---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.parquet
  - split: validation
    path: data/validation.parquet
license: other
pretty_name: BeTraC2026 Augmented
tags:
- medical
- speech
- asr
- soap
- chain-of-thought
- webdataset
---

# BeTraC2026 Augmented

This repository contains a curated Hugging Face version of `KITs BeTraC sumbission`.
It has two layers:

1. `data/train.parquet` and `data/validation.parquet` provide a lightweight
   preview/index table for browsing, filtering, and `load_dataset(...)`.
2. `data/<task>/*.tar` contains WebDataset shards with the real `.opus` audio
   bytes and chat-message JSON needed to replicate the experiments.


## Preview Columns

- `id`: stable example identifier. Uses the source `__key__` when available,
  with task and split metadata prefixed for uniqueness.
- `original_dataset`: source dataset name (`dopaco`, `omi-health`,
  `mts-dialog`, `aci-bench`, `primock57`).
- `split`: original split name (`train` or `dev`). The Hugging Face `dev`
  examples are stored in the `validation` split.
- `task`: normalized task label.
- `input_modality`: input type (`audio` or `asr_text`).
- `output_type`: target output type (`soap`, `cot_soap`,
  `speaker_diarized_asr`).
- `source_file`: original GRUN10 filename.
- `source_key`: source `__key__` if present, otherwise a generated row index.
- `has_audio`: whether the sample has a real audio file.
- `audio_format`: `opus` for audio samples, otherwise `null`.
- `audio_location`: `webdataset` for audio samples, otherwise `null`.
- `audio_shard`: repo-relative tar shard containing the audio sample.
- `audio_key`: filename of the `.opus` member inside the shard.
- `webdataset_key`: shared sample key used for `.json`, `.messages.json`, and
  optional `.opus` members inside WebDataset shards.
- `messages`: chat-style training messages.
- `record`: original JSON object with `messages`, optional `audios`, and
  optional `__key__`.

The Hugging Face Dataset Preview shows metadata and chat messages only. For rows
where `has_audio=true`, the real Ogg/Opus audio is stored in the WebDataset tar
shard referenced by `audio_shard` and `audio_key`.

## Task Mapping

| Source files | `task` | `input_modality` | `output_type` |
| --- | --- | --- | --- |
| `a.<dataset>.<split>.json` | `audio2soap` | `audio` | `soap` |
| `t.<dataset>.<split>.json` | `asr2soap` | `asr_text` | `soap` |
| `cot.a.<dataset>.<split>.json` | `audio2cotsoap` | `audio` | `cot_soap` |
| `cot.t.<dataset>.<split>.json` | `asr2cotsoap` | `asr_text` | `cot_soap` |
| `spk_asr.<dataset>.<split>.json` | `audio2speaker_asr` | `audio` | `speaker_diarized_asr` |

The curated files use the `<think>...</think>` chain-of-thought format. The
`cot.out.*` / `out.cot.*` natural-language-thinking variants are not included.
To derive that style, replace the `<think>` and `</think>` delimiters in
assistant messages with the natural-language thinking markers used by the target
training format.

## Loading the Preview Table

The preview/index table can be loaded directly from Hugging Face:

```python
from datasets import load_dataset

ds = load_dataset("YapayNet/betrac2026-augmented")
print(ds)
print(ds["train"][0]["task"])
```

This is convenient for filtering examples and inspecting messages. It does not
inline or decode the audio in the preview table.

## Replicating Experiments with Audio

For exact replication, download the full repository snapshot so the WebDataset
shards are available locally:

```bash
hf download YapayNet/betrac2026-augmented \
  --repo-type dataset \
  --local-dir betrac2026-augmented
```

Then load the relevant task shard with `webdataset`:

```python
import json
from pathlib import Path
import webdataset as wds

root = Path("betrac2026-augmented")
shards = str(root / "data/audio2soap/train-{00000..00099}.tar")

dataset = wds.WebDataset(shards, shardshuffle=False)
for sample in dataset:
    key = sample["__key__"]
    messages = json.loads(sample["messages.json"])
    metadata = json.loads(sample["json"])
    audio_bytes = sample.get("opus")
    print(key, metadata["task"], len(messages), len(audio_bytes or b""))
    break
```

For audio-input tasks (`audio2soap`, `audio2cotsoap`, and
`audio2speaker_asr`), each WebDataset sample contains:

```text
<webdataset_key>.json
<webdataset_key>.messages.json
<webdataset_key>.opus
```

For text-only tasks (`asr2soap` and `asr2cotsoap`), each sample contains:

```text
<webdataset_key>.json
<webdataset_key>.messages.json
```

The `.json` member contains metadata such as `original_dataset`, `split`,
`task`, `source_file`, `source_key`, `has_audio`, `audio_shard`, and
`audio_key`. The `.messages.json` member contains the chat-style training
messages. The `.opus` member contains the real audio bytes when audio exists.