---
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: 1500
- training_steps: 15000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 5.7778        | 0.0321 | 500   | 2.8852          | 1.0    |
| 1.4914        | 0.0641 | 1000  | 1.2012          | 0.7806 |
| 0.8803        | 0.0962 | 1500  | 1.1212          | 0.7590 |
| 0.7723        | 0.1283 | 2000  | 0.9681          | 0.6770 |
| 0.6988        | 0.1603 | 2500  | 0.9453          | 0.6599 |
| 0.6392        | 0.1924 | 3000  | 0.8691          | 0.6200 |
| 0.6114        | 0.2244 | 3500  | 0.8661          | 0.6192 |
| 0.5807        | 0.2565 | 4000  | 0.7885          | 0.5794 |
| 0.5534        | 0.2886 | 4500  | 0.7739          | 0.5490 |
| 0.5358        | 0.3206 | 5000  | 0.7416          | 0.5415 |
| 0.5189        | 0.3527 | 5500  | 0.7362          | 0.5303 |
| 0.4991        | 0.3848 | 6000  | 0.7188          | 0.5066 |
| 0.48          | 0.4168 | 6500  | 0.6985          | 0.5178 |
| 0.463         | 0.4489 | 7000  | 0.6682          | 0.4933 |
| 0.4477        | 0.4810 | 7500  | 0.6625          | 0.4867 |
| 0.4431        | 0.5130 | 8000  | 0.6374          | 0.4736 |
| 0.4392        | 0.5451 | 8500  | 0.6392          | 0.4772 |
| 0.4197        | 0.5771 | 9000  | 0.6159          | 0.4547 |
| 0.4147        | 0.6092 | 9500  | 0.5995          | 0.4522 |
| 0.3912        | 0.6413 | 10000 | 0.5848          | 0.4286 |
| 0.3742        | 0.6733 | 10500 | 0.5850          | 0.4259 |
| 0.402         | 0.7054 | 11000 | 0.6352          | 0.4489 |
| 0.5746        | 0.7375 | 11500 | 0.7712          | 0.5171 |
| 0.5783        | 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