---
library_name: transformers
tags:
- audio-classification
- generated_from_trainer
datasets:
- voxceleb
metrics:
- accuracy
model-index:
- name: ce-len3-bs256-lr1e-3
  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: 0.9410023545240498
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ce-len3-bs256-lr1e-3

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: 0.2946
- Accuracy: 0.9410

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.6728        | 1.0   | 523  | 4.3456          | 0.1504   |
| 3.224         | 2.0   | 1046 | 2.2589          | 0.5141   |
| 2.3964        | 3.0   | 1569 | 1.4663          | 0.6836   |
| 1.8474        | 4.0   | 2092 | 0.9548          | 0.7927   |
| 1.5275        | 5.0   | 2615 | 0.6698          | 0.8571   |
| 1.248         | 6.0   | 3138 | 0.5270          | 0.8899   |
| 1.0991        | 7.0   | 3661 | 0.4500          | 0.9037   |
| 0.9221        | 8.0   | 4184 | 0.3572          | 0.9267   |
| 0.7997        | 9.0   | 4707 | 0.3138          | 0.9353   |
| 0.7603        | 10.0  | 5230 | 0.2946          | 0.9410   |


### Framework versions

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