8-gen bench (4 emotions × 2 corpora) at seed=42 against firdhokk
Whisper-LV3:
target RAVDESS CREMA-D
happy happy (0.999) ✓ happy (0.999) ✓
angry neutral (0.92) sad (0.99)
fearful happy (0.998) fearful (0.984) ✓
sad angry (0.99) fearful (0.99)
CREMA-D 2/4 vs RAVDESS 1/4. Larger / more naturalistic corpus
produces more class-pure fearful direction. Neither corpus solves
angry or sad — recipe shifts into 'vague expressivity' rather than
class-specific corners.
Practical: prefer CREMA-D when available; A/B both per emotion if
class precision matters.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
Three real bugs found while running a YouTube → train → eval pipeline
end-to-end on real corpora:
1. emotion2vec was producing near-constant logits regardless of input.
Per config.yaml `normalize: true` — data2vec2/emotion2vec expects
per-utterance zero-mean unit-variance normalization on the raw
waveform before the local_encoder. Added inside the EmotionDetector
trait impl so all callers get it.
Verified empirically: 4 different audio inputs (Carlini talk,
audience question, McConaughey speech) now produce different argmax
classes. Before fix: all 4 produced identical logits.
2. The 9→5 emotion fold was collapsing every real-world clip to
[excited]. happy / surprised / other all mapped to Excited covered
~95% of natural speech. Replaced with a direct 9-class identity
mapping; EmotionLabel gained Disgusted, Fearful, Happy, Surprised,
Unk variants. Now: 132 [surprised] + 12 [excited] across the
Carlini corpus instead of 144 [excited].
3. lora_train_emotional --peak-lr / --epochs flags. The canned 3-stage
recipe over-fits on small (~100 clip) corpora at extended rank 8;
users need to tune. (The recipe stays as defaults; flags are pure
overrides.)
Plus diagnostic: examples/emotion2vec_probe — feed real audio files
into emotion2vec and dump per-class logits. Used to find bug #1.
Lib suite still 131/131 (the test that locked the 9→5 fold updated
to lock the new identity mapping).
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
Trained adapters now embed (rank, alpha, target_modules, crate_version)
as JSON in safetensors __metadata__["rtx_csm_lora"]. apply_lora_adapter
reads it at load time so users no longer have to remember matching
--lora-rank/--lora-alpha/--extended-lora flags from training.
LoraAdapterMetadata::is_extended() heuristic: target_modules contains
any MLP path or output_proj or k_proj. Handles both the canonical
extended() preset and future custom configs that overlap it.
apply_lora_adapter rank/alpha/extended params became Option<_>
(None = use file metadata, Some = override). Both callers updated.
save_lora_adapter_with_metadata is the new path used by both trainers;
the plain save_lora_adapter still exists for the metadata-less case
(per-stage curriculum snapshots).
safetensors dep bumped 0.4 → 0.7 to match candle 0.9's transitive pin
so candle's Tensor: View impl is in scope for serialize_to_file
(candle's own save wrapper hardcodes the metadata arg to None).
Backward compat: pre-12.5 adapters load fine when explicit CLI flags
are passed; auto-detect path is skipped silently.
Verified end-to-end: trained adapter saved with metadata,
`generate --lora <path>` (no other flags) auto-detected
rank=8 alpha=16 extended=true and applied. Older metadata-less adapter
still loaded with explicit flags. 3 new unit tests; lib suite 99/99.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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]>
