---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-bretonwelsh-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: cy
      split: test
      args: cy
    metrics:
    - name: Wer
      type: wer
      value: 0.29761332022507164
---

<!-- 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-xls-r-300m-bretonwelsh-colab

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4506
- Wer: 0.2976

## 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: 0.0004
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9503        | 0.98  | 800  | 0.8330          | 0.7296 |
| 0.6531        | 1.95  | 1600 | 0.5592          | 0.5470 |
| 0.4637        | 2.93  | 2400 | 0.4711          | 0.4539 |
| 0.3449        | 3.91  | 3200 | 0.4484          | 0.4116 |
| 0.2694        | 4.88  | 4000 | 0.4313          | 0.3860 |
| 0.2087        | 5.86  | 4800 | 0.4115          | 0.3616 |
| 0.1649        | 6.84  | 5600 | 0.4105          | 0.3378 |
| 0.1313        | 7.81  | 6400 | 0.4409          | 0.3236 |
| 0.1079        | 8.79  | 7200 | 0.4402          | 0.3093 |
| 0.0897        | 9.77  | 8000 | 0.4506          | 0.2976 |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3