---
license: cc-by-4.0
language:
- en
task_categories:
- audio-classification
- text-to-audio
- audio-to-audio
tags:
- audio
- multimodal
- nano4m
- 4m
- vggsound
- encodec
- cosmos-tokenizer
size_categories:
- 1K<n<10K
pretty_name: nano4M-Audio Team (week-1)
---

# nano4M-Audio — Team (week-1)

Week-1 data preparation for **nano4M-Audio**, an extension of EPFL's
[nano4M](https://github.com/com-304/nano4M) (the educational nano version of
[4M](https://github.com/apple/ml-4m) / [4M-21](https://arxiv.org/abs/2406.09406))
that adds **audio** as a fifth modality alongside RGB, depth, surface normals
and captions.

This dataset covers all 12 VGGSound classes assigned to the three-person team:

| person | classes |
|---|---|
| 1 (Hassan) | lions roaring, horse neighing, pig oinking, cow lowing |
| 2 (Ziyad)  | dog barking, cat meowing, coyote howling, elephant trumpeting |
| 3 (Marc)   | chicken clucking, duck quacking, sheep bleating, donkey/ass braying |

It contains the pre-tokenized modalities consumed by nano4M's
`SimpleMultimodalDataset` (`tok_audio@256`, `tok_rgb@256`, `scene_desc`). The
raw WAV+JPG pairs are kept on each teammate's machine and are not redistributed
here; the tokens are the canonical artifact for training.

> **Source attribution.** Audio + image content is derived from
> [VGGSound](https://www.robots.ox.ac.uk/~vgg/data/vggsound/) (Chen et al.,
> ICASSP 2020), whose primary key is `(ytid, start)`. Every file in this
> dataset is a 1.71 s, 24 kHz mono crop centered on the CLAP-best audio peak
> of the corresponding VGGSound clip and the CLIP-best frame of the same
> window. We re-distribute the raw clips here for reproducibility under
> VGGSound's CC-BY-4.0 terms.

## Counts

12 VGGSound classes, ~4.9k clips after link rot + CLIP/CLAP filtering. Per-class
breakdown (see `stats_team.md` for full distributions and qualitative samples):

| class | train OK | test OK | total OK |
|---|---:|---:|---:|
| dog barking         | 599 | 38 | **637** |
| lions roaring       | 639 |  4 | **643** |
| duck quacking       | 501 | 38 | **539** |
| chicken clucking    | 454 | 37 | **491** |
| cat meowing         | 449 | 41 | **490** |
| sheep bleating      | 405 | 38 | **443** |
| coyote howling      | 344 | 41 | **385** |
| horse neighing      | 325 | 13 | **338** |
| donkey/ass braying  | 302 | 33 | **335** |
| pig oinking         | 264 | 12 | **276** |
| elephant trumpeting | 183 | 34 | **217** |
| cow lowing          |  98 | 13 | **111** |
| **TOTAL**           | **4,563** | **342** | **4,905** |

All clips are uniformly **41,040 samples = 1.7100 s @ 24 kHz** mono PCM16
before tokenization (frames sliced by the CLAP-best 1.71 s window from a 10 s
VGGSound clip).

## Files

```
.
├── manifest_team.csv                     canonical join table (6,529 VGGSound rows; 4,905 OK)
├── stats_team.md                         per-class counts + score histograms
├── manifest_person2.csv                  legacy Person-2 manifest (kept for reference)
├── stats_person2.md                      legacy Person-2 stats
└── tokenized/
    ├── train/tok_audio@256/{stem}.npy    EnCodec tokens, shape (1, 256), int16, [0, 1023]
    ├── train/tok_rgb@256/{stem}.npy      Cosmos tokens,  shape (1, 256), int32, [0, 63999]
    ├── train/scene_desc/{stem}.json      JSON list ["<class>"] (K=1 augmentation)
    └── test/...                          same layout
```

`{stem}` is `{ytid}_{start:06d}` — globally unique across the 12-class team
union since `(ytid, start)` is VGGSound's primary key.

## Tokenizer details

| modality | tokenizer | shape | dtype | vocab |
|---|---|---|---|---|
| `tok_audio@256` | [`facebook/encodec_24khz`](https://huggingface.co/facebook/encodec_24khz) at 1.5 kbps, K=2 RVQ codebooks at 75 Hz | (1, 256) | int16 | 1024 |
| `tok_rgb@256`   | [`nvidia/Cosmos-0.1-Tokenizer-DI16x16`](https://huggingface.co/nvidia/Cosmos-0.1-Tokenizer-DI16x16) on 256×256 frames | (1, 256) | int32 | 64,000 |
| `scene_desc`    | not pre-tokenized — captions tokenized at load time by GPT-2 ([SOS] $A [EOS], vocab 50,304) | JSON list[str] | — | — |

## On-disk contract for nano4M

Three details that matter for compatibility with nano4M's `SimpleMultimodalDataset`:

1. **`@256` is part of the directory name.** The dataloader reads from
   `{root}/{split}/{modality_string}/{stem}{ext}` and the modality string in
   the config is literally `"tok_audio@256"`, not `"tok_audio"`.
2. **`scene_desc/*.json` is a JSON list, not a dict.** The dataloader does
   `captions[augmentation_idx]`. Run with `sample_from_k_augmentations=1`.
3. **Captions are tokenized at load time, not pre-tokenized.** The dataloader
   instantiates GPT-2 with `[SOS] $A [EOS]` template (vocab 50,304) on the fly.

## Provenance & limitations

- **Link rot is permanent.** 385 of 2,155 source rows (17.9%) failed
  `yt-dlp` because the YouTube videos are unavailable / private /
  region-blocked. These are listed in `raw/failed.txt` with reason
  `FAIL_DL`. Re-running the downloader at a later date will lose more
  clips, not gain any back — that is why the raw WAV+JPG pairs are
  re-distributed here.
- **CLIP/CLAP filtering.** 26 clips dropped for `FAIL_CLIP` (best frame's
  CLIP score < 0.20 vs class label) and 14 for `FAIL_CLAP` (best 1.71 s
  window's CLAP logit < 0.20 vs class label). 1 `FAIL_PEAKS` (silent /
  sub-1.71 s clip). All in `failed.txt`.
- **Class imbalance.** `elephant trumpeting` has ~3× fewer clips than
  `dog barking` due to lower YouTube availability.
- **Single keyframe per clip.** Only the CLIP-best frame within the chosen
  1.71 s window is provided, not a full video.

## Reproducing

Pipeline scripts and full README are in the project repo (private). Briefly:
download via `yt-dlp` from VGGSound's official CSV → CLAP-pick best 1.71 s
window → CLIP-pick best frame in that window → EnCodec tokenize audio →
Cosmos tokenize image → emit manifest. The Cosmos step requires a CUDA GPU
(this dataset's `tok_rgb@256` was generated on an NVIDIA L40S).

## Citation

If you use VGGSound (the underlying source), please cite:

```bibtex
@inproceedings{chen2020vggsound,
  title={VGGSound: A Large-scale Audio-Visual Dataset},
  author={Chen, Honglie and Xie, Weidi and Vedaldi, Andrea and Zisserman, Andrew},
  booktitle={ICASSP},
  year={2020}
}
```

For 4M / nano4M:

```bibtex
@inproceedings{mizrahi20234m,
  title={4M: Massively Multimodal Masked Modeling},
  author={Mizrahi, David and Bachmann, Roman and Kar, O{\u{g}}uzhan Fatih and Yeo, Teresa and Gao, Mingfei and Dehghan, Afshin and Zamir, Amir},
  booktitle={NeurIPS},
  year={2023}
}
```

## License

Released under **CC-BY-4.0**, inheriting from VGGSound. You must cite
VGGSound and respect YouTube's Terms of Service when using this data.