---
library_name: transformers
tags:
- audio-classification
- generated_from_trainer
datasets:
- voxceleb
metrics:
- accuracy
model-index:
- name: etdnn-voxceleb1
  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: 0.9340733266061217
---

<!-- 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. -->

# etdnn-voxceleb1

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: 0.3594
- Accuracy: 0.9341

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.489         | 1.0   | 523  | 4.2089          | 0.1722   |
| 3.0685        | 2.0   | 1046 | 2.7621          | 0.4110   |
| 2.2892        | 3.0   | 1569 | 1.6627          | 0.6543   |
| 1.7576        | 4.0   | 2092 | 1.1761          | 0.7586   |
| 1.3706        | 5.0   | 2615 | 0.8903          | 0.8204   |
| 1.1258        | 6.0   | 3138 | 0.7555          | 0.8433   |
| 0.9379        | 7.0   | 3661 | 0.5587          | 0.8897   |
| 0.7925        | 8.0   | 4184 | 0.4518          | 0.9117   |
| 0.6733        | 9.0   | 4707 | 0.3889          | 0.9293   |
| 0.6187        | 10.0  | 5230 | 0.3594          | 0.9341   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.0.0+cu117
- Datasets 3.2.0
- Tokenizers 0.21.0