# Tiny Hinglish Turn Detector — ONNX preview

This is a flat, dependency-light Kaggle Models bundle for audio-native
`HOLD`/`END` decisions at VAD pause checkpoints. Upload every file in this
folder as one ONNX model variation.

> **Development preview:** the model was trained on one of 83 upstream training
> shards. The official test remains sealed, the acoustic logistic baseline is
> stronger on the current development split, and no verified Hinglish benchmark
> recordings have been evaluated. Do not claim production or Hinglish accuracy.

## Kaggle model settings

- Framework: **ONNX**
- Suggested variation: `tiny-tcn-fp32-preview`
- Fine-tunable: **No**
- Visibility: **Private** until upstream-derived-weight redistribution rights
  have been reviewed
- License: **Other (specified in description)**. Apache-2.0 covers authored
  code, not the upstream data or derived-weight rights.

## Files needed for inference

- `model.onnx`: 151,812-parameter FP32 TinyTCN
- `model_metadata.json`: frontend, tensor names, threshold, controller policy
- `turn_detector.py`: standalone NumPy + ONNX Runtime inference
- `requirements.txt`: three runtime dependencies

`MODEL_CARD.md`, `DATA_CARD.md`, `development_metrics.json`, and
`benchmark.json` document the limited evidence. `SHA256SUMS` binds the payload.

## Use inside a Kaggle Notebook

```python
from pathlib import Path
import kagglehub

model_dir = Path(kagglehub.model_download(
    "YOUR_USERNAME/tiny-hinglish-turn-detector/onnx/tiny-tcn-fp32-preview"
))

import sys
sys.path.insert(0, str(model_dir))
from turn_detector import TurnDetector

detector = TurnDetector(model_dir)
result = detector.predict_file("/kaggle/input/your-audio/example.wav", silence_ms=300)
print(result)
```

For a downloaded folder outside Kaggle:

```bash
python -m pip install -r requirements.txt
python smoke_test.py
python example_inference.py path/to/audio.wav --silence-ms 300
```

Input audio may be mono or stereo and is resampled deterministically to 16 kHz.
The model uses the most recent four seconds. `p_end` is compared with the
serialized threshold and bounded by the serialized minimum/maximum silence
policy. This helper makes one stateless checkpoint decision; production callers
should retain the stateful controller from the full GitHub repository to latch
`END` and prevent duplicate responses.