laughter-detection / eval /MANIFEST.json
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{
"bootstrap": {
"n": 2000,
"seed": 0,
"unit": "recording"
},
"code_dirty": [
"hf-release-v0.1/config.json",
"training/hf_convert.py",
"training/hq5a_cough_eval.sbatch",
"training/hq5a_evalsets.sbatch",
"training/release_gate.py",
"hf-release-v0.1/release-manifest.json",
"training/hq5a_event_calib.sbatch"
],
"code_revision": "e4db7af66b151e230c6075e28253302b9b48ff6e",
"family": "laughter-v0.1",
"members": [
"laughter-v0.1-seed0",
"laughter-v0.1-seed1",
"laughter-v0.1-seed10",
"laughter-v0.1-seed11",
"laughter-v0.1-seed12",
"laughter-v0.1-seed13",
"laughter-v0.1-seed14",
"laughter-v0.1-seed2",
"laughter-v0.1-seed3",
"laughter-v0.1-seed4",
"laughter-v0.1-seed5",
"laughter-v0.1-seed6",
"laughter-v0.1-seed7",
"laughter-v0.1-seed8",
"laughter-v0.1-seed9"
],
"reproduce": "For each public surface: AP of each probs/<surface>__laughter-v0.1-seedN.npy against target>=0.5 in <surface>.clips.tsv, mean over the 15 seeds = family_mean; margin = family_mean - reference AP; CI by paired recording (speaker on VocalSound) bootstrap, 2000 resamples, seed 0.",
"rule": "internal-and-any-public",
"set_digests": {
"ami-eval-v1": "1ab59b11012d18d9",
"annotation-eval-v5": "2dcdba95c052bbde",
"podcastfillers-eval": "e4deca38c6380c35",
"public-vocalsound": "c4557271c7f68f70",
"fsd50k-panel": "b55cd8789c24fda8"
},
"table": {
"ami-eval-v1": {
"ci_hi": 0.03942520109963582,
"ci_lo": 0.012154065156311903,
"family_mean": 0.9483739882117678,
"floor_ci_upper": 0.0039598598827272615,
"floor_sigma": 0.002556051282820607,
"label": "AMI",
"margin": 0.02538138623377517,
"member_ap": [
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0.9500519992061698,
0.9452210675451327,
0.9503056961627866,
0.9508894430969833,
0.9470331036343588,
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0.9465480039960334,
0.9513989118555961,
0.9483455133944823,
0.9432426745514125,
0.9511137070448046,
0.9483085823703894,
0.9485920621237138,
0.9487550229182443
],
"n": 15,
"n_clips": 3082,
"n_recordings": 24,
"reference_ap": 0.9229926019779926,
"reference_model": "eat"
},
"annotation-eval-v5": {
"ci_hi": 0.023578607063105828,
"ci_lo": -0.014470678820449834,
"family_mean": 0.8996943593326668,
"floor_ci_upper": 0.004720526387522345,
"floor_sigma": 0.003047054159932776,
"label": "internal",
"margin": 0.0028852774625475197,
"member_ap": [
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0.9039947028281456,
0.8983932783475506,
0.8993346068750109,
0.9005183386530095,
0.9016603462847035,
0.9028680945100267,
0.8955774413407963,
0.9002877424905875,
0.9031921793787044,
0.8952398854096171,
0.9011704571931498,
0.8995806005577214,
0.8942340859359875,
0.9018160886188078
],
"n": 15,
"n_clips": 2297,
"n_recordings": 21,
"reference_ap": 0.8968090818701193,
"reference_model": "ced-base",
"_per_clip": "not shipped (internal audio)"
},
"podcastfillers-eval": {
"ci_hi": 0.050521526766975876,
"ci_lo": -0.02075922278013193,
"family_mean": 0.8333527146573345,
"floor_ci_upper": 0.01673321979650875,
"floor_sigma": 0.010801131654468506,
"label": "PF",
"margin": 0.014778337531855201,
"member_ap": [
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0.841598928384278,
0.8411140826012734,
0.8366294402860901,
0.8337691285784827,
0.8382277485746962,
0.8403275294236768,
0.831845088772049,
0.8383355800012807,
0.8350686755602265,
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0.8376021852481,
0.8343551388336643,
0.8279070269767151,
0.8315146434411776
],
"n": 15,
"n_clips": 10398,
"n_recordings": 20,
"reference_ap": 0.8185743771254793,
"reference_model": "gillick"
},
"public-vocalsound": {
"ci_hi": -0.005603668917087099,
"ci_lo": -0.02523479335876062,
"family_mean": 0.9550608658482184,
"floor_ci_upper": 0.007326952674220729,
"floor_sigma": 0.004729477137259021,
"label": "VS",
"margin": -0.015235673719981468,
"member_ap": [
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0.9552183790146299,
0.9540970381377161,
0.9509495673970976,
0.9519229996151687,
0.9537347287207015,
0.9557549955025102,
0.9619265200931599,
0.9544285936941963,
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],
"n": 15,
"n_clips": 3591,
"n_recordings": 574,
"reference_ap": 0.9702965395681998,
"reference_model": "ced-base"
}
},
"tolerance": {
"ap": 1e-12,
"ci": 0.005
},
"verdict": "FAIL: internal",
"z_whole_ci": {
"AMI": 6.409667762358887,
"PF": 0.8831735739787857,
"VS": -2.0794011367900485,
"internal": 0.6112194331068893
},
"curation": {
"by": "release packaging",
"date": "2026-09-05",
"note": "Public bundle, curated from the internal eval bundle: the private internal surface's per-clip data is excluded (its digest and aggregate row are kept so the card's internal row stays auditable), run names are mapped to release naming (laughter-v0.1-seed0..14, order preserved against the member_ap arrays), the fifteen per-seed configs ship once as configs/laughter-v0.1.json (runs are seeds 0-14 of it), and operational fields are removed from that config. eval/podcastfillers-relabel.tsv records the 100 PodcastFillers non-laughter clips both model families scored above 0.9 and the listening verdict on each (97 contain audible laughter); the table above uses the published labels. 2026-09-09: the FSD50K NSV panel added (fsd50k-panel.clips.tsv, probs for the 15 seeds and ced-base); its column is defined under fsd50k_panel. 2026-09-10: the two no-teacher arms retrained on seeds 0-14 so every trained row shares the same seeds; card_table holds every cell."
},
"fsd50k_panel": {
"label": "FSD50K NSV",
"what": "Share of FSD50K non-laughter vocalisation clips (any of cough, sneeze, breathing, gasp, sigh, crying, burping; no Laughter label) scored as laughter, at the threshold where the model recovers 90% of the panel's Laughter clips. Each clip's score is the max over its 4 s windows (hop 4 s). Lower is better.",
"n_laughter": 933,
"n_nsv": 1136,
"n_clips": 4269,
"recall": 0.9,
"family_mean": 0.1459507042253521,
"family_sd": 0.013560746885563212,
"member_nsv_fp": [
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0.15316901408450703,
0.1267605633802817,
0.13556338028169015,
0.1637323943661972,
0.13468309859154928,
0.13556338028169015,
0.1619718309859155,
0.12147887323943662,
0.13908450704225353,
0.16549295774647887,
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0.15933098591549297,
0.14788732394366197,
0.14524647887323944
],
"reference_model": "ced-base",
"reference_nsv_fp": 0.03609154929577465,
"others": {
"eat-lora-no-teacher": {
"nsv_fp": 0.1931338028169014,
"sd": 0.01694497340594801,
"n": 15
},
"eat-full-finetune-teacher": {
"nsv_fp": 0.17951877934272303,
"sd": 0.04972447014311274,
"n": 15
},
"eat-full-finetune-no-teacher": {
"nsv_fp": 0.26085680751173707,
"sd": 0.030258187344898973,
"n": 15
},
"eat": {
"nsv_fp": 0.0801056338028169,
"n": 1
},
"ced-tiny": {
"nsv_fp": 0.07130281690140845,
"n": 1
},
"beats": {
"nsv_fp": 0.14524647887323944,
"n": 1
},
"gillick": {
"nsv_fp": 0.48327464788732394,
"n": 1
}
},
"reproduce": "s = probs/fsd50k-panel__<model>.npy aligned with fsd50k-panel.clips.tsv; thr = the 840th highest score among laughter==1 rows (ceil(0.9*933)); nsv_fp = mean(s[nsv==1] >= thr). Clips are FSD50K dev audio (train and val folds) that carry panel labels; none of it is training data."
},
"card_table": {
"note": "Every cell of the card's results table. Trained rows: mean and sd over seeds 0-14. Off-the-shelf rows: value and 95% bootstrap half-width (2000 draws, seed 0) over recordings (speakers on VocalSound, clips on the FSD50K panel). Built by training.card_results.",
"columns": [
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"podcastfillers-eval",
"ami-eval-v1",
"public-vocalsound",
"fsd50k-panel"
],
"panel_recall": 0.9,
"bootstrap": 2000,
"n_laughter": 933,
"n_nsv": 1136,
"rows": {
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