speaker-segmentation-fine-tuned-callhome-jpn

VarunUOB/speaker-segmentation-fine-tuned-callhome-jpn

speaker-segmentation-fine-tuned-callhome-jpn is a machine learning model from VarunUOB. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

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

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At a glance

License: mit
Model type: pyannet
Access: Public
Created: Sep 1, 2024
Updated: Sep 3, 2024
SHA: c9a3160f

Base models

pyannote/speaker-diarization-3.1

Datasets

diarizers-community/callhome

Model card

From the Hugging Face model README

speaker-segmentation-fine-tuned-callhome-jpn

This model is a fine-tuned version of pyannote/speaker-diarization-3.1 on the diarizers-community/callhome dataset. It achieves the following results on the evaluation set:

Loss: 0.6518
Der: 0.2003
False Alarm: 0.0204
Missed Detection: 0.0126
Confusion: 0.1673

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Der	False Alarm	Missed Detection	Confusion
0.6084	1.0	157	0.6361	0.2140	0.0209	0.0101	0.1829
0.5157	2.0	314	0.6039	0.2079	0.0213	0.0101	0.1765
0.4718	3.0	471	0.6117	0.2094	0.0218	0.0101	0.1775
0.5069	4.0	628	0.6086	0.2129	0.0215	0.0101	0.1813
0.47	5.0	785	0.5974	0.2040	0.0215	0.0101	0.1724
0.4539	6.0	942	0.6047	0.2065	0.0219	0.0102	0.1745
0.4325	7.0	1099	0.5944	0.2009	0.0214	0.0104	0.1691
0.434	8.0	1256	0.6110	0.2059	0.0214	0.0105	0.1740
0.4199	9.0	1413	0.6045	0.2050	0.0212	0.0106	0.1733
0.4479	10.0	1570	0.6101	0.1990	0.0212	0.0105	0.1673
0.392	11.0	1727	0.6106	0.2003	0.0208	0.0107	0.1687
0.3858	12.0	1884	0.6279	0.2009	0.0211	0.0108	0.1689
0.3686	13.0	2041	0.6279	0.1976	0.0209	0.0114	0.1653
0.3963	14.0	2198	0.6263	0.1991	0.0211	0.0112	0.1668
0.3521	15.0	2355	0.6313	0.1970	0.0206	0.0116	0.1649
0.348	16.0	2512	0.6307	0.2001	0.0204	0.0123	0.1673
0.3668	17.0	2669	0.6425	0.2012	0.0206	0.0124	0.1682
0.3592	18.0	2826	0.6328	0.2001	0.0205	0.0124	0.1672
0.3485	19.0	2983	0.6489	0.2006	0.0202	0.0128	0.1675
0.3529	20.0	3140	0.6501	0.2007	0.0206	0.0123	0.1678
0.35	21.0	3297	0.6473	0.2003	0.0205	0.0124	0.1674
0.3549	22.0	3454	0.6518	0.2003	0.0205	0.0126	0.1672
0.3439	23.0	3611	0.6523	0.2002	0.0204	0.0127	0.1671
0.3495	24.0	3768	0.6527	0.2002	0.0204	0.0126	0.1672
0.34	25.0	3925	0.6518	0.2003	0.0204	0.0126	0.1673

Framework versions

Transformers 4.42.4
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1

Type: pyannet
License: mit
Languages: jpn
Created: Sep 1, 2024
Updated: Sep 3, 2024