---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: speech-accent-detection
  results: []
datasets:
- CSTR-Edinburgh/vctk
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# speech-accent-detection

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the VCTK dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0441
- Accuracy: 0.9955

## Model description

I used the wav2vec2 model's weights and fine-tune over my dataset.

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8005        | 1.0   | 2205  | 0.6526          | 0.8270   |
| 0.0508        | 2.0   | 4410  | 0.3466          | 0.9374   |
| 0.3054        | 3.0   | 6615  | 0.2946          | 0.9524   |
| 0.0882        | 4.0   | 8820  | 0.1832          | 0.9737   |
| 0.0006        | 5.0   | 11025 | 0.1539          | 0.9757   |
| 0.0003        | 6.0   | 13230 | 0.0677          | 0.9896   |
| 0.3011        | 7.0   | 15435 | 0.1219          | 0.9859   |
| 0.0001        | 8.0   | 17640 | 0.0695          | 0.9916   |
| 0.0001        | 9.0   | 19845 | 0.0397          | 0.9955   |
| 0.0           | 10.0  | 22050 | 0.0441          | 0.9955   |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0