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: 0.5595
Der: 0.2894
False Alarm: 0.2353
Missed Detection: 0.0536
Confusion: 0.0005

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: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Der	False Alarm	Missed Detection	Confusion
0.3614	1.0	226	0.4962	0.2910	0.2389	0.0520	0.0001
0.3465	2.0	452	0.5067	0.2860	0.2179	0.0679	0.0002
0.3325	3.0	678	0.5343	0.2941	0.2300	0.0636	0.0005
0.3189	4.0	904	0.5613	0.2906	0.2380	0.0522	0.0004
0.3238	5.0	1130	0.5595	0.2894	0.2353	0.0536	0.0005

Framework versions

Transformers 4.45.1
Pytorch 2.4.1
Datasets 3.0.1
Tokenizers 0.20.0

mirari/callhome-RTVE

speaker-segmentation-fine-tuned-callhome-spa

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mirari/
callhome-RTVE