Closes the personal_voice_training_guide.md §4 stack: capacity (12.1) +
control tokens (12.2) + multi-stage curriculum (this).
TrainingExample gains emotion_tag + stage. Trainer::train applies the
tag via the same apply_emotion_hint helper inference uses (now
pub(crate)) — training and inference must use identical prefix
formatting or the adapter won't transfer.
TrainingDataset::load_from_manifest reads JSONL
`{wav, transcript, emotion_tag?, stage?, speaker?}` rows; wav paths
resolve relative to manifest dir.
CurriculumStage + CurriculumTrainer run N stages sequentially against a
shared VarMap. Per stage: filter by ex.stage label, build a transient
sub-dataset, run Trainer, save snapshot if requested. The "*" stage
name is a global catch-all.
examples/lora_train_emotional.rs wraps the canonical 3-stage recipe:
audiobook (3 ep × lr 1e-4) → podcast (1 ep × lr 3e-5) → va (1 ep ×
lr 1e-5). --extended-lora recommended (FFN is the prosodic-style
carrier per the guide).
Verified end-to-end on Metal: 3-row manifest → all 3 stages execute,
checkpoints + final adapter written, prompt-token lengths varied by
emotion-tag length (9 vs 11 for different tags) confirming the tag
flowed through the training tokenization. Lib suite 96/96.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
