speaker-segmentation-fine-tuned-callhome-eng-0927

echojosh/speaker-segmentation-fine-tuned-callhome-eng-0927

speaker-segmentation-fine-tuned-callhome-eng-0927 is a machine learning model from echojosh. 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: Sep 27, 2025
Updated: Sep 27, 2025
SHA: 155fea59

Base models

pyannote/segmentation-3.0

Datasets

diarizers-community/callhome

Model card

From the Hugging Face model README

speaker-segmentation-fine-tuned-callhome-eng-0927

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: 0.4972
Model Preparation Time: 0.007
Der: 0.1808
False Alarm: 0.0619
Missed Detection: 0.0699
Confusion: 0.0489

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: 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: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Der	False Alarm	Missed Detection	Confusion
0.4219	1.0	362	0.4806	0.007	0.1893	0.0543	0.0778	0.0572
0.3851	2.0	724	0.4724	0.007	0.1879	0.0480	0.0817	0.0581
0.3676	3.0	1086	0.4666	0.007	0.1849	0.0588	0.0712	0.0549
0.3554	4.0	1448	0.4667	0.007	0.1831	0.0598	0.0704	0.0530
0.3457	5.0	1810	0.4613	0.007	0.1802	0.0603	0.0692	0.0507
0.3319	6.0	2172	0.4586	0.007	0.1771	0.0541	0.0740	0.0490
0.3126	7.0	2534	0.4743	0.007	0.1787	0.0488	0.0787	0.0512
0.3144	8.0	2896	0.4885	0.007	0.1852	0.0595	0.0736	0.0520
0.3084	9.0	3258	0.4781	0.007	0.1819	0.0624	0.0698	0.0498
0.303	10.0	3620	0.4801	0.007	0.1819	0.0599	0.0727	0.0493
0.2907	11.0	3982	0.4893	0.007	0.1820	0.0581	0.0750	0.0490
0.293	12.0	4344	0.4798	0.007	0.1792	0.0559	0.0739	0.0493
0.2787	13.0	4706	0.4926	0.007	0.1823	0.0642	0.0675	0.0506
0.2741	14.0	5068	0.4928	0.007	0.1813	0.0647	0.0676	0.0490
0.2705	15.0	5430	0.4940	0.007	0.1807	0.0595	0.0717	0.0496
0.2638	16.0	5792	0.4955	0.007	0.1809	0.0632	0.0691	0.0486
0.2623	17.0	6154	0.4999	0.007	0.1808	0.0606	0.0707	0.0494
0.2704	18.0	6516	0.4977	0.007	0.1803	0.0605	0.0707	0.0491
0.2705	19.0	6878	0.4973	0.007	0.1808	0.0616	0.0701	0.0491
0.2683	20.0	7240	0.4972	0.007	0.1808	0.0619	0.0699	0.0489

Framework versions

Transformers 4.56.2
Pytorch 2.7.1+cu118
Datasets 3.6.0
Tokenizers 0.22.1

Library: transformers
Type: pyannet
License: mit
Created: Sep 27, 2025
Updated: Sep 27, 2025