---
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: 1.6956
- Wer: 0.7702

## 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        | 0.1548 | 100  | 3.5587          | 1.0    |
| No log        | 0.3096 | 200  | 3.2506          | 1.0    |
| No log        | 0.4644 | 300  | 2.7740          | 1.0000 |
| No log        | 0.6192 | 400  | 1.1196          | 0.7807 |
| 3.6484        | 0.7740 | 500  | 0.9134          | 0.6539 |
| 3.6484        | 0.9288 | 600  | 0.7675          | 0.5923 |
| 3.6484        | 1.0836 | 700  | 0.7208          | 0.5290 |
| 3.6484        | 1.2384 | 800  | 0.6209          | 0.4745 |
| 3.6484        | 1.3932 | 900  | 0.6220          | 0.4788 |
| 0.6286        | 1.5480 | 1000 | 0.5739          | 0.4588 |
| 0.6286        | 1.7028 | 1100 | 0.5642          | 0.4262 |
| 0.6286        | 1.8576 | 1200 | 0.5512          | 0.4208 |
| 0.6286        | 2.0124 | 1300 | 0.5275          | 0.3865 |
| 0.6286        | 2.1672 | 1400 | 0.4955          | 0.3755 |
| 0.4816        | 2.3220 | 1500 | 0.4909          | 0.3733 |
| 0.4816        | 2.4768 | 1600 | 0.4983          | 0.3728 |
| 0.4816        | 2.6316 | 1700 | 0.4891          | 0.3655 |
| 0.4816        | 2.7864 | 1800 | 0.4796          | 0.3571 |
| 0.4816        | 2.9412 | 1900 | 0.4643          | 0.3592 |
| 0.4017        | 3.0960 | 2000 | 0.5085          | 0.3698 |
| 0.4017        | 3.2508 | 2100 | 0.6755          | 0.4530 |
| 0.4017        | 3.4056 | 2200 | 0.7100          | 0.5108 |
| 0.4017        | 3.5604 | 2300 | 0.8311          | 0.5643 |
| 0.4017        | 3.7152 | 2400 | 0.7032          | 0.5029 |
| 0.6839        | 3.8700 | 2500 | 0.7071          | 0.5007 |
| 0.6839        | 4.0248 | 2600 | 0.8224          | 0.5069 |
| 0.6839        | 4.1796 | 2700 | 0.8344          | 0.5162 |
| 0.6839        | 4.3344 | 2800 | 0.9089          | 0.5620 |
| 0.6839        | 4.4892 | 2900 | 0.9665          | 0.5640 |
| 0.8292        | 4.6440 | 3000 | 0.9128          | 0.5415 |
| 0.8292        | 4.7988 | 3100 | 1.1925          | 0.5939 |
| 0.8292        | 4.9536 | 3200 | 1.4327          | 0.6999 |
| 0.8292        | 5.1084 | 3300 | 1.2741          | 0.7827 |
| 0.8292        | 5.2632 | 3400 | 1.9348          | 0.8742 |
| 1.4131        | 5.4180 | 3500 | 1.9216          | 0.9870 |
| 1.4131        | 5.5728 | 3600 | 1.8565          | 0.9367 |
| 1.4131        | 5.7276 | 3700 | 1.7828          | 0.8240 |
| 1.4131        | 5.8824 | 3800 | 1.6847          | 0.8059 |
| 1.4131        | 6.0372 | 3900 | 1.6440          | 0.7984 |
| 1.7728        | 6.1920 | 4000 | 1.6765          | 0.8053 |
| 1.7728        | 6.3467 | 4100 | 1.6733          | 0.8024 |
| 1.7728        | 6.5015 | 4200 | 1.6601          | 0.7900 |
| 1.7728        | 6.6563 | 4300 | 1.6605          | 0.7973 |
| 1.7728        | 6.8111 | 4400 | 1.6599          | 0.7805 |
| 1.6777        | 6.9659 | 4500 | 1.6359          | 0.7693 |
| 1.6777        | 7.1207 | 4600 | 1.6400          | 0.7651 |
| 1.6777        | 7.2755 | 4700 | 1.6759          | 0.7672 |
| 1.6777        | 7.4303 | 4800 | 1.6849          | 0.7686 |
| 1.6777        | 7.5851 | 4900 | 1.6858          | 0.7690 |
| 1.683         | 7.7399 | 5000 | 1.6956          | 0.7702 |


### Framework versions

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