---
license: apache-2.0
---
# additional-data-a1-plus — tokenized audio + captions (MOSS-Audio-Tokenizer-v2)

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.

`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, 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
```
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.