speaker-segmentation-fine-tuned-callhome-eng-5

tgrhn/speaker-segmentation-fine-tuned-callhome-eng-5

speaker-segmentation-fine-tuned-callhome-eng-5 is a machine learning model from tgrhn. 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 26, 2024
Updated: Apr 26, 2024
SHA: 8e7d20e6

Base models

pyannote/segmentation-3.0

Datasets

diarizers-community/callhome

Model card

From the Hugging Face model README

speaker-segmentation-fine-tuned-callhome-eng-5

This model is a fine-tuned version of pyannote/segmentation-3.0 on the diarizers-community/callhome eng dataset. It achieves the following results on the evaluation set:

Loss: 0.4674
Der: 0.1833
False Alarm: 0.0583
Missed Detection: 0.0725
Confusion: 0.0526

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: 0.0003
train_batch_size: 64
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Der	False Alarm	Missed Detection	Confusion
0.4679	1.0	181	0.4997	0.2011	0.0620	0.0789	0.0602
0.4255	2.0	362	0.4820	0.1948	0.0604	0.0770	0.0574
0.4084	3.0	543	0.4808	0.1920	0.0598	0.0769	0.0553
0.4017	4.0	724	0.4787	0.1906	0.0584	0.0760	0.0562
0.3911	5.0	905	0.4716	0.1885	0.0572	0.0762	0.0552
0.3845	6.0	1086	0.4676	0.1875	0.0618	0.0718	0.0538
0.3877	7.0	1267	0.4682	0.1877	0.0584	0.0739	0.0555
0.3828	8.0	1448	0.4681	0.1849	0.0579	0.0740	0.0530
0.3768	9.0	1629	0.4645	0.1842	0.0581	0.0733	0.0528
0.3697	10.0	1810	0.4662	0.1838	0.0576	0.0734	0.0529
0.3731	11.0	1991	0.4697	0.1852	0.0607	0.0715	0.0530
0.3691	12.0	2172	0.4642	0.1829	0.0572	0.0734	0.0523
0.3663	13.0	2353	0.4701	0.1854	0.0611	0.0708	0.0535
0.3641	14.0	2534	0.4678	0.1835	0.0591	0.0714	0.0530
0.3631	15.0	2715	0.4655	0.1835	0.0583	0.0724	0.0528
0.3685	16.0	2896	0.4693	0.1838	0.0589	0.0720	0.0529
0.365	17.0	3077	0.4675	0.1836	0.0584	0.0724	0.0528
0.3618	18.0	3258	0.4675	0.1834	0.0582	0.0726	0.0526
0.3651	19.0	3439	0.4675	0.1833	0.0582	0.0725	0.0526
0.3583	20.0	3620	0.4674	0.1833	0.0583	0.0725	0.0526

Framework versions

Transformers 4.40.1
Pytorch 2.2.0+cu121
Datasets 2.17.0
Tokenizers 0.19.1

Library: transformers
Type: pyannet
License: mit
Created: Apr 26, 2024
Updated: Apr 26, 2024