Promote derisk_energy checkpoint_epoch=099 (staged, not pushed)

train-repo: Artificial-Humanity/Sonora, MLflow run nimble-duck-437
gate verdict in derisk_energy_gate: all 4 sweep groups pass north-star §7
thresholds (energy rho ~1.000, WER delta <= 0.042, leakage <= 0.091) with
real margin. Sweep renders human-audited 2026-07-16, energy/loudness change
judged clearly discernible and natural.

Staged locally only -- same open question as vocoder-24k-hifigan/: this is
ONE VAT channel (energy), not the full directable actor, so whether a
partial-VAT checkpoint belongs in the public HF repo is a deliberate call,
not a default push. No auto-queued follow-on training this time either --
next steps are scope decisions (full-VAT corpus question, export-harness
adaptation), not mechanical continuations.

derisk-energy-24k/README.md

@@ -0,0 +1,64 @@
+# §7 de-risk: energy channel, 24 kHz, multi-speaker (promoted, NOT yet public)
+
+**Status:** staged locally per chore #7's canonical artifact flow (train → logs (transient) →
+promote → Registry → push HF). **Not yet pushed to the public `artificial-humanity/Sonora` HF
+repo** — same open question as `vocoder-24k-hifigan/`: this validates ONE VAT channel
+(energy/arousal; valence and tension are hardwired to 0 in the v0 corpus labels), not the full
+directable actor. Whether a partial-VAT checkpoint belongs in the public registry, and how it
+should be described if so, is worth a deliberate call rather than a default push.
+
+## What this is
+
+The north-star §7 de-risk experiment (`configs/experiment/derisk_energy.yaml`): one trained FiLM
+channel through the VAT conditioning plumbing, multi-speaker (247 spk, LibriTTS-R train-clean-100),
+native 24 kHz, warm-started from the multi-speaker VCTK checkpoint. Its entire purpose was to prove
+the VAT/FiLM plumbing works end-to-end and is worth the investment of full 3-channel training
+(which needs valence/tension-labeled data the current corpus doesn't have) before committing to
+that larger effort.
+
+## Provenance
+
+* **Training:** `sonora_training` container on `ai-lab-0`, MLflow run `nimble-duck-437`
+  (experiment `lightning_logs`), launched 2026-07-15, warm-started from
+  `derisk_energy_init.ckpt` (`scripts/make_warmstart.py` off `matcha_vctk.ckpt`).
+* **Stopped:** 2026-07-16, at `checkpoint_epoch=099.ckpt`, on a CONVERGED verdict from the
+  automated eval-harness gate (see [training-operations.md
+  §Stop signal](../../../Notes/Sonora/training-operations.md)). Training continued to epoch 106
+  before being stopped (the watcher never auto-stops); epoch 099 is the audited, gated checkpoint
+  promoted here, not the latest one.
+* **Convergence criterion (north star §7, pre-registered):** every sweep group passes
+  Spearman |ρ| ≥ 0.9 (controllability), ECAPA leakage ≤ 0.2 (identity), WER Δ ≤ +0.10 (guardrail).
+  At epoch 099, across 4 speaker/clip groups: **energy ρ ≈ 1.000 in all four** (perfectly
+  monotonic), WER Δ 0.000–0.042, leakage 0.051–0.091 — every group clears its threshold with real
+  margin, not a borderline pass. Full numbers in `eval_report.json`; history in
+  `gate_history.jsonl`.
+* **Sweep samples:** `sweep_samples/` — 20 renders (5 energy values × 4 speaker/clips, energy ∈
+  {−1, −0.5, 0, +0.5, +1}), vocoded with the promoted 24k HiFi-GAN (`../vocoder-24k-hifigan/`).
+  Human-audited 2026-07-16: energy/loudness change judged clearly discernible and natural-sounding
+  across the sweep. `render_meta.json` records the exact render settings (seed, n_timesteps,
+  temperature, length_scale).
+* **License:** LibriTTS-R only (CC-BY-4.0) — **clean, not NC-tainted** (no Expresso). Promotable
+  under the same terms as Phase 0.
+
+## Files
+
+| File | What |
+|---|---|
+| `checkpoint_epoch=099.ckpt` | Full Lightning checkpoint (weights + optimizer state — resumable, unlike the vocoder's inference-only promotion). |
+| `gate_history.jsonl` | Full convergence-watcher history for this run (includes the plumbing-test entry against the zero-init warmstart). |
+| `sweep_samples/` | The 20 energy-sweep renders evaluated at the promoted checkpoint. |
+| `eval_report.json` | Per-group controllability/identity/WER numbers from `scripts/eval_harness.py`. |
+| `render_meta.json` | Exact render settings for reproducibility. |
+
+## Open questions before this goes further
+
+1. **Public HF push** — same call as the vocoder: hold or push as a documented partial-VAT
+   milestone? See [model-size-target-decision.md](../../../Notes/Sonora/model-size-target-decision.md)
+   for the surrounding size/quality target discussion this checkpoint feeds into.
+2. **No auto-queued follow-on training this time.** Unlike vocoder → derisk_energy, there is no
+   preset "next run" waiting behind this one. The real next steps are scope decisions, not
+   mechanical ones: (a) formalize the §7 write-up, (b) decide on the valence/tension corpus
+   question before attempting full 3-channel VAT (Expresso is NC-tainted; a clean labeled
+   alternative doesn't yet exist), (c) begin export-harness adaptation
+   (`/data/toolchain/litert-conversion/` is still hardcoded to the 22.05 kHz v1-ljspeech
+   model + generator_v1 and doesn't cover this checkpoint's 24 kHz/multi-speaker/VAT shape as-is).

derisk-energy-24k/checkpoint_epoch=099.ckpt

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+oid sha256:3c70a316413829c73e18dfaa58e450f82136a48aa8ca530119a5f2ed7cf24141
+size 271379859

derisk-energy-24k/eval_report.json

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+}

derisk-energy-24k/gate_history.jsonl

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