---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- automatic-speech-recognition
- DewiBrynJones/banc-trawsgrifiadau-bangor-clean-with-ccv
- 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 the DEWIBRYNJONES/BANC-TRAWSGRIFIADAU-BANGOR-CLEAN-WITH-CCV - DEFAULT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4404
- Wer: 0.3235

## 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: 64
- 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| No log        | 0.0566 | 100  | 3.5966          | 1.0    |
| No log        | 0.1133 | 200  | 3.5808          | 1.0    |
| No log        | 0.1699 | 300  | 2.2642          | 0.9884 |
| No log        | 0.2265 | 400  | 1.0231          | 0.7426 |
| 3.6826        | 0.2831 | 500  | 0.9161          | 0.6457 |
| 3.6826        | 0.3398 | 600  | 0.7942          | 0.5827 |
| 3.6826        | 0.3964 | 700  | 0.7551          | 0.5417 |
| 3.6826        | 0.4530 | 800  | 0.7243          | 0.5352 |
| 3.6826        | 0.5096 | 900  | 0.6665          | 0.4985 |
| 0.4714        | 0.5663 | 1000 | 0.6403          | 0.4863 |
| 0.4714        | 0.6229 | 1100 | 0.6220          | 0.4727 |
| 0.4714        | 0.6795 | 1200 | 0.6100          | 0.4642 |
| 0.4714        | 0.7361 | 1300 | 0.5863          | 0.4442 |
| 0.4714        | 0.7928 | 1400 | 0.5668          | 0.4384 |
| 0.3661        | 0.8494 | 1500 | 0.5753          | 0.4421 |
| 0.3661        | 0.9060 | 1600 | 0.5641          | 0.4311 |
| 0.3661        | 0.9626 | 1700 | 0.5466          | 0.4153 |
| 0.3661        | 1.0193 | 1800 | 0.5304          | 0.4023 |
| 0.3661        | 1.0759 | 1900 | 0.5280          | 0.3938 |
| 0.3069        | 1.1325 | 2000 | 0.5220          | 0.3902 |
| 0.3069        | 1.1891 | 2100 | 0.5064          | 0.3929 |
| 0.3069        | 1.2458 | 2200 | 0.5033          | 0.3823 |
| 0.3069        | 1.3024 | 2300 | 0.4920          | 0.3773 |
| 0.3069        | 1.3590 | 2400 | 0.4955          | 0.3737 |
| 0.264         | 1.4156 | 2500 | 0.4955          | 0.3739 |
| 0.264         | 1.4723 | 2600 | 0.4846          | 0.3651 |
| 0.264         | 1.5289 | 2700 | 0.4761          | 0.3645 |
| 0.264         | 1.5855 | 2800 | 0.4672          | 0.3627 |
| 0.264         | 1.6421 | 2900 | 0.4645          | 0.3640 |
| 0.2498        | 1.6988 | 3000 | 0.4652          | 0.3589 |
| 0.2498        | 1.7554 | 3100 | 0.4596          | 0.3534 |
| 0.2498        | 1.8120 | 3200 | 0.4505          | 0.3510 |
| 0.2498        | 1.8686 | 3300 | 0.4490          | 0.3522 |
| 0.2498        | 1.9253 | 3400 | 0.4460          | 0.3456 |
| 0.2304        | 1.9819 | 3500 | 0.4440          | 0.3405 |
| 0.2304        | 2.0385 | 3600 | 0.4493          | 0.3425 |
| 0.2304        | 2.0951 | 3700 | 0.4386          | 0.3374 |
| 0.2304        | 2.1518 | 3800 | 0.4424          | 0.3381 |
| 0.2304        | 2.2084 | 3900 | 0.4385          | 0.3373 |
| 0.1947        | 2.2650 | 4000 | 0.4394          | 0.3355 |
| 0.1947        | 2.3216 | 4100 | 0.4354          | 0.3324 |
| 0.1947        | 2.3783 | 4200 | 0.4345          | 0.3325 |
| 0.1947        | 2.4349 | 4300 | 0.4343          | 0.3331 |
| 0.1947        | 2.4915 | 4400 | 0.4517          | 0.3349 |
| 0.1996        | 2.5481 | 4500 | 0.4571          | 0.3328 |
| 0.1996        | 2.6048 | 4600 | 0.4423          | 0.3255 |
| 0.1996        | 2.6614 | 4700 | 0.4405          | 0.3254 |
| 0.1996        | 2.7180 | 4800 | 0.4437          | 0.3238 |
| 0.1996        | 2.7746 | 4900 | 0.4401          | 0.3232 |
| 0.2024        | 2.8313 | 5000 | 0.4404          | 0.3235 |


### Framework versions

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