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---
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: 0.4118
- Wer: 0.3219

## 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: 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: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| No log        | 0.0194 | 100   | 3.5728          | 1.0    |
| No log        | 0.0387 | 200   | 3.0768          | 1.0    |
| No log        | 0.0581 | 300   | 3.5010          | 1.0    |
| No log        | 0.0774 | 400   | 2.0594          | 0.9900 |
| 4.06          | 0.0968 | 500   | 1.4703          | 0.8800 |
| 4.06          | 0.1161 | 600   | 1.2464          | 0.8297 |
| 4.06          | 0.1355 | 700   | 1.0686          | 0.7493 |
| 4.06          | 0.1549 | 800   | 1.0069          | 0.7116 |
| 4.06          | 0.1742 | 900   | 0.9367          | 0.6888 |
| 1.0399        | 0.1936 | 1000  | 0.8961          | 0.6742 |
| 1.0399        | 0.2129 | 1100  | 0.8967          | 0.6413 |
| 1.0399        | 0.2323 | 1200  | 0.8311          | 0.6153 |
| 1.0399        | 0.2516 | 1300  | 0.8019          | 0.5965 |
| 1.0399        | 0.2710 | 1400  | 0.7925          | 0.5927 |
| 0.8395        | 0.2904 | 1500  | 0.8165          | 0.5987 |
| 0.8395        | 0.3097 | 1600  | 0.7696          | 0.6150 |
| 0.8395        | 0.3291 | 1700  | 0.7455          | 0.5624 |
| 0.8395        | 0.3484 | 1800  | 0.7681          | 0.5684 |
| 0.8395        | 0.3678 | 1900  | 0.7292          | 0.5609 |
| 0.7574        | 0.3871 | 2000  | 0.7305          | 0.5534 |
| 0.7574        | 0.4065 | 2100  | 0.7096          | 0.5363 |
| 0.7574        | 0.4259 | 2200  | 0.7108          | 0.5572 |
| 0.7574        | 0.4452 | 2300  | 0.6703          | 0.5175 |
| 0.7574        | 0.4646 | 2400  | 0.6596          | 0.5149 |
| 0.6864        | 0.4839 | 2500  | 0.6846          | 0.5336 |
| 0.6864        | 0.5033 | 2600  | 0.6666          | 0.5286 |
| 0.6864        | 0.5226 | 2700  | 0.6391          | 0.4949 |
| 0.6864        | 0.5420 | 2800  | 0.6296          | 0.4990 |
| 0.6864        | 0.5614 | 2900  | 0.6292          | 0.4957 |
| 0.6734        | 0.5807 | 3000  | 0.6164          | 0.4765 |
| 0.6734        | 0.6001 | 3100  | 0.6180          | 0.4778 |
| 0.6734        | 0.6194 | 3200  | 0.6132          | 0.4909 |
| 0.6734        | 0.6388 | 3300  | 0.6107          | 0.4683 |
| 0.6734        | 0.6581 | 3400  | 0.6068          | 0.4749 |
| 0.6433        | 0.6775 | 3500  | 0.6008          | 0.4773 |
| 0.6433        | 0.6969 | 3600  | 0.5917          | 0.4656 |
| 0.6433        | 0.7162 | 3700  | 0.5885          | 0.4601 |
| 0.6433        | 0.7356 | 3800  | 0.5848          | 0.4482 |
| 0.6433        | 0.7549 | 3900  | 0.5852          | 0.4496 |
| 0.6217        | 0.7743 | 4000  | 0.5772          | 0.4416 |
| 0.6217        | 0.7937 | 4100  | 0.5671          | 0.4469 |
| 0.6217        | 0.8130 | 4200  | 0.5668          | 0.4463 |
| 0.6217        | 0.8324 | 4300  | 0.5558          | 0.4401 |
| 0.6217        | 0.8517 | 4400  | 0.5652          | 0.4307 |
| 0.5954        | 0.8711 | 4500  | 0.5561          | 0.4307 |
| 0.5954        | 0.8904 | 4600  | 0.5432          | 0.4206 |
| 0.5954        | 0.9098 | 4700  | 0.5294          | 0.4137 |
| 0.5954        | 0.9292 | 4800  | 0.5444          | 0.4210 |
| 0.5954        | 0.9485 | 4900  | 0.5291          | 0.4157 |
| 0.5663        | 0.9679 | 5000  | 0.5429          | 0.4140 |
| 0.5663        | 0.9872 | 5100  | 0.5209          | 0.4116 |
| 0.5663        | 1.0066 | 5200  | 0.5282          | 0.4042 |
| 0.5663        | 1.0259 | 5300  | 0.5118          | 0.3918 |
| 0.5663        | 1.0453 | 5400  | 0.5089          | 0.3993 |
| 0.4941        | 1.0647 | 5500  | 0.5011          | 0.3921 |
| 0.4941        | 1.0840 | 5600  | 0.5022          | 0.3887 |
| 0.4941        | 1.1034 | 5700  | 0.5066          | 0.3853 |
| 0.4941        | 1.1227 | 5800  | 0.4907          | 0.3815 |
| 0.4941        | 1.1421 | 5900  | 0.4982          | 0.3809 |
| 0.4628        | 1.1614 | 6000  | 0.4913          | 0.3896 |
| 0.4628        | 1.1808 | 6100  | 0.4826          | 0.3734 |
| 0.4628        | 1.2002 | 6200  | 0.4884          | 0.3740 |
| 0.4628        | 1.2195 | 6300  | 0.4841          | 0.3700 |
| 0.4628        | 1.2389 | 6400  | 0.4828          | 0.3697 |
| 0.4435        | 1.2582 | 6500  | 0.4816          | 0.3739 |
| 0.4435        | 1.2776 | 6600  | 0.4793          | 0.3674 |
| 0.4435        | 1.2969 | 6700  | 0.4744          | 0.3669 |
| 0.4435        | 1.3163 | 6800  | 0.4682          | 0.3609 |
| 0.4435        | 1.3357 | 6900  | 0.4628          | 0.3594 |
| 0.4298        | 1.3550 | 7000  | 0.4663          | 0.3554 |
| 0.4298        | 1.3744 | 7100  | 0.4656          | 0.3584 |
| 0.4298        | 1.3937 | 7200  | 0.4593          | 0.3565 |
| 0.4298        | 1.4131 | 7300  | 0.4599          | 0.3566 |
| 0.4298        | 1.4324 | 7400  | 0.4613          | 0.3521 |
| 0.4292        | 1.4518 | 7500  | 0.4521          | 0.3475 |
| 0.4292        | 1.4712 | 7600  | 0.4512          | 0.3491 |
| 0.4292        | 1.4905 | 7700  | 0.4478          | 0.3518 |
| 0.4292        | 1.5099 | 7800  | 0.4416          | 0.3421 |
| 0.4292        | 1.5292 | 7900  | 0.4427          | 0.3459 |
| 0.4072        | 1.5486 | 8000  | 0.4388          | 0.3457 |
| 0.4072        | 1.5679 | 8100  | 0.4401          | 0.3453 |
| 0.4072        | 1.5873 | 8200  | 0.4365          | 0.3434 |
| 0.4072        | 1.6067 | 8300  | 0.4346          | 0.3397 |
| 0.4072        | 1.6260 | 8400  | 0.4325          | 0.3360 |
| 0.3991        | 1.6454 | 8500  | 0.4320          | 0.3358 |
| 0.3991        | 1.6647 | 8600  | 0.4287          | 0.3355 |
| 0.3991        | 1.6841 | 8700  | 0.4293          | 0.3334 |
| 0.3991        | 1.7034 | 8800  | 0.4272          | 0.3333 |
| 0.3991        | 1.7228 | 8900  | 0.4220          | 0.3303 |
| 0.3916        | 1.7422 | 9000  | 0.4238          | 0.3292 |
| 0.3916        | 1.7615 | 9100  | 0.4215          | 0.3281 |
| 0.3916        | 1.7809 | 9200  | 0.4177          | 0.3266 |
| 0.3916        | 1.8002 | 9300  | 0.4188          | 0.3257 |
| 0.3916        | 1.8196 | 9400  | 0.4164          | 0.3247 |
| 0.3687        | 1.8389 | 9500  | 0.4163          | 0.3243 |
| 0.3687        | 1.8583 | 9600  | 0.4140          | 0.3239 |
| 0.3687        | 1.8777 | 9700  | 0.4132          | 0.3247 |
| 0.3687        | 1.8970 | 9800  | 0.4122          | 0.3224 |
| 0.3687        | 1.9164 | 9900  | 0.4117          | 0.3219 |
| 0.3707        | 1.9357 | 10000 | 0.4118          | 0.3219 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1