ECG RVQ · 64×

Reproduce the ecg-rvq-64x configuration. Prepare its dataset before running the workflow; use the linked configuration to inspect the exact settings.

Overview

The v1.1 bundle corrects the export of the existing checkpoint without retraining. Its physiological scorecard remains historical; see release validation.

Modality: ECG
Structure: codec
Target compression ratio: 64×
Sample rate: 256 Hz
Recipe: train-ecg-rvq
Config: configs/ecg_rvq_256hz_64x_golden.yaml
Run name: ecg_rvq_256hz_64x_golden
Hugging Face target: Ambiq/compressionkit-ecg-64x-v1.1

Dataset & License

Dataset: PTB-XL (dataset_id: ptb-xl)
License: CC BY 4.0 (open)
Notes: Auto-downloaded on first use.

The lifecycle runner pre-flights dataset availability before training (see the dataset contract).

Reproduction

# Single command, end-to-end.
uv run compressionkit golden run ecg-rvq-64x

Results land under results/ecg_rvq_256hz_64x_golden/; deploy artifacts under results/ecg_rvq_256hz_64x_golden/deploy/.

Evaluation Metrics

See the modality model zoo for the full metrics table:

ECG models

Evaluation writes quality_scorecard.json and summary.json under its results/<run>/.

Deploy Artifacts

The deploy package contains the following artifacts, depending on the export configuration:

encoder.tflite / encoder.h — INT8 encoder.
encoder.keras — float32 Python reference encoder.
decoder.tflite / decoder.h — decoder (float32 + optional INT8).
decoder.keras — float32 Python reference decoder.
codebook.npz / codebook.h — RVQ codebook tables.
sample_data.npz — normalized synthetic inputs, targets and reconstructions; sample_stimulus.npz contains raw synthetic waveforms.
model_card.json, deploy_manifest.json — metadata.

Customization Notes

Tweak the YAML to explore neighbouring operating points; copy the file before editing.
For new recipes, prefer the compressionkit/recipes/ package recipes as a starting point.
To resume publishing without retraining, pass --skip-train to compressionkit golden run ecg-rvq-64x --publish.