metadata

library_name: transformers
language:
  - hi
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
  - speaker-diarization
  - speaker-segmentation
  - generated_from_trainer
datasets:
  - diarizers-community/callhome
model-index:
  - name: SpeakerSegmentation_Hindi
    results: []

SpeakerSegmentation_Hindi

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: 0.4294
Model Preparation Time: 0.0006
Der: 0.1343
False Alarm: 0.0233
Missed Detection: 0.0270
Confusion: 0.0840

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 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
0.4597	1.0	194	0.4796	0.0006	0.1635	0.0254	0.0315	0.1066
0.3807	2.0	388	0.4499	0.0006	0.1502	0.0225	0.0312	0.0966
0.379	3.0	582	0.4359	0.0006	0.1400	0.0217	0.0305	0.0878
0.3363	4.0	776	0.4479	0.0006	0.1402	0.0240	0.0278	0.0884
0.3082	5.0	970	0.4358	0.0006	0.1371	0.0245	0.0268	0.0859
0.3125	6.0	1164	0.4287	0.0006	0.1361	0.0214	0.0293	0.0855
0.3143	7.0	1358	0.4247	0.0006	0.1344	0.0233	0.0272	0.0839
0.3081	8.0	1552	0.4211	0.0006	0.1328	0.0230	0.0271	0.0827
0.2999	9.0	1746	0.4298	0.0006	0.1341	0.0233	0.0270	0.0838
0.292	10.0	1940	0.4294	0.0006	0.1343	0.0233	0.0270	0.0840

Framework versions

Transformers 4.49.0.dev0
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0