sbob-datasets — SpongeBob LoRA + TTS pipeline data
Everything needed to rebuild any stage of the sbob project without the original episode files. Private, personal research only — derived from © Nickelodeon material.
| dir | contents |
|---|---|
corpus/ |
lines.jsonl — 129k speaker-attributed transcript lines from 1043 episodes (the scraped source corpus); episodes.jsonl index |
text_datasets/ |
per-character LoRA train/val jsonls (10 chars) + _ensemble/ (103 speakers, 77k records) — completion format, episode-level split; the ensemble adapter trained on this beat every per-char adapter (see corpus_eval/REPORT.md) |
tts_ft_v3/ |
trio voice-FT jsonls, round-3 recipe: fixed slots S1/S2/S3, singles-only, slow-Patrick filter |
tts_clips/ |
the exact 24 kHz mono training clips (cut from demucs vocals at attribution timings) |
media_vocals_flac/ |
demucs (htdemucs) vocal stems of all 41 S01 episodes, lossless FLAC, 48k mono — re-cut clips/refs from these |
refs_v2/ |
frozen cloning refs (vocals-domain) + alternates for the trio — use these with the v3 checkpoints |
refs_v1/ |
original ref pools + manifests (raw-audio domain, ref-grade clips, all attributed chars) |
attribution/ |
SRT × transcript alignments per episode (speaker, start/end, text, score) — the timing ground truth |
eval_probes/ |
the frozen probe/teaser/scene jsonls used for every voice A/B |
outlines/ |
episode "director" scripts: The Laugh Tax, quantum podcast (+ trio cut) |
corpus_eval/ |
text-tune comparison: 12-config × 11-val-set matrix, gold-referenced samples, REPORT.md |
Checkpoints trained on this data: weystrom/sbob-tts-ckpts. Tooling: the sbob repo
(scrape.py, build_dataset.py, tts/attribute_voices.py, tts/build_tts_ft_dataset.py).