| --- |
| 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. |
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