---
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: nan
- Wer: 1.0

## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 600
- training_steps: 15000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 4.6156        | 0.0321 | 500   | 1.5867          | 0.9177 |
| 1.0315        | 0.0641 | 1000  | 1.1748          | 0.7888 |
| 0.834         | 0.0962 | 1500  | 1.0393          | 0.7220 |
| 0.7184        | 0.1283 | 2000  | 0.9616          | 0.6637 |
| 0.6655        | 0.1603 | 2500  | 0.9034          | 0.6331 |
| 0.6193        | 0.1924 | 3000  | 0.8615          | 0.6239 |
| 0.5952        | 0.2244 | 3500  | 0.8161          | 0.5866 |
| 0.5622        | 0.2565 | 4000  | 0.8110          | 0.5851 |
| 0.5341        | 0.2886 | 4500  | 0.7580          | 0.5547 |
| 0.522         | 0.3206 | 5000  | 0.7397          | 0.5412 |
| 0.5123        | 0.3527 | 5500  | 0.7229          | 0.5317 |
| 0.4884        | 0.3848 | 6000  | 0.7235          | 0.5165 |
| 0.4658        | 0.4168 | 6500  | 0.6814          | 0.5117 |
| 0.4471        | 0.4489 | 7000  | 0.6623          | 0.4891 |
| 0.4338        | 0.4810 | 7500  | 0.6450          | 0.4914 |
| 0.4267        | 0.5130 | 8000  | 0.6256          | 0.4685 |
| 0.4283        | 0.5451 | 8500  | 0.6343          | 0.4711 |
| 0.4131        | 0.5771 | 9000  | 0.5989          | 0.4487 |
| 0.4317        | 0.6092 | 9500  | 0.7168          | 0.4920 |
| 0.5904        | 0.6413 | 10000 | nan             | 0.7310 |
| 0.0513        | 0.6733 | 10500 | nan             | 1.0    |
| 0.0           | 0.7054 | 11000 | nan             | 1.0    |
| 0.0           | 0.7375 | 11500 | nan             | 1.0    |
| 0.0           | 0.7695 | 12000 | nan             | 1.0    |
| 0.0           | 0.8016 | 12500 | nan             | 1.0    |
| 0.0           | 0.8337 | 13000 | nan             | 1.0    |
| 0.0           | 0.8657 | 13500 | nan             | 1.0    |
| 0.0           | 0.8978 | 14000 | nan             | 1.0    |
| 0.0           | 0.9298 | 14500 | nan             | 1.0    |
| 0.0           | 0.9619 | 15000 | nan             | 1.0    |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1