Update README to 12-class team scope

README.md

@@ -16,10 +16,10 @@ tags:
 - cosmos-tokenizer
 size_categories:
 - 1K<n<10K
-pretty_name: nano4M-Audio Person 2 (week-1)
+pretty_name: nano4M-Audio Team (week-1)
 ---
 
-# nano4M-Audio — Person 2 (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
@@ -27,10 +27,18 @@ Week-1 data preparation for **nano4M-Audio**, an extension of EPFL's
 that adds **audio** as a fifth modality alongside RGB, depth, surface normals
 and captions.
 
-This dataset covers the 4 VGGSound classes assigned to Person 2 (Ziyad):
-`dog barking`, `cat meowing`, `coyote howling`, `elephant trumpeting`. It
-contains both raw clips and the pre-tokenized modalities consumed by nano4M's
-`SimpleMultimodalDataset`.
+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.,
@@ -42,29 +50,37 @@ contains both raw clips and the pre-tokenized modalities consumed by nano4M's
 
 ## Counts
 
-| class | train OK | test OK | total OK | raw rows | survival |
-|---|---:|---:|---:|---:|---:|
-| dog barking         | 599 | 38 | **637** | 767  | 83.0% |
-| cat meowing         | 449 | 41 | **490** | 588  | 83.3% |
-| coyote howling      | 344 | 41 | **385** | 490  | 78.6% |
-| elephant trumpeting | 183 | 34 | **217** | 310  | 70.0% |
-| **TOTAL**           | **1,575** | **154** | **1,729** | **2,155** | **80.2%** |
-
-CLIP scores μ=0.283, σ=0.025, range [0.20, 0.37]. CLAP raw logits μ=11.71,
-σ=3.02, range [0.28, 20.18]. All 1,729 OK clips are uniformly **41,040
-samples = 1.7100 s @ 24 kHz** mono PCM16.
+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_person2.csv                  canonical join table (1,729 OK rows)
-├── stats_person2.md                      per-class counts + score histograms
-├── raw/
-│   ├── failed.txt                        audit of the 433 link-rotted / filtered IDs
-│   ├── train/{class}/{stem}.wav          24 kHz mono PCM16, exactly 41,040 samples
-│   ├── train/{class}/{stem}.jpg          CLIP-best frame from the same 1.71 s window
-│   └── test/...                          same layout
+├── 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]