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

Loss: 0.2243
Model Preparation Time: 0.0033
Der: 0.0906
False Alarm: 0.0239
Missed Detection: 0.0427
Confusion: 0.0240

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: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Der	False Alarm	Missed Detection	Confusion
No log	1.0	56	0.2303	0.0033	0.0985	0.0298	0.0430	0.0257
0.2116	2.0	112	0.2301	0.0033	0.0968	0.0218	0.0524	0.0227
0.2116	3.0	168	0.2247	0.0033	0.0923	0.0230	0.0462	0.0231
0.1681	4.0	224	0.2244	0.0033	0.0909	0.0246	0.0424	0.0240
0.1681	5.0	280	0.2243	0.0033	0.0906	0.0239	0.0427	0.0240

Framework versions

Transformers 4.52.3
Pytorch 2.6.0+cu126
Datasets 3.6.0
Tokenizers 0.21.1

Lili-O/diarization-fr

speaker-segmentation-simsamu-fra

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Lili-O/
diarization-fr