--- language: - en license: mit tags: - keyword-spotting - wake-word - audio-classification - liquid-neural-network - edge-ai - onnx - awareliquid pipeline_tag: audio-classification --- # O1-Sound **A greeting wake-word spotter built on the O-Series liquid core.** Always-on keyword spotting on a battery: the model listens continuously, holds a fixed-size state no matter how long the microphone has been open, and fires when it hears a greeting — "hello", "hola", "bonjour", "你好". ``` mic → log-mel (fixed, no weights) → liquid core (multi-timescale τ) → wake / not-wake ↑ carried state: 5,120 bytes, constant ``` ## Files | File | Description | |---|---| | `o1sound.pt` | Binary wake/not-wake research checkpoint (Run 7, 2026-08-18), 4.8 MB | | `o1sound_multiclass.pt` | Multiclass experiment checkpoint (2026-09), one class per greeting + "other" | | `o1sound.onnx` | Streaming ONNX graph (fp32, single-frame `step()`), 5.04 MB | | `o1sound.metrics.json` / `o1sound_multiclass.metrics.json` | Per-language metrics for each checkpoint | | `o1sound_multiclass_v2.pt` | Multiclass v2 checkpoint (Run 8, 2026-09-29), 9.8 MB — hidden 896, 40 epochs, 400 negatives | | `o1sound_neg_v1.pt` | Binary v3 checkpoint, real negative audio (Run 9, 2026-10-04), 9.8 MB — FAR ≤ 1% on the held-out test (0.97% @ thr 0.9906) | | `o1sound_neg_v2.pt` | v3 full-negative retrain (9,706 clips) — honest negative result (class imbalance broke recall; dev balanced 0.732) | | `o1sound_neg_v1.metrics.json` / `o1sound_neg_v2.metrics.json` | Dev metrics for the v3 checkpoints | | `test.json` / `test_multiclass.json` / `test_v2.json` / `test_neg_v1_exact.json` | Held-out test results (Run 7 / multiclass / Run 8 / Run 9 exact sweep) | ## Honest status — what is and isn't validated **Validated (runs today, in CI)** - 32 tests pass: streaming `step()` is numerically identical to the batched `forward()`, carried state stays constant over 500 frames. - **ONNX export: 5.03 MB fp32, 1.27 MB int8**, max |onnx − torch| = **3.7e-09**. - **Carried state: 5,120 bytes per stream**, independent of stream length — a property of the recurrence, verified by test. **Measured — the architecture works, the multilingual framing does not (yet)** - **Run 2** (2026-08-01, English only, 301 wake clips): **FRR 0.146 at FAR 0.046** on held-out test; dev accuracy 0.919 against a 0.780 never-fire baseline. - **Run 1** (2026-07-31, 9 languages, 91 wake clips): **FRR 0.909 at FAR 0.049** — worse than saying no unconditionally at that data scale. - **Run 7** (2026-08-18, 10 languages, full MSWC): dev acc **0.885** (balanced 0.849); English is the only language with meaningful data (FRR 0.098 at FAR 0.133, 41 clips). Worst-language FRR is 1.000 (2 positive clips) — the multilingual claim is bounded by per-language data, not the architecture. - **Multiclass experiment** (2026-09, 10 languages, OR-head): dev balanced accuracy 0.84; test FRR 0.35 @ FAR 0.046 overall — multilingual remains unsupported at this data scale. - **Run 8 (v2, 2026-09-29, GPU A100)** — improved recipe (hidden 896, 40 epochs, 400 negatives, multiclass OR inference): dev acc **0.838**, dev balanced **0.8815**, worst-language dev FRR **0.600** (sv-SE; Run 7: 1.000). On the held-out test split **no operating point reaches FAR ≤ 1%** — the production bar was still unmet at this point. Checkpoint: `o1sound_multiclass_v2.pt` (9.8 MB, in this repo). - **Run 9 (v3, 2026-10-04, real negative audio)** — Speech Commands extras + full MSWC negative pool (3,000 balanced clips in training): dev balanced **0.8913** (Run 8: 0.8815), worst-language dev FRR **0.500** (Run 8: 0.600). On the held-out test set (167 pos / 1,859 neg) the **FAR ≤ 1% bar is crossed: FAR 0.97% (18/1,859) at thr 0.9906** — once the threshold sweep is exact (candidates = all unique scores; the earlier "1.02% minimum" was grid quantization, not a model limit). At thr 0.9994, FAR reaches 0 (0/1,859) with FRR 0.81. Honest note: the full 9,706-clip retrain (60 epochs) is a **negative result** — class imbalance (~1:10) collapsed dev balanced accuracy to 0.7319; the balanced 3,000-clip sample (v3) is the best configuration. Checkpoints: `o1sound_neg_v1.pt` (best), `o1sound_neg_v2.pt` (kept for honesty). The multilingual claim remains bounded by per-language data (worst-language test FRR 1.000 at 2–3 clips), independent of this FAR fix. **Not production-grade.** Deployed wake words run single-digit FRR at a false-accept rate quoted per hour, not per clip. Latency and power are unmeasured on real hardware (the Python `step()` loop runs ~0.5 ms/frame on a desktop CPU). On out-of-domain audio (e.g. pure tones) it can false-trigger. Run 9's FAR ≤ 1% is a per-clip held-out test figure; the per-hour field equivalent is not measured. ## Model sizes (measured, n_classes=2) | hidden | layers | params | fp32 | int8 | carried state | |-------:|-------:|-------:|-----:|-----:|--------------:| | 384 | 2 | 483,984 | 1.94 MB | 0.48 MB | 3,072 B | | 512 | 2 | 841,872 | 3.37 MB | 0.84 MB | 4,096 B | | **640** | **2** | **1,298,064** | **5.19 MB** | **1.30 MB** | **5,120 B** | | 512 | 3 | 1,367,184 | 5.47 MB | 1.37 MB | 6,144 B | | 768 | 2 | 1,852,560 | 7.41 MB | 1.85 MB | 6,144 B | `hidden=640, layers=2` is the default: the largest 2-layer configuration that still clears 7 MB in fp32 once ONNX graph overhead is counted. ## Usage The exported ONNX graph is the single-frame `step()` — one frame in, logits and the next state out. Full training / export / streaming-demo code lives in the GitHub repository: - Code, training and streaming demo: [AwareLiquid/O1-Sound](https://github.com/AwareLiquid/O1-Sound) - Research release notes: [release `research-2026-08-18`](https://github.com/AwareLiquid/O1-Sound/releases/tag/research-2026-08-18) ## Why a liquid core Each channel carries its own learnable time constant τ, parameterised as `softplus(log_tau) + tau_min` and initialised geometrically across 10–240 ms. Short-τ channels track the current phoneme; long-τ channels hold the envelope of the whole word — separating a wake phrase from a phonetically close neighbour without stacking depth. Streaming is O(1): the state does not grow with how long the microphone has been open. ## Related - [AwareLiquid/M1](https://github.com/AwareLiquid/M1) — the MT-LNN / O-Series research line this core comes from - [AwareLiquid/M2](https://github.com/AwareLiquid/M2) — next-generation architecture research - [awareliquid.ai](https://awareliquid.ai) — benchmarks and retractions ## Try it - 🎮 Interactive demo (record or upload audio, runs in your browser): https://huggingface.co/spaces/AwareLiquid/O1-Sound-demo ## License MIT.