metadata
language:
- jpn
license: apache-2.0
base_model: openai/whisper-small
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/callhome
model-index:
- name: speaker-segmentation-fine-tuned-callhome-jpn
results: []
speaker-segmentation-fine-tuned-callhome-jpn
This model is a fine-tuned version of openai/whisper-small on the diarizers-community/callhome dataset. It achieves the following results on the evaluation set:
- Loss: 0.7490
- Der: 0.2217
- False Alarm: 0.0465
- Missed Detection: 0.1331
- Confusion: 0.0421
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 | Der | False Alarm | Missed Detection | Confusion |
|---|---|---|---|---|---|---|---|
| 0.575 | 1.0 | 328 | 0.7539 | 0.2338 | 0.0503 | 0.1345 | 0.0489 |
| 0.5261 | 2.0 | 656 | 0.7483 | 0.2256 | 0.0485 | 0.1334 | 0.0436 |
| 0.5048 | 3.0 | 984 | 0.7581 | 0.2248 | 0.0440 | 0.1373 | 0.0435 |
| 0.4911 | 4.0 | 1312 | 0.7467 | 0.2226 | 0.0472 | 0.1330 | 0.0424 |
| 0.5161 | 5.0 | 1640 | 0.7490 | 0.2217 | 0.0465 | 0.1331 | 0.0421 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1