PPG RVQ · 8×
Reproduce the ppg-rvq-8x configuration. Prepare its dataset before running the workflow; use the linked configuration to inspect the exact settings.
Overview
Section titled “Overview”The v1.1 bundle corrects the export of the existing checkpoint without retraining. Its physiological scorecard remains historical; see release validation.
- Modality: PPG
- Structure:
codec - Target compression ratio: 8×
- Sample rate: 64 Hz
- Recipe:
train-ppg-rvq - Config:
configs/ppg_rvq_64hz_08x_golden.yaml - Run name:
ppg_rvq_64hz_08x_golden - Hugging Face target:
Ambiq/compressionkit-ppg-8x-v1.1
Dataset & License
Section titled “Dataset & License”- Dataset: Open unified PPG v1 (
dataset_id: ppg-unified-strict-sanitize-v1) - License: Open (BIDMC, BUT PPG, PPG-DaLiA, WESAD — mixed open licenses, no restricted-access dependency)
- Notes: Sources: BIDMC, BUT PPG, PPG-DaLiA, and WESAD. The saved training configuration uses these open sources; complete pretraining ancestry was not independently audited in the export repair. Build the cache with
scripts/build_ppg_cache.py.
The lifecycle runner pre-flights dataset availability before training (see the dataset contract).
Reproduction
Section titled “Reproduction”# Single command, end-to-end.uv run compressionkit golden run ppg-rvq-8xResults land under results/ppg_rvq_64hz_08x_golden/; deploy artifacts under results/ppg_rvq_64hz_08x_golden/deploy/.
Two-Stage Variant
Section titled “Two-Stage Variant”Paired entropy prior: ppg-rvq-8x-prior. Run via the lifecycle runner to chain codec → prior
and bundle prior_int8.tflite into this experiment’s deploy/.
Evaluation Metrics
Section titled “Evaluation Metrics”See the modality model zoo for the full metrics table:
Evaluation writes quality_scorecard.json and summary.json under its results/<run>/.
Deploy Artifacts
Section titled “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.npzcontains raw synthetic waveforms.model_card.json,deploy_manifest.json— metadata.prior_int8.tflite/prior_int8.h/prior_manifest.json— entropy prior (two-stage only).
Customization Notes
Section titled “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-traintocompressionkit golden run ppg-rvq-8x --publish.