---
task_categories:
- audio-to-audio
language:
- en
tags:
- spatial-audio
- ambisonics
- 360-video
- audio-visual
size_categories:
- 1K<n<10K
---

# Sphere360

Paired **first-order ambisonic audio** and **360° equirectangular video**, sourced
from YouTube. Each pair shares one ID, so the spatial sound field and the visual
sphere are aligned by construction. 791 hours of paired material.

| | Pairs | Audio | Video | Size |
|---|---|---|---|---|
| `train` | 5,469 | 5,469 | 5,469 | 5.24 TB |
| `test` | 162 | 162 | 162 | 0.17 TB |
| `extras` | — | 415 | 110 | 0.25 TB |
| **Total** | **5,631** | **6,046** | **5,741** | **5.66 TB** |

**Audio** — Opus, 48 kHz, 4-channel first-order ambisonics (`ambisonic 1`).
Every audio file in the dataset is verified to carry exactly this layout.
**Video** — VP9 equirectangular. It also carries a stereo track that is *not* the
spatial audio; always take the ambisonics from `audio/`.

## Layout

```
audio/{train,test}/{00..63}/<id>.webm     ambisonic audio
video/{train,test}/{00..63}/<id>.webm     360° video
extras/audio_only/{train,test}/<id>.webm  unpaired
extras/video_only/{train,test}/<id>.webm  unpaired
metadata/{train,test,extras}.jsonl        one row per ID
metadata/stats.json                       counts, and every file excluded and why
```

The 64 shards keep each directory to ~184 files and leave room to grow. Shard is
derived from the ID, so no lookup is needed:

```python
import hashlib
shard = lambda vid: f"{int(hashlib.sha1(vid.encode()).hexdigest()[:8], 16) % 64:02d}"
shard("-EOhDHns4xw")  # -> "00"
```

Shards are hashed rather than cut from the ID's first characters because YouTube
IDs are case-sensitive and would collide on case-insensitive filesystems.

## Use

Extensions vary, so read paths from the metadata rather than building them:

```python
import json
from huggingface_hub import hf_hub_download

R = "OmniAV/Sphere360"
rows = [json.loads(l) for l in open(hf_hub_download(R, "metadata/train.jsonl", repo_type="dataset"))]

r = rows[0]
audio = hf_hub_download(R, r["audio"]["path"], repo_type="dataset")
video = hf_hub_download(R, r["video"]["path"], repo_type="dataset")
```

One row:

```json
{"id": "-EOhDHns4xw", "split": "train", "shard": "00", "paired": true,
 "youtube_url": "https://www.youtube.com/watch?v=-EOhDHns4xw",
 "audio": {"path": "audio/train/00/-EOhDHns4xw.webm", "bytes": 11536470,
           "duration": 255.981, "codec": "opus", "channels": 4,
           "channel_layout": "ambisonic 1", "sample_rate": 48000,
           "sha256": "6e4f14c2cb0c0790..."},
 "video": {"path": "video/train/00/-EOhDHns4xw.webm", "bytes": 557769482,
           "duration": 256.008, "codec": "vp9", "width": 3840, "height": 2160,
           "sha256": "..."}}
```

Every entry carries `bytes` and `sha256`, so a download can be checked without
trusting the transfer:

```python
import hashlib
h = hashlib.sha256(open(audio, "rb").read()).hexdigest()
assert h == r["audio"]["sha256"]
```

Pull one shard instead of 5.66 TB:

```bash
hf download OmniAV/Sphere360 --repo-type dataset --include "audio/train/00/*" "video/train/00/*"
```

## Know before you train

- **Resolution is not uniform** — 56 distinct sizes. 3840×2160 (44%), 3840×1920
  (28%) and 3840×2048 (9%) cover most of it, but the range runs from 1920×960 to
  7680×4320. This reflects the sources, not a processing error. Filter on
  `width`/`height` if your pipeline needs one size.
- **Duration is not uniform** — 12 s to 11.4 h, median 277 s. Clip as needed.
- **`extras/` is unpaired** and excluded from `train`/`test`. Single-modality use
  only.
- **30 files were dropped**, each a low-bitrate video rendition the fetcher had
  written into the audio slot when the ambisonic track was unavailable. They
  contained no audio stream at all. Their IDs are listed in
  `metadata/stats.json` under `rejected_files`; the 27 that still had real video
  moved to `extras/video_only/`.
- Material was fetched twice in parallel. Where both runs returned an ID, the
  higher-resolution copy won, with file size breaking ties at equal resolution.
- Every row carries `youtube_url`, so any file can be traced to its source.

## Availability

All 64 shards are published — 11,787 files, 5.66 TB. `metadata/*.jsonl` lists
only rows whose files are actually present, and `metadata/stats.json` records the
live set under `published_shards` / `published_rows`, so a run driven by the
metadata never asks for a missing file.

## Citation

Derived from the Sphere360 dataset introduced in OmniAudio.

```bibtex
@inproceedings{omniaudio2025,
  title     = {OmniAudio: Generating Spatial Audio from 360-Degree Video},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2025}
}
```