jax-realtime - GitLite

KO | EN

gitlite — search

#c #computer-vision #sql #bash #awesome #tensorflow #ui #video #golang #chatbot

jax-realtime

(★ 9)

Full‑duplex realtime voice assistant in your browser with jax‑js

파일 탐색기

ZIP 다운로드

- - orb.png
- BENCHMARKS.md
- DUPLEX_SPEC.md
- - silero-vad-v5.bin
- - commit.test.ts
  - commit.ts
  - decoding.ts
  - features.ts
  - model.ts
  - streaming.ts
  - tokenizer.ts
  - vad.ts
- - smollm.ts
- - tools.ts
- - audio.ts
  - inference.ts
  - pocket-tts.ts
- - coco.ts
  - detector.ts
  - vision.ts
- duplex.ts
- float16-polyfill.ts
- main.ts
- memory.ts
- mic.ts
- orb.ts
- pipeline.ts
- sentence-split.ts
- style.css
- tunables.ts
- sentence-split.test.ts
.gitignore
.nvmrc
index.html
LICENSE
netlify.toml
package-lock.json
package.json
README.md
tsconfig.json
vite.config.ts

# CDN으로 사용하기

jsDelivr

jsDelivr는 공개 GitHub 리포지토리를 별도 설정 없이 CDN으로 즉시 서빙합니다. 버전과 파일을 고르면 웹페이지에 바로 붙일 수 있는 링크와 예시 코드가 만들어집니다.

버전

파일

링크

예시

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

A real-time, full-duplex voice assistant that runs **entirely in your browser**
on WebGPU, built with [jax-js](https://github.com/ekzhang/jax-js).

Every stage (speech → ASR → LLM → TTS → speech, plus optional vision) runs
locally in the tab; nothing is sent to a server.

<img src="docs/assets/orb.png" alt="jax-realtime: the idle orb, ready to talk" width="75%" />

It's inspired by the Thinking Machines
[interaction model](https://thinkingmachines.ai/blog/interaction-models/) and
[GPT-Live](https://openai.com/index/introducing-gpt-live/), rebuilt as a
small-model cascade that fits in a browser tab. The goal is a conversation
that *feels* live: you can interrupt it mid-sentence, pause mid-thought
without losing your turn, and it keeps searching in the background while you
talk.

| Stage | Model | Runs on |
| --- | --- | --- |
| Ear (ASR) | [Whisper base.en](https://huggingface.co/openai/whisper-base.en) (int8, dequantized to fp16) | WebGPU via jax-js |
| Turn-taking (VAD) | [Silero VAD v5](https://github.com/snakers4/silero-vad), ported to TypeScript | CPU (~2 ms / 32 ms frame) |
| Brain (LLM) | [SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) (fp16) | WebGPU via jax-js |
| Voice (TTS) | [Kyutai Pocket TTS](https://huggingface.co/kyutai/pocket-tts-without-voice-cloning) + [Mimi](https://github.com/kyutai-labs/moshi) codec (fp16) | WebGPU via jax-js |
| Eye (vision) | [D-FINE small](https://huggingface.co/bukuroo/D-FINE-ONNX) (COCO-80) | WebGPU via `@jax-js/onnx` |

Everything shares the single WebGPU device. The streaming ASR lane is paused
while the assistant speaks so it doesn't contend with TTS for the GPU; barge-in
is therefore energy-based (see below), and captions resume the moment the
assistant stops.

## Interaction

- **Full-duplex micro-turns**: a ~150 ms tick loop drives a deterministic,
  priority-ordered policy: adaptive **barge-in** (talk over the assistant,
  including its tool narrations, and the audio cuts in ~300 ms; the threshold
  auto-calibrates to the echo floor of each reply), adaptive **endpointing**,
  and time-awareness timers. A watchdog force-recovers the session if a reply
  ever stalls, so it can't wedge.
- **Continuation-merge**: if the endpoint fires on a mid-thought pause and you
  resume speaking before the reply's first audio, the unheard reply is aborted
  and both halves are answered as one turn ("append, don't restart").
- **Learned turn signal**: a pure-TypeScript port of Silero VAD v5 scores
  P(speech) every 32 ms on the CPU and drives speech onset, silence tracking,
  and the phantom-turn guard: keyboard noise and ambient swells never even
  latch an utterance, near-silence never reaches Whisper (so no hallucinated
  "thank you"s), and quiet speech still passes. A repetition-degeneracy gate
  drops decoder loops on top.
- **Eye (vision)**: enabled by default for webcam context, with a pre-load
  toggle to skip its 42 MB model, camera access, and GPU residency. D-FINE
  runs low-priority object detection (it yields the GPU to audio), smooths the
  person count, and answers
  "what do you see?" / "how many people?" / "tell me about the person"
  directly from the measurements. Proactive interjections (stepped away,
  phone spotted, slouching) are best-effort rule heuristics. The webcam shows
  as a corner PiP with detection boxes.
- **Typed conversation memory**: bounded facts the user explicitly states
  (name, trip, pet, favorite, plans, relationships) are retained and injected
  only when relevant; exact recall bypasses small-model guessing.
- **Two-tier tools**: factual asks are delegated so the small on-device model
  isn't left guessing: weather ("what's the weather in Tokyo" → [open-meteo](https://open-meteo.com/),
  in °F/mph), facts ("who is Ada Lovelace" → Wikipedia), plus instant offline
  **calculator** and **clock/date**. Web lookups speak a holding line and fetch
  in the background, then answer on the next silence and render a card; the
  card clears when the conversation moves on, and the spoken answer can be
  interrupted like any reply.

## Performance (all on jax-js / WebGPU)

The turn-latency floor is set by the single GPU, so the work went into cutting
GPU cost per token/frame rather than overlapping stages (which a single device
can't do; see [`docs/BENCHMARKS.md`](docs/BENCHMARKS.md) for the full
map-reduce campaign log, including the negative results):

- **Fused decode**: the LLM decode step is fused from dozens of per-layer jit
  dispatches into one, and Pocket TTS from ~11 into two, cutting the
  command-buffer submit overhead that dominated per-step cost (~22% each).
- **GPU top-k sampling**: the LLM samples from a device-side top-64 (one small
  readback) instead of transferring the full vocab logits every token,
  folded into the fused step's single dispatch.
- **Stable prefill shapes**: every turn has a different prompt length, which
  otherwise forces jax-js to compile new traces mid-conversation. Prompts use
  256-token buckets, the common buckets are warmed during loading, and the KV
  cache has one fixed capacity, so long conversations never hit a
  multi-second recompile stall mid-turn.
- **Faster confidence-aware ASR**: timestamp-gate candidate reductions are
  reused for confidence scoring instead of scanning the vocabulary again. ASR
  runs 5–7% faster while preserving all 21 paired clean/quiet/distorted
  transcripts; low-confidence failures request a repeat before invoking the LLM.
- **Bounded history window**: the prompt is capped at 8 messages so it never
  leaves the warm prefill buckets: first-token latency stays flat (~500–610 ms)
  across long sessions instead of doubling around turn 6, and the worst turn of
  a 12-turn session sits within ~150 ms of the median.
- **Deterministic memory fast paths**: exact recall and bounded trip, pet, and
  activity follow-ups can answer in a few milliseconds without model generation.
- **Smaller download**: Whisper ships a per-row int8 build (73 MB instead of
  144 MB), while the TTS checkpoint omits 35 MB of audio-encoder weights never
  used for synthesis. The SmolLM2 brain stays full fp16 for conversation quality.

Runtime behaviour is tunable at `src/tunables.ts` (read live, so A/B
experiments don't need a rebuild).

## Hard-won details

- **The assistant used to interrupt itself.** The barge-in threshold
  calibrated its echo floor during the reply's first ticks, which fall in the
  silent LLM/TTS latency gap, so the floor was ambient-level and the
  assistant's own playback echo tripped it. Calibration now only counts ticks
  where the TTS output analyser confirms audio is actually playing.
- **Whisper invents "Thank you." from silence.** Every Whisper size does this
  (silent-outro captions in its training data), and it decodes with *high*
  confidence, so no model swap or confidence gate catches it. The fix is
  signal-side: unvoiced audio never reaches the decoder, on every endpoint
  path, including barge-in continuations, which originally skipped the guard.
- **A mid-sentence TTS cut sounds like a full stop.** Each chunk is
  synthesized as a complete utterance, so flushing at a bare word boundary
  produces sentence-final falling intonation plus a pause in the middle of
  your sentence. Chunks now only split at real punctuation: a measured
  latency cost, paid for prosody.

## Run it

```sh
npm install
npm run dev
```

Open http://localhost:5173 in a WebGPU-capable browser (Chrome/Edge on desktop,
Safari 26+). Click **Load models**: ~1.0 GB on first run, cached in OPFS
afterwards. The Eye is on by default and requests camera access; uncheck it
before loading to skip it. Then press the orb once and just talk, hands-free.
Press it again to end.

Weights come from
[Hugging Face](https://huggingface.co/sachink98/jax-realtime-weights): SmolLM2
at full fp16 for conversation quality, Whisper as a per-row int8 build
dequantized at load (it transcribes the bench suite identically to fp16).

## How it works

The pipeline stages, from microphone to speaker:

| Path | What's there |
| --- | --- |
| `src/mic.ts` | 16 kHz PCM capture via an AudioWorklet. |
| `src/asr/` | Whisper encoder/decoder and features. `streaming.ts` transcribes live (LocalAgreement-2: words lock in once two passes agree) and filters out the assistant's own voice. `vad.ts` is the Silero VAD v5 port. |
| `src/llm/smollm.ts` | SmolLM2-360M forward pass with a KV cache: one fused GPU dispatch per token, bucket-padded prefill so jit traces are reused across turns. |
| `src/memory.ts` | Extraction and deterministic recall of facts the user shared. |
| `src/tts/` | Pocket TTS flow-matching LM + the [Mimi](https://github.com/kyutai-labs/moshi) codec on jax-js, streamed to an `AudioContext` player. `src/sentence-split.ts` chunks reply text at clause boundaries for synthesis. |
| `src/vision/` | D-FINE detector on `@jax-js/onnx`, webcam `VisionSession`, COCO labels, box-dedupe and person-count smoothing. |
| `src/tools/tools.ts` | Keyless intent detection → weather / Wikipedia / calc / clock. |

## License

[MIT](LICENSE). Model inference code is adapted from the
[jax-js repository](https://github.com/ekzhang/jax-js/tree/main/website/src/routes)
by Eric Zhang (MIT licensed); model weights remain under their respective
licenses.