whitneyten/pydiarize-Dataset-2_4

speaker-segmentation-fine-tuned-datasetID-hugging_2_4

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

• Loss: 0.3735
• Model Preparation Time: 0.0099
• Der: 0.1228
• False Alarm: 0.0205
• Missed Detection: 0.0096
• Confusion: 0.0927

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: 43
• 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
0.4492	1.0	570	0.4525	0.0099	0.1515	0.0219	0.0094	0.1202
0.3875	2.0	1140	0.4229	0.0099	0.1403	0.0212	0.0098	0.1093
0.4014	3.0	1710	0.3889	0.0099	0.1324	0.0207	0.0102	0.1016
0.3656	4.0	2280	0.3899	0.0099	0.1305	0.0212	0.0094	0.1000
0.3232	5.0	2850	0.3804	0.0099	0.1283	0.0207	0.0096	0.0981
0.319	6.0	3420	0.3796	0.0099	0.1256	0.0204	0.0097	0.0956
0.3018	7.0	3990	0.3750	0.0099	0.1247	0.0202	0.0101	0.0945
0.2928	8.0	4560	0.3700	0.0099	0.1220	0.0203	0.0097	0.0920
0.3321	9.0	5130	0.3753	0.0099	0.1238	0.0204	0.0098	0.0936
0.2738	10.0	5700	0.3735	0.0099	0.1228	0.0205	0.0096	0.0927

Framework versions

• Transformers 4.50.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1