feat(dots-tts): ship OpenAI-compatible dots.tts TTS stack on irv-ml1:8198

Thin FastAPI wrapper over DotsTtsRuntime (soar, optimize=True, RTF ~0.22), serialized single-consumer; OpenAI /v1/audio/speech (stream + non-stream), voices from the voices/ corpus derived set. Live + healthy alongside chatterbox-fast on the 3090; nothing repointed. Dockerfile needs build-essential (torch.compile/inductor JITs via gcc at runtime) + persisted inductor cache. Remaining Phase-2: ratatoskr client cutover.
2026-08-10 01:07:37 -07:00
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+# dots-tts
+
+OpenAI-compatible zero-shot voice-clone TTS over **dots.tts** (rednote-hilab) —
+2B continuous-AR, native **48kHz**, `optimize=True` CUDA graphs → **RTF ~0.22** on
+the irv-ml1 3090. Thin FastAPI wrapper around `DotsTtsRuntime` (chosen over SGLang
+Omni: Omni's batching is MeanFlow-only and unneeded for a single consumer; the raw
+runtime already streams at the same RTF and is ~100 lines we control).
+
+- **Host:** irv-ml1, port **8198** (chatterbox-fast is :8197 — they co-reside on the 3090)
+- **Model:** `dots-studio/dots.tts-soar`, bf16, num_steps=10
+- **Voices:** every `<name>.wav` (+ `<name>.txt` transcript) in the mounted voices dir,
+  sourced from the [`voices/`](../../voices/) canonical corpus via `derive.py dots`.
+
+## API
+
+```
+GET  /health                 -> {status, model, sample_rate, voices[]}
+GET  /v1/voices              -> {voices[]}
+POST /v1/audio/speech        -> audio
+     body: {input, voice, response_format?("wav"|"pcm"), stream?}
+```
+
+`stream:true` returns a WAV stream (placeholder-header + PCM frames, 48kHz mono
+s16le) — the same shape the Zonos/chatterbox consumers already handle. Non-stream
+returns a complete WAV (or raw PCM with `response_format:"pcm"`).
+
+```bash
+curl -X POST http://10.100.79.3:8198/v1/audio/speech \
+  -H 'Content-Type: application/json' \
+  -d '{"input":"Well, look who finally showed up.","voice":"glados"}' \
+  --output out.wav
+```
+
+## Deploy
+
+Reference sets come from the canonical corpus, not this stack — derive then point
+the mount at them:
+
+```bash
+# 1. produce dots refs from the corpus (on a box with the whisper venv):
+python voices/derive.py dots                 # -> voices/derived/dots/*.{wav,txt}
+
+# 2. build + run on irv-ml1 (cp .env.example .env first; adjust mounts):
+scripts/deploy-stack.sh irv-ml1 dots-tts     # or, on the host:
+docker compose build && docker compose up -d
+```
+
+The model (~5GB) is **not** baked — it's read from the mounted `HF_HOME`
+(`DOTS_HFCACHE_DIR`). First boot downloads it there if absent.
+
+## Voice cloning gotcha
+
+dots.tts clones from `(reference wav + its transcript)` and **leaks reference
+audio into the output** if the transcript is inaccurate or ends mid-clause. The
+`voices/` corpus + `derive.py` handle this (sentence-bounded trim + accurate
+transcript); don't hand this server a raw reference wav without a matching `.txt`.
+
+## Notes
+
+- **GPU:** `NVIDIA_VISIBLE_DEVICES=0` = the 3090 in Docker (PCI order). `optimize=True`
+  is **incompatible with `PYTORCH_CUDA_ALLOC_CONF=expandable_segments`** (CUDA-graph
+  capture error) — don't set it.
+- **Variants:** `dots.tts-mf` (MeanFlow, faster) is a drop-in via `DOTS_MODEL`; soar
+  is the quality pick and single-consumer doesn't need mf's batching.