**Checking dependencies and search methods**

I'm considering a minimal check by running a simple Python command, but it seems like we're missing some dependencies. If required libraries like dotenv and typer aren't installed, that could cause the code to fail. I'll create a `requirements.txt` for installations later, and although `pyproject` could be sufficient, I think we need to ensure a robust search by implementing substring matching. Searching through a large bucket might be slow, but user guidance suggests a recursive search if nothing is found.


**Improving search efficiency**

I'm considering ways to enhance our search method. If the `video_id` has 11 characters, I want to explore using a prefix. However, S3 only allows prefix searches from the start, so a substring search would mean scanning all items, which could be hefty. While the user mentioned that compute isn't constrained, scanning millions of objects might still be significant. I think we could introduce more efficient heuristics, like checking if the video ID appears near the end of the key. If the user provides a prefix, I’d use that when available.


**Planning download and extraction process**

I'm considering adding a CLI option to specify a key or prefix, allowing users to override prefixes and perform dry runs. I also want to include a `--key` option to skip the search step entirely. For the `download_to` function, I plan to ensure it uses the base name of the key. When it comes to extracting tar files, I need to address security concerns around symlinks and hardlinks, ensuring they don’t affect the extraction process. Next, I'll move on to implementing robust R2 download features, including recursive search and caching.


Output: Plan updated


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.


**Improving R2 Retrieval and Extraction**

I’m focusing on enhancing the R2 retrieval and extraction for datasets that include audio files structured into tar folders. I’ll implement functions to locate the tar object based on video_id, considering possible formats like `.tar.gz` or `.tgz`. We need to ensure that we can find the nested segments directory properly. I’ll create methods in the R2Client for listing and finding videos, possibly using a paginator to manage larger datasets. Furthermore, I’ll enhance the tar extraction process to prevent symlink and absolute path issues.


**Supporting Extraction Improvements**

I want to ensure we can extract only under the specified extraction directory while verifying for symlinks. I also think we need to add detection for the segments folder, which means creating `maya_transcribe/dataset.py` with helpful functions. Step 2 should include implementing the `download` command properly so that after extraction, the user can easily find the segments folder. I'll also handle cases where the tar doesn’t exist and show error messages. Furthermore, I'll allow users to specify the R2 key directly, minimizing searches and implementing optional Supabase integration later.


**Patching the Download Command**

I realize we can enhance the `download` command by allowing it to accept a key. This seems like a straightforward improvement that can streamline the user experience, making it easier for users to specify their R2 key directly. I’ll go ahead and patch this functionality. Keeping it simple is key here, but I want to ensure that it’s clear to the user how to use this feature effectively. Let's make this happen!


Input:
*** Begin Patch
*** Update File: maya_transcribe/tar.py
@@
 def safe_extract_tar(tar_path: Path, extract_dir: Path) -> None:
     extract_dir.mkdir(parents=True, exist_ok=True)

     with tarfile.open(tar_path, "r:*") as tf:
         members = tf.getmembers()
         for m in members:
+            if m.islnk() or m.issym():
+                raise RuntimeError(f"Refusing to extract links from tar: {m.name}")
+            if m.name.startswith("/") or m.name.startswith("\\"):
+                raise RuntimeError(f"Refusing to extract absolute path from tar: {m.name}")
             member_path = extract_dir / m.name
             if not _is_within_directory(extract_dir, member_path):
                 raise RuntimeError(f"Refusing to extract outside target dir: {m.name}")
         tf.extractall(path=extract_dir)
*** End Patch


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


Summary: auto


Input:
*** Begin Patch
*** Add File: maya_transcribe/dataset.py
+from __future__ import annotations
+
+from pathlib import Path
+
+
+def find_segments_dir(root: Path) -> Path | None:
+    """
+    Return the first directory named 'segments' under `root` (including `root`).
+    """
+    if root.name == "segments" and root.is_dir():
+        return root
+    for path in root.rglob("segments"):
+        if path.is_dir():
+            return path
+    return None
+
*** End Patch


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


I'm thinking about updating the command line interface (CLI) to utilize the segments directory properly. It seems like a straightforward task, but I need to make sure I also include an option to accept the r2_key. I wonder if there's anything else I should consider during this update, like potential errors or how it might affect existing users. It’s always best to think through all possible outcomes before proceeding, so I’m just double-checking everything in my mind.


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
 from maya_transcribe.config import Settings
+from maya_transcribe.dataset import find_segments_dir
 from maya_transcribe.r2 import R2Client
 from maya_transcribe.tar import safe_extract_tar

 app = typer.Typer(add_completion=False, no_args_is_help=True)
@@
 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)."),
     prefix: str | None = typer.Option(None, help="Optional R2 prefix override."),
     dry_run: bool = typer.Option(False, help="Only show matched key(s); do not download."),
 ) -> None:
@@
     r2 = R2Client.from_settings(settings)

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

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

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

     if len(matches) > 1:
         typer.echo("Multiple matches found; downloading the shortest key (most specific).")

     key = sorted(matches, key=len)[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}")

 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}}


**Refining search functionality**

I'm considering an update to the search functionality so it can filter by suffix, like `.tar`, `.tar.gz`, and other related formats. Right now, the `find` function matches any key with `video_id`, but I think we can make it better by implementing a `prefer_suffixes` list. I should also modify `find_keys_containing` to accept optional suffixes for filtering. To improve performance, focusing on listing only tar objects might be beneficial. I'll get started on this patch!


Input:
*** Begin Patch
*** Update File: maya_transcribe/r2.py
@@
     def find_keys_containing(self, video_id: str, prefix: str | None = None, limit: int = 5000) -> list[str]:
         s3 = self._client()
         continuation_token: str | None = None
         matches: list[str] = []
@@
-            for obj in resp.get("Contents", []):
+            for obj in resp.get("Contents", []):
                 key = obj.get("Key")
                 if not key:
                     continue
                 if video_id in key:
                     matches.append(key)
                     if len(matches) >= limit:
                         return matches
@@
         return matches
*** End Patch


Output: {"output":"Success. Updated the following files:\nM maya_transcribe/r2.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Considering suffix filter**

I realize we haven’t added a suffix filter yet. I should think about how to implement that by patching the existing code with a new parameter. This makes sense, right? I mean, including a suffix filter could enhance functionality. I wonder what kind of suffixes would be useful. It feels a bit rushed, but it’s important to ensure all bases are covered for whatever’s coming next. I’ll get on that!


Input:
*** Begin Patch
*** Update File: maya_transcribe/r2.py
@@
-    def find_keys_containing(self, video_id: str, prefix: str | None = None, limit: int = 5000) -> list[str]:
+    def find_keys_containing(
+        self,
+        video_id: str,
+        prefix: str | None = None,
+        limit: int = 5000,
+        suffixes: tuple[str, ...] | None = (".tar", ".tar.gz", ".tgz"),
+    ) -> list[str]:
         s3 = self._client()
         continuation_token: str | None = None
         matches: list[str] = []
@@
-                if video_id in key:
+                if video_id in key:
+                    if suffixes and not key.endswith(suffixes):
+                        continue
                     matches.append(key)
                     if len(matches) >= limit:
                         return matches
*** End Patch


Output: {"output":"Success. Updated the following files:\nM maya_transcribe/r2.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Reviewing R2 implementation steps**

Great, the CLI now uses a default suffix filter. Maybe we should add an optional command to list keys, like `search`. For step 2, we’ve implemented R2 download and extraction, but perhaps we could make extraction more robust. Testing isn't strictly necessary now, but I should install dependencies and test the `download` command later. We need to ensure our code is using the required environment variables. I'm considering marking step 2 as complete, but we should add proper error messages for missing variables.


**Considering Boto3 region settings**

I’m looking into whether Boto3 needs `region_name="auto"` for R2. It seems I could set it to "auto" or "us-east-1." Without specifying a region, Boto3 might default to a certain region, which could lead to errors. My experience suggests that the Boto3 client for S3 could work without explicitly stating a region, but it defaults to 'us-east-1.' I plan to check this by creating a Boto3 client in Python, although I still need to install it first. Setting `region_name` to "auto" seems like a good idea.


**Patching Boto3 configuration**

To avoid any potential issues, I think it’s best to set `region_name="auto"`. That might just work perfectly! I’ll also update the client accordingly. Since Cloudflare R2 uses signature version v4, and Boto3 defaults to that, we may still need `config=Config(signature_version='s3v4')` for some scenarios. It seems like patching the code will look like this:

```python
from botocore.config import Config
return boto3.client("s3", endpoint_url=..., aws_access_key_id=..., aws_secret_access_key=..., region_name="auto", config=Config(signature_version="s3v4"))
```

I’ll implement that patch now.


Input:
*** Begin Patch
*** Update File: maya_transcribe/r2.py
@@
     def _client(self):
         import boto3
+        from botocore.config import Config

         return boto3.client(
             "s3",
             endpoint_url=self.endpoint_url,
             aws_access_key_id=self.access_key_id,
             aws_secret_access_key=self.secret_access_key,
+            region_name="auto",
+            config=Config(signature_version="s3v4"),
         )
*** End Patch


Output: {"output":"Success. Updated the following files:\nM maya_transcribe/r2.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Planning audio processing**

Okay, step 2 is solid, and I need to make sure `pyproject.toml` includes `botocore` since it's a dependency of `boto3`. Now, I'll implement step 3, focusing on audio quality control (QC) and voice activity detection (VAD) trimming. I plan to create an audio reading module named `maya_transcribe/audio.py` with functions like `probe_audio()` for getting audio info, `normalize_audio()` for adjusting audio properties, and `compute_qc_metrics()` for calculating metrics like RMS and peak clipping. I'll also consider implementing silence trimming using ffmpeg’s `silenceremove` filter for now.


**Implementing VAD trim and QC**

I'm thinking about implementing the VAD trim by first converting audio to a 16k mono WAV file in a temporary location. I can use webrtcvad to detect voiced frames for trimming, but since I already have ffmpeg installed, I might as well call it to trim silence using the `silenceremove` parameters. Energy thresholds could vary, so I could also set up `vad_trim` with Silero VAD if torch becomes available. For now, I’ll focus on adding audio quality control (QC) and VAD trim, using pure ffmpeg and numpy while ensuring the audio is in the correct format for models. I'll implement an `AudioInfo` dataclass and use `ffprobe` for audio information, aiming to get important QC metrics like RMS and peak levels.


**Deciding on audio dependencies**

I’m considering how to read audio samples for quality control (QC) and VAD. While I could use the `soundfile` library, it's currently an optional dependency in the `asr` extras. QC may need it, so I’m thinking it might be best to move `soundfile` into core dependencies. This way, I can use it without extra hassle, even though QC could be done using ffmpeg’s `astats` filter or `ffprobe`, which lacks the extra dependency. I'll make sure to account for libsndfile, which might need installation on systems like Ubuntu.


**Considering audio quality options**

I’m weighing whether to keep the base minimal by keeping `soundfile` as optional while implementing quality control (QC) using ffmpeg. However, for a more robust pipeline, including `soundfile` would allow for accurate metrics, even if the user hasn't installed anything yet. I'll check if `libsndfile` is installed, but if it's not, using `pip install soundfile` may require additional installation steps. I realize soundfile usually bundles the necessary library on many Linux distributions, but I might prefer to avoid it for the core. Instead, I could use `ffmpeg` to handle audio reading and metric computation directly.


**Implementing audio reading functions**

I’m planning to implement the `read_audio_mono_16k(path)` function using ffmpeg with a command that converts audio to a 16kHz mono format without needing `soundfile`. I can utilize subprocess to run ffmpeg and capture the output as an array in numpy int16 format. This approach seems robust, and since segments up to 60 seconds will only take about 2MB, it’s manageable.

I'll create several functions, like `decode_audio_to_pcm16()` for reading and processing, converting to float32, and computing QC metrics. Additionally, I’ll add options for trimming silence with ffmpeg’s `silenceremove`. I should also handle any potential errors and set things up for later steps involving LID and ASR backends. For now, I’ll focus on creating the audio module and necessary functions.


Input:
*** Begin Patch
*** Add File: maya_transcribe/audio.py
+from __future__ import annotations
+
+import json
+import subprocess
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+
+
+@dataclass(frozen=True)
+class AudioInfo:
+    path: str
+    duration_sec: float | None
+    sample_rate: int | None
+    channels: int | None
+
+
+@dataclass(frozen=True)
+class AudioQCMetrics:
+    duration_sec: float
+    rms_dbfs: float
+    peak_dbfs: float
+    clipping_fraction: float
+
+
+def ffprobe(path: Path) -> AudioInfo:
+    cmd = [
+        "ffprobe",
+        "-v",
+        "error",
+        "-show_entries",
+        "format=duration",
+        "-show_entries",
+        "stream=sample_rate,channels",
+        "-of",
+        "json",
+        str(path),
+    ]
+    proc = subprocess.run(cmd, check=True, capture_output=True, text=True)
+    data = json.loads(proc.stdout or "{}")
+
+    duration_sec = None
+    try:
+        duration_sec = float(data.get("format", {}).get("duration"))
+    except (TypeError, ValueError):
+        duration_sec = None
+
+    sample_rate = None
+    channels = None
+    streams = data.get("streams") or []
+    if streams:
+        s0 = streams[0] or {}
+        try:
+            sample_rate = int(s0.get("sample_rate")) if s0.get("sample_rate") else None
+        except (TypeError, ValueError):
+            sample_rate = None
+        try:
+            channels = int(s0.get("channels")) if s0.get("channels") else None
+        except (TypeError, ValueError):
+            channels = None
+
+    return AudioInfo(path=str(path), duration_sec=duration_sec, sample_rate=sample_rate, channels=channels)
+
+
+def decode_pcm16(path: Path, sample_rate: int = 16000, mono: bool = True) -> tuple[np.ndarray, int]:
+    """
+    Decode audio with ffmpeg into signed 16-bit PCM (optionally mono) at `sample_rate`.
+    Returns (float32 waveform in [-1, 1], sample_rate).
+    """
+    cmd = ["ffmpeg", "-v", "error", "-i", str(path), "-f", "s16le", "-ar", str(sample_rate)]
+    if mono:
+        cmd += ["-ac", "1"]
+    cmd += ["-"]
+
+    proc = subprocess.run(cmd, check=True, capture_output=True)
+    pcm = np.frombuffer(proc.stdout, dtype=np.int16)
+    if pcm.size == 0:
+        return np.zeros((0,), dtype=np.float32), sample_rate
+    wav = pcm.astype(np.float32) / 32768.0
+    return wav, sample_rate
+
+
+def compute_qc(path: Path, sample_rate: int = 16000) -> AudioQCMetrics:
+    wav, sr = decode_pcm16(path, sample_rate=sample_rate, mono=True)
+    duration_sec = float(wav.shape[0]) / float(sr) if sr else 0.0
+    if wav.size == 0:
+        return AudioQCMetrics(duration_sec=0.0, rms_dbfs=float("-inf"), peak_dbfs=float("-inf"), clipping_fraction=0.0)
+
+    peak = float(np.max(np.abs(wav)))
+    rms = float(np.sqrt(np.mean(np.square(wav))))
+
+    peak_dbfs = 20.0 * float(np.log10(max(peak, 1e-12)))
+    rms_dbfs = 20.0 * float(np.log10(max(rms, 1e-12)))
+
+    clipping_threshold = 0.999
+    clipping_fraction = float(np.mean(np.abs(wav) >= clipping_threshold))
+
+    return AudioQCMetrics(
+        duration_sec=duration_sec,
+        rms_dbfs=rms_dbfs,
+        peak_dbfs=peak_dbfs,
+        clipping_fraction=clipping_fraction,
+    )
+
+
+def normalize_audio(
+    in_path: Path,
+    out_path: Path,
+    sample_rate: int = 16000,
+    mono: bool = True,
+    trim_silence: bool = True,
+    silence_threshold_db: float = -40.0,
+    min_silence_sec: float = 0.15,
+) -> Path:
+    """
+    Convert audio to 16kHz mono WAV and optionally trim leading/trailing silence.
+    """
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+
+    cmd = ["ffmpeg", "-v", "error", "-y", "-i", str(in_path)]
+
+    if trim_silence:
+        # Trim leading/trailing silence; tuned for already-clean segments.
+        # https://ffmpeg.org/ffmpeg-filters.html#silenceremove
+        thr = f"{silence_threshold_db}dB"
+        sil = (
+            f"silenceremove=start_periods=1:start_duration={min_silence_sec}:start_threshold={thr}:"
+            f"stop_periods=1:stop_duration={min_silence_sec}:stop_threshold={thr}"
+        )
+        cmd += ["-af", sil]
+
+    cmd += ["-ar", str(sample_rate)]
+    if mono:
+        cmd += ["-ac", "1"]
+    cmd += ["-c:a", "pcm_s16le", str(out_path)]
+
+    subprocess.run(cmd, check=True)
+    return out_path
+
*** End Patch


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


**Implementing audio QC steps**

I’m focusing on implementing step 3 more fully, specifically involving audio Quality Control (QC) and Voice Activity Detection (VAD) trim in our pipeline, although the pipeline isn't ready yet. I think it's worth updating the CLI to include a `preprocess` command for normalization and QC. This step will involve locating audio segments in the segments folder, enabling us to apply normalization and eventually integrate it into the pipeline. For now, let’s plan for this `preprocess` command.


**Updating progress on tasks**

I've added some code, and there's a possibility we could update the CLI to call it or incorporate it into the pipeline later in steps 4 or 5. For now, I'll keep it pending until it’s integrated. It looks like step 3 can be in progress when the code is used. I think step 2 is completed since we have the R2 download and extraction command; we just need to ensure robust output and proper extraction. I'll mark step 2 as done and step 3 as in progress.


Output: Plan updated


**Integrating audio processing**

I need to integrate audio quality control and voice activity detection into the processing pipeline. Step 3 will involve the ability to preprocess audio segments, so I’ll implement a `preprocess` command.

The input will be the path to `segments_dir`, with the output going to `out_dir`, defaulting to `outputs/preprocessed`. I'll loop through each audio file, perhaps in formats like .wav or .mp3, call `normalize_audio`, and compute quality control metrics to save in jsonl.

I'll ensure concurrency and make this a part of the `maya_transcribe/preprocess.py` module, returning segment metadata. Also, I'll decide to use the segment ID as the file stem and manage various audio extensions.


**Implementing audio preprocessing**

I’m planning to store results in `out_dir/qc.jsonl`, with each record containing the original and normalized paths, metrics, and possibly ffprobe info. For now, I think it's best to keep it simple and focus on implementing the CLI.

In the `preprocess` command, I’ll take parameters like `segments_dir`, output directory, sample rate, and a flag for trimming silence. I'll ensure the files are sorted and process them accordingly, computing quality control for the normalized output.

I’ll also need to write a helper function to handle JSONL storage, and I’ll create `maya_transcribe/io.py` for that purpose. Time to apply patches and update the CLI!


Input:
*** Begin Patch
*** Add File: maya_transcribe/io.py
+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
+
*** End Patch


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


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
 from pathlib import Path

 import typer

+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.io import write_jsonl
 from maya_transcribe.r2 import R2Client
 from maya_transcribe.tar import safe_extract_tar

 app = typer.Typer(add_completion=False, no_args_is_help=True)
@@
     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:
+        segment_id = src.stem
+        dst = norm_dir / f"{segment_id}.wav"
+        normalize_audio(
+            src,
+            dst,
+            sample_rate=sample_rate,
+            mono=True,
+            trim_silence=trim_silence,
+            silence_threshold_db=silence_threshold_db,
+            min_silence_sec=min_silence_sec,
+        )
+        info = ffprobe(dst)
+        qc = compute_qc(dst, sample_rate=sample_rate)
+        rows.append(
+            {
+                "segment_id": segment_id,
+                "src_path": str(src),
+                "wav_path": str(dst),
+                "ffprobe": {
+                    "duration_sec": info.duration_sec,
+                    "sample_rate": info.sample_rate,
+                    "channels": info.channels,
+                },
+                "qc": {
+                    "duration_sec": qc.duration_sec,
+                    "rms_dbfs": qc.rms_dbfs,
+                    "peak_dbfs": qc.peak_dbfs,
+                    "clipping_fraction": qc.clipping_fraction,
+                },
+            }
+        )
+
+    write_jsonl(out / "qc.jsonl", rows)
+    typer.echo(f"Wrote: {out / 'qc.jsonl'}")
+    typer.echo(f"Normalized audio: {norm_dir}")
+
+
 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 processing steps**

I've implemented the preprocess command, but there's a potential issue with our `segments_dir.rglob(ext)`. It uses patterns, and while nested directories are fine, I might want to include uppercase extensions as well. Step 3, which covers audio quality control and VAD trimming, is done. Now, I’ll mark step 3 as completed and move on to making progress on step 4. Let's update the plan accordingly!


Output: Plan updated


Summary: auto


**Structuring ASR Data Classes**

I'm planning to compute average log probabilities for segments in the ASR process. I think I’ll create dataclasses `ASRSegment` and `ASRResult` in `maya_transcribe/schemas.py`, though it seems we haven't done that yet. For now, I could return simple dictionaries instead. Eventually, I need to design `maya_transcribe/types.py` and define the pipeline output schema. This includes details like `LanguagePrediction` and `LIDResult`, while simplifying the segments list for ASR to include just what's essential. I'll also implement text normalization as part of this process.


**Mapping Language Codes**

I'm looking into the language codes used in LID and Whisper. LID returns ISO 639-3 codes (three letters), while Whisper appears to use two-letter language tokens like 'en', 'hi', and 'ta'. For Faster Whisper, I need to implement a mapping from the ISO3 codes to Whisper's two-letter format. The target languages include Malayalam, Hindi, Punjabi, Tamil, Kannada, Gujarati, Telugu, Bengali, Odia, Marathi, Assamese, and English. I'll create a clear mapping to organize this. There’s a bit of uncertainty regarding the correct code for Odia, so I’ll double-check that!


**Clarifying Language Codes**

I'm sorting out the codes for Oriya. I think 'ori' is the ISO 639-2 code, while the 639-3 might be 'ory'. I also need to check that! For the mappings, I’ll use 'mar' for Marathi, 'asm' for Assamese, and 'eng' for English. I plan to implement the mapping in `maya_transcribe/language.py`.

As for the LID model, I might eventually add an optional second model, like SpeechBrain, plus an interface for WhisperLID. For language detection, I want to create a function called `detect_language(audio_path)` to handle incoming audio efficiently.


**Implementing LID and ASR Modules**

For the LID ensemble, I'll implement MMS-LID alongside Whisper's language detection as a fallback. I plan to create the `lid.py` file with a class called `LIDEnsemble`. I also want to add new commands for `lid` and `asr` to the CLI, so I can run predictions with a command like `maya-transcribe lid --audio path.wav`.

I’ll create several files, including `maya_transcribe/language.py`, `maya_transcribe/lid.py`, and `maya_transcribe/asr/base.py` to keep things organized. Additionally, I’ll design `maya_transcribe/asr.py` with relevant classes, like `FasterWhisperASR`, ensuring everything is clear and structured!


**Implementing ASR with Transformers**

I’m realizing that the `transformers` library can run Whisper for tasks like IndicWhisper, though it might be a bit heavier and slower. I plan to create a `TransformersASR` class that uses the `automatic-speech-recognition` pipeline, specifying models and languages as needed.

I’ll implement `FasterWhisperASR` with a default model size of "large-v3", allowing a `--model` option too. For computation, I’ll set it to `float16` on CUDA and `float32` on CPU for quality.

Finally, I’ll create the `MMSLID` class for handling predictions and add `lid` and `asr` commands to the CLI while keeping it manageable. It looks like I have quite a bit to do, but I’m excited about the progress!


Input:
*** Begin Patch
*** Add File: maya_transcribe/language.py
+from __future__ import annotations
+
+ISO3_TO_WHISPER_LANG: dict[str, str] = {
+    "eng": "en",
+    "hin": "hi",
+    "mar": "mr",
+    "guj": "gu",
+    "pan": "pa",
+    "tam": "ta",
+    "tel": "te",
+    "kan": "kn",
+    "mal": "ml",
+    "ben": "bn",
+    "asm": "as",
+    "ori": "or",
+    "ory": "or",
+}
+
+
+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)
+
*** End Patch


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


Input:
*** Begin Patch
*** Add File: maya_transcribe/lid.py
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+
+from maya_transcribe.audio import decode_pcm16
+
+
+@dataclass(frozen=True)
+class LanguagePrediction:
+    code: str
+    prob: float
+
+
+@dataclass(frozen=True)
+class LIDResult:
+    model: str
+    predictions: list[LanguagePrediction]
+
+
+class MMSLID:
+    def __init__(self, model_id: str = "facebook/mms-lid-256", device: str | None = None) -> None:
+        try:
+            import torch
+            from transformers import AutoModelForAudioClassification, AutoProcessor
+        except Exception as e:  # pragma: no cover
+            raise RuntimeError(
+                "MMS-LID requires the optional dependencies: torch + transformers. "
+                "Install with: pip install -e '.[torch]'"
+            ) from e
+
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        self.model_id = model_id
+        self.device = device
+        self._torch = torch
+        self._processor = AutoProcessor.from_pretrained(model_id)
+        self._model = AutoModelForAudioClassification.from_pretrained(model_id).to(device)
+        self._model.eval()
+
+    @property
+    def name(self) -> str:
+        return self.model_id
+
+    def predict(self, audio_path: Path, top_k: int = 5) -> LIDResult:
+        wav, sr = decode_pcm16(audio_path, sample_rate=16000, mono=True)
+        if wav.size == 0:
+            return LIDResult(model=self.model_id, predictions=[])
+
+        inputs = self._processor(wav, sampling_rate=sr, return_tensors="pt")
+        inputs = {k: v.to(self.device) for k, v in inputs.items()}
+
+        with self._torch.no_grad():
+            logits = self._model(**inputs).logits
+            probs = self._torch.softmax(logits, dim=-1)[0].detach().cpu().numpy().astype(np.float32)
+
+        top_idx = np.argsort(probs)[::-1][: max(top_k, 1)]
+        id2label = getattr(self._model.config, "id2label", {}) or {}
+
+        preds: list[LanguagePrediction] = []
+        for idx in top_idx:
+            label = id2label.get(int(idx), str(int(idx)))
+            preds.append(LanguagePrediction(code=str(label), prob=float(probs[int(idx)])))
+
+        return LIDResult(model=self.model_id, predictions=preds)
+
*** End Patch


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


**Implementing ASR Backend**

I’ve imported numpy, which is great since it's a core dependency. This means I'm on the right track! Now, I need to focus on implementing the ASR (Automatic Speech Recognition) backend. It's crucial to make sure everything integrates smoothly. I feel like having numpy in place really helps with data handling and processing. Let’s keep going and tackle this next step effectively! I’m excited to see how this all comes together!


Input:
*** Begin Patch
*** Add File: maya_transcribe/asr.py
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from maya_transcribe.language import to_whisper_lang
+
+
+@dataclass(frozen=True)
+class ASRSegment:
+    start: float
+    end: float
+    text: str
+    avg_logprob: float | None = None
+    no_speech_prob: float | None = None
+    compression_ratio: float | None = None
+
+
+@dataclass(frozen=True)
+class ASRResult:
+    model: str
+    text: str
+    language: str | None
+    language_probability: float | None
+    segments: list[ASRSegment]
+    meta: dict[str, Any]
+
+
+class FasterWhisperASR:
+    def __init__(
+        self,
+        model_size_or_path: str = "large-v3",
+        device: str | None = None,
+        compute_type: str | None = None,
+    ) -> None:
+        try:
+            from faster_whisper import WhisperModel
+        except Exception as e:  # pragma: no cover
+            raise RuntimeError(
+                "faster-whisper is not installed. Install with: pip install -e '.[asr]'"
+            ) from e
+
+        if device is None:
+            try:
+                import torch
+
+                device = "cuda" if torch.cuda.is_available() else "cpu"
+            except Exception:
+                device = "cpu"
+
+        if compute_type is None:
+            compute_type = "float16" if device == "cuda" else "float32"
+
+        self.model_size_or_path = model_size_or_path
+        self.device = device
+        self.compute_type = compute_type
+        self._model = WhisperModel(model_size_or_path, device=device, compute_type=compute_type)
+
+    @property
+    def name(self) -> str:
+        return f"faster-whisper:{self.model_size_or_path}"
+
+    def transcribe(
+        self,
+        audio_path: Path,
+        language: str | None = None,
+        beam_size: int = 5,
+        best_of: int = 5,
+        temperature: float = 0.0,
+        vad_filter: bool = False,
+    ) -> ASRResult:
+        lang = to_whisper_lang(language)
+
+        segments_iter, info = self._model.transcribe(
+            str(audio_path),
+            language=lang,
+            beam_size=beam_size,
+            best_of=best_of,
+            temperature=temperature,
+            vad_filter=vad_filter,
+        )
+
+        segments: list[ASRSegment] = []
+        texts: list[str] = []
+        for s in segments_iter:
+            texts.append(s.text)
+            segments.append(
+                ASRSegment(
+                    start=float(s.start),
+                    end=float(s.end),
+                    text=str(s.text),
+                    avg_logprob=getattr(s, "avg_logprob", None),
+                    no_speech_prob=getattr(s, "no_speech_prob", None),
+                    compression_ratio=getattr(s, "compression_ratio", None),
+                )
+            )
+
+        return ASRResult(
+            model=self.name,
+            text="".join(texts).strip(),
+            language=getattr(info, "language", None),
+            language_probability=getattr(info, "language_probability", None),
+            segments=segments,
+            meta={
+                "device": self.device,
+                "compute_type": self.compute_type,
+                "beam_size": beam_size,
+                "best_of": best_of,
+                "temperature": temperature,
+                "vad_filter": vad_filter,
+            },
+        )
+
*** End Patch


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


**Updating CLI commands**

I'm planning to update the CLI to include `lid` and `asr` commands. I'll import the optional modules as needed, although the CLI file already imports some audio functions. It's good to know that if the optional dependencies aren't installed, the `lid` command will still handle it. I'll import `MMSLID` and `FasterWhisperASR` specifically within the command to avoid import issues. Time to patch the cli.py and add those commands!


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
 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.io import write_jsonl
 from maya_transcribe.r2 import R2Client
 from maya_transcribe.tar import safe_extract_tar

 app = typer.Typer(add_completion=False, no_args_is_help=True)
@@
 def preprocess(
@@
     typer.echo(f"Wrote: {out / 'qc.jsonl'}")
     typer.echo(f"Normalized audio: {norm_dir}")

+
+@app.command()
+def lid(
+    audio: Path = typer.Option(..., exists=True, dir_okay=False, help="Audio file path."),
+    model_id: str = typer.Option("facebook/mms-lid-256", help="HuggingFace model id."),
+    top_k: int = typer.Option(5, help="Return top-K predictions."),
+) -> None:
+    """
+    Spoken language identification (MMS-LID).
+    """
+    from maya_transcribe.lid import MMSLID
+
+    model = MMSLID(model_id=model_id)
+    res = model.predict(audio_path=audio, top_k=top_k)
+    for p in res.predictions:
+        typer.echo(f"{p.code}\t{p.prob:.4f}")
+
+
+@app.command()
+def asr(
+    audio: Path = typer.Option(..., exists=True, dir_okay=False, help="Audio file path."),
+    language: str | None = typer.Option(None, help="Language hint (ISO-639-1 or ISO-639-3)."),
+    model: str = typer.Option("large-v3", help="Whisper model size or path for faster-whisper."),
+    beam_size: int = typer.Option(5, help="Beam size."),
+    best_of: int = typer.Option(5, help="Best-of (sampling candidates)."),
+    temperature: float = typer.Option(0.0, help="Temperature."),
+) -> None:
+    """
+    Transcribe a single file with faster-whisper.
+    """
+    from maya_transcribe.asr import FasterWhisperASR
+
+    asr_backend = FasterWhisperASR(model_size_or_path=model)
+    res = asr_backend.transcribe(
+        audio_path=audio, language=language, beam_size=beam_size, best_of=best_of, temperature=temperature
+    )
+    typer.echo(res.text)
+

 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}}


**Integrating backends**

Now, I've added the LID and ASR backends, but I might also consider integrating Whisper's built-in LID or a LID ensemble later on. That could potentially be part of step 5. The master plan suggests a spoken LID ensemble, but for now, we have MMS-LID and can use Whisper's data. Should I mark step 4 as complete? I still need to implement some indicators, but the plan for adding multiple backends remains. For step 5, I want to support multiple models eventually.


**Implementing ASR diversity**

I realize I can treat the same model with different decoding parameters as multiple hypotheses. For a true ensemble, I want at least two backends. I could include a second backend using Whisper with different compute settings. Maybe I could also implement a `FasterWhisperASR` with options like the small or medium model. However, I need to ensure there's enough diversity. I might consider adding a `TransformersWhisperASR` to allow for different models, like IndicWhisper, and let the user specify the model ID. I’ll also aim to implement a new ASR backend using a "automatic-speech-recognition" pipeline for completeness.


**Implementing TransformersASR Class**

I'm setting up the `TransformersASR` class, which will initialize with model_id, device, and dtype (float16). The `transformers.pipeline` for automatic speech recognition will consider the device type as an integer for specifying GPU. For Whisper models, I may need to pass in `generate_kwargs` for the language and task as transcription.

Since I'm unsure about the exact syntax, I'll refer to the documentation. For now, I can implement a minimal pipeline setup without added complexity, passing necessary language hints with `generate_kwargs`.


**Implementing TransformersWhisperASR**

I’m going to implement the `TransformersWhisperASR` class using `AutoModelForSpeechSeq2Seq` and `AutoProcessor`. I'll set it up to allow control over processing audio input, ensuring it works with Whisper models. For audio, we'll need a 16000 sampling rate for compatibility.

While we're focusing on Whisper seq2seq models now, we should keep in mind that other models may not fit neatly into this setup. The class will leverage forced decoder IDs based on the specified language for multilingual detection, returning just the full transcribed text. I'll implement this in the `asr.py` for a second backend.


**Patching asr.py**

I need to ensure that any optional dependency errors provide a clear message instructing users to install with `.[torch]`. That way, it’s user-friendly and guides them on how to resolve the issue. I’ll patch `asr.py` accordingly to implement this. This small adjustment will help prevent confusion and make the experience smoother for anyone interacting with the code. It's all about making it easier for users to get set up!


Input:
*** Begin Patch
*** Update File: maya_transcribe/asr.py
@@
 class FasterWhisperASR:
@@
         return ASRResult(
             model=self.name,
             text="".join(texts).strip(),
             language=getattr(info, "language", None),
             language_probability=getattr(info, "language_probability", None),
             segments=segments,
             meta={
                 "device": self.device,
                 "compute_type": self.compute_type,
                 "beam_size": beam_size,
                 "best_of": best_of,
                 "temperature": temperature,
                 "vad_filter": vad_filter,
             },
         )
+
+
+class TransformersWhisperASR:
+    """
+    Whisper-family ASR using HuggingFace Transformers (useful for fine-tuned checkpoints like IndicWhisper).
+    """
+
+    def __init__(
+        self,
+        model_id: str,
+        device: str | None = None,
+        dtype: str | None = None,
+    ) -> None:
+        try:
+            import torch
+            from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
+        except Exception as e:  # pragma: no cover
+            raise RuntimeError(
+                "TransformersWhisperASR requires the optional dependencies: torch + transformers. "
+                "Install with: pip install -e '.[torch]'"
+            ) from e
+
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+        if dtype is None:
+            dtype = "float16" if (device == "cuda") else "float32"
+
+        torch_dtype = torch.float16 if dtype == "float16" else torch.float32
+
+        self.model_id = model_id
+        self.device = device
+        self.dtype = dtype
+        self._torch = torch
+        self._processor = AutoProcessor.from_pretrained(model_id)
+        self._model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype).to(device)
+        self._model.eval()
+
+    @property
+    def name(self) -> str:
+        return f"transformers:{self.model_id}"
+
+    def transcribe(
+        self,
+        audio_path: Path,
+        language: str | None = None,
+        max_new_tokens: int = 256,
+        num_beams: int = 5,
+    ) -> ASRResult:
+        from maya_transcribe.audio import decode_pcm16
+
+        wav, sr = decode_pcm16(audio_path, sample_rate=16000, mono=True)
+        if wav.size == 0:
+            return ASRResult(
+                model=self.name,
+                text="",
+                language=language,
+                language_probability=None,
+                segments=[],
+                meta={"max_new_tokens": max_new_tokens, "num_beams": num_beams},
+            )
+
+        inputs = self._processor(wav, sampling_rate=sr, return_tensors="pt")
+        input_features = inputs.get("input_features")
+        if input_features is None:
+            raise RuntimeError("Processor did not return input_features")
+        input_features = input_features.to(self.device)
+
+        forced_decoder_ids = None
+        whisper_lang = to_whisper_lang(language)
+        if whisper_lang:
+            get_ids = getattr(self._processor, "get_decoder_prompt_ids", None)
+            if callable(get_ids):
+                forced_decoder_ids = get_ids(language=whisper_lang, task="transcribe")
+
+        with self._torch.no_grad():
+            generated_ids = self._model.generate(
+                input_features,
+                forced_decoder_ids=forced_decoder_ids,
+                max_new_tokens=max_new_tokens,
+                num_beams=num_beams,
+            )
+
+        text = self._processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        return ASRResult(
+            model=self.name,
+            text=text.strip(),
+            language=whisper_lang or language,
+            language_probability=None,
+            segments=[],
+            meta={
+                "device": self.device,
+                "dtype": self.dtype,
+                "max_new_tokens": max_new_tokens,
+                "num_beams": num_beams,
+            },
+        )
*** End Patch


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


**Updating command process**

I'm noting that the CLI's `asr` command now uses FasterWhisper and that there's an option to specify a transformers backend by providing a model ID. I think it's best to keep this option in place. Now, step 4 might also involve incorporating LID ensemble, or perhaps that's more suited to step 5. I’ll mark step 4 as completed and move on to step 5, adjusting the plan accordingly.


Output: Plan updated


rg -n "ai4bharat/indicwhisper" -S docs master.md task.md Prompt.md Agents.md || true


Output:
Exit code: 0
Wall time: 0 seconds
Output:
docs/Reference3.md:80:1. **Primary Model:** **AI4Bharat/IndicWhisper** (HuggingFace). This is non-negotiable for high-quality Indic TTS data.
docs/Reference5.md:147:Primary ASR: IndicWhisper (ai4bharat/indicwhisper-{lang})


**Designing ASR Pipeline**

Given the limitations of our environment, it seems like we might not run large models due to the absence of a GPU. So, I'll opt for a smaller model for demonstration, like `tiny`, but the user wants a more robust solution. I’ll implement a pipeline that supports multiple ASR models, defaulting to whisper large-v3 for its versatility. This includes using techniques like beam search and sampling to create diversity. Implementing text normalization is essential, like applying Unicode normalization and adjusting punctuation based on context.


**Normalizing Text for ASR**

To ensure robustness in our ASR pipeline, I'll implement a `normalize_text(text, lang)` function. This will include using `unicodedata.normalize("NFKC", text)` to standardize the text, replacing various spaces with regular ones, and removing zero-width joiners. I’ll also consider standardizing digits to ASCII and convert the text to lowercase for English. Punctuation removal will be done using regex, and I’ll align tokens to avoid mismatches. Lastly, I'll implement the ROVER algorithm with input for progressive alignment as a list of tokens and optional weights.


**Aligning Transcripts for ROVER**

I'm working on the ROVER algorithm, where each slot will map tokens to their weight sums, possibly also keeping track of token counts. The idea is to use the base transcript tokens from the first model and initialize the network, treating each token as a slot with its weight. For subsequent transcripts, I’ll align their tokens to the network using dynamic programming. This approach might feel complex, but aligning to the best output tokens simplifies it. I'll also consider a centroid approach where we use a reference transcript to align others, needing to compute pairwise Word Error Rates (WER) quickly. To start, I’ll tokenize each normalized hypothesis into words by splitting on whitespace.


**Computing Transcript Alignment**

I need to compute the pairwise Word Error Rate (WER) distance between all transcripts, defined as WER = edit distance divided by the length of the reference. It would make sense to normalize this distance. To find the centroid, I’ll use the transcript with the minimal average normalized edit distance to others, based on tokens. This centroid will act as the skeleton for alignment. I'll align other transcripts' tokens using Levenshtein alignment, applying dynamic programming to compute edit operations. Finally, I’ll construct confusion network slots by maintaining counts of alternatives for skeleton tokens and managing insertions and deletions.


**Implementing ROVER for Confusion Networks**

To handle transcript alignment in the ROVER framework, I'll represent skeleton slots alongside insertion slots. I'll use an alignment algorithm to produce pairs that indicate aligned operations between the skeleton and hypotheses. The plan is to start with skeleton tokens and iteratively build a confusion network from each aligned hypothesis. By aligning skeleton tokens to hypotheses using dynamic programming, I’ll manage insertions effectively while keeping track of the relative positions. The ultimate goal is to create a robust confusion network that includes all necessary tokens and adjustments for any insertions, ensuring clarity and accuracy in the alignment process.


**Refining Insertion Handling in ROVER**

I'm planning how to handle insertions in our ROVER implementation. For deletions, I'll use a special `<del>` token. When aligning hypotheses to the skeleton, if the skeleton token is `None`, that indicates an insertion. I’ll create slots for these insertions and ensure we maintain the order of tokens. The challenge will be avoiding excessive duplication in insertion slots across different hypotheses. I can simplify this by using consensus insertion sequences for gaps between skeleton tokens, calculating them after processing all hypotheses.

To execute this, I'll focus on grouping identical insertions and deriving a centroid sequence to ensure quality. The core algorithm will involve choosing a skeleton based on the centroid hypothesis, aligning each hypothesis accordingly, and then voting on the best tokens. We’ll track insertions for each gap, aiming to create a cohesive final transcript.


**Implementing ROVER and Alignment Logic**

I’m strategizing how to implement a robust ROVER-like system that computes token alignments. The basic idea is to use edit distances to align sequences and determine the final token sequence with gaps filled by inserting tokens as needed. Each hypothesis will contribute to consensus, and I’ll generate a disagreement map to flag where the hypotheses diverge.

For the verification process, I'm looking to calculate various confidence metrics and establish thresholds for acceptance or rejection of segments. This involves implementing features like a function for scoring individual segments, assessing quality checks, and allowing for optional retries with adjusted parameters.

My implementation will include several files to organize the code effectively, focusing on token alignment using Levenshtein distance while maintaining a clear pipeline for processing audio segments. I'll need to design a function for computing edit distances and alignments to further this goal, ensuring I cover all necessary steps in the pipeline efficiently.


**Normalizing and Implementing ROVER**

I’m starting with normalizing text, focusing on Unicode normalization and whitespace handling. I want to remove punctuation while potentially keeping meaningful symbols, then ensure the digits are unified to ASCII.

For ROVER, I’m creating a `RoverResult` dataclass that will include combined text, tokens, and other metadata like disagreement rates and agreement scores. Aligning hypotheses with a skeleton model will help identify gaps and decide on consensus insertion sequences based on weighted votes.

I’ll also set verification thresholds for the tasks, like checking the language identification confidence and agreement scores. This way, I can effectively process and evaluate segments!


**Implementing ROVER and Pipeline Logic**

I'm focusing on utilizing edit distance for measuring ROVER disagreement rates and computing ASR log probabilities for results. Various conditions will guide decision-making—like rejecting segments that are too short or uncertain, especially when disagreement rates exceed allowed thresholds.

I’m structuring the pipeline for processing audio segments, which involves several steps, including normalizing audio, running speech recognition models, and collecting assessment metrics for each segment. I'll keep the implementation modular, ensuring that I gracefully handle dependencies while maintaining robust functionality.


Input:
*** Begin Patch
*** Add File: maya_transcribe/align.py
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class AlignmentResult:
+    distance: int
+    aligned: list[tuple[str | None, str | None]]
+
+
+def align_tokens(ref: list[str], hyp: list[str]) -> AlignmentResult:
+    """
+    Levenshtein alignment between token sequences.
+    Returns edit distance and an alignment as (ref_token|None, hyp_token|None) pairs.
+    """
+    n = len(ref)
+    m = len(hyp)
+    dp = [[0] * (m + 1) for _ in range(n + 1)]
+
+    for i in range(1, n + 1):
+        dp[i][0] = i
+    for j in range(1, m + 1):
+        dp[0][j] = j
+
+    for i in range(1, n + 1):
+        ri = ref[i - 1]
+        for j in range(1, m + 1):
+            hj = hyp[j - 1]
+            sub_cost = 0 if ri == hj else 1
+            dp[i][j] = min(
+                dp[i - 1][j] + 1,  # deletion
+                dp[i][j - 1] + 1,  # insertion
+                dp[i - 1][j - 1] + sub_cost,  # substitution/match
+            )
+
+    aligned: list[tuple[str | None, str | None]] = []
+    i, j = n, m
+    while i > 0 or j > 0:
+        if i > 0 and j > 0:
+            ri = ref[i - 1]
+            hj = hyp[j - 1]
+            sub_cost = 0 if ri == hj else 1
+            if dp[i][j] == dp[i - 1][j - 1] + sub_cost:
+                aligned.append((ri, hj))
+                i -= 1
+                j -= 1
+                continue
+        if i > 0 and dp[i][j] == dp[i - 1][j] + 1:
+            aligned.append((ref[i - 1], None))
+            i -= 1
+            continue
+        if j > 0 and dp[i][j] == dp[i][j - 1] + 1:
+            aligned.append((None, hyp[j - 1]))
+            j -= 1
+            continue
+
+        # Fallback (shouldn't happen): prefer diagonal, then deletion, then insertion.
+        if i > 0 and j > 0:
+            aligned.append((ref[i - 1], hyp[j - 1]))
+            i -= 1
+            j -= 1
+        elif i > 0:
+            aligned.append((ref[i - 1], None))
+            i -= 1
+        else:
+            aligned.append((None, hyp[j - 1]))
+            j -= 1
+
+    aligned.reverse()
+    return AlignmentResult(distance=dp[n][m], aligned=aligned)
+
+
+def edit_distance(a: list[str], b: list[str]) -> int:
+    return align_tokens(a, b).distance
+
*** End Patch


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


Summary: auto


Input:
*** Begin Patch
*** Add File: maya_transcribe/textnorm.py
+from __future__ import annotations
+
+import unicodedata
+
+
+_ZERO_WIDTH = {
+    "\u200b",  # zero width space
+    "\u200c",  # zero width non-joiner
+    "\u200d",  # zero width joiner
+    "\ufeff",  # byte order mark
+}
+
+
+def normalize_text(text: str, lang: str | None = None) -> str:
+    """
+    Conservative normalization for hypothesis alignment/comparison (not for publishing).
+    - Unicode NFKC
+    - Drop punctuation
+    - Normalize all Unicode digits to ASCII
+    - Collapse whitespace
+    - Lowercase for English only
+    """
+    if not text:
+        return ""
+
+    text = unicodedata.normalize("NFKC", text)
+
+    out_chars: list[str] = []
+    for ch in text:
+        if ch in _ZERO_WIDTH:
+            continue
+
+        cat = unicodedata.category(ch)
+        if cat.startswith("P"):
+            continue
+
+        if cat == "Zs" or ch in ("\t", "\n", "\r"):
+            out_chars.append(" ")
+            continue
+
+        if cat == "Nd":
+            try:
+                out_chars.append(str(unicodedata.digit(ch)))
+                continue
+            except (TypeError, ValueError):
+                pass
+
+        out_chars.append(ch)
+
+    norm = "".join(out_chars)
+    norm = " ".join(norm.split())
+
+    if (lang or "").lower() in {"en", "eng"}:
+        norm = norm.lower()
+
+    return norm
+
+
+def tokenize_words(text: str) -> list[str]:
+    text = " ".join((text or "").split())
+    if not text:
+        return []
+    return text.split(" ")
+
*** End Patch


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


Input:
*** Begin Patch
*** Add File: maya_transcribe/rover.py
+from __future__ import annotations
+
+from dataclasses import dataclass
+from math import ceil
+from typing import Any
+
+from maya_transcribe.align import align_tokens, edit_distance
+from maya_transcribe.textnorm import tokenize_words
+
+
+@dataclass(frozen=True)
+class RoverDisagreement:
+    kind: str  # "token" | "gap"
+    index: int
+    chosen: Any
+    options: Any
+
+
+@dataclass(frozen=True)
+class RoverResult:
+    text: str
+    skeleton_key: str
+    disagreement_rate: float
+    disagreements: list[RoverDisagreement]
+
+
+def _avg_norm_distance(tokens_a: list[str], tokens_b: list[str]) -> float:
+    if not tokens_a and not tokens_b:
+        return 0.0
+    denom = max(1, (len(tokens_a) + len(tokens_b)) // 2)
+    return float(edit_distance(tokens_a, tokens_b)) / float(denom)
+
+
+def choose_centroid(hyps: dict[str, list[str]]) -> str:
+    keys = list(hyps.keys())
+    if not keys:
+        raise ValueError("No hypotheses provided")
+    if len(keys) == 1:
+        return keys[0]
+
+    avg: dict[str, float] = {}
+    for k in keys:
+        dsum = 0.0
+        for k2 in keys:
+            if k2 == k:
+                continue
+            dsum += _avg_norm_distance(hyps[k], hyps[k2])
+        avg[k] = dsum / float(len(keys) - 1)
+    return min(avg.items(), key=lambda kv: kv[1])[0]
+
+
+def rover_combine(
+    hypotheses: dict[str, str],
+    weights: dict[str, float] | None = None,
+    min_gap_support: float = 0.5,
+) -> RoverResult:
+    """
+    Lightweight ROVER-style combination:
+    - pick a centroid hypothesis as skeleton
+    - align others to skeleton
+    - vote per skeleton token position
+    - for gap insertions, keep a centroid insertion sequence if it has >= min_gap_support weight
+    """
+    if not hypotheses:
+        return RoverResult(text="", skeleton_key="", disagreement_rate=0.0, disagreements=[])
+
+    weights = weights or {}
+    tokenized: dict[str, list[str]] = {k: tokenize_words(v) for k, v in hypotheses.items()}
+    skel_key = choose_centroid(tokenized)
+    skel_tokens = tokenized[skel_key]
+
+    keys = list(tokenized.keys())
+    total_weight = sum(float(weights.get(k, 1.0)) for k in keys)
+    if total_weight <= 0:
+        total_weight = float(len(keys))
+
+    aligned_tokens: dict[str, list[str | None]] = {k: [None] * len(skel_tokens) for k in keys}
+    gap_inserts: dict[str, list[list[str]]] = {k: [[] for _ in range(len(skel_tokens) + 1)] for k in keys}
+
+    # Skeleton aligns to itself.
+    for j, tok in enumerate(skel_tokens):
+        aligned_tokens[skel_key][j] = tok
+
+    # Align all others to skeleton.
+    for k in keys:
+        if k == skel_key:
+            continue
+        hyp_tokens = tokenized[k]
+        alignment = align_tokens(skel_tokens, hyp_tokens).aligned
+
+        skel_pos = 0
+        for ref_tok, hyp_tok in alignment:
+            if ref_tok is None:
+                if hyp_tok is not None:
+                    gap_inserts[k][skel_pos].append(hyp_tok)
+                continue
+            # ref_tok is not None
+            if skel_pos >= len(skel_tokens):
+                break
+            aligned_tokens[k][skel_pos] = hyp_tok
+            skel_pos += 1
+
+    disagreements: list[RoverDisagreement] = []
+
+    # Combine gap insertions.
+    chosen_gaps: list[list[str]] = [[] for _ in range(len(skel_tokens) + 1)]
+    for gap_idx in range(len(skel_tokens) + 1):
+        sequences: list[tuple[str, tuple[str, ...], float]] = []
+        present_weight = 0.0
+        for k in keys:
+            w = float(weights.get(k, 1.0))
+            seq = tuple(gap_inserts[k][gap_idx])
+            if seq:
+                present_weight += w
+            sequences.append((k, seq, w))
+
+        if present_weight < (min_gap_support * total_weight):
+            chosen_gaps[gap_idx] = []
+            continue
+
+        # Choose centroid sequence among non-empty candidates (weighted by average distance).
+        candidates = [s for s in sequences if s[1]]
+        if not candidates:
+            chosen_gaps[gap_idx] = []
+            continue
+
+        best_seq: tuple[str, ...] | None = None
+        best_score: float | None = None
+        for _, seq, _w in candidates:
+            score = 0.0
+            for _, seq2, w2 in candidates:
+                score += _avg_norm_distance(list(seq), list(seq2)) * w2
+            score /= max(1e-9, present_weight)
+            if best_score is None or score < best_score:
+                best_score = score
+                best_seq = seq
+
+        chosen = list(best_seq or ())
+        chosen_gaps[gap_idx] = chosen
+
+        unique = {s[1] for s in candidates}
+        if len(unique) > 1:
+            disagreements.append(
+                RoverDisagreement(
+                    kind="gap",
+                    index=gap_idx,
+                    chosen=chosen,
+                    options={k: list(seq) for k, seq, _w in candidates},
+                )
+            )
+
+    # Combine skeleton positions.
+    chosen_tokens: list[str] = []
+    for j in range(len(skel_tokens)):
+        votes: dict[str, float] = {}
+        for k in keys:
+            tok = aligned_tokens[k][j]
+            if tok is None:
+                continue
+            tok = tok.strip()
+            if not tok:
+                continue
+            votes[tok] = votes.get(tok, 0.0) + float(weights.get(k, 1.0))
+
+        if not votes:
+            continue
+
+        max_vote = max(votes.values())
+        top = [t for t, v in votes.items() if v == max_vote]
+        if len(top) == 1:
+            chosen_tok = top[0]
+        else:
+            chosen_tok = skel_tokens[j] if skel_tokens[j] in top else sorted(top)[0]
+
+        if len(votes) > 1:
+            disagreements.append(
+                RoverDisagreement(kind="token", index=j, chosen=chosen_tok, options=dict(sorted(votes.items())))
+            )
+
+        chosen_tokens.append(chosen_tok)
+
+    # Assemble final transcript with gap insertions.
+    final_tokens: list[str] = []
+    for j in range(len(skel_tokens)):
+        final_tokens.extend(chosen_gaps[j])
+        if j < len(chosen_tokens):
+            final_tokens.append(chosen_tokens[j])
+    final_tokens.extend(chosen_gaps[len(skel_tokens)])
+
+    text = " ".join(final_tokens).strip()
+    total_positions = len(skel_tokens) + (len(skel_tokens) + 1)
+    disagreement_rate = float(len(disagreements)) / float(max(1, total_positions))
+
+    return RoverResult(
+        text=text,
+        skeleton_key=skel_key,
+        disagreement_rate=disagreement_rate,
+        disagreements=disagreements,
+    )
+
*** End Patch


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


**Evaluating imports and logic**

I noticed that the unused import `ceil` should be removed to avoid linting warnings, even though it's not crucial. We also imported `Any`, which is used in the dataclass. In assembling the final transcript, there's a concern with the `chosen_tokens` list potentially being shorter than `skeleton tokens` if there are no votes due to all `None`. However, `skeleton tokens` should always have some token. It seems the logic around `chosen_tokens` and appending is generally consistent, but I need to double-check to confirm there are no bugs.


**Reviewing token assembly**

I put together `final_tokens` with gap insertions, then pulled from `chosen_tokens`. However, I noticed I might have appended incorrectly: the condition `if j < len(chosen_tokens)` may confuse things a bit. There’s a bug where I add `chosen_gaps[j]` and then `chosen_tokens[j]`, but the appending occurs in the same order. We’re tracking disagreements for token positions where votes exceed one, which makes sense, but I might need to adjust how that’s applied regarding strong weights. Now I’ll need to check `verify.py`.


Input:
*** Begin Patch
*** Add File: maya_transcribe/verify.py
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+from maya_transcribe.lid import LIDResult
+
+
+@dataclass(frozen=True)
+class Verification:
+    decision: str  # "accept" | "human" | "reject"
+    reasons: list[str]
+    scores: dict[str, Any]
+
+
+def lid_confidence(lid: LIDResult | None) -> dict[str, Any]:
+    if not lid or not lid.predictions:
+        return {"available": False, "top1": None, "top1_prob": None, "margin": None, "confident": False}
+    top1 = lid.predictions[0]
+    top2_prob = lid.predictions[1].prob if len(lid.predictions) > 1 else 0.0
+    margin = float(top1.prob) - float(top2_prob)
+    return {
+        "available": True,
+        "top1": top1.code,
+        "top1_prob": float(top1.prob),
+        "margin": margin,
+        "confident": False,
+    }
+
+
+def verify_segment(
+    *,
+    qc: dict[str, Any],
+    lid: LIDResult | None,
+    rover: dict[str, Any],
+    asr_summaries: dict[str, Any],
+    thresholds: dict[str, Any] | None = None,
+) -> Verification:
+    """
+    Conservative verification gate (precision-first).
+    """
+    thresholds = thresholds or {}
+    min_duration = float(thresholds.get("min_duration_sec", 0.8))
+    max_clipping = float(thresholds.get("max_clipping_fraction", 0.01))
+    max_disagreement = float(thresholds.get("max_rover_disagreement_rate", 0.03))
+    lid_min_prob = float(thresholds.get("lid_min_prob", 0.90))
+    lid_min_margin = float(thresholds.get("lid_min_margin", 0.20))
+
+    reasons: list[str] = []
+    decision = "accept"
+
+    duration = float(qc.get("duration_sec") or 0.0)
+    clipping = float(qc.get("clipping_fraction") or 0.0)
+    if duration < min_duration:
+        decision = "reject"
+        reasons.append("too_short")
+    if clipping > max_clipping:
+        decision = "human" if decision != "reject" else decision
+        reasons.append("clipping")
+
+    lidc = lid_confidence(lid)
+    if lidc["available"]:
+        lidc["confident"] = bool(lidc["top1_prob"] >= lid_min_prob and lidc["margin"] >= lid_min_margin)
+        if not lidc["confident"]:
+            decision = "human" if decision != "reject" else decision
+            reasons.append("lid_uncertain")
+    else:
+        reasons.append("lid_missing")
+
+    disagreement_rate = float(rover.get("disagreement_rate") or 0.0)
+    if disagreement_rate > max_disagreement:
+        decision = "human" if decision != "reject" else decision
+        reasons.append("asr_disagreement")
+
+    # Whisper-style safety: flag if any decoder claims "no speech" with high probability.
+    no_speech_probs = []
+    for summary in asr_summaries.values():
+        p = summary.get("no_speech_prob_mean")
+        if p is None:
+            continue
+        no_speech_probs.append(float(p))
+    if no_speech_probs and max(no_speech_probs) > 0.8:
+        decision = "reject"
+        reasons.append("no_speech")
+
+    scores = {
+        "duration_sec": duration,
+        "clipping_fraction": clipping,
+        "rover_disagreement_rate": disagreement_rate,
+        "lid": lidc,
+        "no_speech_prob_max": max(no_speech_probs) if no_speech_probs else None,
+    }
+
+    return Verification(decision=decision, reasons=reasons, scores=scores)
+
*** End Patch


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


**Designing the Processing Pipeline**

It looks like I'm working on a system where the `lid_confidence` always returns `False`, but I see that the `verify_segment` sets it properly, which is good. In the `pipeline.py`, I need to design an orchestration function. This function should work with the `segments_dir` and output directory, while also calling the internal `preprocess`. I want the `TranscriptionPipeline` class to handle this by returning a results list and managing model instantiation while tracking timing using `time.perf_counter()`.


**Creating the Processing Pipeline**

I’m focusing on building a processing pipeline. The function should take `segments_dir`, `out_dir`, and optional parameters like `video_id` and `lid_model`. I’ll discover audio files in the `segments_dir`, similar to the preprocessing steps. For each file, I’ll measure time using `perf_counter()` and run the audio normalization. If the lid model is enabled, I'll also run LID predictions.

After gathering ASR results, I'll normalize transcripts and combine them. To optimize memory, I plan to implement streaming in output writing and create an `append_jsonl` function for efficient record writing.


**Implementing the JSONL Function**

I want to add an `append_jsonl(path, row)` function in `io.py`. To keep things simple, I can open the file within the pipeline. I’m considering whether to store results in a separate directory for each segment—it's not strictly necessary, but it could help with caching and resuming later if I check for existing records. For now, I'll pass on that.

I also need to handle exceptions during segment processing by recording errors and continuing. Finally, I'll update the CLI with a `run` command that hooks into the pipeline after it's implemented.


Input:
*** Begin Patch
*** Add File: maya_transcribe/pipeline.py
+from __future__ import annotations
+
+import json
+import time
+from dataclasses import asdict
+from pathlib import Path
+from typing import Any, Iterable
+
+from rich.progress import track
+
+from maya_transcribe.asr import ASRResult, FasterWhisperASR, TransformersWhisperASR
+from maya_transcribe.audio import compute_qc, ffprobe, normalize_audio
+from maya_transcribe.lid import LIDResult, MMSLID
+from maya_transcribe.rover import rover_combine
+from maya_transcribe.textnorm import normalize_text
+from maya_transcribe.verify import verify_segment
+
+
+def _iter_audio_files(segments_dir: Path) -> list[Path]:
+    audio_files: list[Path] = []
+    for ext in ("*.wav", "*.mp3", "*.m4a", "*.flac", "*.ogg", "*.opus"):
+        audio_files.extend(sorted(segments_dir.rglob(ext)))
+    return sorted(set(audio_files))
+
+
+def _asr_summary(result: ASRResult) -> dict[str, Any]:
+    avg_logprobs = [s.avg_logprob for s in result.segments if s.avg_logprob is not None]
+    no_speech_probs = [s.no_speech_prob for s in result.segments if s.no_speech_prob is not None]
+    compression_ratios = [s.compression_ratio for s in result.segments if s.compression_ratio is not None]
+
+    def mean(xs: list[float]) -> float | None:
+        return (sum(xs) / len(xs)) if xs else None
+
+    return {
+        "language": result.language,
+        "language_probability": result.language_probability,
+        "avg_logprob_mean": mean([float(x) for x in avg_logprobs]),
+        "no_speech_prob_mean": mean([float(x) for x in no_speech_probs]),
+        "compression_ratio_mean": mean([float(x) for x in compression_ratios]),
+    }
+
+
+def run_pipeline(
+    *,
+    segments_dir: Path,
+    out_dir: Path,
+    lid_model_id: str = "facebook/mms-lid-256",
+    whisper_model: str = "large-v3",
+    extra_transformers_whisper_models: list[str] | None = None,
+    limit: int | None = None,
+    thresholds: dict[str, Any] | None = None,
+) -> Path:
+    """
+    End-to-end run: preprocess -> LID -> ASR ensemble -> normalize -> ROVER -> verify.
+    Writes `out_dir/results.jsonl` and returns that path.
+    """
+    thresholds = thresholds or {}
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    audio_files = _iter_audio_files(segments_dir)
+    if not audio_files:
+        raise RuntimeError(f"No audio files found under: {segments_dir}")
+    if limit is not None:
+        audio_files = audio_files[: max(0, int(limit))]
+
+    # Instantiate models once.
+    lid_backend: MMSLID | None
+    try:
+        lid_backend = MMSLID(model_id=lid_model_id)
+    except Exception:
+        lid_backend = None
+
+    whisper_backend = FasterWhisperASR(model_size_or_path=whisper_model)
+
+    extra_transformers_whisper_models = extra_transformers_whisper_models or []
+    transformers_backends: list[TransformersWhisperASR] = []
+    for model_id in extra_transformers_whisper_models:
+        transformers_backends.append(TransformersWhisperASR(model_id=model_id))
+
+    results_path = out_dir / "results.jsonl"
+    summary_path = out_dir / "summary.json"
+
+    counts = {"accept": 0, "human": 0, "reject": 0, "error": 0}
+    timings_total: list[float] = []
+
+    with results_path.open("w", encoding="utf-8") as f:
+        for src in track(audio_files, description="Transcribing"):
+            segment_id = src.stem
+            t0 = time.perf_counter()
+
+            try:
+                # 1) Preprocess
+                t_pre0 = time.perf_counter()
+                wav_path = out_dir / "audio" / f"{segment_id}.wav"
+                normalize_audio(src, wav_path, trim_silence=True)
+                info = ffprobe(wav_path)
+                qc = compute_qc(wav_path)
+                t_pre1 = time.perf_counter()
+
+                # 2) LID
+                t_lid0 = time.perf_counter()
+                lid: LIDResult | None = None
+                if lid_backend is not None:
+                    lid = lid_backend.predict(wav_path, top_k=5)
+                t_lid1 = time.perf_counter()
+
+                # Route language hint (only if confident).
+                route_lang = None
+                if lid and lid.predictions:
+                    top1 = lid.predictions[0]
+                    top2p = lid.predictions[1].prob if len(lid.predictions) > 1 else 0.0
+                    if float(top1.prob) >= float(thresholds.get("lid_min_prob", 0.90)) and (
+                        float(top1.prob) - float(top2p)
+                    ) >= float(thresholds.get("lid_min_margin", 0.20)):
+                        route_lang = top1.code
+
+                # 3) ASR ensemble
+                t_asr0 = time.perf_counter()
+                asr_results: dict[str, ASRResult] = {}
+
+                # Whisper: deterministic + a small sampling diversity pass.
+                det = whisper_backend.transcribe(wav_path, language=route_lang, beam_size=5, best_of=5, temperature=0.0)
+                asr_results[f"{det.model}|det"] = det
+                samp = whisper_backend.transcribe(
+                    wav_path, language=route_lang, beam_size=5, best_of=10, temperature=0.2
+                )
+                asr_results[f"{samp.model}|samp_t0.2"] = samp
+
+                for tf_backend in transformers_backends:
+                    res = tf_backend.transcribe(wav_path, language=route_lang, num_beams=5, max_new_tokens=256)
+                    asr_results[res.model] = res
+
+                t_asr1 = time.perf_counter()
+
+                # 4) Normalize hypotheses
+                t_norm0 = time.perf_counter()
+                norm_hyps: dict[str, str] = {}
+                for k, res in asr_results.items():
+                    lang_hint = res.language or route_lang
+                    norm_hyps[k] = normalize_text(res.text, lang=lang_hint)
+                t_norm1 = time.perf_counter()
+
+                # 5) ROVER
+                t_rover0 = time.perf_counter()
+                rover = rover_combine(norm_hyps)
+                t_rover1 = time.perf_counter()
+
+                # 6) Verify
+                t_ver0 = time.perf_counter()
+                asr_summaries = {k: _asr_summary(v) for k, v in asr_results.items()}
+                verification = verify_segment(
+                    qc=asdict(qc),
+                    lid=lid,
+                    rover={
+                        "text": rover.text,
+                        "disagreement_rate": rover.disagreement_rate,
+                        "skeleton_key": rover.skeleton_key,
+                    },
+                    asr_summaries=asr_summaries,
+                    thresholds=thresholds,
+                )
+                t_ver1 = time.perf_counter()
+
+                record = {
+                    "segment_id": segment_id,
+                    "src_path": str(src),
+                    "wav_path": str(wav_path),
+                    "ffprobe": asdict(info),
+                    "qc": asdict(qc),
+                    "lid": (
+                        None
+                        if lid is None
+                        else {
+                            "model": lid.model,
+                            "predictions": [{"code": p.code, "prob": p.prob} for p in lid.predictions],
+                        }
+                    ),
+                    "route": {"language_hint": route_lang},
+                    "asr": {
+                        k: {
+                            "model": v.model,
+                            "text": v.text,
+                            "language": v.language,
+                            "language_probability": v.language_probability,
+                            "segments": [asdict(s) for s in v.segments],
+                            "meta": v.meta,
+                        }
+                        for k, v in asr_results.items()
+                    },
+                    "normalized": norm_hyps,
+                    "rover": {
+                        "text": rover.text,
+                        "skeleton_key": rover.skeleton_key,
+                        "disagreement_rate": rover.disagreement_rate,
+                        "disagreements": [
+                            {"kind": d.kind, "index": d.index, "chosen": d.chosen, "options": d.options}
+                            for d in rover.disagreements
+                        ],
+                    },
+                    "verification": {
+                        "decision": verification.decision,
+                        "reasons": verification.reasons,
+                        "scores": verification.scores,
+                    },
+                    "timing": {
+                        "preprocess_sec": t_pre1 - t_pre0,
+                        "lid_sec": t_lid1 - t_lid0,
+                        "asr_sec": t_asr1 - t_asr0,
+                        "normalize_sec": t_norm1 - t_norm0,
+                        "rover_sec": t_rover1 - t_rover0,
+                        "verify_sec": t_ver1 - t_ver0,
+                        "total_sec": time.perf_counter() - t0,
+                    },
+                }
+
+                f.write(json.dumps(record, ensure_ascii=False) + "\n")
+                decision = verification.decision
+                counts[decision] = counts.get(decision, 0) + 1
+                timings_total.append(record["timing"]["total_sec"])
+
+            except Exception as e:  # pragma: no cover
+                counts["error"] += 1
+                f.write(
+                    json.dumps(
+                        {"segment_id": segment_id, "src_path": str(src), "error": str(e)}, ensure_ascii=False
+                    )
+                    + "\n"
+                )
+
+    summary = {
+        "segments_total": len(audio_files),
+        "counts": counts,
+        "avg_total_sec": (sum(timings_total) / len(timings_total)) if timings_total else None,
+        "p50_total_sec": _percentile(timings_total, 0.50),
+        "p90_total_sec": _percentile(timings_total, 0.90),
+    }
+    summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
+    return results_path
+
+
+def _percentile(xs: list[float], q: float) -> float | None:
+    if not xs:
+        return None
+    xs_sorted = sorted(xs)
+    idx = int(round((len(xs_sorted) - 1) * q))
+    idx = max(0, min(len(xs_sorted) - 1, idx))
+    return float(xs_sorted[idx])
+
*** End Patch


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


**Evaluating audio processing pipeline**

I'm reviewing the pipeline where we're using `asdict` from dataclasses for AudioInfo and AudioQCMetrics. This includes fields like `path`, and in `ffprobe`, we return values such as `duration`. The import of `Iterable` isn’t being utilized, though, so I’ll clean that up later. In the `verify_segment` call, I'm passing `qc=asdict(qc)`, which is working well. A potential issue arises with `normalize_audio`; the default parameters are fine, but I should ensure all necessary arguments are passed to avoid errors with missing libraries.


**Updating CLI for run command**

I need to update the command-line interface (CLI) to include the new run command. It’s essential to patch this in properly, so I’ll make sure to address any potential issues during the update process. I should review how this run command fits into the existing structure and functionality, ensuring everything works seamlessly afterward. Making these updates efficiently is crucial for a smooth user experience! Let's patch it accordingly.


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
 def asr(
@@
     typer.echo(res.text)

+
+@app.command()
+def run(
+    segments_dir: Path = typer.Option(..., exists=True, file_okay=False, help="Directory containing audio segments."),
+    out: Path = typer.Option(Path("outputs/run"), help="Output directory."),
+    whisper_model: str = typer.Option("large-v3", help="Whisper model size/path for faster-whisper."),
+    lid_model_id: str = typer.Option("facebook/mms-lid-256", help="HuggingFace MMS-LID model id."),
+    transformers_whisper_model: list[str] = typer.Option(
+        [], help="Extra whisper-family HF models to run via transformers (repeatable)."
+    ),
+    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
+
+    results_path = run_pipeline(
+        segments_dir=segments_dir,
+        out_dir=out,
+        lid_model_id=lid_model_id,
+        whisper_model=whisper_model,
+        extra_transformers_whisper_models=transformers_whisper_model,
+        limit=limit,
+    )
+    typer.echo(f"Wrote: {results_path}")
+    typer.echo(f"Wrote: {out / 'summary.json'}")
+

 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}}


**Finalizing step 5**

I'm wrapping up step 5, which involves ROVER and the verification gate. I need to ensure the code compiles without any missing imports or unused elements. We've used `ceil`, but it's not critical. Once I confirm everything works after some testing, I'll mark this step as complete since we've successfully implemented ROVER and the verification gate. Then, I'll move on to step 6.


Output: Plan updated


**Considering Supabase Integration**

I'm thinking about integrating Supabase to fetch references, but I'm unsure about the schema. It could be useful to implement a generic Supabase client that allows the user to query specific tables and columns via CLI. I might create a `supabase_export_refs` command, but since the schema is unknown, a `supabase_query` command could let users specify their requirements. While I’m contemplating a `maya-transcribe supabase ls-tables` command, it might not be necessary. I'll focus first on implementing the Supabase config loader for the URL and keys, which currently only includes R2.


**Implementing Evaluation Steps**

I'm noting that step 6 involves WER evaluation and a latency report, but that's separate from Supabase. Since we already have a latency summary, I’ll focus on implementing the evaluation. I’m planning to create an `eval.py` script and add a corresponding CLI command for it. This way, the user can evaluate performance effectively while keeping the Supabase integration distinct from this evaluation process. It's important to keep things organized!


Input:
*** Begin Patch
*** Add File: maya_transcribe/eval.py
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from jiwer import wer
+
+from maya_transcribe.io import read_jsonl
+from maya_transcribe.textnorm import normalize_text
+
+
+@dataclass(frozen=True)
+class EvalRow:
+    segment_id: str
+    language: str | None
+    decision: str | None
+    ref: str
+    hyp: str
+    wer: float
+
+
+def load_refs(refs_path: Path) -> dict[str, dict[str, Any]]:
+    """
+    Read a JSONL file of references.
+    Expected keys (any one): `text`, `transcript`, `reference`.
+    Required key: `segment_id` (or `id`).
+    """
+    refs: dict[str, dict[str, Any]] = {}
+    for row in read_jsonl(refs_path):
+        segment_id = str(row.get("segment_id") or row.get("id") or "").strip()
+        if not segment_id:
+            continue
+        refs[segment_id] = row
+    return refs
+
+
+def _get_text(row: dict[str, Any], keys: tuple[str, ...]) -> str | None:
+    for k in keys:
+        v = row.get(k)
+        if isinstance(v, str) and v.strip():
+            return v
+    return None
+
+
+def evaluate(results_path: Path, refs_path: Path, out_path: Path | None = None) -> dict[str, Any]:
+    refs = load_refs(refs_path)
+    results = read_jsonl(results_path)
+
+    rows: list[EvalRow] = []
+    for r in results:
+        segment_id = str(r.get("segment_id") or "").strip()
+        if not segment_id or segment_id not in refs:
+            continue
+
+        ref_row = refs[segment_id]
+        ref_text = _get_text(ref_row, ("text", "transcript", "reference"))
+        if ref_text is None:
+            continue
+
+        # Use ROVER output as hypothesis by default.
+        hyp_text = None
+        rover = r.get("rover") or {}
+        if isinstance(rover, dict):
+            hyp_text = rover.get("text")
+        if not isinstance(hyp_text, str):
+            continue
+
+        lang = None
+        route = r.get("route") or {}
+        if isinstance(route, dict):
+            lang = route.get("language_hint")
+
+        decision = None
+        ver = r.get("verification") or {}
+        if isinstance(ver, dict):
+            decision = ver.get("decision")
+
+        ref_norm = normalize_text(ref_text, lang=lang)
+        hyp_norm = normalize_text(hyp_text, lang=lang)
+        w = float(wer(ref_norm, hyp_norm))
+
+        rows.append(
+            EvalRow(
+                segment_id=segment_id,
+                language=lang,
+                decision=str(decision) if decision is not None else None,
+                ref=ref_text,
+                hyp=hyp_text,
+                wer=w,
+            )
+        )
+
+    by_lang: dict[str, list[float]] = {}
+    by_dec: dict[str, list[float]] = {}
+    for row in rows:
+        if row.language:
+            by_lang.setdefault(row.language, []).append(row.wer)
+        if row.decision:
+            by_dec.setdefault(row.decision, []).append(row.wer)
+
+    summary = {
+        "pairs_evaluated": len(rows),
+        "wer_mean": (sum(r.wer for r in rows) / len(rows)) if rows else None,
+        "wer_by_language_mean": {k: sum(v) / len(v) for k, v in sorted(by_lang.items())},
+        "wer_by_decision_mean": {k: sum(v) / len(v) for k, v in sorted(by_dec.items())},
+    }
+
+    if out_path is not None:
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+        out_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
+
+    return summary
+
*** End Patch


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


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
 def run(
@@
     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."),
+) -> None:
+    """
+    Compute WER for `rover.text` 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)
+    typer.echo(json.dumps(summary, ensure_ascii=False, indent=2))
+

 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}}


**Fixing import issue**

I notice that the CLI currently uses the json module but hasn't imported it at the top of cli.py. Right now, we're importing Path, typer, and some audio components, but json is missing. That absence is definitely going to cause an error. I'll need to add `import json` at the beginning of the file. Additionally, I'm calling `typer.echo(json.dumps(...))`, which also needs patching. Let's make these updates!


Input:
*** Begin Patch
*** Update File: maya_transcribe/cli.py
@@
 from __future__ import annotations

+import json
 from pathlib import Path

 import typer
*** End Patch


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


**Planning and testing process**

I've finished the evaluation step, and now I'm thinking about updating the README with new commands—though that's optional. I should mark plan step 6 as completed after a quick run. The user needs me to implement, test, and report WERs and latencies. I realize we haven't tested on actual data yet. I should run some minimal tests with R2 and our models, even though the environment feels heavy. I need to install several dependencies and choose a video ID from the R2 bucket. Luckily, I have the credentials!


**Testing pipeline options**

For testing the pipeline, I think a minimal approach makes sense—starting with just R2 listing, downloading, and preprocessing. However, the user requested to test the pipeline and report WERs and latencies. Running ASR models could be heavy, but I might work with a smaller model to ensure everything functions properly. The environment might lack GPU support, so a large model on CPU could be slow. Using a smaller model like `tiny` or `base` would allow for a quick test, even if latencies won’t be fully representative. I really need to check if there are human transcripts in Supabase to compute WER accurately.


**Exploring Supabase for transcripts**

I’m considering querying Supabase to retrieve reference transcripts for segments and make comparisons, but I’m unsure of the schema. Alternatively, I could attempt computing pseudo-WER between different models, but it sounds a bit tricky since WER requires an actual reference. The user likely expects real WERs from the evaluation harness, so I'll really need to integrate Supabase to fetch those transcripts. I’ll explore the metadata in Supabase to find the transcripts, connecting using the .env variables, like `URL` and `SUPABASE_ADMIN`. I'll check the .env file while keeping values masked.


**Connecting to Supabase**

To connect to Supabase, I can use the Python Supabase client, which requires the `supabase` library that I should install. Alternatively, I could query Supabase with SQL using the `DATABASE_URL`, likely through psycopg. I need to inspect the .env file to clarify what `URL` refers to. I could use `grep` to show variable names and masked values without revealing any secrets. I see variables like `HOST`, `PORT`, and `USER`, along with `SUPABASE_ANON` and `SUPABASE_ADMIN`, which seem crucial. I suspect `URL` might include something like `https://<project>.supabase.co`. I'll need that!


**Implementing Supabase client**

I can set up the Supabase client using environment variables like `SUPABASE_URL` or `URL`. To figure out what’s in the `.env` file, I should check lines that contain `SUPABASE` or `URL=`. I can use `awk` to print the keys and mask the values, maybe just showing the first eight characters. I’ll run a Python script to read the `.env`, parse the key-value pairs, and print out the keys along with the length of their values. Let's get that done!


python3 - <<'PY'
from pathlib import Path
import re
p=Path('.env')
for line in p.read_text().splitlines():
    line=line.strip()
    if not line or line.startswith('#') or '=' not in line:
        continue
    k,v=line.split('=',1)
    k=k.strip()
    v=v.strip().strip('"').strip("'")
    if k in {'SUPABASE_ADMIN','SUPABASE_ANON','URL','DATABASE_URL','R2_ENDPOINT_URL','R2_BUCKET','R2_PREFIX'}:
        print(k, 'len', len(v), 'sample', (v[:10]+'...' if len(v)>10 else v))
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
R2_ENDPOINT_URL len 65 sample https://cb...
R2_BUCKET len 4 sample test
DATABASE_URL len 110 sample postgresql...
SUPABASE_ANON len 208 sample eyJhbGciOi...
SUPABASE_ADMIN len 219 sample eyJhbGciOi...
URL len 40 sample https://ex...
R2_ENDPOINT_URL len 65 sample https://cb...
R2_BUCKET len 4 sample test
R2_PREFIX len 0 sample


Summary: auto