| --- |
| 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} |
| } |
| ``` |
|
|
