metadata
library_name: transformers
language:
- hi
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- Samyak29/synthetic-speaker-diarization-dataset-hindi-large
model-index:
- name: speaker-segmentation-fine-tuned-hindi
results: []
speaker-segmentation-fine-tuned-hindi
This model is a fine-tuned version of pyannote/speaker-diarization-3.1 on the Samyak29/synthetic-speaker-diarization-dataset-hindi-large dataset. It achieves the following results on the evaluation set:
- Loss: 0.4284
- Model Preparation Time: 0.0095
- Der: 0.1417
- False Alarm: 0.0235
- Missed Detection: 0.0281
- Confusion: 0.0901
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: 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 | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
|---|---|---|---|---|---|---|---|---|
| 0.4708 | 1.0 | 194 | 0.4808 | 0.0095 | 0.1613 | 0.0255 | 0.0323 | 0.1035 |
| 0.388 | 2.0 | 388 | 0.4553 | 0.0095 | 0.1499 | 0.0225 | 0.0314 | 0.0960 |
| 0.3654 | 3.0 | 582 | 0.4368 | 0.0095 | 0.1433 | 0.0242 | 0.0278 | 0.0913 |
| 0.363 | 4.0 | 776 | 0.4296 | 0.0095 | 0.1410 | 0.0239 | 0.0279 | 0.0893 |
| 0.3388 | 5.0 | 970 | 0.4284 | 0.0095 | 0.1417 | 0.0235 | 0.0281 | 0.0901 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1