Prepare minimum submission bundle
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{
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"config": {
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"schema": "q1-v2-source-first-2026-09",
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"source_cache_schema": "q1-b0b4-v4-openface",
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"common_step_s": 0.1,
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"multi_context_windows_s": [
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0.1,
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0.3,
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0.7
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],
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"relative_progress_bins": 50,
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"text_encoder": "google-bert/bert-base-uncased; frozen last-four-layer mean per whitespace word; window=510, stride=384",
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"alignment": "fixed-transcript CTC monotone state graph; hard Viterbi path plus stored forward-backward occupancy; scores uncalibrated",
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"audio": "74-D: 40 log-Mel + 13 MFCC + 13 two-sided local-linear delta MFCC + [log-energy, log-F0, librosa.pyin voiced probability, spectral centroid, bandwidth, flux, zero-crossing rate, HNR dB]; 16 kHz, 400-sample window, 160-sample hop, FFT 512; HNR autocorrelation uses n=1024 at detected F0; preserve per-field validity; librosa.pyin warns cycle support is short at fmin=80 Hz for a 25 ms frame",
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"vision": "OpenFace 2.2.0 native-frame output: 17 AU intensities, Pose6, Gaze6, Geometry6; confidence threshold 0.8; actual frame PTS and indices retained",
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"quality": "B0 uses raw CTC word-path score in [0,1]; unavailable quality falls back to q*=1 and is flagged; audio/vision retain explicit unknown or detector-confidence states",
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"content_probe": "disabled; physical time/query maps remain separate from content similarity",
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"time": "left-closed/right-open seconds relative to video stream origin; source indices retained",
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"branch_states": {
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"sec": "primary B0 view and materialized",
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"B1_vision_geometry": "geometry-aware 0.1 s view materialized",
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"word": "materialized",
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"phase50": "materialized",
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"multi": "derived from native rows on demand by q1_io",
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"posterior": "native CTC posterior stored; sec projection available through q1_io",
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"dynamics": "materialized if continuous support is sufficient",
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"b4_speech": "source-cache-backed on demand through q1_io to stay within the 50 MiB feature-package cap",
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"query_word": "physical H_time matrices materialized",
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"content_probe": "disabled"
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}
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},
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"config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617",
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"source_run_manifest": "source_cache_manifest.json",
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"source_cache_schema": "q1-b0b4-v4-openface",
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"source_label_sha256": "827334f782b1f242c84ad944a657bc7f7c63643f71ae9c977811a3eecdd3ab33",
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"python": "3.14.7 (main, Aug 10 2026, 00:00:00) [GCC 16.1.1 20260515 (Red Hat 16.1.1-2)]",
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"packages": {
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"torch": "2.14.0+cu130",
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"transformers": "5.17.0",
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"numpy": "2.5.3",
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"scipy": "1.18.1",
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"scikit-learn": "1.9.1",
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"opencv-python-headless": "5.0.0.93",
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"librosa": "1.0.0",
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"openpyxl": "3.1.5",
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"matplotlib": "3.11.2"
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},
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"models": {
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"text_id": "google-bert/bert-base-uncased",
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"text_revision": "86b5e0934494bd15c9632b12f734a8a67f723594",
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"speech_id": "facebook/wav2vec2-base-960h",
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"speech_revision": "22aad52d435eb6dbaf354bdad9b0da84ce7d6156",
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"speech_stride_samples": 320,
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"speech_receptive_field_samples": 400
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},
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"samples": 100,
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"modalities": 300,
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"total_bytes": 44321567
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}
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