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---
license: apache-2.0
tags:
- generated_from_trainer
base_model: facebook/wav2vec2-xls-r-300m
datasets:
- common_voice_15_0
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-br
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: common_voice_15_0
type: common_voice_15_0
config: br
split: None
args: br
metrics:
- type: wer
value: 41
name: WER
- type: cer
value: 14.7
name: CER
language:
- br
pipeline_tag: automatic-speech-recognition
---
# wav2vec2-xls-r-300m-br
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on Mozilla Common Voice 15 Breton dataset and [Roadennoù](https://github.com/gweltou/roadennou) dataset. It achieves the following results on the MCV15-br test set:
- Wer: 41.0
- Cer: 14.7
## Model description
This model was trained to assess the performance wav2vec2-xls-r-300m for fine-tuning a Breton ASR model.
## Intended uses & limitations
This is a research model. Usage for production is not recommended.
## Training and evaluation data
The training dataset consists of MCV15-br train dataset and 90% of the Roadennoù dataset.
The validation dataset consists of MCV15-br validation dataset and the remaining 10% of the Roadennoù dataset.
The final test dataset consists of MCV15-br test dataset.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- 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: 40
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.39.1
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2 |