Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
errors_sample_count 5
{'segment_id': 'Kathbath__bengali__valid__1433__844424932436436-1174-m.m4a-5747f41e0ce6e0c8', 'src_path': 'data/bench/maya_hf_v2/audio/ben/Kathbath__bengali__valid__1433__844424932436436-1174-m.m4a.wav', 'error': "'or' is not a valid language code (accepted language codes: af, am, ar, as, az, ba, be, bg, bn, bo, br, bs, ca, cs, cy, da, de, el, en, es, et, eu, fa, fi, fo, fr, gl, gu, ha, haw, he, hi, hr, ht, hu, hy, id, is, it, ja, jw, ka, kk, km, kn, ko, la, lb, ln, lo, lt, lv, mg, mi, mk, ml, mn, mr, ms, mt, my, ne, nl, nn, no, oc, pa, pl, ps, pt, ro, ru, sa, sd, si, sk, sl, sn, so, sq, sr, su, sv, sw, ta, te, tg, th, tk, tl, tr, tt, uk, ur, uz, vi, yi, yo, zh, yue)"}
{'segment_id': 'Kathbath__bengali__valid__1628__844424931280735-278-m.m4a-c1d4be38037dff38', 'src_path': 'data/bench/maya_hf_v2/audio/ben/Kathbath__bengali__valid__1628__844424931280735-278-m.m4a.wav', 'error': "'or' is not a valid language code (accepted language codes: af, am, ar, as, az, ba, be, bg, bn, bo, br, bs, ca, cs, cy, da, de, el, en, es, et, eu, fa, fi, fo, fr, gl, gu, ha, haw, he, hi, hr, ht, hu, hy, id, is, it, ja, jw, ka, kk, km, kn, ko, la, lb, ln, lo, lt, lv, mg, mi, mk, ml, mn, mr, ms, mt, my, ne, nl, nn, no, oc, pa, pl, ps, pt, ro, ru, sa, sd, si, sk, sl, sn, so, sq, sr, su, sv, sw, ta, te, tg, th, tk, tl, tr, tt, uk, ur, uz, vi, yi, yo, zh, yue)"}
{'segment_id': 'Kathbath__bengali__valid__1767__844424932350287-1174-m.m4a-d06d6022d8156b37', 'src_path': 'data/bench/maya_hf_v2/audio/ben/Kathbath__bengali__valid__1767__844424932350287-1174-m.m4a.wav', 'error': "'or' is not a valid language code (accepted language codes: af, am, ar, as, az, ba, be, bg, bn, bo, br, bs, ca, cs, cy, da, de, el, en, es, et, eu, fa, fi, fo, fr, gl, gu, ha, haw, he, hi, hr, ht, hu, hy, id, is, it, ja, jw, ka, kk, km, kn, ko, la, lb, ln, lo, lt, lv, mg, mi, mk, ml, mn, mr, ms, mt, my, ne, nl, nn, no, oc, pa, pl, ps, pt, ro, ru, sa, sd, si, sk, sl, sn, so, sq, sr, su, sv, sw, ta, te, tg, th, tk, tl, tr, tt, uk, ur, uz, vi, yi, yo, zh, yue)"}
{'segment_id': 'Kathbath__bengali__valid__2172__844424931098526-99-m.m4a-d6c0e7b3e93239dd', 'src_path': 'data/bench/maya_hf_v2/audio/ben/Kathbath__bengali__valid__2172__844424931098526-99-m.m4a.wav', 'error': "'or' is not a valid language code (accepted language codes: af, am, ar, as, az, ba, be, bg, bn, bo, br, bs, ca, cs, cy, da, de, el, en, es, et, eu, fa, fi, fo, fr, gl, gu, ha, haw, he, hi, hr, ht, hu, hy, id, is, it, ja, jw, ka, kk, km, kn, ko, la, lb, ln, lo, lt, lv, mg, mi, mk, ml, mn, mr, ms, mt, my, ne, nl, nn, no, oc, pa, pl, ps, pt, ro, ru, sa, sd, si, sk, sl, sn, so, sq, sr, su, sv, sw, ta, te, tg, th, tk, tl, tr, tt, uk, ur, uz, vi, yi, yo, zh, yue)"}
{'segment_id': 'Kathbath__odia__valid__1002__844424930473235-692-f.m4a-4c66d6d85750d3da', 'src_path': 'data/bench/maya_hf_v2/audio/ory/Kathbath__odia__valid__1002__844424930473235-692-f.m4a.wav', 'error': "'or' is not a valid language code (accepted language codes: af, am, ar, as, az, ba, be, bg, bn, bo, br, bs, ca, cs, cy, da, de, el, en, es, et, eu, fa, fi, fo, fr, gl, gu, ha, haw, he, hi, hr, ht, hu, hy, id, is, it, ja, jw, ka, kk, km, kn, ko, la, lb, ln, lo, lt, lv, mg, mi, mk, ml, mn, mr, ms, mt, my, ne, nl, nn, no, oc, pa, pl, ps, pt, ro, ru, sa, sd, si, sk, sl, sn, so, sq, sr, su, sv, sw, ta, te, tg, th, tk, tl, tr, tt, uk, ur, uz, vi, yi, yo, zh, yue)"}


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.


**Clarifying language codes**

I realize that MMS-LID actually returns ISO 639-3 codes, which are three letters long, like 'ory', instead of the two-letter codes like 'or'. So, that means the route_lang from LID can't be 'or'. It seems `_infer_lang_from_relpath` might use folder names in the dataset path to determine the language. For instance, in the path `data/bench/maya_hf_v2/audio/ory/...`, it probably infers 'ory', not 'or'. I need to check `_infer_lang_from_relpath` in pipeline.py to confirm this.


set -euo pipefail
rg -n "def _infer_lang_from_relpath" -n maya_transcribe/pipeline.py
nl -ba maya_transcribe/pipeline.py | sed -n '1,120p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
45:def _infer_lang_from_relpath(relpath: str) -> str | None:
     1	from __future__ import annotations
     2
     3	import json
     4	import time
     5	from dataclasses import asdict
     6	from pathlib import Path
     7	from typing import Any
     8
     9	from rich.progress import track
    10
    11	from maya_transcribe.asr import ASRResult, FasterWhisperASR, TransformersWhisperASR, TransformersWhisperASRRouter
    12	from maya_transcribe.audio import compute_qc, ffprobe, normalize_audio
    13	from maya_transcribe.ids import segment_identity
    14	from maya_transcribe.language import LANG_CODE_TO_NAME
    15	from maya_transcribe.lid import LIDResult, MMSLID
    16	from maya_transcribe.rover import rover_combine
    17	from maya_transcribe.textnorm import normalize_text
    18	from maya_transcribe.verify import verify_segment
    19
    20
    21	def _iter_audio_files(segments_dir: Path) -> list[Path]:
    22	    audio_files: list[Path] = []
    23	    for ext in ("*.wav", "*.mp3", "*.m4a", "*.flac", "*.ogg", "*.opus"):
    24	        audio_files.extend(sorted(segments_dir.rglob(ext)))
    25	    return sorted(set(audio_files))
    26
    27
    28	def _asr_summary(result: ASRResult) -> dict[str, Any]:
    29	    avg_logprobs = [s.avg_logprob for s in result.segments if s.avg_logprob is not None]
    30	    no_speech_probs = [s.no_speech_prob for s in result.segments if s.no_speech_prob is not None]
    31	    compression_ratios = [s.compression_ratio for s in result.segments if s.compression_ratio is not None]
    32
    33	    def mean(xs: list[float]) -> float | None:
    34	        return (sum(xs) / len(xs)) if xs else None
    35
    36	    return {
    37	        "language": result.language,
    38	        "language_probability": result.language_probability,
    39	        "avg_logprob_mean": mean([float(x) for x in avg_logprobs]),
    40	        "no_speech_prob_mean": mean([float(x) for x in no_speech_probs]),
    41	        "compression_ratio_mean": mean([float(x) for x in compression_ratios]),
    42	    }
    43
    44
    45	def _infer_lang_from_relpath(relpath: str) -> str | None:
    46	    parts = [p.strip().lower() for p in (relpath or "").split("/") if p.strip()]
    47	    for p in parts[:-1]:
    48	        if p in LANG_CODE_TO_NAME:
    49	            return p
    50	    return None
    51
    52
    53	def run_pipeline(
    54	    *,
    55	    segments_dir: Path,
    56	    out_dir: Path,
    57	    lid_model_id: str = "facebook/mms-lid-256",
    58	    whisper_model: str = "large-v3",
    59	    extra_transformers_whisper_models: list[str] | None = None,
    60	    transformers_whisper_model_map: dict[str, str] | None = None,
    61	    transformers_whisper_default_model: str | None = None,
    62	    gemini_model: str | None = None,
    63	    gemini_prompt_path: Path | None = None,
    64	    gemini_max_output_tokens: int = 512,
    65	    gemini_hard_only: bool = True,
    66	    ctc_model: str | None = None,
    67	    ctc_model_map: dict[str, str] | None = None,
    68	    require_lid_backend: bool = True,
    69	    trim_silence: bool = True,
    70	    silence_threshold_db: float = -40.0,
    71	    min_silence_sec: float = 0.15,
    72	    limit: int | None = None,
    73	    thresholds: dict[str, Any] | None = None,
    74	) -> Path:
    75	    """
    76	    End-to-end run: preprocess -> LID -> ASR ensemble -> normalize -> ROVER -> verify.
    77	    Writes `out_dir/results.jsonl` and returns that path.
    78	    """
    79	    thresholds = thresholds or {}
    80	    if (ctc_model or ctc_model_map) and "require_ctc" not in thresholds:
    81	        thresholds["require_ctc"] = True
    82	    if (ctc_model or ctc_model_map) and "min_ctc_margin" not in thresholds:
    83	        thresholds["min_ctc_margin"] = 0.05
    84	    out_dir.mkdir(parents=True, exist_ok=True)
    85
    86	    audio_files = _iter_audio_files(segments_dir)
    87	    if not audio_files:
    88	        raise RuntimeError(f"No audio files found under: {segments_dir}")
    89	    if limit is not None:
    90	        audio_files = audio_files[: max(0, int(limit))]
    91
    92	    # Instantiate models once.
    93	    lid_backend: MMSLID | None
    94	    try:
    95	        lid_backend = MMSLID(model_id=lid_model_id)
    96	    except Exception as e:
    97	        if require_lid_backend:
    98	            raise RuntimeError(
    99	                "Failed to initialize MMS-LID. Install torch/transformers deps (pip install -e '.[torch]') "
   100	                "or run with require_lid_backend=False."
   101	            ) from e
   102	        lid_backend = None
   103
   104	    whisper_backend = FasterWhisperASR(model_size_or_path=whisper_model)
   105
   106	    extra_transformers_whisper_models = extra_transformers_whisper_models or []
   107	    transformers_backends: list[TransformersWhisperASR] = []
   108	    for model_id in extra_transformers_whisper_models:
   109	        transformers_backends.append(TransformersWhisperASR(model_id=model_id))
   110
   111	    transformers_router = None
   112	    if transformers_whisper_model_map:
   113	        transformers_router = TransformersWhisperASRRouter(
   114	            model_map=transformers_whisper_model_map, default_model=transformers_whisper_default_model
   115	        )
   116
   117	    ctc_scorer = None
   118	    rank_by_ctc = None
   119	    if ctc_model or ctc_model_map:
   120	        from maya_transcribe.ctc_fit import CTCFitScorer, rank_candidates_by_ctc


Summary: auto


**Investigating language mapping errors**

It looks like there’s an issue with route_lang being set as 'ory', but to_whisper_lang returns None since there’s no mapping for that. Previously, it might have passed an incorrect mapping like 'ori', which could cause a crash. I’m wondering if the input language was 'or' instead of 'ory', since 'ory' is for Odia while 'ori' is sometimes used incorrectly. It seems the error message indicates that the segment is Bengali, suggesting incorrect predictions might be at play here.


**Clarifying language code predictions**

I’m thinking about why route_lang might be set to 'or' during different runs. It seems like for Bengali segments the path should contain 'ben', which makes 'or' seem unlikely. But could the LID system have predicted 'or'? It's weird since LID usually deals with ISO3 codes like 'ory'. The errors from the whisper results show that 'or' isn't valid. I need to understand how route_lang gets set in pipeline.py. I'll check the MMS-LID output codes next to confirm this.


**Examining LID language codes**

I've determined that MMS-LID outputs ISO3 codes, so for Odia it should be 'ory' and not 'or'. This means route_lang from LID can't be 'or' unless there's a conversion happening somewhere else. However, they seem to set route_lang directly from top1.code, which should be ISO3. If LID outputs 'ori', then I should check that against the dataset languages. It appears that if top1.code is 'ori', to_whisper_lang processes it and returns None, not 'or', unless someone mistakenly passed a 2-letter code.


**Analyzing route_lang determination**

It seems that route_lang could be set based on the path, which uses 2-letter codes like 'hi' or 'bn'. LANG_CODE_TO_NAME includes 'or', but I don't see 'hi' in the dataset paths, since they use ISO3 directories like 'hin'. For downloaded YouTube segments, the base directories might not really contain 'or' either. I wonder if route_lang is influenced by the CLI 'run' command with a `--language` parameter. The error lines show 'route' contains 'language_hint_source', which indicates they're using path inference for route_lang, especially when LID isn’t confident.


**Exploring route_lang mapping**

In the indic_whisper run, I’m considering if they set `route_lang_from_path` to true, which would mean using path inference when LID isn't confident. For Bengali, that should set route_lang to 'ben', and for Odia to 'ory'. But the error shows 'or' is invalid, raising questions about whether path inference returned 'or'. I’m curious if there's a directory or filename containing 'or'.

However, it looks like the dataset uses ISO3 'ory', not 'or'. I wonder if route_lang could stem from the LANG_CODE_TO_NAME mapping instead, which includes 'or': 'Odia'. Although LID predictions typically show ISO3 codes, maybe MMLS-LID uses BCP47 codes, which could complicate things. I guess I should check the `maya_transcribe/lid.py` for further clarification on the mappings!


set -euo pipefail
nl -ba maya_transcribe/lid.py | sed -n '1,220p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
     1	from __future__ import annotations
     2
     3	from dataclasses import dataclass
     4	from pathlib import Path
     5
     6	import numpy as np
     7
     8	from maya_transcribe.audio import decode_pcm16
     9
    10
    11	@dataclass(frozen=True)
    12	class LanguagePrediction:
    13	    code: str
    14	    prob: float
    15
    16
    17	@dataclass(frozen=True)
    18	class LIDResult:
    19	    model: str
    20	    predictions: list[LanguagePrediction]
    21
    22
    23	class MMSLID:
    24	    def __init__(self, model_id: str = "facebook/mms-lid-256", device: str | None = None) -> None:
    25	        try:
    26	            import torch
    27	            from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
    28	        except Exception as e:  # pragma: no cover
    29	            raise RuntimeError(
    30	                "MMS-LID requires the optional dependencies: torch + transformers. "
    31	                "Install with: pip install -e '.[torch]'"
    32	            ) from e
    33
    34	        if device is None:
    35	            device = "cuda" if torch.cuda.is_available() else "cpu"
    36
    37	        self.model_id = model_id
    38	        self.device = device
    39	        self._torch = torch
    40	        self._processor = AutoFeatureExtractor.from_pretrained(model_id)
    41	        self._model = AutoModelForAudioClassification.from_pretrained(model_id).to(device)
    42	        self._model.eval()
    43
    44	    @property
    45	    def name(self) -> str:
    46	        return self.model_id
    47
    48	    def predict(self, audio_path: Path, top_k: int = 5) -> LIDResult:
    49	        wav, sr = decode_pcm16(audio_path, sample_rate=16000, mono=True)
    50	        if wav.size == 0:
    51	            return LIDResult(model=self.model_id, predictions=[])
    52
    53	        inputs = self._processor(wav, sampling_rate=sr, return_tensors="pt")
    54	        inputs = {k: v.to(self.device) for k, v in inputs.items()}
    55
    56	        with self._torch.no_grad():
    57	            logits = self._model(**inputs).logits
    58	            probs = self._torch.softmax(logits, dim=-1)[0].detach().cpu().numpy().astype(np.float32)
    59
    60	        top_idx = np.argsort(probs)[::-1][: max(top_k, 1)]
    61	        id2label = getattr(self._model.config, "id2label", {}) or {}
    62
    63	        preds: list[LanguagePrediction] = []
    64	        for idx in top_idx:
    65	            label = id2label.get(int(idx), str(int(idx)))
    66	            preds.append(LanguagePrediction(code=str(label), prob=float(probs[int(idx)])))
    67
    68	        return LIDResult(model=self.model_id, predictions=preds)


set -euo pipefail
python3 - <<'PY'
from transformers import AutoModelForAudioClassification
m=AutoModelForAudioClassification.from_pretrained('facebook/mms-lid-256')
labels=set(m.config.id2label.values())
print('num_labels', len(labels))
print('has_or', 'or' in labels)
print('has_ory', 'ory' in labels)
print('has_ori', 'ori' in labels)
# print any 2-letter codes
short=[l for l in labels if isinstance(l,str) and len(l)==2]
print('num_2letter', len(short))
print('sample_2letter', sorted(short)[:20])
PY


Output:
Exit code: 0
Wall time: 8.6 seconds
Output:
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
2026-01-14 00:04:42.105872: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/ubuntu/.local/lib/python3.10/site-packages/google/api_core/_python_version_support.py:266: FutureWarning: You are using a Python version (3.10.12) which Google will stop supporting in new releases of google.api_core once it reaches its end of life (2026-10-04). Please upgrade to the latest Python version, or at least Python 3.11, to continue receiving updates for google.api_core past that date.
  warnings.warn(message, FutureWarning)
num_labels 256
has_or False
has_ory True
has_ori False
num_2letter 0
sample_2letter []


set -euo pipefail
ls -1 data/bench/maya_hf_v2/audio | head -n 50


Output:
Exit code: 0
Wall time: 0 seconds
Output:
asm
ben
eng
guj
hin
kan
mal
mar
ory
pan
tam
tel


**Analyzing language processing errors**

I’m digging into the `maya-transcribe` command's operation and the implications of working in an editable setup. The outputs include useful features like `route_lang_source` from `pipeline.py`, raising questions about whether these changes reflect properly. I wonder if 'or' errors stem from `language.py`, particularly that `to_whisper_lang` function, which handles certain language codes. Could there be issues related to whitespace or case sensitivity? It’s fascinating to explore how file names might impact this too!


**Investigating error sources**

I'm pondering that any errors might partly arise from outdated file names, suggesting that the `route_lang` was used incorrectly as a 2-letter code like 'or'. It seems our current setup doesn’t pass that anymore; perhaps fixes were made after this run. Also, when checking for faster-whisper language codes, 'or' wasn’t listed. The `to_whisper_lang` function seems designed to return None for cases like 'or' and checks that before any mapping occurs. So it can’t map 'ory' to 'or'; that doesn’t seem right.


**Verifying version discrepancies**

I’m considering that if the special-case wasn’t present at runtime, it suggests that the earlier version simply mapped 'ory' to 'or' and caused a crash by passing 'or'. The later fix seems to have introduced this special-case, but the errors during the indic_whisper run indicate it might not have been active. I’m trying to piece together timelines: the run time shows 19:15, while the file modification was at 09:21, which was after the run, confirming the fix came earlier. I wonder if they didn’t reinstall the updated code afterward. How can I check that?


**Checking the installed file**

I'm considering running a command like `python3 -c "import maya_transcribe, inspect; print(maya_transcribe.__file__)"` to see exactly which file Python is using for the `maya_transcribe` module. This could help me determine if the correct version is being utilized. It's a straightforward approach that could clarify if they're working with the latest updates or an older installation. Let's see what this reveals!


set -euo pipefail
python3 - <<'PY'
import maya_transcribe
import maya_transcribe.language as lang
import inspect
print('maya_transcribe package file:', maya_transcribe.__file__)
print('language file:', lang.__file__)
print('to_whisper_lang source:')
print(inspect.getsource(lang.to_whisper_lang))
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
maya_transcribe package file: /home/ubuntu/maya-transcribe/maya_transcribe/__init__.py
language file: /home/ubuntu/maya-transcribe/maya_transcribe/language.py
to_whisper_lang source:
def to_whisper_lang(lang_code: str | None) -> str | None:
    if not lang_code:
        return None
    lang_code = lang_code.strip().lower()
    if len(lang_code) == 2:
        return lang_code
    return ISO3_TO_WHISPER_LANG.get(lang_code, None)


**Investigating file discrepancies**

I'm trying to figure out why the file content isn't matching what I'm inspecting. It seems like there could be multiple `language.py` files in different directories. Maybe changes were made to the one on disk, but I was looking at an older version? I wonder if Python loaded a stale `.pyc` from `__pycache__`. Actually, `inspect.getsource` should read from the source file, using `linecache`. But if `mtime` changed and `linecache` isn't updated, that could cause issues. I'm really trying to make sense of this!


**Confirming file content**

I think I can check the file now to confirm whether it lacks any special cases. It's possible that I saw different content earlier because of caching issues. To really see the actual content, I could use `sed` to manipulate the file around `to_whisper_lang`. This way, I'll get the most up-to-date version. It’s always helpful to verify assumptions to avoid miscommunication. Let's dive in and inspect the file!


set -euo pipefail
nl -ba maya_transcribe/language.py | sed -n '55,100p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    55	    "Marathi": "Devanagari",
    56	    "Gujarati": "Gujarati",
    57	    "Punjabi": "Gurmukhi",
    58	    "Tamil": "Tamil",
    59	    "Telugu": "Telugu",
    60	    "Kannada": "Kannada",
    61	    "Malayalam": "Malayalam",
    62	    "Bengali": "Bengali",
    63	    "Assamese": "Assamese",
    64	    "Odia": "Odia",
    65	}
    66
    67	# Coarse script expectations for filtering/normalization heuristics.
    68	# Assamese uses the Bengali Unicode block.
    69	LANG_CODE_TO_SCRIPT: dict[str, str] = {
    70	    "en": "Latin",
    71	    "eng": "Latin",
    72	    "hi": "Devanagari",
    73	    "hin": "Devanagari",
    74	    "mr": "Devanagari",
    75	    "mar": "Devanagari",
    76	    "gu": "Gujarati",
    77	    "guj": "Gujarati",
    78	    "pa": "Gurmukhi",
    79	    "pan": "Gurmukhi",
    80	    "ta": "Tamil",
    81	    "tam": "Tamil",
    82	    "te": "Telugu",
    83	    "tel": "Telugu",
    84	    "kn": "Kannada",
    85	    "kan": "Kannada",
    86	    "ml": "Malayalam",
    87	    "mal": "Malayalam",
    88	    "bn": "Bengali",
    89	    "ben": "Bengali",
    90	    "as": "Bengali",
    91	    "asm": "Bengali",
    92	    "or": "Oriya",
    93	    "ori": "Oriya",
    94	    "ory": "Oriya",
    95	    "ur": "Arabic",
    96	    "urd": "Arabic",
    97	}
    98
    99
   100	def expected_script(lang_code: str | None) -> str | None:


Summary: auto


**Inspecting file changes**

Now I see the file has significantly more content than before. It seems like we didn't display the `to_whisper_lang` function, which might have been earlier, maybe above line 55. Oh wait, the file has changed drastically, and now it includes script maps among other things. This could be a new version from another agent's modifications. I should inspect the earlier parts to find `to_whisper_lang` since it still appears simple compared to the current content. Let's search for `def to_whisper_lang` lines to clarify.


set -euo pipefail
rg -n "def to_whisper_lang" maya_transcribe/language.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
158:def to_whisper_lang(lang_code: str | None) -> str | None:


set -euo pipefail
nl -ba maya_transcribe/language.py | sed -n '150,200p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
   150	            if ("LATIN" in unicodedata.name(ch, "")) or ("A" <= ch <= "z"):
   151	                bump("Latin")
   152
   153	    if total_letters < int(min_letters):
   154	        return None
   155	    return max(counts.items(), key=lambda kv: kv[1])[0] if counts else None
   156
   157
   158	def to_whisper_lang(lang_code: str | None) -> str | None:
   159	    if not lang_code:
   160	        return None
   161	    lang_code = lang_code.strip().lower()
   162	    if len(lang_code) == 2:
   163	        return lang_code
   164	    return ISO3_TO_WHISPER_LANG.get(lang_code, None)
   165
   166
   167	def to_language_name(lang_code: str | None) -> str | None:
   168	    if not lang_code:
   169	        return None
   170	    lang_code = lang_code.strip().lower()
   171	    return LANG_CODE_TO_NAME.get(lang_code)
   172
   173
   174	def to_prompt_language_hint(lang_code: str | None) -> str:
   175	    """
   176	    Return a human-friendly language hint string for prompting audio-native models.
   177	    """
   178	    if not lang_code:
   179	        return "Unknown (auto-detect primary spoken language)"
   180	    lang_code_norm = lang_code.strip().lower()
   181	    name = LANG_CODE_TO_NAME.get(lang_code_norm)
   182	    if not name:
   183	        return f"{lang_code_norm} (auto-detect primary spoken language)"
   184	    script = LANG_NAME_TO_SCRIPT.get(name)
   185	    if script:
   186	        return f"{name} ({script} script)"
   187	    return name


set -euo pipefail
nl -ba maya_transcribe/language.py | sed -n '1,80p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
     1	from __future__ import annotations
     2
     3	import unicodedata
     4
     5	ISO3_TO_WHISPER_LANG: dict[str, str] = {
     6	    "eng": "en",
     7	    "hin": "hi",
     8	    "mar": "mr",
     9	    "guj": "gu",
    10	    "pan": "pa",
    11	    "tam": "ta",
    12	    "tel": "te",
    13	    "kan": "kn",
    14	    "mal": "ml",
    15	    "ben": "bn",
    16	    "asm": "as",
    17	    "urd": "ur",
    18	    "san": "sa",
    19	    "ori": "or",
    20	    "ory": "or",
    21	}
    22
    23	# Human-friendly names and scripts (for prompts/UI).
    24	LANG_CODE_TO_NAME: dict[str, str] = {
    25	    "eng": "English",
    26	    "en": "English",
    27	    "hin": "Hindi",
    28	    "hi": "Hindi",
    29	    "mar": "Marathi",
    30	    "mr": "Marathi",
    31	    "guj": "Gujarati",
    32	    "gu": "Gujarati",
    33	    "pan": "Punjabi",
    34	    "pa": "Punjabi",
    35	    "tam": "Tamil",
    36	    "ta": "Tamil",
    37	    "tel": "Telugu",
    38	    "te": "Telugu",
    39	    "kan": "Kannada",
    40	    "kn": "Kannada",
    41	    "mal": "Malayalam",
    42	    "ml": "Malayalam",
    43	    "ben": "Bengali",
    44	    "bn": "Bengali",
    45	    "asm": "Assamese",
    46	    "as": "Assamese",
    47	    "ori": "Odia",
    48	    "ory": "Odia",
    49	    "or": "Odia",
    50	}
    51
    52	LANG_NAME_TO_SCRIPT: dict[str, str] = {
    53	    "English": "Latin",
    54	    "Hindi": "Devanagari",
    55	    "Marathi": "Devanagari",
    56	    "Gujarati": "Gujarati",
    57	    "Punjabi": "Gurmukhi",
    58	    "Tamil": "Tamil",
    59	    "Telugu": "Telugu",
    60	    "Kannada": "Kannada",
    61	    "Malayalam": "Malayalam",
    62	    "Bengali": "Bengali",
    63	    "Assamese": "Assamese",
    64	    "Odia": "Odia",
    65	}
    66
    67	# Coarse script expectations for filtering/normalization heuristics.
    68	# Assamese uses the Bengali Unicode block.
    69	LANG_CODE_TO_SCRIPT: dict[str, str] = {
    70	    "en": "Latin",
    71	    "eng": "Latin",
    72	    "hi": "Devanagari",
    73	    "hin": "Devanagari",
    74	    "mr": "Devanagari",
    75	    "mar": "Devanagari",
    76	    "gu": "Gujarati",
    77	    "guj": "Gujarati",
    78	    "pa": "Gurmukhi",
    79	    "pan": "Gurmukhi",
    80	    "ta": "Tamil",


**Identifying mapping errors**

I think I've spotted the issue: the mapping includes `"ory": "or"`, causing errors because faster-whisper doesn’t support 'or'. The user mentioned a fix that should prevent Odia from crashing with an error, but it seems like there was an attempt to fix it that inadvertently reintroduced the mapping. So, I can explain to the user that these mapping errors lead to increased error counts and affect performance, while keeping the focus on giving an explanation rather than a patch suggestion.


**Proposing next steps**

I see that while the user only asked for an explanation, I could still suggest a patch. However, I need to follow the system's guidance and focus on explaining instead of modifying code unless directly requested. There's also a significant issue with the FLEURS audio being too quiet and trimmed away, resulting in zero duration audio. That seems to be more about configuration than a code problem. I should note this while preparing to explain WER and related details to the user.


**Explaining recent experiments**

The user mentioned that another model has been running for a while without clarity on its goals. I’ll check the output directory for recent experiments like indic_whisper and per_lang_vaani. The per_lang_vaani run attempts to perform per-language mapping for ARTPARK Vaani models, but it faced significant errors due to GPU out-of-memory issues and Odia language code mismatches. They tried limit runs to debug selection preferences. I’ll need to inspect the per_lang_vaani_limit directories and check the results.jsonl or configuration records for any changes made to thresholds or settings.


**Summarizing pipeline issues**

I noticed that the pipeline record includes configurations like trim_silence and silence thresholds, but it doesn't mention transformer models. It also involves ASR keys, which could help inform our understanding. The user primarily wants an explanation of the experiments, so I should summarize key points.

The results indicate that using Indic-whisper harmed performance, and the per-language Vaani attempt failed due to out-of-memory errors. The WER is unusually high at around 0.8 because of dataset issues and silent segments. The single baseline model may not perform well for certain languages, and using word-level evaluation might inflate WER, making CER a potentially better metric.


**Checking evaluation metrics**

I need to look into the `outputs/bench/maya_hf_v2/indic_whisper/eval.json` file to check the WER by language mean and other related metrics. It’s important to inspect the results on a per-language basis to understand the performance better. This should give me a clearer picture of how well the model is doing across different languages. Let’s dive in and see what the data reveals!


set -euo pipefail
python3 - <<'PY'
import json
from pathlib import Path
j=json.loads(Path('outputs/bench/maya_hf_v2/indic_whisper/eval.json').read_text(encoding='utf-8'))
print('wer_by_language_mean:')
by=j.get('wer_by_language_mean') or {}
for k in sorted(by):
    print(k, by[k])
print('\nwer_by_decision_mean:', j.get('wer_by_decision_mean'))
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
wer_by_language_mean:
asm 1.0578857154789767
ben 0.8953795354305555
eng 0.9215304733130019
guj 0.6010849705849705
hin 0.625640859140859
kan 0.8795423604173603
mal 1.138755827505828
mar 0.9549586940836938
ory 1.0721972471972472
pan 0.8645189948939946
tam 0.5913566156066155
tel 0.922344669219668

wer_by_decision_mean: {'accept': 0.594883999638253, 'human': 0.8157604352520663, 'reject': 1.074516223257519}


**Analyzing WER scores**

I’m looking at WER scores across languages, seeing that some like Tamil and Gujarati are quite low, while others like Malayalam and English are significantly higher. But I note that English segments often have zero duration due to trimming, which inflates the WER. The user is curious about WER comparisons, like what 0.812 vs. 0.998 means. They also want to know if industry-standard ASR models typically reach these scores or if it’s our ensembling affecting the results.


**Explaining the WER metrics**

I'll clarify that a WER of 0.8 is quite poor, as strong models usually score below 0.1 on clean data. For challenging low-resource languages, scores might be around 0.2-0.5, while 0.8 suggests serious issues like poor audio quality or language mismatches. This isn’t due to ensembling, but rather problems with the base ASR or data preprocessing. Also, remember WER can exceed 1.0, indicating significant insertions, particularly for languages like Malayalam. The user is also curious why these tests are so lengthy.


**Detailing the testing delays**

I'll explain that the long test durations stem from the extensive processing pipeline. Each segment goes through the `faster-whisper large-v3` model, additional sampling, and transformers—not to mention audio normalization and language identification, which all add significant time. With 2,400 segments, this adds up quickly! It takes about 4.25 seconds per segment, totalling roughly 2.83 hours. Plus, we can see that errors are affecting the process, with 190 segments crashing due to language issues or other factors.


**Examining evaluation and processes**

I noticed that many segments are being evaluated as human due to ASR disagreement. However, the evaluation doesn't filter by decision; the WER mean includes all decisions. In `eval.json`, we see that 2,210 out of 2,400 pairs were evaluated, which is affected by errors or missing hypotheses. The user also mentioned another model running a long time. That could relate to the per_lang_vaani experiments, so I’ll check for any ongoing processes with `ps` for `maya-transcribe` or Python processes.


set -euo pipefail
ps aux | rg -n "maya-transcribe|maya_transcribe|bench run" | head -n 50 || true


Output:
Exit code: 0
Wall time: 0 seconds
Output:
268:ubuntu   1197202  150  4.5 201002500 5615696 ?   Rl   Jan13  35:31 /usr/bin/python3 /home/ubuntu/.local/bin/maya-transcribe bench run --bench-dir data/bench/maya_hf_v2 --out outputs/bench/maya_hf_v2/per_lang_vaani_limit250_finalpref --whisper-model large-v3 --transformers-whisper-model-map data/bench/model_maps/whisper_vaani_map.json --limit 250
292:ubuntu   1225633  0.0  0.0   7504  3712 ?        S    00:07   0:00 /bin/bash -lc set -euo pipefail ps aux | rg -n "maya-transcribe|maya_transcribe|bench run" | head -n 50 || true
294:ubuntu   1225638  0.0  0.0   9900  4480 ?        S    00:07   0:00 rg -n maya-transcribe|maya_transcribe|bench run


set -euo pipefail
out=outputs/bench/maya_hf_v2/per_lang_vaani_limit250_finalpref
ls -la "$out" || true
if [ -f "$out/results.jsonl" ]; then
  echo "lines=$(wc -l < "$out/results.jsonl")"
  tail -n 2 "$out/results.jsonl" | python3 - <<'PY'
import sys, json
for line in sys.stdin:
    r=json.loads(line)
    print(r.get('segment_id'), 'error' in r)
PY
fi


Output:
Exit code: 141
Wall time: 0 seconds
Output:
total 1208
drwxrwxr-x  3 ubuntu ubuntu    4096 Jan 13 23:44 .
drwxrwxr-x 15 ubuntu ubuntu    4096 Jan 13 23:44 ..
drwxrwxr-x  2 ubuntu ubuntu   20480 Jan 14 00:07 audio
-rw-rw-r--  1 ubuntu ubuntu 1200321 Jan 14 00:07 results.jsonl
lines=162


Summary: auto


**Analyzing command exit issues**

I'm seeing that exit code 141 might indicate a broken pipe, possibly because we piped the tail output to a Python process that exited. It printed lines=162, suggesting that after 35 minutes, only 162 out of a 250 limit were processed, which seems slow with the heavy model. I should check if it's making progress by looking at the last modification time. The results.jsonl file was 1.2MB at this time, so it could still be writing. I'll inform the user about the ongoing process and its slow progress due to GPU memory issues and processing time estimates.


**Checking GPU and model specifics**

I can conclude that the process isn’t stuck; it’s just heavy. I might want to check GPU usage or memory status, so I could run `nvidia-smi` to get that information, although it’s not entirely necessary. Additionally, it’s worth inspecting the `data/bench/model_maps/whisper_vaani_map.json` file to see which models are being used. It could be mapping per language to Vaani or others, which might help explain what's happening. I’ll take a look at that file.


set -euo pipefail
cat data/bench/model_maps/whisper_vaani_map.json


Output:
Exit code: 0
Wall time: 0 seconds
Output:
{
  "hin": "ARTPARK-IISc/whisper-large-v3-vaani-hindi",
  "tel": "ARTPARK-IISc/whisper-large-v3-vaani-telugu",
  "ory": "ARTPARK-IISc/whisper-large-v3-vaani-odia",
  "ben": "ARTPARK-IISc/whisper-medium-vaani-bengali",
  "kan": "ARTPARK-IISc/whisper-medium-vaani-kannada",
  "tam": "vasista22/whisper-tamil-large-v2",
  "mal": "DrishtiSharma/whisper-large-v2-malayalam",
  "mar": "durgesh10/whisper-large-v3-marathi",
  "guj": "vasista22/whisper-gujarati-medium",
  "pan": "DrishtiSharma/whisper-large-v2-punjabi",
  "asm": "DrishtiSharma/whisper-large-v2-assamese-5k-steps"
}


**Explaining benchmarks and experiments**

I need to clarify the current benchmarks and experiments done by other models. It seems like the goal is to summarize each output directory found under outputs/bench/maya_hf_v2, which includes the WER (Word Error Rate) and other relevant data. I should be organizing this information so it’s clear and useful. There’s data available from previous summaries that I can reference, so I’ll be sure to utilize that effectively!


**Gathering experiment data**

Let’s collect the details for each experiment. For indic_whisper, the WER is 0.8606 with 190 errors, likely due to the Odia language code. The per_lang_vaani shows a higher WER of 0.9472 but has significant errors, indicating possible invalidation because of OOM issues. Then, per_lang_vaani_v2 needs inspection as it lacks a summary. The limit250 version has a WER of 0.9813 and is limited for debugging. It’s crucial to check all these results thoroughly!


**Analyzing WER results**

I’m looking at the WER results for several models. The per_lang_vaani_limit20_routerpref has a WER of 0.834, while per_lang_vaani_limit20_finalpref is at 0.796. The smoke_vaani models show errors, with smoke_vaani3 having a WER of 0.898 and only 5 pairs. It seems these iterations are examining aspects like language models, script filtering, and decoder preferences. The user asked about discrepancies between earlier WERs of 0.812 and 0.998 versus the full run, so I’ll need to compute results for the full indic_whisper to clarify.


set -euo pipefail
python3 - <<'PY'
from pathlib import Path
from maya_transcribe.eval import evaluate

results=Path('outputs/bench/maya_hf_v2/indic_whisper/results.jsonl')
refs=Path('data/bench/maya_hf_v2/refs.jsonl')

for h in ['final','asr:faster-whisper:large-v3|det','asr:transformers:Vignesh-M/Indic-whisper']:
    s=evaluate(results, refs, hypothesis=h, oracle=False, language_source='refs')
    print(h, 'pairs', s['pairs_evaluated'], 'wer_mean', s['wer_mean'], 'cer_mean', s['cer_mean'])
PY


Output:
Exit code: 0
Wall time: 1.9 seconds
Output:
final pairs 2210 wer_mean 0.8606466002054953 cer_mean 0.6257349337304581
asr:faster-whisper:large-v3|det pairs 2210 wer_mean 0.854401599552134 cer_mean 0.6130518916585745
asr:transformers:Vignesh-M/Indic-whisper pairs 2210 wer_mean 0.947894708095563 cer_mean 0.9960492446207443


**Evaluating WER results**

I'm looking at the WER numbers: faster-whisper is at 0.8544 and Indic whisper at 0.9479, with a final WER of 0.8606, indicating that Indic performs worse. The previous metrics at 0.812 and 0.998 were partial, while now I've got 0.854 and 0.948. I should explain how silent trimmed clips and error mapping for Odia contribute to high WER. There might be issues with normalization affecting the baseline large-v3’s score of 0.85 WER, which is quite poor.


**Analyzing WER per language**

The WER values per language in the evaluation seem unusually high, which raises some questions. Are we really evaluating the correct transcripts? The dataset is constructed from valid splits, but we did use `normalize_audio` with `trim_silence` set to true. This could have removed important parts of the audio, especially if it treated very quiet audio as silence, leading to zero durations. This obviously influences the high WER for English but doesn’t fully explain why languages like Hindi show a WER of 0.625. It could also relate to the normalization process affecting the final transcripts.


**Evaluating text normalization**

In the evaluation process, `extract_hypothesis_text` pulls from `result_row["final"]["text"]`, which is set to `rover.text`, using normalized candidates. The function `normalize_text` processes this, which means we're using normalized text rather than the originals. While normalization doesn’t change the core content, it can lead to loss of information. The reference transcripts from Kathbath and FLEURS are also normalized. So why is the WER so high? It might be because Whisper is producing outputs in the incorrect script for some languages due to the language hint mappings.


**Examining Odia language processing**

For the Assamese language, the mapping from `route_lang 'asm'` to 'as' is correct. However, for Odia, mapping `route_lang 'ory'` to 'or' is invalid, possibly causing processing errors for many Odia segments. Yet, the evaluation shows a WER of 1.07 for Odia, indicating some segments were processed after all. Maybe the `route_lang` wasn't correctly set to 'ory' due to a language identification prediction, or it could have returned `None`, leading to errors in the segments that should’ve been identified as Odia.


**Proposing improvements for ASR**

I think we should focus on enhancing pre-processing and language handling instead of relying on untested models. For instance, applying normalization to audio loudness can enhance ASR performance across languages without requiring new models. We should consider disabling aggressive trimming and improve segment splitting. Evaluating WER for segments with a duration over zero could help. Using bigger models like Whisper large-v3 or trying the turbo version may also yield better results.

Improvements in selection algorithms are crucial since they might be responsible for high WERs. The selection mechanisms should ideally pick the best candidates, and I need to clarify that WER relates to error rates rather than accuracy. The user has questions about long test durations, and I want to reassure them that we are making progress!


**Discussing WER implications**

High WER signifies poor ASR performance, but since the pipeline verification is strict, many segments are labeled as 'human' or 'reject', which is expected. To get a clearer picture of quality, I should compute WER only for segments marked as `accept`. For the indic_whisper evaluation, the WER for accepted segments is around 0.595, which, while high, is better than the overall rate.

It’s important to compare this to industry standards, where a typical WER is below 0.2. Adjusting for silence trimming could improve our WER, so we might compute figures again while excluding nonsignificant segments to show our argument effectively.


set -euo pipefail
python3 - <<'PY'
import json
from pathlib import Path
from jiwer import wer
from maya_transcribe.textnorm import normalize_text

results_path=Path('outputs/bench/maya_hf_v2/indic_whisper/results.jsonl')
refs_path=Path('data/bench/maya_hf_v2/refs.jsonl')

refs={}
for line in refs_path.read_text(encoding='utf-8').splitlines():
    r=json.loads(line)
    refs[r['segment_id']]=r

wers_all=[]
wers_nonzero=[]
count0=0
for line in results_path.read_text(encoding='utf-8').splitlines():
    r=json.loads(line)
    sid=r.get('segment_id')
    ref=refs.get(sid)
    if not ref or ref.get('lang')!='eng':
        continue
    asr=r.get('asr') or {}
    det=asr.get('faster-whisper:large-v3|det') or {}
    hyp=det.get('text') or ''
    qc=r.get('qc') or {}
    dur=qc.get('duration_sec')
    try:
        dur=float(dur)
    except Exception:
        dur=None
    ref_n=normalize_text(ref.get('text') or '', lang='eng')
    hyp_n=normalize_text(hyp, lang='eng')
    w=float(wer(ref_n, hyp_n))
    wers_all.append(w)
    if dur and dur>0:
        wers_nonzero.append(w)
    else:
        count0 += 1
print('eng total', len(wers_all))
print('eng wer_mean_all', sum(wers_all)/len(wers_all))
print('eng dur0', count0)
print('eng wer_mean_dur>0', sum(wers_nonzero)/len(wers_nonzero))
PY


Output:
Exit code: 0
Wall time: 0.3 seconds
Output:
eng total 200
eng wer_mean_all 0.9220567891024756
eng dur0 146
eng wer_mean_dur>0 0.7113214411202803


**Analyzing experiment results**

I'm looking into some experiments related to various models and their performance. It seems there were issues with duplicate IDs in v0 causing mismatches, which makes the WER less reliable. When experimenting with v2, I found different outcomes for segments labeled smoke_vaani and indic_whisper. The WER varied significantly, indicating some attempts were not reliable. Also, per_lang_vaani runs had issues due to languages and errors, suggesting more testing might be needed. Each variant seems to have its complexities.


**Clarifying model details**

I'm analyzing the per_lang_vaani_limit250_finalpref experiment, which is currently processing segments. About 162 out of 250 have been processed so far, taking roughly 8.6 seconds each. I need to understand how it differs from the previous limit250 version; it seems to select final transcripts differently. The user asked for clarification about the WER and the progress, so I’ll explain the WER formula and mention that this run is using large transformer models, taking about 35 minutes to complete.


**Exploring ASR scores**

I’m considering whether to stop the process due to out-of-memory issues, but the user is more focused on understanding the duration instead. They also asked about industry standard models in ASR and whether they typically see scores around 0.8. I’ll explain that 0.8 is unusually high, suggesting major issues with the pipeline configuration. We can point out that datasets like Kathbath and FLEURS are cleaner, so more errors are concerning. Some segments are very short, contributing to higher WER. I’ll check the `wer_by_duration_bucket_mean` for indic_whisper to provide more insights.


set -euo pipefail
python3 - <<'PY'
import json
from pathlib import Path
j=json.loads(Path('outputs/bench/maya_hf_v2/indic_whisper/eval.json').read_text(encoding='utf-8'))
print('wer_by_duration_bucket_mean', j.get('wer_by_duration_bucket_mean'))
print('cer_by_duration_bucket_mean', j.get('cer_by_duration_bucket_mean'))
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
wer_by_duration_bucket_mean {'0-3s': 0.9965252447220982, '10-30s': 1.1627607876239885, '3-10s': 0.7246950654767635}
cer_by_duration_bucket_mean {'0-3s': 0.874305301165092, '10-30s': 1.0184238676363668, '3-10s': 0.38311378004324514}


Summary: auto


**Analyzing performance and testing**

I’m exploring the industry-standard models and considering if our low scores might be due to ensembling. I need to provide clarity on the testing duration too. For the indic_whisper, the average time is 4.25 seconds over 2400 segments, plus the transformer model is inherently slow.

It's worth noting that running the transformers Whisper uses PyTorch, which is 2-5 times slower than alternatives. The evaluation indicates the Vignesh model isn't bridging the gap, so instead, faster-whisper is a better choice.


**Exploring language models and performance**

I think we should mention that some per-language specialized models might be beneficial. It's also important to emphasize that the pipeline uses a "verbatim transcription" prompt, although that might not be directly relevant.

Next, I'll check the per_lang_vaani_limit20_* directories for any changes. It seems they may have implemented script filtering to select candidates in the correct script, which could be an improvement.

For per_lang_vaani_limit20_scriptfilter, the WER is 0.796. Interestingly, it matches the oracle best score, suggesting their selection was effective—albeit based on only 20 samples, so that might not generalize. In contrast, per_lang_vaani_limit20_newdecode shows a WER of 1.126, with the selection gap indicating a decline in performance. This suggests they are indeed exploring selection heuristics.


**Interpreting language model performance**

It seems they created a per-language model map with multiple models. However, naive selection might lead to picking the wrong candidate or script, while the script filter aims to select the candidate that matches the expected script, which looks like an improvement.

That said, the underlying models might still struggle, especially with out-of-memory (OOM) issues. The full run of per_lang_vaani faced significant OOM problems, making it unreliable. However, the per_lang_vaani_limit250 and limit20 runs completed without errors but were slower, possibly due to relying on CPU or being less efficient. I’ll check the results in per_lang_vaani_limit250 to see if it includes ASR outputs for additional insights.


set -euo pipefail
python3 - <<'PY'
import json
from pathlib import Path
p=Path('outputs/bench/maya_hf_v2/per_lang_vaani_limit250/results.jsonl')
line=p.read_text(encoding='utf-8').splitlines()[0]
r=json.loads(line)
print('keys', r.keys())
print('route', r.get('route'))
print('asr_keys', list((r.get('asr') or {}).keys())[:10])
print('final_source', (r.get('final') or {}).get('source'))
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
keys dict_keys(['schema_version', 'segment_id', 'base_id', 'relpath', 'uid', 'src_path', 'wav_path', 'src_ffprobe', 'ffprobe', 'qc', 'lid', 'route', 'asr', 'asr_summary', 'asr_errors', 'ensemble', 'normalized', 'rover', 'ctc', 'final', 'verification', 'config', 'timing'])
route {'language_hint': 'asm', 'language_hint_source': 'lid'}
asr_keys ['faster-whisper:large-v3|det', 'faster-whisper:large-v3|samp_t0.2', 'transformers:DrishtiSharma/whisper-large-v2-assamese-5k-steps']
final_source rover


**Evaluating model limitations**

I’m looking into the per_lang_vaani_limit250, which includes three hypotheses: faster-whisper det, samp, and a specific transformer model for Assamese. The idea is that the router selects a model per language and returns one per segment, which should prevent memory overload. But, it might still cache and load multiple models over time, risking out-of-memory situations, especially with more languages in play. For a more efficient approach, I need to consider how to manage model loading effectively.


**Explaining performance results**

The user asked if the low score is due to ensembling. I can explain that on the indic_whisper run, the final Word Error Rate (WER) is 0.860, which is close to faster-whisper's 0.854. So, ensembling didn’t degrade performance much; rather, a problematic model increased disagreement without affecting the output. The high WER is mostly from the base ASR struggling with the dataset or pipeline issues, not from ensembling itself. I'll clarify that a lower WER indicates better performance, which suggests that the baseline model performed better on this subset.


**Discussing WER improvements**

I should mention that the `oracle_best_wer_mean` suggests potential improvements if we could select the best candidate per segment, which is around 0.786. With better selection methods, like CTC fit scoring, we might reduce the WER by about 0.075, though it would still be relatively high. The user inquired about industry standard models, and I can say that typical WER for Whisper might be in the single-digit to tens of percent range. WER of 0.86 indicates a concerning number of errors, so we need to refine our evaluation methods.


**Explaining WER context**

I can mention some info without directly citing it, referring back to the Vistaar README that shows a WER of 24.6%. I might say, "IndicWhisper reported around 0.25 WER on Vistaar, so 0.86 indicates an issue." It's crucial to note that WER numbers aren't directly comparable due to dataset differences and normalization. Also, WER can exceed 1.0, with values going up to 1.14, highlighting that these numbers aren't bounded. When discussing the 81%, I'll clarify it's an error rate, not accuracy, where a WER of 0 means perfection.


**Summarizing benchmarks and experiments**

The user wants explanations on current benchmarks and the experiments conducted with different models. I'll provide a summary table highlighting the reliability issues such as high errors in the per_lang_vaani experiments. It's important to treat these results as diagnostic, as we have loudness and trimming issues to fix. Typical WER for good ASR systems is much lower than 0.8, which suggests pipeline and audio issues. It's crucial to address improvements in preprocessing, especially for the Odia language, and propose next steps for running benchmarks effectively.


**Explaining WER and Experiments**

WER of 0.86 translates to approximately 86 errors per 100 words, while 0.998 means essentially 1 error per word; it’s important to note this is an error rate, not an accuracy measure. In our experiments:

- `indic_whisper`: WER 0.861, 190 errors.
- `per_lang_vaani`: faced significant OOM issues, not meaningful results.
- `per_lang_vaani_limit20_*`: WER 0.796–1.126, small test.
- `per_lang_vaani_limit250`: WER 0.981.
- `smoke_vaani*`: errors noted.

The long test times are due to processing overhead and issues such as language code bugs and audio quality. If needed, I can stop the current run to conserve resources.


**What WER Means**
- `WER = (substitutions + deletions + insertions) / (# reference words)`.
- `WER 0.812` means ~81.2 word-level errors per 100 reference words (not “81% accuracy”). `WER 0.998` is ~99.8 errors per 100 words (worse). WER can be `> 1.0` if insertions are high.

**What The Current Runs Show (v2 bench)**
- `outputs/bench/maya_hf_v2/indic_whisper/eval.json`: `WER(final)=0.861`, while `WER(asr:faster-whisper:large-v3|det)=0.854` and `WER(asr:transformers:Vignesh-M/Indic-whisper)=0.948` → the extra model is worse; “ensembling” didn’t cause 0.8, the baseline is already high.
- This run also had `error=190/2400` (see `outputs/bench/maya_hf_v2/indic_whisper/summary.json`), largely from the Odia mapping bug (`ory -> or`) which crashes faster-whisper (`'or' is not a valid language code`).
- English is heavily distorted by preprocessing: in this run **146/200 FLEURS English clips became 0s audio** after trimming (very quiet audio + `silenceremove` at `-40dB`), which inflates WER a lot.

**Why Some Experiments Look Worse / Take Forever**
- `outputs/bench/maya_hf_v2/per_lang_vaani` is not a valid comparison: it hit **CUDA OOM** and logged `error=1708/2400`, so its `WER=0.947` is on a biased subset.
- There is still a long job running: `maya-transcribe bench run ... per_lang_vaani_limit250_finalpref` (PID `1197202`), currently ~`162/250` rows written; these runs are slow because transformer Whisper checkpoints + beam search are much slower than `faster-whisper` (CTranslate2), and model-loading across languages is expensive.

**Are “0.8 WER” Scores Normal?**
- No for clean benchmark speech; values around `0.8–1.1` usually mean “ASR is mostly failing” and/or the benchmark is broken by issues like silence-trim deleting audio, wrong language tokens (Odia), ultra-low volume, or severe domain mismatch.


# Context from my IDE setup:

## Open tabs:
- results.jsonl: outputs/bench/maya_hf_v2/indic_whisper/results.jsonl
- summary.json: outputs/bench/maya_hf_v2/indic_whisper/summary.json
- eval.json: outputs/bench/maya_hf_v2/indic_whisper/eval.json
- results.jsonl: outputs/bench/maya_hf_v2/per_lang_vaani/results.jsonl
- summary.json: outputs/test_run5/summary.json

## My request for Codex:
what scores are best for high quality TTS data and ASR training data


It depends on what you mean by “scores” (dataset quality vs model quality). Common, practical targets:

**For ASR training data (dataset quality gates)**
- `SNR / loudness`: avoid ultra-quiet clips; typical keep range is roughly `rms_dbfs ≈ -35 to -12` (below ~`-45 dBFS` is often unusably quiet unless you normalize).
- `Clipping`: `clipping_fraction` ideally `0.0`; reject if `> 0.1–1%` depending on tolerance.
- `Duration`: usually keep `1–30s` for generic ASR; reject near-zero or mostly-silence.
- `Language ID`: high confidence if you use it (e.g., top1 prob ≥ `0.9` and margin ≥ `0.2`).
- `ASR self-check (if you run a baseline model)`: keep if `WER` is low against trusted references; if you don’t have refs, use proxy signals (stable transcript across augmentations/models, low disagreement, no `<unk>`).

**For ASR model quality (with references)**
- Clean read speech: WER often in single digits to teens (≈ `0.03–0.15`).
- Conversational / noisy / code-mixed: WER can be higher (≈ `0.15–0.35+`).
- WER around `0.8–1.0` typically indicates failure or evaluation/pipeline problems.

**For TTS training data (dataset quality gates)**
- Alignment quality (forced alignment / CTC-fit): very high coverage; token/word coverage ideally `> 0.95` (or near-100% for clean studio data).
- Transcript exactness: for high-quality TTS, transcripts should be near-perfect; if you compute ASR-on-TTS or ASR-on-recordings WER vs transcript, you generally want very low WER (≈ `0.00–0.05` for studio, maybe up to `0.10` for less controlled).
- Audio quality: no clipping, minimal background noise, consistent mic/room; avoid long leading/trailing silence.
- Speaker consistency: single speaker per dataset (or well-labeled multi-speaker), stable speaking rate and pronunciation.

If you tell me whether you mean “our pipeline’s metrics” (rms_dbfs/clipping/LID/CTC-fit) or “benchmark metrics like WER/CER”, I can map exact numeric thresholds to your repo’s fields.


# Context from my IDE setup:

## Open tabs:
- results.jsonl: outputs/bench/maya_hf_v2/indic_whisper/results.jsonl
- summary.json: outputs/bench/maya_hf_v2/indic_whisper/summary.json
- eval.json: outputs/bench/maya_hf_v2/indic_whisper/eval.json
- results.jsonl: outputs/bench/maya_hf_v2/per_lang_vaani/results.jsonl
- summary.json: outputs/test_run5/summary.json

## My request for Codex:
but doesnt indicwhisper and whisperv3 large and other ASR models mention they have 0.08 ish WER scores right ? why is our ensemble pipelines giving 80 and 99 on same benchmarks.


**Analyzing WER metrics**

I'm looking at a word error rate (WER) of 0.86 on 2210 pairs, which is still quite high. There was some earlier confusion between 0.812 and 0.998, stemming from different hypotheses. Normally, a WER of around 0.08 is typical for benchmarks with proper normalization. For Indic languages, I anticipate a slightly higher WER, but certainly not as high as 0.8. There’s a few key reasons for the inflated WER: empty clips, language mismatches, and how tokenization varies across different scripts. I think using character error rate (CER) might be more effective in some cases.


**Exploring WER and CER issues**

I'm examining how the normalization process collapses whitespace, leading to potential issues with word segmentation in languages where words aren't separated. If the entire line is treated as a single token, any mismatch would inflate the word error rate (WER) to 1.0. ASR outputs can vary, and while FLEURS transcripts have spaces, if a model outputs without them, it can further complicate things. Using character error rate (CER) could be more reliable, especially since our CER mean is still high at 0.625. I realize that the low WER numbers are for fine-tuned models, not baseline ones.


**Addressing benchmark issues**

I'm reviewing our benchmark, which includes multiple languages but lacks per-language fine-tuning. We're using a quicker model that isn't fine-tuned, and some language hints might be wrong due to language identification errors. This can lead to incorrect tokens, affecting results. Additionally, our pipeline has two passes, but they aren't improving outcomes. The reported WER of 0.08 indicates an 8% error rate, while ours reflects around 80% errors because of misconfigurations, trimming issues, and domain mismatches. I'll recommend running without trimming silence and computing WER based only on accepted results while considering character-level metrics.


**Clarifying WER calculations**

I'm preparing to provide a quick command, possibly a Python snippet, to compute the word error rate (WER) on non-zero durations. However, the user expected a detailed explanation based on their eval.json numbers, noting a WER of 0.86 instead of 0.08. I need to clarify that "IndicWhisper" isn't being run; they've used an unofficial version, which isn't the same as the official one trained on Vistaar. I'll outline specific reasons for the high WER, such as silence trimming issues, invalid code mapping errors, the difference in WER across evaluations, and the model mismatch.


**Suggesting reliable models**

I’m thinking about recommending "battle-tested" models like OpenAI's whisper-large-v3, which is great with the right language hints. For Indic languages, using the official AI4B IndicWhisper or ARTPARK Vaani could be better. However, if the user expresses skepticism about trusting these, I can also mention alternatives like Google Gemini. I’ll also clarify why the tests might be slow: multiple models for each segment, combined with CPU/GPU processing, slower transformers, and preprocessing steps with ffmpeg. No additional tools are necessary since I've already checked the stats.


Those “0.08 WER” claims are **0.08 = 8% word error rate** on a *specific setup* (dataset + language + normalization + decoding settings). Your runs are showing **~0.86 = 86% word error rate**, which is “mostly wrong”, and it’s happening for a few concrete reasons:

- **You didn’t run “AI4Bharat IndicWhisper”** in `indic_whisper/`. You ran `transformers:Vignesh-M/Indic-whisper` (unofficial), and in this benchmark it’s worse than `faster-whisper:large-v3` (`WER ≈ 0.948` vs `0.854` on the same evaluated pairs).
- **Benchmark corruption from silence trimming**: for FLEURS English, **146/200 clips became 0.0s audio** after `silenceremove` at `-40dB` (the audio is extremely quiet). Those segments inevitably get empty transcripts → WER near 1.0. This alone can drag averages toward ~0.8–1.0 even if the model is decent.
- **Wrong language-code handling for Odia** in this repo version: `ISO3_TO_WHISPER_LANG` maps `ory -> "or"`, but faster-whisper **does not accept `"or"`**, so you get many hard errors; your `indic_whisper` run has **190 errors** and `403 rejects` in `summary.json`.
- **You’re averaging over everything (including rejects/human)**. In `outputs/bench/maya_hf_v2/indic_whisper/eval.json`, `wer_by_decision_mean` shows `accept ≈ 0.595` but `reject ≈ 1.075`. If your goal is “auto-accepted quality”, you should report WER on `decision=accept` separately.
- **WER is brittle for some scripts/spacing** (ASR outputs may omit spaces; references include them). CER is usually more stable across Indic scripts.

So no, it’s not that “ensembling makes WER 80%”; it’s mostly **pipeline + benchmark configuration issues (trim + lang codes) + evaluating hard/empty clips**, and also **the added model isn’t the one whose 0.08 numbers you’re thinking of**.

Why tests take so long:
- Each segment runs `ffmpeg` preprocess + MMS-LID + **two** faster-whisper decodes (det + samp) + optional transformers model; transformers Whisper is much slower than faster-whisper and can OOM, which your `per_lang_vaani` run shows (`error=1708/2400`).


# Context from my IDE setup:

## Open tabs:
- results.jsonl: outputs/bench/maya_hf_v2/indic_whisper/results.jsonl
- summary.json: outputs/bench/maya_hf_v2/indic_whisper/summary.json
- eval.json: outputs/bench/maya_hf_v2/indic_whisper/eval.json
- results.jsonl: outputs/bench/maya_hf_v2/per_lang_vaani/results.jsonl
- summary.json: outputs/test_run5/summary.json

## My request for Codex:
then why tf is my other agent working so fking jard with all those stupid models. can you instruct it to stick with standard models and all these chapri models dont work well or out of the box ? and when tf did we deviate from original plan. our original plan was [master.md](master.md)  and i dont remember we have language specific models and each model for each language.


Summary: auto


You’re right to be skeptical, and you should tell the other agent to stop chasing random checkpoints.

**What to tell your other agent (copy/paste)**
- “Stop adding/benchmarking unofficial whisper fine-tunes (random HF users). They’re not improving WER here and they increase failure modes (script mismatch, decoding quirks, OOM).”
- “Return to the plan in `master.md`: baseline must be `faster-whisper large-v3` + MMS-LID + quality gating + (optional) audio-grounded CTC-fit scoring + (optional) Gemini on hard segments only.”
- “Only use ‘standard’ models from top orgs if we add diversity: OpenAI Whisper (`faster-whisper large-v3`) and Meta MMS/Seamless (for LID/CTC fit or alternate ASR). No per-language zoo unless we have strong evidence and reproducible wins.”
- “Fix benchmark validity first: rerun benches with `--trim-silence false` (or much lower threshold) because FLEURS audio is very quiet and current trimming zeros out 146/200 English clips, inflating WER.”
- “Report metrics correctly: WER overall + WER for `decision=accept` only; log error-rate and reasons. Don’t compare runs with huge `error` counts.”

**When/why we deviated**
- The deviation happened while trying to lower WER quickly by adding an extra hypothesis model, then it snowballed into a per-language router experiment (`per_lang_vaani*`). That’s not required by `master.md` and it’s currently causing lots of OOM/errors and noisy conclusions.

If you want, I can point to the exact evidence you can cite: `outputs/bench/maya_hf_v2/indic_whisper/eval.json` shows the added `transformers:Vignesh-M/Indic-whisper` is worse than `faster-whisper:large-v3|det`, and `outputs/bench/maya_hf_v2/per_lang_vaani/summary.json` shows massive errors (OOM) making those runs invalid for decisions.


# Context from my IDE setup:

## Open tabs:
- master.md: master.md
- results.jsonl: outputs/bench/maya_hf_v2/fw_largev3_baseline_ntrim_limit650/results.jsonl
- results.jsonl: outputs/bench/maya_hf_v2/indic_whisper/results.jsonl
- summary.json: outputs/bench/maya_hf_v2/indic_whisper/summary.json
- eval.json: outputs/bench/maya_hf_v2/indic_whisper/eval.json

## My request for Codex:
i've got a different model to get things back to align with the plan at [master.md](master.md) .

Summary of Changes

  I've reviewed the pipeline against master.md and fixed the following gaps:

  Issues Found & Fixed

  1. Added MMS ASR Backend (asr.py:371-494)
    - Implemented MMSASR class using facebook/mms-1b-all (CTC-based)
    - Added MMSASRRouter for language-specific tokenization
    - Provides architectural diversity from Whisper's encoder-decoder approach
  2. Added IndicConformer Backend (asr.py:525-679)
    - Implemented IndicConformerASR using NVIDIA NeMo
    - Added IndicConformerASRRouter with default per-language model mappings
    - Uses Conformer architecture (different from both Whisper and MMS)
  3. Updated Pipeline (pipeline.py)
    - Integrated MMS ASR and IndicConformer into the ensemble step
    - Added new parameters: mms_asr_model, indicconformer_model_map, indicconformer_default_model
  4. Updated CLI (cli.py)
    - Added --mms-asr-model for MMS ASR CTC-based diversity
    - Added --indicconformer-model-map and --indicconformer-default-model for Conformer diversity
  5. Updated Dependencies (pyproject.toml)
    - Added [nemo] optional dependency for IndicConformer support

  Pipeline Alignment with master.md
  ┌─────────────────────┬────────────────────────┬─────────────────────────────────────┐
  │      Component      │          Plan          │               Status                │
  ├─────────────────────┼────────────────────────┼─────────────────────────────────────┤
  │ Pre-QC + VAD trim   │ ✓                      │ Implemented                         │
  ├─────────────────────┼────────────────────────┼─────────────────────────────────────┤
  │ MMS-LID             │ Primary                │ Implemented                         │
  ├─────────────────────┼────────────────────────┼─────────────────────────────────────┤
  │ VoxLingua107        │ Optional 2nd opinion   │ Not implemented (optional)          │
  ├─────────────────────┼────────────────────────┼─────────────────────────────────────┤
  │ Whisper large-v3    │ Generalist             │ Implemented                         │
  ├─────────────────────┼────────────────────────┼─────────────────────────────────────┤
  │ IndicWhisper        │ Specialist             │ Implemented via transformers router │
  ├─────────────────────┼────────────────────────┼─────────────────────────────────────┤
  │ IndicConformer      │ Architecture diversity │ Now implemented                     │
  ├─────────────────────┼────────────────────────┼─────────────────────────────────────┤
  │ MMS ASR             │ Optional diversity     │ Now implemented                     │
  ├─────────────────────┼────────────────────────┼─────────────────────────────────────┤
  │ ROVER               │ System combination     │ Implemented                         │
  ├─────────────────────┼────────────────────────┼─────────────────────────────────────┤
  │ CTC verification    │ Audio-grounded gate    │ Implemented                         │
  ├─────────────────────┼────────────────────────┼─────────────────────────────────────┤
  │ Accept/Human/Reject │ Precision-first        │ Implemented                         │
  └─────────────────────┴────────────────────────┴─────────────────────────────────────┘
  Ready for Benchmarking

  The benchmark corpus is already set up at data/bench/maya_hf_v0/ with 200 samples per language for all 12 target languages.

  Example benchmark commands:

  # Basic benchmark with Whisper large-v3 only
  maya-transcribe bench run \
    --bench-dir data/bench/maya_hf_v0 \
    --whisper-model large-v3

  # Full ensemble with per-language fine-tuned Whisper + MMS ASR
  maya-transcribe bench run \
    --bench-dir data/bench/maya_hf_v0 \
    --whisper-model large-v3 \
    --transformers-whisper-model-map data/bench/model_maps/whisper_vaani_map.json \
    --mms-asr-model facebook/mms-1b-all \
    --ctc-model facebook/mms-1b-all

  # With IndicConformer (requires NeMo: pip install -e '.[nemo]')
  maya-transcribe bench run \
    --bench-dir data/bench/maya_hf_v0 \
    --whisper-model large-v3 \
    --transformers-whisper-model-map data/bench/model_maps/whisper_vaani_map.json \
    --mms-asr-model facebook/mms-1b-all \
    --indicconformer-default-model ai4bharat/indicconformer_stt_hi_hybrid_rnnt_large

  The pipeline is now aligned with the master.md plan for minimal WER through:
  - Correct routing via MMS-LID
  - Diverse hypotheses via ensemble (Whisper + IndicWhisper + MMS ASR + IndicConformer)
  - System combination via ROVER
  - Audio-grounded verification via CTC scoring
  - Precision-first acceptance with human review for uncertain cases

What would you like me tell the agent to bench first. let's start one language first, fix if WER is large, if satisfied, scale with more samples, then run with "few" languages and move to all languages eventually. running all languages from first gives more testing time, less iterations over fixing the pipeline and ends up less productive in reaching where we want to be.


**Planning language priorities**

I'm thinking it makes sense to start with Gujarati or Tamil after Hindi since their scripts are distinct but still somewhat common. I could add English to validate and use `--trim-silence false` or set a low threshold for clearer results. I wonder if tools are really necessary here, since the user asked what to tell the agent; I can respond directly without requiring specific tools. I'll keep things concise!


Tell the agent to bench **Hindi first (Kathbath)** on a *small, clean, single-language corpus*, with **only faster-whisper large-v3**, and no new ASR backends yet.

**Why Hindi first**
- It’s the most likely to behave “normally” (good LID, plentiful training coverage), so if WER is still bad it points to *pipeline/eval* problems, not low-resource effects.

**Step 0 (don’t use v0)**
- Use `data/bench/maya_hf_v2`, not `v0`. `v0` had duplicate/unstable `segment_id` issues and polluted earlier comparisons.

**Step 1: build a Hindi-only bench (fast iteration)**
- `maya-transcribe bench build --out data/bench/maya_hf_hin_v2 --lang hin --samples-per-lang 200 --force`

**Step 2: baseline run (no ensemble, no CTC, no extra models)**
- `maya-transcribe bench run --bench-dir data/bench/maya_hf_hin_v2 --out outputs/bench/maya_hf_hin_v2/fw_largev3_baseline --whisper-model large-v3`
- Evaluate specifically the primary hypothesis (to avoid ROVER weirdness early):
  - `maya-transcribe eval --results outputs/bench/maya_hf_hin_v2/fw_largev3_baseline/results.jsonl --refs data/bench/maya_hf_hin_v2/refs.jsonl --hypothesis asr:faster-whisper:large-v3|det --language-source refs`

**What to look for before adding anything**
- If Hindi WER isn’t in a sane range (you should not be seeing ~0.8), stop and fix pipeline/eval.
- Confirm **no** `'or' is not a valid language code` errors.
- Confirm trimming isn’t deleting audio (Hindi shouldn’t, but verify `qc.duration_sec > 0` almost always).

**Only if baseline looks sane, add one thing at a time**
1) Add **CTC-fit scoring only** (don’t add MMS ASR yet): `--ctc-model facebook/mms-1b-all`
2) Then consider adding **one** extra ASR hypothesis (either MMS ASR or one trusted Whisper checkpoint), but avoid NeMo/IndicConformer until you’ve proven you’re already in the right ballpark—those will slow runs massively and add OOM risk.

**Second language after Hindi**
- Do **Tamil** or **Gujarati** (different scripts), then **English (FLEURS)** specifically to validate `--trim-silence false` (FLEURS is very quiet and your current trim settings can zero out clips).


# Context from my IDE setup:

## Open tabs:
- master.md: master.md
- results.jsonl: outputs/bench/maya_hf_v2/fw_largev3_baseline_ntrim_limit650/results.jsonl
- results.jsonl: outputs/bench/maya_hf_v2/indic_whisper/results.jsonl
- summary.json: outputs/bench/maya_hf_v2/indic_whisper/summary.json
- eval.json: outputs/bench/maya_hf_v2/indic_whisper/eval.json

## My request for Codex:
Hindi Baseline Results Summary

  ✅ Pipeline Sanity Verified
  ┌──────────────────┬────────┬─────────────────────────────────────────────────┐
  │      Check       │ Status │                     Details                     │
  ├──────────────────┼────────┼─────────────────────────────────────────────────┤
  │ Error rate       │ ✅     │ 0/200 errors                                    │
  ├──────────────────┼────────┼─────────────────────────────────────────────────┤
  │ Audio duration   │ ✅     │ All segments >0s (range 2-10s)                  │
  ├──────────────────┼────────┼─────────────────────────────────────────────────┤
  │ Language routing │ ✅     │ All 200 routed via path (correct for benchmark) │
  ├──────────────────┼────────┼─────────────────────────────────────────────────┤
  │ WER range        │ ✅     │ ~30% (not ~80%, pipeline is working)            │
  └──────────────────┴────────┴─────────────────────────────────────────────────┘
  Baseline Metrics (faster-whisper large-v3 only)
  ┌──────────────┬───────────────┐
  │    Metric    │     Value     │
  ├──────────────┼───────────────┤
  │ WER mean     │ 30.0%         │
  ├──────────────┼───────────────┤
  │ CER mean     │ 11.2%         │
  ├──────────────┼───────────────┤
  │ WER p50      │ 27.3%         │
  ├──────────────┼───────────────┤
  │ WER p90      │ 53.3%         │
  ├──────────────┼───────────────┤
  │ Accept rate  │ 112/200 (56%) │
  ├──────────────┼───────────────┤
  │ Human review │ 88/200 (44%)  │
  └──────────────┴───────────────┘
  LID Analysis

  - 164/200 (82%) correctly detected as Hindi
  - Some confusion with Urdu (10), Gujarati (8), Hindi dialects
  - 137/200 (68.5%) confident (>=0.90 prob)
  - Path-based routing correctly overrides uncertain LID

  Typical Error Types

  Errors are normal ASR mistakes, not pipeline issues:
  - Spelling variations (समन्वयक→समनवैक)
  - Nuqta handling (क़ुरआन→कुराने)
  - Word boundaries (रेतकर→रहत कर)
  - Code-switching (पीएम→PM)

  Next Steps

  The baseline is sane. Ready to add one thing at a time:

  1. Add CTC-fit scoring only (no MMS ASR yet):
  maya-transcribe bench run \
    --bench-dir data/bench/maya_hf_hin_v2 \
    --out outputs/bench/maya_hf_hin_v2/fw_largev3_ctc \
    --whisper-model large-v3 \
    --ctc-model facebook/mms-1b-all

  Would you like me to proceed with adding CTC verification?

would you like me to proceed ?


Yes—proceed with **CTC-fit scoring only** next, but do it in a way that answers one clear question: *does CTC improve selection/gating without hurting WER?*

Tell the agent to run:

1) **CTC run**
- `maya-transcribe bench run --bench-dir data/bench/maya_hf_hin_v2 --out outputs/bench/maya_hf_hin_v2/fw_largev3_ctc --whisper-model large-v3 --ctc-model facebook/mms-1b-all`

2) **Evaluate both hypotheses (so we can see if “final” is better than plain det)**
- `maya-transcribe eval --results outputs/bench/maya_hf_hin_v2/fw_largev3_ctc/results.jsonl --refs data/bench/maya_hf_hin_v2/refs.jsonl --hypothesis asr:faster-whisper:large-v3|det --language-source refs`
- `maya-transcribe eval --results outputs/bench/maya_hf_hin_v2/fw_largev3_ctc/results.jsonl --refs data/bench/maya_hf_hin_v2/refs.jsonl --hypothesis final --language-source refs`

3) **Compare acceptance behavior**
- Pull from `summary.json`: `accept/human/reject` counts, and top `verification.reasons` frequency.

If CTC doesn’t improve `final` WER or it tanks accept-rate, stop and tune only `min_ctc_margin` / coverage thresholds—don’t add new ASR backends yet.


# Context from my IDE setup:

## Open tabs:
- master.md: master.md
- results.jsonl: outputs/bench/maya_hf_v2/fw_largev3_baseline_ntrim_limit650/results.jsonl
- results.jsonl: outputs/bench/maya_hf_v2/indic_whisper/results.jsonl
- summary.json: outputs/bench/maya_hf_v2/indic_whisper/summary.json
- eval.json: outputs/bench/maya_hf_v2/indic_whisper/eval.json

## My request for Codex:
CTC Analysis Results

  ❌ CTC Tanks Accept Rate
  ┌────────┬──────────┬──────────┐
  │ Metric │ Baseline │ With CTC │
  ├────────┼──────────┼──────────┤
  │ Accept │ 112      │ 0        │
  ├────────┼──────────┼──────────┤
  │ Human  │ 88       │ 200      │
  ├────────┼──────────┼──────────┤
  │ Reject │ 0        │ 0        │
  └────────┴──────────┴──────────┘
  Root Cause: Threshold Mismatch

  The MMS CTC model produces very different score ranges for Hindi:
  ┌──────────────────┬──────────────┬───────────┬────────────────────┐
  │      Score       │ Actual (p50) │ Threshold │       Result       │
  ├──────────────────┼──────────────┼───────────┼────────────────────┤
  │ token_coverage   │ 0.23         │ 0.85      │ ❌ 199/200 fail    │
  ├──────────────────┼──────────────┼───────────┼────────────────────┤
  │ token_score_mean │ -6.4         │ -3.0      │ ❌ 199/200 fail    │
  ├──────────────────┼──────────────┼───────────┼────────────────────┤
  │ margin           │ 0.07         │ 0.05      │ ✅ ~50% would pass │
  └──────────────────┴──────────────┴───────────┴────────────────────┘
  Verification Reasons

  BASELINE:
    lid_uncertain: 63
    asr_disagreement: 32

  WITH CTC:
    ctc_low_coverage: 199   ← killing everything
    ctc_low_token_score: 199 ← killing everything
    lid_uncertain: 63
    asr_disagreement: 38
    ctc_ambiguous: 15

  WER Impact
  ┌────────────┬──────────┬──────────┬───────┐
  │ Hypothesis │ Baseline │ With CTC │ Delta │
  ├────────────┼──────────┼──────────┼───────┤
  │ det        │ 30.0%    │ 30.0%    │ 0     │
  ├────────────┼──────────┼──────────┼───────┤
  │ final      │ 31.7%    │ 31.7%    │ 0     │
  └────────────┴──────────┴──────────┴───────┘
  WER unchanged (CTC isn't selecting better), but accept rate tanked 112→0.

  Recommendation: Tune Thresholds

  The default CTC thresholds are miscalibrated for Hindi/MMS. Options:

  1. Disable coverage/token_score checks (only use margin):
  {
    "min_ctc_token_coverage": 0.0,
    "min_ctc_token_score_mean": -999.0,
    "min_ctc_margin": 0.05
  }

  2. Lower thresholds to match actual distribution:
  {
    "min_ctc_token_coverage": 0.15,
    "min_ctc_token_score_mean": -7.0,
    "min_ctc_margin": 0.05
  }

  3. Try a different CTC model (e.g., ai4bharat/indicwav2vec-hindi)

  Should I re-run with relaxed thresholds to see if CTC margin alone helps selection?


**Evaluating Hindi model improvements**

For the Hindi model, if I’m using just the baseline, the det and samp are identical, and CTC can’t improve that. To evaluate selection improvement, I need at least two candidates that differ significantly. I think I could add the MMS ASR or transformer whisper, but they prefer standard models. It might be best to add only MMS ASR, even if it requires more computing power. There’s also an "accept rate 0" for the bench run due to certain thresholds needing adjustment. The next step could be treating CTC as advisory rather than gating it, maintaining baseline accept.


**Crafting the message for adjustments**

I want to suggest sending a message about rerunning with relaxed thresholds, but I need to fix the metrics or adjust the gating first. I'd propose these threshold adjustments in the JSON file: set require_ctc to false, min_ctc_token_coverage to 0.0, min_ctc_token_score_mean to -999, set fail_on_ctc_span_mismatch to false, and set min_ctc_margin to 0.05. After that, I’ll compare the final WER with the det and accept rates. If CTC is meant to help, using a per-language model instead of mms-1b-all could be better.


Yes, rerun — but **not** to “see if margin alone helps selection” (it probably won’t yet). Rerun to validate CTC can be included **without destroying routing/acceptance**, and to collect sane distributions for calibration.

**Why CTC didn’t help (and why accept went to 0)**
- Your current `token_coverage` / `token_score_mean` signals are **not calibrated for `facebook/mms-1b-all`** (and may be structurally mismatched because they’re derived from `torchaudio.merge_tokens(...)` spans, which often won’t correspond 1:1 with target tokens for subword-ish tokenizers). With defaults like `min_ctc_token_coverage=0.85`, everything gets flagged → all “human”.
- WER didn’t change because in your Hindi baseline there’s basically **nothing for CTC to choose between** (det vs samp are often identical; ROVER equals det). CTC can’t improve selection if candidates don’t differ.

**What to rerun (recommended thresholds)**
Use CTC as “advisory”, not a hard gate:

```json
{
  "require_ctc": false,
  "min_ctc_margin": 0.05,
  "min_ctc_token_coverage": 0.0,
  "min_ctc_token_score_mean": -999.0,
  "fail_on_ctc_span_mismatch": false
}
```

That will:
- keep your baseline accept-rate intact,
- still log CTC scores/margins so you can study them,
- avoid the current “ctc_low_coverage kills everything”.

**If you want CTC to actually reduce WER**
- Add **a genuinely different hypothesis** first (e.g., MMS ASR as a second candidate), then let CTC pick between *meaningfully different* outputs.
- Or switch CTC model to a Hindi-specialized wav2vec2/CTC (AI4Bharat indicwav2vec hindi) if you have access; multilingual MMS-1B is a blunt tool.

So: rerun with the “advisory CTC” thresholds above. If accept-rate recovers and CTC margins look sensible, then we can decide whether to (a) fix the `token_coverage` computation properly, or (b) use a better per-language CTC model.


# Context from my IDE setup:

## Open tabs:
- master.md: master.md
- results.jsonl: outputs/bench/maya_hf_v2/fw_largev3_baseline_ntrim_limit650/results.jsonl
- results.jsonl: outputs/bench/maya_hf_v2/indic_whisper/results.jsonl
- summary.json: outputs/bench/maya_hf_v2/indic_whisper/summary.json
- eval.json: outputs/bench/maya_hf_v2/indic_whisper/eval.json

## My request for Codex:
CTC Advisory Run Summary

  ✅ Accept-Rate Recovered
  ┌────────┬──────────┬──────────┬──────────────┐
  │ Metric │ Baseline │ CTC Hard │ CTC Advisory │
  ├────────┼──────────┼──────────┼──────────────┤
  │ Accept │ 112      │ 0        │ 107          │
  ├────────┼──────────┼──────────┼──────────────┤
  │ Human  │ 88       │ 200      │ 93           │
  └────────┴──────────┴──────────┴──────────────┘
  Verification Reasons (Advisory)

  lid_uncertain: 63      (same as baseline)
  asr_disagreement: 40   (up from 32)
  ctc_ambiguous: 23      (new - margin < 0.05)

  CTC Distributions for Calibration
  ┌──────────────────┬───────┬───────┬───────┬────────────────────┐
  │      Score       │  p10  │  p50  │  p90  │ Current Threshold  │
  ├──────────────────┼───────┼───────┼───────┼────────────────────┤
  │ loss             │ 5.94  │ 6.45  │ 7.08  │ N/A                │
  ├──────────────────┼───────┼───────┼───────┼────────────────────┤
  │ margin           │ 0.01  │ 0.05  │ 0.15  │ 0.05 ✅            │
  ├──────────────────┼───────┼───────┼───────┼────────────────────┤
  │ token_coverage   │ 0.19  │ 0.23  │ 0.28  │ 0.85 ❌ (disabled) │
  ├──────────────────┼───────┼───────┼───────┼────────────────────┤
  │ token_score_mean │ -6.94 │ -6.41 │ -5.89 │ -3.0 ❌ (disabled) │
  └──────────────────┴───────┴───────┴───────┴────────────────────┘
  Key Findings

  1. CTC can't improve selection yet - only 41/200 samples have margin (different candidates). CTC selected rover 183x, samp 17x, det 0x. With a single hypothesis, there's nothing to choose between.
  2. Gating works correctly - WER by decision:
  | Decision | Baseline | CTC Advisory |
  |----------|----------|--------------|
  | accept   | 0.270    | 0.261 ✓      |
  | human    | 0.377    | 0.383        |

  2. Lower-error samples → accept, higher-error → human. Gate is routing correctly.
  3. MMS coverage/token_score miscalibrated - the torchaudio.merge_tokens spans don't align with MMS subword tokenization. Coverage at 0.23 (vs 0.85 threshold) means these signals are structurally mismatched for this model.

  Next Steps Options

  1. Add a genuinely different hypothesis (MMS ASR) so CTC has meaningful candidates to choose between
  2. Try Hindi-specialized CTC (ai4bharat/indicwav2vec-hindi) if available
  3. Fix token_coverage computation to handle subword tokenizers properly

  Recommendation: The advisory CTC baseline is now stable. To see if CTC actually helps WER, add MMS ASR as a second candidate that produces genuinely different output. Want me to proceed with that?

✻ Brewed for 10m 58s


Yes—proceed, but do it **tightly scoped** so we learn something actionable:

**1) Add MMS ASR as the second hypothesis (Hindi only), keep CTC advisory**
- Run: `faster-whisper large-v3` + `MMS ASR (facebook/mms-1b-all)` + `CTC advisory (facebook/mms-1b-all)`
- Keep thresholds exactly as in the advisory run (`require_ctc=false`, coverage/token_score disabled, `min_ctc_margin=0.05`).

**2) What to measure (must report these)**
- `WER(final)` vs `WER(asr:faster-whisper:large-v3|det)` vs `WER(asr:mms:<...>)` (whatever key you log for MMS ASR).
- `oracle_best_wer_mean` and `selection_gap_wer_mean` (to see if MMS adds useful diversity even if selection isn’t perfect yet).
- Accept/human counts and top `verification.reasons` (watch that we don’t tank accept-rate again).

**3) Success criteria**
- MMS hypothesis alone doesn’t need to beat Whisper, but **oracle_best WER must improve** (meaning MMS sometimes wins), and **final WER should not get worse**.
- If oracle improves but final doesn’t: selection logic needs work (CTC margin only may not be enough; may need better alignment signal or per-language CTC).

If MMS ASR adds no oracle improvement on Hindi, don’t take it to more languages; switch to a Hindi-specialized CTC/ASR model instead.


# Context from my IDE setup:

## Open tabs:
- master.md: master.md
- results.jsonl: outputs/bench/maya_hf_v2/fw_largev3_baseline_ntrim_limit650/results.jsonl
- results.jsonl: outputs/bench/maya_hf_v2/indic_whisper/results.jsonl
- summary.json: outputs/bench/maya_hf_v2/indic_whisper/summary.json
- eval.json: outputs/bench/maya_hf_v2/indic_whisper/eval.json

## My request for Codex:
MMS ASR Results Summary

  ❌ MMS ASR Failed for Hindi
  ┌───────────────┬────────┬────────────────────────────────┐
  │  Hypothesis   │  WER   │             Notes              │
  ├───────────────┼────────┼────────────────────────────────┤
  │ det (Whisper) │ 30.0%  │ Baseline                       │
  ├───────────────┼────────┼────────────────────────────────┤
  │ MMS ASR       │ 100.9% │ Garbage output                 │
  ├───────────────┼────────┼────────────────────────────────┤
  │ final (ROVER) │ 30.6%  │ ROVER correctly picked Whisper │
  ├───────────────┼────────┼────────────────────────────────┤
  │ oracle_best   │ 29.7%  │ No improvement from MMS        │
  └───────────────┴────────┴────────────────────────────────┘
  MMS Output Sample

  Whisper: इस बकाया रकम में ब्याज की राशी भी सामे जाता है
  MMS:     ि ैब् बो ो ैवि निव िो  (garbage)

  Why Accept Rate Tanked (Again)
  ┌────────────────┬────────┬───────┬───────────────────────┐
  │      Run       │ Accept │ Human │        Reason         │
  ├────────────────┼────────┼───────┼───────────────────────┤
  │ Baseline       │ 112    │ 88    │ -                     │
  ├────────────────┼────────┼───────┼───────────────────────┤
  │ + MMS (no CTC) │ 0      │ 200   │ asr_disagreement: 200 │
  └────────────────┴────────┴───────┴───────────────────────┘
  ROVER disagreement rate: 28-48% (vs threshold 3%) because MMS garbage disagrees with Whisper on nearly every token.

  CTC Bias Problem (With CTC)

  When CTC was enabled, it selected MMS garbage over Whisper because the MMS CTC model naturally scores its own output lower:
  ┌───────────────────┬────────────────┐
  │     Candidate     │    CTC Loss    │
  ├───────────────────┼────────────────┤
  │ MMS (garbage)     │ 1.7 ← selected │
  ├───────────────────┼────────────────┤
  │ Whisper (correct) │ 5.2            │
  └───────────────────┴────────────────┘
  Conclusions

  1. MMS ASR does NOT add useful oracle diversity for Hindi - it never wins
  2. Don't take MMS ASR to more languages - fundamental issue
  3. CTC + same-model ASR creates selection bias - can't use MMS for both

  Recommendations

  1. Try Hindi-specialized ASR instead (e.g., ai4bharat/indicwav2vec-hindi, or one of the fine-tuned Whisper models from whisper_vaani_map.json)
  2. Use different model for CTC than ASR if you want CTC selection to work
  3. Enable script filtering (filter_hypotheses_by_script: true) to catch garbage outputs

  Should I try adding one of the fine-tuned Hindi Whisper models (from your existing whisper_vaani_map.json) as the second hypothesis instead? That would provide genuine diversity within the same architecture.

Should we try voxtral LLM or Gemma3n LLM even tho if it takes much compute, if WER is very less is what we need right ? should we involve them yet or can we go even lower


Don’t jump to Voxtral/Gemma-3n yet. You can almost certainly get big gains first by fixing **benchmark validity + selection signals + adding one trusted specialist**, without paying LLM latency/cost or introducing hallucination risk.

**Why MMS ASR failed (and what it implies)**
- `facebook/mms-1b-all` is **CTC** and multilingual; using it as “ASR output generator” is brittle (tokenization/script issues → garbage).
- Using the *same* family for **CTC selection** and **ASR candidate** creates a built-in bias (it will often score its own garbage lowest).

**What to do next (in order)**
1) **Add script filtering now** (cheap, high-signal): reject/ignore hypotheses whose output script doesn’t match the expected script for the routed language. This will prevent “MMS garbage” from poisoning ROVER and `asr_disagreement`.
2) **Keep CTC advisory**, but only as *gating* when candidates are in the correct script; don’t use MMS for both ASR + CTC.
3) **Second hypothesis: use one trusted specialist model**.
   - If you want to stay “standard”, the safest move is a well-known, widely used checkpoint: `openai/whisper-large-v3` via transformers (or `large-v2`) as a second backend to catch engine differences; it won’t be huge diversity but it’s stable.
   - If you accept “specialist but reputable org”: use ARTPARK/AI4Bharat-style models (your `whisper_vaani_map.json`). That *is* language-specific, but you can treat it as **optional** and only enable per-language when it *proves* oracle gain on that language.

**When to involve audio LLMs (Gemini / Voxtral / etc.)**
Use them only if:
- Baseline is already sane (Hindi WER ~0.30 is sane), and
- You still have hard segments where accept-rate is low and you want fewer “human” routes,
- And you can enforce strict “no guessing” + `<unk>` rules (LLMs can hurt WER if they “fill in”).
They’re best as **hard-segment fallback**, not as a baseline.

**Concrete next experiment**
- Hindi again: add **one** extra trusted whisper-family model (preferably from `ARTPARK-IISc/whisper-large-v3-vaani-hindi` if you allow it), enable script filtering, keep CTC advisory, and check `oracle_best_wer_mean` improves without hurting accept-rate. If oracle doesn’t improve, don’t scale.

If you want “no language-specific models at all”, then skip Vaani and instead do:
- `faster-whisper large-v3` + `transformers openai/whisper-large-v3` (or `large-v2`) + script filtering + CTC advisory. This is slower but stays “standard”.