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---
library_name: transformers
tags:
- audio-classification
- generated_from_trainer
datasets:
- voxceleb
metrics:
- accuracy
model-index:
- name: ecapa-tdnn-voxceleb1-c512-aam
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: confit/voxceleb
type: voxceleb
config: verification
split: train
args: verification
metrics:
- name: Accuracy
type: accuracy
value: 0.9757901815736382
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ecapa-tdnn-voxceleb1-c512-aam
This model is a fine-tuned version of [](https://huggingface.co/) on the confit/voxceleb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5840
- Accuracy: 0.9758
## 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: 0.0005
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 9.047 | 1.0 | 575 | 8.3662 | 0.4304 |
| 5.3508 | 2.0 | 1150 | 4.0252 | 0.8191 |
| 3.3124 | 3.0 | 1725 | 2.1083 | 0.9260 |
| 2.3212 | 4.0 | 2300 | 1.2224 | 0.9435 |
| 1.6276 | 5.0 | 2875 | 0.8229 | 0.9677 |
| 1.1418 | 6.0 | 3450 | 0.5840 | 0.9758 |
| 1.0484 | 7.0 | 4025 | 0.5781 | 0.9738 |
| 0.0 | 8.0 | 4600 | nan | 0.0007 |
| 0.0 | 9.0 | 5175 | nan | 0.0007 |
| 0.0 | 10.0 | 5750 | nan | 0.0007 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.0.0+cu117
- Datasets 3.2.0
- Tokenizers 0.21.0
|