---
license: cc-by-4.0
task_categories:
- text-to-speech
- audio-classification
language:
- en
tags:
- vocal-bursts
- speech
- webdataset
- synthetic
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: shard-*.tar
---

# Vocal-burst scenes — audio, annotated

73,500 generated scenes across **49 vocal-burst classes** that our
burst adapters handle badly or not at all — 500 prompts per class, each rendered at **3 seeds**,
so every prompt appears three times and a class effect can be told apart from a sampling accident.

**Every clip now carries a Gemini 3.8 Flash annotation**: where the bursts are and what they are.
123,004 events over 73,500 clips (1.674 per clip),
97 distinct labels.

Generated at the generator's default settings on 32 GH200 nodes (1 h 49 m, 128 ranks, zero failures). Prompts published
separately at
[`laion/voice-acting-burst-prompts`](https://huggingface.co/datasets/laion/voice-acting-burst-prompts).

## Why this exists

Our own burst detector emitted `Shriek` **zero times** across a 60-clip audit, used 8 of its 83
labels, and put the requested burst in its top-3 on 3 of 60 clips. Every burst adapter this
project ships was trained on rows that detector labelled. This corpus gets burst audio from a
*different* generator, densely and on purpose, and labels it with a *different* annotator.

**The annotation is a second model's opinion, not verified ground truth.** No human has listened
to these. A prompt asking for a shriek is not evidence a shriek was produced, and neither is a
label saying so.

## The annotation

| | |
|---|--:|
| clips annotated | 73,500 |
| successful responses | 73,500 |
| errors / unparsable | 0 |
| clips with no response | 0 |
| burst events found | 123,004 |
| events per clip | 1.674 |
| clips where the model heard no burst | 394 |
| distinct labels used | 97 |
| median event length | 1.27 s |
| spans clamped to the clip | 0 |

Model `gemini-3.8-flash`, **`thinkingLevel: "low"`**, temperature 0, JSON schema enforced, run
through the Batch API. Measured cost: 100.8 M input and
12.6 M output tokens, **2,362,228 thinking tokens**.

Each clip is shown to the model **whole**, never pre-cut: this model's labels are
context-dependent, and on excerpts it had itself left unannotated it returned Scream or Shriek
eleven times out of twenty. A span reaching past the end of its own audio is **clamped and marked
`out_of_range`, never dropped**, so the counts above reconcile exactly.

### The 25 most common labels

| label (first choice) | events |
|---|--:|
| Deep Breath | 18,121 |
| Exasperated Sigh | 17,771 |
| Humming | 10,449 |
| Chuckle | 9,893 |
| Relief Sigh | 6,043 |
| Sharp Inhale | 5,937 |
| Yawn | 4,089 |
| Scream | 3,637 |
| Cackle | 2,434 |
| Hiss | 2,199 |
| Displeased Grunt | 1,991 |
| Frustrated Groan | 1,810 |
| Surprised Gasp | 1,760 |
| Affirmative Grunt | 1,699 |
| Hiccup | 1,691 |
| Growl | 1,633 |
| Contented Sigh | 1,621 |
| Guffaw | 1,602 |
| Breathy Giggle | 1,601 |
| Soft Hum | 1,527 |
| Coughing | 1,522 |
| Wistful Sigh | 1,478 |
| Exhausted Groan | 1,440 |
| Childlike Giggle | 1,423 |
| Sniff | 1,416 |

## Files and layout

**81 files at the repository root.**

| path | count | what it is |
|---|--:|---|
| `shard-00000.tar` … `shard-00073.tar` | 74 | **the audio**, plus two JSON sidecars per clip |
| `metadata.parquet` | 1 | one row per clip, 7 columns — filter the corpus without opening a tar |
| `stats.json` | 1 | corpus totals and the per-class / per-gender / per-age counts |
| `annotation_stats.json` | 1 | the Gemini annotation run: label histogram, event counts, token usage |
| `vocal_burst_groups.json`, `GROUPS.md` | 2 | the 23-group scheme over 118 burst label strings |
| `README.md` | 1 | this card |

1000 samples per shard, 500 in the last. Shard order is a fixed seeded permutation, so **one shard
already contains the whole distribution** — no large shuffle buffer needed.

### Inside a tar — three members per clip, one stem

| member | format | detail |
|---|---|---|
| `<key>.mp3` | **MPEG-1 Layer III, 48 kHz, 160 kbps CBR, mono** | as written by the generator — not re-encoded, and not re-encoded again when the annotations were added |
| `<key>.json` | JSON | the generation record |
| `<key>.gemini.json` | JSON | the Gemini annotation |

Keys are `<class>__<prompt index>__s<seed>`, e.g. `affirmative_grunt__0378__s5678`.

**Read back from the shipped bytes**, not assumed: every file's MPEG frame header was checked
during packing; all 73,500 passed and all 73,500 keys round-tripped out of the written shards
exactly once. The clips' own `<key>.json` records the same thing as
`{"codec": "mp3", "bitrate_kbps": 160, "channels": 1, "sr": 48000}`.

### There are **no** codec tokens here

This repository ships **no MOSS codes, no `.moss.npy`, no embeddings and no `.vclap.npy`** — audio
and two JSON sidecars, that is the whole payload. If you need MOSS tokens for this material you
must encode it yourself with
[`OpenMOSS-Team/MOSS-Audio-Tokenizer-v2`](https://huggingface.co/OpenMOSS-Team/MOSS-Audio-Tokenizer-v2).
There is no transcript column either: the spoken line is inside `prompt` / `spec`, as it was asked
for, not as it came out.

### `<key>.json` — the generation record

| key | meaning |
|---|---|
| `key` | the clip key; also the tar member stem |
| `cls` | the vocal-burst class this clip was generated for (one of 49) |
| `idx` | prompt index within the class, 0..499 |
| `seed` | generation seed; each prompt is rendered at 3 seeds |
| `prompt` | the full text prompt handed to the generator |
| `gender`, `age_band` | the requested speaker gender and age band |
| `spec` | the structured prompt specification: `age`, `register`, `place`, `topic`, `trigger`, `phon` (the burst as written), `position`, `return_to_calm`, `intensity` |
| `settings` | generator settings: `cfg_scale`, `stg_scale`, `duration_multiplier`, `gen_duration`, `ref_duration` |
| `audio` | `{codec, bitrate_kbps, channels, sr}` of the file beside it |
| `dur_s`, `peak` | duration in seconds and the peak sample value |

### `<key>.gemini.json` — the annotation

| key | meaning |
|---|---|
| `no_burst` | the model heard no vocal burst at all in the clip |
| `events` | list of `{start_s, end_s, labels[1-3], confidence, description}` — `labels[0]` is the first choice |
| `overall` | one sentence describing the whole clip |
| `usage` | token counts for the call |
| `model`, `thinking_level` | `gemini-3.8-flash`, `low` |

A span reaching past the end of its own audio is **clamped and marked `out_of_range`, never
dropped**, so the counts in this card reconcile exactly.

### `metadata.parquet` — the column reference

**7 columns, 73,500 rows** — one per clip, read from the shipped schema:

| column | type | meaning |
|---|---|---|
| `key` | `string` | the clip key. **This is the tar member stem.** |
| `cls` | `string` | the vocal-burst class the clip was generated for. 49 distinct values, **1,500 clips each, exactly** |
| `seed` | `int64` | generation seed. Exactly three values — `1234`, `5678`, `9012` — 24,500 clips each |
| `gender` | `string` | requested speaker gender — `male` / `female`, 36,750 each |
| `age_band` | `string` | requested age band — `young_adult` 24,696 / `older_adult` 24,402 / `middle_aged` 24,402 |
| `shard` | `string` | **the tar file name, with the `.tar` extension**, e.g. `shard-00000.tar`. 74 distinct values |
| `prompt` | `string` | the full text prompt that produced the clip |

Note the spelling of `shard`: it carries the extension. Use it as the file name directly, do not
append `.tar` a second time.

## The join

```python
import tarfile, io, json
import pyarrow.parquet as pq

meta = pq.read_table("metadata.parquet")
row  = meta.slice(0, 1).to_pylist()[0]

with tarfile.open(row["shard"]) as tf:            # "shard-00000.tar" — extension included
    stem  = row["key"]                            # the member stem
    audio = tf.extractfile(stem + ".mp3").read()
    gen   = json.loads(tf.extractfile(stem + ".json").read())
    ann   = json.loads(tf.extractfile(stem + ".gemini.json").read())

print(row["cls"], ann["events"][0]["labels"], gen["audio"])
```

Verified on `shard-00000`: the parquet's first rows for that shard name members that are in the
tar, in the tar's own order.

Or stream the whole thing and ignore the parquet:

```python
import webdataset as wds
ds = wds.WebDataset("shard-{00000..00073}.tar").decode()
for r in ds:
    audio, meta, ann = r["mp3"], r["json"], r["gemini.json"]
```

## Balance

Held by construction, not by luck:

| | |
|---|---|
| male / female | 36,750 / 36,750 |
| young / middle-aged / older adult | 24,696 / 24,402 / 24,402 |
| per class | 1,500 (500 prompts × 3 seeds) |
| audio | 32.8 GB |

## Audio format

160 kbit/s mono 48 kHz MP3, **as written by the generator — not re-encoded**, and not re-encoded
again when the annotations were added. Every file's MPEG frame header was read back out of the
bytes during packing; all 73,500 passed and all 73,500 keys round-tripped out
of the written shards exactly once.

## Related

* [`laion/voice-acting-burst-prompts`](https://huggingface.co/datasets/laion/voice-acting-burst-prompts) — the prompts
* [`laion/vocal-bursts-gemini-segments`](https://huggingface.co/datasets/laion/vocal-bursts-gemini-segments) — real-speech burst segments, CC BY 4.0
* [`laion/vocal-burst-classifier-gemini`](https://huggingface.co/laion/vocal-burst-classifier-gemini) — the classifier trained on those

## Licence

Audio, prompts, annotations and packaging are released under **CC BY 4.0**.

---

### Correction (2026-09-04)

An earlier version of this card said the shipped `vocal-burst-detector-v2` "used 8 of its 83
labels" and "emitted `Shriek` zero times". That was measured on a **60-clip audit** and does not
generalise. Run over both full corpora (72,500 synthetic clips and 3,598 real utterances), the same
detector emits **41 distinct labels on the synthetic half and 36 on the real half, and it does emit
`Shriek`** — on 50 synthetic clips and 49 real ones.

What the 60-clip audit actually saw is how *concentrated* the distribution is: `Contented Sigh`
alone accounts for 16,694 of 72,500 synthetic clips, `Surprised Gasp` 7,451, `Wistful Sigh` 5,437.
The correct statement is that the detector's **effective** vocabulary is small, not that its
emitted vocabulary is 8. The conclusions drawn elsewhere in this card — that the detector is the
bottleneck, and that its failure is granularity rather than deafness — are unaffected and are
independently supported by the cross-source results in §62.

<!-- vocal-burst-groups-23:start -->

### Class groups

`vocal_burst_groups.json` and `GROUPS.md` carry a **23-group** scheme over 117 burst label strings, grouping names that denote the same or a very similar sound (`snicker`/`chuckle`, `shriek`/`scream`, `cough`/`coughing`). Scoring the same predictions at group level raises the mean generation hit rate from 0.302 to 0.537; a random grouping with identical group sizes reaches 0.355, so **+0.182 of it is the grouping being right and the rest is arithmetic**. Groups were checked with directed lift rather than raw confusion, because two labels account for 29 % of all annotator top-1 calls whatever was requested and merging on raw confusion books a generation failure as a hit.

For **training** the classifier, keep the fine classes: collapsing them raises raw accuracy only because chance rises with it. Group at evaluation time — that can be done at any point, the reverse cannot.

<!-- vocal-burst-groups-23:end -->

---

The audio in this dataset was generated with a voice-acting AI model.