This model is a fine-tuned version of pyannote/segmentation-3.0 on the objects76/synthetic-ja4-speaker-overlap-6400 dataset. It achieves the following results on the evaluation set:

Loss: 0.4142
Der: 0.1244
False Alarm: 0.0536
Missed Detection: 0.0531
Confusion: 0.0177

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 2048
eval_batch_size: 2048
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 200

Training results

Training Loss	Epoch	Step	Confusion	Der	False Alarm	Validation Loss	Missed Detection
No log	1.0	7	0.0594	0.2999	0.1200	0.9837	0.1206
No log	2.0	14	0.0651	0.2710	0.0724	0.8711	0.1335
No log	3.0	21	0.0775	0.2659	0.0140	0.8192	0.1744
0.9138	4.0	28	0.0714	0.2422	0.0283	0.7813	0.1425
0.9138	5.0	35	0.0569	0.2231	0.0685	0.7536	0.0977
0.9138	6.0	42	0.0564	0.2136	0.0681	0.7049	0.0891
0.9138	7.0	49	0.0509	0.2050	0.0815	0.6687	0.0726
0.7407	8.0	56	0.0470	0.2004	0.0872	0.6428	0.0661
0.7407	9.0	63	0.0459	0.1950	0.0827	0.6224	0.0664
0.7407	10.0	70	0.0430	0.1915	0.0874	0.6045	0.0611
0.6308	11.0	77	0.0418	0.1877	0.0839	0.5877	0.0620
0.6308	12.0	84	0.0391	0.1828	0.0812	0.5714	0.0625
0.6308	13.0	91	0.0356	0.1771	0.0820	0.5571	0.0595
0.6308	14.0	98	0.0335	0.1698	0.0755	0.5410	0.0608
0.5637	15.0	105	0.0310	0.1642	0.0753	0.5294	0.0579
0.5637	16.0	112	0.0293	0.1588	0.0691	0.5173	0.0604
0.5637	17.0	119	0.0278	0.1551	0.0687	0.5070	0.0586
0.5103	18.0	126	0.0279	0.1517	0.0634	0.4972	0.0604
0.5103	19.0	133	0.0272	0.1493	0.0625	0.4876	0.0596
0.5103	20.0	140	0.0264	0.1490	0.0643	0.4819	0.0583
0.5103	21.0	147	0.0260	0.1471	0.0605	0.4732	0.0606
0.4692	22.0	154	0.0251	0.1453	0.0607	0.4689	0.0595
0.4692	23.0	161	0.0250	0.1435	0.0601	0.4639	0.0584
0.4692	24.0	168	0.0238	0.1417	0.0622	0.4591	0.0556
0.4454	25.0	175	0.0241	0.1406	0.0571	0.4529	0.0594
0.4454	26.0	182	0.0234	0.1404	0.0612	0.4527	0.0558
0.4454	27.0	189	0.0229	0.1392	0.0560	0.4459	0.0602
0.4454	28.0	196	0.0217	0.1388	0.0634	0.4473	0.0536
0.423	29.0	203	0.0223	0.1375	0.0580	0.4434	0.0572
0.423	30.0	210	0.0225	0.1368	0.0564	0.4398	0.0579
0.423	31.0	217	0.0215	0.1361	0.0599	0.4379	0.0547
0.423	32.0	224	0.0220	0.1350	0.0553	0.4340	0.0578
0.3906	33.0	231	0.0216	0.1352	0.0561	0.4334	0.0575
0.3906	34.0	238	0.0211	0.1353	0.0602	0.4348	0.0541
0.3906	35.0	245	0.0211	0.1326	0.0551	0.4274	0.0565
0.3918	36.0	252	0.0214	0.1336	0.0577	0.4280	0.0545
0.3918	37.0	259	0.0220	0.1328	0.0545	0.4297	0.0562
0.3918	38.0	266	0.0220	0.1333	0.0572	0.4315	0.0541
0.3918	39.0	273	0.0220	0.1324	0.0533	0.4287	0.0571
0.3811	40.0	280	0.0202	0.1310	0.0577	0.4222	0.0531
0.3811	41.0	287	0.0202	0.1298	0.0540	0.4178	0.0556
0.3811	42.0	294	0.0201	0.1296	0.0545	0.4218	0.0549
0.3707	43.0	301	0.0189	0.1280	0.0535	0.4176	0.0556
0.3707	44.0	308	0.0189	0.1287	0.0556	0.4156	0.0542
0.3707	45.0	315	0.0192	0.1267	0.0524	0.4142	0.0551
0.3707	46.0	322	0.0188	0.1269	0.0529	0.4141	0.0552
0.3636	47.0	329	0.0190	0.1283	0.0571	0.4188	0.0522
0.3636	48.0	336	0.0191	0.1264	0.0503	0.4129	0.0569
0.3636	49.0	343	0.0182	0.1276	0.0574	0.4167	0.0520
0.354	50.0	350	0.0191	0.1264	0.0511	0.4141	0.0562
0.354	51.0	357	0.0184	0.1264	0.0555	0.4132	0.0526
0.354	52.0	364	0.0193	0.1265	0.0510	0.4137	0.0562
0.354	53.0	371	0.0187	0.1267	0.0560	0.4153	0.0520
0.3411	54.0	378	0.4133	0.1255	0.0540	0.0534	0.0182
0.3411	55.0	385	0.4143	0.1251	0.0515	0.0549	0.0187
0.3411	56.0	392	0.4136	0.1254	0.0544	0.0524	0.0186
0.3411	57.0	399	0.4118	0.1253	0.0526	0.0538	0.0189
0.3413	58.0	406	0.4134	0.1259	0.0532	0.0541	0.0187
0.3413	59.0	413	0.4137	0.1255	0.0529	0.0542	0.0184
0.3413	60.0	420	0.4119	0.1243	0.0509	0.0547	0.0186
0.3381	61.0	427	0.4117	0.1249	0.0532	0.0534	0.0183
0.3381	62.0	434	0.4092	0.1258	0.0504	0.0570	0.0184
0.3381	63.0	441	0.4122	0.1254	0.0548	0.0529	0.0177
0.3381	64.0	448	0.4121	0.1253	0.0502	0.0560	0.0190
0.3351	65.0	455	0.4131	0.1255	0.0572	0.0511	0.0172
0.3351	66.0	462	0.4094	0.1241	0.0516	0.0551	0.0173
0.3351	67.0	469	0.4123	0.1246	0.0549	0.0525	0.0172
0.3296	68.0	476	0.4107	0.1243	0.0533	0.0536	0.0173
0.3296	69.0	483	0.4096	0.1233	0.0504	0.0552	0.0177
0.3296	70.0	490	0.4126	0.1250	0.0518	0.0552	0.0181
0.3296	71.0	497	0.4127	0.1248	0.0509	0.0558	0.0181
0.3242	72.0	504	0.4142	0.1244	0.0536	0.0531	0.0177

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

objects76/full-ja4-2.25sec-big-rf-250513_1709

full-ja4-2.25sec-big-rf

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

objects76/
full-ja4-2.25sec-big-rf-250513_1709