memory: snapshot — chatterbox-fast streaming build (adaptive buffer-ratchet chunking); native-turbo-streaming abandoned; fish reference_id fix + glados transcript + NFS/smithy + references-togglable; add design doc
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# Chatterbox-Fast — Streaming TTS Engine (design)
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**Status:** Draft / pre-contract design. Spike-validated 2026-06-02.
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**Owner:** infra-ops · **Workload (operator-confirmed):** single-stream interactive.
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## 1. Goal
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Make Chatterbox-Turbo our **primary interactive TTS engine** by cutting
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time-to-first-audio from today's ~2.5 s to ~0.3–0.5 s via **true
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incremental streaming**, while preserving turbo's quality, inline
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paralinguistic tags, and voice cloning.
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## 2. Why now — the proven result
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Spike (2026-06-02, turbo on the A6000), proper CUDA-sync timing:
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| | time-to-first-audio |
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|---|---|
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| today (devnen, no real streaming) | **~2.5 s** |
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| windowed generate, K=20 tokens | **0.31 s** (delivers ~0.96 s audio) |
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| K=30 | 0.41 s |
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| K=50 | 0.57 s |
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~**8× TTFB win.** Decode cost is ~constant **0.12 s** regardless of chunk
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size (turbo's 2-step decoder), so first-chunk time is dominated by
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generating the first K tokens — smaller K = faster first audio. Full
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generation runs at **4.2× realtime**, so once the first chunk plays the
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generator stays well ahead of playback and the stream never starves.
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## 3. Non-goals
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- High-concurrency **batch throughput** — that's a separate base-on-vLLM
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lane; deferred (research: vLLM port doesn't support turbo).
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- **Multilingual** — turbo is EN-only; out of scope.
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- **Quantization** — research flags it high-risk (gibberish < Q8, Q8-CUDA
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broken); deprioritized.
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## 4. Background — turbo internals (from the spike)
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- `generate()` = `t3.inference_turbo()` (AR loop, all tokens) →
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`s3gen.inference(..., n_cfm_timesteps=2)` (flow + HiFT vocoder, all tokens).
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- `inference_turbo` is a **plain-Python `for` loop** with a KV cache, a
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`max_gen_len` param, and a stop-token break — cleanly hookable.
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- `s3gen` decode is cheap per-window (~0.12 s constant).
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## 5. Architecture
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A **lean, purpose-built FastAPI server on the `chatterbox` library** —
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*not* a fork of devnen. devnen buffers the entire synthesis before
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emitting (proven: opus/mp3/wav all return first byte at full-synth time);
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we need to own the generate loop. Components:
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1. **Model holder** — `ChatterboxTurboTTS` loaded once at startup, warmed.
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2. **Streaming generate** — windowed wrapper around `inference_turbo`:
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yields token windows; each window decoded via `s3gen` → audio chunk →
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streamed. (~40 lines on top of the lib, per the spike.)
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3. **Voice management** — predefined voices (dir of wavs) + clone refs
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(the `prepare_conditionals` path); reuse chatterbox's `Conditionals`.
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4. **HTTP API** — `POST /tts` (streaming + non-streaming), optional
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OpenAI-compat `/v1/audio/speech`, `/health`.
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5. **Watermark** — Resemble PerTh (mandatory); applied per-chunk or post.
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### Decision 1 — streaming transport
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**Recommend: HTTP chunked transfer of raw PCM** (client plays chunks as
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they arrive). Lowest latency, trivial client. Offer opus for
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bandwidth-constrained callers. *Not* websocket (one-way; overkill).
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### Decision 2 — seam handling (the key productionization detail)
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Independent per-window decode (as in the spike) can leave faint **seams**
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at chunk boundaries because the vocoder has receptive-field context.
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**Approach: overlap-discard** — decode each window with a small lookback
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of the previous window's trailing tokens, discard that overlap's audio,
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keep only the new window's output. (The `davidbrowne17/chatterbox-streaming`
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fork uses this pattern.) Tune the overlap for inaudible seams vs latency.
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**Validate** by ear + a spectral seam check.
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### Decision 3 — chunk schedule
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First chunk **small** (K≈20–25 → ~0.3 s first audio); subsequent chunks
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**larger** (K≈50–100) for decode efficiency, since after chunk 1 we're
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ahead of playback. A simple ramp.
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## 6. Performance levers (fold in, measure each)
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- **bf16** (Ampere-safe), **TF32** (matmul), **SDPA/flash** backend on the
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Llama backbone — low-risk, measure the delta.
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- **torch.compile** — **DEFER.** Research flags a real batch-1 regression
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risk (documented 0.85× at batch-1). Benchmark separately; adopt only if
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it beats eager on our hardware. Not on the critical path.
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## 7. Deployment
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- New stack **`chatterbox-fast` deployed ALONGSIDE** the existing
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`chatterbox` (zero disruption; A/B then cut over).
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- **Port:** 8197 (next free on irv-ml1).
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- **GPU placement (decided 2026-06-02):** **3090 (device 0) if it fits,
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else A6000 (device 1).** The GPU stack is a shared dev stack — workloads
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float across cards, so the 20.5 GB-at-idle on the 3090 is expected
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residency, not a blocker. Fit is borderline: turbo is ~2.5 GB but the
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3090 currently shows ~3.5 GB free, so the deploy step **tries the 3090,
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falls back to the A6000 (device 1, ~30 GB free, shares with Fish) on
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OOM.** Pin via `device_ids` in compose per fleet convention.
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- **From-source Dockerfile** (chatterbox lib + our server), pinned.
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## 8. Benchmark / A-B gate (deploy guard, like the Fish reference_id gate)
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- **first-audio (TTFB)** under target (e.g. < 0.6 s on the deployment GPU).
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- **realtime factor** maintained (> 3×).
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- **quality parity** vs current chatterbox — ECAPA speaker-sim for clone
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voices, listen test for predefined, spectral **seam** check.
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- Wire as a hard gate in the deploy playbook.
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## 9. Risks / open questions
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1. **Seam artifacts** — mitigation: overlap-discard decode; validate by ear + spectral.
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2. **torch.compile batch-1 regression** — mitigation: benchmark, optional.
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3. **3090's 20.5 GB-at-idle** — RESOLVED (non-issue): shared dev stack, expected residency. Placement decided (§7): 3090-if-fits-else-A6000.
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4. **PerTh watermark on short chunks** — confirm no artifacts per-chunk.
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5. **Paralinguistic tags across chunk boundaries** — confirm a tag split
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across windows doesn't break delivery.
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## 10. Build plan (phases)
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0. **Spike** — DONE, proven (§2).
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1. **Streaming server MVP** — windowed generate + overlap-discard seam
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handling + `/tts` streaming endpoint; bench first-audio + seam quality.
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2. **Parity + perf** — predefined + clone voice management; bf16/TF32/SDPA;
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per-chunk watermark.
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3. **Containerize + deploy** — from-source Dockerfile; deploy `chatterbox-fast`
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alongside; wire the A-B gate.
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4. **Cutover** — switch the catalog route; burn-in; deprecate the old stack.
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## 11. Open decisions for operator
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- ~~GPU placement~~ — **DECIDED (2026-06-02):** 3090 if it fits, else A6000 (§7).
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- ~~Cutover strategy~~ — **DECIDED (2026-06-02): parallel catalog entry**,
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burn-in beside the live `chatterbox`, then flip the route once it earns
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trust. Phases 1–3 are cutover-agnostic; the flip happens in Phase 4.
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