amity-diarization-v02

aongwachi/amity-diarization-v02

amity-diarization-v02 is a machine learning model from aongwachi. 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 17, 2025
Updated: Apr 17, 2025
SHA: ef67d409

Base models

pyannote/segmentation-3.0

Datasets

amitysolution/sample-voice-dataset

Model card

From the Hugging Face model README

amity-diarization-v02

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

Loss: 0.3846
Model Preparation Time: 0.0077
Der: 0.1686
False Alarm: 0.0769
Missed Detection: 0.0777
Confusion: 0.0140

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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use 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: 15.0

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Der	False Alarm	Missed Detection	Confusion
0.7157	0.6173	300	0.5912	0.0077	0.2733	0.0783	0.1634	0.0315
0.5807	1.2346	600	0.5261	0.0077	0.2517	0.0783	0.1451	0.0283
0.5114	1.8519	900	0.4810	0.0077	0.2299	0.0841	0.1207	0.0250
0.4601	2.4691	1200	0.4629	0.0077	0.2098	0.0910	0.0960	0.0227
0.426	3.0864	1500	0.4443	0.0077	0.2016	0.0909	0.0894	0.0214
0.4077	3.7037	1800	0.4391	0.0077	0.1946	0.0866	0.0888	0.0192
0.3818	4.3210	2100	0.4287	0.0077	0.1891	0.0863	0.0839	0.0189
0.3687	4.9383	2400	0.4214	0.0077	0.1848	0.0838	0.0821	0.0188
0.357	5.5556	2700	0.4135	0.0077	0.1802	0.0849	0.0777	0.0175
0.3533	6.1728	3000	0.4106	0.0077	0.1768	0.0796	0.0809	0.0163
0.3357	6.7901	3300	0.3981	0.0077	0.1732	0.0821	0.0754	0.0157
0.3317	7.4074	3600	0.3957	0.0077	0.1724	0.0800	0.0777	0.0146
0.3278	8.0247	3900	0.3884	0.0077	0.1710	0.0800	0.0761	0.0148
0.3193	8.6420	4200	0.3859	0.0077	0.1696	0.0787	0.0765	0.0144
0.3218	9.2593	4500	0.3842	0.0077	0.1687	0.0790	0.0755	0.0142
0.3244	9.8765	4800	0.3795	0.0077	0.1674	0.0781	0.0751	0.0142
0.3121	10.4938	5100	0.3827	0.0077	0.1685	0.0762	0.0780	0.0144
0.31	11.1111	5400	0.3825	0.0077	0.1688	0.0768	0.0779	0.0140
0.3131	11.7284	5700	0.3855	0.0077	0.1688	0.0772	0.0775	0.0141
0.3108	12.3457	6000	0.3836	0.0077	0.1685	0.0772	0.0773	0.0141
0.3093	12.9630	6300	0.3853	0.0077	0.1687	0.0769	0.0779	0.0139
0.3131	13.5802	6600	0.3855	0.0077	0.1688	0.0767	0.0782	0.0139
0.3012	14.1975	6900	0.3847	0.0077	0.1687	0.0769	0.0777	0.0140
0.3108	14.8148	7200	0.3846	0.0077	0.1686	0.0769	0.0777	0.0140

Framework versions

Transformers 4.51.2
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

Library: transformers
Type: pyannet
License: mit
Created: Apr 17, 2025
Updated: Apr 17, 2025