metadata
datasets:
- voxceleb
library_name: transformers
metrics:
- accuracy
tags:
- audio-classification
- generated_from_trainer
model-index:
- name: ecapa-tdnn-voxceleb1-c512-aam
results:
- task:
type: audio-classification
name: Audio Classification
dataset:
name: confit/voxceleb
type: voxceleb
config: verification
split: train
args: verification
metrics:
- type: accuracy
value: 0.8030272452068618
name: Accuracy
ecapa-tdnn-voxceleb1-c512-aam
This model is a fine-tuned version of on the confit/voxceleb dataset. It achieves the following results on the evaluation set:
- Loss: 4.7003
- Accuracy: 0.8030
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: 0.0001
- 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 |
|---|---|---|---|---|
| 11.3851 | 1.0 | 523 | 11.0293 | 0.1806 |
| 9.7596 | 2.0 | 1046 | 9.1401 | 0.3850 |
| 8.7136 | 3.0 | 1569 | 7.8821 | 0.5242 |
| 7.848 | 4.0 | 2092 | 6.9451 | 0.6144 |
| 7.1912 | 5.0 | 2615 | 6.2630 | 0.6821 |
| 6.6763 | 6.0 | 3138 | 5.7182 | 0.7292 |
| 6.3112 | 7.0 | 3661 | 5.2653 | 0.7632 |
| 6.0255 | 8.0 | 4184 | 4.9663 | 0.7826 |
| 5.8091 | 9.0 | 4707 | 4.7787 | 0.7957 |
| 5.7269 | 10.0 | 5230 | 4.7003 | 0.8030 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.0.0+cu117
- Datasets 3.2.0
- Tokenizers 0.21.0