PPG RVQ · 4× with entropy prior
Reproduce the ppg-rvq-4x-prior configuration. Prepare its dataset before running the workflow; use the linked configuration to inspect the exact settings.
Overview
Section titled “Overview”- Modality: PPG
- Structure:
two_stage - Target compression ratio: 4×
- Sample rate: 64 Hz
- Recipe:
train-rvq-prior - Config:
configs/ppg_rvq_64hz_04x_golden_prior.yaml - Run name:
ppg_rvq_64hz_04x_golden - Hugging Face target:
Ambiq/compressionkit-ppg-4x-v1.0
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-4x-priorResults land under results/ppg_rvq_64hz_04x_golden/; deploy artifacts under results/ppg_rvq_64hz_04x_golden/deploy/.
Parent codec
Section titled “Parent codec”The parent codec now uses corrected v1.1 exports. This prior remains on its historical v1.0 track and has not been requalified for that release.
This entry is the entropy-prior stage paired with ppg-rvq-4x.
The historical codec and prior artifacts use the v1.0 HuggingFace repo
(Ambiq/compressionkit-ppg-4x-v1.0).
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. - Publication is blocked until the prior is requalified against its parent’s corrected v1.1 release.