---
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: 0.4290
- Wer: 0.3378

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2600
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| No log        | 0.0774 | 100  | 3.5325          | 1.0    |
| No log        | 0.1549 | 200  | 2.9652          | 1.0    |
| No log        | 0.2323 | 300  | 2.8521          | 1.0    |
| No log        | 0.3097 | 400  | 1.2473          | 0.8265 |
| 3.7403        | 0.3871 | 500  | 0.9730          | 0.7234 |
| 3.7403        | 0.4646 | 600  | 0.8328          | 0.6178 |
| 3.7403        | 0.5420 | 700  | 0.7426          | 0.5505 |
| 3.7403        | 0.6194 | 800  | 0.7127          | 0.5540 |
| 3.7403        | 0.6969 | 900  | 0.6692          | 0.5080 |
| 0.7271        | 0.7743 | 1000 | 0.6376          | 0.5256 |
| 0.7271        | 0.8517 | 1100 | 0.6119          | 0.4706 |
| 0.7271        | 0.9292 | 1200 | 0.5987          | 0.4651 |
| 0.7271        | 1.0066 | 1300 | 0.5614          | 0.4267 |
| 0.7271        | 1.0840 | 1400 | 0.5463          | 0.4229 |
| 0.5511        | 1.1614 | 1500 | 0.5232          | 0.4079 |
| 0.5511        | 1.2389 | 1600 | 0.5185          | 0.4029 |
| 0.5511        | 1.3163 | 1700 | 0.5090          | 0.4042 |
| 0.5511        | 1.3937 | 1800 | 0.4785          | 0.3851 |
| 0.5511        | 1.4712 | 1900 | 0.4775          | 0.3803 |
| 0.4529        | 1.5486 | 2000 | 0.4677          | 0.3722 |
| 0.4529        | 1.6260 | 2100 | 0.4574          | 0.3544 |
| 0.4529        | 1.7034 | 2200 | 0.4473          | 0.3562 |
| 0.4529        | 1.7809 | 2300 | 0.4437          | 0.3470 |
| 0.4529        | 1.8583 | 2400 | 0.4353          | 0.3450 |
| 0.4149        | 1.9357 | 2500 | 0.4300          | 0.3401 |
| 0.4149        | 2.0132 | 2600 | 0.4290          | 0.3378 |


### Framework versions

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