metadata
datasets:
- voxceleb
library_name: transformers
metrics:
- accuracy
tags:
- audio-classification
- generated_from_trainer
model-index:
- name: xvector-voxceleb1
results:
- task:
type: audio-classification
name: Audio Classification
dataset:
name: confit/voxceleb
type: voxceleb
config: verification
split: train
args: verification
metrics:
- type: accuracy
value: 0.9405314497140935
name: Accuracy
xvector-voxceleb1
This model is a fine-tuned version of on the confit/voxceleb dataset. It achieves the following results on the evaluation set:
- Loss: 0.2981
- Accuracy: 0.9405
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: 256
- eval_batch_size: 1
- seed: 914
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.6869 | 1.0 | 523 | 4.1199 | 0.1960 |
| 3.2423 | 2.0 | 1046 | 2.2824 | 0.5047 |
| 2.4164 | 3.0 | 1569 | 1.4862 | 0.6816 |
| 1.8625 | 4.0 | 2092 | 0.9794 | 0.7917 |
| 1.5637 | 5.0 | 2615 | 0.7048 | 0.8490 |
| 1.265 | 6.0 | 3138 | 0.5389 | 0.8862 |
| 1.0888 | 7.0 | 3661 | 0.4364 | 0.9101 |
| 0.9296 | 8.0 | 4184 | 0.3617 | 0.9265 |
| 0.8066 | 9.0 | 4707 | 0.3207 | 0.9353 |
| 0.7675 | 10.0 | 5230 | 0.2981 | 0.9405 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.0.0+cu117
- Datasets 3.2.0
- Tokenizers 0.21.0