---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-base-voxceleb1
  results: []
---

<!-- 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. -->

# wav2vec2-base-voxceleb1

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0316
- Accuracy: 0.8311

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 1
- seed: 1016
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 5.7408        | 1.0   | 8648  | 6.0003          | 0.0198   |
| 3.0546        | 2.0   | 17296 | 3.9534          | 0.2351   |
| 1.3947        | 3.0   | 25944 | 2.4203          | 0.4975   |
| 0.5969        | 4.0   | 34592 | 1.5746          | 0.6645   |
| 0.2713        | 5.0   | 43240 | 1.3448          | 0.7310   |
| 0.1379        | 6.0   | 51888 | 1.2183          | 0.7664   |
| 0.0555        | 7.0   | 60536 | 1.1041          | 0.7956   |
| 0.0268        | 8.0   | 69184 | 1.0941          | 0.8104   |
| 0.0122        | 9.0   | 77832 | 1.0580          | 0.8256   |
| 0.0053        | 10.0  | 86480 | 1.0316          | 0.8311   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.0.0+cu117
- Datasets 3.1.0
- Tokenizers 0.20.3