feat(voices): canonical voice corpus + dots.tts-optimized refs

Engine-agnostic voice corpus: canonical source clip + transcript per voice,
per-engine reference sets derived by derive.py from engines.yaml profiles.
First residents donut/glados/emmie/miranda optimized + verified clean for
dots.tts (sentence-bounded ref + accurate transcript — dots leaks reference
audio into output otherwise). canonical/ + transcripts/ tracked; derived/
gitignored (regenerable). Records the dots.tts burn-in in persistent-memory.

persistent-memory.md

@@ -128,6 +128,8 @@ _As of 2026-08-08 — long session; all major arcs LANDED (full detail per arc i
 
 ## Recent decisions
 
+- `[2026-08-09→10]` **dots.tts (rednote-hilab) TTS burn-in on irv-ml1 + canonical voice corpus built (`voices/`).** Operator-directed eval to potentially replace chatterbox-fast. **dots.tts VERIFIED real** (canonical HF ns `dots-studio/`, `rednote-hilab/dots.tts-*` redirects there; Apache-2.0; PyPI `dots.tts` 0.2.1; 2B continuous-AR = semantic enc + Qwen2.5-1.5B LLM + flow-matching acoustic head over 48kHz AudioVAE; zero-shot clone from wav+transcript). **Runs on Ampere 3090** (sm_86, bf16, no fp8 dep); **optimized RTF 0.22** at num_steps=10 (`from_pretrained(..., optimize=True)` CUDA graphs — raw unoptimized was 1.21), **~6GB VRAM**, 48kHz, streams (`generate_stream`). Venv+cache at `irv-ml1:/home/lkraven/dots-tts` (~10GB). **Operator design calls:** SGLang Omni serving (OpenAI `/v1/audio/speech`), transcribe-refs-first, `soar` variant. ⚠ Omni serves soar but its continuous-batching + streaming opts are **mf-only** (soar = single-request) — non-issue for ratatoskr's single-consumer RP surface. **KEY FINDING — dots is highly sensitive to an accurate AND sentence-bounded reference transcript:** mismatched transcript → 0.16s collapse; over-long/messy transcript → reference-audio BLEEDS as an output prefix; mid-clause trim → dangling-word leak (glados "we'll", emmie "And,"). RECIPE (baked into `voices/derive.py`): trim ref to a clean ~6–10s clip ending on a sentence boundary + accurate transcript of exactly that clip. **CANONICAL VOICE CORPUS** stood up in eshpfi `voices/` (operator idea): engine-agnostic `canonical/<v>.wav` + `transcripts/<v>.txt` → per-engine ref sets DERIVED by `derive.py` reading `engines.yaml` profiles (dots/chatterbox/zonos); canonical wavs git-tracked (small/curated), `derived/` gitignored. **4 voices optimized + verified CLEAN for dots: donut, glados, emmie, miranda** (glados canonical is low-SR 16kHz — flagged upgrade candidate). ⚠ GPU GOTCHA: irv-ml1 native CUDA orders **A6000=device0** (ComfyUI-full) — pin the 3090 with `CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0`; and `PYTORCH_CUDA_ALLOC_CONF=expandable_segments` CONFLICTS with `optimize=True` CUDA graphs (curr_block error). Booths: `dots-vs-chatterbox`, `dots-voices-optimized`. **PENDING: operator A/B ear-verdict → Phase 2** (containerize SGLang Omni serving dots.tts-soar on the 3090 alongside chatterbox; ratatoskr client cutover to `/v1/audio/speech`); wrapper-vs-Omni serving-layer choice deferred to Phase 2. **OPEN operator call:** corpus home = eshpfi `voices/` (my rec) vs spin-out `vh/voice-corpus`. [[reference_chatterbox_fast_repo]] [[reference_zonos_tts_stack]] [[reference_verify_hf_repo_ids_before_pull]]
+
 - `[2026-08-08]` **worldtree-dev #400 CLOSED → fiction-decomp snapshot cleared from nh3-dev.** worldtree-dev signaled #400 done (shipped v1.0.0b185; exact-lexical efficacy 79%→12% on ratatoskr's gate, brokkr no-harm bracket green both ends; the snapshot served 4 probe rounds — rank decomposition, promoted-vs-gold annotation, tie-set falsification, A0/A1/A2 mechanism probe). Cleared `~/snapshots/worldtree-400-fiction-decomp` (208M: chroma + manifest/provenance/stamp) — a read-only rsync copy of PERSONAL Worldtree's Chroma (source on corviduo-dev, so safe to remove). **LEFT INTACT:** `rex393-fiction-index`/`rex393-fiction-snapshot` (separate operator KEEP word, unchanged) + `r42-gate-*`. No config deltas rode this train. Only remaining non-blocking await = ratatoskr-dev's chatterbox-fast knob revert. Replied confirming (`01KZJ9GMCC…`).
 
 - `[2026-08-07]` **chatterbox-fast "broken audio" root-caused (T3 AR tail over-run) + FIXED (max_chunk_chars=250 cap, :v2 deployed).** Long saga, operator-driven clean diagnosis. **Symptom:** ratatoskr's migrated RP-surface TTS "swaps to German" / "dead air" / "garbage" on long turns. **NOT** German-leak (Turbo `generate()` has NO language param — plain AutoTokenizer, no `language_id`; the multilingual `language_id="en"` lever lives only in the separate `ChatterboxMultilingualTTS`), **NOT** OOM alone. **Real cause:** the Chatterbox **Turbo T3 model OVER-RUNS its generation tail** — a long single `generate()` degrades into garble/dead-air in its final ~2-3s (lib filters OOV tokens `<6561` + pads silence = messy AR tail). The scheduler's buffer-ratchet builds 300-600 char mega-chunks that land in that zone; streaming concatenates each bad tail (worst case). **ratatoskr's anti-"German" knobs (top_k=80/temp=0.5) made it WORSE** — tight sampling pulls the degradation onset SHORTER (~200 chars vs ~300 at default knobs). **Diagnosis method** (deterministic, no ears-only): single-shot length sweep + **amplitude-gated voiced-ZCR** (garble spikes ZCR; must gate on |x|>500 else trailing silence confounds it) — degraded voiced-tail = 1.58× mid, clean = ~0.64-1.1×. **FIX:** server-side `max_chunk_chars=250` cap on the scheduler (`:v2` image, `CBF_MAX_CHUNK_CHARS=250` env) — bounds each generation to just under the ~300-char onset → clean **3-4 sentence** chunks (max prosodic arc while clean). Operator ear-confirmed clean audio + clean joins; **chatterbox's low emotiveness keeps chunk joins smooth** (the harsh joins that got Zonos rejected are absent — operator's key call). **ratatoskr TODO (relayed msg `01KZER9X7S`):** revert knobs to default (top_k→1000, temp→0.8), send full text (server chunks internally), keep the 503-on-empty guard. **Cap value tunable** per-request (`max_chunk_chars`) + env. **Deeper prosody** (if ever wanted) = scheduler Phase-2 context-priming at joins (feed prior sentence as discarded-audio context; +latency). **⚠ FOOT-GUNS:** (1) acoustic tail-trim is UNRELIABLE — sibilants ('s'/'sh'/'f') spike ZCR like garble, can't cleanly detect the speech→garble boundary. (2) **build-context vs image drift** — the `:v2` image was built from cap source, but after a `:v1` rollback the build context held `:v1` source → a `docker compose build` would've silently produced a cap-less `:v2`; re-synced the flat cap source to `/opt/docker/compose/chatterbox-fast/` (rebuild-verified). **⚠ DIVERGENCE (follow-up):** deployed build context is FLAT (`app.py`/`scheduler.py`, `from scheduler import`, thin-overlay `FROM local/chatterbox:v1`, cap-only) vs the `vh/chatterbox-fast` REPO which is PACKAGE-layout (`chatterbox_fast/`, `from chatterbox_fast.scheduler`, self-contained Dockerfile) + has `norm_loudness` (repo commit `6bc7bf0` = cap; deployed omits norm_loudness deliberately to keep the ear-test unconfounded). Reconcile the two layouts so a repo-based rebuild matches deploy. Rollback: `.bak-cap-20260807-104850` backups on irv-ml1 + `:v1` image both retained. [[reference_chatterbox_fast_repo]] [[reference_zonos_tts_stack]]