Rziane/speaker_seg_ft_eslotest_mm03082025

pyannote/segmentation-3.0

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

• Loss: 0.8139
• Model Preparation Time: 0.0035
• Der: 0.5111
• False Alarm: 0.1728
• Missed Detection: 0.2406
• Confusion: 0.0978

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 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Der	False Alarm	Missed Detection	Confusion
0.8517	1.0	300	0.8676	0.0035	0.5466	0.1920	0.2425	0.1121
0.7998	2.0	600	0.8499	0.0035	0.5307	0.1640	0.2628	0.1039
0.7867	3.0	900	0.8529	0.0035	0.5366	0.1602	0.2767	0.0997
0.7777	4.0	1200	0.8351	0.0035	0.5296	0.1912	0.2333	0.1050
0.7596	5.0	1500	0.8185	0.0035	0.5118	0.1817	0.2239	0.1062
0.7591	6.0	1800	0.8083	0.0035	0.5101	0.1655	0.2540	0.0906
0.7555	7.0	2100	0.8141	0.0035	0.5109	0.1711	0.2396	0.1001
0.7394	8.0	2400	0.8145	0.0035	0.5119	0.1726	0.2405	0.0988
0.7458	9.0	2700	0.8138	0.0035	0.5107	0.1721	0.2403	0.0983
0.705	10.0	3000	0.8139	0.0035	0.5111	0.1728	0.2406	0.0978

Framework versions

• Transformers 4.51.0
• Pytorch 2.7.0+cu126
• Datasets 3.5.0
• Tokenizers 0.21.1