---
model-index:
- name: mHuBERT-147-br
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_15_0
      type: common_voice_15_0
      config: br
      split: test
      args: br
    metrics:
    - name: WER
      type: wer
      value: 47.0
    - name: CER
      type: cer
      value: 16.7
language:
- br
metrics:
- wer
base_model: utter-project/mHuBERT-147
pipeline_tag: automatic-speech-recognition
datasets:
- mozilla-foundation/common_voice_15_0
---


# mHuBERT-147-br

This model is a fine-tuned version of [utter-project/mHuBERT-147](https://huggingface.co/utter-project/mHuBERT-147) on Mozilla Common Voice 15 Breton dataset and [Roadennoù](https://github.com/gweltou/roadennou) dataset.
It achieves the following results on the validation set:
- Loss: 0.7331
- Wer: 50.09
- Cer: 16.45

## Model description

This model was trained to assess the performance mHubert-147 for finetuning a Breton ASR model. 

## Intended uses & limitations

This model is a research model and shouldn't be used in production.

## Training and evaluation data

90% of the Roadennoù dataset was used for training, the remaining 10% was used for validation in addition to MCV15-br validation dataset.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3.8e-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: 52
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.39.1
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2