extra_transformers_whisper_models=transformers_whisper_model,
*** End Patch


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


**Planning ensemble script and ASR updates**

I'm preparing to update the ASR usage in the pipeline for proofreading to produce extra keys and adjust the summary generation accordingly. Next, I'll implement a one-off ensemble merging script referencing existing combine functions to generate ensemble results for documenting in results.md, delaying adding permanent CLI commands for now.


**Confirming proofread evaluation setup**

I'm verifying that proofread outputs use a distinct key format compatible with evaluation and planning to run the bench with Voxtral proofread including default prompt. Next, I'll run WER evaluation using the exact proofread key formatted as expected by the eval tool, checking code to confirm hypothesis key syntax.


rg -n "def extract_hypothesis_text" -S maya_transcribe/eval.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
218:def extract_hypothesis_text(result_row: dict[str, Any], hypothesis: str) -> str | None:


Summary: auto


sed -n '218,260p' maya_transcribe/eval.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
def extract_hypothesis_text(result_row: dict[str, Any], hypothesis: str) -> str | None:
    """
    hypothesis:
      - "final": uses result_row["final"]["text"] (falls back to rover if missing)
      - "rover" (default): uses result_row["rover"]["text"]
      - "asr:<key>": uses result_row["asr"][<key>]["text"]
    """
    hypothesis = (hypothesis or "rover").strip()
    if hypothesis == "final":
        final = result_row.get("final") or {}
        if isinstance(final, dict) and isinstance(final.get("text"), str):
            return final["text"]
        hypothesis = "rover"
    if hypothesis == "rover":
        rover = result_row.get("rover") or {}
        if isinstance(rover, dict) and isinstance(rover.get("text"), str):
            return rover["text"]
        return None

    if hypothesis.startswith("asr:"):
        key = hypothesis[len("asr:") :].strip()
        asr = result_row.get("asr") or {}
        if isinstance(asr, dict):
            entry = asr.get(key) or {}
            if isinstance(entry, dict) and isinstance(entry.get("text"), str):
                return entry["text"]
        return None

    return None

def extract_all_candidates(result_row: dict[str, Any]) -> dict[str, str]:
    """
    Collect all text candidates available in a pipeline result row.
    Keys are stable and namespaced (final/rover/asr:<key>).
    """
    candidates: dict[str, str] = {}

    final = extract_hypothesis_text(result_row, "final")
    if final is not None:
        candidates["final"] = final

    rover = extract_hypothesis_text(result_row, "rover")


**Preparing to run proofread evaluation**

I'm setting up to run the evaluation with the proofread model and plan to implement the ensemble script afterward, with results updates and compilation checks scheduled after running the proofread.


python3 -m maya_transcribe.cli bench run --bench-dir data/bench/maya_hf_hin_v2 --out outputs/bench/maya_hf_hin_v2/voxtral_small_vllm_proofread --skip-whisper --vllm-asr-model mistralai/Voxtral-Small-24B-2507 --vllm-gpu-memory-utilization 0.9 --vllm-max-model-len 4096 --vllm-proofread


Output:
Exit code: 1
Wall time: 0.1 seconds
Output:
Traceback (most recent call last):
  File "/usr/lib/python3.10/runpy.py", line 187, in _run_module_as_main
    mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
  File "/usr/lib/python3.10/runpy.py", line 157, in _get_module_details
    code = loader.get_code(mod_name)
  File "<frozen importlib._bootstrap_external>", line 1017, in get_code
  File "<frozen importlib._bootstrap_external>", line 947, in source_to_code
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/home/ubuntu/maya-transcribe/maya_transcribe/cli.py", line 222
    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
    ^^^^^^^^^^^^^^^^^^^^
SyntaxError: duplicate argument 'vllm_proofread' in function definition


**Investigating duplicate argument error**

There's a syntax error from a duplicated argument in the function definition around line 222; I'll check nearby lines to confirm if the argument was accidentally added twice to the same function.


sed -n '210,250p' maya_transcribe/cli.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    out: Path = typer.Option(Path("outputs/run"), help="Output directory."),
    whisper_model: str = typer.Option("large-v3", help="Whisper model size/path for faster-whisper."),
    skip_whisper: bool = typer.Option(False, help="Skip faster-whisper decoding (use only other ASR backends)."),
    vllm_asr_model: str | None = typer.Option(
        None, help="vLLM multimodal ASR model id (e.g., mistralai/Voxtral-Mini-3B-2507)."
    ),
    vllm_gpu_memory_utilization: float | None = typer.Option(
        None, help="vLLM GPU memory utilization fraction (e.g., 0.15 for low free memory)."
    ),
    vllm_max_model_len: int | None = typer.Option(None, help="Override vLLM max_model_len to reduce KV cache size."),
    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
    lid_model_id: str = typer.Option("facebook/mms-lid-256", help="HuggingFace MMS-LID model id."),
    require_lid_backend: bool = typer.Option(True, help="Fail if MMS-LID backend can't initialize."),
    transformers_whisper_model: list[str] = typer.Option(
        [], help="Extra whisper-family HF models to run via transformers (repeatable)."
    ),
    transformers_whisper_model_map: Path | None = typer.Option(
        None, help="JSON map {lang_code: hf_model_id} for per-language Whisper-family HF models (transformers)."
    ),
    # MMS ASR for CTC-based model diversity (per master.md plan).
    mms_asr_model: str | None = typer.Option(
        None, help="Optional MMS ASR model id (e.g., facebook/mms-1b-all) for CTC-based diversity."
    ),
    # IndicConformer for Conformer architecture diversity (per master.md plan).
    indicconformer_model_map: Path | None = typer.Option(
        None, help="JSON map {lang_code: nemo_model_name} for per-language IndicConformer models."
    ),
    indicconformer_default_model: str | None = typer.Option(
        None, help="Default IndicConformer NeMo model name for architecture diversity."
    ),
    gemini_model: str | None = typer.Option(None, help="Optional Gemini model name (adds Gemini as a hypothesis)."),
    gemini_prompt_file: Path | None = typer.Option(Path("Prompt.md"), help="Prompt file for Gemini."),
    gemini_hard_only: bool = typer.Option(True, help="Only call Gemini on 'hard' segments."),
    ctc_model: str | None = typer.Option(None, help="Optional HF CTC model id for audio-grounded fit scoring."),
    ctc_model_map: Path | None = typer.Option(None, help="JSON map {lang_code: hf_model_id} for CTC scoring."),
    require_ctc: bool = typer.Option(True, help="When CTC is enabled, require a CTC score to accept."),
    min_ctc_margin: float = typer.Option(0.05, help="Min (second-best - best) CTC loss margin to auto-accept."),
    trim_silence: bool = typer.Option(True, help="Trim leading/trailing silence (ffmpeg silenceremove)."),


**Removing duplicate proofread lines**

I'm patching the run command to remove the duplicated vllm_proofread lines, keeping just one pair to clean up the code.


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
-    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
-    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
-    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
-    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
+    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
+    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
*** End Patch


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


**Inspecting bench_run signature duplicates**


sed -n '380,440p' maya_transcribe/cli.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    skip_whisper: bool = typer.Option(False, help="Skip faster-whisper decoding (use only other ASR backends)."),
    vllm_asr_model: str | None = typer.Option(
        None, help="vLLM multimodal ASR model id (e.g., mistralai/Voxtral-Mini-3B-2507)."
    ),
    vllm_gpu_memory_utilization: float | None = typer.Option(
        None, help="vLLM GPU memory utilization fraction (e.g., 0.15 for low free memory)."
    ),
    vllm_max_model_len: int | None = typer.Option(None, help="Override vLLM max_model_len to reduce KV cache size."),
    lid_model_id: str = typer.Option("facebook/mms-lid-256", help="HuggingFace MMS-LID model id."),
    require_lid_backend: bool = typer.Option(True, help="Fail if MMS-LID backend can't initialize."),
    transformers_whisper_model: list[str] = typer.Option(
        [], help="Extra whisper-family HF models to run via transformers (repeatable)."
    ),
    transformers_whisper_model_map: Path | None = typer.Option(
        None, help="JSON map {lang_code: hf_model_id} for per-language Whisper-family HF models (transformers)."
    ),
    # MMS ASR for CTC-based model diversity (per master.md plan).
    mms_asr_model: str | None = typer.Option(
        None, help="Optional MMS ASR model id (e.g., facebook/mms-1b-all) for CTC-based diversity."
    ),
    # IndicConformer for Conformer architecture diversity (per master.md plan).
    indicconformer_model_map: Path | None = typer.Option(
        None, help="JSON map {lang_code: nemo_model_name} for per-language IndicConformer models."
    ),
    indicconformer_default_model: str | None = typer.Option(
        None, help="Default IndicConformer NeMo model name for architecture diversity."
    ),
    ctc_model: str | None = typer.Option(None, help="Optional HF CTC model id for audio-grounded fit scoring."),
    ctc_model_map: Path | None = typer.Option(None, help="JSON map {lang_code: hf_model_id} for CTC scoring."),
    require_ctc: bool = typer.Option(True, help="When CTC is enabled, require a CTC score to accept."),
    min_ctc_margin: float = typer.Option(0.05, help="Min (second-best - best) CTC loss margin to auto-accept."),
    trim_silence: bool = typer.Option(
        False, help="Trim leading/trailing silence (ffmpeg silenceremove). Off by default for benchmarking."
    ),
    silence_threshold_db: float = typer.Option(-40.0, help="Silence threshold in dB for trimming."),
    min_silence_sec: float = typer.Option(0.15, help="Min silence duration for trimming."),
    limit: int | None = typer.Option(None, help="Process only the first N segments."),
    thresholds_json: Path | None = typer.Option(None, help="Optional JSON file of threshold overrides (verify + routing)."),
    hypothesis: str = typer.Option("final", help='Hypothesis to evaluate: "final", "rover", or "asr:<key>".'),
) -> None:
    """
    Run the full pipeline on a local benchmark corpus and write eval.json.
    """
    from datetime import datetime

    from maya_transcribe.eval import evaluate
    from maya_transcribe.pipeline import run_pipeline

    audio_dir = bench_dir / "audio"
    refs_path = bench_dir / "refs.jsonl"
    if not audio_dir.exists():
        raise typer.BadParameter(f"Missing: {audio_dir}")
    if not refs_path.exists():
        raise typer.BadParameter(f"Missing: {refs_path}")

    if out is None:
        ts = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
        out = Path("outputs/bench") / bench_dir.name / f"run_{ts}"

    ctc_map = None
    if ctc_model_map is not None:


**Adding missing vllm_proofread option**

The vllm_proofread option was missing from bench_run, likely due to an earlier patch not applying; I'll add it after vllm_max_model_len to fix this.


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
     vllm_gpu_memory_utilization: float | None = typer.Option(
         None, help="vLLM GPU memory utilization fraction (e.g., 0.15 for low free memory)."
     ),
     vllm_max_model_len: int | None = typer.Option(None, help="Override vLLM max_model_len to reduce KV cache size."),
+    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
+    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
*** End Patch


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


**Verifying parameter passing in pipeline call**

I'm confirming the run_pipeline call in bench_run includes vllm_proofread without duplicates and verifying its placement relative to vllm_max_model_len.


rg -n "vllm_proofread" -n maya_transcribe/cli.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
220:    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
221:    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
222:    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
223:    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
299:        vllm_proofread=vllm_proofread,
300:        vllm_proofread_prompt=vllm_proofread_prompt,
479:        vllm_proofread=vllm_proofread,
480:        vllm_proofread_prompt=vllm_proofread_prompt,


**Reevaluating duplicate removal**

Duplicates still appear around line 220 in the run function, suggesting the previous patch didn't completely remove them; I'll inspect lines 214-230 to pinpoint leftover duplicates.


sed -n '214,232p' maya_transcribe/cli.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
        None, help="vLLM multimodal ASR model id (e.g., mistralai/Voxtral-Mini-3B-2507)."
    ),
    vllm_gpu_memory_utilization: float | None = typer.Option(
        None, help="vLLM GPU memory utilization fraction (e.g., 0.15 for low free memory)."
    ),
    vllm_max_model_len: int | None = typer.Option(None, help="Override vLLM max_model_len to reduce KV cache size."),
    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
    lid_model_id: str = typer.Option("facebook/mms-lid-256", help="HuggingFace MMS-LID model id."),
    require_lid_backend: bool = typer.Option(True, help="Fail if MMS-LID backend can't initialize."),
    transformers_whisper_model: list[str] = typer.Option(
        [], help="Extra whisper-family HF models to run via transformers (repeatable)."
    ),
    transformers_whisper_model_map: Path | None = typer.Option(
        None, help="JSON map {lang_code: hf_model_id} for per-language Whisper-family HF models (transformers)."
    ),
    # MMS ASR for CTC-based model diversity (per master.md plan).


**Removing duplicate command pairs**

Duplicates persist in the run command with two pairs present; I'll patch the code to keep only one pair and remove the redundant duplicates.


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
-    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
-    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
-    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
-    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
+    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
+    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
*** End Patch


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


**Reassessing patch effect on duplicates**

The patch removed four lines and replaced them with two, so duplicates should be eliminated; I'll re-check the snippet to confirm this.


sed -n '214,228p' maya_transcribe/cli.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
        None, help="vLLM multimodal ASR model id (e.g., mistralai/Voxtral-Mini-3B-2507)."
    ),
    vllm_gpu_memory_utilization: float | None = typer.Option(
        None, help="vLLM GPU memory utilization fraction (e.g., 0.15 for low free memory)."
    ),
    vllm_max_model_len: int | None = typer.Option(None, help="Override vLLM max_model_len to reduce KV cache size."),
    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
    lid_model_id: str = typer.Option("facebook/mms-lid-256", help="HuggingFace MMS-LID model id."),
    require_lid_backend: bool = typer.Option(True, help="Fail if MMS-LID backend can't initialize."),
    transformers_whisper_model: list[str] = typer.Option(
        [], help="Extra whisper-family HF models to run via transformers (repeatable)."
    ),
    transformers_whisper_model_map: Path | None = typer.Option(
        None, help="JSON map {lang_code: hf_model_id} for per-language Whisper-family HF models (transformers)."


Summary: auto


**Verifying duplicate removal**

I'm rerunning the search for duplicate pairs to confirm only one remains in each of run and bench-run.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
220:    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
221:    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
297:        vllm_proofread=vllm_proofread,
298:        vllm_proofread_prompt=vllm_proofread_prompt,
477:        vllm_proofread=vllm_proofread,
478:        vllm_proofread_prompt=vllm_proofread_prompt,


**Verifying bench-run signature insertion**

I'm checking whether the earlier insertion of the new item into the bench-run signature actually took effect, as ripgrep only shows one occurrence and none near the expected line, suggesting the patch might not have applied or was removed.


sed -n '372,410p' maya_transcribe/cli.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    typer.echo(json.dumps({"wrote": str(out), "total_rows": manifest.get("total_rows"), "counts": manifest.get("counts")}, indent=2))

@bench_app.command("run")
def bench_run(
    bench_dir: Path = typer.Option(Path("data/bench/maya_hf_v0"), exists=True, file_okay=False, help="Benchmark corpus directory."),
    out: Path | None = typer.Option(None, help="Output directory (defaults under outputs/bench/)."),
    whisper_model: str = typer.Option("large-v3", help="Whisper model size/path for faster-whisper."),
    skip_whisper: bool = typer.Option(False, help="Skip faster-whisper decoding (use only other ASR backends)."),
    vllm_asr_model: str | None = typer.Option(
        None, help="vLLM multimodal ASR model id (e.g., mistralai/Voxtral-Mini-3B-2507)."
    ),
    vllm_gpu_memory_utilization: float | None = typer.Option(
        None, help="vLLM GPU memory utilization fraction (e.g., 0.15 for low free memory)."
    ),
    vllm_max_model_len: int | None = typer.Option(None, help="Override vLLM max_model_len to reduce KV cache size."),
    lid_model_id: str = typer.Option("facebook/mms-lid-256", help="HuggingFace MMS-LID model id."),
    require_lid_backend: bool = typer.Option(True, help="Fail if MMS-LID backend can't initialize."),
    transformers_whisper_model: list[str] = typer.Option(
        [], help="Extra whisper-family HF models to run via transformers (repeatable)."
    ),
    transformers_whisper_model_map: Path | None = typer.Option(
        None, help="JSON map {lang_code: hf_model_id} for per-language Whisper-family HF models (transformers)."
    ),
    # MMS ASR for CTC-based model diversity (per master.md plan).
    mms_asr_model: str | None = typer.Option(
        None, help="Optional MMS ASR model id (e.g., facebook/mms-1b-all) for CTC-based diversity."
    ),
    # IndicConformer for Conformer architecture diversity (per master.md plan).
    indicconformer_model_map: Path | None = typer.Option(
        None, help="JSON map {lang_code: nemo_model_name} for per-language IndicConformer models."
    ),
    indicconformer_default_model: str | None = typer.Option(
        None, help="Default IndicConformer NeMo model name for architecture diversity."
    ),
    ctc_model: str | None = typer.Option(None, help="Optional HF CTC model id for audio-grounded fit scoring."),
    ctc_model_map: Path | None = typer.Option(None, help="JSON map {lang_code: hf_model_id} for CTC scoring."),
    require_ctc: bool = typer.Option(True, help="When CTC is enabled, require a CTC score to accept."),
    min_ctc_margin: float = typer.Option(0.05, help="Min (second-best - best) CTC loss margin to auto-accept."),


**Retrying patch application with context**


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
     vllm_gpu_memory_utilization: float | None = typer.Option(
         None, help="vLLM GPU memory utilization fraction (e.g., 0.15 for low free memory)."
     ),
     vllm_max_model_len: int | None = typer.Option(None, help="Override vLLM max_model_len to reduce KV cache size."),
+    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
+    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
     lid_model_id: str = typer.Option("facebook/mms-lid-256", help="HuggingFace MMS-LID model id."),
*** End Patch


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


**Verifying presence of target code**


Output:
Exit code: 0
Wall time: 0 seconds
Output:
220:    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
221:    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
297:        vllm_proofread=vllm_proofread,
298:        vllm_proofread_prompt=vllm_proofread_prompt,
388:    vllm_proofread: bool = typer.Option(False, help="Run vLLM proofread pass when supported (audio + transcript)."),
389:    vllm_proofread_prompt: str | None = typer.Option(None, help="Override the proofread prompt template."),
479:        vllm_proofread=vllm_proofread,
480:        vllm_proofread_prompt=vllm_proofread_prompt,


**Verifying transcription method updates**

I'm confirming the new parameters in the transcription method and searching for all direct calls, especially outside the pipeline, to update them accordingly and avoid signature mismatches.


rg -n "VllmTranscriptionASR" -S maya_transcribe


Output:
Exit code: 0
Wall time: 0 seconds
Output:
maya_transcribe/asr.py:354:class VllmTranscriptionASR:
maya_transcribe/pipeline.py:18:    VllmTranscriptionASR,
maya_transcribe/pipeline.py:184:    vllm_backend: VllmTranscriptionASR | None = None
maya_transcribe/pipeline.py:187:            vllm_backend = VllmTranscriptionASR(


**Updating CLI and preparing rerun**

I've updated the CLI run command to pass the new options and adjusted the type hint for the prompt in asr.py. Next, I'll rerun the benchmark for proofreading and evaluation before updating results.md.