document caption columns

README.md

@@ -1,20 +1,27 @@
 ---
 license: apache-2.0
 ---
-# additional-data-a1-plus — tokenized audio (MOSS-Audio-Tokenizer-v2)
+# additional-data-a1-plus — tokenized audio + captions (MOSS-Audio-Tokenizer-v2)
 
-MOSS-Audio-Tokenizer-v2 codes (12-codebook RVQ @ 48 kHz) for `scientifi-papers/a1-plus`,
-for MOSS-TTS-Local-Transformer (moss 1.5 local) training. **No MP3s** — only tokens.
+MOSS-Audio-Tokenizer-v2 codes (12-codebook RVQ @ 48 kHz) **with captions** for
+`scientifi-papers/a1-plus`, ready for MOSS-TTS-Local-Transformer (moss 1.5 local)
+training. **No MP3s** — tokens + text + captions only.
 
-Per row (`key` joins to a1-plus):
+`tokenized_audio.parquet`, per row (`key` joins to a1-plus):
 - `target_codes` : int16 bytes, reshape `[target_frames, 12]`
 - `ref_codes`    : int16 bytes, reshape `[ref_frames, 12]` (same-speaker reference; may be null)
+- `text`                 : transcript (what is said)
+- `voice_acting_caption` : natural GENERAL + SCRIPT caption with `[pause X.Xs]` (how it is said)
+- `procedural_caption`   : terse tag-style caption
+- `bude_caption`         : BUD-E voice description
 
 ```python
-import numpy as np, pandas as pd
+import numpy as np, pandas as pd, torch
 d = pd.read_parquet("tokenized_audio.parquet")
 r = d.iloc[0]
 tgt = np.frombuffer(r.target_codes, np.int16).reshape(r.target_frames, 12)   # [T,12]
+prompt = r.voice_acting_caption          # instruction/caption for training
 # decode: MossAudioTokenizer.batch_decode([torch.tensor(tgt.T)])  -> 48kHz audio
 ```
-Password `acting` (a1-plus). Reconstruction verified at 0.994 correlation.
+Full VoiceNet/EmoNet/blend/genuineness scores + same-speaker `ref_spk_sim` live in the
+`scientifi-papers/a1-plus` metadata (password `acting`). Reconstruction verified at 0.994 correlation.