README.md

---
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.