README.md

---
license: cc-by-nc-sa-4.0
language:
- en
task_categories:
- automatic-speech-recognition
- text-generation
tags:
- dialogue
- task-oriented-dialogue
- speech
- asr
- conversational
- repeats-and-rephrases
pretty_name: 'OD3: Open Directed Dialogue Dataset'
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: od3_train.jsonl
  - split: validation
    path: od3_dev.jsonl
  - split: test
    path: od3_test.jsonl
---

# OD3: Open Directed Dialogue Dataset

OD3 (Open Directed Dialogue Dataset) is designed to allow the community to explore further
research into leveraging flawed conversational interactions to improve model performance.
OD3 is a collection of 63K conversations (600K turns, 1,172 hours of audio) drawn from existing
natural language task-oriented dialog datasets, and augmented with synthetic audio. OD3 is
further augmented with turns containing repeats and rephrases of previous failed utterances.

For more details, check out [the paper](https://arxiv.org/abs/2401.02417).

- **Paper:** [Task Oriented Dialogue as a Catalysis for Self-Supervised Automatic Speech Recognition](https://arxiv.org/abs/2401.02417) (ICASSP 2024)
- **Original repository:** [amazon-science/amazon-od3](https://github.com/amazon-science/amazon-od3)
- **Contact:** [davidchan@berkeley.edu](mailto:davidchan@berkeley.edu)
- **License:** CC-BY-NC-SA-4.0

## Dataset statistics

Computed directly from the released annotation files:

| Split      | Conversations | Turns   | Audio clips | Repeat/rephrase turns | Distinct voices |
| ---------- | ------------- | ------- | ----------- | --------------------- | --------------- |
| train      | 47,702        | 472,347 | 638,851     | 9,199                 | 82,527          |
| validation | 6,437         | 64,631  | 95,778      | 1,375                 | 12,470          |
| test       | 8,821         | 85,610  | 126,945     | 1,816                 | 17,108          |
| **Total**  | **62,960**    | **622,588** | **861,574** | **12,390**        | —               |

### Seed dataset composition (conversations)

| Source dataset | train  | validation | test  | total  |
| -------------- | ------ | ---------- | ----- | ------ |
| DSTC11-Track-5 | 25,579 | 4,085      | 5,342 | 35,006 |
| MultiWOZ 2.2   | 8,425  | 1,000      | 999   | 10,424 |
| SIMMC 2.1      | 7,306  | 563        | 1,687 | 9,556  |
| NOESIS         | 3,967  | 487        | 489   | 4,943  |
| KVRET          | 2,425  | 302        | 304   | 3,031  |

## Data structure

Each line of a `.jsonl` file is one conversation:

```json
{
  "sample_id": "b4bbb5b2-c075-4a8b-a953-8b14cd861ead",
  "source_dataset": "DSCT11-Track-5",
  "is_llm_generated": false,
  "meta": {"knowledge": null, "target": false},
  "turns": [
    {
      "text": "I am looking for a certain hotel...",
      "speaker_id": "0d6bb10a-76da-4f7e-bd90-2416a4449a8c",
      "turn_id": "aa0a0989-b545-4620-a7d9-47e7e0abf82d",
      "is_agent": false,
      "turn_is_repeat_rephrase": false,
      "meta": {
        "is_induced_by_repeat_rephrase": false,
        "is_machine_generated": false,
        "repeat_rephrase_type": null
      },
      "audio": {
        "25218975": {
          "path": "dev/b4/b4bbb5b2-.../aa0a0989-...+25218975.wav",
          "voice_id": "25218975",
          "universal_agent": false,
          "asr_transcript": " I am looking for a certain hotel...",
          "normalized_ground_truth": "i am looking for a certain hotel...",
          "normalized_asr_transcript": "i am looking for a certain hotel..."
        }
      }
    }
  ]
}
```

### Fields

**Conversation level**
- `sample_id` — unique conversation identifier (UUID).
- `source_dataset` — the seed dataset this conversation was drawn from.
- `is_llm_generated` — whether the conversation itself was LLM-generated.
- `meta` — source-specific metadata (e.g. `knowledge`, `target`).

**Turn level**
- `text` — the ground-truth utterance text.
- `speaker_id` — synthetic speaker identity, stable within a conversation.
- `turn_id` — unique turn identifier (UUID).
- `is_agent` — `true` for agent turns, `false` for user turns.
- `turn_is_repeat_rephrase` — whether this turn is a repeat/rephrase of a previous failed turn.
- `meta.repeat_rephrase_type` — the kind of repeat/rephrase, when applicable.
- `audio` — map of `voice_id` → audio rendition.

**Audio rendition**
- `path` — path within the audio tree: `<split>/<first-2-chars-of-sample_id>/<sample_id>/<turn_id>+<voice_id>.wav`.
- `voice_id` — the synthetic voice used (cloned from CommonVoice).
- `asr_transcript` — transcript produced by running ASR over the synthesized audio.
- `normalized_ground_truth` / `normalized_asr_transcript` — normalized text for WER computation.
- `word_error_rate` — WER of this rendition's ASR transcript against the ground truth.
- `nbest` — up to 8 n-best ASR hypotheses for the clip.

The gap between `normalized_ground_truth` and `normalized_asr_transcript` is what makes OD3
useful for studying self-supervised ASR improvement from dialogue signals.

## Usage

### Annotations only

```python
from datasets import load_dataset

ds = load_dataset("davidchan/od3")          # train / validation / test
print(ds["train"][0]["sample_id"])
```

### Conversations with audio

The audio is published as **conversation-grained WebDataset shards** under `audio/`:
**one WebDataset sample is one complete conversation**, not one clip.

```python
import json, webdataset as wds

url = "https://huggingface.co/datasets/davidchan/od3/resolve/main/audio/train-00000.tar"
for sample in wds.WebDataset(url, shardshuffle=False):
    conv = json.loads(sample["json"])       # the whole conversation
    print(conv["sample_id"], len(conv["turns"]), "turns,", conv["n_audio"], "clips")

    for field, meta in conv["audio_files"].items():
        wav = sample[field]                 # raw RIFF/WAVE bytes
        turn = conv["turns"][meta["turn_index"]]
        print("  ", turn["text"][:50], "| voice", meta["voice_id"], "|", len(wav), "bytes")
    break
```

## Audio

```
audio/train-00000.tar ... train-00099.tar   100 shards
audio/dev-00000.tar   ... dev-00014.tar      15 shards
audio/test-00000.tar  ... test-00019.tar     20 shards
```

Each shard holds whole conversations (~1GB per shard). A conversation is **never split across
shards**. Within a sample:

| File | Contents |
| ---- | -------- |
| `<split>/<sample_id>.json` | the complete conversation record — every turn, all metadata (including DSTC11 `knowledge` grounding), plus `split`, `n_audio`, and `audio_files` |
| `<split>/<sample_id>.000.wav` | clips, numbered in turn order then voice order |
| `<split>/<sample_id>.001.wav` | … |

The WebDataset `__key__` is `<split>/<sample_id>`.

`audio_files` maps each wav field to its place in the dialogue, which is how you attach a clip
to its turn:

```json
"audio_files": {
  "000.wav": {"path": "train/e9/.../3da7d281-...+24797540.wav",
              "turn_index": 0, "turn_id": "3da7d281-...", "voice_id": "24797540"}
}
```

### Things to know

- **`sample_id` is unique only within a split.** 486 conversations appear in *both* `dev` and
  `test` — same turns and text, resynthesized with different voices. Key on
  `(split, sample_id)`, and be aware of this overlap if you evaluate on both splits.
- **A turn may have 0, 1, or 2 renditions** (different synthetic voices): 299,136 turns have
  one, 281,219 have two, and 42,233 are text-only. Conversations run up to 72 clips.
- **Text-only conversations are not in the shards.** 4,649 `train` conversations have no audio
  at all; they appear in `od3_train.jsonl` only. The shards cover 58,311 conversations and all
  861,574 clips.
- **`dev_mini` is not included.** The original `audio.tar.gz` contains a small `dev_mini/`
  directory (5 conversations) holding an extra voice rendering of a handful of `dev`
  conversations. No annotation file references those paths, and the renditions have no
  `voice_id`, ASR transcript, or WER, so they are omitted here. The shards contain exactly the
  861,574 clips described by the annotations.
- **Use the `webdataset` reader for audio.** Because samples have a variable number of `.wav`
  fields, the `datasets` WebDataset builder (which infers one fixed schema) is a poor fit here;
  read the shards with `webdataset` and use `load_dataset` for the JSONL annotations.

### Original tarball

The audio was originally distributed as a single `audio.tar.gz` (93.3&nbsp;GB) from CloudFront.
That object exceeds CloudFront's 50&nbsp;GiB per-object limit, so a plain `curl -O` returns
**HTTP 400**; only bounded HTTP range requests succeed. The shards here contain byte-for-byte
the same WAV files, regrouped by conversation.

## Citation

```bibtex
@inproceedings{chan2023domain,
  title={Task Oriented Dialogue as a Catalysis for Self-Supervised Automatic Speech Recognition},
  author={Chan, David M and Ghosh, Shalini and Tulsiani, Hitesh and Rastrow, Ariya and Hoffmeister, Bj{\"o}rn},
  booktitle={ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2024},
  organization={IEEE}
}
```

## License and attribution

Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

OD3 is released under [CC-BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
This repository mirrors the dataset originally released at
[amazon-science/amazon-od3](https://github.com/amazon-science/amazon-od3).

OD3 is derived from DSTC11 (Track 5), KVRET, MultiWOZ 2.2, NOESIS, and SIMMC 2, and uses the
Mozilla CommonVoice corpus (`cv-corpus-14.0-2023-06-23`) for voice cloning. Each seed dataset
remains subject to its own license.