---
license: apache-2.0
tags:
- generated_from_trainer
base_model: facebook/wav2vec2-large-xlsr-53
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-53-breton
  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-large-xlsr-53-breton

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co./facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9840
- Wer: 0.5852
- Cer: 0.2130

## 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: 6e-05
- 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_ratio: 0.08
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 11.8947       | 2.56  | 250  | 3.4769          | 1.0    | 0.9862 |
| 3.1668        | 5.13  | 500  | 3.0459          | 1.0    | 0.9862 |
| 2.6491        | 7.69  | 750  | 1.6416          | 0.9319 | 0.4441 |
| 1.4107        | 10.26 | 1000 | 1.1000          | 0.7751 | 0.2852 |
| 0.9989        | 12.82 | 1250 | 0.9827          | 0.7092 | 0.2578 |
| 0.8238        | 15.38 | 1500 | 0.9543          | 0.6864 | 0.2476 |
| 0.7193        | 17.95 | 1750 | 0.9241          | 0.6547 | 0.2371 |
| 0.6377        | 20.51 | 2000 | 0.9296          | 0.6452 | 0.2352 |
| 0.5865        | 23.08 | 2250 | 0.9287          | 0.6320 | 0.2301 |
| 0.541         | 25.64 | 2500 | 0.9359          | 0.6205 | 0.2231 |
| 0.4988        | 28.21 | 2750 | 0.9850          | 0.6149 | 0.2244 |
| 0.4691        | 30.77 | 3000 | 0.9566          | 0.6065 | 0.2192 |
| 0.4568        | 33.33 | 3250 | 0.9653          | 0.6019 | 0.2175 |
| 0.4485        | 35.9  | 3500 | 0.9760          | 0.5949 | 0.2175 |
| 0.4219        | 38.46 | 3750 | 0.9824          | 0.5926 | 0.2177 |
| 0.397         | 41.03 | 4000 | 0.9669          | 0.5885 | 0.2138 |
| 0.3912        | 43.59 | 4250 | 0.9857          | 0.5908 | 0.2145 |
| 0.3764        | 46.15 | 4500 | 0.9937          | 0.5886 | 0.2145 |
| 0.3742        | 48.72 | 4750 | 0.9840          | 0.5852 | 0.2130 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0