Output:
Exit code: 0
Wall time: 79.7 seconds
Output:
loading fleurs as_in test

Generating train split: 0 examples [00:00, ? examples/s]
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Dataset({
    features: ['id', 'num_samples', 'path', 'audio', 'transcription', 'raw_transcription', 'gender', 'lang_id', 'language', 'lang_group_id'],
    num_rows: 984
})
columns ['id', 'num_samples', 'path', 'audio', 'transcription', 'raw_transcription', 'gender', 'lang_id', 'language', 'lang_group_id']
first {'id': 'int', 'num_samples': 'int', 'path': 'str', 'audio': 'dict', 'transcription': 'str', 'raw_transcription': 'str', 'gender': 'int', 'lang_id': 'int', 'language': 'str', 'lang_group_id': 'int'}
text প্ৰতিবেদনটোত ইৰাকৰ কাৰ্যপালিকাৰ বৰ্তমানৰ প্ৰতিটো নীতিৰ অতি বেছি সমালোচনা কৰা হৈছে আৰু ইয়াত ততালিকে নিৰ্দেশ সলনি কৰাৰ বাবে অনুৰোধ জনোৱা হৈছে
audio {'path': 'test/10007973808011083086.wav', 'array': array([0.        , 0.        , 0.        , ..., 0.00035185, 0.00030065,
       0.00047541]), 'sampling_rate': 16000}


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.


**Evaluating dataset processing**

I’m generating train, validation, and test splits for a dataset: 2812 for training, 418 for validation, and 984 for testing. That seems manageable! I can sample from the test or validation sets, but if the user needs 1000-2000 examples, I might need to include some from the training set too. I’ll also handle audio processing, preparing it as 16k wav files for our pipeline that requires a segments directory. I must remember to create the references file keyed by segment ID.


**Creating audio file paths**

I’m considering how to establish a clear naming convention for audio files in the segments directory, using stable names based on dataset IDs. These could look something like `kathbath-hindi-valid-<fname>`. The `segment_identity` will generate an ID from the relative path, but I need to remember the pipeline’s structure. It should identify segments uniquely, even if IDs repeat. I also want to map languages to ISO-3 codes, keeping in mind that LID may misdetect some routes.


**Improving evaluation methods**

I'm evaluating how to effectively use `route_lang` for normalization. If it's missing or incorrect, there could be minor issues with normalization, like improper English lowercasing. For better accuracy, I think I should consider passing the known language code from the dataset config rather than relying solely on the pipeline route. It seems like storing the language in references and using that would enhance evaluation quality. So, I may modify the evaluation to use the reference language instead of the predicted one to account for potential LID errors.


**Addressing LID and normalization errors**

LID errors are part of the pipeline, and while they may impact WER by routing the wrong model, evaluation should assess WER independently. Normalization differences could influence WER, especially since lowercasing remains crucial for English. If LID mispredicts a language, we might skip lowercasing, which could slightly inflate WER. I plan to adjust the evaluation method to check for the known language in the reference row first, ensuring that we use stable and correct information. Additionally, storing ISO-3 codes could help streamline our processes.


**Implementing a benchmarking system**

The pipeline uses CTC scoring only if the model is enabled. It selects the best candidate without gating and sets the final text based on the candidate with the best loss, but it could encounter issues with too many unknown tokens, possibly skipping some candidates. To improve WER, I’ll enable the correct CTC model per language or utilize MMS. I plan to automate benchmarking by creating a Python script to handle multiple languages, allowing for flexible configurations. Each language will have its own segments directory for ease of use and accurate reference matching.


**Optimizing audio handling**

I’m considering storing raw audio files at the original sample rate, but since the pipeline will convert them to 16k using ffmpeg, I think that’s fine. It's important for the audio processing to work seamlessly, maintaining quality while being efficient.

I'll implement a function that helps with this conversion, ensuring all audio types are handled correctly, whether they come as an array, file path, or bytes. I'll also create stable file naming conventions to avoid duplicates and prepare for evaluations across languages. Using a combined segments directory for multiple languages during benchmarking seems like a better approach for model reuse.


**Analyzing WER improvements**

I'm planning to compute segment IDs when building the dataset, ensuring we have clarity on language sample sizes as well. After running the pipeline and assessing the WER, I need to investigate any languages with high error rates. Possible causes could include incorrect language identification, model mismatches, text normalization issues, or format discrepancies in transcripts.

To enhance performance, I'll consider incorporating different model configurations, possibly running benchmarks that compare WER across these setups. This systematic approach is essential as I aim for improvements, noting user requests for reproducible results and a robust manifest for potential open benchmarking efforts.


**Building and pushing datasets**

I’m planning to create a dataset for Kathbath and FLEURS, combining different sources to ensure I reach my target sample count for each language. Once I’ve structured the dataset, I’ll push it to Hugging Face. However, I need to decide whether to include audio files alongside the transcripts or just a manifest to reference them.

For user convenience, I’ll default to including audio unless they specify otherwise. I also want to implement robust benchmarking to refine our model's performance and reduce WER. I'll consider the optimal number of samples from each language to run tests efficiently.


**Testing pipeline speed**

I’m estimating that the processing time for 1200 audio segments could be around 10 minutes at 10x real-time on an A100. That’s aiming for efficient speed! For LID, I'm considering that it might slow things down a bit since it uses transformers. For our ASR models, starting with the baseline of faster-whisper large-v3 makes sense, but I might explore adding additional models later for better WER. I also need to enable CTC scoring, as it might help improve accuracy, provided all necessary libraries like torch and transformers are installed.


python3 -c "import torchaudio; print(torchaudio.__version__)"


Output:
Exit code: 0
Wall time: 1.7 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]
2.9.0+cu128


Summary: auto


**Optimizing audio scoring**

I’m noticing that computing audio logits for each candidate text is rather costly. In the `rank_candidates_by_ctc`, we call `scorer.score` for every candidate, which decodes audio and computes logits repeatedly. That's inefficient! We should optimize by computing audio logits just once and reusing them across multiple candidates; this could really speed up CTC scoring and might even help with WER indirectly. If we check the `_CTCModel.score`, it looks like we’re evaluating the model multiple times unnecessarily, which adds up significantly. Refactoring could save time and resources!


**Implementing log probability optimization**

I’m thinking we can create a method called `_CTCModel.compute_log_probs(audio_path)` to streamline our process for each candidate. It would be great to compute log probabilities just once, especially since forced alignment relies on them too.

I could implement a new method to return log probabilities and input lengths as tensors. However, we also need to tokenize text for each candidate and compute ctc_loss, as it’s dependent on the targets.

So, we could restructure it to allow the use of precomputed log probabilities for each candidate while addressing their specific needs. This could improve efficiency significantly!


**Implementing candidate ranking optimization**

Okay, this adjustment will really speed up the `rank_candidates_by_ctc` process. I’m considering whether we might also cache audio log probabilities per segment, but that’s not necessary right now. Instead, I plan on modifying `rank_candidates_by_ctc` to compute log probabilities once and score each candidate efficiently.

Since it currently uses `CTCFitScorer`, I can create a new method to rank candidates based on the audio and language. I’ll ensure we set the target language with the tokenizer correctly before tokenization. This should help with the overall optimization and benchmarking!


**Optimizing benchmarking process**

I’m circling back to benchmarking and realizing that the pipeline run might be pretty heavy already. With CTC scoring, repeated forward passes could make it even heavier, but my optimization should help.

There are tasks to implement, like patching `ctc_fit.py` to add the MMS target language and optimize those forward passes. I also want to create a benchmark module with CLI commands for dataset sampling, pipeline running, evaluating, and analyzing, plus the option to push the dataset to HF private. This will require a structured plan with steps, but first, I’ll confirm any AGENTS instructions to follow. Let’s check the existing outputs directory just in case, even though it might not be crucial!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 44
drwxrwxr-x 11 ubuntu ubuntu 4096 Jan 13 06:15 .
drwxrwxr-x  8 ubuntu ubuntu 4096 Jan 13 06:26 ..
drwxrwxr-x  3 ubuntu ubuntu 4096 Jan 13 04:33 test_run
drwxrwxr-x  3 ubuntu ubuntu 4096 Jan 13 04:36 test_run2
drwxrwxr-x  3 ubuntu ubuntu 4096 Jan 13 05:31 test_run3
drwxrwxr-x  3 ubuntu ubuntu 4096 Jan 13 05:34 test_run4
drwxrwxr-x  3 ubuntu ubuntu 4096 Jan 13 06:12 test_run5
drwxrwxr-x  3 ubuntu ubuntu 4096 Jan 13 06:15 test_run6
drwxrwxr-x  3 ubuntu ubuntu 4096 Jan 13 05:41 test_run_ctc


**Planning analysis tool features**

I want to ensure that dataset sampling is safe regarding size, and I'll implement caching to avoid rewriting existing bench audio, allowing for resumption. Initially, I think multi-processing might not be necessary. Now, I need to focus on implementing the analysis tool with the command `maya-transcribe bench analyze --results ... --refs ...`. This should provide a summary, possibly a breakdown by language, reason counts, and highlight top error cases, all while remaining concise. The user asked to analyze high results and suggest fixes or tweaks.


**Implementing analysis function**

I'm planning an analysis function that reads results.jsonl and refs.jsonl for each segment. I’ll identify both the reference and hypothesis texts, normalizing them based on the language from the reference row. Then, I’ll compute the Word Error Rate (WER) and Character Error Rate (CER), determine the predicted language, and make verification decisions, including error reasons.

Next, I'll aggregate data by language, counting metrics like mean WER and coverage. Also, I'll check for Language Identification (LID) mismatches to aid in debugging. Finally, I’ll compile a list of top segments with WER for saving to a JSON file and run benchmarks on sample counts.


**Deciding on dataset sample size**

I’m figuring out how to implement this with the bench command or run commands directly using the new dataset. The user mentioned wanting "a few hundreds of samples for each language" and overall around 1000-2000 samples per language. I think we could start with a baseline of 200 samples per language. Aiming for 1000 samples, totaling about 12,000, seems ambitious but possibly doable on A100. However, it might take around an hour or more. So, I’ll go ahead with 200 samples per language for the initial run and adjust based on available time.


**Planning code changes and steps**

I need to implement some code changes, which might extend the run time. However, running tasks concurrently isn't an option here, so we can proceed after the code modifications. We’re in a limited codex environment, but I'll give it a shot. First, I need to produce a patch that includes the bench commands and a fix for CTC.

Next, I’ll build the dataset and run the pipeline with a moderate sample size to generate results in the outputs directory. The user requested a new HF dataset upload, which may complicate things if we run large, but I’ll try to tackle it step by step.

My plan includes patching CTC scoring, implementing benchmarking utilities like the dataset sampler, bench runner, and analysis summarizer, and finally adding CLI `bench` commands to call these utilities.


**Generating benchmark subset dataset**

I’m planning to generate a benchmark subset dataset with about 200 samples per language using Kathbath and FLEURS, storing it under `bench/maya_bench_v0`. Then, I’ll run the pipeline and evaluation on this subset, producing a summary for analysis.

There’s an option to create a private HF dataset repo and push either the manifest or audio, but we may skip some steps if they’re too heavy. I’ll also want to check for a `.gitignore` file in the repository; if it’s not there, I might add one to ignore generated data like `bench/` or `bench_runs/`. However, it’s not strictly required since the repo might already handle ignoring outputs. Let’s take a look at the `.gitignore` to see.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
.DS_Store
__pycache__/
*.pyc
*.pyo
*.pyd
.pytest_cache/
.mypy_cache/
.ruff_cache/
.venv/
venv/
dist/
build/
*.egg-info/

data/
outputs/
cache/


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

import json
from pathlib import Path
from typing import Any, Iterable

def write_jsonl(path: Path, rows: Iterable[dict[str, Any]]) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    with path.open("w", encoding="utf-8") as f:
        for row in rows:
            f.write(json.dumps(row, ensure_ascii=False) + "\n")

def read_jsonl(path: Path) -> list[dict[str, Any]]:
    rows: list[dict[str, Any]] = []
    with path.open("r", encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            rows.append(json.loads(line))
    return rows


**Implementing bench module**

I need to implement the bench module, focusing on reading and writing functionality. I’m thinking it could be helpful to add `maya_transcribe/config.py` to manage settings and environment variables, though that's not strictly necessary. Also, I want to inspect `config.py` to ensure it's set up properly for retrieving the HF token. This part seems crucial for smooth functionality, so I’ll take a closer look at how to optimize that.


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

from dataclasses import dataclass
from pathlib import Path
from typing import Final

from dotenv import load_dotenv

@dataclass(frozen=True)
class Settings:
    r2_endpoint_url: str
    r2_access_key_id: str
    r2_secret_access_key: str
    r2_bucket: str = "1-cleaned-data"
    r2_prefix: str | None = None

    @staticmethod
    def load(env_path: str | Path = ".env") -> "Settings":
        load_dotenv(dotenv_path=env_path, override=False)

        import os

        def required(name: str) -> str:
            value = os.getenv(name)
            if not value:
                raise RuntimeError(f"Missing required env var: {name}")
            return value

        endpoint_url: Final[str] = required("R2_ENDPOINT_URL")
        access_key_id: Final[str] = required("R2_ACCESS_KEY_ID")
        secret_access_key: Final[str] = required("R2_SECRET_ACCESS_KEY")
        bucket: Final[str] = os.getenv("R2_BUCKET") or "1-cleaned-data"
        prefix = os.getenv("R2_PREFIX") or None

        return Settings(
            r2_endpoint_url=endpoint_url,
            r2_access_key_id=access_key_id,
            r2_secret_access_key=secret_access_key,
            r2_bucket=bucket,
            r2_prefix=prefix,
        )


**Considering dataset extraction**

I need to extract examples and compute hashed keys for each dataset. First, I’ll determine unique IDs using keys like 'id', 'fname', or 'path', or I might default to an index. Then, I'll build a list of either (sort_key, index) or (hash, example) for all examples, which may involve reading the entire dataset. There could be concerns about memory since audio arrays might load heavily. I’m wondering if I can avoid this by using the dataset's `Audio` column with decode=False.


**Evaluating audio dataset management**

When the dataset is loaded, the audio isn't decoded until I access that specific column. To avoid decoding all audio arrays—which can be heavy—I should only select a specific number of examples using the `select` method with indices. To choose those indices deterministically, I need a sort key based on metadata rather than audio data. I could potentially use the example ID field 'fname' since it’s a string and doesn't require audio decoding. However, iteration over the dataset might still trigger audio decoding for each accessed row.


**Refining audio retrieval strategy**

I can retrieve specific columns from the dataset without decoding audio by setting `decode=False` for the audio column. This is crucial! By casting the audio column to `Audio(decode=False)`, I might get the file path or bytes without decoding. For selection, I should focus on the text and ID fields without loading audio. Using `ds.to_pandas()` is heavy, so a simpler approach is to just take the first N examples with `ds[:N]`, though it won't be random. If the user wants randomness, I could consider sampling by hashing the ID field and using the dataset’s `sort` method, but that might involve reading everything.


**Planning dataset selection and shuffling**

I can manage 3151 examples, but sorting the dataset might slow things down as it copies data to disk. A simpler approach is to use the first N examples from each source. However, I’ll also add an option for `--shuffle` to allow for randomness while defaulting to the first N to maintain reproducibility. To generate indices, I can use `random.Random(seed).sample(range(len(ds)), k=N)`, which won’t require reading audio data. While the dataset might have a large length (like 91,752 for training), sampling from a valid subset should keep things manageable, especially since I’ll only need about 1000 examples for each language.


**Designing sampling and evaluation process**

I can use random sampling to avoid decoding audio, which is great! To ensure stability across runs, I’ll add a seed for randomness. My algorithm will look like this: for each language, I’ll load the dataset and determine available and needed counts. If I don't want to shuffle, I’ll just take the first examples. If shuffling, I’ll sample indices using a hash-based seed. For each selected index, I'll extract the text and audio, compute a segment ID, and keep track of progress.

Now regarding file management, I'll create an organized directory structure under `bench_dir/audio`, and save files in a specific naming format to ensure segment identity remains unique. Additionally, I’ll write a reference file and a manifest that captures configuration and counts. The pipeline will be run with specific directory paths, and I need to evaluate the results and analyze metrics like WER by language. I plan on updating the evaluation process to support both route and reference languages for better analysis. It might require some dataclass adjustments, but that should be manageable!


**Implementing evaluation and scoring mechanisms**

I'm working on the evaluate function: first, I'll determine the language for scoring based on whether it uses reference or route languages. The goal is to allow benchmarking while ensuring reproducibility.

In the CTC optimization patch, I’ll create an internal method for computing log probabilities from audio, ensuring audio is only decoded once per candidate audio file. Additionally, I’ll set up a structure for tokenizing targets and calculating necessary metrics for scoring, including handling unknown ratios and forced alignments, which might need to be optional due to potential costs. I'll implement scoring candidates by using prepared log probabilities without re-decoding audio. This method will return scores and handle any exceptions during the scoring process.


**Implementing Language Normalization and Dataset Building**

I'm setting up a function to normalize language codes. It should handle both ISO languages and BCP-47 codes, ensuring we can always get the correct three-letter format. I'll also handle potential exceptions while setting the target language for the tokenizer.

Now, I'm moving on to creating the benchmark module to build a dataset. This involves defining a function that can shuffle and filter samples from selected sources while ensuring we meet the required sample count per language. As I implement audio extraction and writing, I'll handle potential variations in audio formats while keeping it simple by just writing raw audio data without extra processing steps, like normalization.


**Considering WAV and FLAC Formats and Further Implementations**

I'm weighing the choice between writing audio files in WAV format versus FLAC for space efficiency. Although WAV might take up more space, it’s straightforward, and I could use a tool like ffmpeg for compression, though that's not necessary right now.

After creating the benchmark dataset, I'll run the pipeline, taking into account limits on the dataset size. Next, I’ll analyze results by comparing reference languages, computing metrics like WER, and identifying any selection issues.

Then, I'll add options for adjusting thresholds through a JSON file in the CLI, providing users with more flexibility.


Explanation: Implement robust benchmarking + dataset tooling, and fix MMS CTC language handling + efficiency so benchmarks are meaningful and fast.


Output: Plan updated


Summary: auto


**Considering audio processing**

I need to compute the unknown tokens ratio for each candidate. Okay, I also must consider memory usage since log probabilities can get large for long audio clips. Segments are about 60 seconds, which could still be manageable. However, I worry that CTC scoring might be heavy on resources. Alright, so I guess it’s time to implement this. Let's patch it and see how it goes!


Input:
*** Begin Patch
*** Update File: maya_transcribe/ctc_fit.py
@@
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Any

 from maya_transcribe.audio import decode_pcm16
+from maya_transcribe.language import ISO3_TO_WHISPER_LANG
 from maya_transcribe.textnorm import normalize_text

 @dataclass(frozen=True)
 class CTCScore:
@@
     span_count: int | None
     span_mismatch: bool | None

+_WHISPER_TO_ISO3: dict[str, str] = {v: k for k, v in ISO3_TO_WHISPER_LANG.items()}
+
+
+def _to_iso3(lang: str | None) -> str | None:
+    if not lang:
+        return None
+    code = lang.strip().lower()
+    if not code:
+        return None
+    # Handle common BCP-47 forms (e.g., hi_IN / hi-IN).
+    code = code.replace("-", "_")
+    if "_" in code:
+        code = code.split("_", 1)[0]
+    if len(code) == 3:
+        return code
+    if len(code) == 2:
+        return _WHISPER_TO_ISO3.get(code, None)
+    return code
+
+
 class _CTCModel:
     def __init__(self, model_id: str, device: str | None = None) -> None:
         try:
             import torch
             from transformers import AutoModelForCTC, AutoProcessor
@@
         if blank is None and getattr(self._processor, "tokenizer", None) is not None:
             blank = getattr(self._processor.tokenizer, "pad_token_id", None)
         self.blank_id = int(blank) if blank is not None else 0

+        self._target_lang: str | None = None
+
+    def _maybe_set_target_lang(self, lang: str | None) -> None:
+        tokenizer = getattr(self._processor, "tokenizer", None)
+        set_lang = getattr(tokenizer, "set_target_lang", None) if tokenizer is not None else None
+        if not callable(set_lang):
+            return
+
+        iso3 = _to_iso3(lang)
+        if not iso3:
+            return
+        if self._target_lang == iso3:
+            return
+        try:
+            set_lang(iso3)
+        except Exception as e:
+            raise RuntimeError(f"set_target_lang_failed:{iso3}:{e}") from e
+        self._target_lang = iso3
+
+    def _compute_log_probs(self, audio_path: Path):
+        wav, sr = decode_pcm16(audio_path, sample_rate=16000, mono=True)
+        if wav.size == 0:
+            raise ValueError("empty_audio")
+
+        inputs = self._processor(wav, sampling_rate=sr, return_tensors="pt")
+
+        # Common key names: input_values (wav2vec2) or input_features (some processors).
+        if "input_values" in inputs:
+            model_inputs = {"input_values": inputs["input_values"].to(self.device)}
+        elif "input_features" in inputs:
+            model_inputs = {"input_features": inputs["input_features"].to(self.device)}
+        else:
+            # Best-effort: move all tensors to device.
+            model_inputs = {k: v.to(self.device) for k, v in inputs.items() if hasattr(v, "to")}
+
+        with self._torch.no_grad():
+            logits = self._model(**model_inputs).logits  # (B,T,C)
+            log_probs = logits.log_softmax(dim=-1)  # (B,T,C)
+
+        input_lengths = self._torch.tensor([log_probs.size(1)], dtype=self._torch.long, device=log_probs.device)
+        return log_probs, input_lengths
+
+    def _score_from_log_probs(
+        self,
+        *,
+        log_probs,
+        input_lengths,
+        text: str,
+        lang: str | None,
+        max_unk_ratio: float,
+        token_logp_threshold: float,
+    ) -> CTCScore:
+        import torch.nn.functional as F
+        import warnings
+
+        if not text:
+            raise ValueError("empty_text")
+        if "<unk>" in text or "<inaudible>" in text:
+            raise ValueError("contains_unk")
+
+        # Conservative normalization helps tokenizer compatibility.
+        text_norm = normalize_text(text, lang=lang)
+        if not text_norm:
+            raise ValueError("empty_text")
+        if self._force_uppercase:
+            text_norm = text_norm.upper()
+
+        # Tokenize targets (MMS multilingual tokenizers require set_target_lang()).
+        tokenizer = getattr(self._processor, "tokenizer", None)
+        if tokenizer is None:
+            raise ValueError("missing_tokenizer")
+        self._maybe_set_target_lang(lang)
+        tokenized = tokenizer(text_norm, add_special_tokens=False)
+        target_ids = tokenized.get("input_ids")
+        if not target_ids:
+            raise ValueError("empty_targets")
+
+        unk_id = getattr(tokenizer, "unk_token_id", None)
+        unk_ratio: float | None = None
+        if unk_id is not None:
+            unk_count = sum(1 for tid in target_ids if int(tid) == int(unk_id))
+            unk_ratio = float(unk_count) / float(len(target_ids))
+            if unk_ratio > max_unk_ratio:
+                raise ValueError(f"too_many_unk_tokens:{unk_ratio:.3f}")
+
+        # torch ctc_loss expects (T,N,C)
+        log_probs_tnc = log_probs.transpose(0, 1)
+        device = log_probs_tnc.device
+        targets = self._torch.tensor(target_ids, dtype=self._torch.long, device=device)
+        target_lengths = self._torch.tensor([targets.numel()], dtype=self._torch.long, device=device)
+
+        # Reduction=mean normalizes by target length; good for comparability.
+        loss = F.ctc_loss(
+            log_probs_tnc,
+            targets,
+            input_lengths,
+            target_lengths,
+            blank=self.blank_id,
+            reduction="mean",
+            zero_infinity=True,
+        )
+        loss_value = float(loss.detach().cpu().item())
+
+        # Forced alignment token scores (coverage/fit). Best-effort: alignment may fail for short audio vs long text.
+        token_score_mean = None
+        token_score_min = None
+        token_coverage = None
+        span_count = None
+        span_mismatch = None
+
+        try:
+            import torchaudio
+
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", message=".*forced_align.*deprecated.*", category=UserWarning)
+                aligned_tokens, aligned_scores = torchaudio.functional.forced_align(
+                    log_probs,
+                    targets.unsqueeze(0),
+                    input_lengths=input_lengths,
+                    target_lengths=target_lengths,
+                    blank=self.blank_id,
+                )
+
+            spans = torchaudio.functional.merge_tokens(
+                aligned_tokens[0].detach().cpu(), aligned_scores[0].detach().cpu(), blank=self.blank_id
+            )
+            span_scores = [float(s.score) for s in spans]
+            span_count = len(span_scores)
+            span_mismatch = (span_count != int(target_lengths.item()))
+            if span_scores:
+                token_score_mean = sum(span_scores) / float(len(span_scores))
+                token_score_min = min(span_scores)
+                token_coverage = sum(1 for s in span_scores if s >= token_logp_threshold) / float(len(span_scores))
+        except Exception:
+            pass
+
+        return CTCScore(
+            model_id=self.model_id,
+            loss=loss_value,
+            text=text,
+            unk_ratio=unk_ratio,
+            target_len=int(target_lengths.item()),
+            token_score_mean=token_score_mean,
+            token_score_min=token_score_min,
+            token_coverage=token_coverage,
+            token_logp_threshold=token_logp_threshold,
+            span_count=span_count,
+            span_mismatch=span_mismatch,
+        )
+
     def score(
         self,
         audio_path: Path,
         text: str,
         lang: str | None = None,
         *,
         max_unk_ratio: float = 0.2,
         token_logp_threshold: float = -3.0,
     ) -> CTCScore:
-        import torch.nn.functional as F
-        import warnings
-
-        if not text:
-            raise ValueError("empty_text")
-        if "<unk>" in text or "<inaudible>" in text:
-            raise ValueError("contains_unk")
-
-        wav, sr = decode_pcm16(audio_path, sample_rate=16000, mono=True)
-        if wav.size == 0:
-            raise ValueError("empty_audio")
-
-        # Conservative normalization helps tokenizer compatibility.
-        text_norm = normalize_text(text, lang=lang)
-        if not text_norm:
-            raise ValueError("empty_text")
-        if self._force_uppercase:
-            text_norm = text_norm.upper()
-
-        inputs = self._processor(wav, sampling_rate=sr, return_tensors="pt")
-
-        # Common key names: input_values (wav2vec2) or input_features (some processors).
-        if "input_values" in inputs:
-            model_inputs = {"input_values": inputs["input_values"].to(self.device)}
-        elif "input_features" in inputs:
-            model_inputs = {"input_features": inputs["input_features"].to(self.device)}
-        else:
-            # Best-effort: move all tensors to device.
-            model_inputs = {k: v.to(self.device) for k, v in inputs.items() if hasattr(v, "to")}
-
-        with self._torch.no_grad():
-            logits = self._model(**model_inputs).logits  # (B,T,C)
-            log_probs = logits.log_softmax(dim=-1)  # (B,T,C)
-
-        # Tokenize targets.
-        tokenizer = getattr(self._processor, "tokenizer", None)
-        if tokenizer is None:
-            raise ValueError("missing_tokenizer")
-        tokenized = tokenizer(text_norm, add_special_tokens=False)
-        target_ids = tokenized.get("input_ids")
-        if not target_ids:
-            raise ValueError("empty_targets")
-
-        unk_id = getattr(tokenizer, "unk_token_id", None)
-        unk_ratio: float | None = None
-        if unk_id is not None:
-            unk_count = sum(1 for tid in target_ids if int(tid) == int(unk_id))
-            unk_ratio = float(unk_count) / float(len(target_ids))
-            if unk_ratio > max_unk_ratio:
-                raise ValueError(f"too_many_unk_tokens:{unk_ratio:.3f}")
-
-        # torch ctc_loss expects (T,N,C)
-        log_probs_tnc = log_probs.transpose(0, 1)
-        device = log_probs_tnc.device
-        input_lengths = self._torch.tensor([log_probs_tnc.size(0)], dtype=self._torch.long, device=device)
-        targets = self._torch.tensor(target_ids, dtype=self._torch.long, device=device)
-        target_lengths = self._torch.tensor([targets.numel()], dtype=self._torch.long, device=device)
-
-        # Reduction=mean normalizes by target length; good for comparability.
-        loss = F.ctc_loss(
-            log_probs_tnc,
-            targets,
-            input_lengths,
-            target_lengths,
-            blank=self.blank_id,
-            reduction="mean",
-            zero_infinity=True,
-        )
-        loss_value = float(loss.detach().cpu().item())
-
-        # Forced alignment token scores (coverage/fit). Best-effort: alignment may fail for short audio vs long text.
-        token_score_mean = None
-        token_score_min = None
-        token_coverage = None
-        span_count = None
-        span_mismatch = None
-
-        try:
-            import torchaudio
-
-            with warnings.catch_warnings():
-                warnings.filterwarnings("ignore", message=".*forced_align.*deprecated.*", category=UserWarning)
-                aligned_tokens, aligned_scores = torchaudio.functional.forced_align(
-                    log_probs,
-                    targets.unsqueeze(0),
-                    input_lengths=input_lengths,
-                    target_lengths=target_lengths,
-                    blank=self.blank_id,
-                )
-
-            spans = torchaudio.functional.merge_tokens(
-                aligned_tokens[0].detach().cpu(), aligned_scores[0].detach().cpu(), blank=self.blank_id
-            )
-            span_scores = [float(s.score) for s in spans]
-            span_count = len(span_scores)
-            span_mismatch = (span_count != int(target_lengths.item()))
-            if span_scores:
-                token_score_mean = sum(span_scores) / float(len(span_scores))
-                token_score_min = min(span_scores)
-                token_coverage = sum(1 for s in span_scores if s >= token_logp_threshold) / float(len(span_scores))
-        except Exception:
-            pass
-
-        return CTCScore(
-            model_id=self.model_id,
-            loss=loss_value,
-            text=text,
-            unk_ratio=unk_ratio,
-            target_len=int(target_lengths.item()),
-            token_score_mean=token_score_mean,
-            token_score_min=token_score_min,
-            token_coverage=token_coverage,
-            token_logp_threshold=token_logp_threshold,
-            span_count=span_count,
-            span_mismatch=span_mismatch,
-        )
+        log_probs, input_lengths = self._compute_log_probs(audio_path)
+        return self._score_from_log_probs(
+            log_probs=log_probs,
+            input_lengths=input_lengths,
+            text=text,
+            lang=lang,
+            max_unk_ratio=max_unk_ratio,
+            token_logp_threshold=token_logp_threshold,
+        )
+
+    def score_candidates(
+        self,
+        *,
+        audio_path: Path,
+        candidates: dict[str, str],
+        lang: str | None,
+        max_unk_ratio: float,
+        token_logp_threshold: float,
+    ) -> tuple[list[tuple[str, CTCScore]], dict[str, str]]:
+        log_probs, input_lengths = self._compute_log_probs(audio_path)
+
+        scored: list[tuple[str, CTCScore]] = []
+        errors: dict[str, str] = {}
+
+        for key, text in candidates.items():
+            text = (text or "").strip()
+            if not text:
+                continue
+            try:
+                score = self._score_from_log_probs(
+                    log_probs=log_probs,
+                    input_lengths=input_lengths,
+                    text=text,
+                    lang=lang,
+                    max_unk_ratio=max_unk_ratio,
+                    token_logp_threshold=token_logp_threshold,
+                )
+                scored.append((key, score))
+            except Exception as e:
+                errors[key] = f"{type(e).__name__}:{e}"
+
+        return scored, errors
@@
 class CTCFitScorer:
@@
     def score(self, audio_path: Path, text: str, lang: str | None) -> CTCScore | None:
         model_id = self._get_model_id(lang)
         if not model_id:
             return None
@@

         return model.score(
             audio_path=audio_path,
             text=text,
             lang=lang,
             max_unk_ratio=self.max_unk_ratio,
             token_logp_threshold=self.token_logp_threshold,
         )

+    def score_candidates(
+        self, audio_path: Path, candidates: dict[str, str], lang: str | None
+    ) -> tuple[list[tuple[str, CTCScore]], dict[str, str]] | None:
+        model_id = self._get_model_id(lang)
+        if not model_id:
+            return None
+
+        model = self._cache.get(model_id)
+        if model is None:
+            model = _CTCModel(model_id=model_id)
+            self._cache[model_id] = model
+
+        return model.score_candidates(
+            audio_path=audio_path,
+            candidates=candidates,
+            lang=lang,
+            max_unk_ratio=self.max_unk_ratio,
+            token_logp_threshold=self.token_logp_threshold,
+        )
+

 def rank_candidates_by_ctc(
     scorer: CTCFitScorer, audio_path: Path, candidates: dict[str, str], lang: str | None
 ) -> dict[str, Any]:
-    scored: list[tuple[str, CTCScore]] = []
-    errors: dict[str, str] = {}
-
-    for key, text in candidates.items():
-        text = (text or "").strip()
-        if not text:
-            continue
-        try:
-            s = scorer.score(audio_path=audio_path, text=text, lang=lang)
-            if s is None:
-                continue
-            scored.append((key, s))
-        except Exception as e:
-            errors[key] = f"{type(e).__name__}:{e}"
+    scored: list[tuple[str, CTCScore]] = []
+    errors: dict[str, str] = {}
+
+    batch = scorer.score_candidates(audio_path=audio_path, candidates=candidates, lang=lang)
+    if batch is not None:
+        scored, errors = batch

     scored.sort(key=lambda kv: kv[1].loss)
     best = scored[0] if scored else None
*** End Patch


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


Output: Plan updated


**Implementing benchmarking functions**

I need to create a bench module with specific functions. The `build_hf_benchmark` function will generate necessary directories and files, using built-in sources for each language. If `overwrite` is activated and the `bench_dir` already exists, I'm considering whether to clear it or skip existing files, which seems destructive. Instead, I’ll just raise an error if the directory isn’t empty unless the `--force` option is enabled. This feels like a safer approach for building the benchmark.


**Implementing safe benchmark functions**

I need to tread carefully here; destructive actions like deleting directories could be risky. Even though the user might request it, I should prioritize safe methods. I can implement an `--force` option that allows the deletion of an existing `bench_dir`, but only if absolutely necessary. Alternatively, I could create a new `bench_dir` each time. I'll also work on the `run_benchmark` function and analyze features inside the benchmark module while ensuring that heavy libraries aren’t imported at the start to keep the command line responsive.


**Setting up benchmarking commands**

I’m adding commands for the benchmarking utilities to the application. Specifically, I’ll implement a `bench_app` using Typer, which will include a `bench_build` command. In this function, I’ll make sure to import necessary components and define some options. These options will cover output paths, sample targets, shuffling behavior, and language limitations. It looks like I can use the `typer.Option` to allow multiple languages with the `--lang` argument being repeatable. I’m excited to get this organized!


**Configuring trust options and run parameters**

I’m considering the `--trust-remote-code` option for FLEURS. It makes sense to default it to `True` since FLEURS requires it, but I’m also worried about security, so I might consider setting it to `False` by default. However, since users need it for FLEURS to work, I’ll keep it as `True` and mention the security aspect. For the `bench_run` command, I'm thinking of default paths like `outputs/bench/<bench_dir.name>/run_<timestamp>` to ensure reproducibility while still allowing the user to set their own output paths if they wish.


**Designing the benchmark module**

I’m thinking through how to manage the output path for users while allowing default settings. I’ll set the default to `outputs/bench/run`, but need to compute it if `out` is None since Typer doesn’t support dynamic defaults. I'll also add various options for the pipeline, focusing on key ones instead of including everything like Gemini, and introduce an evaluation step. Now, for the `bench push` command, I’ll create logic to determine the repo ID and handle the audio inclusion based on user preferences. Finally, I’ll implement a function to build the dataset correctly.


Input:
*** Begin Patch
*** Add File: maya_transcribe/bench.py
+from __future__ import annotations
+
+import json
+import os
+import random
+import re
+import wave
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+from maya_transcribe.ids import segment_identity
+from maya_transcribe.io import read_jsonl, write_jsonl
+
+
+@dataclass(frozen=True)
+class HFSource:
+    dataset: str
+    config: str | None
+    split: str
+    audio_key: str
+    text_key: str
+    id_key: str | None = None
+    trust_remote_code: bool = False
+    name: str | None = None
+
+
+_SAFE_NAME_RE = re.compile(r"[^a-zA-Z0-9._-]+")
+
+
+def _safe_name(value: str) -> str:
+    value = (value or "").strip()
+    value = value.replace("/", "_")
+    value = value.replace("\\", "_")
+    value = _SAFE_NAME_RE.sub("_", value)
+    value = value.strip("._-")
+    return value or "x"
+
+
+def _ensure_hf_token_env() -> None:
+    token = os.getenv("HUGGINGFACE_HUB_TOKEN") or os.getenv("HF_TOKEN")
+    if token and not os.getenv("HUGGINGFACE_HUB_TOKEN"):
+        os.environ["HUGGINGFACE_HUB_TOKEN"] = token
+
+
+def _write_wav_mono16(path: Path, audio: np.ndarray, sampling_rate: int) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    wav = np.asarray(audio, dtype=np.float32)
+    if wav.ndim == 2:
+        wav = wav.mean(axis=1)
+    wav = np.clip(wav, -1.0, 1.0)
+    pcm16 = (wav * 32767.0).astype(np.int16)
+
+    with wave.open(str(path), "wb") as wf:
+        wf.setnchannels(1)
+        wf.setsampwidth(2)
+        wf.setframerate(int(sampling_rate))
+        wf.writeframes(pcm16.tobytes())
+
+
+def _extract_audio(example: dict[str, Any], audio_key: str) -> tuple[np.ndarray, int] | None:
+    audio = example.get(audio_key)
+    if not isinstance(audio, dict):
+        return None
+    arr = audio.get("array")
+    sr = audio.get("sampling_rate")
+    if arr is None or sr is None:
+        return None
+    try:
+        sr_i = int(sr)
+    except (TypeError, ValueError):
+        return None
+    return np.asarray(arr, dtype=np.float32), sr_i
+
+
+def _extract_text(example: dict[str, Any], text_key: str) -> str | None:
+    text = example.get(text_key)
+    if not isinstance(text, str):
+        return None
+    text = text.strip()
+    return text or None
+
+
+def default_sources() -> dict[str, list[HFSource]]:
+    """
+    Default benchmark sources for the 12 target languages:
+    Malayalam, Hindi, Punjabi, Tamil, Kannada, Gujarati, Telugu, Bengali, Odia, Marathi, Assamese, English.
+    """
+    kathbath = "ai4bharat/Kathbath"
+    fleurs = "google/fleurs"
+
+    # Canonical ISO-639-3 codes aligned to MMS-LID output (and language.py mappings).
+    return {
+        "hin": [HFSource(dataset=kathbath, config="hindi", split="valid", audio_key="audio_filepath", text_key="text")],
+        "tam": [HFSource(dataset=kathbath, config="tamil", split="valid", audio_key="audio_filepath", text_key="text")],
+        "tel": [HFSource(dataset=kathbath, config="telugu", split="valid", audio_key="audio_filepath", text_key="text")],
+        "kan": [HFSource(dataset=kathbath, config="kannada", split="valid", audio_key="audio_filepath", text_key="text")],
+        "mal": [
+            HFSource(dataset=kathbath, config="malayalam", split="valid", audio_key="audio_filepath", text_key="text")
+        ],
+        "ben": [HFSource(dataset=kathbath, config="bengali", split="valid", audio_key="audio_filepath", text_key="text")],
+        "guj": [
+            HFSource(dataset=kathbath, config="gujarati", split="valid", audio_key="audio_filepath", text_key="text")
+        ],
+        "pan": [
+            HFSource(dataset=kathbath, config="punjabi", split="valid", audio_key="audio_filepath", text_key="text")
+        ],
+        "mar": [
+            HFSource(dataset=kathbath, config="marathi", split="valid", audio_key="audio_filepath", text_key="text")
+        ],
+        "ory": [HFSource(dataset=kathbath, config="odia", split="valid", audio_key="audio_filepath", text_key="text")],
+        # Kathbath doesn't include Assamese/English; use FLEURS (requires trust_remote_code=True).
+        "asm": [
+            HFSource(
+                dataset=fleurs,
+                config="as_in",
+                split="test",
+                audio_key="audio",
+                text_key="transcription",
+                trust_remote_code=True,
+            ),
+            HFSource(
+                dataset=fleurs,
+                config="as_in",
+                split="validation",
+                audio_key="audio",
+                text_key="transcription",
+                trust_remote_code=True,
+            ),
+            HFSource(
+                dataset=fleurs,
+                config="as_in",
+                split="train",
+                audio_key="audio",
+                text_key="transcription",
+                trust_remote_code=True,
+            ),
+        ],
+        "eng": [
+            HFSource(
+                dataset=fleurs,
+                config="en_us",
+                split="test",
+                audio_key="audio",
+                text_key="transcription",
+                trust_remote_code=True,
+            ),
+            HFSource(
+                dataset=fleurs,
+                config="en_us",
+                split="validation",
+                audio_key="audio",
+                text_key="transcription",
+                trust_remote_code=True,
+            ),
+            HFSource(
+                dataset=fleurs,
+                config="en_us",
+                split="train",
+                audio_key="audio",
+                text_key="transcription",
+                trust_remote_code=True,
+            ),
+        ],
+    }
+
+
+def build_benchmark_corpus(
+    *,
+    out_dir: Path,
+    samples_per_lang: int = 200,
+    seed: int = 0,
+    shuffle: bool = True,
+    langs: list[str] | None = None,
+    min_duration_sec: float = 1.0,
+    max_duration_sec: float = 30.0,
+    force: bool = False,
+) -> dict[str, Any]:
+    """
+    Materialize a local benchmark corpus:
+      - `out_dir/audio/<lang>/*.wav`
+      - `out_dir/refs.jsonl` with `segment_id`, `text`, `lang`, `audio_relpath`, `source_*`
+      - `out_dir/manifest.json` with build config + counts
+    """
+    _ensure_hf_token_env()
+
+    if out_dir.exists():
+        existing = [p for p in out_dir.iterdir()]
+        if existing and not force:
+            raise RuntimeError(f"Refusing to overwrite non-empty directory: {out_dir} (pass force=True)")
+        if force:
+            for p in sorted(out_dir.rglob("*"), reverse=True):
+                if p.is_file() or p.is_symlink():
+                    p.unlink()
+                elif p.is_dir():
+                    p.rmdir()
+
+    out_dir.mkdir(parents=True, exist_ok=True)
+    audio_root = out_dir / "audio"
+
+    sources_by_lang = default_sources()
+    target_langs = langs or sorted(sources_by_lang.keys())
+    target_langs = [l.strip().lower() for l in target_langs if l.strip()]
+
+    try:
+        from datasets import load_dataset
+    except Exception as e:  # pragma: no cover
+        raise RuntimeError("Benchmark corpus build requires `datasets`. Install with: pip install -e '.[bench]'") from e
+
+    rng = random.Random(int(seed))
+
+    rows: list[dict[str, Any]] = []
+    counts: dict[str, int] = {}
+    skipped: dict[str, int] = {}
+
+    for lang in target_langs:
+        sources = sources_by_lang.get(lang)
+        if not sources:
+            continue
+        remaining = int(samples_per_lang)
+        written = 0
+        skipped_lang = 0
+
+        for src in sources:
+            if remaining <= 0:
+                break
+
+            ds = load_dataset(
+                src.dataset,
+                src.config,
+                split=src.split,
+                trust_remote_code=bool(src.trust_remote_code),
+            )
+            idxs = list(range(len(ds)))
+            if shuffle:
+                # Stable per-source shuffling (seeded by global seed + source identity).
+                s = f"{seed}|{lang}|{src.dataset}|{src.config}|{src.split}"
+                srng = random.Random(int.from_bytes(s.encode("utf-8"), "little") % (2**32))
+                srng.shuffle(idxs)
+
+            for i in idxs:
+                if remaining <= 0:
+                    break
+
+                ex = ds[int(i)]
+                if not isinstance(ex, dict):
+                    continue
+
+                text = _extract_text(ex, src.text_key)
+                if not text:
+                    skipped_lang += 1
+                    continue
+
+                audio_ex = _extract_audio(ex, src.audio_key)
+                if audio_ex is None:
+                    skipped_lang += 1
+                    continue
+                audio_arr, sr = audio_ex
+                dur = float(audio_arr.shape[0]) / float(sr) if sr else 0.0
+                if dur < float(min_duration_sec) or dur > float(max_duration_sec):
+                    skipped_lang += 1
+                    continue
+
+                src_id = None
+                if src.id_key and isinstance(ex.get(src.id_key), (str, int)):
+                    src_id = str(ex.get(src.id_key))
+                elif isinstance(ex.get("id"), (str, int)):
+                    src_id = str(ex.get("id"))
+                elif isinstance(ex.get("fname"), str):
+                    src_id = ex.get("fname")
+                elif isinstance(ex.get("path"), str):
+                    src_id = ex.get("path")
+
+                src_name = src.name or _safe_name(src.dataset.split("/")[-1])
+                lang_dir = audio_root / lang
+                fname = _safe_name(f"{src_name}__{src_id or i}")
+                wav_path = lang_dir / f"{fname}.wav"
+
+                _write_wav_mono16(wav_path, audio_arr, sr)
+                ident = segment_identity(wav_path, root=audio_root)
+
+                audio_relpath = wav_path.relative_to(audio_root).as_posix()
+                rows.append(
+                    {
+                        "segment_id": ident.segment_id,
+                        "text": text,
+                        "lang": lang,
+                        "audio_relpath": audio_relpath,
+                        "duration_sec": dur,
+                        "source_dataset": src.dataset,
+                        "source_config": src.config,
+                        "source_split": src.split,
+                        "source_id": src_id,
+                        "source_index": int(i),
+                    }
+                )
+
+                remaining -= 1
+                written += 1
+
+        counts[lang] = written
+        skipped[lang] = skipped_lang
+
+    refs_path = out_dir / "refs.jsonl"
+    write_jsonl(refs_path, rows)
+
+    manifest = {
+        "out_dir": str(out_dir),
+        "audio_root": str(audio_root),
+        "samples_per_lang": int(samples_per_lang),
+        "seed": int(seed),
+        "shuffle": bool(shuffle),
+        "langs": target_langs,
+        "min_duration_sec": float(min_duration_sec),
+        "max_duration_sec": float(max_duration_sec),
+        "counts": counts,
+        "skipped": skipped,
+        "total_rows": len(rows),
+        "sources_by_lang": {
+            k: [vars(s) for s in v] for k, v in sources_by_lang.items() if (not langs or k in set(target_langs))
+        },
+    }
+    (out_dir / "manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
+    return manifest
+
+
+def push_benchmark_to_hub(
+    *,
+    bench_dir: Path,
+    repo_id: str,
+    private: bool = True,
+    include_audio: bool = False,
+    split: str = "test",
+) -> str:
+    """
+    Push the benchmark corpus as a Hugging Face dataset repo.
+
+    If `include_audio` is False, uploads only the JSONL manifest rows (no audio redistribution).
+    """
+    _ensure_hf_token_env()
+    token = os.getenv("HUGGINGFACE_HUB_TOKEN") or os.getenv("HF_TOKEN")
+    if not token:
+        raise RuntimeError("Missing HF auth token (set HF_TOKEN or HUGGINGFACE_HUB_TOKEN).")
+
+    try:
+        from huggingface_hub import HfApi
+    except Exception as e:  # pragma: no cover
+        raise RuntimeError("Push-to-hub requires `huggingface_hub`. Install with: pip install -e '.[bench]'") from e
+
+    try:
+        from datasets import Audio, Dataset, DatasetDict
+    except Exception as e:  # pragma: no cover
+        raise RuntimeError("Push-to-hub requires `datasets`. Install with: pip install -e '.[bench]'") from e
+
+    refs_path = bench_dir / "refs.jsonl"
+    if not refs_path.exists():
+        raise RuntimeError(f"Missing refs.jsonl: {refs_path}")
+    rows = read_jsonl(refs_path)
+
+    audio_root = bench_dir / "audio"
+    if include_audio and not audio_root.exists():
+        raise RuntimeError(f"Missing audio/ directory: {audio_root}")
+
+    records: dict[str, list[Any]] = {
+        "segment_id": [],
+        "lang": [],
+        "text": [],
+        "audio_relpath": [],
+        "duration_sec": [],
+        "source_dataset": [],
+        "source_config": [],
+        "source_split": [],
+        "source_id": [],
+        "source_index": [],
+    }
+    audio_paths: list[str] = []
+
+    for r in rows:
+        records["segment_id"].append(r.get("segment_id"))
+        records["lang"].append(r.get("lang"))
+        records["text"].append(r.get("text"))
+        records["audio_relpath"].append(r.get("audio_relpath"))
+        records["duration_sec"].append(r.get("duration_sec"))
+        records["source_dataset"].append(r.get("source_dataset"))
+        records["source_config"].append(r.get("source_config"))
+        records["source_split"].append(r.get("source_split"))
+        records["source_id"].append(r.get("source_id"))
+        records["source_index"].append(r.get("source_index"))
+
+        if include_audio:
+            rel = str(r.get("audio_relpath") or "")
+            audio_paths.append(str(audio_root / rel))
+
+    if include_audio:
+        records["audio"] = audio_paths
+
+    ds = Dataset.from_dict(records)
+    if include_audio:
+        ds = ds.cast_column("audio", Audio())
+
+    api = HfApi(token=token)
+    api.create_repo(repo_id=repo_id, repo_type="dataset", private=bool(private), exist_ok=True)
+
+    dsd = DatasetDict({split: ds})
+    dsd.push_to_hub(repo_id=repo_id, token=token, private=bool(private))
+    return repo_id
+
*** End Patch


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


**Evaluating deletion logic**

I need to check the `force` deletion logic in my `bench.py`. It deletes all files and directories under `out_dir` using rglob with reverse sorting, but it might fail if a directory isn't empty. We’re using `rglob("*")`, which doesn’t include the root itself, and removing directories after their children, which should be fine. However, while reverse sorting helps, path ordering may not be guaranteed. Sorting by path length could be better, as the current method feels a bit risky.


**Checking code updates**

I’m realizing that the `force` option is used correctly, but there might be a bug in the seed for the per-source shuffle. We use `int.from_bytes(s.encode("utf-8"), "little") % (2**32)` to keep things within 32-bit limits, which is good. However, we have an unused `rng` variable that I can remove to clean up the code, even if it isn't necessary. I’ll patch to eliminate that. Now, I’ll update the CLI to add the bench commands in `maya_transcribe/cli.py` after the main app creation, being careful about the large file and truncated lines.


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

import json
from pathlib import Path

import typer
from dotenv import load_dotenv

from maya_transcribe.audio import compute_qc, ffprobe, normalize_audio
from maya_transcribe.config import Settings
from maya_transcribe.dataset import find_segments_dir
from maya_transcribe.ids import segment_identity
from maya_transcribe.io import write_jsonl
from maya_transcribe.r2 import R2Client
from maya_transcribe.tar import safe_extract_tar

load_dotenv(dotenv_path=Path(".env"), override=False)

app = typer.Typer(add_completion=False, no_args_is_help=True)

@app.command()
def download(
    video_id: str = typer.Option(..., help="YouTube video id (used to locate the tar in R2)."),
    out: Path = typer.Option(Path("data"), help="Output directory."),
    r2_key: str | None = typer.Option(None, help="Exact R2 object key (skips search)."),
    bucket: str | None = typer.Option(None, help="R2 bucket override (defaults to env R2_BUCKET)."),
    prefix: str | None = typer.Option(None, help="Optional R2 prefix override."),
    use_db: bool = typer.Option(False, help="Fetch `r2_tar_key` from Supabase Postgres (DATABASE_URL)."),
    dry_run: bool = typer.Option(False, help="Only show matched key(s); do not download."),
) -> None:
    """
    Download the cleaned/segmented tar for a video from R2 and extract it.
    """
    settings = Settings.load()
    r2 = R2Client.from_settings(settings)
    if bucket:
        r2 = R2Client(
            endpoint_url=r2.endpoint_url,
            access_key_id=r2.access_key_id,
            secret_access_key=r2.secret_access_key,
            bucket=bucket,
        )

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

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

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

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

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

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

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

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

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

    rows = []
    norm_dir = out / "audio"
    for src in audio_files:
        ident = segment_identity(src, root=segments_dir)
        segment_id = ident.segment_id
        dst = norm_dir / f"{segment_id}.wav"
        normalize_audio(
            src,
            dst,
            sample_rate=sample_rate,
            mono=True,
            trim_silence=trim_silence,
            silence_threshold_db=silence_threshold_db,
            min_silence_sec=min_silence_sec,
        )
        info = ffprobe(dst)
        qc = compute_qc(dst, sample_rate=sample_rate)


**Planning app modifications**

I'm considering adding bench subcommands right after app creation, following `app = typer.Typer`. I want to ensure we avoid making large changes, so I'll define `bench_app = typer.Typer(help="Benchmarking utilities.")` and add it to the app's main commands. I also need to think about defining the bench commands at the end of the file and importing necessary modules like `datetime` for implementing timestamp functionality. I’ll make sure our CLI remains functional as I patch everything.


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
 load_dotenv(dotenv_path=Path(".env"), override=False)

 app = typer.Typer(add_completion=False, no_args_is_help=True)
+bench_app = typer.Typer(add_completion=False, no_args_is_help=True, help="Benchmarking utilities.")
+app.add_typer(bench_app, name="bench")
@@
 if __name__ == "__main__":
     app()
*** End Patch


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


**Planning command structure**

I see that we've added bench_app but haven’t set up any commands yet. My plan is to add those commands right before `if __name__ == "__main__":`, so I should place them after the eval command. I want to patch this near the bottom of cli.py, focusing on that area around the eval command. It's all about making sure everything flows smoothly and functions as intended!


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

@app.command()
def run(
    segments_dir: Path = typer.Option(..., exists=True, file_okay=False, help="Directory containing audio segments."),
    out: Path = typer.Option(Path("outputs/run"), help="Output directory."),
    whisper_model: str = typer.Option("large-v3", help="Whisper model size/path for faster-whisper."),
    lid_model_id: str = typer.Option("facebook/mms-lid-256", help="HuggingFace MMS-LID model id."),
    require_lid_backend: bool = typer.Option(True, help="Fail if MMS-LID backend can't initialize."),
    transformers_whisper_model: list[str] = typer.Option(
        [], help="Extra whisper-family HF models to run via transformers (repeatable)."
    ),
    gemini_model: str | None = typer.Option(None, help="Optional Gemini model name (adds Gemini as a hypothesis)."),
    gemini_prompt_file: Path | None = typer.Option(Path("Prompt.md"), help="Prompt file for Gemini."),
    gemini_hard_only: bool = typer.Option(True, help="Only call Gemini on 'hard' segments."),
    ctc_model: str | None = typer.Option(None, help="Optional HF CTC model id for audio-grounded fit scoring."),
    ctc_model_map: Path | None = typer.Option(None, help="JSON map {lang_code: hf_model_id} for CTC scoring."),
    require_ctc: bool = typer.Option(True, help="When CTC is enabled, require a CTC score to accept."),
    min_ctc_margin: float = typer.Option(0.05, help="Min (second-best - best) CTC loss margin to auto-accept."),
    trim_silence: bool = typer.Option(True, help="Trim leading/trailing silence (ffmpeg silenceremove)."),
    silence_threshold_db: float = typer.Option(-40.0, help="Silence threshold in dB for trimming."),
    min_silence_sec: float = typer.Option(0.15, help="Min silence duration for trimming."),
    limit: int | None = typer.Option(None, help="Process only the first N segments."),
) -> None:
    """
    End-to-end pipeline: preprocess -> LID -> ASR ensemble -> ROVER -> verify.
    """
    from maya_transcribe.pipeline import run_pipeline

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

    thresholds = {}
    if ctc_model or ctc_map:
        thresholds["require_ctc"] = require_ctc
        thresholds["min_ctc_margin"] = float(min_ctc_margin)

    results_path = run_pipeline(
        segments_dir=segments_dir,
        out_dir=out,
        lid_model_id=lid_model_id,
        require_lid_backend=require_lid_backend,
        whisper_model=whisper_model,
        extra_transformers_whisper_models=transformers_whisper_model,
        gemini_model=gemini_model,
        gemini_prompt_path=gemini_prompt_file,
        gemini_hard_only=gemini_hard_only,
        ctc_model=ctc_model,
        ctc_model_map=ctc_map,
        trim_silence=trim_silence,
        silence_threshold_db=silence_threshold_db,
        min_silence_sec=min_silence_sec,
        limit=limit,
        thresholds=thresholds,
    )
    typer.echo(f"Wrote: {results_path}")
    typer.echo(f"Wrote: {out / 'summary.json'}")

@app.command()
def eval(
    results: Path = typer.Option(..., exists=True, dir_okay=False, help="Pipeline results JSONL (outputs/run/results.jsonl)."),
    refs: Path = typer.Option(..., exists=True, dir_okay=False, help="Reference transcripts JSONL."),
    out: Path | None = typer.Option(None, help="Write summary JSON to this path."),
    hypothesis: str = typer.Option('final', help='Hypothesis to evaluate: "final", "rover", or "asr:<key>".'),
    oracle: bool = typer.Option(True, help="Compute oracle-best and selection-gap stats across all candidates."),
) -> None:
    """
    Compute WER/CER for a hypothesis against a reference JSONL keyed by `segment_id`.
    """
    from maya_transcribe.eval import evaluate

    summary = evaluate(results_path=results, refs_path=refs, out_path=out, hypothesis=hypothesis, oracle=oracle)
    typer.echo(json.dumps(summary, ensure_ascii=False, indent=2))

if __name__ == "__main__":
    app()


**Implementing bench commands**

I need to insert bench_app commands between eval and if __name__. Let's add three commands: `bench build`, `bench run`, and `bench push`. For imports, maybe I'll use `import time` or `datetime` to set a default output directory, but I'll do imports inside the function. I also need to update the eval function signature for language_source later. For now, the bench run command should call `evaluate(..., language_source='refs')` after that change. I'll implement the bench run command for now and adjust later.


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
 def eval(
     results: Path = typer.Option(..., exists=True, dir_okay=False, help="Pipeline results JSONL (outputs/run/results.jsonl)."),
     refs: Path = typer.Option(..., exists=True, dir_okay=False, help="Reference transcripts JSONL."),
     out: Path | None = typer.Option(None, help="Write summary JSON to this path."),
     hypothesis: str = typer.Option('final', help='Hypothesis to evaluate: "final", "rover", or "asr:<key>".'),
     oracle: bool = typer.Option(True, help="Compute oracle-best and selection-gap stats across all candidates."),
 ) -> None:
@@
     summary = evaluate(results_path=results, refs_path=refs, out_path=out, hypothesis=hypothesis, oracle=oracle)
     typer.echo(json.dumps(summary, ensure_ascii=False, indent=2))

+
+@bench_app.command("build")
+def bench_build(
+    out: Path = typer.Option(Path("data/bench/maya_hf_v0"), help="Output benchmark corpus directory."),
+    samples_per_lang: int = typer.Option(200, help="Target samples per language."),
+    seed: int = typer.Option(0, help="Shuffle seed."),
+    shuffle: bool = typer.Option(True, help="Shuffle within each source before selecting."),
+    lang: list[str] = typer.Option([], help="Limit to ISO-639-3 language codes (repeatable)."),
+    min_duration_sec: float = typer.Option(1.0, help="Min audio duration."),
+    max_duration_sec: float = typer.Option(30.0, help="Max audio duration."),
+    force: bool = typer.Option(False, help="Delete and rebuild output directory if it exists."),
+) -> None:
+    """
+    Build a local benchmark corpus from Hugging Face datasets.
+    """
+    from maya_transcribe.bench import build_benchmark_corpus
+
+    manifest = build_benchmark_corpus(
+        out_dir=out,
+        samples_per_lang=samples_per_lang,
+        seed=seed,
+        shuffle=shuffle,
+        langs=lang or None,
+        min_duration_sec=min_duration_sec,
+        max_duration_sec=max_duration_sec,
+        force=force,
+    )
+    typer.echo(json.dumps({"wrote": str(out), "total_rows": manifest.get("total_rows"), "counts": manifest.get("counts")}, indent=2))
+
+
+@bench_app.command("run")
+def bench_run(
+    bench_dir: Path = typer.Option(Path("data/bench/maya_hf_v0"), exists=True, file_okay=False, help="Benchmark corpus directory."),
+    out: Path | None = typer.Option(None, help="Output directory (defaults under outputs/bench/)."),
+    whisper_model: str = typer.Option("large-v3", help="Whisper model size/path for faster-whisper."),
+    lid_model_id: str = typer.Option("facebook/mms-lid-256", help="HuggingFace MMS-LID model id."),
+    require_lid_backend: bool = typer.Option(True, help="Fail if MMS-LID backend can't initialize."),
+    transformers_whisper_model: list[str] = typer.Option(
+        [], help="Extra whisper-family HF models to run via transformers (repeatable)."
+    ),
+    ctc_model: str | None = typer.Option(None, help="Optional HF CTC model id for audio-grounded fit scoring."),
+    ctc_model_map: Path | None = typer.Option(None, help="JSON map {lang_code: hf_model_id} for CTC scoring."),
+    require_ctc: bool = typer.Option(True, help="When CTC is enabled, require a CTC score to accept."),
+    min_ctc_margin: float = typer.Option(0.05, help="Min (second-best - best) CTC loss margin to auto-accept."),
+    trim_silence: bool = typer.Option(True, help="Trim leading/trailing silence (ffmpeg silenceremove)."),
+    silence_threshold_db: float = typer.Option(-40.0, help="Silence threshold in dB for trimming."),
+    min_silence_sec: float = typer.Option(0.15, help="Min silence duration for trimming."),
+    limit: int | None = typer.Option(None, help="Process only the first N segments."),
+    hypothesis: str = typer.Option("final", help='Hypothesis to evaluate: "final", "rover", or "asr:<key>".'),
+) -> None:
+    """
+    Run the full pipeline on a local benchmark corpus and write eval.json.
+    """
+    from datetime import datetime
+
+    from maya_transcribe.eval import evaluate
+    from maya_transcribe.pipeline import run_pipeline
+
+    audio_dir = bench_dir / "audio"
+    refs_path = bench_dir / "refs.jsonl"
+    if not audio_dir.exists():
+        raise typer.BadParameter(f"Missing: {audio_dir}")
+    if not refs_path.exists():
+        raise typer.BadParameter(f"Missing: {refs_path}")
+
+    if out is None:
+        ts = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
+        out = Path("outputs/bench") / bench_dir.name / f"run_{ts}"
+
+    ctc_map = None
+    if ctc_model_map is not None:
+        ctc_map = json.loads(ctc_model_map.read_text(encoding="utf-8"))
+        if not isinstance(ctc_map, dict):
+            raise typer.BadParameter("ctc_model_map must be a JSON object {lang_code: model_id}")
+
+    thresholds: dict[str, object] = {}
+    if ctc_model or ctc_map:
+        thresholds["require_ctc"] = require_ctc
+        thresholds["min_ctc_margin"] = float(min_ctc_margin)
+
+    results_path = run_pipeline(
+        segments_dir=audio_dir,
+        out_dir=out,
+        lid_model_id=lid_model_id,
+        require_lid_backend=require_lid_backend,
+        whisper_model=whisper_model,
+        extra_transformers_whisper_models=transformers_whisper_model,
+        ctc_model=ctc_model,
+        ctc_model_map=ctc_map,
+        trim_silence=trim_silence,
+        silence_threshold_db=silence_threshold_db,
+        min_silence_sec=min_silence_sec,
+        limit=limit,
+        thresholds=thresholds,
+    )
+
+    eval_path = out / "eval.json"
+    summary = evaluate(results_path=results_path, refs_path=refs_path, out_path=eval_path, hypothesis=hypothesis, oracle=True)
+    typer.echo(f"Wrote: {results_path}")
+    typer.echo(f"Wrote: {out / 'summary.json'}")
+    typer.echo(f"Wrote: {eval_path}")
+    typer.echo(json.dumps(summary, ensure_ascii=False, indent=2))
+
+
+@bench_app.command("push")
+def bench_push(
+    bench_dir: Path = typer.Option(Path("data/bench/maya_hf_v0"), exists=True, file_okay=False, help="Benchmark corpus directory."),
+    repo_id: str = typer.Option(..., help="Hugging Face dataset repo id (e.g., org/name)."),
+    private: bool = typer.Option(True, help="Create/push as a private dataset repo."),
+    include_audio: bool = typer.Option(False, help="Include audio bytes in the pushed dataset (check dataset licenses!)."),
+    split: str = typer.Option("test", help="Split name to use on the Hub."),
+) -> None:
+    """
+    Push a local benchmark corpus to the Hugging Face Hub as a (private) dataset.
+    """
+    from maya_transcribe.bench import push_benchmark_to_hub
+
+    repo = push_benchmark_to_hub(bench_dir=bench_dir, repo_id=repo_id, private=private, include_audio=include_audio, split=split)
+    typer.echo(f"Pushed: {repo}")
+

 if __name__ == "__main__":
     app()
*** End Patch


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


**Updating code for efficiency**

I'm focusing on step 2, where I need to patch the code to remove any unused rng variables. My plan is to update the bench.py file accordingly. I'll search for instances of rng in the code to make sure I correctly identify where it needs to be removed. It feels a bit tedious, but optimizing the code will definitely make it cleaner and more efficient in the long run! I'll just take it one step at a time.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
213:    rng = random.Random(int(seed))
241:                srng = random.Random(int.from_bytes(s.encode("utf-8"), "little") % (2**32))


**Cleaning up code**

I see that there's an unused variable named `rng` at line 213. It looks like it doesn't serve any purpose, so I'm planning to remove it. I'll make sure to patch the code afterward to keep it clean and functional. It's always important to tidy up and eliminate any unnecessary components to ensure everything runs smoothly!


Input:
*** Begin Patch
*** Update File: maya_transcribe/bench.py
@@
     try:
         from datasets import load_dataset
     except Exception as e:  # pragma: no cover
         raise RuntimeError("Benchmark corpus build requires `datasets`. Install with: pip install -e '.[bench]'") from e
-
-    rng = random.Random(int(seed))

     rows: list[dict[str, Any]] = []
     counts: dict[str, int] = {}
     skipped: dict[str, int] = {}
*** End Patch


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


**Updating benchmarking tools**

I'm currently working on step two, and I need to update the optional dependencies in the pyproject file, specifically for the bench module. The plan is to add some benchmarking tools, which is really exciting! I should make sure that the bench module and its command line interface are compiling correctly. I think it would be a good idea to run `python3 -m py_compile` for the changed files just to be sure everything is in order.


python3 -m py_compile maya_transcribe/ctc_fit.py maya_transcribe/bench.py maya_transcribe/cli.py


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:


**Updating project plans**

I'm checking for any syntax errors, and now it seems I need to update the plan for step 2 to potentially include modifications for eval and thresholds. For step 3, I want to keep it separate. I also think we should adjust `bench` to handle loading the dataset. We could add an optional dependency group `bench` to the pyproject as part of step 2. I’ll patch the `pyproject.toml` to include `bench` with specific dependencies, and maybe we’ll update the `full` extras to include it too.


Input:
*** Begin Patch
*** Update File: pyproject.toml
@@
 [project.optional-dependencies]
 supabase = ["supabase>=2.6.0"]
 db = ["psycopg2-binary>=2.9.9"]
 gemini = ["google-genai>=1.54.0"]
+bench = [
+  "datasets>=2.19.0",
+  "huggingface_hub>=0.23.0",
+]
 asr = [
   "faster-whisper>=1.0.3",
   "soundfile>=0.12.1",
 ]
@@
 full = [
+  "datasets>=2.19.0",
+  "huggingface_hub>=0.23.0",
   "supabase>=2.6.0",
   "psycopg2-binary>=2.9.9",
   "google-genai>=1.54.0",
   "faster-whisper>=1.0.3",
*** End Patch


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


Summary: auto


**Updating project steps**

Alright, for step 2, I should update the README to include bench commands, but that part might be optional for later. I think it’s reasonable to mark step 2 as complete now and move on to step 3. However, I still need to implement the threshold config in the CLI as part of step 3. So, I’ll update the plan accordingly: step 2 is done, and I’m in progress with step 3!


Output: Plan updated


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
 def run(
     segments_dir: Path = typer.Option(..., exists=True, file_okay=False, help="Directory containing audio segments."),
     out: Path = typer.Option(Path("outputs/run"), help="Output directory."),
     whisper_model: str = typer.Option("large-v3", help="Whisper model size/path for faster-whisper."),
     lid_model_id: str = typer.Option("facebook/mms-lid-256", help="HuggingFace MMS-LID model id."),
     require_lid_backend: bool = typer.Option(True, help="Fail if MMS-LID backend can't initialize."),
@@
     min_ctc_margin: float = typer.Option(0.05, help="Min (second-best - best) CTC loss margin to auto-accept."),
     trim_silence: bool = typer.Option(True, help="Trim leading/trailing silence (ffmpeg silenceremove)."),
     silence_threshold_db: float = typer.Option(-40.0, help="Silence threshold in dB for trimming."),
     min_silence_sec: float = typer.Option(0.15, help="Min silence duration for trimming."),
     limit: int | None = typer.Option(None, help="Process only the first N segments."),
+    thresholds_json: Path | None = typer.Option(None, help="Optional JSON file of threshold overrides (verify + routing)."),
 ) -> None:
@@
     ctc_map = None
     if ctc_model_map is not None:
         ctc_map = json.loads(ctc_model_map.read_text(encoding="utf-8"))
         if not isinstance(ctc_map, dict):
             raise typer.BadParameter("ctc_model_map must be a JSON object {lang_code: model_id}")

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

     if ctc_model or ctc_map:
         thresholds["require_ctc"] = require_ctc
         thresholds["min_ctc_margin"] = float(min_ctc_margin)
*** End Patch


Output:
apply_patch verification failed: Failed to find expected lines in /home/ubuntu/maya-transcribe/maya_transcribe/cli.py:
    ctc_map = None
    if ctc_model_map is not None:
        ctc_map = json.loads(ctc_model_map.read_text(encoding="utf-8"))
        if not isinstance(ctc_map, dict):
            raise typer.BadParameter("ctc_model_map must be a JSON object {lang_code: model_id}")

    thresholds = {}

    if ctc_model or ctc_map:
        thresholds["require_ctc"] = require_ctc
        thresholds["min_ctc_margin"] = float(min_ctc_margin)


**Investigating patch failure**

It seems like the patch failed due to differences in formatting. I’ll start by looking at the specific part of the run command to figure this out. I might need to check around the run definition lines, maybe around line 200 or so. Using tools like `rg -n` or `nl` should help me pinpoint the exact lines. Let’s see what I can find and hopefully resolve this issue!


rg -n "def run\(" -n maya_transcribe/cli.py && nl -ba maya_transcribe/cli.py | sed -n '180,280p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
208:def run(
   180
   181	    asr_backend = FasterWhisperASR(model_size_or_path=model)
   182	    res = asr_backend.transcribe(
   183	        audio_path=audio, language=language, beam_size=beam_size, best_of=best_of, temperature=temperature
   184	    )
   185	    typer.echo(res.text)
   186
   187
   188	@app.command()
   189	def gemini(
   190	    audio: Path = typer.Option(..., exists=True, dir_okay=False, help="Audio file path."),
   191	    language: str | None = typer.Option(None, help="Language hint (e.g., hin/tam/eng)."),
   192	    model: str = typer.Option("gemini-2.0-flash", help="Gemini model name."),
   193	    prompt_file: Path | None = typer.Option(Path("Prompt.md"), help="Prompt file (defaults to Prompt.md)."),
   194	    max_output_tokens: int = typer.Option(512, help="Max output tokens."),
   195	) -> None:
   196	    """
   197	    Transcribe a single file with Gemini (audio-native LLM). Output is parsed/validated strictly.
   198	    """
   199	    from maya_transcribe.gemini_asr import GeminiASR, GeminiConfig, load_prompt_template
   200
   201	    prompt = load_prompt_template(prompt_file)
   202	    backend = GeminiASR(config=GeminiConfig(model=model, max_output_tokens=max_output_tokens), prompt_template=prompt)
   203	    res = backend.transcribe(audio_path=audio, language=language)
   204	    typer.echo(res.text)
   205
   206
   207	@app.command()
   208	def run(
   209	    segments_dir: Path = typer.Option(..., exists=True, file_okay=False, help="Directory containing audio segments."),
   210	    out: Path = typer.Option(Path("outputs/run"), help="Output directory."),
   211	    whisper_model: str = typer.Option("large-v3", help="Whisper model size/path for faster-whisper."),
   212	    lid_model_id: str = typer.Option("facebook/mms-lid-256", help="HuggingFace MMS-LID model id."),
   213	    require_lid_backend: bool = typer.Option(True, help="Fail if MMS-LID backend can't initialize."),
   214	    transformers_whisper_model: list[str] = typer.Option(
   215	        [], help="Extra whisper-family HF models to run via transformers (repeatable)."
   216	    ),
   217	    gemini_model: str | None = typer.Option(None, help="Optional Gemini model name (adds Gemini as a hypothesis)."),
   218	    gemini_prompt_file: Path | None = typer.Option(Path("Prompt.md"), help="Prompt file for Gemini."),
   219	    gemini_hard_only: bool = typer.Option(True, help="Only call Gemini on 'hard' segments."),
   220	    ctc_model: str | None = typer.Option(None, help="Optional HF CTC model id for audio-grounded fit scoring."),
   221	    ctc_model_map: Path | None = typer.Option(None, help="JSON map {lang_code: hf_model_id} for CTC scoring."),
   222	    require_ctc: bool = typer.Option(True, help="When CTC is enabled, require a CTC score to accept."),
   223	    min_ctc_margin: float = typer.Option(0.05, help="Min (second-best - best) CTC loss margin to auto-accept."),
   224	    trim_silence: bool = typer.Option(True, help="Trim leading/trailing silence (ffmpeg silenceremove)."),
   225	    silence_threshold_db: float = typer.Option(-40.0, help="Silence threshold in dB for trimming."),
   226	    min_silence_sec: float = typer.Option(0.15, help="Min silence duration for trimming."),
   227	    limit: int | None = typer.Option(None, help="Process only the first N segments."),
   228	) -> None:
   229	    """
   230	    End-to-end pipeline: preprocess -> LID -> ASR ensemble -> ROVER -> verify.
   231	    """
   232	    from maya_transcribe.pipeline import run_pipeline
   233
   234	    ctc_map = None
   235	    if ctc_model_map is not None:
   236	        ctc_map = json.loads(ctc_model_map.read_text(encoding="utf-8"))
   237	        if not isinstance(ctc_map, dict):
   238	            raise typer.BadParameter("ctc_model_map must be a JSON object {lang_code: model_id}")
   239
   240	    thresholds = {}
   241	    if ctc_model or ctc_map:
   242	        thresholds["require_ctc"] = require_ctc
   243	        thresholds["min_ctc_margin"] = float(min_ctc_margin)
   244
   245	    results_path = run_pipeline(
   246	        segments_dir=segments_dir,
   247	        out_dir=out,
   248	        lid_model_id=lid_model_id,
   249	        require_lid_backend=require_lid_backend,
   250	        whisper_model=whisper_model,
   251	        extra_transformers_whisper_models=transformers_whisper_model,
   252	        gemini_model=gemini_model,
   253	        gemini_prompt_path=gemini_prompt_file,
   254	        gemini_hard_only=gemini_hard_only,
   255	        ctc_model=ctc_model,
   256	        ctc_model_map=ctc_map,
   257	        trim_silence=trim_silence,
   258	        silence_threshold_db=silence_threshold_db,
   259	        min_silence_sec=min_silence_sec,
   260	        limit=limit,
   261	        thresholds=thresholds,
   262	    )
   263	    typer.echo(f"Wrote: {results_path}")
   264	    typer.echo(f"Wrote: {out / 'summary.json'}")
   265
   266
   267	@app.command()
   268	def eval(
   269	    results: Path = typer.Option(..., exists=True, dir_okay=False, help="Pipeline results JSONL (outputs/run/results.jsonl)."),
   270	    refs: Path = typer.Option(..., exists=True, dir_okay=False, help="Reference transcripts JSONL."),
   271	    out: Path | None = typer.Option(None, help="Write summary JSON to this path."),
   272	    hypothesis: str = typer.Option('final', help='Hypothesis to evaluate: "final", "rover", or "asr:<key>".'),
   273	    oracle: bool = typer.Option(True, help="Compute oracle-best and selection-gap stats across all candidates."),
   274	) -> None:
   275	    """
   276	    Compute WER/CER for a hypothesis against a reference JSONL keyed by `segment_id`.
   277	    """
   278	    from maya_transcribe.eval import evaluate
   279
   280	    summary = evaluate(results_path=results, refs_path=refs, out_path=out, hypothesis=hypothesis, oracle=oracle)