| # LATENCY — benchmarking methods & budget tables |
|
|
| ## MEASURED — MVP slice, 2026-10-04, cpu-upgrade (8 vCPU cgroup quota, CPU-only) |
|
|
| End-to-end run `ml-intern-freestream-e2e-speculative` (final: job `6ac2e756404719ba37651973`, |
| two turns, speculative dispatch ON, ASR tiny.en @1 thread, clause-level chunking): |
| livekit-server v1.13.7 dev mode in-container, bot joined via the real CLI, |
| test client asked spoken questions, timings measured at the receiving client. |
|
|
| | Metric | Measured | Target | Verdict | |
| |---|---|---|---| |
| | **t_perceived, turn 1** (speculative + cached disclosure) | **2.94s** | ≤ 4.0s (win condition) | **PASS** 🔥 | |
| | t_perceived, turn 2 (speculative, dynamic reply) | 5.79s | — | both turns via speculative path | |
| | t_perceived, roleplay mode (LLM agent in the loop) | 7.1–7.4s | relaxed (12s) | HF router generation dominates | |
| | t_ready (CLI join → bot ready, incl. warmup) | 12.5s | — | — | |
| | ASR finalize (tiny.en int8, 1 thread) | 1.25–1.46s | 400–700ms | over; standalone RTF 0.25 | |
| | First-chunk synth, turn 2 | 4.09s | 200–400ms | clause-capped (was 7.47s) | |
| | bot→client transport gap | 0.04s (t1) / 0.45s (t2) | — | negligible | |
| | cgroup CPU throttling during turns | **0 periods, 0ms** | — | contention myth settled | |
| | Mouth movement | 13/13 checks incl. both turns | >0 | PASS | |
| | Replay logs | both hash chains verify | valid | PASS | |
|
|
| Findings from the profiling campaign: |
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|
| 1. **cgroup oversubscription is the #1 CPU pitfall**: the container reports |
| `os.cpu_count()=64` under an 8-core quota. Whisper tiny at 8 threads |
| measured **39.5s** vs **1.16s** at 1 thread (same transcript). Thread |
| counts are pinned low (FREESTREAM_WHISPER_THREADS). |
| 2. **The fixed consent disclosure is pre-synthesizable** (CONSENT.md #2): |
| caching it at session start removes Piper entirely from the first turn's |
| audio path. |
| 3. **bot→client transport is nearly free (0.04–0.5s)** — measured with |
| absolute cross-process timestamps. |
| 4. **Speculative dispatch works** (concept mitigation #1): dispatching ASR + |
| agent + first-chunk synth at ~300ms of silence (with cancellation on |
| speech resume and dedup against the final event) took turn 1 from 3.12s |
| to **2.94s**. Both measured turns completed via the speculative path; the |
| cancellation path is exercised by the segmenter's "resumed" event. |
| 5. **The contention question is settled: zero cgroup throttling** |
| (cpu.stat deltas across turns: 0 periods, 0ms). The in-context vs |
| standalone RTF gap is not quota starvation — it is cold-start effects |
| (page cache, allocator warmup) and benign thread scheduling. The ORT |
| thread-cap countermeasure was not needed (knob FREESTREAM_ORT_THREADS |
| remains for other hosts). |
| 6. **First-chunk synth length was the real turn-2+ bottleneck**: a 99-char |
| first sentence cost 7.47s of synthesis. Clause-level chunking (cap ~60 |
| chars at comma boundaries) cut it to ~4s. Later chunks pipeline behind |
| playback. |
| 7. **Quantization verdict — asymmetric**: int8-dynamic-quantizing the Piper |
| voice (63.2 → 18.7 MB) made TTS **3–4× slower** (RTF 2.50–2.98 vs 0.79–0.86 |
| fp32): dequant overhead dominates the conv-heavy VITS graph on this CPU |
| (no VNNI). Rejected here; artifact + verdict archived at |
| [LoveLogicAI/piper-en_US-amy-medium-int8](https://huggingface.co/LoveLogicAI/piper-en_US-amy-medium-int8). |
| ASR was already int8 (CTranslate2) and got faster via English-only tiny.en |
| @1 thread. |
| 8. **whisper tiny.en vs base accuracy**: base transcribed the bot name |
| "Streamy" correctly; tiny.en hears "Streami". The latency profile uses |
| tiny.en; base (+~2s ASR) is the accuracy upgrade path. |
|
|
| Remaining levers, in order of expected yield: (a) the ~0.9s of unexplained |
| utterance-finalization lag beyond the 600ms hangover; (b) in-context ASR/synth |
| contention (standalone RTF 0.25/0.79 vs in-context ~0.5/~1.2) — the render |
| loop is exonerated, so the suspect is WebRTC encode + thread interference; |
| (c) the concept's speculative dispatch at ~600ms silence (overlaps agent |
| response with utterance tail) — the largest structural win for turn-2+, |
| but a bigger build. |
|
|
| The tables below are the original design targets, kept for reference. |
|
|
| ## Budget (from the concept package; measured results above) |
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|
| These are the *targets* the MVP slice was designed against; the MEASURED |
| section above is the current scorecard against them. |
|
|
| | Stage | Local CPU-only | Local + GPU/ANE | Pika (published) | |
| |---|---|---|---| |
| | ASR finalize | 400–700ms | 150–250ms | — | |
| | Agent first token | 300–900ms | 150–400ms | — | |
| | TTS first chunk | 200–400ms | 80–150ms | — | |
| | Avatar render | 500–900ms | 200–350ms | — | |
| | **Perceived turn** | **~2.5–4.5s** | **~1.2–2.0s** | **~1.5s** | |
|
|
| ## The mitigations that make 2.5s feel like 1.5s |
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|
| 1. **Speculative dispatch at ~600ms of silence** — partial ASR transcripts |
| go to the agent before the utterance finalizes; resumed speech cancels |
| the generation. Saves 200–600ms. |
| 2. **Sentence-level pre-roll** — the agent's first sentence starts TTS |
| while later sentences are still generating. Saves one TTS round-trip. |
| 3. **Lead-out motion** — avatar mouth motion starts ~150ms before audio |
| playout, perceptually erasing the render handshake. |
| 4. **Cached identity** — avatar/voice assets are loaded and warmed at |
| session start, never per turn. |
|
|
| ## Fidelity ladder rungs & FPS targets |
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|
| | Rung | Name | Target FPS | Host | |
| |---|---|---|---| |
| | 4 | FULL_PORTRAIT | 20–24 | GPU / Apple ANE | |
| | 3 | VISEME_STILL | ≥15 (MVP) | CPU | |
| | 2 | AUDIO_REACTIVE_WAVE | ≥10 | CPU (weak) | |
| | 1 | STATIC_CARD | n/a | anything | |
| |
| Rung selection is `select_rung()` at join time; runtime degradation |
| triggers when measured FPS < target for 5 consecutive seconds (event: |
| `persona.rung_change`). |
|
|
| ## Benchmark harness (MVP acceptance) |
|
|
| `python benchmarks/turn_latency.py` (lands with the MVP slice) measures, |
| per turn over ≥50 synthetic Q&A turns: |
|
|
| - `t_asr_final`: speech-end → final transcript |
| - `t_first_token`: transcript → agent first sentence |
| - `t_tts_chunk`: sentence → first audio chunk |
| - `t_avatar_frame`: audio chunk → first frame on the virtual camera |
| - `t_perceived`: speech-end → first audible+visible output |
|
|
| Win condition from the concept: **agent joins, answers within 4s with a |
| moving mouth, leaves, and produces a valid replay log** — i.e. |
| `t_perceived` p50 < 4.0s on CPU at rung 3, and |
| `EventLog(...).verify() == (True, None)`. |
|
|
| ## Reporting rules |
|
|
| - Report p50/p95, machine, and library versions with every number. |
| - Faster-whisper int8, Piper medium voices; note thread counts. |
| - If FULL_PORTRAIT can't hold 20 FPS on Apple Silicon, video becomes a |
| cloud-burst feature and rung 3 becomes the local ceiling — that is a |
| finding to report, not a failure to hide. |
