---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-ft-btb-ccv-cy
  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-xlsr-53-ft-btb-ccv-cy

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5355
- Wer: 0.4186

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| No log        | 0.0194 | 100  | 3.5494          | 1.0    |
| No log        | 0.0387 | 200  | 3.0426          | 1.0    |
| No log        | 0.0581 | 300  | 2.8965          | 1.0    |
| No log        | 0.0774 | 400  | 1.8263          | 0.9829 |
| 3.9715        | 0.0968 | 500  | 1.3860          | 0.8749 |
| 3.9715        | 0.1161 | 600  | 1.3084          | 0.8153 |
| 3.9715        | 0.1355 | 700  | 1.0550          | 0.7337 |
| 3.9715        | 0.1549 | 800  | 1.0012          | 0.7190 |
| 3.9715        | 0.1742 | 900  | 0.9137          | 0.6752 |
| 1.0155        | 0.1936 | 1000 | 0.8486          | 0.6469 |
| 1.0155        | 0.2129 | 1100 | 0.8535          | 0.6112 |
| 1.0155        | 0.2323 | 1200 | 0.8350          | 0.6193 |
| 1.0155        | 0.2516 | 1300 | 0.7681          | 0.5670 |
| 1.0155        | 0.2710 | 1400 | 0.7377          | 0.5559 |
| 0.7987        | 0.2904 | 1500 | 0.7130          | 0.5437 |
| 0.7987        | 0.3097 | 1600 | 0.7040          | 0.5452 |
| 0.7987        | 0.3291 | 1700 | 0.6729          | 0.5051 |
| 0.7987        | 0.3484 | 1800 | 0.6646          | 0.5113 |
| 0.7987        | 0.3678 | 1900 | 0.6531          | 0.4969 |
| 0.6851        | 0.3871 | 2000 | 0.6414          | 0.5038 |
| 0.6851        | 0.4065 | 2100 | 0.6109          | 0.4677 |
| 0.6851        | 0.4259 | 2200 | 0.6035          | 0.4692 |
| 0.6851        | 0.4452 | 2300 | 0.5802          | 0.4590 |
| 0.6851        | 0.4646 | 2400 | 0.5720          | 0.4455 |
| 0.5979        | 0.4839 | 2500 | 0.5695          | 0.4426 |
| 0.5979        | 0.5033 | 2600 | 0.5557          | 0.4351 |
| 0.5979        | 0.5226 | 2700 | 0.5499          | 0.4270 |
| 0.5979        | 0.5420 | 2800 | 0.5451          | 0.4258 |
| 0.5979        | 0.5614 | 2900 | 0.5383          | 0.4217 |
| 0.5753        | 0.5807 | 3000 | 0.5355          | 0.4186 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1