| --- |
| 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. |
|
|
