synthetic-all-2.25sec-250416_1332

objects76/synthetic-all-2.25sec-250416_1332

synthetic-all-2.25sec-250416_1332 is a machine learning model from objects76. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: mit
Model type: pyannet
Access: Public
Created: Apr 16, 2025
Updated: Apr 16, 2025
SHA: d20946fc

Base models

pyannote/segmentation-3.0

Datasets

objects76/synthetic-all2

Model card

From the Hugging Face model README

synthetic-speaker-all2

This model is a fine-tuned version of pyannote/segmentation-3.0 on the objects76/synthetic-all2 dataset. It achieves the following results on the evaluation set:

Loss: 0.2023
Der: 0.0619
False Alarm: 0.0207
Missed Detection: 0.0284
Confusion: 0.0127

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: 1536
eval_batch_size: 1536
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: 70

Training results

Training Loss	Epoch	Step	Validation Loss	Der	False Alarm	Missed Detection	Confusion
No log	1.0	6	0.6450	0.1856	0.0603	0.0739	0.0514
No log	2.0	12	0.4891	0.1391	0.0450	0.0605	0.0336
No log	3.0	18	0.4150	0.1212	0.0229	0.0661	0.0323
No log	4.0	24	0.3674	0.1107	0.0246	0.0579	0.0283
0.5602	5.0	30	0.3349	0.1019	0.0354	0.0432	0.0233
0.5602	6.0	36	0.3133	0.0965	0.0367	0.0390	0.0208
0.5602	7.0	42	0.3003	0.0903	0.0280	0.0435	0.0189
0.5602	8.0	48	0.2887	0.0859	0.0263	0.0411	0.0185
0.3151	9.0	54	0.2791	0.0837	0.0310	0.0351	0.0176
0.3151	10.0	60	0.2727	0.0823	0.0326	0.0325	0.0172
0.3151	11.0	66	0.2672	0.0805	0.0251	0.0374	0.0180
0.3151	12.0	72	0.2632	0.0790	0.0254	0.0357	0.0179
0.2678	13.0	78	0.2586	0.0778	0.0298	0.0313	0.0167
0.2678	14.0	84	0.2527	0.0758	0.0273	0.0320	0.0165
0.2678	15.0	90	0.2470	0.0742	0.0242	0.0342	0.0158
0.2678	16.0	96	0.2454	0.0737	0.0247	0.0331	0.0160
0.2448	17.0	102	0.2461	0.0741	0.0262	0.0315	0.0164
0.2448	18.0	108	0.2434	0.0733	0.0266	0.0302	0.0165
0.2448	19.0	114	0.2396	0.0722	0.0246	0.0312	0.0163
0.2448	20.0	120	0.2344	0.0706	0.0248	0.0301	0.0157
0.2261	21.0	126	0.2315	0.0714	0.0239	0.0309	0.0165
0.2261	22.0	132	0.2302	0.0712	0.0223	0.0323	0.0167
0.2261	23.0	138	0.2298	0.0712	0.0238	0.0309	0.0165
0.2261	24.0	144	0.2253	0.0696	0.0234	0.0307	0.0155
0.2139	25.0	150	0.2235	0.0682	0.0220	0.0313	0.0148
0.2139	26.0	156	0.2203	0.0667	0.0214	0.0313	0.0139
0.2139	27.0	162	0.2192	0.0662	0.0240	0.0288	0.0134
0.2139	28.0	168	0.2166	0.0650	0.0239	0.0284	0.0127
0.2139	29.0	174	0.2158	0.0647	0.0214	0.0303	0.0129
0.1974	30.0	180	0.2148	0.0650	0.0218	0.0299	0.0133
0.1974	31.0	186	0.2155	0.0648	0.0240	0.0273	0.0134
0.1974	32.0	192	0.2151	0.0644	0.0242	0.0266	0.0137
0.1974	33.0	198	0.2118	0.0644	0.0197	0.0307	0.0140
0.1897	34.0	204	0.2134	0.0650	0.0204	0.0300	0.0146
0.1897	35.0	210	0.2132	0.0648	0.0219	0.0284	0.0145
0.1897	36.0	216	0.2115	0.0648	0.0216	0.0289	0.0143
0.1897	37.0	222	0.2110	0.0644	0.0208	0.0297	0.0140
0.1883	38.0	228	0.2106	0.0635	0.0211	0.0288	0.0136
0.1883	39.0	234	0.2136	0.0642	0.0222	0.0281	0.0140
0.1883	40.0	240	0.2138	0.0644	0.0213	0.0292	0.0139
0.1883	41.0	246	0.2133	0.0640	0.0218	0.0286	0.0137
0.1808	42.0	252	0.2102	0.0633	0.0224	0.0276	0.0133
0.1808	43.0	258	0.2079	0.0626	0.0222	0.0274	0.0130
0.1808	44.0	264	0.2087	0.0628	0.0220	0.0275	0.0133
0.1808	45.0	270	0.2095	0.0632	0.0220	0.0274	0.0138
0.1734	46.0	276	0.2061	0.0627	0.0202	0.0289	0.0136
0.1734	47.0	282	0.2029	0.0620	0.0196	0.0293	0.0131
0.1734	48.0	288	0.2026	0.0620	0.0204	0.0287	0.0129
0.1734	49.0	294	0.2028	0.0620	0.0213	0.0278	0.0129
0.1715	50.0	300	0.2034	0.0622	0.0217	0.0275	0.0130
0.1715	51.0	306	0.2029	0.0620	0.0214	0.0277	0.0128
0.1715	52.0	312	0.2024	0.0618	0.0210	0.0281	0.0127
0.1715	53.0	318	0.2017	0.0617	0.0204	0.0287	0.0126
0.1715	54.0	324	0.2017	0.0618	0.0204	0.0288	0.0126
0.1708	55.0	330	0.2015	0.0617	0.0210	0.0282	0.0125
0.1708	56.0	336	0.2013	0.0618	0.0212	0.0279	0.0126
0.1708	57.0	342	0.2021	0.0620	0.0213	0.0280	0.0128
0.1708	58.0	348	0.2025	0.0621	0.0214	0.0280	0.0128
0.172	59.0	354	0.2026	0.0620	0.0212	0.0281	0.0127
0.172	60.0	360	0.2024	0.0621	0.0211	0.0283	0.0127
0.172	61.0	366	0.2023	0.0618	0.0208	0.0284	0.0126
0.172	62.0	372	0.2027	0.0620	0.0208	0.0284	0.0127
0.1692	63.0	378	0.2029	0.0621	0.0208	0.0285	0.0128
0.1692	64.0	384	0.2025	0.0619	0.0207	0.0285	0.0127
0.1692	65.0	390	0.2024	0.0619	0.0207	0.0285	0.0127
0.1692	66.0	396	0.2023	0.0619	0.0207	0.0285	0.0127
0.1711	67.0	402	0.2023	0.0619	0.0207	0.0285	0.0127
0.1711	68.0	408	0.2023	0.0619	0.0207	0.0284	0.0127
0.1711	69.0	414	0.2023	0.0619	0.0207	0.0285	0.0127
0.1711	70.0	420	0.2023	0.0619	0.0207	0.0284	0.0127

Framework versions

Transformers 4.51.0
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

Library: transformers
Type: pyannet
License: mit
Created: Apr 16, 2025
Updated: Apr 16, 2025